Handbook of Clinical QEEG and Neurotherapy 9781138802643, 9781315754093, 9781317623083, 9781317623076

Table of contents :
Cover
Title
Copyright
CONTENTS
List of Figures
List of Tables
List of Contributors
Preface
Introduction
PART I Clinical Practice of Neurofeedback
1 A Brain Functional Dynamic Approach to Counseling and Therapy
2 Evolving as a Neurotherapist: Integrating Psychotherapy and Neurofeedback
3 Variables Related to Neurotherapy Success/Failure
4 Neuromeditation: An Introduction and Overview
5 Investigating the Neuroplasticity of Chronic Pain Utilizing Biofeedback Procedures
6 Working with Forensic Populations: Incorporating Peripheral Biofeedback and Brainwave Biofeedback into Your Organization or Practice
PART II Pediatric Neurofeedback
7 Training Children Younger Than 6 Years of Age
8 QEEG and 19 Channel Neurofeedback as a Clinical Evaluation Tool for Children with Attention, Learning and Emotional Problems
PART III The Neurologist’s Perspective
9 QEEG-Guided Neurofeedback to Normalize Brain Function in Various Disorders
10 QEEG (Brain Mapping) and LORETA Z-Score Neurofeedback in Neuropsychiatric Practice
11 Concussionology: Sport Concussion Management
PART IV Dyslexia and Reading
12 Rhythms of Dyslexia: EEG, ERP and Neurofeedback
13 Normal and Abnormal Reading Processes in Children: Neuropsychophysiological Studies
14 The Electrophysiological Coordinated Allocation of Resource (CAR) Model of Effective Reading in Children, Adolescents and Adults
PART V sLORETA/LORETA and Z-Score Training
15 sLORETA in Clinical Practice: Not All ROIs Are Created Equal
16 sLORETA Neurofeedback as a Treatment for PTSD
17 The Efficacy of Z-Score Neurofeedback Training
18 Introduction to the Concepts and Clinical Applications of Multivariate Live Z-Score Training, PZOK and sLORETA Feedback
PART VI QEEG and Brain Dynamical Approaches
19 Perspective and Method for a QEEG Based Two Channel Bi-Hemispheric Compensatory Model of Neurofeedback Training
20 Neurotherapy for Clinicians in the Trenches: The ClinicalQ and Braindriving
21 The Use of Surface 19-Channel Z-Score Training to Ameliorate Symptoms Remaining or Apparently Caused after Withdrawal from Those Medications
22 Raw EEG Biomarkers, Z-Scored Normative Analysis, and the Diagnosis of Neurobehavioral Disorders
PART VII Traditional Alpha/Theta/Beta Protocols
23 Neurofeedback as a Treatment for Anxiety in Adolescents and Young Adults
24 Alpha-Theta-Based Clinical Outreach
PART VIII Emerging Paradigms
25 EEG State Discrimination and the Phenomenal Correlates of Brainwave States
26 Infra-Slow Fluctuation (ISF) Training for Autism Spectrum Disorders
27 Transcranial Direct Current Stimulation in Rehabilitation
28 An Integrative Approach to Optimizing Neural Function: Exploring the Brain–Gut Connection
Index

Citation preview

HANDBOOK OF CLINICAL QEEG AND NEUROTHERAPY

This book is an essential resource describing a wide range of approaches and technologies in the areas of quantitative EEG (QEEG) and neurotherapy including neurofeedback and neuromodulation approaches. It emphasizes practical, clinically useful methods, reported by experienced clinicians who have developed and used these approaches first hand. These chapters describe how the authors approach and use their particular combinations of technology, and how clients are evaluated and treated. This resource, which is encyclopedic in scope, provides a valuable and broad, yet sufficiently detailed account, to help clinicians guide the future directions in client assessment and neurotherapeutic treatment. Each contribution includes literature citations, practical information related to clinical interventions, and clinical outcome information. Thomas F. Collura, PhD, QEEG-D, BCN, NCC, LPCC, is Clinical Director, the Brain Enrichment Center, and President, BrainMaster Technologies, Inc., in Bedford, Ohio. He is a Past President of the International Society for Neurofeedback and Research (ISNR) and is President of the Association for Applied Psychophysiology and Biofeedback (AAPB). Jon A. Frederick, PhD, BCN, is assistant Professor of Psychology at St. Cloud State University, St. Cloud, MN. He serves on the editorial board of Neuroregulation and the board of directors for the Foundation for Neurofeedback and Neuromodulation Research.

“The Handbook of QEEG and Neurotherapy is an outstanding introduction to a very diverse field by many authors who are well known and have contributed immeasurably to the development of neurofeedback. The chapters range from basic introduction to neurofeedback to neurofeedback applications for all age groups and in particular for disorders such as dyslexia, addiction, and other clinical entities. The newest areas such as LORETA Z score based training is included in this volume as well as traditional protocols that date back to the 1970s. This Handbook will be very useful for newcomers to the field as well as seasoned practitioners.” —Joel F. Lubar PhD, Professor Emeritus, University of Tennessee; Director, Southeastern, Neurofeedback Institute Inc.; BCIA Senior Fellow-EEG, BCN—Board Certified Neurofeedback; QEEG Diplomate “Clinical rather than scientific in tone, practical with useful anecdotal information that will appeal to practitioners . . . . authors [are] well respected and recognized in their clinical work.” —David Trudeau, MD, Editor Emeritus, Journal of Neurotherapy; President, ISNR Research Foundation; former director, Neurofeedback Lab, Department of Psychiatry, Minneapolis Veterans Affairs Medical Center

HANDBOOK OF CLINICAL QEEG AND NEUROTHERAPY

Edited by Thomas F. Collura and Jon A. Frederick

First published 2017 by Routledge 711 Third Avenue, New York, NY 10017 and by Routledge 2 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN Routledge is an imprint of the Taylor & Francis Group, an informa business © 2017 Taylor and Francis The right of the editors to be identified as the authors of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. The purchase of this copyright material confers the right on the purchasing institution to photocopy pages which bear the photocopy icon and copyright line at the bottom of the page. No other parts of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging in Publication Data Names: Collura, Thomas F., editor. | Frederick, Jon A., editor. Title: Handbook of clinical QEEG and neurotherapy / edited by Thomas F. Collura and Jon A. Frederick. Description: New York, NY : Routledge, 2016. Includes bibliographical references and index. Identifiers: LCCN 2016010490 | ISBN 9781138802643 (alk. paper) | ISBN 9781315754093 (ebk) Subjects: | MESH: Neurofeedback | Electroencephalography | Case Reports Classification: LCC RC386.6.E43 NLM WM 425.5.B6 DDC 616.8/047547—dc23 LC record available at http://lccn.loc.gov/2016010490 ISBN: 978-1-138-80264-3 (hbk) ISBN: 978-1-315-75409-3 (ebk) Typeset in Bembo by Apex CoVantage, LLC

CONTENTS

List of Figures List of Tables List of Contributors Preface Introduction

ix xix xxi xxv xxvii

PART I

Clinical Practice of Neurofeedback

1

1 A Brain Functional Dynamic Approach to Counseling and Therapy Thomas F. Collura, Christen H. Stahl, Benjamin A. Berry, and Justin R. Leiter-Mcbeth

3

2 Evolving as a Neurotherapist: Integrating Psychotherapy and Neurofeedback Glenn Weiner

45

3 Variables Related to Neurotherapy Success/Failure James R. Evans, Mary Blair Dellinger, Ann Guyer, and Jane Price

55

4 Neuromeditation: An Introduction and Overview Jeffrey M. Tarrant

64

5 Investigating the Neuroplasticity of Chronic Pain Utilizing Biofeedback Procedures Stuart Donaldson, Mary Donaldson and Doneen Moran

82

6 Working with Forensic Populations: Incorporating Peripheral Biofeedback and Brainwave Biofeedback into Your Organization or Practice Robert E. Longo and G. Michael Russo

92

v

Contents

PART II

Pediatric Neurofeedback

107

7 Training Children Younger Than 6 Years of Age Merlyn Hurd 8 QEEG and 19 Channel Neurofeedback as a Clinical Evaluation Tool for Children with Attention, Learning and Emotional Problems Theresia Stöckl-Drax

109

134

PART III

The Neurologist’s Perspective

147

9 QEEG-Guided Neurofeedback to Normalize Brain Function in Various Disorders Jonathan E. Walker

149

10 QEEG (Brain Mapping) and LORETA Z-Score Neurofeedback in Neuropsychiatric Practice J. Lucas Koberda

158

11 Concussionology: Sport Concussion Management Harry Kerasidis

184

PART IV

Dyslexia and Reading

211

12 Rhythms of Dyslexia: EEG, ERP and Neurofeedback Tony Steffert and Beverly Steffert

213

13 Normal and Abnormal Reading Processes in Children: Neuropsychophysiological Studies Giuseppe A. Chiarenza

235

14 The Electrophysiological Coordinated Allocation of Resource (CAR) Model of Effective Reading in Children, Adolescents and Adults Kirtley E. Thornton and Dennis P. Carmody

250

PART V

sLORETA/LORETA and Z-Score Training

281

15 sLORETA in Clinical Practice: Not All ROIs Are Created Equal Mark Llewellyn Smith

283

vi

Contents

16 sLORETA Neurofeedback as a Treatment for PTSD Nir Getter, Zeev Kaplan and Doron Todder

300

17 The Efficacy of Z-Score Neurofeedback Training Joseph Guan

312

18 Introduction to the Concepts and Clinical Applications of Multivariate Live Z-Score Training, PZOK and sLORETA Feedback Penijean A. Gracefire

326

PART VI

QEEG and Brain Dynamical Approaches

385

19 Perspective and Method for a QEEG Based Two Channel Bi-Hemispheric Compensatory Model of Neurofeedback Training Richard Soutar

387

20 Neurotherapy for Clinicians in the Trenches: The ClinicalQ and Braindriving Paul G. Swingle 21 The Use of Surface 19-Channel Z-Score Training to Ameliorate Symptoms Remaining or Apparently Caused after Withdrawal from Those Medications Kathy Abbott 22 Raw EEG Biomarkers, Z-Scored Normative Analysis, and the Diagnosis of Neurobehavioral Disorders Leonardo Mascaro

404

421

431

PART VII

Traditional Alpha/Theta/Beta Protocols

453

23 Neurofeedback as a Treatment for Anxiety in Adolescents and Young Adults Cynthia Kerson

455

24 Alpha-Theta-Based Clinical Outreach Lincoln Stoller

464

PART VIII

Emerging Paradigms

475

25 EEG State Discrimination and the Phenomenal Correlates of Brainwave States Jon A. Frederick

477

vii

Contents

26 Infra-Slow Fluctuation (ISF) Training for Autism Spectrum Disorders Mark Llewellyn Smith, Leonardo M. Leiderman, and Jackie de Vries

488

27 Transcranial Direct Current Stimulation in Rehabilitation Roger H. Riss and Frederick Ulam

500

28 An Integrative Approach to Optimizing Neural Function: Exploring the Brain–Gut Connection Nancy E. White and Leonard M. Richards

531

Index

545

viii

FIGURES

1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 1.10 1.11 1.12 1.13 1.14 1.15 1.16 1.17 1.18 1.19 1.20 1.21 1.22 1.23 1.24 1.25 1.26 1.27 1.28 1.29 1.30 1.31 1.32

Eyes closed (QEEGPro) pre-treatment. Post-treatment. Eyes closed (Neuroguide) pre-treatment. Post-treatment. Eyes closed (BrainDx) pre-treatment. Post-treatment. Eyes closed (QEEGPro) pre-treatment. Post-treatment. Eyes closed (Neuroguide) pre-treatment. Post-treatment. Eyes closed (BrainDx) pre-treatment. Post-treatment. Eyes closed (QEEGPro) pre-treatment. Post-treatment. Eyes closed (Neuroguide) pre-treatment. Post-treatment. Eyes closed (BrainDx) pre-treatment. Post-treatment. Eyes closed (QEEGPro) post-20 treatment. Post-40 treatment. Eyes closed (Neuroguide) post-20 treatment. Post-40 treatment. Eyes closed (Brain Dx) post-20 treatment. Post-40 treatment. CNS-VS graph. Eyes closed (QEEGPro) pre-treatment. Post-20 treatment. Eyes closed (Neuroguide) pre-treatment. Post-20 treatment. Eyes closed (BrainDx) pre-treatment. Post-20 treatment. EYES Closed (QEEG-Pro) post 20-treatment. ix

11 12 13 14 15 15 17 18 19 20 21 21 23 24 25 26 27 27 28 29 30 31 32 32 33 34 35 36 37 38 38 39

Figures

1.33 1.34 1.35 1.36 1.37 1.38 4.1 4.2 4.3 4.4a 4.4b 4.5 6.1 7.1 7.2

Post-40 treatment. Eyes closed (Neuroguide) post-20 treatment. Post-40 treatment. Eyes closed (BrainDx) post-20 treatment. Post-40 treatment. CNS-VS graph. ACC theta and Precuneus during an OM neuromeditation session. Precuneus image in BrainMaster BrainAvatar training screen. ACC and insula gamma during an LK-C neuromeditation. Z-score FFT absolute power meditation. Peak meditation. Absolute power difference between peak meditation and meditation Mv (sq). An example of a QEEG. Intake information. Eyes closed Dynamic FFT analysis of absolute power and Z score of absolute power. 7.3 The picture on the left is one that has been artifacted and the one on the right uses all data without artifacting. 7.4a and 7.4b These are the summaries of the child being discussed. The picture above is a referential summary of absolute power, relative power, amplitude asymmetry, coherence and phase lag. The picture on the next page is a Laplacian summary of the absolute power, relative power and amplitude asymmetry. 7.5 This is a comparison of the pre and post QEEG on the child after 11 months of C4 SMR training. The picture on the left is the pre (age 2.83 years) and the one on the right is post (age 5.06 years). 7.6 A sample of eyes closed showing the dominant frequency is in the Theta range. 7.7 A sample of the eyes open condition showing similar patterns as in the eyes closed condition. 7.8 An analysis of the eyes closed (dotted light grey line) and eyes open (dotted dark grey line) in the child’s dominant frequency of 4–8 Hz. 7.9 Summary of eyes closed and eyes opened of child being discussed at age 2.86 months. 7.10 The map shows in the dominant frequency distribution considerable suppression in all except Fp2, T4, F7 and Fp1. 7.11 When compared to the first map the changes are obvious. The areas which remain outside the norm is in the Beta area in the central, parietal and left posterior. Amplitude Asymmetry has changed to being mainly in the Delta frequency; the coherence is between the prefrontal and central regions in Theta and the Phase Lag shows Theta, Beta and high Beta essentially in the right hemisphere as being mixed. Speed of processing is too fast for recruitment of resources. 7.12 The map shows more activation and flexibility across the cortex with the exceptions being the Beta ranges. The child’s concept development was still below average. 7.13 Both pictures are of eyes opened. Eyes opened 1st QEEG age 2.86 years (left picture) and at age 9 (right picture) after 19 channel and Neurofield trainings. 8.1 Z-scores and display of racing game as a challenge condition. 8.2 Z-scores displayed as numbers. x

40 41 42 43 43 44 70 71 75 75 75 76 100 112 117 118

119

121 122 123 124 125 127

128

129 132 136 137

Figures

8.3 8.4 8.4a 8.4b

Z-scores displayed as instant brain maps. Z-scores displayed as plots (power, coherence, phase). Example for actual activation without stimulus (slight hyperactivation). Activation mentioning “reading” in a dyslexic child (pronounced hyperactivation and hypercoherence). 8.4c Activation mentioning “reading with mom” in the same child (more pronounced hyperactivation esp. in the higher frequencies). 8.4d Activation “let the brain make it as white as possible” (shows how easily the brain can regulate itself—this is a powerful experience for many children). 8.5 BrainAvatar voxels with left and right shifting of gamma activation. 8.5a Neutral without any stimulus, gamma being equally distributed rt/lt. 8.5b Thinking about favorite dish. Gamma more to the left. 8.5c Thinking about boy in class who is bullying him. Gamma more to the right. 8.6 Z-scores and movie feedback: the different movie sizes reflect the amount of Z-scores being within the chosen limits, and of course the children want to see it big. 8.7 Session trend over a training period with video feedback of 13 minutes at the end of the evaluative session, showing how the slow waves regulate. 10.1 A 17-year-old with prior concussions and overlapping ADD and behavioral problems—QEEG showed elevated frontal and temporal theta and alpha power (in red), increased frontal beta power and right temporal delta power. 10.2a and 10.2b LORETA of 17-year-old male with prior concussions, and ADD-area of electrical dysregulation of the right temporal lobe is shown in red (a) and anterior cingulate in blue (b). 10.3 A 69-year-old with prior CVA. QEEG showed increased right temporal delta and theta power. 10.4a and 10.4b LORETA showed several areas of electrical dysregulation, including right temporal lobe BA-36 and cingulate gyrus BA-24 (in red). 10.5 A 36-year-old female with trigeminal neuralgia. Pre-treatment QEEG showed marked frontal and temporal increase in delta and theta power (in red) and frontal beta power (in yellow). 10.6 A 36-year-old female with trigeminal neuralgia. Pre-treatment LORETA showing left insular cortex electrical dysregulation (in red). 10.7 A 36-year-old female with with trigeminal neuralgia. Post-NFB treatment QEEG showed partial correction of previously identified abnormalities. 10.8 A 36-year-old female with trigeminal neuralgia. Post-NFB treatment LORETA showed resolution of previously identified insular electric dysregulation. 10.9a A LORETA imaging of 19-year-old patient showing area of electrical dysregulation of anterior cingulate (AC) region BA-32 (in red). 10.9b Resolution of AC dysregulation after 20 sessions of NFB. 10.10 QEEG of 19-year-old with cognitive problems before initiation of NFB (a) and after a course of NFB sessions (b). 10.10a QEEG before NFB—noticeable area of frontal increase in delta power. 10.10b QEEG after NFB—an improvement in previously overexpressed frontal delta power is recorded. 10.11 Executive testing (NeuroTrax) before NFB (March 2013) and after each round of 10 sessions of NFB (in May 2013, October 2013 and January 2014)— gradual increase in executive function was recorded. xi

137 138 138 138 139

139 140 140 141 141 142 142

162

163 164 165

168 168 169 169 170 171 171 171 172

173

Figures

10.12 A 64-year-old male with AD. Initial QEEG showed central increase of delta and beta power (in yellow) as well as coherence abnormalities. 10.13 A 64-year-old with AD. LORETA detected several areas of electrical dysregulation (in red) including anterior cingulate and other BA (not shown). 10.14 Summary of frequently identified QEEG/LORETA abnormalities in patients suffering from depression and anxiety. 10.15 Orbitofrontal electrical (area in red) dysregulation (BA-11) seen in one of the patients with depression and anxiety. 10.16 A 58-year-old female: LORETA imaging before NFB shows two areas of electrical dysregulation (in red), including the cingulate cortex and left temporal region. 10.17 This is a 12-year-old boy with ASD. Pre-NFB QEEG shows increased central and temporal beta power (in red). 10.18 Pre-NFB LORETA showed area (in blue) of anterior cingulate electrical dysregulation in theta frequency. 10.19 A 15-year-old male with AS/ADHD. LORETA showed electrical dysregulation of BA-9 (in red) in delta frequency. 10.20 Illustrates lower than expected cognitive score, especially memory and attention, as well as information processing speed (too low to be scored). 10.21 An 18-year-old female with medication resistant epilepsy. LORETA showed several areas of electrical dysregulation including left temporal region (in red). 11.1 The high resolution frequency spectra are shown below at each scalp location for QEEG Z-Score Log Power Spectra. The cursor is at 7.42 Hz. 11.2 The high resolution frequency spectra are shown below at each scalp location for QEEG Z-Score Log Power Spectra. The cursor is at 14.06 Hz. 11.3 sLORETA pre-treatment excess frontal alpha following motor vehicle accident (MVA). 11.4 sLORETA pre-treatment excess beta in parietal and precuneus following MVA. 11.5 The high resolution frequency spectra are shown at each scalp location for QEEG Magnitude Spectra. The cursor is at 8.20 Hz. 11.6 The high resolution frequency spectra are shown at each scalp location for QEEG Magnitude Spectra. The cursor is at 9.38 Hz. 11.7 The high resolution frequency spectra are shown at each scalp location for QEEG Z-Score Log Power Spectra. The cursor is at 7.81 Hz. 11.8 The high resolution frequency spectra are shown at each scalp location for QEEG Z-Score Log Power Spectra. The cursor is at 13.67 Hz. 11.9 sLORETA post-treatment reduced frontal alpha after live Z-score training following MVA. 11.10 A summary of the QEEG results for this patient is provided by these topographic images, displaying the Z-scored features computed from 19 standardized electrode positions, as viewed from above with the nose at top, and left on the left. The scale is set at +/- 3.0 Z. 11.11 A summary of the QEEG results for this patient is provided by these topographic images, displaying the Z-scored features computed from 19 standardized electrode positions, as viewed from above with the nose at top, and left on the left. The scale is set at +/- 3.0 Z. 11.12 sLORETA pre-treatment excess posterior delta following train accident. 11.13 sLORETA post-treatment showing reduced posterior delta following train accident. xii

175 175 176 176

177 179 180 180 182 182 201 201 202 202 203 204 204 205 205

206

207 208 208

Figures

12.1

Magnocellular and parvocellular are two separate visual pathways going from the eyes to the Lateral Geniculate Nucleus (LGN) of the thalamus and on to the visual cortex. 12.2 Shows two superimposed EEG spectra of 35 subjects who had been diagnosed with visual dyslexia. 12.3 Shows the topographic maps of alpha power (9 to 11 Hz) of 35 visual dyslexia subjects with visual dyslexia reading with and without coloured glasses. 12.4a Shows a stereotypical Evoked and Event-Related response. 12.4b Evoked and Event-Related Potentials can reflect the time course of sensory and cognitive processing and can help to distinguish between sensory and cognitive deficits even when there is no external behavioural response such as a button push. 12.5 This Event-Related Potential is a GO/NOGO, Visual Continuous Performance Task (VCPT). 12.6 The Independent Component (IC) of the P3b posterior of the GO trial, in the Visual Continuous Performance Task (VCPT), Event-Related Potentials. 12.7 Raw EEG bipolar P3-Cz, shows the excess of alpha. 12.8a and 12.8b Show the difference between the client and age matched norms. 12.9a, 12.9b and 129c Shows the deviation from norms. Left: topographic map. Middle: spectra, the grey shaded area shows the excess alpha activity at P3. Right: sLORETA image of Independent Component (IC). 12.10a, 12.10b and 12.10c ERPs. Left: topographic map of the largest deviations from normality at 456 ms. Middle: time course of ERP. Right: sLORETA image of Independent Component (IC) of the ERP. 12.11a and 12.11b Spectra: pre and post. 12.12a, 12.12b and 12.12c Pre and post ERP for GO trial of the VCPT. 13.1 Average movement-related brain potentials elicited during the execution of a complex motor-perceptual task. 13.2 Average of movement-related brain potentials associated with target performance in healthy subjects (dashed line) and dyslexic subjects (continuous line). 13.3 Average of movement-related brain potentials associated with non-target performance in healthy subjects (dashed line) and dyslexic subjects (continuous line). 13.4 Chronology of reading-related potentials. 13.5 Significant differences (paired t-test: P < 0.01) between sLORETA maps. 13.6 Superimposition of functional activations on inflated anatomical cortical surface: lateral and medial views of both hemispheres. 13.7 Significant differences (unpaired t-test: P < 0.05) between sLORETA maps. 14.1 Participant characteristics. 14.2 Group 1—reading condition all participants—all ages. 14.3 Group 1—immediate recall reading condition all participants—all ages. 14.4 Group 1—delayed recall reading condition all participants—all ages. 14.5 Group 2 (children—non-clinical and clinical): Developmental changes. 14.6 Group 2 (children—non-clinical and clinical): Positive and negative relations between QEEG variables and memory performance during reading task. 14.7 Group 2 (children—non-clinical and clinical): Recall developmental trends. 14.8 Group 2 (children—non-clinical and clinical): Reading immediate recall relations. xiii

215 220 220 221

222 225 225 226 226

227

227 228 229 239

240

241 243 244 245 246 256 257 258 259 260 261 263 264

Figures

14.9

Group 2 (children—non-clinical and clinical): CAR patterns of delayed recall of reading material. 14.10 Group 3 (adolescents and adults—non-clinical and clinical): Developmental trends. 14.11 Group 3 (adolescents and adults—non-clinical and clinical): Correlates of memory during reading. 14.12 Group 3 (adolescents and adults—non-clinical and clinical): Immediate recall correlates. 14.13 Group 4 (normative group—children and adults): Unique developmental patterns during reading. 14.14 Group 4 (normative group—children, adolescents, and adults): Reading memory correlates during input task. 14.15 Group 4 (normative group—children, adolescents, and adults): Immediate recall. 14.16 Group 4 (normative group—children, adolescents, and adults): Delayed recall. 14.17 Group 5 (non-clinical child): Shows the developmental relations. 14.18 Group 5 (child normative group): Relations during the reading task. 14.19 Group 6 (adolescent and adult normative group): Developmental trends during reading task. 14.20 Group 6 (adolescent and adult normative group): Relations during reading task. 14.21 Significant relations between locations across frequencies. 14.22 All participants—encoding stage—correlates with reading memory. 14.23 All participants—immediate recall stage—correlates with reading memory. 14.24 All participants—delayed recall stage—QEEG correlates with delayed recall reading memory. 15.1 Pre-treatment QEEG. 15.2 Insufficient amplitude of Current Source Density (CSD) at 1 Hertz in the right Superior Frontal Gyrus. 15.3 Pre-treatment LORETA analysis revealing insufficient Current Source Density (CSD) in the Anterior Cingulate Gyrus at 11 Hertz. The Z-score is 1.6 standard deviations less than the mean. 15.4 Post-treatment QEEG reveals delta band resolution and improvements in network information sharing. 15.5a and 15.5b Pre- and post-treatment LORETA analysis reveals an increase of 0.8 STD in alpha power at 11 Hertz. 15.6 Pre/Post ISF training brain maps. 15.7 Raw traces of EEG during training. Note the complete lack of alpha morphology. 15.8 BrainAvatar real-time analytics revealing Z-scored global insufficient absolute power in the alpha band during training. 15.9 Z-scored sLORETA Projector identifying insufficient power in alpha band in the Precuneus. 15.10 Depicts a client during a moment of Z-scored low alpha power with right lateral frontal region hypocoherence. 15.11 Depicts the client during a moment of “normal” alpha power. 16.1 Alona’s neurocognitive normalized score at baseline compared to follow-up assessments. Higher scores reflects better performance. xiv

265 266 267 267 268 269 269 270 271 271 272 273 274 275 276 277 285 286

286 289 290 292 294 295 295 296 297 308

Figures

17.1 17.2 17.3 17.4 17.5 17.6 18.1 18.2 18.3

18.4 18.5 18.6 18.7 18.8 18.9 18.10 18.11 18.12 18.13 18.14 18.15 18.16

18.17 18.18 18.19 18.20 18.21 18.22 18.23 18.24

Pre-treatment QEEG. Bipolar client post-test August 2013. ADHD client pre-treatment QEEG brain map February 2012. ADHD client interim QEEG brain map December 2012. A QEEG brain map was done in March 2013. Autistic boy’s QEEG (post-treatment). Diagram of EEG sine wave: amplitude and frequency. BrainAvatar live sLORETA projector software displaying CSD estimation of theta amperage in the parahippocampal gyrus. Illustration of power measurements: surface EEG estimated in amplitude (height) at each electrode location, sLORETA EEG estimated in amperage (volume) using a composite of 19 electrodes across the entire scalp. 17 yr old male with 11 Hz alpha in excess of 5.93 standard deviations in Brodmann Area 9. 17 yr old male from Figure 18.4—post 8 sessions of sLORETA amperage training with alpha in Brodmann area 9 now at 1.61 standard deviations. 39 yr old female with 4 Hz theta in excess of 5.97 standard deviations in Broadmann area 6. 39 yr old female from Figure 18.6—post 11 sessions of sLORETA amperage training with alpha in Brodmann area 9 now at 1.86 standard deviations. Comparison of two QEEG maps to illustrate potential clinical applications when considering neurofeedback strategies. Illustration of the four locations in which live z-scores are observed during PZOK feedback. Example of a 4 channel PZOK electrode placement strategy which potentially targets improved focus and attention. Pre and post treatment maps of 7 yr old male with ADHD. Screenshot of BrainMaster live z-score monitoring display. 19 yr old male, hx brain injury and trauma, progression with 4 channel PZOK neurofeedback at F3-F4-P3-P4. 44 yr old female, hx of trauma, before and after PZOK training, F3-F4-P3-P4. 55 yr old female with 28 Hz beta in excess of 3.28 standard deviations in her cingulate gyrus. 55 yr old female from Figure 18.15—post 40 sessions of 4 channel PZOK training Cz-Pz-P3-P4—28 Hz beta in cingulate gyrus reduced to 1.39 standard deviations. 12 yr old male, pre and post training alpha coherence maps, 4 channel PZOK, F3-F4-C3-C4. 12 yr old male, pre and post 40 sessions PZOK, 1 Hz bins, magnitude. Three cases with PTSD, TBI and anxiety all exhibiting atypical amounts of 2–3 Hz and low amplitudes in alpha. Screenshot from BrainMaster software of 9 channel PZOK setup. Screenshot from BrainMaster software of 9 channel PZOK setup. 32 yr old female, severe anxiety, pre and post 5 sessions of 9 channel PZOK. 24 yr old male, severe anxiety, pre and post 5 sessions of 9 channel PZOK. 24 yr old male with 19 Hz beta in excess of 2.96 standard deviations in his Brodmann area 24. xv

317 318 319 320 321 323 328 330

331 332 333 333 334 336 337 338 339 341 342 343 345

346 347 347 348 351 352 353 355 356

Figures

18.25 24 yr old male from Figure 18.24—post 5 sessions of 9 channel PZOK—19 Hz beta in Brodman area 24 reduced to 0.14 standard deviation. 18.26 47 yr old male, severe depression, pre and post 5 training sessions, 9 channel PZOK. 18.27 47 yr old male with 6 Hz theta in excess of 4.57 standard deviations in his cingulate gyrus. 18.28 47 yr old male with 6 Hz theta in excess of 4.57 standard deviations in his cingulate gyrus. 18.29 Screenshot from BrainMaster software of 19 channel PZOK setup. 18.30 9 yr old male, developmental delays, pre and post 10 sessions of 19 channel PZOK. 18.31 9 yr old male, developmental delays, pre and post 10 sessions of 19 channel PZOK. 18.32 14 yr old male, developmental delays, pre and post 5 sessions of 19 channel PZOK. 18.33 14 yr old male with 5 Hz theta in deficit of − 2.47 standard deviations in his temporal lobe and supramarginal gyrus. 18.34 14 yr old male from Figure 18.33—post 5 sessions of 9 channel PZOK — 6 Hz theta increased to − 1.36 standard deviations. 18.35 Screenshot from BrainAvatar software of some of the sLORETA regions of interest available for selection when designing a feedback program. 18.36 44 yr old male with 14 Hz in excess of 5.2 standard deviations in Brodmann area 6. 18.37 44 yr old male from Figure 18.36—post 2 sessions of sLORETA z-score training—14 Hz in Brodmann area 6 decreased to 2.1 standard deviations. 18.38 Screenshot from BrainAvatar software of an sLORETA z-score display. 18.39 Image from a QEEGPro sLORETA extreme z-score summary report. 18.40 47 yr old male, anxiety, rumination; pre and post 5 sessions of sLORETA z-score training. 18.41 47 yr old male, anxiety, rumination; pre and post 5 sessions of sLORETA z-score training. 18.42 47 yr old male, anxiety, rumination; pre and post 5 sessions of sLORETA z-score training. 18.43 ROIs selected for the sLORETA z-score protocol used to achieve the results observed in the pre-post maps in Figures 18.40–18.42. 18.44 47 yr old male, anxiety, rumination; pre (left) and post 5 sessions of sLORETA z-score training (right). 18.45 Illustration of Z-Plus metrics: PZMO and PZME. 18.46 6 yr old male, IBS, learning delays, OCD; pre and post 8 sessions with 19 channel PZOK, PZMO, PZME. 18.47 6 yr old male, IBS, learning delays, OCD; pre and post 8 sessions with 19 channel PZOK, PZMO, PZME—z-scored alpha phase lock duration. 19.1 The Cognitive Emotional Checklist. 19.2 The Pre-Post assessment compares the first QEEG with those that follow. 19.3 Time series analysis shows client symptom severity based on a 10 point Likert rating scale. 19.4 Bar graph shows symptom reductions of 60% or more on average with each symptom.

xvi

356 358 359 359 361 362 364 365 366 366 368 369 370 371 372 373 374 375 377 378 380 380 381 397 398 399 399

Figures

19.5

Pre-post QEEG compares the last two maps and shows a consistent 36% overall change. 19.6 Asymmetry headmaps. 20.1 Client’s (M21) self-reported conditions. 20.2 ‘Genetic’ depression 20.3 Reactive depression (Alpha) 20.4 Reactive depression (Theta) 20.5 Trauma triggered depression 20.6 Trauma based depression 20.7 Anxiety based depression 20.8 Braindriving Beta down @ O1 with H6 stimulated > T 20.9 Braindriving Low Alpha down @ Fz with OMNI and 11Hz visual > T 20.10 Braindriving Theta down @ Cz with 16 Hz and OMNI > T 20.11 Braindriving Alpha @ Pz with 11 Hz and Serene < T 21.1 Client G z-scored FFT summary information showing excess beta at Fz. 22.1 Initial QEEG shows a map that would be considered a normal one except for the fact that the patient was under medication and for the presence of a whole head hypercoherent pattern at Delta, and a hypocoherent one in the midline of the head, at High Beta. 22.2 Functional deficiencies in frontal and frontal-limbic structures, in the lower half of the frequency spectrum (Figure 22.2a) and upper half of the frequency spectrum (Figure 22.2b), confirming attentional and memory problems. 22.3 Raw EEG signature for OCD at F3-F4, on and off medication. 22.4 Raw EEG signature for enuresis. Site is O1-O2. Montage is bipolar. Scale is 20μV. 22.5 Z-scored FFT Absolute Power Individual head bins confirming the presence of excessive high voltage Alpha at O1-O2. Condition is eyes-open (EO). 22.6 LORETA localization of dysregulations found in the patient’s brain activity at 10Hz. 22.7 Z-scored FFT Absolute Power Individual head bins show the significant reduction of almost one standard deviation of Alpha activity, at O1-O2. Condition is eyes-open (EO). 22.8a and 22.8b Keeping the color scale set to 2.5 standard deviations (Figure 22.8a) does not allow for the remaining brain dysregulations to be seen unless one sets the color scale towards 1.5 standard deviations (Figure 22.8b). 22.9 A snapshot of change. 23.1a By session 9, you can see somewhat more typical magnitude levels except for theta and beta. 23.1b The slower frequencies are at what may be considered as too low magnitude, despite the proper proportion to the overall energy. 23.2a 20-minute eyes-closed session number 16. Peak-to-peak graph (magnitudes). 23.2b 20-minute eyes-closed session number 16. FFT graph (relative power). 23.3 Session 21. Instances of the theta/alpha crossover. Y-axis is magnitude and X-axis is time in minutes. 23.4 Session 24. Longer instances of the theta/alpha crossover. 25.1 Learning effect (effect of session number) on session averages in subjects who achieved criterion on the task. 25.2 Effect of percentile amplitude on discrimination task performance.

xvii

400 400 409 412 412 412 412 412 412 416 416 416 416 424

436

437 441 445 446 446

447

448 449 459 460 461 461 462 462 481 482

Figures

25.3 25.4 26.1

26.2 26.3

26.4 26.5

26.6

26.7 27.1 27.2

Effect of stimulus duration or EEG smoothing average on discrimination task performance. Effect of relative versus absolute amplitude on discrimination task performance. Pre-treatment QEEG summary maps. Excess theta band absolute power. Hypocoherence in all bands but the beta band. Most deviance in the delta and theta bands with telescoping hypocoherence from right hemisphere parietal and temporal areas. Pre-treatment QEEG single Hertz bins revealing near global excess absolute power in the theta band at 5–7 Hertz. Post-treatment QEEG. Complete regulation of absolute power in the theta band. Complete normalization of coherence in the delta, theta, and alpha bands. Some minor hypocoherence in the beta and high beta bands. Post-treatment QEEG. Near total normalization of power in the theta band single Hertz bins at 5–7 Hertz. QEEG summary maps at intake. Note the hypercoherence in all bands with a global expression in the beta and high beta bands. This map reveals excess high beta absolute power. 2nd QEEG. Mid-treatment summary maps. Note the normalization of delta absolute power, the virtually normalized excess high beta and substantial regulation of coherence abnormalities in the beta and high beta bands. Post-treatment QEEG. Note the normalization of coherence values, particularly in the beta and high beta bands. Cumulative changes in alpha relative power from the F3 electrode during 10 sessions of active anodal versus sham tDCS. Number of significantly improved neuropsychological test performances by EEG-defined subgroups.

xviii

482 482

491 491

492 493

494

495 496 516 517

TABLES

6.1 7.1 7.2 7.3 9.1 10.1 10.2 10.3 10.4 10.5 10.6 10.7 12.1 14.1 14.2 14.3 14.4 16.1 18.1 20.1 20.2

ACE study Neurotherapy Institute An analysis of the individual sites with the principal functions and other functions associated with the sites. Patient treatment history Published series by other investigators of cases successfully treated with QEEG-guided neurofeedback for various disorders. A 17-year-old with prior concussions and ADD. Computerized cognitive testing (NeuroTrax) of a 69-year-old with prior CVA before and after 10 sessions of NFB. Summary of headache patients. A 64-year-old with AD. A 58-year-old female cognitive testing results before (left column) and after (right column) 10 sessions of NFB. ASD patients’ demographics and other clinical information. Summary of patients suffering from seizures and subjected to NFB therapy. Shows the number of participants in the dyslexic and control groups for each age range. Participant characteristics. Tasks presented and analyzed by groups. Age and memory scores. Group 2 (children—non-clinical and clinical): Intercorrelations between variables. Alona’s CAPS score at baseline (Pre) compared to follow-up assessment (Post). Higher score mean more severe and frequent PTSD symptoms. Summary of characteristics primary to each neurofeedback technique discussed in Chapter 18. Heritability of schizophrenia. Client M21. Full 19-site QEEG report from independent service using normative data base.

xix

97 110 115 131 151 161 165 167 174 178 178 181 225 254 255 255 262 309 327 405 410

Tables

20.3a, 20.3b, 20.3c and 20.3d ClinicalQ for client M21. 20.4a and 20.4b Nine-year-old male child—potential bully victim. 20.5a and 20.5b Nine-year-old male child—potential bully victim. 20.6 EEG of client going into sleep state. 23.1 Results of 2-channel pre-assessment. 27.1 Relationship of headache subtype to tDCS treatment montage.

xx

410 413 414 416 458 509

CONTRIBUTORS

Kathy Abbott, Psy.D., PC Licensed Clinical Psychologist Evergreen Park, IL Benjamin A. Berry, B.S. Brain Enrichment Center Bedford, OH Dennis P. Carmody, Ph.D. Rutgers University School of Nursing Newark, NJ Giuseppe A. Chiarenza, M.D. Centro Internazionale Disturbi di Apprendimento Attenzione e Iperattività (CIDAAI) Milano, Italy Thomas F. Collura, Ph.D., MSMHC, QEEG-D, BCN, NCC, LPC BrainMaster Technologies, Inc. & the Brain Enrichment Center Bedford, OH Jackie de Vries, M.S. Crossroads Center of New Jersey Ridgewood, NJ Mary Blair Dellinger, B.A. Neurodevelopmental Disorders Lab University of South Carolina Columbia, SC Mary Donaldson, M.Ed. Myosymmetries Calgary Calgary, Alberta, Canada Stuart Donaldson, Ph.D. Myosymmetries Calgary Calgary, Alberta, Canada xxi

Contributors

James R. Evans, Ph.D. Department of Psychology University of South Carolina Columbia, SC Jon A. Frederick, Ph.D. St. Cloud State University St. Cloud, MN Nir Getter, Ph.D. Beer Sheva Mental Health Center Ben Gurion University Israel Penijean A. Gracefire, LMHC, BCN Private Practice Tampa, FL StressTherapy Solutions, Inc. Cleveland, OH Joseph Guan, Ph.D. Clinical Director, Brain Enhancement Centre Private Limited Singapore Ann Guyer, BSN, RN University of South Carolina Columbia, SC Merlyn Hurd, Ph.D., QEEGD, BCN Senior Fellow Private Practice New York, NY Zeev Kaplan, Ph.D. Beer Sheva Mental Health Center Ben Gurion University Israel Harry Kerasidis, M.D. Chesapeake Neurology Associates Prince Frederick, MD Cynthia Kerson, Ph.D., QEEGD, BCN, BCB Adjunct Professor, Saybrook University Oakland, CA Director of Education, APed San Ramon, CA, Jerome F. Kiffer, MA Psych and Psych, LLC Private Practice Cleveland, Ohio J. Lucas Koberda, M.D., Ph.D. CEO-Brain Enhancement, Inc. Professor of Neurology xxii

Contributors

Director-Tallahassee NeuroBalance Center Tallahassee, FL Leonardo M. Leiderman, Psy.D., ABPP, CGP Neurofeedback and Psychological Services Purchase, NY Justin R. Leiter-Mcbeth Brain Enrichment Center Bedford, OH Robert E. Longo, MRC, LPC, BCN Serendipity Healing Arts Lexington, NC Leonardo Mascaro, Psychologist, MNeuroSci, BCN Brain Tech Inc. São Paulo, Brazil Doneen Moran B.A. Myosymmetries Calgary Calgary, Alberta, CANADA Jane Price, M.A., BCN Sterlingworth Center of the Upstate Greenville, SC Leonard M. Richards, Th.D. Unique MindCare Houston, TX Roger H Riss, Psy.D. Madonna Rehabilitation Hospital Lincoln, NE G. Michael Russo, B.A. University of Texas at San Antonio San Antonio, TX Mark Llewellyn Smith, LCSW, BCN, QEEGT Neurofeedback Services of New York New York, NY Richard Soutar, Ph.D. New Mind Center Roswell, GA Christen H. Stahl, M.A., BCN Brain Enrichment Center Bedford, OH Beverly Steffert, Ph.D. Chartered Psychologist & Neuropsychologist Associate Fellow of the British Psychological Society AFBPsS Learning Recovery Cambridge, England xxiii

Contributors

Tony Steffert, Ph.D. Researcher in QEEG, Neurofeedback & Physiological Sonification The Open University Milton Keynes, England Theresia Stöckl-Drax, Dr. med., BCIA, BCN Pediatric Neurodevelopmental Clinic, Director Gauting/Munich, Germany Lincoln Stoller, Ph.D. Mind Strength Balance, Inc., Shokan, NY Paul G. Swingle, Ph.D., R. Psych. Swingle Clinic Vancouver, Canada Jeff M. Tarrant, Ph.D., BCN CEO, NeuroMeditation Institute Corvallis, OR Kirtley E. Thornton, Ph.D. The Neuroscience Center Charlotte, NC Doron Todder, M.D., Ph.D. Beer Sheva Mental Health Center Ben Gurion University Israel Fred Ulam, Ph.D., BCN, QEEG Burrell Behavioral Health Springfield MO Jonathan E. Walker, M.D. Neurotherapy Center of Dallas Dallas, TX Glenn Weiner, Ph.D., BCN. Dominion Behavioral Healthcare of Chesterfield Midlothian, VA Nancy E. White, Ph.D. Unique MindCare Houston, TX

xxiv

PREFACE Handbook of Clinical QEEG and Neurotherapy

At times the germ of an idea takes only a split second, but the realization of that thought may take days, weeks, months, or years. This compilation is one such example. As the monograph “Technical Foundations of Neurofeedback” went into press in late 2013, there was an evident need for clinically oriented information that would help to put the concepts of neurofeedback into practice. In order to achieve this goal, what better way than to contact practitioners “in the trenches” and ask them to share three key pieces of information. These were, “what is your evidence base?” “what is it that you do?” and “how is that working out for you?” Each of the chapters in this edition of the Handbook of Clinical QEEG and Neurotherapy provides the opportunity for a different practitioner or group to report on their clinical experiences with QEEG and neurofeedback. This project was undertaken without any bias with regard to approaches, philosophy, equipment, or methods. Therefore, there is considerable variety in the topics covered, and no particular interest is being served. The priority in this volume was to allow clinicians to share with their peers, and provide information that might not be in the published literature, because it is oriented not toward research, but toward clinical practice. Indeed, many of the authors herein have had minimal presence in peer-reviewed press, for any of several reasons. One is that they are not necessarily doing research of the type that can be controlled and published under intense scrutiny. Another is that their priorities, often consuming well over a typical 40-hour week, are on clients, not paper output. Of the potential authors invited to contribute to this edition, approximately one-third completed the process of writing, editing, and revising manuscripts that have made it into the volume. This self-selection process has, in my view, produced material that is characterized by the authors’ commitment to sharing, clarity of writing, and ability to prioritize the time required for the task. About half way into the projects, when the first round of chapters was being compiled, it was realized that the task of editing and interacting with a couple dozen authors was beyond the scope on one person. I therefore contacted Jon Frederick, and asked him to contribute his time, experience and skills to take the helm reviewing the submissions, commenting on them, and working with contributors on revising their work. Jon has been an invaluable resource and friend, as we embarked on what would be a larger task than either of us probably imagined at the time. As we began to close in on the final versions, it became further apparent that a third, experienced individual could make a material contribution in such things as final reviewing, organizing sections and chapter order, writing the introduction, managing abstracts and keywords and the table of contents for this project. I have known Jerry Kiffer since the 1980s, and as one of the most experienced and skilled biofeedback clinicians I knew, he was ideal to take on this effort as an “outsider” to the worlds of QEEG and neurofeedback. xxv

Preface

What is put forth here is hopefully the first in a continuing series with periodic updates as the years progress. This volume can set an example for open and frank communication of clinically relevant information whose focus is to allow the fields to grow and diversify, rather than to put forth any particular bent. The authors herein have set an example that can be followed by others, with the potential of producing a wide-ranging and relevant reference base that is clinician-driven, and clinician-focused. All royalties that proceed from the sale of this book are being contributed to the Foundation for Neurofeedback and Neuromodulation Research (FNNR). This organization, formerly the ISNR Research Foundation, has a history of supporting research and communication, and I and the editors are confident that the contributions that accrue to them will be put to good use in fostering the field and growing awareness and acceptance of these growing disciplines. Edited by Thomas F. Collura and Jon A. Frederick Organized and with an Introduction by Jerome Kiffer

xxvi

INTRODUCTION

Neurofeedback comes of age by evolving from stand-alone machines to PC-based alpha-theta protocols to QEEG with normative databases to 3-D LORETA real-time visualizations of network connectivity hubs and Brodmann ROIs. Now, take this latest brain technology and science and put it in the hands of clinicians. Let them use it for a while to help clients/patients and the result is the current book. Experienced and intermediate clinicians will find this book to be invaluable as neurofeedback often takes place in the historical context of biofeedback and psychotherapy. Beginning clinicians can read this as they integrate new methods or work in hospital settings with neuroscience centers. This book provides an essential clinician’s resource describing a wide range of approaches and technologies in the areas of quantitative EEG (QEEG), neurofeedback, and neurotherapy. It emphasizes practical clinically useful methods, reported by experienced clinicians who have developed and used these approaches first hand. Each of the chapter authors is an experienced practitioner. These chapters describe how the authors approach and use their particular combinations of EEG technology, how clients are evaluated and treated, and details of outcomes. This resource, which is encyclopedic in scope, provides a valuable and broad, yet sufficiently detailed, account to help clinicians guide future directions in client assessment and neurotherapeutic treatment. There is an urgent non-drug clinical need to help people suffering from what, in current parlance, may be called brain circuitry disorders and mind-body-behavior syndromes. As technically sophisticated and precise as that sounds there is an “in the trenches” clinician’s view. As an example, here is part of an email from an author of a chapter in this book during our correspondence: “I conduct QEEG’s and use all the most up to date instruments and software to address the needs of the clients.” How beautiful a statement describing a clinician’s heartfelt drive to learn the newest neurofeedback technology and methods, all in the service of providing the best help possible to clients. This is a book written for those, and by those, clinicians that champion this philosophy. Contained herein is the state of the art and state of the science of neurofeedback, neurotherapy, and QEEG, primarily from a clinician’s viewpoint. This is a book born out of the marriage of the history of applied psychophysiology and present-day applied neuroscience cloaked in the historical veil of psychotherapy. This book captures the rich threads of methods and protocols and weaves a masterful tapestry that illuminates the current status of neurofeedback and neurotherapy. Follow the threads below on a tour of the eight parts of the book. The first and longest part is “Clinical Practice of Neurofeedback.” Thomas F. Collura, Christen H. Stahl, Benjamin A. Berry, and Justin R. Leiter-Mcbeth describe the EEG data collection and assessment procedure of a clinical practice along with presenting case studies (Chapter 1). Intriguing xxvii

Jerome F. Kiffer

are the findings about how various QEEG databases match up, not only between databases but also between the clinical symptoms and the QEEG mapping reports. Glenn Weiner details the journey of integrating psychotherapy and neurofeedback (Chapter 2). He has a beautiful phrase: “Neurofeedback is at times a Trojan horse for psychotherapy.” I would add that this applies to all biofeedback over the past 40 years. Once psychotherapy combines with neurofeedback (neurotherapy), there are now two intersecting vectors of interference or progress: the psychological and the neurophysiological. James R. Evans, Mary Blair Dellinger, Ann Guyer, and Jane Price explain the variables related to success or failure in neurotherapy (Chapter 3). This is a real-life reminder of the myriad intrapsychic, interpersonal, and environmental factors that influence clinical outcomes. An enthralling discussion follows by Jeffrey M. Tarrant about four types of meditation practices (Chapter 4). This overview of neuromeditation discusses the use of standard or LORETA protocols in clinical applications to aid a client toward their own peak meditation brainwave signature. The previous chapters highlight the positive potential of change due to neuroplasticity. The neuroplasticity model provides the basis for the efficacy of psychotherapy. However, what is the maladaptive downside of neuroplasticity? Stuart Donaldson, Mary Donaldson, and Doneen Moran explore this downside when expressed as chronic pain syndromes (Chapter 5). They utilize EMG biofeedback to train operantly the peripheral nervous system and QEEG-guided neurotherapy for CNS training. This combined clinical biofeedback approach is illustrated with a case study which has, among other events, a history of abuse. Forensic populations frequently have histories of abuse and Robert E. Longo and G. Michael Russo describe the use of biofeedback and neurofeedback to treat this group (Chapter 6). They use the Adverse Childhood Events (ACE) questionnaire to quantify the history of childhood abuse. The ACE is available at the Centers for Disease Control website (along with references, prevalence data, and editorial comments suggesting that a higher number of ACEs were associated with premature mortality of up to 20 years of life lost). This rich data harkens back to the foundational work of Allan Schore in the ’90s regarding the devastating impact of trauma on a developing brain. The second part is “Pediatric Neurofeedback.” This part is precious to all who have children. Moreover, we are not just talking about latency age or teenage ADHD cases, of which there are many throughout this book. Read Merlyn Hurd’s delightful chapter as she details training children younger than 6 years of age (Chapter 7). She presents case studies on children as young as 3 years of age, outcomes for epileptic children, QEEG maps, EEG data, and clinical forms to facilitate working with children and parents. All work with children requires more assessment than with adults. In addition, that assessment needs to be individualized and client specific with the goal of optimal treatment planning. Theresia Stöckl-Drax does just that as she shares how to incorporate QEEG and neurofeedback as a clinical evaluation tool (Chapter 8). These chapters should be required reading for beginning clinicians as an example of how neurofeedback procedures can benefit even children as young as 3 years old. “The Neurologist’s Perspective” is the third part of the book. Three neurologists contribute their clinical work findings using QEEG and neurofeedback to diagnosis and treat medical, psychiatric, and psychological disorders. They are prominent clinicians and authors sharing their clinical experiences with the latest protocols and equipment. Jonathan E. Walker provides a literature review of successfully treated disorders using QEEG-guided neurofeedback (Chapter 9). In it, he presents a tabular list of clinical conditions, the characteristic QEEG abnormalities of each condition, along with the effective neurofeedback protocols used for training these conditions. This list will be a reference favorite for all practitioners. J. Lucas Koberda presents the use of QEEG/LORETA for diagnostic confirmation along with summarizing LORETA Z-score therapy on 260 clinical cases (Chapter 10). He reports a subjective response of 70+ per cent to Z-score LORETA neurofeedback within 10 sessions. Harry Kerasidis provides a brief, basic overview of the neurological aspects of concussion and

xxviii

Introduction

post-concussion syndrome (Chapter 11). Case studies illustrate treatment interventions using QEEGdriven selection of training targets used for Z-score neurofeedback and neuromodulation. The fourth part, “Dyslexia and Reading,” focuses on how QEEG is used to evaluate reading disorders that affect all ages. Reading is one of the most complex higher cognitive skills and the distinguished authors tackle this complexity with stunning expert analyses. The ongoing advances in QEEG analysis has moved from local power analyses to measures of brain connectivity in identifying brain activation during reading tasks. Starting with a discussion of Evoked Potentials (EPs) and Evoked Response Potentials (ERPs), Tony Steffert and Beverly Steffert dissect the brain’s responses to reading stimuli with illustrative QEEG brain maps (Chapter 12). A retrospective QEEG/ERP case series of 41 dyslexics is presented. A case study shows the use of neurofeedback protocols in treating dyslexia with pre and post QEEGs. Giuseppe A. Chiarenza describes neuropsychophysiogical studies of normal and abnormal reading processes in children (Chapter 13). He describes EEG findings for the existence of subtypes of developmental dyslexia. Kirtley E. Thornton and Dennis P. Carmody have a brilliant review of QEEG correlates of reading that highlights the importance of memory retrieval mechanisms in children and adults (Chapter 14). There is a conceptual and theoretical elegance to the Coordinated Allocation of Resource (CAR) model fully described in this chapter. Amongst the interesting findings is the central processing unit concept of the frontal lobe in reading memory with the dominant frequency of alpha (phase and coherence) in the left frontal location. Their chapter is paradigmatic in showing how QEEG has advanced our understanding of not only brain connectivity networks but also the incredible complexity of globally distributed patterns in memory processes. Any chink in the armor of connected brain networks has ripple effects in the efficiency of higher cognitive functions. Techniques to increase the efficiency of these networks are presented in the next part of the book. “sLORETA/LORETA and Z-Score Training” is the fifth part of the book. Here you find four clinical chapters detailing the latest neurofeedback protocols. This part illustrates the maturation of neurofeedback from its infancy as single channel training to its maturity in using multiple channels and QEEG-driven protocols. QEEG normative databases have additional power by combining clients’ symptoms so that hypotheses can be formed to guide intervention to improve brain network functioning. Mark Llewellyn Smith shows how sLORETA regions of interest (ROIs) can help when QEEG-driven protocols do not always match the clients’ symptom reports (Chapter 15). Clinical wisdom and decision-making is required to adjust what might be too rigid of a protocol. Furthermore, clinical expertise decides which parameters to measure and when to adjust to a different protocol. He illustrates this with two clinical cases. Nir Getter, Zeev Kaplan, and Doron Todder describe the efficacy of sLORETA in resetting the fronto-temporal limbic network to a healthier level by focusing on vmPFC theta band power (Chapter 16). They present the case of a client suffering from PTSD that exhibited dysregulation and decreased connectivity in the fronto-limbic network. In the next chapter by Joseph Guan, the efficacy of Z-score training using 4-channel neurofeedback is presented via several case studies (Chapter 17). Penijean A. Gracefire gives an up-to-date overview of clinical applications of 19-channel Z-score training along with a summary of five types of neurofeedback (Chapter 18). Replete with brain maps, this chapter draws from her expertise in breaking down complex material into compelling graphics. Here you will find descriptions of the newest software training protocols with vivid clinical data. The sixth part is “QEEG and Brain Dynamical Approaches.” Richard Soutar reviews a basic 2-channel approach pulled from its traditional moorings by basing it on QEEG (Chapter 19). From thousands of cases using this approach, he describes the evolution of a broad clinical intervention involving methods of biopsychosocial assessment and symptom tracking. Convincingly, he grounds this approach with the latest neuroscientific findings, arousal theory, affect regulation research findings, and regional network dynamics in selected bilateral networks. Considering a more “bottom

xxix

Jerome F. Kiffer

up” rather than top-down approach, Paul G. Swingle gives an overview of his ClinicalQ database gathered from Cz, F3, F4, Fz, and O1 sites (Chapter 20). The ClinicalQ database contains more than 1,400 clinical clients and is organized around clinical conditions. He provides several examples of braindriving protocols based on classical rather than instrumental conditioning. Next, Kathy Abbott reports on three cases benefiting from 19-channel Z-score training as they were reducing prescription medication (Chapter 21). She attributes part of this benefit to neurofeedback pushing the relative power of various frequencies closer to their pre-medication levels. This highlights the importance of baseline EEG recordings. Leonardo Mascaro examines the raw EEG closely as he describes his work with QEEG, LORETA, and Z-scored methods (Chapter 22). He discusses the importance of integrating all of these measurements as he presents two case studies. “Traditional Alpha/Theta/Beta Protocols,” the seventh part, illustrates the need for clinician flexibility to consider these protocols given the context of the client’s presenting symptoms. Cynthia Kerson describes the value of traditional protocols due to clinical presentation and the nature of anxiety disorders (Chapter 23). A successful case study describes her clinical treatment approach. Lincoln Stoller narrates his journey of giving public presentations using a portable neurofeedback workstation (Chapter 24). He uses an alpha-theta protocol in a single session to promote the field of neurofeedback to the public. It is a reminder of the importance of simplifying the complex to communicate effectively with clients who have no prior exposure to brain science. For many, the brain remains a mystery that is difficult to describe . . . and that includes many clinicians! The final part is “Emerging Paradigms,” which covers EEG state discrimination, Infra-Slow Fluctuation (ISF) training, transcranial direct current stimulation (tDCS), and an integrative brain–gut approach. Jon A. Frederick traces volitional control issues back to Joe Kamiya’s pioneering work in the 1960s on alpha state training (Chapter 25). The chapter explicates the psychological states of awareness of brainwave responses, analyzed with rigor. Next, Mark Llewellyn Smith, Leonardo M. Leiderman, and Jackie de Vries examine the slowest CNS frequencies, the infra-slow oscillations, in treating two case studies of autism (Chapter 26). ISF training has correlates with the very same networks thought to be dysregulated in autism. Hence, the putative effect of ISF training. In a superb review of tDCS, Roger H. Riss and Frederick Ulam write in comprehensive detail about the history, basic principles, clinical applications, and emerging technological advances (Chapter 27). Nancy E. White and Leonard M. Richards explore expanding the scope of a neurofeedback practice to include an integrative approach (Chapter 28). The recent model of the brain–gut axis is used as the basis for focusing on the effects of dietary nutrients on the brain via autonomic nervous system communication to the brain. As you can see from the preceding description, the forging of psychotherapy, neuroscience, and technology creates a powerful tool, now being wielded in prime time. Neurofeedback has become dramatically more precise. When the method of delivery is through clinical psychotherapy, it is more potent and efficient. In coming of age, neurofeedback became strategic and technical in utilizing sLORETA and QEEG to visualize EEG signatures and link clinical symptoms to deviant brain patterns. However, as much as things change, the more they stay the same. What stays the same is the deft and artful way of the clinician’s humanistic approach with the client. What stays the same is the clinical decision-making of what, where, and why to use certain protocols. What remains the same is hypothesis generation of what is making the client ill. Matching symptoms to the clinical approach taken will always be a topic of discussion. Neurofeedback tools are more advanced but clinical decision-making variables, interpersonal variables, the process of psychotherapy (i.e., transference and counter-transference) variables, along with non-specific variables, all remain the same as the field advances. Likely, the underlying principles for the efficacy of combined psychotherapy and neurofeedback are operant conditioning, neuroplasticity, adaptation (compensatory mechanisms), and selfregulation. Exciting research results appear monthly pointing at the above. Combining psychological xxx

Introduction

self-regulation theory with precise identification of structural brain networks of self-regulation is on the horizon. Neurofeedback training is literally consciousness observing itself, a feat never before accomplished in human history. This can be a game-changer. If not now, the authors and editors will continue efforts to make it happen soon, along with the neurofeedback community around the world. Jerome F. Kiffer 8–11–2015

xxxi

PART I

Clinical Practice of Neurofeedback

1 A BRAIN FUNCTIONAL DYNAMIC APPROACH TO COUNSELING AND THERAPY Thomas F. Collura, Christen H. Stahl, Benjamin A. Berry, and Justin R. Leiter-Mcbeth Abstract This chapter describes the brain-based approach used in the Brain Enrichment Center. Our goal is to increase self-efficacy, flexibility, and effectiveness for our clients. We combine theoretical perspectives in a postmodern approach that emphasizes neuroscience, as well as the best clinical evidence-based approaches. We target clients’ issues and concerns with regard to the content (the what), the actual brain functioning (the why), and the tone (the feeling) of the client’s internal world. Our evaluations take advantage of a combination of symptom- and function-based questions, as well as neurocognitive testing and QEEG analysis. We incorporate a comprehensive evaluation of the clients’ pasts, as well as medical and environmental factors influencing the presenting problems. We find that encouraging clients to address brain function in an objective manner is an empowering and facilitating approach that works in concert with many other interventions that a client may or may not pursue. We present case studies illustrating the decision-making and therapeutic processes as they are reflected in individual clients, and how their treatment outcomes are achieved and evaluated. The Brain Enrichment Center uses a brain-based, systematic, and evidence-based approach to pursue wellness for each client. We use diverse theoretical perspectives and thorough assessments to create individualized treatment plans targeting both the content and the tone of each client’s functioning, with the ultimate goal of aligning healthy brain activity and well-adjusted, goal-oriented behavior. We address client issues from a dynamic, brain functional approach. This is an evidence-based and systemic approach that takes into account the broad range of factors that enter into the client’s physical, mental, and emotional state, as well as the dynamic processes that produce what we experience as thoughts and behaviors. The brain is seen as a goal-seeking mechanism that is tasked with continuous pattern recognition. We are therefore not solely interested in the client’s presenting problem, but rather, we pursue the multifaceted relationship between, on one hand, what the client thinks of as patterns and goals, and, on the other, what the brain is recognizing and pursuing. Within the therapist–client relationship, modifying these functional aspects of the client, and simultaneously connecting them, moves the client toward a non-pharmaceutical way to increase self-regulation. This facilitates flexibility and relaxation, thus improving existing skills while helping to create new skills. The therapist–client interaction is not simply that of being a “neurofeedback tech” who connects sensors and operates the equipment. As shown by Sedlacek and Taub (1996),

3

Thomas F. Collura et al.

of the major factors affecting outcome, the nature of the patient interaction is the most important. While methodology and the environment are important and must be properly addressed, without an effective client interaction, even the best feedback methods and equipment can be of limited value. Sedlacek and Taub showed that the difference in effectiveness between an impersonal attitude, versus an informal and friendly one, can change positive outcomes from as low as 10% to as high as over 90%, based solely on the therapist attitude and behavior. Therefore, our technicians, who may or may not be licensed therapists, generally have a counseling or psychology background, and are trained in how to interact with clients in a relaxed, friendly, and supportive manner. Our basic view of human nature is that human beings are goal-seeking organisms that combine logical information with affective tone, in the pursuit of goals. However, various limitations can hinder this pursuit. For example, sometimes the brain is not working properly, and functions such as attention, planning, logic, and goal-selection may be hindered by biological or psychological factors. Our approach seeks to identify the hindrances in both the functioning of the brain and in what it is thinking, and to help modify and coordinate each, thus enabling the client to seek goals consistently and effectively. Maladaptive symptoms such as impulsivity can often be traced to brain functions that cause the individual to seek unhealthy goals and behaviors in pursuit of satisfying physiological needs that are inconsistent with the individual’s normal behavior. In other words, “the brain has a mind of its own.” This reflects the fact that the goal-seeking behaviors implemented by the brain are not necessarily in alignment with the individual’s well-being. Our approach is fundamentally humanistic, but it is not “mushy” or “intuitive.” Rather, it respects the human aspects of the individual, and recognizes that the individual possesses a brain, an organ that carries out tasks including sensation, perception, reason, memory, planning, decision-making, and action. By bringing the brain into alignment with the individual’s overall needs and goals, the client’s personal integrity, autonomy, and self-efficacy can be brought to greater fulfillment. The underlying principles that account for behavior modification are self-regulation, integrating the mind/body connection, and general cognitive changes through operant learning, coaching, and talk therapy. We have recently begun to explore alpha/theta deep states and hypnotherapy as interventions in cases where these are evidence-based interventions. These include clients who experience anxiety, panic, stress, cravings, or related factors that are known to respond to interventions of these types. One in particular is that of “Heart-Centered Hypnotherapy,” as described by Zimberoff (2013) and taught at the Cleveland Clinic Wellness Institute in Lyndhurst, Ohio. Our approach involves multiple treatment methodologies, including person-centered, cognitive behavioral, family systems, existential, reality, and postmodern principles to treating clients (Palmo, Weikel, & Borsos, 2006). Tenets from each of these are integrated into a robust and individualized brain-based approach, addressing a client’s presenting problem(s) in a goal-directed manner. Selfesteem is improved by strengthening self-awareness and self-control by addressing brain dysregulation. This concept rests in person-centered theory. Addressing incongruities in brain function due to functional disconnection, one can help to ameliorate irrational or illogical thoughts, which is a principle of cognitive behavioral therapy. Family systems theory is incorporated by addressing behaviors that affect the family dynamics, in addition to attending to multiple aspects of a client’s life during treatment such as relational, emotional, physical, and psychological factors, knowing that the client, as a subsystem of multiple other systems will be affected by any change whatsoever (Anderson & Sabatelli, 2007; Corey, 2009; Neukrug, 2007). In this, the client can be assisted in taking personal responsibility for the self, a component of existential therapy. In accordance with reality therapy principles, we avoid diagnosing clients, and aim for clients to learn to be successful in decision-making (Corey, 2009). Lastly, the postmodern approach (Corey, 2009) reflects the belief that the client is the “master” of his or her own life, and has the innate mechanisms to succeed and thrive. We look for dysfunctional systems and relationships that undermine personal efficacy, but which can be recognized and corrected with the help of neurofeedback, among other factors. The extent of the brain’s 4

A Brain Functional Dynamic Approach

plasticity has become increasingly recognized (Davidson & Begley, 2012; Doidge, 2007) and is foundational to our treatment process. We believe that it is important that there be a match between the client and the approach. Our primary intent is to help them find non-pharmaceutical approaches that take advantage of their strengths, and which also give them tools to build or rebuild the functional capabilities that are compromised. We do not expect all clients to be the same, and even when using QEEG-based approaches, we do not take the position that everyone should have the same norms. Rather, QEEG databases are useful to identify typical patterns, but that is not to assume that everyone should leave the clinic with the same standard EEG pattern (Collura, 2014). Paramount amidst each methodology is the importance of the therapist–client relationship; although complex, it requires both the human aspects of comfort and trust, and also the elements of logic and consistency. The key functions of the therapist are to uncover the divergence between the client’s goals and the goals of his or her brain, to reveal the limitations in the processing of those goal-seeking behaviors, and then to help the client identify and explore goals in a manner that is consistent with his or her overall well-being. In this way, it is useful to understand that the client has both tone, which is content-free, and content, which is independent of tone. Tone includes elements such as mood, anxiety, focus, attention, and drive, or the client’s affective condition—feeling good, bad, angry, and so on. Content includes beliefs and experiences, lessons learned, and reasoning processes. Content is addressed by therapeutic approaches because it relates to what the client is thinking, how he or she is judging and evaluating himself or herself and others, and how his or her plans and expectations take form. Content and tone interact in a manner that is not generally revealed in an approach using one technique. For example, a client may feel bad, and believe that they feel bad for a reason. A cognitive approach would pursue the reasons for the feeling, and what might be done to alleviate the situation; however, the cause may be due to hidden, underlying factors. Until the true influences are determined, the cognitive approach would have limited benefits. Research has shown, for example, that individuals may be influenced by subliminal information, and that this can affect decision-making and judgments (Marzouki & Marzouki, 2010). This tendency to respond to unconscious stimuli can undermine information-based approaches. Nonetheless, talk therapy is essential to addressing content. For example, behavioral and cognitive therapies are important for relearning and overcoming false beliefs, while various forms of biofeedback are useful for addressing tone, stress, relaxation, and other nonverbal elements. Therefore, to ignore either content or tone by using one therapeutic framework exclusively is to work in partial blindness, which runs the risk of working endlessly at difficult or potentially fruitless pursuits. Another fundamental concept is that of brain functional resources, or the allocation, organization, and use of a client’s natural resources. It is recognized that the brain is a coordinated system of complex, interrelated processing elements, and that these elements must work together in combination in order to perform a task. In addition, an individual has only a certain set of resources, and once a resource is occupied with a task, it is unavailable for other tasks. If a resource is chronically impaired, this will manifest in the client’s behavior and subjective world. A key therapeutic goal in the context of brain functional resources is the development, allocation, and use of resources in a manner that is effective and consistent with the client’s goals. The counselor helps the client to identify key functions that are involved with the thought process, choices made, and how behaviors are pursued. Aberrant or negative feelings or behaviors such as chronic anger, resentment, subversion, addiction, self-defeating strategies, or denial, for example, are seen as arising from some internal strategy that the individual has developed in response to past environments and experiences. However, rather than specifically seeing the client as responsible for his or her decisions and condition, this approach sees the client as responding to some extent to automatic or uncontrolled factors, which hinders the client’s ability and his or her brain’s ability to relax, be flexible, and self-regulate. 5

Thomas F. Collura et al.

When treating a child with ADHD, neurofeedback provides an important nonverbal element to facilitate change toward flexibility and self-regulation. One can tell a child to “sit still,” “relax,” and “pay attention” endlessly, but what is needed is an affective change, a shift in tone, and a gaining of skills. We have experienced a child who, after using biofeedback for relaxation training, said, “I understand this now. Why didn’t you just tell me what to do?” When asked how they would have described it, they replied, “Yes, you’re right, there are no words for it” (see Pigott and Cannon (2014) for a discussion on neurofeedback as a treatment for ADHD). It is important to note that our approach is not only humanistic and systemic, it is also humane. It is not reasonable to expect “perfect” behavior from a child without attention to other factors such as diet, exercise, sleep, and other influences. One of our clinicians told us a story of a boy who was diagnosed with ADHD, but who suddenly had a great day at school. When asked what had changed, the boy replied, My father usually gets home at 10:00 pm and we start playing [a violent video game]. We play video games until about 1:00 am and I go to bed. My mother normally wakes me up at 6:00 am to take me to day care so she can to go work. Well, last night, my father just went to bed, so I went to bed at 9:00 pm, and my mother was sick this morning, so I slept in until after 8:00 am. In our view, to be diagnosing and potentially medicating a child who is suffering from environmental disturbances that are out of his control is not humane. Nor does it respect the whole child, who is, in part, a product of his environment (Hartmann, 1992). Generally, our view is that any attempt to treat a client without regard for diet, exercise, sleep, life stressors, or daily routine and other environmental factors is futile. The underlying mechanisms that dominate mood, desire, energy, and motivation are key elements in the human equation and should, therefore, not be ignored. Specific populations that will benefit from our approach include those with dysfunctions such as attention-deficit hyperactivity disorder (ADHD), post-traumatic stress disorder (PTSD), anxiety, and depression. These client populations will often manifest physiological changes that are associated with their concerns, and that respond to brain modification (Doidge, 2007). Such clients will benefit from relaxation training, breathing exercises, cognitive rehabilitation and restructuring, and biofeedback. The most responsive populations will be those whose disorder has been learned through life experiences, and we believe that a significant amount of ADHD, PTSD, anxiety, and depression are learned behaviors that can be unlearned. Surprisingly, the connectivity deficits evident in the EEGs of children with autism do respond to operant conditioning, with a concomitant improvement in symptoms (Coben & Padolsky, 2007; Collura, Guan, Tarrant, Bailey, & Starr, 2010; Collura, Thatcher, Smith, Lambos, & Stark, 2009). It is conceivable that essentially complete remediation can be achieved in some of these populations, based upon well-selected and well-administered interventions. Populations that will be less likely to benefit are those with severe disabilities that will not manifest in brain plasticity, such as those with severe chemical imbalances, deep-seated issues, or genetically based problems such as Reye’s syndrome. Difficult disorders would include borderline personality disorder, bipolar disorder, and schizophrenia. In summary, the client is a goal-seeking organism, but the goals may not be clear, the behavior needed to achieve the goals may not be accessible, and there may be additional factors that hinder progress and self-realization. Our comprehensive brain functional approach, which includes both the biological substrate of the brain and personal mental content, allows the client to be understood as a system. By helping the client align and use his or her brain and body as agents toward achievement and change, the therapist can help to maximize his or her physical, mental and emotional well-being, thus moving the client toward his or her goals. 6

A Brain Functional Dynamic Approach

Our Approach: What We Do at the Brain Enrichment Center In order to assess and treat each client effectively, the treatment team at the Brain Enrichment Center incorporates information about the client from a variety of sources, including psychosocial assessments and an intake interview, the client’s raw EEG waveforms, and reports generated from quantified EEG data, calculated with reference to multiple databases. Clientele at the Brain Enrichment Center range in age from roughly 8 or 9 years old to 65 years old or older. On average, the minor clients are upper elementary school age and the adult clients are middle-aged.

Consultation The first interaction with a client is usually a phone call, and often followed by a free, one-hour informational consultation. Because few clients already understand neurofeedback, this consultation is crucial in their decision to invest in neurofeedback training. Another important component of this consultation hour is the client’s brief description of their situation, and interest, in neurofeedback. This allows the clinicians at the Brain Enrichment Center to explain neurofeedback as it relates to the client, discuss research relevant to the client, and also to explain each part of the treatment process. Sample reports are made available to give the client further awareness of what to expect from their QEEG. Clients generally appreciate the clarity that comes with the consultation, and are comforted by learning about both the treatment process, and neurofeedback’s scientific basis.

Initial Assessment The full initial assessment includes multiple steps: intake interview, QEEG, assessments, questionnaires taken both inside and outside of the office, generation of brain maps from multiple databases, and a review of the results. Included in the price of this initial assessment is a second QEEG, a set of post-treatment brain maps, and an in-office review of results after training has been completed. Some clients do not require extensive assessment or symptom tracking. A simplified treatment package is available for these clients at a reduced price, including the pre- and post-treatment QEEGs, brain maps, and reviews of results, but no additional testing or assessment. The initial assessment typically occurs at the first appointment, and is several hours in length. To accommodate the client’s needs, the initial assessment may also be completed in multiple steps.

Intake Interview The intake interview is central to synthesizing all information into a cohesive treatment plan for each client. Biopsychosocial information is gathered, along with information along sexual and spiritual dimensions when appropriate, to provide the clinicians with a broad understanding of the client’s history and current presenting problem(s). Family/childhood, educational, physical health, occupational, substance use, social/sexual adjustment, and overall mental status information is gathered.

Quantitative Electroencephalogram (QEEG) The client is fitted with an appropriately sized electrode cap (Electrode-Cap Inc.; Eaton, OH). All impedances are reduced to below 10 kilohms before the recording takes place. Once all channels are ready, the clinician begins the software and checks the clarity of the EEG. The client will help with this process, learning that blinking, jaw clenching, and other forms of movement cause EEG artifact. The client is then instructed to sit still with guidance on how to relax their face if muscle tension is present. Eyes-open and eyes-closed recordings are taken. The clinician monitors the EEG 7

Thomas F. Collura et al.

throughout, both in raw form and in real-time quantified form, watching for early indications of the anticipated findings of the QEEG reports.

QEEG Reports In order to quantify the recorded waveforms, both eyes-closed and eyes-open recordings are uploaded to QEEGPro, producing two sets of reports using both the Neuroguide database and the QEEGPro database. QEEGPro’s Standardized Artifact Rejection Algorithm (SARA), an automated artifacting tool, allows this abundance of data to be generated without great demand on the clinician’s time. The artifacted SARA file provided by QEEGPro can also be used to generate standardized reports through NewMind, BrainDx, and BrainAvatar, allowing the treatment team a wide range of approaches by which to assess brain function. For further discussion on QEEG and its role in neurofeedback, see Budzynski, Budzynski, Evans, and Arbanal (2009).

CNS-Vital Signs (CNS-VS) The CNS-VS instrument is a computerized neurocognitive test battery that was developed as a routine clinical screening instrument. It consists of interactive tests including: verbal and visual memory, finger tapping, symbol digit coding, the Stroop Test, and tests of shifting attention and continuous performance (Gualtieri & Johnson, 2006; http://www.cnsvs.com). It has been validated using 1,069 subjects aged 7–90, the results of which have shown reliability and repeatability of the test and its respective normative database. It provides individual scores in domains such as memory, executive function, attention, and, also, an overall neurocognitive index. The test report includes validity indicators for each domain, responsive to whether the subject understood the test, put forth their best effort, or has a clinical condition requiring further evaluation. This test is used during baseline (pre-neurofeedback training) to give the clinicians a better understanding of the client’s functionality compared to his or her peers, as well as post-neurofeedback training to see if there was accompanying improvement in untargeted areas of functioning.

Comprehensive Neurodiagnostic Checklist-1020 (CNC-1020) The CNC-1020 is a computerized checklist of 300 items addressing concerns related to a variety of neurocognitive functions created through a collaboration of EEG Professionals and Brownback, Mason and Associates. It queries the participant to answer questions using a scale of 0 to 8, fleshing out 42 different neuropathologies such as ADD, depression, OCD, and GAD, and is specifically developed for the professional neurofeedback practice. It is administered online, and provides an automated assessment that shows the brain locations most likely to be implicated by the reported items. Suggested neuropathologies are calculated using the International 10–20 system, and suggestions are included as to which placements should be considered for assessment or treatment. The construct and predictive validities of the CNC-1020 were reported by Helgers, Brownback, Mulder, and Fonteijn (2011) using 1733 clients aged 6–80, and included clients with one or more guided psychosomatic complaints or clinical diagnoses. It has been successfully compared with the Symptom Checklist (SCL-90) and the Child Behavior Checklist (CBCL) for construct validity and predictive validity. It can be completed by the primary client, as well as three additional family members, teachers, or other close associates, providing differing perceptions of the client’s issues. The CNC-1020 also includes the Comprehensive Tracking Checklist (CTC), wherein each participant completes a brief survey weekly (timing is customizable), enabling the clinician to keep track of individualized and specific items throughout the treatment process. For example, each week a client who came in with anxiety

8

A Brain Functional Dynamic Approach

would rate his or her agreement with a statement such as “I feel restless, keyed up or on edge,” which would be expected to decrease throughout neurofeedback training.

Symptom Checklist-90-Revised (SCL-90-R) For clients at least 13 years old, the SCL-90-R (Derogatis & Savitz, 2000) evaluates a broad range of psychological symptoms (i.e. anxiety, depression, hostility, obsessive compulsive). The 90 items, rated on a 5-point scale, take about 15 minutes to complete, and are used as a baseline measure preneurofeedback training, and as a measure of progress post-neurofeedback training.

Review of Information Before neurofeedback training begins, the Brain Enrichment Center invites the client into the office for a one-hour review of that client’s relevant information which has been synthesized, or analyzed and integrated by the clinicians with suggested number of sessions, type(s) of neurofeedback training, and overall treatment plan. The client’s maps, z-scores, and assessment results are summarized, and it is demonstrated to the client that objective evidence correlates with their presenting problem(s). This oftentimes is empowering to the client and those involved with them, such as a spouse or parent, because the client’s symptomatology has been re-framed as a particular brain dysfunction, thus providing newfound hope for a future without the presented symptoms.

Neurofeedback Training Our clients receive individualized treatment; each client undergoes training with a neurofeedback protocol built, and customized, for their specific needs. This is done using a few foundational types of neurofeedback. Power training, Z-score training (PZOK), sLORETA current source density training, and sLORETA Z-score training are all utilized. For each client, clinicians decide which would be most efficacious. Consideration is given to the importance of using more than 1 channel, up to 19, for neurofeedback. The client’s ability or willingness to tolerate more sensors is considered. Some clients begin with 1 or 2 channels, when power abnormalities are well defined on the scalp and are a priority. When connectivity issues are concerned, we may go to 4-channel LZT training, which is a useful bridge to whole-head work. Perhaps 50% of our clients are trained with a full 19 sensors, either using a cap or free sensors. When localized training is indicated, a full head of sensors permits sLORETA power training or Z-score training. Power training generally includes up and/or down training, rather than inhibits. Training the current source density of one or multiple region(s) of interest (ROIs) is also utilized. Dynamic Z-score training is also frequently used and usually involves at least 4 channels, but more often all 19 channels.

Post-Neurofeedback Training After the client has completed the first set of sessions, the QEEG, CNS-VS, and SCL-90 are all administered again, noting that the CNC-1020 is used throughout the process while employing the CTC during neurofeedback training. Another review of information then occurs, which includes suggestions, and a plan for moving forward, and often advises the client to continue with more neurofeedback training sessions, as clients typically complete this step having only completed 10 or 20 sessions.

9

Thomas F. Collura et al.

Case Studies 0092 A 31-year-old female previously diagnosed with bipolar disorder, rapid cycling, ADHD, inattentive type along with anxiety (panic and phobia). She presented with low self-esteem, low motivation, social ineptitudes, and a history of a delusional episode when she was 14 or 15 years of age. Due to these diagnoses and the delusional episode, she was on a variety of medications including Depakote, Adderall, Wellbutrin, Paxil, Atenolol, and Necon. She is fully supported financially and emotionally by her parents, who brought her in and bribed her to participate in treatment. She is a talkative, but pleasant, individual and is obsessed with cats. She has a very good memory and precise hearing. She was adopted as a newborn by the great aunt of her biological mother (the great aunt will henceforth be referred to as the client’s mother). Her mother’s husband, now her father, did not want her as the couple already had three girls and wanted a boy, not a girl. The parents recall that she did not smile until she was a teenager on medication. The client completed a total of 40 sessions, which included 21 sessions of 19-channel PZOK, mostly with eyes closed, a few sessions of PZOK at F3F4P3P4, and 16 sessions of ROI training, which included the following ROIs: Frontal Alpha up on the right side, and parietal beta2 up on the right side. The client reported significant subjective improvement. She experienced a dramatic improvement in memory, particularly dreams. She had not recalled a dream in 10 years, but now recalled vivid and moving images. She reported being able to control her panic, she gained social skills, she showed empathy, and she began a new friendship with a peer. In one episode, she reported, “My stepfather tried to get me angry and I walked away.” When asked why she walked away, she reported, “I remembered I didn’t want to ruin it for my brother.” This was a new level of consideration and behavior control for her. In addition, she was calmer, was less easily angered, showed lessened tendency toward procrastination and increased initiative, and also discussed cats much less. Her ability to hold, follow, and continue a conversation improved exponentially. With her successful subjective report, her brain maps surprisingly showed few improvements, if any, potentially due to medications. General agreement between databases is evident in the client’s maps. In the pre-treatment maps, all databases indicate a left-frontal theta deficit and a diffuse beta deficit most prominent frontally. Diffuse delta deficits are indicated by QEEGPro and BrainDx, but not by Neuroguide, and a frontocentral gamma deficit is indicated by Neuroguide and BrainDx, but not by QEEGPro. All databases indicate little change between pre- and post-treatment maps except a general intensifying of most deviations. Some database agreement is also evident in the connectivity maps.

10

Figure 1.1

Eyes closed (QEEGPro) pre-treatment.

Figure 1.2

Post-treatment.

Figure 1.3

Eyes closed (Neuroguide) pre-treatment.

Figure 1.4

Post-treatment.

Figure 1.5

Eyes closed (BrainDx) pre-treatment.

Figure 1.6

Post-treatment.

Thomas F. Collura et al.

0097 A 66-year-old male was self-referred for volatile eruptions of anger and anxiety. He was a wealthy man who was divorced, with two grown children. He was frequently involved in a familial dispute often centered on his children’s disapproval of his current relationship with a 32-year-old woman, who has a young daughter. His job takes him out of the country (usually to the Middle East) for months and sometimes years at a time. In the office he received 13 sessions of neurofeedback: seven sessions of 19-channel PZOK, five sessions of 4-channel PZOK, and one session of high beta down at Cz. In addition, due to his being out of the country, he completed nine clinician-guided remote sessions of 2- or 4-channel PZOK and numerous other sessions on his own. Client reported being much calmer and having an improved ability to maintain composure during difficult situations. In general, client reported feeling “lighter, less burdened, more in control of [his] emotions, and better able to handle life situations.” He also gained a better understanding of himself, while realizing and working through past hurts. He improved his boundaries, and explored his tendencies toward self-sabotage. General coping skills improved as well. His most notable findings, according to the eyes-closed brain maps, include a normalization of gamma excesses and global hypercoherence and hypophase in gamma. General agreement between databases is evident in the client’s maps. In the pre-treatment maps, all databases indicate a clear pattern of delta excess, most prominent at electrode site F3. All databases also indicate a pre-treatment diffuse alpha excess, more prominent on the left side, and high beta excesses, most prominent centrally and at electrode site T6. All databases indicate a post-treatment alleviation of the high-frequency excesses, and a broadening of the low- and midrange-frequency excesses. Strong database agreement is also evident in the connectivity maps, especially between QEEGPro and Neuroguide.

16

Figure 1.7

Eyes closed (QEEGPro) pre-treatment.

Figure 1.8

Post-treatment.

Figure 1.9

Eyes closed (Neuroguide) pre-treatment.

Figure 1.10

Post-treatment.

Figure 1.11

Eyes closed (BrainDx) pre-treatment.

Figure 1.12

Post-treatment.

Thomas F. Collura et al.

0129 A 10-year-old male presented with a diagnosis of high-functioning autism spectrum disorder, along with various psychological concerns including inattention, anxiety, sleep issues, problems with sensory integration, and oppositional behavior. The client’s parents, who were generally resistant to mainstream medicine, had established behavioral interventions, and reported an improvement in oppositional behavior. Free electrodes were used in place of the full electrode cap for the initial QEEG, as the client was unable to tolerate the full cap. Excess frontal delta and high beta frequencies, as well as a deficit in alpha frequencies, were observed in the initial QEEG. A training protocol was tailored to address these deviations by training delta down, alpha up, and high beta down at Cz with eyes open. After the initial 20 sessions the client was able to tolerate a full electrode cap. Behavioral changes in the client were assessed by reports from his parents and teachers. A representative of the client’s individualized education program (IEP) at school reported dramatic improvements in his educational performance, particularly in reading comprehension. The client’s parents reported: a slight reduction in general anxiety, and fear of thunderstorms; decreased frequency and intensity of oppositional behaviors; improved ability to sustain attention; an increase in appropriate eye contact; and social approach-type behaviors. Improvements were also evident in the client’s brain maps, including normalization in alpha and beta frequencies. Excesses in frontal delta and high beta frequencies were attenuated, although not to normal levels. The appearance of over-connectedness/hypercoherence was observed which, upon the normalization of problematic frequencies, may be the brain’s compensatory coping mechanism. Following the post-20 neurofeedback session review, the treatment protocol was focused at the continual decrease of delta and high beta, as well as increasing alpha through power training. Four-channel Z-score neurofeedback, at Fp1, Fp2, F3, and F4, was added which allowed for the training of hypercoherence in addition to training all bands. During these next 20 sessions the client’s parents reported an increase in self-regulation, described as “waking-up,” and better retention of neurofeedback gains after 40 sessions. The client also moved into the regular classroom with peers; access to an aide was provided, if needed. His parents reported the very first successful sleepover, pointing to improvements with interpersonal relationships. Resistance to neurofeedback was reduced over time, and the client was able to start participating in HRV biofeedback. Upon the post-40 treatment QEEG, global hypercoherence trended towards normalization along with delta, theta, and high beta frequencies. Alpha was completely normalized. After the first 20 sessions, CNS-VS showed there were improvements in verbal (1st to 9th percentile) and composite memory (1st to 4th percentile). After 40 sessions further improvements were seen in composite memory (4th to 16th percentile), visual memory (6th to 42nd percentile), processing speed (1st to 13th percentile), and in psychomotor speed (1st to 6th percentile). Although found invalid in the first two CNS-VS assessments, working memory and sustained attention were both found valid upon the post-40 treatment CNS-VS, and ranked at the 32nd and 30th percentile respectively; placing sustained attention and working memory within the average category. These results provide a useful metric with which to assess the client’s progress; it should be noted, however, that this test is designed for use in neurotypical adults and children, and the large amount of variability in the results, in this case, is likely related to the client’s autism spectrum disorder and/or his other social and emotional concerns. Strong agreement between the analyses using each database is evident in the client’s maps. In the pre-treatment maps, all databases indicated highly localized frontal slow-frequency excesses and diffuse alpha and beta deficits. Pre-treatment gamma activity was reported as a diffuse excess by both Neuroguide and BrainDx. However, pre-treatment gamma activity was reported as normal, but framed with slight deficits by QEEGPro. Despite the differences in analysis using each database,

22

A Brain Functional Dynamic Approach

the shape of the gamma activity is nearly identical across all three. In the post-20 treatment maps, all databases indicate delta excesses on the left and right sides, and a diffuse central gamma excess. Although a change in color palette caused by a BrainDx update makes the post-treatment BrainDx maps slightly more difficult to interpret, post-treatment QEEGPro and Neuroguide maps both also indicate slight theta and alpha deficits and a frontal beta excess. General database agreement is also evident in the connectivity maps, especially between QEEGPro and Neuroguide. All three databases continue in agreement into the post-40 treatment maps. Both Neuroguide and QEEGPro show frontal excesses in delta, and all three show posterior excesses in theta; the shape of the excess in high beta/gamma is also very similar.

Figure 1.13

Eyes closed (QEEGPro) pre-treatment.

23

Figure 1.14

Post-treatment.

Figure 1.15

Eyes closed (Neuroguide) pre-treatment.

Figure 1.16

Post-treatment.

Figure 1.17

Eyes closed (BrainDx) pre-treatment.

Figure 1.18

Post-treatment.

Figure 1.19

Eyes closed (QEEGPro) post-20 treatment.

Figure 1.20

Post-40 treatment.

Figure 1.21

Eyes closed (Neuroguide) post-20 treatment.

Figure 1.22

Post-40 treatment.

Figure 1.23

Eyes closed (Brain Dx) post-20 treatment.

Figure 1.24

Post-40 treatment.

A Brain Functional Dynamic Approach

Figure 1.25

CNS-VS graph.

0131 A 39-year-old female presented with severe anxiety, mild dissociative episodes, and a diagnosis of PTSD. The client reported a history of sexual trauma in early childhood and a resultant pattern of anxiety, depression, and mild dissociative episodes which were occasionally triggered by sexual activity. Her depression had been successfully treated with a combination of medication and psychotherapy, but the client continued to experience daily anxiety at varying degrees of intensity. The client initially completed 20 sessions aimed at bringing theta down, and beta up, at Pz with eyes closed. After the first 20 sessions she reported a steady reduction in anxiety symptoms, and her treatment team noted that her prior state of perceptible nervousness and hyperarousal had abated. As her symptoms reduced she was able to work with her psychiatrist to reduce the dosage of both her anxiolytic medication and a tricyclic antidepressant she was taking for sleep. Although the client was primarily seeking treatment for her anxiety, she also experienced improvements in her dissociative symptoms. After 13 sessions she was able to predict, and sometimes prevent, episodes of dissociation. Her post-20 treatment brain maps indicate normalization of delta, beta, and high beta frequencies; however an increased excess in theta frequencies was also observed. As beta was improved to be within normal range, subsequent training was focused solely on the reduction of theta at F4. As training progressed the client reported continued improvement in her anxiety symptoms, as well as continued reduction of her medication dosages. Although the absolute power maps show the overall amplitude increasing, normalization can be observed within the relative power maps. Specifically, theta is reduced, and normalization occurs in frontal alpha, beta, and high beta. Post-20 treatment neurocognitive testing, via CNS-VS, showed improvement in reaction time (1st to 42nd percentile), as well as in the neurocognitive index (18th to 42nd percentile). After 40 sessions, further improvements were observed in verbal memory (4th to 16th percentile) moving from low to low average, and composite memory (10th to 45th percentile) moving from low average to average. Processing speed markedly improved as well, moving from the 45th to 99th percentile. The number of domains rated as above average increased from 1 to 5 with no domains rated as low or very low, and only one low average remaining (verbal memory). The neurocognitive index score also further improved, moving from the 42nd to 70th percentile. With some small exceptions, general agreement between databases is evident in the client’s maps. In the pre-treatment absolute power maps, all databases indicate a strong frontal theta excess, mild delta excesses frontally and at electrode site O1, and a diffuse beta deficit most prominent in the rear. The QEEGPro maps differ slightly from the other databases in this case, indicating delta and gamma deficits of greater intensity than is reported by both Neuroguide and BrainDx. More uniform agreement between databases is evident in the post-20 treatment absolute power maps, with the 33

Thomas F. Collura et al.

exception of QEEGPro, which showed a diffuse delta deficit. All databases indicate the post-treatment emergence of a diffuse alpha excess not present pre-treatment, and post-20 treatment frontal theta excesses of greater intensity than those found pre-treatment. All databases also indicate widespread normalization in high-frequency activity. The post-40 treatment absolute power maps show general agreement, across all databases, in the distribution and intensity of alpha, beta, and high beta frequencies. However, there are some notable exceptions to the database agreement. BrainDx reports normal delta activity, while both Neuroguide and QEEGPro indicate a deficit in delta at T3. Conversely, BrainDx and QEEGPro agree on theta activity; both show similar shape and intensity of theta frequencies. In this case it is Neuroguide that shows a slight deviation in the shape of theta activity compared to the other two databases, but Neuroguide does show a similar intensity of theta activity to BrainDx and QEEGPro.

Figure 1.26

Eyes closed (QEEGPro) pre-treatment.

34

Figure 1.27

Post-20 treatment.

Figure 1.28

Eyes closed (Neuroguide) pre-treatment.

Figure 1.29

Post-20 treatment.

Figure 1.30

Eyes closed (BrainDx) pre-treatment.

Figure 1.31

Post-20 treatment.

Figure 1.32 EYES Closed (QEEG-Pro) post 20-treatment.

Figure 1.33

Post-40 treatment.

Figure 1.34 Eyes closed (Neuroguide) post-20 treatment.

Figure 1.35

Post-40 treatment.

Figure 1.36 Eyes closed (BrainDx) post-20 treatment.

Figure 1.37

Post-40 treatment.

Thomas F. Collura et al.

Figure 1.38

CNS-VS graph.

References Anderson, S. A., & Sabatelli, R. M. (2007). Family interaction: A multigenerational developmental perspective. New York: Pearson Education, Inc. Budzynski, T., Budzynski, H., Evans, W., & Arbanal, A. (Eds.) (2009). Introduction to QEEG and neurofeedback: Advanced theory and applications (2nd ed.). New York: Elsevier. Coben, R., & Padolsky, I. (2007). Assessment-guided neurofeedback for autistic spectrum disorder. Journal of Neurotherapy, 11(1), 5–23. Collura, T. F. (2014). Specifying and developing references for Live Z-Score neurofeedback. Neuroconnections, 9(1) (Spring), 26–39. Collura, T. F., Guan, J., Tarrant, J., Bailey, J., & Starr, F. (2010). EEG biofeedback case studies using Live Z-Score Training (LZT) and a normative database. Journal of Neurotherapy, 14(2), 22–46. Collura, T. F., Thatcher, R. W., Smith, M. L., Lambos, W. A., & Stark, C. R. (2009). EEG biofeedback training using Z-scores and a normative database. In T. Budzynski, H. Budzynski, W. Evans, & A. Arbanal (Eds.), Introduction to QEEG and neurofeedback: Advanced theory and applications (2nd ed.) (pp. 103–141). New York: Elsevier. Corey, G. (2009). Theory and practice of counseling and psychotherapy (8th ed.). Belmont, CA: Brooks/Cole. Davidson, R., & Begley, S. (2012). The emotional life of your brain. New York: Hudson Street Press. Derogatis, L. R. & Savitz, K. L. (2000). The SCL-90-R and the Brief Symptom Inventory (BSI) in primary care In M. E. Maruish (Ed.), Handbook of psychological assessment in primary care settings, Volume 236 (pp. 297–334). Mahwah, NJ: Lawrence Erlbaum Associates. Doidge, N. (2007). The brain that changes itself. London: Penguin. Gualtieri, C. T., & Johnson, L. G. (2006). Reliability and validity of a computerized neurocognitive test battery, CNS Vital Signs. Archives of Clinical Neuropsychology, 21(7), 623–643. Hartmann, T. (1992). Attention deficit disorder: A different perception. Nevada City, CA: Underwood. Helgers, N. A. H., Brownback, T. S., Mulder, D., & Fonteijn, W. A. (2011). Construct and predictive validity of the comprehensive neurodiagnostic checklist 10/20 (CNC). Presented at the International Society for Neurofeedback and Research. Retrieved from http://www.eegprofessionals.nl/cnc1020_isnr_2011.pdf/ Marzouki, I. S., & Marzouki, Y. (2010). Subliminal emotional priming and decision making in a simulated hiring situation. Swiss Journal of Psychology, 69(4), 213–219. Neukrug, E. (2007). The world of the counselor: An introduction to the counseling profession (3rd ed.). Belmont, CA: Thomson Brooks/Cole. Palmo, A. J., Weikel, W. J., & Borsos, D. B. (2006). Foundations of mental health counseling (3rd ed.). Springfield, IL: Charles C Thomas Publisher, Ltd. Pigott, H. E., & Cannon, R. (2014). Neurofeedback is the best available first-line treatment for ADHD: What is the evidence for this claim? NeuroRegulation, 1(1), 4. Sedlacek, K., & Taub, E. (1996). Biofeedback treatment of Raynaud’s disease. Professional Psychology: Research and Practice, 27, 548–553. Zimberoff, D. (2013). Breaking free from the victim trap. Issaquah, WA: Wellness Press.

44

2 EVOLVING AS A NEUROTHERAPIST Integrating Psychotherapy and Neurofeedback Glenn Weiner

Abstract Clinicians who are adding neurofeedback services to their psychotherapy practice are faced with a number of challenges. Neurotherapy, the combination of psychotherapy and neurofeedback, requires all the skills of the psychotherapist plus some additional ones. This chapter is an effort to try to define some of the theoretical constructs and tactics involved in providing neurotherapy in an optimal way. Time is spent in defining tactics for developing a working neurotherapeutic relationship as well as how to explain the rationale and process of neurofeedback to patients. It is important to discuss with patients the potential for negative effects as well as the impact of neurofeedback upon medication effects. Assessment and progress monitoring are important aspects of providing clinical care. The specific types of data that have usefulness for neurofeedback progress monitoring include: (1) standardized rating scales and inventories; (2) neuropsychological tests; (3) behavioral ratings; and (4) EEG measures. There are a range of choices in neurofeedback approaches and protocols and some of the options will be discussed. Neurotherapy provides some unique ethical challenges which will be presented. Finally, given the complexity of this work, and the relative newness of the field, there is a need for continuing professional education and support.

Intro I am writing this chapter in somewhat of a personal way. I am assuming that the reader is an experienced psychotherapist, yet relatively new to providing neurofeedback services. I am hoping that the more experienced neurofeedback clinician may also find some value here in that there are so many different ways that this work can be done and the field is evolving too rapidly for anyone to keep up with all aspects of it. The intention is to help clinicians more easily navigate the process of developing a neurotherapy practice. While I aspire to practice at the level that I am trying to describe here, the real world of clinical work is challenging. Equipment breaks down. Stressors and trauma affect patients. We make technical errors. We don’t monitor progress as well as we wish due to lack of time. We get stuck and do not always seek consultation. This is challenging and humbling work. We are working with the most complex object in the universe, the human brain. It is quite frustrating when patients do not make sufficient improvement. It is wonderful when they do and even miraculous at times. I started my career as a psychologist in the public sector but have spent my last 30 years in the private practice world. For many years, my practice consisted of providing traditional counseling and 45

Glenn Weiner

testing services. My graduate school base of cognitive behavioral therapy and family therapy was added to over the years with various therapeutic approaches (e.g., solution-focused therapy; EMDR; hypnosis, etc.). As the research demonstrating the effectiveness of neurofeedback for ADD became clearer, I decided that it was important to add this service to my practice. Over the years, I have had a number of wonderful teachers. In 1999, I began my initial neurofeedback training with the Lubars. After practicing with my new single channel Autogenics machine on family and friends, I began to introduce neurofeedback to a select number of patients in my practice. I learned about arousal-based training models from the Othmers and learned to integrate peripheral biofeedback from the Thompsons. Roger deBeus patiently taught me to edit Quantitative EEGs (QEEGs) and Jay Gunkelman and Bob Thatcher helped refine my analytical skills. Len Ochs exposed me to a whole new way of treating patients and thinking about them with his Low Frequency Neurofeedback System. I learned about Z score training from Tom Collura and the Brainmaster team. There has been exposure to many technologies and treatment approaches at formal and informal conferences. Currently, I am a senior partner in a large multidisciplinary outpatient-based practice, where I provide a mix of individual and family therapy, psychological testing, neurofeedback, Quantitative EEG, and mentoring services. I utilize one neurofeedback technician who is also my psychometrician.

Neurofeedback in the Context of a Psychotherapeutic Relationship Neurofeedback might conceivably be viewed as a standalone intervention if doing peak performance training with someone, yet when we are using this to treat problems it is part of a more complex psychotherapeutic relationship. The psychotherapy part is to generate hope, teach them skills, come up with ways to help them get behaviorally “unstuck,” to give them an emotionally corrective experience and add structure to their lives. We are trying to help people increase their sensitivity, awareness and responsibility for their behaviors and relationships. A family systems perspective would say that our mission is to change family communication and relational patterns which contribute to the problems and prevent better solutions. Patients don’t come in for neurofeedback; they come in to solve serious problems. They want help managing anxiety, depression, attention, pain or other difficulties. Just as when doing psychotherapy alone, I am interested in obtaining a good understanding of people from multiple theoretical dimensions including developmentally, medically, neurologically, behaviorally and from a family systems perspective. The intake session is all about establishing a working therapeutic alliance. Perhaps a mantra for the first session might be “connect, connect, connect, and then get a good history.” This includes connecting with all family members present to increase our chances for success. It is always good to get everyone’s definition of the problems, their theory, and understand what previous efforts to solve it resulted in what outcomes. Carl Rogers (1980) described core facilitative conditions of effective helping: genuineness, unconditional positive regard and empathy. They continue to be necessary but are not sufficient. The structural family therapist pioneers added the importance of conceptualizing the patterns/structures of family systems. They added awareness of the value of identifying and intervening when there are hierarchy problems, enmeshment/disengagement and communication triangulation. The strategic family therapy pioneers (Haley (1976), Watzlawick, Weakland, and Fish (1974)) added other useful constructs for understanding families such as problem maintaining behaviors and pattern interruption techniques. The diffusion of innovation research (French & Raven, 1959) can guide us to consciously decide how much of an “expert” our patient needs us to be vs. how similar to them they need us to be (referent power). Understanding therapeutic stages of change (Prochaska & DiClemente 1983) and understanding optimal ways to engage people such as from a motivational interviewing perspective 46

Evolving as a Neurotherapist

are valuable skills for a Neurotherapist to be maximally effective. We also want to utilize the placebo effect (Hammond, 2011) in a skilled and ethical way that is helpful for our patients.

Conducting the Intake Interview(s) I particularly like the way that Jay Haley (1976) wrote about some of the issues involved in skillfully doing the initial interview. He did a nice job of trying to define what the informational goals of the session are. As noted above, developing a good therapeutic alliance to start outweighs gaining the assessment information that we would like to have. We don’t have to get it all figured out in the first session; in fact we really cannot as things are too complex. We want to begin the assessment process, begin to orient them about neurofeedback, and end with what the provisional plan will be going forward.

Explaining the Rationale and Process of Neurofeedback Patients come in with varying degrees of information and understanding as to the rationale and process about what neurofeedback is. Sometimes, I start by asking what their understanding is as to what we are going to do and why. I explain how some problems are completely brain based; others are completely environmentally based with this being on a continuum. I then differentiate between how MRIs and CT scans reveal structural problems while Quantitative EEG measures pick up more subtle brain functioning problems. We discuss what neurologists do when they do a clinical EEG and how that is different than the EEG measures we will be doing. I might say that “the clinical EEGs are trying to identify seizure activity, your brain at its worse. The neurologist may flash bright light in your eyes, have you hyperventilate or do the test in a sleep deprived condition when your brain is particularly weak. Our QEEG is meant to be a measure of your well rested brain and we have the option to identify more subtle problems in brain functioning due to the ability to compare you to a database of normals.” I remind them that they have a pretty good brain which enables them to do all of the complex things that they do every day: drive a car, study, read people and interact complexly, etc. We talk about how their difficulties may be related to brain dysregulation and what that means and how we might measure it. I might say that in computer terms the problem is more of a software problem than a hardware problem. We talk about how the training works, what we do in a session and the rationale for it. The typical questions follow: What is the total cost for the training? How many sessions? Is it covered by insurance, and why not? What is the success rate? When would we know if it is not working? Have I ever worked with someone like them? I tell patients that two out of three people get a good result from the training. Those who do feel it was well worth the money and time invested. It is important to take all of the time to answer all of these questions even if not asked directly. Having a good website with information that is accessible to them can help a great deal. Giving them some things to read also helps. Cory Hammond’s article: What Is Neurofeedback: An Update is often shared. I think that we do best when we describe the alternative treatment choices and not “oversell” ourselves.

The Overtraining and Medication Changes Discussion Prior to initiating treatment it is helpful and ethically mandated to discuss issues regarding overtraining or side effects. Some of these issues have been systematically written about (Hammond & Kirk (2008), Matthews (2007)) with the key points being to openly discuss the risks and make sure there is a written consent policy that is discussed. Dr. Len Ochs of Och Labs has developed a model of 47

Glenn Weiner

conceptualizing a patient’s perceptual sensitivity, physiological reactivity and overall level of hardiness/fragility as a way to help figure out how likely and how strongly someone is to have a negative reaction if the “dosing” or training effect is too strong for them. I tell patients that I am not aware of any side effects or overtraining effects that have lasted and talk about what the short-term overtraining or side effects have looked like in other patients. We discuss the possibility that if we do too much (as in dosing), then there is a tendency for patients to temporarily be “overly tired” or “overly wired”: “overly tired” being fatigued, foggy or irritable; “overly wired” being overly anxious, energetic or irritable. I then state that most people do fine, and that these overtraining effects tend to fade out with sleep or the next day unless you don’t tell me and we keep overtraining. Patients often come for neurofeedback because they would like to minimize or avoid the use of psychotropic medications. The general message that I give is that medication may be an alternative to neurofeedback and explain the advantages and disadvantages of both approaches. I let people know about the limits of research on medications and how no one really knows the mechanisms or outcomes when multiple psychotropic medicines are used. I tell people that they can’t change medication and start the neurofeedback at the same time because we won’t know what is doing what. If they want to make medication changes, or if there is any kind of ambivalence about starting the neurofeedback, I encourage them to see how good a result they can get with medication changes prior to beginning the neurofeedback. Once their medication is stabilized, I tell them and communicate with their physician that neurofeedback often potentiates medication effects and, therefore, at some point if the neurofeedback is going well, they will need less medication. Thus, they will respond however they might respond if they were on too high a dose of medication. This may look like overtraining effects of “overly tired” or “overly wired.” By predicting in advance this situation, they are more likely to reduce medication at that time rather than unnecessarily add additional medication. Many patients come for neurofeedback in hopes of reducing or eliminating medication. I encourage them to share with their physician their intention to begin to gradually titrate down their medications as soon as we see some consistent beginning gains and/or possibly if we see an overly tired or overly wired response that was not seen at the start of training.

Assessment and Progress Monitoring As in psychotherapy alone, assessment is not a one-time process when doing neurotherapy. The goal is to gather sufficient data to make treatment decisions and have data to use to monitor progress. In the spirit of wanting to know all that we can about a patient that is relevant to treatment planning, at times more traditional intellectual testing or personality assessment may be utilized. Such testing is necessary when trying to obtain special education support services for children, as an example. Specific types of data that have usefulness for neurofeedback progress monitoring include: (1) standardized rating scales and inventories; (2) neuropsychological tests; (3) behavioral ratings; and (4) EEG measures. Standardized rating scales are useful for both assessment and treatment monitoring. I like to utilize a broadband scale and a narrow band scale whenever possible. There are some general, multidimensional broadband scales that look at a variety of emotional and behavioral difficulties, such as the Behavior Assessment Scale for Children, Personality Inventory for Children, MMPI or SCL90-R. Narrow band scales are for specific disorders such as the Conner’s Rating Scales, which enable multiple raters to respond. The Multidimensional Anxiety Scale for Children, Beck Anxiety Scale, Burns Anxiety Scale, Beck Depression Inventory, Children’s Depression Scale, Gilliam Asperger’s Disorder Scale, and Social Rating Scales are also routinely utilized in my practice. Neuropsychological tests that I most commonly use are the Integrated Visual and Auditory Attention Test (IVA), Test of Variables of Attention (TOVA) and the battery of neurocognitive measures 48

Evolving as a Neurotherapist

that compose the CNS Vitals test battery. We utilize Wechsler Scales, Woodcock Johnsons and other traditional measures for differential diagnosis and forensic work yet not typically to measure treatment progress. Behavioral ratings are what I find the most valuable metric for adjusting protocols. Every neurotherapy patient that I see has a list of treatment targets that we monitor on a weekly basis. Early in treatment I ask them what things would look like when we are done with treatment. This is a variation of the “miracle question” in psychotherapy (Milwaukee Brief Family Therapy Center). It is also helpful to ask what it would take for you to feel that this treatment was worth your money and time investment. Dr. Matt Alexander, an experienced neurotherapy lecturer, developed a neurofeedback progress tracking system called “Results” and I have modified his idea to customize it for myself using Excel. Essentially, I find it most helpful to develop a list of treatment targets with each patient with around six or so representative problem areas. I help them to generate an operational definition of each target, including frequency, duration, and intensity whenever possible. I often ask patients to type up a description of how each of the problem areas currently impacts them at the start of treatment. We then assign each target a value of zero and track each session. If the week was a little bit better, the score goes up ½ point; if a little bit worse, down ½ point. If it was a better week, it goes up a point, and if it was a worse week, down a point. Tracking this over time is very helpful in adjusting treatment protocols. EEG measures: Most of the neurofeedback software systems have the ability to track this data yet I find it easier to more effectively do quality reviews on cases by having all of the progress data in one Excel document. I use the second tab in the spreadsheet to track average microvolts for the session to help see if there is a learning curve. Given the number of EEG variables, we generally track the band or two that we are most interested in modifying. While this creates an excellent system of documentation, more importantly it enables us to try to more objectively see the impacts of our training. There are many options with regard to using QEEG measures to measure progress. Depending upon the neurofeedback training or analysis software, amplitudes can be tracked or converted to Z scores.

Navigating the Choices of Equipment and General Neurofeedback Approaches Today there are more equipment options to do neurofeedback than ever before, and more ways to do it. Our field is still in its childhood and the treatment options have far outpaced not only the research but the field’s clinical experience. Essentially the research is supportive of treating ADHD and Seizure Disorders. All of the other disorders which we treat have only minimal research support, typically small group case studies. Fortunately most experienced clinicians report successfully treating a broad variety of patients in their practice. The field has evolved from well researched single channel training along the central strip for ADHD and Seizure Disorders, but the research has not kept pace due to funding problems. Pigott et al. (2013) have written a comprehensive article about the insurance companies’ bias against neurofeedback. Many clinicians report good results with multichannel training, bi-hemispheric arousal-based protocols including infra-low training, multichannel Z score training and whole head 19 channel training. Slow Cortical Potential Training and Alpha-Theta training also are helpful, and there is some supportive research for their effectiveness. While the level of research is only at a case study level (e.g. Larsen, 2006), there are a number of clinicians who have found the Low Energy Neurofeedback System (LENS) to be a powerful and time efficient way to work with people. There is controversy as to whether this is a form of neurofeedback as it is not operant conditioning. I perceive it as a brain disentrainment neurofeedback as the barely measureable electrical feedback is based upon the patient’s EEG. I have found it to be very helpful to a number of patients over the years. 49

Glenn Weiner

One of the current cutting edges of the field is providing 19 channel training with either surface neurofeedback training or Low Resolution Electromagnetic Tomography (LORETA) training. LORETA utilizes EEG signals from 19 channels on the scalp to mathematically determine the source of the EEG within brain voxels (7 × 7 × 7 mm cubes). These voxels combine to form Brodmann areas or Regions of Interest which combine to form networks. Functional MRI (fMRI) research has been identifying networks correlated with various functions. There is a theoretical elegance in trying to find the overlap of EEG abnormalities within the brain networks which have been identified in fMRI research as involving the patient’s problems and then training those specific voxels which are identified as problematic using a normative database. LORETA neurofeedback enables very precise training and the ability to train deeper brain structures and the connections between structures. This adds a demand for clinicians to try to learn about brain networks and fMRI neurofeedback research. There are many choices for how clinicians choose equipment. ISNR has established some standards for equipment. In choosing one’s initial equipment it is important to consider not only the hardware and software but the community of clinicians who are using that approach and opportunities for initial training and continuing education.

Neurotherapeutic Tactics A common but enjoyable experience is helping a family see their child’s challenges in a more loving and supportive light. Prior to evaluating Billy the parents clarified that the dad doesn’t really believe in his son having ADHD or in medication for what he called a “flimsily defined disorder,” attributing a low work ethic and low persistence to be the real cause of his son’s difficulties. After doing formal attention testing and reviewing the Quantitative EEG with the parents the dad began to get teary when he realized how there were neurophysiological reasons why his son was not succeeding as well as he might. He said they have been through several evaluations and experts but this was the first objective physiological measure. It is more helpful for parents and child to think about him having a brain that makes sleepy brain waves at times, rather than as being lazy or dull. Neurofeedback is at times a Trojan horse for psychotherapy. Some individuals cannot tolerate the intensity of looking at their difficulties or the intimacy of a psychotherapeutic relationship. Neurofeedback enables us to titrate the interpersonal intensity down. One adolescent could tolerate limited discussion while he and his parents verbally fill in the weekly progress grid. This enables talking about some of the problems in ways that were previously met with resistance. Sometimes it helps to ask which treatment target they think will be the easiest to improve over the next week, and how would things look differently if there was a very minimal, barely noticeable change vs. a more noticeable change. What would they notice, how would it feel, what would others see? We can set parents up to give positive feedback that is necessary to balance the negative feedback, which might then be heard and perhaps owned a little bit more. A couple who wanted the husband to work on his low frustration tolerance meltdowns were not at the point where they were ready to pursue couples therapy. We could do some of the beginning relational work by including the wife in the behavioral ratings process and provide successive approximation reinforcement for gains. The husband could ask for how he wanted her to support him in working toward his goals in the context of helping the neurofeedback work. There have been a number of children with anxious pressuring parents who are initially unable to look at their role in the problematic transactional patterns at home. Having them involved in the progress reporting of neurofeedback while gradually discussing alternative ways to parent is something that they are more amenable to in the context of neurofeedback than in more traditional counseling. It is not unusual for parents to want to do neurofeedback themselves after they have seen the changes in their child. While neurofeedback is a fairly standardized treatment, we are limited only by our creativity as clinicians in enhancing its effects psychotherapeutically. One traumatized woman found it helpful for 50

Evolving as a Neurotherapist

us to read exposure vignettes which typically cause strong emotional reactions while doing training. Over time she could learn to have an optimized brain while thinking about the traumatic events. It was fascinating for a severely traumatized patient and me to watch the EEG show dysregulation in the form of high amplitude beta bursts a second before she would have a flashback. The feedback enabled her to get more aware of the nuances of the experiences and ultimately much better control. One adolescent found it very helpful to significantly reduce his marijuana usage when we compared QEEG measures on the day he “mistakenly” came in after smoking earlier in the day and his baseline measures. Working with pain patients while training to notice the rising and falling and variability of pain is a helpful step in helping them move toward more helpful cognitive tactics for dealing with pain, such as differentiating sensation from pain. Neurofeedback works well when interspersed with many other psychotherapeutic techniques including EMDR and Exposure/Ritual Prevention for OCD. Utilizing Alpha-Theta training with optimal functioning/idealized scripts can be very powerful in helping people change long-term patterns. Kelly’s (1955) fixed-role therapy gives a great deal of detail in this approach.

Ethical Challenges As I have been trying to express, neurofeedback and psychotherapy are so interwoven that neurotherapy presents all of the ethical challenges of psychotherapy and the additional challenges that neurofeedback adds. Obviously, breaking sexual boundaries, confidentiality and fraudulent billing are not OK. However, neurofeedback adds some additional ethical challenges. Each clinician must decide on the issue of billing insurance. Most insurance carriers do not currently reimburse for neurofeedback, which is a type of biofeedback. Some very caring clinicians bill neurofeedback under the psychotherapy CPT codes arguing that they are counseling people, working on solving mental health problems and it is really a form of cognitive behavioral therapy. They further state that without doing billing in this manner their patients would not be able to afford this necessary treatment. Clinicians should ask themselves how they might respond to an insurance fraud investigation question asking why they billed the procedure code for psychotherapy (such as 90837, which is typically covered) when they were aware of the biofeedback code (90901) or the biofeedback and psychotherapy code (90876), which are not typically covered.

Treating Family Members and Friends It gets particularly challenging when sorting out Dual Relationships issues as there are often very few local clinicians who we can refer our family and friends to whom we would like to get the benefits of neurofeedback. Most State Licensure Laws are consistent with the American Psychological Association’s Code of Ethics. Other medical and behavioral healthcare professions have a very similar version. The APA manual states that: A psychologist refrains from entering into a multiple relationship if the multiple relationships could reasonably be expected to impair the psychologist’s objectivity, competence or effectiveness in performing his or her functions as a psychologist, or otherwise risks exploitation or harm to the person with whom the professional relationship exists. Multiple relationships that would not reasonably be expected to cause impairment or risk exploitation or harm are not unethical. 51

Glenn Weiner

One of the challenges is in defining the relationship and the potential to do harm. It is obvious that we do not treat family members with psychotherapy. Unfortunately, that is even a little fuzzy, as at times we advise relatives who could benefit by our knowledge and experience. Certainly we all want to be as loving and supportive to relatives and friends as we can. Someone once said that neurofeedback is only good for the difficulties that the brain is involved in. We might smile and say but wait, the brain is involved in almost every problem or the problematic response/adaptation to a stressor. We might also say that successful neurofeedback makes you cognitively sharper. Gains in attention, sleep and emotional regulation are common. The challenge is that we want these benefits for our family members and friends. When we are getting started we also need to practice on someone. Providing these services for free doesn’t completely absolve us from the primary ethical requirement of doing no harm to patients. Can we be objective enough? One test is to mentally try on and discuss the worst scenario you can think of, and how that might affect your relationship. One dichotomy is looking at neurofeedback as treatment if there is a “Disorder” and optimal performance training if there is not a formal Disorder. However, that line is very fuzzy. When a family member or friend has a Disorder perhaps that would argue more strongly for another clinician to work with them. Unfortunately, there is a shortage of clinicians so often it is neurofeedback with you or not. When trying to decide complex ethical issues, it is always a good idea to get a consultation with a colleague and document in writing that discussion including the pros, cons and solutions to try to minimize potential harm. Utilize your same informed consent forms even if it is a close family member. Keep records in the same way that you would if it were a paying client. Use the same progress measures. Always present all options for alternative treatments and clinicians. Over time there may be increased malpractice risks associated with doing neurotherapy. There is a thin line between what is medical and what is psychotherapeutic. The relational demands for doing neurotherapy are higher than when just doing psychotherapy. We are utilizing instruments and approaches that are difficult to understand by many patients. We decrease our risk of malpractice by being transparent about what we do, clarifying what the research support is or is not, not overpromising and working hard at establishing a good working psychotherapeutic alliance. Physicians who connect with patients get sued much less often than those with poor bedside manner.

Honing Our Craft and Continuing Education This is very complex work that is not easy to do. It requires a blend of strong relational and technical skills. Given the newness of the field and rapid changes in technology and approaches, continued learning and support is a necessity. Fortunately this has been become increasingly easier over the years. When I was studying for the exam as part of the certification process for Biofeedback Certification International Alliance (www.bcia.org), there was only one published textbook about neurofeedback (Evans & Abarbanel, 1999). Now there are many. The International Society for Neurofeedback and Research has a wonderful annual conference with many workshops and lectures. There are also a number of very active internet listservs with a great deal of daily activity. GoToMeeting and similar videoconferencing enable long-distance mentoring and webinars to be accessible, and can be very cost-effective. The ability to share computer desktops enables two people to look at raw EEG, assessment and progress data simultaneously. Fees for these professional mentoring services vary. I am trying to encourage others that a portion of these mentoring services be provided at half professional fees to make them more affordable to newer clinicians. For about eight years I have led a small mentoring group in my office on occasional Saturday mornings. Part of the interest was to help them obtain necessary hours for BCIA certification, but 52

Evolving as a Neurotherapist

participants have remained involved well after their certification as value is seen in doing small group case consultation. I also think there is benefit in meeting all of the local clinicians in your area. I don’t believe that there is a shortage of patients anywhere. There is plenty of suffering and much value in collaboration and cross referring by specialty. Collaboration makes all of us better clinicians. One of the challenges that our field brings is that we are still at a descriptive level of research in many ways. It is not clear exactly which is the best way to do neurofeedback currently. We, as evolving Neurotherapists, are continually challenging our ways of thinking about the world from a mechanistic standpoint to a non-linear dynamic standpoint. There is a tendency for Neurotherapists to have crises of imposterism (Leary, Patton, Orlando & Funk, 2000). When I talk to clinicians at conferences, many privately express that they think that other clinicians are getting better results. Perhaps that is related to success cases being what is presented at conferences or discussed in the hallways. Successful outcome research is what gets submitted to the journals. It is also hard for newer clinicians to feel confident when the technological treatment options are being developed faster than research or even faster than the experiences of a large number of clinicians. It is impossible for any individual clinician to learn, better yet master all of the newer approaches that are being developed given the speed of innovation and change. Insurance inconsistently and generally poorly covers our services. Neurofeedback as a field has significant financial challenges to overcome. This is often a time-intensive process and there is considerable expense for equipment and ongoing continuing education. Sessions may be covered at such a low rate that clinicians drop off of insurance panels. There may be micromanagement of a limited number of sessions. My hope is that over time that the real value of neurotherapy will be recognized and fairly compensated for.

Taking Care of Ourselves and Developing a Support Team In closing, I want to remind the reader that we are trying to treat the most complex object in the universe. Neurotherapy is hard work. It is confusing, and there are technological challenges. However, it is the best way that many of us have found to help many patients and in ways that other approaches cannot. It is important for us to model what we want our patients to do: exercise, eat reasonably well, take our fish oil, get enough sleep and have and model a balanced life. Yoga, meditation (with or without God’s acknowledged presence) is also good for us and our patients. It is really necessary to have access to regular mentoring support which later may be replaced by small group peer consultation. I wish you good luck in moving along this path.

References Evans, J., & Abarbanel, A. (1999). An introduction to quantitative EEG and neurofeedback. San Diego: Academic Press. French, J.R.P., & Raven, B. (1959). The bases of social power. In D. Cartwright & A. Zander (Eds.), Group dynamics. Ann Arbor: University of Michigan. Haley, J. (1976). Problem solving therapy. San Francisco: Josey Bass. Hammond, D. C. (2011). Placebos and neurofeedback: A case for facilitating and maximizing placebo response in neurofeedback treatments. Journal of Neurotherapy, 15(2), 94–114. Hammond, D. C., & Kirk, L. (2008). First, do no harm: Adverse effects and the need for practice standards in neurofeedback. Journal of Neurotherapy, 12(1), 79–88. Kelly, G. (1955). Psychology of personal constructs. London: Routledge. Larsen, S. (2006). The healing power of neurofeedback. Rochester: Healing Arts Press. Leary, M. R., Patton, K., Orlando, A., & Funk, W. W. (2000). The impostor phenomenon: Self-perceptions, reflected appraisals, and interpersonal strategies. Journal of Personality, 68(4), 725–756. Matthews, T. V. (2007). Neurofeedback overtraining and the vulnerable patient. Journal of Neurotherapy, 11(3), 63–66.

53

Glenn Weiner Pigott, E. H., Bodenhamer-Davis, E., Davis, R. E., & Harbin, H. (2013). Ending the evidentiary & insurance reimbursement bias against neurofeedback to treat ADHD: It will take clinician action in addition to the compelling science. Journal of Neurotherapy, Spring, 17, 93–105. Prochaska, J. O., & DiClemente, C. C. (1983). Stages and processes of self-change in smoking: Toward an integrative model of change. Journal of Consulting and Clinical Psychology, 51, 390–395. Rogers, C. (1980). Way of being. New York: Houghton Mifflin. Watzlawick, P., Weakland, J. H., & Fish, R. (1974). Change: Principles of problem formation and problem resolution. New York: Norton.

54

3 VARIABLES RELATED TO NEUROTHERAPY SUCCESS/FAILURE James R. Evans, Mary Blair Dellinger, Ann Guyer, and Jane Price

Abstract In all forms of therapy there are many variables which can affect treatment progress, either positively or negatively, but which are extraneous to the specific procedures employed by the therapist. This soon becomes obvious to the experienced neurotherapist. However, many inexperienced or narrowly trained persons may not be aware of the large number of possible influences. Such lack of awareness can be very detrimental to both clients and practitioners. Here, the authors, each of whom is an experienced neurotherapist, cite examples of extraneous variables which may intervene during training to impede or halt training progress (whether or not targeted EEG changes occur), or which, in contrast, may unexpectedly facilitate symptom reduction despite no apparent EEG regulation. Examples are divided into those traditionally considered primarily psychological, socio-cultural or biological in nature. Suggestions are offered for actions a neurotherapist could consider taking to help a client, e.g., referral for individual counseling or psychotherapy, family therapy, vision training, medical procedures. The importance is stressed of neurotherapists being able to recognize, and appreciate the relevance of, extraneous variables impacting training, and being willing and able to make referrals to various other professionals as needed.

Introduction Neurotherapy does not occur in a vacuum; many factors influence results. The authors of this chapter have had experience with neurotherapy ranging from one to over twenty years, and all agree on the truth and significance of this statement. Of course, it is true for any type therapy, and is a well known fact and concern for any experienced and effective therapist. However, new or narrowly trained therapists may not be aware of, or understand, the many variables with potential to affect training outcome. For example, many neurofeedback practitioners accept as clients persons with a wide range of symptoms or diagnoses, often involving mental health, yet have little or no training regarding the psychological, socio-cultural and biological factors involved in mental illness. The main purpose of this chapter is to point out and briefly describe examples of variables with potential to impact therapeutic progress. Although recognizing that treatment-specific variables such as quality of equipment, therapist skill, and training protocols used also are important for success, emphasis will be upon nonspecific (extraneous) variables.

55

James R. Evans et al.

The chapter is organized into three sections. The first section concerns extraneous variables to be considered in cases where targeted symptoms are not decreasing in severity or number as had been expected, new undesirable symptoms have developed, and/or the client prematurely ceases treatment. This may be despite desired changes in EEG measures. The second section considers variables which may be involved when targeted symptoms are alleviated despite no expected changes (normalization; regulation) in the EEG. In the final section the authors summarize chapter content, with emphasis on the need for a multidisciplinary approach in the field of neurotherapy.

Neurotherapy Progress Is Slow, Does Not Occur, or Ceases Psychological Variables (a) It is not at all unusual for a client to have multiple diagnoses, including some with major psychological components. Depression, post-traumatic stress disorder, and generalized anxiety disorder are common examples. Clients seen for attention deficit/hyperactivity disorder may be especially likely to present with multiple diagnoses which need to be recognized and addressed if training is to proceed in an optimal fashion. (b) Successful neurotherapy, of course, leads to change, e.g., positive developments such as decreased anxiety and better ability to focus. Some clients, however, become very uncomfortable with their changes, and some may even label them negative side effects. Some years ago, a client of one of the authors expressed this in his statement: “I’m feeling anxious about no longer being anxious!” Happily, after a short counseling session, that client agreed to resume neurotherapy, and eventually was pleased with results. Recall of earlier traumatic experiences also can occur during the course of neurotherapy and be a source of distress. It may prove helpful to mention to clients in advance of treatment that having some new, usually temporary, but perhaps alarming, experiences is not uncommon, and usually are indications of therapy progress. This might be included in an initial informed consent. Of course, if a client’s fear or distress is great, and continues to interfere with progress, referral for professional counseling/psychotherapy or other treatment is called for. (c) The authors have encountered clients who, with or without conscious awareness, appeared to want to “keep” their symptoms. This often falls under the concept of “secondary gain.” An example would be a person receiving disability payments for a condition or injury which would continue so long as related symptoms continue. Such persons may have been instructed by an insurance company or a social services agency to try neurotherapy as a treatment for their symptoms. Other examples of secondary gain are cases where one’s symptoms are keeping them in the role of “the sick one” in a family, which, while likely very costly to self-esteem, nevertheless has its benefits in freedom from responsibility and/or extra attention from parents, spouse, or other caregivers. The authors have observed examples of this in adults as well as children. In most cases the client was pressured by family members to seek neurotherapy and agreed to do so. Although they may on the surface appear to be cooperating, and targeted EEG changes may occur, the “rewards” of maintaining symptoms hinder training progress. (d) A psychological phenomena, related to terms such as “you can lead a horse to water, but can’t make it drink,” rather frequently is seen with teen-age neurotherapy clients. This is especially common if they are angry with one or both parents and/or have been coerced into coming for treatment, e.g., be “off restriction” as long as they come for the sessions. Again, targeted EEG changes may occur, but behavioral symptoms persist. This, of course, is not limited to parent– child conflict, and has been observed in cases of marital conflict, court-imposed treatment, and other situations where a client perceives external pressure to change. Relevant to this, the senior author recalls how readily he was able during a psychology class to “shape” a rat’s behavior using operant conditioning procedures, but when applying such procedures to high school students 56

Neurotherapy Success/Failure

in his class it was not so easy. It seems there was an intervening variable referred to variously by terms such as consciousness, ego, self-determination. Unlike the rat, students apparently acted on thoughts such as “he’s trying to manipulate me,” or “I like me the way I am,” and behavioral change did not come easy (for teacher or students!). The authors occasionally have had clients ask specifically: “Will this change my personality?” (e) Readers who accept and understand the significance of unconscious motivation and dynamics can appreciate still other psychological variables which the authors believe are involved in some cases of slow or non-existent neurotherapy progress. This is suspected, for example, in cases where clients seem bent on “self-sabotage.” They usually strongly deny such motivation, yet their histories contain multiple instances of developments such as losing good jobs, dropping out of college with only one semester (or class) to go, failing, despite multiple warnings, to make mandatory reports to parole officers and thus being returned to prison. Mental health practitioners can speculate on causation in such cases (e.g., guilt and a need for punishment; need to maintain a “loser” self-concept), and may recommend psychodynamic type psychotherapy. Whatever the true dynamics may be, the neurotherapist often notes slow or variable progress, sometimes with partial or total remission of symptoms which later return with equal or greater severity than originally. (f ) Another situation where unconscious motivation may hinder neurotherapy has been observed at times with clients diagnosed with obsessive-compulsive disorder (OCD). Although OCD presently is seen primarily as having a neurophysiological basis, and best treated with cognitive behavior therapy using exposure and response prevention (e.g., Foa, Yadkin, & Lichner, 2012), it has been the senior author’s experience that unconscious dynamics can be involved. For example, a compulsive behavior may serve to “correct for” a strongly anxiety-provoking obsessive thought of unconscious origin. In such cases more common psychodynamic approaches generally prove futile. And, to the degree that unconscious motivation is involved in perpetuating symptoms, neurotherapy progress also is likely to slow or be non-existent. Of course, in cases where some aspects of OCD have a basis in neurophysiology, such aspects may prove responsive to neurotherapy (perhaps especially if used in conjunction with exposure and response prevention). (g) Finally, significant unconscious dynamics can occur in regard to client–therapist relationships, and hinder progress. In the fields of counseling and psychotherapy client–therapist “fit” has long been recognized as critical to success. Sometimes a lack of “fit” is obvious to one or both parties, e.g., when a therapist shows little patience with a hyperactive child or rebellious teen-age client. At other times, though, there is a lack of awareness of why the “fit” just does not feel right—as captured in the rhyme “I do not like thee Ms. Fell; why I cannot tell, but I do not like thee Ms. Fell.” (A possible variation on this could be “I like thee too much Ms. Fell . . . ,” as in transference.) Related to these examples are cases where clients become overly dependent on, or enamored with neurotherapy (or with the neurotherapist), and insist on continuing treatment even though symptoms have been alleviated and further progress is not occurring. This of course, raises ethical questions about when and how a therapist should terminate training. Socio-Cultural Variables (a) Early practitioners of neurotherapy recognized the potential effects, both inhibitory and facilitative, of family dynamics. For example, Judith Lubar, a social worker by training, realized that a child with ADHD-type symptoms was less likely to respond to training if the home environment was chaotic. She sometimes refused to take such children as clients until home environmental change occurred through family therapy (Lubar & Lubar, 1999). (b) When a client’s symptoms respond favorably to training, parents (siblings, spouse, peers) may be uncomfortable with the “new” child, and fail to reinforce or actively discourage the behavioral 57

James R. Evans et al.

(c)

(d)

(e)

(f )

changes. Examples include parent(s) who need a “sick” child to gain sympathy from associates or divert attention from their own marital relationship problems, the domineering husband whose formerly depressed wife became more assertive and less dependent upon him, and the friends of the formerly impulsive “class clown” they now perceive as boring. Occasionally the parents (or spouse, etc.) of a client whose symptoms are diminishing, and which were the major originally stated reason for referral, will express disappointment (or report no perceived progress), and may add additional “symptoms” to the list of those they wish to be targeted for change. This often seems to reflect an attitude of “we want a perfect child” (or their version of an ideal child). In some such cases parents actually may have perceived some progress, and realized the potential for much more. Wanting the best for their child in today’s highly competitive society, they then added to the “desirable” traits what they would like to see developed. Considering the successes some neurotherapy practitioners have had in developing one’s “peak performance,” such motivation may be laudable. However, the client may not perceive it that way, and begin to resent coming for therapy. In such cases progress may cease, or original symptoms return. Major environmental events perceived by the client as traumatic and/or disruptive can interfere significantly with neurotherapy progress. Some of the more commonly encountered ones include death or serious illness or injury in the client’s family, separation or divorce, family moves, transfer to a new school, bullying, and break-up with a boyfriend or girlfriend. Occasionally therapists encounter cases where a client experiences sexual abuse, rape, onset of physical or emotional abuse (as by a new step-parent or spouse) during the course of therapy. In such cases progress may cease or regression occur, and it may become necessary to suspend neurotherapy until the trauma is sufficiently resolved, whether through grief counseling, psychotherapy, environmental change, or other means. Occasionally a neurofeedback client is making good progress but becomes concerned about what it is costing whoever is paying for the sessions. The authors have suspected this in child and teen-age clients who may begin feeling guilty about what it is costing their parents. Perhaps they overheard parents arguing about or otherwise expressing concern about the cost. They then may make a decision to resume symptoms, hoping that this would lead parents to believe the sessions are not being effective and cease training. And in some cases it may be that the cost of treatment actually is contributing to marital problems and family discord which, in turn, inhibits therapy progress. Of course, financial concerns affecting progress are not restricted to children. Adults paying out-of-pocket for therapy may begin to experience financial pressures, and feel a need to cease training. Some will openly admit to this, but others may prefer to deny progress and cease training rather than admit to financial difficulties. Those of us working in the field of neurotherapy are well aware of its limited acceptance by mainstream medicine, education, and other fields. This cultural situation has the potential to “sabotage” a client’s neurotherapy progress. One of the authors reported once having had a young client who was making good progress. When, during an office visit to their family physician, the child’s parents showed him the “brain map” based on QEEG findings, he glanced at it, tore it to pieces, and put it in a trash can, saying, “That is what that is worth.” In this case the parents disagreed with his view, and successfully continued their child’s training sessions. Others, however, might have been motivated to discontinue the sessions. Hopefully, such an extreme reaction of the part of a health care professional is rare and becoming increasingly infrequent. Overall, it is our recent experience that this is true. It remains common, however, to hear from clients that they were neither strongly discouraged nor encouraged to seek neurotherapy training by the health care professional(s) or educator(s) consulted. Since the opinions of family physicians and teachers generally are well respected, any lack of endorsement of neurotherapy on their part could readily become a source of motivation for a parent (or adult client) to prematurely cease training. This may be especially likely if symptom reduction is not obvious within a few sessions. 58

Neurotherapy Success/Failure

A related situation is where a dissatisfied former neurotherapy client actively denounces the treatment as a “waste of time and money,” perhaps on internet posts read by a present client. Or a client may read one or more of the now common advertisements which claim that brain change and symptom reduction can be attained quicker and at lower cost by any one of a myriad of other techniques such as yoga, mindfulness meditation, magnetic bracelets, etc., etc. Again, if this happens and the client is disappointed that anticipated symptoms reduction has not yet occurred, he or she may abruptly cease training. Biological Variables (a) One of the most commonly reported variables involved in poor progress during neurotherapy is medication effects. Large numbers of clients are under a physician’s care and using prescribed psychotropic medications. It is very common to find that during neurotherapy a medication and/or dosage which once may (or may not) have been effective in reducing a client’s symptoms becomes an impediment to progress. In such cases medication adjustment is needed for optimal progress to occur. Unless the neurotherapist is a physician or other professional with prescription privileges, this requires the cooperation of the prescribing physician, and this sometimes is difficult to obtain. When the authors have a prospective neurotherapy client who is taking medication we explain this to them (or their parents), and recommend that they discuss it with the prescribing physician in the hope that she or he will understand and be willing to entertain medication adjustment if needed. A variation on this situation occurs when a client (usually an adult) makes a unilateral decision to cease taking or to self-adjust dosage of prescribed medications. Not only can this be dangerous, but, especially when done in an “off and on again” manner, can make it extremely difficult or impossible for the clinician to discriminate medication effects from those of the neurotherapy training. And, of course, it is not only prescription drugs which can impede progress. Sometimes a client is using illegal substances or excessive alcohol without the therapist’s awareness, or resumes such use during the course of therapy. This makes it difficult or impossible to determine sources of changes in symptoms and EEG measures, and accurately adjust training protocols. (b) Neurotherapists sometimes are surprised to discover that in a large number of cases where inattention and low academic achievement motivation are prominent symptoms, there also are visual, auditory, and/or other sensory disorders. While these often may have correlates in dysregulated cortical brain electrical activity, and thus be at least partially responsive to appropriate neurotherapy, some may have a more peripheral physical source such as defective muscles controlling eye movement, or middle ear dysfunction, which need to be addressed. For instance, Granet, Gomi, Ventura, and Miller-Scholte (2005) report “an apparent three-fold greater incidence of ADHD among patients with convergence insufficiency when compared with the incidence of ADHD in the general US population.” And Nash (2014) notes the many successes he has had combining neurotherapy with vision training, especially in cases of clients presenting with ADHD-type symptoms. The possibility needs to be considered that sensory problems are important contributors to, or even main causes of, the symptoms being addressed by neurotherapy. When any are suspected based on diagnostic interviews, reviews of medical history, behavioral observations, test results, or failure to make expected progress during neurotherapy, referral to appropriate health care professionals is necessary. The authors often have discovered the value of referral for vision assessment and training (usually done under supervision of a developmental optometrist), audiological assessment of hearing acuity and central auditory processing disorder (CAPD), or assessment/training of proprioception or tactile sensitivity abnormality (usually done under supervision of a specially trained occupational therapist). 59

James R. Evans et al.

(c) Much emphasis recently has been placed by various media on poor nutrition, and lack of sufficient sleep of many persons—both adults and children. Blame often is placed on the “hectic” life pace of many children (extensive homework, extra-curricular activities, etc.) and adults (workplace pressures, single mothers’ duties, etc.), along with the ready availability of “fast foods” lacking in nutritional value, and sleep needs competing with social media for nighttime hours. Whatever the socio-cultural causes, large numbers of health professionals are of the opinion that the biological consequences of poor nutrition and sleep habits are major contributing factors to many of the symptoms for which persons seek neurotherapy, e.g., difficulty focusing attention, anxiety, limited motivation, depression-related feelings. The senior author worked for several years with a neurotherapist who requested that all clients concurrently take a dietary supplement consisting of various vitamins, minerals, amino acids and antioxidants. Her belief was that this supplement interacted positively with neurofeedback, and was a major factor in her many therapeutic successes. The authors do not advocate this for all clients, but when neurotherapy progress is slower than expected, we consider diet worth discussing with a nutrition specialist or other qualified health professional. It has been the experience of many clinicians that improved sleep is a common benefit of neurotherapy, whatever the symptoms for which a client sought help (e.g., Hammer, Colbert, Brown, & Ilioi, 2011). This has led to some speculation that in such cases symptom reduction actually may have been due to the improvement in sleep rather than directly due to modification of targeted EEG dysregulation. However, when persistent sleep disorders are present, and little or no progress is being made with neurotherapy, lack of appropriate sleep becomes a variable which needs to be addressed. (d) Clients with epilepsy can present unique challenges for neurotherapists. It is a biological variable which, if not recognized and controlled, may inhibit training progress. There is increasing evidence that appropriate neurotherapy can be beneficial with various seizure disorders (Frey & Koberda, 2015). This is not surprising since abnormal brain electrical activity is a defining feature of epilepsy. However, epilepsy is considered a medical disorder, can have life-threatening consequences, and its treatment ethically (and legally in many places) has to be by, or under the direct supervision of, a physician (usually a neurologist). There is wide variation among seizure disorders in terms of cause and severity. Some of the less obvious types are especially likely to be encountered by neurotherapists. For example, the momentary lapses in consciousness which characterize what once were referred to as “petit mal” seizures (and now usually referred to as absence seizures) often are mistaken as symptoms of ADHD. Unless the neurotherapist is alerted to such lapses by observation or case history report, he or she may proceed with training for ADHD which is less effective (or even dangerous) than if referral to a neurologist had been made and supplemental or alternative therapy provided. (e) There are, of course, many medical conditions (pre-existing or developing during therapy) with potential to interfere with neurotherapy progress. Training of any sort can be more difficult when one is not feeling well. However, there are some chronic disorders known to have symptoms which are among those often seen in persons seeking neurotherapy. For example, a dysfunctional thyroid gland can be a basis for hyperactivity (hyper-thyroidism), and impede neurotherapy training for clients possibly mis-labeled as having ADHD. And symptoms such as sluggishness and low motivation due primarily to hypo-thyroidism could interfere with neurotherapy progress. Often when a client is referred by a pediatrician or other medical practitioner the majority of biological variables have been assessed, and treated. Nevertheless, when slow or unexpectedly variable therapy progress is observed, medical referral may lead to an accurate biological explanation and effective treatment. 60

Neurotherapy Success/Failure

Targeted EEG Metrics Do Not Change, but Symptoms Diminish or Resolve A key underlying assumption with neurotherapy is that it is training (regulating, normalizing, etc.) the brain as evidenced by desired changes in aspects of brain electrical activity (EEG), which, in turn, leads to resolution of undesired symptoms. Whether the “brain wave” changes themselves directly cause the behavior changes may be debated, but a basic assumption is that there will be some correlation between EEG change and behavior change. It has been the authors’ experience that over the course of therapy such correlation usually develops, although not always immediately. There have been cases where the correlation was delayed and noted only after twenty (or even many more) sessions, as well as instances of almost immediate symptom change, but no observed change in targeted EEG measures. The dynamics behind the delayed type cases may (or may not) be similar to those regularly noted with some pharmacological treatments, e.g., neurotransmitter changes occurring rapidly with use of anti-depressant medication, but symptom changes not observed for several weeks. However, in this section we address primarily the latter situation, i.e., symptom change occurring well before EEG change (or even with no EEG change ever). In the following paragraphs we speculate on causes of such instances. Psychological Variables (a) A favorite argument of critics of neurotherapy always has been that positive results are due to client expectation of success and the placebo effect. They regularly cite studies indicating that the majority of published neurotherapy research did not involve placebo controls (and/ or lacked random assignment of subjects, and poor control of other possibly confounding variables). Many neurotherapists discount such criticism, noting that, given the many variables which can impact client progress, successful clinical outcomes require a personalized approach, and often do not occur when there is emphasis on unrealistic attempts at tight control of all variables. Nevertheless, placebo effects consistently are found in regard to all types of therapy, including medical treatment, and most will admit that neurotherapy is no exception. A more appropriate question may be, “How much do placebo effects contribute to the many positive results?” The authors often have heard other neurotherapists and clients make comments such as, “I wonder if that critic actually ever experienced a few sessions of neurotherapy. If he or she had done so, they could have experienced the changes first hand, and realized there is something more going on than placebo.” However, there are instances where expectation and placebo appear to be accounting for most, if not all, positive effects. One such situation is when a client’s symptoms appear to resolve very early in treatment (at times even a few minutes into the initial session), only to return after a few subsequent sessions, at times with even greater than original severity. Some have referred to this as the “honeymoon effect.” Some neurotherapists not only recognize that placebo phenomena can positively impact results, but even encourage it in their practices by such behaviors as showing, and discussing the merits of, their “high tech” equipment, using impressive, “scientific sounding” vocabulary (e.g., “cortical networks”; “neural plasticity”), or wearing white lab coats and furnishing offices to emulate those of physicians. Marketing specialists and website designers, in attempts to attract customers, perhaps unwittingly also contribute to placebo effects by use of attractive websites, using terms such as “clinically proven,” listing multiple suffixes after providers names (Ph.D. ABC, etc., etc.), or making references to allegedly supportive research at prestigious universities. Prospective clients may not consider that the clinic in which neurotherapy supposedly was proven was not named; or that the initials after names do not necessarily mean the person is a licensed professional with training and experience in neurotherapy and closely related fields, but may have “earned” some of those initials from 61

James R. Evans et al.

(b)

(c)

(d)

(e)

participation in a short internet or conference workshop; or that the scientific neuroscience research cited may have no, or only very indirect, relevance to the practice of this particular clinic or clinician. What is important for the present discussion is that such advertising can be expected to result in high expectations and related placebo effects for many clients, not only initially, but over the entire course of therapy. And this may occur with or without associated changes in targeted EEG measure. A recent review of placebo phenomena is provided by Benedetti (2014). Neurotherapy usually occurs over several months during which a client at least periodically is the center of attention from parents, teachers, or a spouse. It is possible that this increases motivation for positive behavior change, perhaps with accompanying desire to “please” the attentiongiver. Related to this, clinicians may speculate that a sense of being loved or cared for develops, which leads to greater motivation to make needed behavior changes independently of targeted EEG changes. It is possible that realizing one is able to modify her or his own brain electrical activity may facilitate a sense of self-empowerment and improved self-concept which then may generalize to a sense of self-control over other behaviors, including control of the symptoms for which the client was receiving training. Skeptics of neurotherapy could claim that resolution of symptoms without evidence of EEG change are due simply to a client wishing to “escape” from the time and/or financial obligations of therapy, and proceeding to use will power to consciously inhibit the symptoms for which they sought, or were brought in for, neurotherapy. While this occasionally may be true (especially for some teen-age clients), it has been the authors’ experience that most clients report enjoying the sessions, and do not wish to quit. Especially with clients being seen for ADHD symptoms, it could be speculated that progress primarily is due to the requirement across training sessions of sitting quietly and attending for increasingly longer periods of time while receiving feedback (reward), i.e., it becomes a learned behavior which then may generalize to school and home situations.

Socio-Cultural Variables (a) Situations where a client makes unexpectedly strong progress prior to targeted EEG changes could be due to significant environmental change such as removal from a psychologically and emotionally “toxic” living situation (home, marriage, etc.). Biological Variables (a) There are many possibilities for unexpected progress in this category. The authors have worked with clients who initially were so hyperactive, inattentive, or anxious that it seemed unlikely they would be able to focus long enough to profit from this type of training. In many such cases parents did not want to have their child take medication and, therefore, had requested neurotherapy. In some of those situations we have suggested that parents ask their pediatrician if he or she would be willing to prescribe medication on a temporary basis to help gain sufficient control of behavior to enable proceeding with training. At times, when they and the physician agreed to do so, we have seen dramatic progress, with the physician later deleting medication as the child continued to progress. (b) 0ther possibilities in this category include: (1) dietary changes or improved sleep patterns facilitating unexpected bursts in progress; (2) spontaneous or medication-related recovery from a disease causally related to the symptoms for which the client sought neurotherapy, e.g., a thyroid abnormality is corrected. 62

Neurotherapy Success/Failure

Summary and Implications In this chapter we have cited and briefly discussed multiple extraneous variables with potential to interfere with or facilitate neurotherapy progress. Readers who have experience as neurotherapists will readily relate to most, and undoubtedly think of more which could have been included. A major purpose of preparing the chapter was to give newcomers to the field an awareness of the complex and broad bio-psycho-social context in which neurotherapy takes place. However, whatever their training background or experience, it is hoped that readers will recognize the need for broad training of neurotherapists, as well as for cooperative interaction with health care providers from multiple disciplines. Some may feel that this chapter has exaggerated the need for consideration of the complexity of the therapist–client–environment interaction. They may cite client success experiences where no EEG measures were taken, minimal or no background information was gathered, and their “onesize-fits-all” single channel neurofeedback approach was all that was used. The authors are aware of such occurrences, not only with neurotherapy, but also with hypnosis, a single counseling session, ingestion of a single placebo “sugar pill,” and every “alternative medicine” approach in existence. We also realize that some clients are “ideal” candidates for neurotherapy, perhaps being truly motivated to get rid of rather clear-cut symptoms; coming from a harmonious, supportive, and well adjusted family; and being free from histories of emotionally or physically traumatic injury. In those cases a very high success rate following relatively few sessions by a minimally trained therapist using almost any one of various types of neurotherapy could be expected; perhaps placebo, perhaps not. In our experience, however, the majority of clients need (and deserve) a broadly trained therapist, willing and able to consult with other professionals as needed, and providing specific personalized training protocols based on results of a thorough diagnostic interview, neuropsychological test results as needed, and a QEEG assessment involving a well developed database. There are no vacuums when it comes to therapy!

References Benedetti, F. (2014). Placebo effects: Understanding the mechanisms in health and disease. New York: Oxford University Press. Foa, E. B., Yadkin, E., & Lichner, T. K. (2012). Exposure and response (ritual) prevention for obsessive-compulsive disorder: Therapist guide (2nd ed.). New York: Oxford University Press. Frey, L. C., & Koberda, J. L. (2015). LORETA Z-score neurofeedback in patients with medically refractory epilepsy. Journal of Neurology and Neurobiology, 1, 1–4. Granet, D. B., Gomi, C. F., Ventura, R., & Miller-Scholte, A. (2005). The relationship between convergence insufficiency and ADHD. Strabismus, 13(4), 163–168. Hammer, B. U., Colbert, A. P., Brown, K. A., & Ilioi, E. C. (2011). Neurofeedback for insomnia: A pilot study of Z-score SMR and individualized protocols. Applied Psychophysiology and Biofeedback, 36(4), 251–264. Lubar, J. F., & Lubar, J. O. (1999). Neurofeedback assessment and treatment for attention deficit/hyperactivity disorders. In J. R. Evans & A. Abarbanel (Eds.), Introduction to quantitative EEG and neurofeedback (pp. 103–143). San Diego: Academic Press. Nash, J. K. (2014). Vision therapy as a complementary procedure during neurotherapy. In D. S. Cantor & J. R. Evans (Eds.), Clinical neurotherapy: Application of techniques for treatment (pp. 383–396). San Diego: Academic Press.

63

4 NEUROMEDITATION An Introduction and Overview Jeffrey M. Tarrant

Abstract Despite the obvious appeal and increased accessibility of meditation training with programs such as mindfulness-based stress reduction (MBSR), it remains a significant challenge for many individuals to maintain a consistent practice (Brandmeyer & Delorme, 2013). Early meditators often complain that they do not know if they are “doing it right” or give up before realizing any significant benefits. By providing the meditator with immediate feedback on their brainwave state, a neurotherapist can help define and refine the process, potentially increasing motivation, interest and impact. This chapter outlines four different types of meditation practices based on the role of attention, intention, brainwave states and brain regions involved; these include Focused Attention, Open Monitoring, Automatic Self-Transcending and Lovingkindness/Compassion. This information is used to provide introductory approaches to standard and LORETA neuromeditation protocols that can be used to achieve deeper states of meditation or as a treatment intervention for mental health conditions such as ADHD, anxiety, depression, personality disorders or addictions. In addition, a methodology is provided to create an individualized neuromeditation protocol for meditation practices that do not easily fit one of the four categories.

Background The past twenty years has witnessed a dramatic increase in interest and research concerning the potential benefits of meditation and mindfulness practices for medical and mental health conditions. A meta-analysis of studies examining the impact of mindfulness training concluded that effect sizes across a wide range of populations suggest that this type of training may be a powerful tool to address both general well-being as well as serious physical and mental health concerns (Grossman, Niemann, Schmidt & Walach, 2004). Improvements were consistently demonstrated across a range of measures including depression, anxiety, coping style, medical symptomatology, sensory pain, physical impairment and quality-of-life estimates. Together with these findings has been an increasing number of brain imaging studies examining the process and outcome of meditative practices from a neurological perspective (Cahn & Polich, 2006; Lutz, Greischar, Rawlings, Ricard & Davidson, 2004). This research has emphasized the plasticity of the brain and the power of these seemingly simple practices to exert significant change over a relatively

64

Neuromeditation

brief period. For example, a recent study examined changes in brain density before and after an 8-week mindfulness training and demonstrated significant increases in several brain regions including the left hippocampus, posterior cingulate cortex, temporo-parietal junction and the cerebellum (Hölzel et al., 2011). Sparking this surge in interest and research has been a variety of secularized methods for learning meditation, such as mindfulness-based stress reduction. This and similar programs have become increasingly popular and are now widely available in hospitals, medical centers and university campuses (Kabat-Zinn & Chapman-Waldrop, 1988; Kabat-Zinn, Lipworth & Burney, 1985). Despite the obvious appeal and increased accessibility of meditation training, it remains a significant challenge for many individuals in Western societies to maintain a consistent practice (Brandmeyer & Delorme, 2013). Learning to meditate takes time and patience. It requires the individual to intentionally minimize external stimulation and learn to manage a mind that seems bent on anticipating and disrupting every effort at control and which simultaneously resists letting go. Many early meditators complain that they do not know if they are “doing it right” or they experience boredom and consequently give up before realizing any significant benefits. Incorporating neurofeedback into a meditation practice can potentially help with these concerns. By giving the meditator immediate feedback on their brainwave state, a neurotherapist can help define and refine the process, potentially increasing motivation, interest and impact. In addition, because meditation and neurofeedback are both involved in the training of mental states, it seems obvious that these practices could be used to enhance each other, either in an attempt to achieve deeper states of meditation or by combining them as a treatment intervention for specific mental health conditions such as ADHD, anxiety or depression (Brandmeyer & Delorme, 2013). This chapter serves as a foundation for further explorations into the methods and potential applications of neuromeditation. Combining neurofeedback with meditation has its roots in the early development of the field. In fact, some of the initial neurofeedback protocols were designed to replicate many of the effects commonly experienced during a meditative state (Crane, 2007). Having observed that the practice of meditation often led to an increase in alpha power, this frequency band was the focus of a great deal of interest and attention (Aftanas & Golocheikine, 2001; Anand, Chhina & Singh, 1961; Arambula, Peper, Kawakami & Hughes Gibney, 2001; Banquet, 1973; Deepak, Manchanda & Maheshwari, 1994; Dunn, Hartigan & Mikulas, 1999; Echenhofer, Coombs & Samten, n.d.; Ghista et al., 1976; Kasamatsu & Hirai, 1966; Khare & Nigam, 2000; Lee et al., 1997; Litscher, Wenzel, Niederwieser & Schwarz, 2001; Saletu, 1987; Taneli & Krahne, 1987; R. K. Wallace, 1970; R. Wallace, 1971; Wenger & Bagchi, 2007). Researchers have clearly established that increases in alpha power play a role in both state and trait changes associated with meditation practice (Deepak et al. 1994; Fenwick, 1987; M. A. West, 2009). Not surprisingly, neurofeedback protocols that reward increases in alpha power consistently appear to assist the user in achieving lower levels of anxiety, feelings of calm and positive emotional states (Hardt & Kamiya, 1978; Kamiya, 1969). While increasing the amplitude of alpha wave activity can certainly benefit many people and often leads to feelings of peace and relaxation, conceptualizing meditation as “increasing alpha” is far too simplistic. The role of alpha in meditation seems to be, at least in part, related to the degree of relaxation and type of attentional processes used during the practice. It has long been known that many relaxation practices that result in reduced sensory stimulation and/or mental processing are also related to increases in alpha power (Marshall & Bentler, 1976). Complicating matters, certain types of meditation actually lead to decreases in alpha power (Başar, Schürmann, Başar-Eroğlu & Karakaş, 1997; Niedermeyer & Lopes da Silva, 2005; Schürmann & Başar, 2001). Thus, increases in alpha power may be associated with some, but not all, meditation practices (Arambula, Peper, Kawakami & Hughes Gibney, 2001; Morse, Martin, Furst & Dublin, 1977). Our understanding of meditative states and associated neurobiology has grown a great deal since the early days of neurofeedback, allowing for more sophisticated approaches that take into account

65

Jeffrey M. Tarrant

individual variables, current brain imaging research, the type of meditation practice and the goals of such a practice. Through integrating research across fields, we can identify which meditations might be better for a certain set of symptoms and which neurofeedback protocols might enhance those meditative practices. The neuromeditation protocols offered here can be used to facilitate a new meditation practice, deepen an existing practice or as a specific treatment modality for mental health concerns. The remainder of this chapter will describe a classification system of meditation types, the brainwaves and brain regions involved and offer specific neuromeditation protocols and clinical applications for each.

Types of Meditation While perhaps not absolutely inclusive, researchers have proposed three distinct categories of meditation practices based on the brainwave patterns they facilitate and the attentional processes involved (Cahn & Polich, 2006; Raffone & Srinivasan, 2010). The three categories of meditation described include focused attention (FA), open awareness (OA) and automatic self-transcending (AST; Travis & Shear, 2010). Meditation practices geared toward producing a specific emotional state, such as lovingkindness or compassion (LK-C) have sometimes been considered a subcategory of focused attention. While LK-C practices certainly involve a specific focus of attention, they also involve the cultivation of intense feeling states, making them different from more traditional practices of FA (Carter et al., 2005; Lutz et al., 2004). Consequently, LK-C practices will be treated as a fourth category of meditation in this discussion.

Description of Meditation Types Focused Attention: Voluntary control of attention and cognitive processes Open Monitoring: Dispassionate, non-evaluative awareness of ongoing experience Automatic Self-Transcending: Automatic transcending of the procedures of the meditation practice Lovingkindness/Compassion: Specific focus on an “unrestricted readiness and availability to help all living beings” (Lutz et al., 2004, p. 16369)

Focused Attention (FA) Focused attention meditation involves voluntary and sustained attention on a chosen object. An example of this type of meditation would include concentration practices that require the meditator to maintain attention on a single object, such as the breath, a part of the body, a strong visual image or a word or phrase (Travis & Shear, 2010). When the attention wanders from this object, the goal is to recognize this as soon as possible and without judgment return attention to the original focus. Studies examining FA forms of meditation have shown increases in frontal-parietal gamma coherence and power as well as increases in beta2 (20–30 Hz) activity (Travis & Shear, 2010). Because attention is multifaceted and involves several areas of the brain and at least two different networks (Petersen & Posner, 2012), it is difficult to simply identify one area of the brain that may be “the most” important to include in an FA neuromeditation. In a study comparing novice and expert meditators during a concentration meditation practice, fMRI brain images showed that several areas were involved in the maintenance of focused attention (Brefczynski-Lewis, Lutz, Schaefer, Levinson & Davidson, 2007); these included the dorsolateral prefrontal cortex (involved in monitoring), the visual cortex (engaging attention) and the superior frontal sulcus and intraparietal sulcus (attentional orienting). Other research suggests that certain brain areas involved in a concentration practice are directly related to the sensory focus of attention. Lehmann et al. (2001) found increases in gamma activity in an advanced meditator that shifted brain areas depending on 66

Neuromeditation

the type of processing involved. When the Buddha was visualized, visual centers were activated. When the monk focused on the verbalization of a mantra, verbal areas were activated, and when he concentrated on dissolution and reconstitution of the self, frontal areas were activated. Further complicating our analysis, some brain imaging studies examine expert meditators, while others look at novices. Studies vary in the strategies used to sample brainwave activity during the meditation process and many do not utilize a methodology that clearly connects the brainwave state to a subjective state of consciousness. In addition, the existing studies showing increased beta and/or gamma represent a wide range of FA meditation practices, including lovingkindness/compassion, Qigong, Zen-3rd ventricle, and Diamond Way Buddhism (Huang & Lo, 2009; Lehmann et al., 2001; Litscher et al., 2001; Lutz et al., 2004). The variety of existing FA practices along with the range of potential brain regions involved make it important to consider which brain regions may be similarly activated among traditions. While the specific sensory modality and use of active versus passive forms of concentration may vary among traditions, all FA practices will require sustained attention and re-orientation after episodes of mind wandering. This is true whether the specific focus of the meditation is the flow of breath, an image of a deity or the flow of energy (qi, prana) around the body. One brain area that is consistently involved in tasks requiring self-regulation of cognition and emotions is the Anterior Cingulate Cortex (ACC). Crottaz-Herbette and Menon (2006) indicate that the ACC shows increased activation during tasks that require selective attention to a stimulus or inhibiting a response to a stimulus. This appears to closely match the sustained attention requirement of an FA meditation. Additional support for the role of the ACC can be found in lesion studies showing that dysfunctions in this brain region lead to executive and attention deficits (Cohen, Kaplan, Moser, Jenkins & Wilkinson, 1999; Ochsner et al., 2001; Swick & Turken, 2002). The ACC has also demonstrated different connectivity patterns depending on the sensory modality of the attention task involved. Within these various patterns, it was discovered that the ACC was the major generator of attention-related activation regardless of the specific sensory modality (Crottaz-Herbette & Menon, 2006). Consequently, the ACC may be an ideal target for an FA meditative task regardless of the specific focus of the meditation. Sustaining attention, a critical component of FA meditation, is made difficult by the mind’s tendency to drift or lose focus, referred to as “mind wandering.” An examination of brain regions involved in mind wandering consistently implicate the Default Mode Network (DMN; Buckner, 2008; Mason, Norton & Van Horn, 2007). This area of the brain is discussed in further detail later in this chapter, but typically becomes more active in the absence of any external, goal-oriented activity and is associated with self-referential thought. In the case of mind wandering, it is also involved in the mind shifting off of its target. This makes perfect sense when considered in the context of FA meditation. When engaging in a FA meditative practice, the mind inevitably drifts off task onto some other task or topic, which is inevitably linked to some aspect of self and increased activation of the DMN. Researchers have previously established that sustained attention during an FA meditation is typically not a single state, but a fluctuation between focused attention and mind wandering (Hasenkamp & Wilson-Mendenhall, 2012). Attention becomes fixed on the object of the meditation and inevitably drifts into some thought process(es) that relate to a sense of self. This process is observed through self-monitoring, and attention is returned to the original target. In addition to the roles in attention processing already noted, the ACC has been shown to become active during awareness of mind wandering (Craigmyle, 2013). It appears that the process of FA meditation involves a dance of activation in the ACC and deactivation in the DMN. This process can be acknowledged and rewarded with a neurofeedback protocol designed to reward beta or gamma in the ACC and reward alpha in the Posterior Cingulate Cortex (PCC) or the Precuneus. You will notice during this type of neuromeditation training that there is a distinct mind state that is necessary to maintain both activation of the ACC and deactivation of the Precuneus. To maintain 67

Jeffrey M. Tarrant

this state, the neuromeditator must maintain a focus, but in a relaxed manner. A client engaged in this type of training described the process in the following way: If I focus too much or too narrowly, the feedback stopped, and when both (tones) had been going for a while, I felt very relaxed but still had to maintain some focus/attention on the tone or breath, otherwise one of the tones would stop. To keep both of them (tones), it was a balance. What makes this protocol so powerful is that, as the meditator, you receive immediate feedback (lack of tone) as soon as the mind wanders and becomes distracted. This change in stimulus immediately brings your attention to the fact that you no longer have a single point of attention, allowing you to quickly return to the focus of the meditation.

Focused Attention (FA) Neuromeditation Protocols Standard Protocol Reward beta2 (20–30 Hz) or gamma (30–40 Hz) at FZ Reward alpha (8–12 Hz) or alpha1 (8–10 Hz) at PZ

LORETA Protocol Reward beta2 (20–30 Hz) or gamma (30–40 Hz) at Anterior Cingulate Cortex (ACC) Reward alpha (8–12 Hz) or alpha1 (8–10 Hz) at Precuneus or Posterior Cingulate Cortex (PCC)

Experience with the above protocols reveals that it is often best to begin training with either the beta/gamma reward or the alpha reward, but not both. Each of these placements and trainings in conjunction with an FA meditation allows the meditator some “wiggle room” in their level of concentrated focus, helping them test the parameters before they are tightened. In addition, it is often helpful for novice meditators to begin with a series of short sessions (three minutes each) with an opportunity to discuss their observations and receive verbal guidance between runs. Using LORETA neurofeedback to train the deeper brain structures (ACC, PCC, Precuneus) provides an additional level of specificity which often helps clarify the desired mental state for the meditator. It has also been noted that both novice and advanced meditators tend to prefer threshold settings that offer a sustained feedback tone with high levels of reward (70–90%). Too many interruptions to the feedback are often perceived as distracting in the beginning phases of using this protocol. Finally, as with any form of neurofeedback, it is important to discover the nuances and individual differences of each person, making adjustments as necessary to provide optimal feedback.

Open Monitoring (OM) Open monitoring meditation does not involve an explicit attentional focus. Instead, it is characterized by an open presence and a non-judgmental awareness of sensory, cognitive and affective experiences as they arise in the present moment. This type of meditation is most often referred to as mindfulness meditation (Cahn & Polich, 2006). From a Buddhist perspective, the OM form of meditation practice is called Vipassana or insight practice and can be practiced separately or in conjunction with FA meditation. As stated by Lutz, Dunne and Davidson (2006), “Tibetan theorists maintain that the highest forms of Buddhist meditation must integrate the qualities of samatha (FA) and vipasyana (OM) into a single practice” (p. 504). While FA practices are concerned with attentional focus and stability, in OM meditation the object of focus is gradually replaced by an effortless sustaining of an 68

Neuromeditation

open background of awareness without an explicit attentional selection (Lutz, Brefczynski-Lewis, Johnstone & Davidson, 2008). There is no attachment to a specific object of attention; instead there is a non-reactive monitoring of the content of experience; a moment-to-moment meta-awareness of each thought, bodily sensation and/or feeling state. The handful of research studies examining EEG activity during OM meditation practice consistently report increases in frontal theta power as well as increased frontal theta coherence in selected studies. These findings were consistent among a variety of traditions including Vipassana (Cahn, Delorme & Polich, 2010), Zazen (Murata et al., 1994), Sahaja Meditation (Aftanas & Golocheikine, 2001; Baijal & Srinivasan, 2010), and Concentrative Qigong (Pan, Zhang & Xia, 1994). Contrary to popular notions about the role of theta in deep states of meditation, a narrow frequency of theta band power has also been shown to increase with task demands and is related to orienting (Dietl, Dirlich, Vogl, Lechner & Strian, 1999), attention (Başar, Schürmann & Sakowitz, 2001; Dietl et al., 1999), memory (Klimesch, 1997; Klimesch, 1999) and affective processing (Aftanas, Lotova, Koshkarov & Popov, 1998; Aftanas, Varlamov & Pavlov, 2001). Specifically, the task-related theta appears in frontal midline areas during performance of mental tasks or meditative concentration and is referred to as the frontal midline theta rhythm (FM theta; Aftanas & Golocheikine, 2001). Other research suggests that the attentional networks of the anterior frontal lobes, and the ACC specifically, are involved in the generation of this activity. Interestingly, during consecutive mental tasks, FM theta reflects alternating activities of the medial prefrontal cortex and the ACC (Asada, Fukuda, Tsunoda, Yamaguchi & Tonoike, 1999). In a study examining a “thoughtless awareness” form of meditation from the Sahaja Yoga meditation tradition, it was discovered that long-term meditators showed different theta activation patterns than short-term meditators. Specifically, long-term meditators demonstrated theta power increases over anterior midline electrodes that suggested both general theta and FM theta processes. The authors suggested that this finding may reflect “recruitment of theta oscillating networks in memory, focused attention, and positive emotional experience mechanisms, associated with the meditative process” (Aftanas & Golocheikine, 2001, p. 59). Short-term meditators did not show midline theta increases which were explained as a result of excessive alertness and anxious/frustrated feeling states related to difficulty attaining the desired meditative state. These findings are consistent with the task of open monitoring meditations. Attention processes are still involved, but rather than reflecting a more narrow focus as utilized in FA meditations, this form of meditation is relaxed and inclusive. Also, different from FA practices, this form of meditation is involved in the ongoing monitoring of self. As such, we would expect that the DMN would be highly engaged in this process. Not surprisingly, frontal theta activity is negatively correlated with the DMN resting state (Scheeringa & Bastiaansen, 2008). A relatively straightforward protocol to utilize during an OM practice might involve rewarding FM theta (4–8 Hz) activity at FZ or ACC with or without an inhibit of alpha (8–12 Hz) activity at PZ or Precuneus.

Open Monitoring Neuromeditation Protocols Standard Protocol Reward theta (4–8 Hz) at FZ Inhibit alpha (8–12 Hz) or alpha1 (8–10 Hz) at PZ OR reward beta2 (20–30 Hz) at PZ

LORETA Protocol Reward theta (4–8 Hz) at ACC Inhibit alpha (8–12 Hz) or alpha1 (8–10 Hz) at Precuneus or PCC OR reward beta 2 (20–30 Hz) at Precuneus or PCC

69

Jeffrey M. Tarrant

Figure 4.1

ACC theta and Precuneus during an OM neuromeditation session.

One neuromeditator who reports a consistent meditation practice in a tradition similar to OM shared the following comments regarding his experience with the protocol inhibiting alpha at Precuneus and rewarding theta at ACC: This protocol, or at least the way I was approaching the session led to a very mellow, pleasant state of mind. Very calming, slow and relaxed. I just let go of any thoughts and don’t try to force anything to happen or to not happen. I seemed to receive the reward when I was a little bit more focused rather than so easygoing as my typical meditation is, or as I would like it to be. I came out of this session not wanting it to end nor wanting to speak or verbalize my experience. A session review graph, after a period to allow for adjustment to the protocol, clearly shows a gradual increase in FM theta at ACC and a gradual decrease of alpha at Precuneus.

Automatic Self-Transcending (AST) Meditations in this category involve moving beyond the procedures of the meditation. Most research involving this style of meditation have examined the practice of transcendental meditation (TM; Travis & Shear, 2010). While TM certainly appears to be an FA form of meditation due to the focus on a mantra, the actual practice reveals that it is a technique for transcending its own procedures; moving from a state of sustained attention to mental silence (Yogi, 1997). This type of meditation has been studied extensively and consistently results in increased low alpha (8–10 Hz) power and coherence (Travis & Shear, 2010). This makes sense because an increase in lower band alpha activity is associated with reduced external attention, vigilance and expectancy (Klimesch, 1999; Klimesch, Doppelmayr, Russegger, Pachinger & Schwaiger, 1998). As we learn to increase low alpha during a meditative practice, the mind becomes more settled and is less involved in the seeking of stimuli. This can be experienced fairly easily by rewarding alpha at PZ, eyes closed, with a gentle, sustained sound when alpha is above a certain threshold. This area (PZ) is essentially 70

Figure 4.2

Precuneus image in BrainMaster BrainAvatar training screen.

Jeffrey M. Tarrant

just anterior to the Precuneus, which has been described as the primary node or hub in the brain’s Default Mode Network (DMN; Mantini & Vanduffel, 2013). The Precuneus may be particularly important in meditations that involve “dropping the self.” This is because the Precuneus is clearly involved in self-centered mental imagery and episodic memory retrieval. This area is also involved in reflective self-awareness and social comparison/judging. Along with the Posterior Cingulate Cortex (PCC), this area is involved in conscious information processing. Based on the apparent central role of the Precuneus in processes associated with identity, this seems an obvious location to involve in AST neuromeditation. In addition to electrode placement at PZ, training involving LORETA neurofeedback could directly train the PCC and/or the Precuneus. As a quieting of internal mentation is a goal of AST meditation practice, it is logical to assume that involving more of the DMN would help to enhance this state. For example, another primary component of the DMN is the Medial Frontal Cortex (MFC). Damage to this portion of the network has been shown to produce an absence of spontaneous thought and a sensation of “mental emptiness” (Damasio & Van Hoesen, 1983). Consistent with this interpretation, research on Zen meditators, who often focus on meditative states such as “no mind,” have regularly demonstrated increases in alpha amplitude, a slowing of the alpha frequency and alpha activity spreading frontally (Kasamatsu & Hirai, 1966; Murata et al., 2004; Takahashi et al., 2005). The descriptions of both the meditative state and the brainwave processes involved appear to have much in common with alpha synchrony training as developed by Les Fehmi (Fehmi & Robbins, 2007). When describing the practice of alpha synchrony, Fehmi states, “objectless imagery—the multisensory experience and awareness of space, nothingness, or absence almost always elicits large amplitude and prolonged periods of phase-synchronous alpha activity” (Fehmi & Robbins, 2007, p. 36). Alpha synchrony neurofeedback training involves rewarding simultaneous alpha activity in multiple regions. Synchrony training with the Open Focus Synchrony Trainer is a 5-channel device that produces a single output that is the sum of the input channels. The traditional electrode placements with this system include FPz, Oz, T3, T4 and Cz with ears used as reference and ground. BrainMaster Technologies provides instructions on their knowledge database for training synchrony in a range of metrics using 2 or 4 channels. Based on our knowledge of the DMN and specific states of meditation, a powerful alpha synchrony training might directly involve the DMN by using placements at F1 (between F3 and FZ), F2 (between F4 and FZ), P3 and P4. This state may be enhanced or encouraged by simultaneously engaging in a meditation based on Fehmi’s Open Focus techniques. In his words, “Seeing, hearing, tasting, feeling, smelling and thinking of space, basking in it—while simultaneously experiencing timelessness—is a powerful way to let go, the most powerful way that I know” (Fehmi & Robbins, 2007, p. 37).

Automatic Self-Transcending (AST) Neuromeditation Protocols Standard Protocol Reward alpha (8–12 Hz) or alpha1 (8–10 Hz) at PZ

LORETA Protocol Reward alpha (8–12 Hz) or alpha1 (8–10 Hz) at PCC or Precuneus

Synchrony Reward alpha or alpha1 coherence between F1, F2, P3 and P4

72

Neuromeditation

Lovingkindness/Compassion (LK-C) Lovingkindness and other compassion-based meditative practices (LK-C), involve a focus of attention on an “unrestricted readiness and availability to help living beings” (Lutz et al., 2004, p. 16369). Because these practices involve a specific focus of attention and share some EEG characteristics with other FA practices, LK-C meditations are sometimes described as a sub-component of FA practices (Travis & Shear, 2010). For example, research examining changes in brainwave activity of experienced and novice meditators engaged in LK-C meditations from a Tibetan tradition have shown significant increases in frontal-parietal gamma coherence and power (Lutz et al., 2004). While there are certainly some similarities between the two styles of practice, including the voluntary control of attention and cognitive processes (Travis & Shear, 2010), LK-C practices are distinct from other forms of FA meditation in the intentional generation of feelings of caring, love and compassion. Lutz et al. (2006) make this clear when they describe LK-C as the generation of a state in which an unconditional feeling of lovingkindness and compassion pervades the whole mind as a way of being, with no other consideration, reasoning, or discursive thoughts . . . the practitioner is not focused upon particular objects during this state. (pp. 540–541) Based on this description, the feeling state in LK-C becomes the point of focus, which involves the activation of positive emotions as well as the ability to understand the feelings of others. Lutz and colleagues (2008) demonstrated that LK-C meditations consistently activate specific brain regions known to be involved in the perception of another’s emotional state. Two areas in particular, the right anterior insula and the ACC, have been found to be related to empathy for others’ suffering and are activated in both novice and experienced meditators when they are engaged in a compassion meditation. Other research has shown that both experienced and empathic pain activate these same areas, supporting the notion that they are important in subjective feeling states (Craig, 2009; Singer et al., 2004). In another study comparing eight long-term meditators with eight novice meditators, there were common activation patterns found in the adepts when engaged in an LK-C meditation. Areas of the brain that were consistently activated included the striatum, ACC, somatosensory cortex, anterior insula and the left prefrontal cortex. A deactivation pattern was consistently found in the right inferior parietal. These patterns were modulated by the degree of expertise, showing that length of practice correlated to the degree of activation (Brefczynski-Lewis et al., 2007; Lutz, Dunne, & Davidson, 2007). It has been noted by Craig (2009) that most studies examining activation patterns of the anterior insula also observe activation of the ACC. Joint activation of the ACC and insula supports the idea that these two areas largely serve as complementary regions that work together with few exceptions. The left frontal activation noted above is consistent with a large body of research connecting this pattern to the presence of positive emotional states (Davidson, 2000). As an indicator of hypoactivation, alpha dominance in the right frontal pole is associated with positive mood states and approach behaviors, while alpha dominance in the left hemisphere is associated with negative affective states and avoidance. These patterns have been thoroughly explored in the literature and reveal both state and trait aspects (see Hammond & Baehr, 2009 for a review). The alpha asymmetry findings have led to the development of specific neurofeedback protocols designed to shift this balance in depressed individuals toward more left activation. Baehr and Baehr (1997) and Baehr, Rosenfeld, Baehr and Earnest (1999) used an alpha asymmetry protocol in two different sets of case studies to successfully treat depression in patients also being treated with psychotherapy. In these protocols, active sites were at F3 and F4 with a reference at CZ and a ground at FZ.

73

Jeffrey M. Tarrant

Training was geared toward increasing the proportion of alpha in the right hemisphere in relation to the left. Other research has taken the same theory, but applied it by rewarding left frontal beta activity (15–18 Hz for 20–22 minutes and 12–15 Hz for 8–10 minutes) while inhibiting alpha and theta activity at FP1 and F3 (Hammond, 2000, 2005). Based on the available research and our understanding of the brain circuitry involved, LK-C forms of meditation appear to involve activation of the anterior insula and ACC as well as left prefrontal regions. In addition, deactivation of the right parietal region may also play a significant role. This provides a range of options to include in an LK-C neuromeditation practice and may depend on the form the meditation takes.

Lovingkindness/Compassion (LK-C) Neuromeditation Protocols Standard Protocol Reward alpha (8–12 Hz) at F4 AND Inhibit alpha (8–12 Hz) at F3 OR reward 15–20 Hz F3

LORETA Protocol Reward beta2 (20–30 Hz) or gamma (30–40 Hz) at right insula Reward beta2 (20–30 Hz) or gamma (30–40 Hz) at ACC

After completing an 18-minute LK-C meditation with a protocol rewarding gamma at ACC and gamma at right insula, an experienced meditator who lived in a Buddhist monastery for eight years made the following comments: During the first session (1st 9 minute run), I found the feedback tones quite distracting to the meditative concentration I was trying to achieve. Because of this, I had to put a considerable amount of effort into keeping my concentration on the content of my meditation. Over the course of the session, this became easier to do, and my state of concentration gradually required less effort. At a certain point, I was aware that the tones were relatively constant, and this corresponded to my own feeling at having achieved a degree of meditative stability. At this point, the feedback tones became more supportive to my meditation practice, as I could tell that when a discrete thought or external stimuli caught my attention and distracted me, one of the tones would cut out. This provided effective feedback that my attention had veered, and gave me impetus to put some effort into letting go of the distraction, or consciously bringing it into my meditation. During the second session (2nd 9 minute run), I could tell that I was beginning with more meditative stability than I had started with in the first one. And the feedback tones seemed to confirm that. During the session, I could once again tell when my attention would become too preoccupied with something, like when someone started talking on the phone in the other room. The feedback tones also indicated this, and once again I had to put some effort into negotiating that phenomena within the meditation, and I found myself using the feedback tones, in part, to do this. Also, when the sirens of the ambulance sounded, my compassion meditation expanded as I found myself incorporating people suffering in this way as subjects of the meditation, if that makes sense. 74

Neuromeditation

Figure 4.3

ACC and insula gamma during an LK-C neuromeditation.

Individualized Neuromeditation From the protocol descriptions listed above, it should be clear that there is a great deal of individual difference in the application of any particular neuromeditation practice. While I have attempted to simplify the issues involved by categorizing types of meditation and the potential brain regions involved, it is critical that a neuromeditation practitioner also accommodate individual differences. The meditative process used by an individual does not always neatly fit into one of the structures offered in this chapter. In such an event, there are a few ways to proceed. When working with someone who has an existing meditative practice that does not easily fit into a specific category, it may be best to examine what is happening for them during the peak of their meditative state and seek to enhance this pattern. Comparing Quantitative EEG recordings between baseline and meditative states can be very helpful in this regard. After recording a baseline eyes closed Quantitative EEG (QEEG), conduct a second recording where the client is engaged in

Figure 4.4a

Z-score FFT absolute power meditation.

Figure 4.4b

Peak meditation.

75

Jeffrey M. Tarrant

Figure 4.5

Absolute power difference between peak meditation and meditation Mv (sq).

their meditative practice of choice. Prior to beginning, ask them to signal, by raising a finger, when they feel they are in the desired state. The clinician must be astute during this recording, noting each time the client indicates a peak state. These states are typically fleeting, lasting only seconds. Once this recording is completed, several comparisons can be made. By separating the “peak” moments of the meditation from the remainder, it becomes possible to compare a sequence of EEG changes: baseline eyes closed, meditation and peak meditation. The emerging pattern is often quite telling and may, by itself, offer hints as to the type of meditation the client is practicing or prefers. Through discussion with the client and analysis of the EEG trends, it is possible to construct a neuromeditation protocol that is specific to that individual and their meditative practice. Comparing the absolute power values of peak meditation segments to non-peak segments using the Neurostat program in Neuroguide (Thatcher, 2012), the practitioner can identify the strongest areas of change between the two conditions. In this case, it is noted that alpha increased dramatically in central/frontal areas, particularly on the right side. In addition, theta increased in frontal central areas. Based on the discussion of meditation types, it seems likely that this individual is engaging in some form of meditative practice that has similarities to both AST and OM traditions. A similar, but more inclusive individualized program involves utilizing the z-builder function available in BrainMaster live z-score neurofeedback systems. In traditional live z-score neurofeedback, the client receives information about how similar (or dissimilar) their brainwave activity is compared to a normative database (Collura, Thatcher, Smith, Lambos & Stark, 2009). The clinician is able to choose which metrics, locations and EEG bands are targeted in the training, with the goal of encouraging the client’s brainwave patterns toward those observed in the normative group. The z-builder program essentially allows the clinician to create a dataset to be used in place of a normative database for live z-score training. Applied to neuromeditation, it is possible to select the “peak” meditation samples described above and use this as the live z-score dataset. This allows the clinician a range of flexible ways to encourage a neuromeditation client toward their own peak meditation brainwave conditions. Combining this approach with the others offered in this chapter can be very powerful for the meditator and lends itself to continual refinement as the client learns, adapts and eventually engages in slightly different meditative states that can become the next target in a neuromeditation practice.

Clinical Applications of Neuromeditation There is abundant research showing that even a relatively brief training in meditation results in improvements in a range of conditions including anxiety, depression and pain (see Cahn & Polich, 2006 for a review). Unfortunately, much of the current brain imaging research has been conducted 76

Neuromeditation

based on the mindfulness-based stress reduction model. While this model is clearly effective, it is taught in many different formats and includes exposure to a variety of different meditation methods that may include elements of all meditation forms discussed in this chapter. Consequently, it is difficult to determine from the existing literature which meditation forms may be most effective for specific mental health and/or medical conditions. We can speculate that FA and OM strategies may be the most appropriate for ADHD as they both train aspects of attention. Specifically, FA may be ideal for training the ability to maintain focus and minimize mind wandering or distractions. OM training may be ideal for improving self-monitoring and executive skills associated with self-awareness. In addition, OM practices may be particularly beneficial in the treatment of anxiety. Previous research has established that persons demonstrating greater theta activity tend to have lower state and trait anxiety scores (Inanaga, 1998). Not surprisingly, increased frontal theta during meditation has been associated with decreases in both state and trait anxiety levels (Shapiro, Jr., 2008; M. West, 1987). A comparison study, examining the EEG signatures of a concentrative (FA) practice versus a mindfulness meditation, found that the mindfulness (OM) practice resulted in higher levels of frontal theta (Dunn et al., 1999). AST protocols may be best for psychological disorders involving disruptions in a sense of self. Interestingly, some neurofeedback practitioners have already begun using a form of this training (reward alpha at Precuneus) in the treatment of Personality Disorders and addictions. Mark Smith noted that he has found this form of training to be the most responsive to producing a calm sense of integrity in these clients (personal communication, Mark Smith, January 14, 2013). Perhaps, in the individual with a poor sense of self, quieting the illogical or distorted perceptions of self allows for a broader perspective which is beneficial in the development of a healthy ego. There is some evidence for this in a recent study which found higher levels of thought-action fusion positively correlated to activation of the Precuneus (Jones & Bhattacharya, 2014). Once the ego is developed, this same process of quieting our internal, self-involved stories may allow us to “let go” and move beyond our sense of self which has become “small” in relation to a broader, more spacious form of consciousness or spirituality. LK-C practices, with an emphasis on establishing positive affect and empathy toward others, may be ideally suited as a treatment for depression. Interestingly, a study comparing FA and LK-C meditations found the two different forms to be equally successful at shifting frontal alpha dominance toward the right. Upon closer examination, it was discovered that subjects with higher levels of brooding were less successful at using the LK-C meditation, but were more successful at the FA practice. Those with lower levels of brooding had the reverse pattern (Barnhofer et al., 2010). Other research examining the impact of meditation on depression and brain activation patterns has found that even a relatively short-term practice of FA meditation significantly alters frontal alpha asymmetry in a direction associated with more positive emotion (Moyer et al., 2011). Thus, to optimize success, the type of meditation utilized may depend on symptom expression and personality characteristics (Bernhardt & Singer, 2012; Stinson & Arthur, 2013).

General Guidelines The ideas presented in this chapter are based in current understandings of the brain and meditation processes as well as the clinical experience of the author. The proposed practices presented in this chapter have not been fully evaluated and will require examination through research and continual refinement as our knowledge unfolds. Neurotherapists are urged to take the ideas developed here as a “starting point” rather than the “final word.” In addition, the information offered here is designed to be used in conjunction with, rather than a substitute for, specific meditation practices. Each area of the brain is responsible for multiple coordinated functions and can be activated by many different 77

Jeffrey M. Tarrant

mechanisms. Simply recreating a specific brainwave signature is not necessarily the same thing as specific meditation approaches and does not necessarily result in the same outcomes. The protocols suggested in this chapter should be utilized flexibly with attention to the individual differences of each meditator. It is important to understand the function of the neural mechanisms targeted as well as the goal of the specific meditation tradition and the way they are applied by the individual meditator. With this attitude, the process of neuromeditation becomes an opportunity for both the clinician and meditator to learn a great deal about the mind and its actions.

References Aftanas, L., & Golocheikine, S. (2001). Human anterior and frontal midline theta and lower alpha reflect emotionally positive state and internalized attention: High-resolution EEG investigation of meditation. Neuroscience Letters, 310(1), 57–60. doi:10.1016/S0304–3940(01)02094–8 Aftanas, L., Lotova, N., Koshkarov, V., & Popov, S. (1998). Non-linear dynamical coupling between different brain areas during evoked emotions: An EEG investigation. Biological Psychology, 48(2), 121–138. doi:10.1016/ S0301–0511(98)00015–5 Aftanas, L., Varlamov, A., & Pavlov, S. (2001). Affective picture processing: Event-related synchronization within individually defined human theta band is modulated by valence dimension. Neuroscience Letters, 303(2), 115–118. doi:10.1016/S0304–3940(01)01703–7 Anand, B., Chhina, G., & Singh, B. (1961). Some aspects of electroencephalographic studies in yogis. Electroencephalography and Clinical Neurophysiology, 13(3), 452–456. doi:10.1016/0013–4694(61)90015–3 Arambula, P., Peper, E., Kawakami, M., & Hughes Gibney, K. (2001). The physiological correlates of Kundalini Yoga meditation: A study of a yoga master. Applied Psychophysiology and Biofeedback, 26(2), 147–153. doi:10.1023/A:1011343307783 Asada, H., Fukuda, Y., Tsunoda, S., Yamaguchi, M., & Tonoike, M. (1999). Frontal midline theta rhythms reflect alternative activation of prefrontal cortex and anterior cingulate cortex in humans. Neuroscience Letters, 274(1), 29–32. doi:10.1016/S0304–3940(99)00679–5 Baehr, E., & Baehr, R. (1997). The use of brainwave biofeedback as an adjunctive therapeutic treatment for depression: Three case studies. Biofeedback, 25(1), 10–11. Retrieved from http://scholar.google.com/scholar ?hl=en&q=The+use+of+brainwave+biofeedback+as+an+adjunctive+therapeutic+treatment+for+depressi on:++Three+case+studies.++&btnG=&as_sdt=1,26&as_sdtp=#2 Baehr, F., Rosenfeld, J. P., Baehr, R., & Earnest, C. (1999). Clinical use of an alpha asymmetry protocol in treatment of mood disorders. In J. R. Evans & A. Abarbanel (Eds.), Introduction to quantitative EEG and neurofeedback (pp. 181–201). New York: Academic Press. Baijal, S., & Srinivasan, N. (2010). Theta activity and meditative states: Spectral changes during concentrative meditation. Cognitive Processing, 11(1), 31–38. doi:10.1007/s10339–009–0272–0 Banquet, J. (1973). Spectral analysis of the EEG in meditation. Electroencephalography and Clinical Neurophysiology, 35(2), 143–151. doi:10.1016/0013–4694(73)90170–3 Barnhofer, T., Chittka, T., Nightingale, H., Visser, C., & Crane, C. (2010). State effects of two forms of meditation on prefrontal EEG asymmetry in previously depressed individuals. Mindfulness, 1, 21–27. Başar, E., Schürmann, M., Başar-Eroğlu, C., & Karakaş, S. (1997). Alpha oscillations in brain functioning: An integrative theory. International Journal of Psychophysiology, 26(1–3), 5–29. doi:10.1016/S0167–8760(97)00753–8 Başar, E., Schürmann, M., & Sakowitz, O. (2001). The selectively distributed theta system: Functions. International Journal of Psychophysiology, 39(2–3), 197–212. doi:10.1016/S0167–8760(00)00141–0 Bernhardt, B., & Singer, T. (2012). The neural basis of empathy. Annual Review of Neuroscience, 35, 1–23. doi:10.1146/annurev-neuro-062111–150536 Brandmeyer, T., & Delorme, A. (2013). Meditation and neurofeedback. Frontiers in Psychology, 4(October), 688. doi:10.3389/fpsyg.2013.00688 Brefczynski-Lewis, J. A., Lutz, A., Schaefer, H. S., Levinson, D. B., & Davidson, R. J. (2007). Neural correlates of attentional expertise in long-term meditation practitioners. Proceedings of the National Academy of Sciences of the United States of America, 104(27), 11483–11488. doi:10.1073/pnas.0606552104 Buckner, R. (2008). The brain’s default network. Annals of the New York Academy of Sciences, 1124, 1–38. Retrieved from http://onlinelibrary.wiley.com/doi/10.1196/annals.1440.011/full Cahn, B. R., Delorme, A., & Polich, J. (2010). Occipital gamma activation during Vipassana meditation. Cognitive Processing, 11(1), 39–56. doi:10.1007/s10339–009–0352–1 Cahn, B. R., & Polich, J. (2006). Meditation states and traits: EEG, ERP, and neuroimaging studies. Psychological Bulletin, 132(2), 180–211. doi:10.1037/0033–2909.132.2.180

78

Neuromeditation Carter, O. L., Presti, D. E., Callistemon, C., Ungerer, Y., Liu, G. B., & Pettigrew, J. D. (2005). Meditation alters perceptual rivalry in Tibetan Buddhist monks. Current Biology, 15(11), R412–R413. doi:10.1016/j. cub.2005.05.043 Cohen, R. A., Kaplan, R. F., Moser, D. J., Jenkins, M. A., & Wilkinson, H. (1999). Impairments of attention after cingulotomy. Neurology, 53(4), 819. doi:10.1212/WNL.53.4.819 Collura, T., Thatcher, R., Smith, M. L., Lambos, W., & Stark, C. (2009). EEG biofeedback training using live Z-scores and a normative database. In T. H. Budzynski, H. K. Budzynski, J. R. Evans, & A. Abarbanel (Eds.), Introduction to quantitative EEG and neurofeedback: Advanced theory and applications (2nd ed.) (pp. 103–140). New York: Academic Press. Craig, A.D.B. (2009). How do you feel—now? The anterior insula and human awareness. Nature Reviews Neuroscience, 10(1), 59–70. doi:10.1038/nrn2555 Craigmyle, N. A. (2013). The beneficial effects of meditation: Contribution of the anterior cingulate and locus coeruleus. Frontiers in Psychology, 4, 731. doi:10.3389/fpsyg.2013.00731 Crane, R. (2007). Infinite potential: A neurofeedback pioneer looks back and ahead. In T. H. M. Press (Ed.), Handbook of neurofeedback: Dynamics and clinical applications: Haworth series in neurotherapy (pp. 3–21). Binghamton, NY: CYC Press. Crottaz-Herbette, S., & Menon, V. (2006). Where and when the anterior cingulate cortex modulates attentional response: Combined fMRI and ERP evidence. Journal of Cognitive Neuroscience, 18(5), 766–780. doi:10.1162/ jocn.2006.18.5.766 Damasio, A., & Van Hoesen, G. (1983). Emotional disturbances associated with focal lesions of the limbic frontal lobe. In K. M. Heilman & P. Satz (Eds.), Neuropsychology of human emotion (pp. 85–110). New York: Guilford. Davidson, R. J. (2000). Affective style, psychopathology, and resilience: Brain mechanisms and plasticity. American Psychologist, 55(11), 1196–1214. doi:10.1037/0003–066X.55.11.1196 Deepak, K. K., Manchanda, S. K., & Maheshwari, M. C. (1994). Meditation improves clinicoelectroencephalographic measures in drug-resistant epileptics. Biofeedback and Self-Regulation, 19(1), 25–40. doi:10.1007/ BF01720668 Dietl, T., Dirlich, G., Vogl, L., Lechner, C., & Strian, F. (1999). Orienting response and frontal midline theta activity: A somatosensory spectral perturbation study. Clinical Neurophysiology, 110(7), 1204–1209. doi:10.1016/ S1388–2457(99)00057–7 Dunn, B., Hartigan, J., & Mikulas, W. (1999). Concentration and mindfulness meditations: Unique forms of consciousness? Applied Psychophysiology and Biofeedback, 24(3), 147–165. doi:10.1023/A:1023498629385 Echenhofer, F., Coombs, M., & Samten, L. (n.d.). EEG and P300 differences during meditation and rest in advanced Tibetan Buddhist and beginning meditators. Paper presented at the annual meeting of the Society for Psychophysical Research. San Diego, CA. Fehmi, L., & Robbins, J. (2007). The open-focus brain: Harnessing the power of attention to heal mind and body. Boston, MA: Trumpeter Books. Fenwick, P. (1987). Meditation and the EEG. In M. A. West (Ed.), The psychology of meditation (pp. 104–117). New York: Clarendon Press/Oxford University Press. Ghista, D. N., Nandagopal, D., Ramamurthi, B., Das, A., Mukherji, A., & Srinivasan, T. M. (1976). Physiological characterisation of the “meditative state” during intuitional practice (the Ananda Marga system of mediation) and its therapeutic value. Medical & Biological Engineering, 14(2), 209–214. doi:10.1007/BF02478750 Grossman, P., Niemann, L., Schmidt, S., & Walach, H. (2004). Mindfulness-based stress reduction and health benefits. Journal of Psychosomatic Research, 57(1), 35–43. Retrieved from http://www.sciencedirect.com/science/ article/pii/S0022399903005737 Hammond, D. C. (2000). Neurofeedback treatment of depression with the Roshi. Journal of Neurotherapy, 4(2), 45–56. Hammond, D. C. (2005). Neurofeedback treatment of depression and anxiety. Journal of Adult Development, 12(2/3), 131–137. Hammond, D. C., & Baehr, E. (2009). Neurofeedback for the treatment of depression: Current status of theoretical issues and clinical research. In T. H. Budzynski, H. K. Budzynski, J. R. Evans, & A. Abarbanel (Eds.), Introduction to quantitative EEG and neurofeedback: Advanced theory and applications (2nd ed., pp. 295–313). New York: Academic Press. Hardt, J., & Kamiya, J. (1978). Anxiety change through electroencephalographic alpha feedback seen only in high anxiety subjects. Science, 201(4350), 79–81. doi:10.1126/science.663641 Hasenkamp, W., & Wilson-Mendenhall, C. (2012). Mind wandering and attention during focused meditation: A fine-grained temporal analysis of fluctuating cognitive states. Neuroimage. Retrieved from http://www. sciencedirect.com/science/article/pii/S1053811911007695 Hölzel, B. K., Carmody, J., Vangel, M., Congleton, C., Yerramsetti, S. M., Gard, T., & Lazar, S. W. (2011). Mindfulness practice leads to increases in regional brain gray matter density. Psychiatry Research, 191(1), 36–43. doi:10.1016/j.pscychresns.2010.08.006

79

Jeffrey M. Tarrant Huang, H., & Lo, P. (2009). EEG dynamics of experienced Zen meditation practitioners probed by complexity index and spectral measure. Journal of Medical Engineering & Technology, 33(4), 314–321. doi:10.1080/ 03091900802602677 Inanaga, K. (1998). Frontal midline theta rhythm and mental activity. Psychiatry and Clinical Neurosciences, 52(6), 555–566. doi:10.1046/j.1440–1819.1998.00452.x Jones, R., & Bhattacharya, J. (2014). A role for the precuneus in thought-action fusion: Evidence from participants with significant obsessive-compulsive symptoms. NeuroImage: Clinical, 4, 112–121. doi:10.1016/j. nicl.2013.11.008 Kabat-Zinn, J., & Chapman-Waldrop, A. (1988). Compliance with an outpatient stress reduction program: Rates and predictors of program completion. Journal of Behavioral Medicine, 11(4), 333–352. doi:10.1007/ BF00844934 Kabat-Zinn, J., Lipworth, L., & Burney, R. (1985). The clinical use of mindfulness meditation for the selfregulation of chronic pain. Journal of Behavioral Medicine, 8(2), 163–190. doi:10.1007/BF00845519 Kamiya, J. (1969). Operant control of the EEG alpha rhythm and some of its reported effects on consciousness. In C. Tart (Ed.), Altered states of consciousness (pp. 489–501). New York: Wiley. Kasamatsu, A., & Hirai, T. (1966). An electroencephalographic study on the Zen meditation (Zazen). Psychiatry and Clinical Neurosciences, 20(4), 315–336. doi:10.1111/j.1440–1819.1966.tb02646.x Khare, K., & Nigam, S. (2000). A study of electroencephalogram in mediators. Indian Journal of Physiology and Pharmacology, 44, 173–178. Retrieved from www.ijpp.com/IJPP%20archives/2000_44_2/173-178.pdf Klimesch, W. (1997). EEG-alpha rhythms and memory processes. International Journal of Psychophysiology, 26(1–3), 319–340. doi:10.1016/S0167–8760(97)00773–3 Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Research Reviews, 29(2–3), 169–195. doi:10.1016/S0165–0173(98)00056–3 Klimesch, W., Doppelmayr, M., Russegger, H., Pachinger, T., & Schwaiger, J. (1998). Induced alpha band power changes in the human EEG and attention. Neuroscience Letters, 244(2), 73–76. doi:10.1016/S0304–3940 (98)00122–0 Lee, M. S., Bae, B. H., Ryu, H., Sohn, J. H., Kim, S. Y., & Chung, H. T. (1997). Changes in alpha wave and state anxiety during ChunDoSunBup Qi-training in trainees with open eyes. The American Journal of Chinese Medicine, 25(3–4), 289–299. doi:10.1142/S0192415X97000329 Lehmann, D., Faber, P., Achermann, P., Jeanmonod, D., Gianotti, L. R. R., & Pizzagalli, D. (2001). Brain sources of EEG gamma frequency during volitionally meditation-induced, altered states of consciousness, and experience of the self. Psychiatry Research: Neuroimaging, 108(2), 111–121. doi:10.1016/S0925–4927(01)00116–0 Litscher, G., Wenzel, G., Niederwieser, G., & Schwarz, G. (2001). Effects of QiGong on brain function. Neurological Research, 23(5), 501–505. doi:10.1179/016164101101198749 Lutz, A., Brefczynski-Lewis, J., Johnstone, T., & Davidson, R. J. (2008). Regulation of the neural circuitry of emotion by compassion meditation: Effects of meditative expertise. PLoS ONE, 3(3), e1897. doi:10.1371/ journal.pone.0001897 Lutz, A., Dunne, J., & Davidson, R. (2006). Meditation and the neuroscience of consciousness. In P. Zelazo, M. Moscovitch, & E. Thompson (Eds.), Cambridge handbook of consciousnes (pp. 499–554). New York: Cambridge University Press. Lutz, A., Dunne, J., & Davidson, R. (2007). Meditation and the neuroscience of consciousness: An introduction. In P. D. Zelazo, M. Moscovitch, & E. Thompson (Eds.), The Cambridge Handbook of Consciousness (pp. 499–554). New York: Cambridge University Press. Lutz, A., Greischar, L. L., Rawlings, N. B., Ricard, M., & Davidson, R. J. (2004). Long-term meditators selfinduce high-amplitude gamma synchrony during mental practice. Proceedings of the National Academy of Sciences of the United States of America, 101(46), 16369–16373. doi:10.1073/pnas.0407401101 Mantini, D., & Vanduffel, W. (2013). Emerging roles of the brain’s default network. The Neuroscientist, 19(1), 76–87. doi:10.1177/1073858412446202 Marshall, M. S., & Bentler, P. M. (1976). The Effects of deep physical relaxation and low-frequency-alpha brainwaves on alpha subjective reports. Psychophysiology, 13(6), 505–516. doi:10.1111/j.1469–8986.1976. tb00870.x Mason, M., Norton, M., & Horn, J. Van (2007). Wandering minds: The default network and stimulus-independent thought. Science, 315(5810), 393–395. Retrieved from http://www.sciencemag.org/content/315/5810/393.short Morse, D. R., Martin, J. S., Furst, M. L., & Dublin, L. L. (1977). A physiological and subjective evaluation of meditation, hypnosis, and relaxation. Psychosomatic Medicine, 39(5), 304–324. Retrieved from http://www. psychosomaticmedicine.org/content/39/5/304.short Moyer, C. A., Donnelly, M.P.W., Anderson, J. C., Valek, K. C., Huckaby, S. J., Wiederholt, D. A., Doty, R. L., Rehlinger, A. S., & Rice, B. L. (2011). Frontal electroencephalographic asymmetry associated with positive emotion is produced by very brief meditation training. Psychological Science, 22(10), 1277–1279.

80

Neuromeditation Murata, T., Koshino, Y., Omori, M., Murata, I., Nishio, M., Sakamoto, K., . . . Isaki, K. (1994). Quantitative EEG study on Zen meditation (Zazen). Psychiatry and Clinical Neurosciences, 48(4), 881–890. doi:10.1111/ j.1440–1819.1994.tb03090.x Murata, T., Takahashi, T., Hamada, T., Omori, M., Kosaka, H., Yoshida, H., & Wada, Y. (2004). Individual trait anxiety levels characterizing the properties of zen meditation. Neuropsychobiology, 50(2), 189–194. doi:10.1159/000079113 Niedermeyer, E., & Lopes da Silva, F. H. (2005). Electroencephalography: Basic principles, clinical applications, and related fields. Philadelphia, PA: Lippincott Williams & Wilkins. Ochsner, K. N., Kosslyn, S. M., Cosgrove, G. R., Cassem, E. H., Price, B. H., Nierenberg, A. A., & Rauch, S. L. (2001). Deficits in visual cognition and attention following bilateral anterior cingulotomy. Neuropsychologia, 39(3), 219–230. doi:10.1016/S0028–3932(00)00114–7 Pan, W., Zhang, L., & Xia, Y. (1994). The difference in EEG theta waves between concentrative and nonconcentrative qigong states—a power spectrum and topographic mapping study. Journal of Traditional Chinese Medicine, 14(3), 212–218. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/7799657 Petersen, S. E., & Posner, M. I. (2012). The attention system of the human brain: 20 years after. Annual Review of Neuroscience, 35, 73–89. doi:10.1146/annurev-neuro-062111–150525 Raffone, A., & Srinivasan, N. (2010). The exploration of meditation in the neuroscience of attention and consciousness. Cognitive Processing, 11(1), 1–7. doi:10.1007/s10339–009–0354-z Saletu, B. (1987). Brain function during hypnosis, acupuncture and transcendental meditation: Quantitative EEG studies. Advances in Biological Psychiatry, 16, 18–40. Retrieved from http://psycnet.apa.org/ psycinfo/1988–31900–001 Scheeringa, R., & Bastiaansen, M. (2008). Frontal theta EEG activity correlates negatively with the default mode network in resting state. International Journal of Psychophysiology, 67(3), 242–251. Retrieved from http://www. sciencedirect.com/science/article/pii/S0167876007001638 Schürmann, M., & Başar, E. (2001). Functional aspects of alpha oscillations in the EEG. International Journal of Psychophysiology, 39(2–3), 151–158. doi:10.1016/S0167–8760(00)00138–0 Shapiro, Jr., D. H. (2008). Meditation: Self-regulation strategy and altered state of consciousness. Piscataway, NJ: AldineTransaction. Singer, T., Seymour, B., O’Doherty, J., Kaube, H., Dolan, R. J., & Frith, C. D. (2004). Empathy for pain involves the affective but not sensory components of pain. Science, 303(5661), 1157–1162. doi:10.1126/ science.1093535 Stinson, B., & Arthur, D. (2013). A novel EEG for alpha brain state training, neurobiofeedback and behavior change. Complementary Therapies in Clinical Practice, 19(3), 114–118. doi:10.1016/j.ctcp.2013.03.003 Swick, D., & Turken, A. U. (2002). Dissociation between conflict detection and error monitoring in the human anterior cingulate cortex. Proceedings of the National Academy of Sciences of the United States of America, 99(25), 16354–16359. doi:10.1073/pnas.252521499 Takahashi, T., Murata, T., Hamada, T., Omori, M., Kosaka, H., Kikuchi, M., . . . Wada, Y. (2005). Changes in EEG and autonomic nervous activity during meditation and their association with personality traits. International Journal of Psychophysiology, 55(2), 199–207. doi:10.1016/j.ijpsycho.2004.07.004 Taneli, B., & Krahne, W. (1987). EEG changes of transcendental meditation practitioners. Advances in Biological Psychiatry, 16, 41–47. Retrieved from http://psycnet.apa.org/psycinfo/1988-31913-001 Thatcher, R. (2012). Handbook of quantitative electroencephalography and EEG biofeedback (1.0 ed.). St. Petersburg: Anipublishing Co. Travis, F., & Shear, J. (2010). Focused attention, open monitoring and automatic self-transcending: Categories to organize meditations from Vedic, Buddhist and Chinese traditions. Consciousness and Cognition, 19(4), 1110–1118. doi:10.1016/j.concog.2010.01.007 Wallace, R. (1971). A wakeful hypometabolic physiologic state. American Journal of Physiology, 221(3), 795–799. Retrieved from http://ajplegacy.physiology.org/content/221/3/795.short Wallace, R. K. (1970). Physiological effects of transcendental meditation. Science, 167(3926), 1751–1754. doi:10.1126/science.167.3926.1751 Wenger, M. A., & Bagchi, B. K. (2007). Studies of autonomic functions in practitioners of yoga in India. Behavioral Science, 6(4), 312–323. doi:10.1002/bs.3830060407 West, M. (1987). The psychology of meditation. New York: Clarendon Press/Oxford University Press. West, M. A. (2009). Meditation and the EEG. Psychological Medicine, 10(2), 369. doi:10.1017/S0033291700044147 Yogi, M. M. (1997). Celebrating perfection in education: Maharishi Vedic (2nd ed.). Noida, India: Maharishi Vedic University Press.

81

5 INVESTIGATING THE NEUROPLASTICITY OF CHRONIC PAIN UTILIZING BIOFEEDBACK PROCEDURES Stuart Donaldson, Mary Donaldson and Doneen Moran Abstract Norman Doidge in his book The Brain That Changes Itself (2007) describes chronic pain as a negative consequence of neuroplasticity or neuroplasticity gone wrong. Using their clinical experiences, the authors explore the issues involving chronic pain and its relevance to neuroplasticity. Changes in the peripheral and central nervous systems are examined through the use of surface electromyography (SEMG), quantitative electroencephalography (QEEG) and stress profiling techniques. The use of these techniques is combined with relevant research literature to produce a working model of the complex phenomena known as neuroplasticity. A case study concludes the presentation. Neuroplasticity, also known as brain plasticity, is an umbrella term that encompasses both synaptic plasticity and non-synaptic plasticity—it refers to changes in neural pathways and synapses which are due to changes in behavior, environment and neural processes, as well as changes resulting from bodily injury (Pascual-Leone et al., 2011). Neuroplastic changes may occur on a number of different physiological levels from cellular to cortical remapping after injury and is also involved in normal learning processes, performance enhancement and growth (maturation). Clinically the neuroplasticity model provides a theoretical model in which to explore and understand the neurological changes that occur with chronic pain. Chronic pain is thought to represent a maladaptation of the nervous system to repeated stimulation which in time produces a change in the nervous system itself. Examination of these changes requires an extensive knowledge of the three branches of the nervous system: (a) the central nervous system, (b) the peripheral nervous system and (c) the autonomic nervous system and the disturbances in one system that can cause alterations in the other systems. In most cases chronic pain initially involves repeated stimulation at a peripheral site which involves noxious stimuli and inflammation. This repeated stimulation over time elicits a neuroplastic response at the cortical level leading to changes in somatotopic organization including central sensitization (Seifert & Maihöfner, 2011). Norman Doidge introduced the concept of neuroplasticity in its numerous forms in his book The Brain That Changes Itself (Doidge, 2007). Outlining the numerous ways changes in the central nervous can enhance the quality of life, he also points out that these changes can be maladaptive, as seen in chronic pain.

82

The Neuroplasticity of Chronic Pain

Doidge postulates that when a part of a body is lost the brain hungers for stimulation, releasing little sprouts to grow and develop looking for nerves or sites in the brain that are similar in type to those lost. Often this occurs but occasionally cross wiring errors happen leading to a mixing of nerve impulses. Also the brain appears to be constantly changing (Doidge, 2007) as repeated mapping of the brain shows that it changes its contours dependent upon the stimulation. Doidge suggests that these factors play a role in the development of the neuroplastic aspects of chronic pain. In a few rare cases repeated stimulation is not evident, being replaced by singular overwhelming trauma such as a motor vehicle accident. Numerous researchers have demonstrated these central nervous system changes. Maihöfner, Handwerker, Neundorfer and Birklein (2003) demonstrated that individuals experiencing complex regional pain syndrome (in the hand) demonstrate a diminished cortical somatotopic representation of the hand contralaterally as well as a decreased spacing between the representation of the hand and the mouth in the somatosensory cortex. Reduction of the volume of gray matter in the prefrontal cortex and thalamus was reported by Apkarian et al. (2004) in chronic pain. Similar results have been reported for phantom limb pain (Karl, Birbaumer, Lutzenberger, Cohen & Flor, 2001), chronic low back pain (Flor, Braun, Elbert & Birbaumer, 1997) and carpal tunnel syndrome (Napadow et al., 2006). The peripheral nervous system also appears to be impacted or involved with neuroplasticity on two levels. Evidence from numerous authors suggests that the peripheral nerves also respond to repeated stimulation demonstrating that dendrites grow in diameter and length while their axons spread the field of innervation. For example, recent research (Navarro, Vivo & Valero-Cabre, 2007) suggests that peripheral nerve injury induces a cascade of events at the systemic, cellular and molecular levels progressing throughout the spinal cord to the brainstem relay nuclei, thalamus and cortex. This brief overview of the literature is not intended to be comprehensive but to give the reader an idea of the theoretical basis for the working model and rationale for the assessment of chronic pain.

Clients During the year 2013 there were 251 new clients seen ranging from children with Attention Deficit Disorder to adults with severe chronic pain of various etiologies. Of the clients seen 133 were of the chronic pain variety with the majority suffering from fibromyalgia or myofascial pain syndromes. These chronic pain sufferers were on the average 47.8 years old, had been in pain for 2–30 years (median 5 years) and were predominantly female and right-handed. The clinic is private in nature with the client responsible for their own expenses. This produces a skewed distribution of type of client, usually upper middle to upper class with a higher education. Participation in the clinic programs was voluntary in nature with a full disclosure of treatment effects and risks occurring before participation.

Evaluation The clinical investigation is directed towards understanding all the factors which contribute to the presence and maintenance of the chronic pain. The question to be addressed is: how does neuroplasticity develop and how is it then maintained in chronic pain? To address this question a five-part evaluation is conducted. The five parts include: (a) presenting problem, (b) history as relevant to the presenting problem, (c) central nervous system contributions, (d) peripheral nervous system contributions and (e) autonomic nervous system contributions.

Presenting Problem(s) The chronic-pain patients seen are usually suffering from: (a) chronic muscle pain (i.e. headaches, low back pain and muscle pain throughout the body) with (b) cognitive complications or issues (i.e.

83

Stuart Donaldson et al.

problems with concentration, loss of focus, poor memory) and/or (c) emotional issues (anxiety or depression). During this phase of the interview these three primary tracks are being explored. Approximately 90% of these individuals report that they have been diagnosed by their health care provider as having fibromyalgia. The emphasis on assessing and treating chronic muscle pain did not take off until the introduction of the concept of fibromyalgia in the late 1990s. The release of the Task Force Report by Wolfe et al. (1990) started the trend to diagnose most chronic muscle pain as fibromyalgia. Most patients start the interview stating, “I want to get rid of my pain and get back to the way I was before the start of the pain.” Time is spent discussing their pain, describing it in detail and exploring how it started and when it started. This emphasis on the pain at the start serves to validate the individual as most people have been told it’s in their head and there is no objective data to support their complaints. The second thing that happens is somebody is listening in a nonjudgmental way. As the interview proceeds the ideas of trigger points and tender points are introduced as the cause of their pain, plus the concepts of myofascial pain syndromes and of fibromyalgia are introduced (if needed). The textbooks of Myofascial Pain (Travell & Simons, 1983) are introduced, with individuals shown pictures of what is expected to be the source of their pain (i.e. trigger points) and the referred pain patterns. This is very validating to individuals who feel neglected by the health care system, with more than a few tears seen at this point. It is a big help to have a diagram of a human body with views of both front and back. Most people will circle specific areas indicating back pain, or headaches or whatever. Some will circle the entire body, both front and back, while others will circle several specific areas of pain. This represents the first clues for diagnosis. The person who circles the whole body probably has some central summation process occurring, probably involving allodynia and hyperalgesia consistent with fibromyalgia. The person with a specific site or two of pain probably has a myofascial pain syndrome involving the peripheral nervous system. The individual with multiple but specific sites presents a greater challenge as there may be central, peripheral and autonomic issues which need further investigation. Time is spent exploring when and where the pain developed. Answers to this question are varied, ranging from on such and such a date when the motor vehicle accident happened, to that of being not sure as there was more of an insidious onset. The fixed date answer represents more of a challenge to determine the time of onset because the fixed date of the accident represents the date of the onset of acute but not chronic pain. This is important to remember because the development of trigger points (myofascial pain syndrome) takes about six weeks to occur after the trauma and the length of time to show marked neuroplastic changes is not well known. The focus of the interview then changes to that of establishing how they are doing cognitively. Issues concerning memory, focus, concentration and the ability to multi-task are discussed. Decreased abilities in these areas are red flags for a need for further investigation of central processes and how the brain is functioning. Most people are confused by the noted cognitive decline, thinking they may be developing Alzheimer’s disease. If the onset is associated with a trauma (i.e. motor vehicle accident) it is important to detail if there was a loss of consciousness or if they were dazed or violently thrown about as this points to a possible closed head injury or whiplash involving the neck. While all this is occurring a mental status examination is being conducted. It is important to know the psychiatric status of the person as individuals with significant emotional issues are screened out at this time. The clinic’s focus is on rehabilitation of chronic pain, not the cure of psychiatric disorders. People eliminated from the program at this point are usually (a) those delusional about putting electrical signals into their brain, (b) those grossly angry with control issues (usually directed at insurance companies) and (c) individuals on certain types of medications that interfere with the biofeedback signals. 84

The Neuroplasticity of Chronic Pain

Conversations after this point are wide ranging and varied, discussing how the pain has impacted their lives in any area the patient wants to discuss and includes their interests and hobbies. All the time the question in the background is: what factors are causing and or maintaining the pain?

History During the wide ranging conversation a review of the individual’s medical, social and employment history will be conducted. Details of these parts of people’s lives are important to neuroplasticity and how it has developed. The medical background is reviewed to see if a pattern of illness existed before the development of the chronic pain. The thinking is, the more the individual was ill before the onset of the pain, the greater the likelihood that the nervous system is wired to be responsive to the new pain (directly reflecting the concepts of allodynia and hyperalgesia). Also at the same time factors pertaining to secondary gain can be explored. Secondary gain is not considered as important as the number of times the individual got sick or suffered some severe illness except in extreme cases such as Munchhausen by Proxy. Illness such as severe viral infections can cause an increase in Theta activity during the course of the illness (Westmoreland, 1993). Patients will report that after the illness was over and they had fully recovered, cognitively they felt different with some type of diminished cognitive capacity. Also their reaction to immunizations is explored as a few chronic pain sufferers report an extreme reaction to the shots. Another issue examined is the number of general anesthetics they received. Social and employment issues are examined, exploring what type of social network they have and if the network is meeting their needs. Socially isolated individuals seem to take longer to recover. The thinking is that having others around, if nothing else, serves as a form of distraction from the pain. Getting people back to work is extremely important, for not only do they have the social network that work provides, but being active reduces the impact of depression. A key predictor as to whether or not the individual will recover from a trauma type onset of the chronic pain is the level of anger or rage the person feels. Often they have feelings of being victimized, of being in the wrong place at the wrong time and yet having to prove they were injured and they are not faking it. They want their just reward and the insurance company is going to pay. These cases usually involve a lawyer as well, introducing a secondary factor as to how much the lawyer reinforces the complainant. On the basis of the information as gathered above the individual is given the option of being evaluated or they can drop out. A comprehensive evaluation involves: (a) a quantitative electroencephalograph (QEEG), (b) a mini cognitive functioning test, (c) a trigger point and dolorimeter examination, (d) a surface electromyographic (SEMG) evaluation and (e) a psychophysiological stress test. Points (a) and (b) are designed to assess central nervous system functioning, points (c) and (d) are designed to assess peripheral nervous system functioning and point (e) is designed to assess autonomic functioning.

Central Nervous System Contributions A routine QEEG is performed on any person with suspected central nervous system (CNS) contributions to the chronic pain. Indications for testing include but are not limited to: (a) cognitive issues such as decreased memory, needing to reread articles several times, unable to recall words when appropriate, poor focus and concentration, and decreased ability to multi-task; (b) changes in emotional status such as increased irritability, depression and anxiety; and (c) poor results from numerous physical therapies. (Patients usually report they have seen 3 to 5 therapists, usually reporting an initial decrease in pain of 30% with any treatment, then plateauing and not responding to further treatments.) 85

Stuart Donaldson et al.

The QEEG is performed following standard protocols and utilizes the Neuroguide database as developed by Thatcher (2012) for data interpretation. The reader is referred to Thatcher (2012) for details of the procedures and database. The raw data from the QEEG is inspected, examining for epileptiform phenomena. With any evidence of this in the tracings the patient is immediately referred back to the physician for investigation by a neurologist and a recommendation that treatment can continue. If the raw tracings are acceptable the entire QEEG is reviewed for deviance from normal. There is no one pattern that is associated with chronic pain so all aspects of the data are examined. Deviances are most commonly found in absolute power, on the Mild Traumatic Brain Discriminate Index (MTBI), coherence, to a lesser extent in phase lag and in Brodmann areas 21 and 23 on the LORETA. Absolute power most often shows increased Beta activity (18–25 Hz) frontally in the fibromyalgia population (Donaldson, Donaldson, Mueller & Sella, 2003). Consistent with this is decreased Delta throughout the cortex. In the myofascial population that is resistant to change, increased Beta activity of 25 Hz is seen specifically at Cz with physical improvement not noted until this activity is decreased. In general, deviant activity in absolute power needs to be altered (normalized) for both fibromyalgia and myofascial pain before physical therapies start to work and the pain changes. Patterns of coherence, phase lag and MTBI results are more difficult to relate to chronic pain as a large percentage of our sample is post motor vehicle accident. Clinical observations by Donaldson suggests that changes in the right hemisphere in coherence and phase lag (to a lesser extent) are associated with a decrease in chronic pain. Specific sites affected include F8, F4, C4, P4, T4 and T6. At this time it is not known if these results are causal or correlational. Data from the MTBI is utilized in a couple of different ways. First it is used to confirm that there is a probability of membership in the closed head injury population. This is particularly important for those individuals thinking they have Alzheimer’s disease or think they are imagining their cognitive problems. The severity index gives them an understanding as to the nature and extent of the injury. The actual results are not used to specifically direct treatment. It is important these results are communicated not only with the patient but with a significant other or spouse as recent research shows that within 5 years 17–48% (Arango-Lasprilla et al., 2008; Kreutzer, Marwitz, Hsu, Williams & Riddick, 2007) of individuals sustaining a closed head injury in an MVA are divorced. The MTBI results are an attempt to help the other understand what the patient is going through, is not faking or malingering and suggest resources to help them cope. Results of the cognitive functioning testing are introduced at this point to reinforce and to connect the noted cognitive dysfunctions to the findings of the QEEG. Data produced by LORETA shows numerous areas can be affected by trauma, particularly Brodmann areas 21 (lateral temporal cortex) and 23 (posterior cingulate gyrus). This is not surprising considering their anatomical locations. However, what is not known is how trauma to these areas affects chronic pain. The neuroplasticity model predicts that the brain will change in response to changes in stimulation, whether external to itself or to changes that occur within the brain’s structure itself. Once these changes occur, as seen in chronic pain patients, it is believed that the changes have to be altered to break up the reinforcement patterns that exist to cause and maintain the pain. The QEEG serves this purpose well by showing exactly which areas and pathways have been altered.

Peripheral Nervous System Contributions The acquisition of motor skills (i.e. learning to walk) is a demonstration of the effect of the peripheral nervous system upon the central nervous system. The continued feedback of the movement and its outcome shapes neural pathways that are retained for the rest of one’s life. While clumsy at the start, as repetitions occur the movement becomes more refined and efficient. Leonard, Moritani, 86

The Neuroplasticity of Chronic Pain

Hirschfeld, and Forssberg (1990) studied the H reflex in children as they grew up from birth to age five, comparing children suffering from cerebral palsy to those with normal development milestones. He found that by age five the H reflex had been suppressed in the normal sample while still present in the cerebral palsy sample. Grouped muscle pattern activity in children was studied by Janda and Stara (1965). They demonstrated that predictable patterns of coactivation existed in younger children throughout the body even in muscles far removed from the site of study. As the children matured this activity disappeared and was absent in normal adults. Under psychophysiological stress these patterns reappeared. The authors went on to demonstrate that these reappearing patterns can then be inhibited and disappear with biofeedback training. All of these results were reproduced by Gate (1967) replicating the effect of maturation upon motor control. Thus skill acquisition appears to be a learned behavior in which the inhibition of excessive or inappropriate muscle activity leads to the development and improvement of motor control and the desired movements (Donaldson, Nelson & Schulz, 1998). As indicated, the neuroplastic model suggests that changes in the central nervous system are associated with repeated stimulation from the peripheral nervous system. This may be both advantageous and harmful. Travell and Simons (1983) indicate that pain arising from muscle trauma affecting the neural activity at the dorsal horn for that vertebral body is 90 times greater and lasts 15 msec. longer when compared to pain from skin abrasions. Thus pain produced from muscular dysfunction(s) can potentially be a major factor in causing neuroplastic changes. As a tool for understanding the effects of neuroplasticity on the peripheral nervous system, surface electromyography (SEMG) procedures offer a vast wealth of information. SEMG techniques indicate (in no particular order) information about (a) muscle tonicity (hyper and hypo) (Sella, 2000), (b) force generation (Basmajian & DeLuca, 1985), (c) amount of force required to generate movement (Basmajian & DeLuca, 1985), (d) interactions with other muscles (Basmajian & DeLuca, 1985) and (e) timing of muscle interactions (Bolek, 2003) and their kinesiological properties. Disruptions in the inhibitory activity produces muscle dysfunctions which have been associated with numerous types of chronic pain including: (a) carpal tunnel (Skubick, Clasby, Donaldson & Marshall, 1993), (b) low back pain (Sihvonen, Partanen, Hanninen & Soimaakallio (1991), (c) headaches (Donaldson, Rozell, Moran & Harlow, 2012), (d) fibromyalgia (Donaldson, Snelling, MacInnis, Sella & Mueller, 2002), (e) trigger points (Donaldson, Skubick, Clasby & Cram, 1994) and (f ) cerebral palsy (Bolek, 2003). Presently, no other tool can offer such information which is so important in understanding the development of neuroplastic changes associated with chronic pain. Each one of the conditions listed above can result in altered neurological afferent sent back to the brain which in turn alters its systems. The continued presence of the disturbed signal leads to the development of trigger points and tender points, causing and maintaining the chronic pain and altering the signals in the brain, producing neuroplastic changes. SEMG assessment follows standardized protocols (Donaldson, 2003) in which electrodes are placed over the targeted muscles. Individuals are asked to sit or stand still and then to perform movements primary for that muscle. Data is captured at a sampling rate of 2048 samples per second for 10 muscles simultaneously as the person performs various movements primary for that muscle. Data is captured in Root Mean Square (RMS) format and analyzed for each muscle for: (a) level of activity at rest, (b) level of activity at rest as compared to the homologous partner, (c) maximum activity during unresisted movement, (d) maximum activity during unresisted movement as compared to its homologous partner, (e) activity as part of the myotatic unit and (f ) as part of the kinesiological functioning of the body. The SEMG signal provides the information that is going back to the brain. Over time the crossed extensor pathway becomes altered (increased and decreased afferent), affecting the learned inhibitory pattern leading to a change in the systems and neuroplastic based dysfunctions in both the central and peripheral systems. 87

Stuart Donaldson et al.

Psychophysiological Stress Profiling The activity of the autonomic nervous system (ANS) is studied through the use of psychophysiological stress profiling (PSP). The ANS functions automatically, controlling blood pressure, heart rate and rate of breathing, amongst other systems. While generally considered as an unconscious function, its activity can be brought under voluntary control through various conditioning techniques. The main concern in studying this system is to understand how its activity may be helping to maintain and enhance the chronic pain. Generally, increased autonomic activity will enhance the activity of the peripheral system in several different ways. Numerous physiological systems are monitored simultaneously, examining for significant deviances from established norms. The physiological systems monitored include (a) heart rate, (b) heart rate variability, (c) respiration, (d) peripheral hand temperature, (e) galvanic skin response, (f) muscle tension in the face and (g) muscle tension in the shoulders (upper trapezius). In addition, brain wave activity is monitored. The test follows a standardized protocol. The individual is hooked up to a computer. Then a baseline of activity is recorded for 2 minutes with eyes open, followed by 2 minutes of eyes closed. Then a computerized task is performed, followed by a rest period. This procedure is repeated for 8 tasks and recovery periods. Data is examined for deviations from normal, reaction to the stressors and the ability to recover. Each deviant measure is thought to play a role in the development of chronic pain or neuroplasticity. It is believed that the impact is significant on both the central and peripheral systems exacerbating dysfunctions or altered neural pathways. For example, increased heart rate, increased respiration and decreased peripheral hand temperature are all thought to exacerbate muscle pain and are associated with anxiety. Increased rate of breathing is thought to increase frontal Alpha. A heightened galvanic skin response prepares the body for a flight or fight response, creating a cascade of reactions peripherally and centrally. This factor is considered a major factor in numerous diseases associated with stress. In conclusion, the ANS has a major impact upon all the systems and needs to be included in the evaluation of any chronic pain situation.

Headaches: An Example of Neuroplasticity An example of how all these ideas culminate in creating chronic pain now follows. The individual is a middle-aged high performance executive who has suffered from chronic headaches for over 30 years. The headaches occurred daily, varying in location but more often in the temples. While diagnosed as migraines she did not respond well to migraine medications, often becoming nauseated with them, but persisted in taking them. Her history is that of abuse coupled with numerous falls off of horses, including one in which she was knocked unconscious. As it was thought that her sinuses were causing the problem, surgery to correct this problem was performed without success. A comprehensive evaluation showed the following results: QEEG: absolute power—decreased (2SDs) Alpha activity throughout, relative power—increased (2SDs) posterior Theta and central High Beta, phase lag issues involving right hemisphere, MTBI positive at 97.5% level with severity index of 2.33 principally affecting T4. SEMG: whiplash involving left sternomastoids, right cervical paraspinals, left scalenes, right lower trapezius. 88

The Neuroplasticity of Chronic Pain

PSP: low heart rate variability, rapid respiration, decreased peripheral hand temperature, facial muscle tension. (Please note the above results represent only information relevant to this discussion. Please see Donaldson et al. (2012) for the complete details of this evaluation) A routine course of treatment lasting six weeks was initiated. This included: a)

b) c) d)

EEG neurotherapy 1. utilizing LENS techniques to normalize the activity at T4, and 2. routine EEG neurotherapy to down train the excessive High Beta activity (> 20 Hz) at Cz. SEMG neuromuscular retraining techniques (Donaldson et al., 1998) were utilized to rebalance the activity of the sternomastoids, cervical paraspinals, scalenes and lower trapezius. Massage therapy was concurrently utilized to deactivate the associated trigger points. Heart rate variability training was also employed.

Headaches had diminished in six weeks and she was withdrawn from her migraine medications at that time. One year follow-up showed no presence of excessive High Beta activity at Cz and a normalization of the Z scores at T4. The neck muscles remained balanced and heart rate variability training was utilized as needed. Presently, she is almost completely symptom free except when the weather changes and chinooks occur. Also improved cognitive functioning is reported.

Neuroplasticity: A Factor in the Development of Motor Control As previously stated, neuroplasticity is an umbrella term that encompasses both synaptic plasticity and non-synaptic plasticity—it refers to changes in neural pathways and synapses which are due to changes in behavior, environment and neural processes, as well as changes resulting from bodily injury (Pascual-Leone et al., 2011). The acquisition of motor skills (i.e. learning to walk) is a demonstration of the effect of the peripheral nervous system upon the central nervous system supporting the concept of neuroplasticity. The continued feedback of the movement and its outcome shape neural pathways that are retained for the rest of one’s life. However, various authors (Gate, 1967; Janda & Stara, 1965; Leonard et al., 1990) suggest that skill acquisition occurs when neuromuscular reflexes and patterns are inhibited. It would appear that these reflexes and patterns are only inhibited and lie dormant as they reappear during periods of stress and disease (Janda & Stara, 1965). The question becomes: what part of the chronic pain process does neuroplasticity play? a)

b) c)

If neuroplasticity reflects changes in the system, in the above example, what part does the abnormal power at T4 play? Does it reflect an injury to the brain producing fewer cells to control that part of the brain or producing decreased ability to compensate or inhibit, or is it an incidental finding? Does the appearance of increased High Beta activity at Cz reflect a loss of inhibitory control or increased compensatory activity? Janda and Stara’s 1965 work would suggest the former. What does the appearance of increased activity in the Theta frequencies in the posterior part of the brain indicate? Any of the above reasoning could be applied to this finding. 89

Stuart Donaldson et al.

d)

e)

The correction of the muscle imbalances in the neck would indicate that correct inhibitory processes in the peripheral control mechanisms had been re-established. These changes are consistent with the ideas of neuroplasticity affecting the peripheral mechanisms, but can the muscle changes be an explanation for the noted changes in brain wave activity? The noted changes in autonomic activity are also evident. Does the improvement in muscle control and changes in brain wave activity lead to a decreased autonomic activity, or does the opposite happen?

Conclusion Neuroplasticity is basically a mechanism that allows for an organism to adapt to changes in the environment. While this phenomenon is occurring daily in minuscule amounts, major changes in the processing of environmental stimuli through alterations in the functioning of different parts of the brain have been shown to be possible. These changes, while generally positive in nature, can also have negative consequences as seen in the development of chronic pain. The development of chronic pain is seen in changes occurring in the central, peripheral and autonomic nervous systems. A change in one of these systems not only affects its own functioning but the functioning of the other parts of the nervous system. Biofeedback procedures offer the health care provider an insight into these systems individually and collectively. Too often, the health care provider is trained in understanding only one system, often ignoring the impact of the remaining unchanged systems. Perhaps this is why short-term benefits (pain reduction) are reported but long-term results show the need for continued treatment or a relapse to the chronic pain cycle. Treatment programs which utilize multi-modality techniques are now demonstrating significant long-term outcomes (Flor, Fydrichc & Turk, 1992). Biofeedback techniques offer the health care provider a unique ability to see into the functioning of the three parts of the nervous system. No other techniques offer this ability. It is incumbent for the practitioner to be aware of the strengths and limitations of one’s techniques and refer to others who have different skills.

References Apkarian, A. V., Sosa, Y., Sonty, S., Levy, R. M., Harden, R. N., Parrish, T. B., & Gitelman D. R. (2004). Chronic back pain is associated with decreased prefrontal and thalamic gray matter density. Journal of Neuroscience, 24, 10410–10415. Arango-Lasprilla, J. C., Ketchum, J., Dezfulian, T., Kreutzer, J., ONeil-Pirozzi, T., Hammond, F., & Jha, A. (2008). Predictors of marital stability two years following brain injury. Brain Injury, 22(7–8), 565–574. Basmajian, J., & DeLuca, C. (1985). Muscles alive: Their function revealed by electromyography (5th ed.). Baltimore: Williams & Wilkins. Bolek, J. E. (2003, June). A preliminary study of modification of gait in real-time using surface electromyography. Applied Psychophysiology Biofeedback, 28(2), 129–138. Doidge, N. (2007). The brain that changes itself. London: Penguin Books. Donaldson, C.C.S. (2003). Guest editor special edition of surface electromyography. Journal of Applied Psychophysiology and Biofeedback, 28(2), 121–122. Donaldson, C.C.S., Nelson, D. V., & Schulz, R. (1998). Disinhibition in the gamma motoneuron circuitry: a neglected mechanism for understanding myofascial pain syndromes. Applied Psychophysiology and Biofeedback, 23(1), 43–56. Donaldson, C.C.S., Rozell, C., Moran, D., & Harlow, E. (2012). Multi-modal assessment and treatment of chronic headache. Biofeedback, 40(2), 67–74. Donaldson, C.C.S., Skubick, D., Clasby, R., & Cram, J. (1994). The evaluation of trigger-point activity using dynamic EMG techniques. American Journal of Pain Management, 4(3), 118–122. Donaldson, C.C.S., Snelling, L. S., MacInnis, A. L., Sella, G. E., & Mueller, H. H. (2002, February). Diffuse muscular coactivation (DMC) as a potential source of pain in fibromyalgia—part 1. Neurorehabilitation, 17(1), 33–39.

90

The Neuroplasticity of Chronic Pain Donaldson, M., Donaldson, C.C.S., Mueller, H. H., & Sella, G. (2003, April). QEEG patterns, psychological status and pain reports of fibromyalgia sufferers. American Journal of Pain Management, 13(2), 60–73. Flor, H., Braun, C., Elbert T., & Birbaumer, N. (1997). Extensive reorganization of primary somatosensory cortex in chronic back pain patients. Neuroscience Letters, 224, 5–8. Flor, H., Fydrichc, T., & Turk, D. C. (1992, May). Efficacy of multidisciplinary pain treatment centers: A metaanalytic review. Pain: The Journal of the International Association for the Study of Pain, 49(2), 221–230. Gate, V. (1967). Studies of the electrical activity of the antagonistic muscles of the arm in normal children aged between 1 and 5 months. Comptes Rendus de l’Académie Bulgare des Sciences, 20, 743–747. Janda, V., & Stara, V. (1965). The role of thigh adductors in movement patterns of the hip and knee joint. Courrier (Le Centre International de l’Enfance), 15, 1–3. Karl, A., Birbaumer, N., Lutzenberger, W., Cohen, L.G., & Flor, H. (2001). Reorganization of motor and somatosensory cortex in upper extremity amputees with phantom limb pain. Journal of Neuroscience, 21, 3609–3618. Kreutzer, J., Marwitz, J., Hsu, N., Williams, J., & Riddick, A. (2007). Marital stability after injury: An investigation and analysis. NeuroRehabilitation, 22(1), 53–59. Leonard, C., Moritani, T., Hirschfeld, H., & Forssberg, H. (1990). Deficits in reciporal inhibition of children with cerebral palsy as revealed by H reflex testing. Developmental Medicine and Child Neurology, 32, 974–984. Maihöfner, C., Handwerker, H. O., Neundorfer, B., Birklein, F. (2003). Patterns of cortical reorganization in complex regional pain syndrome. Neurology, 61, 1707–1715. Napadow, V., Kettner, N., Ryan, A., Kwong, K. K., Audette, J., & Hui, K. K. (2006). Somatosensory cortical plasticity in carpal tunnel syndrome: a cross-sectional fMRI evaluation. Neuroimage, 31, 520–530. Navarro, X., Vivo, M., & Valero-Cabre, A. (2007). Neural plasticity after peripheral nerve injury and regeneration. Progress in Neurobiology, 82(4), pp. 163–201. Pascual-Leone, A., Freitas, C., Oberman, L., Horvath, J. C., Halko, M., Eldaief, M., Bashir, S., Vernet, M., Shafi, M., Westover, B., Vahabzadeh-Hagh, A. M., & Rotenberg, A. (2011). Characterizing brain cortical plasticity and network dynamics across the age-span in health and disease with TMS-EEG and TMS-fMRI. Brain Topography, 24, 302–315. doi 10.1007/s10548–011–0196–8 Seifert, F., & Maihöfner, C. (2011). Functional and structural imaging of pain-induced neuroplasticity. Current Opinion in Anaesthesiology, 24, 515–523. Sella, G. E., (2000). Muscular dynamics: Electromyography assessment of energy and motion. Martins Ferry, OH: GENMED Publishing. Sihvonen, T., Partanen, J., Hanninen, O., & Soimaakallio, S. (1991). Electric behavior of low back muscles during lumbar pelvic rhythm in low back pain patients and healthy controls. Archives of Physical Medicine and Rehabilitation, 72, 1080–1086. Skubick, D., Clasby, R., Donaldson, C.C.S., & Marshall, W. (1993). Carpal tunnel syndrome as an expression of muscular dysfunction in the neck. Journal of Occupational Rehabilitation, 3(1), 31–44. Thatcher, R. W. (2012). Handbook of quantitative electroencephalography and EEG biofeedback. St. Petersburg, FL: Anipublishing Co. Travell, J., & Simons, D. (1983). Myofascial pain and dysfunction: The trigger point manual. Baltimore/London: Williams & Wilkins. Westmoreland, B. (1993). The EEG in cerebral inflammatory processes. In E. Niedermeyer & F. Da Silva (Eds.), Electromyography basic principles, clinical applications, and related fields (pp. 291–304). New York: Williams & Wilkins. Wolfe, F., Smythe, H., Yunus, M., Bennett, R., Bombardier, C., Goldenberg, D. L., Tugwell, P., Campbell, S. M., Abeles, M., & Clark, P. (1990). The American college of rheumatology 1990 criteria for the classification of fibromyalgia: Report of the multicenter criteria committee. Arthritis & Rheumatism, 33, 160–172.

91

6 WORKING WITH FORENSIC POPULATIONS Incorporating Peripheral Biofeedback and Brainwave Biofeedback into Your Organization or Practice Robert E. Longo and G. Michael Russo Abstract Since the turn of the century, one of the core themes in mental health has been the treatment of trauma especially from a brain-based perspective. The verification that the brain has plasticity opened new avenues for treatment and reinforced the use of biofeedback and brainwave biofeedback (neurofeedback) techniques as viable treatment options. The principle author has specialized in the fields of treating victims and perpetrators of sexual abuse, and began adapting treatment interventions addressing self-regulation skills for traumatized victims and interventions focusing on impulsivity and decision making skills for perpetrators. These physiological assessment and treatment modalities have direct application to most, if not all, forensic populations. This paper provides a brief overview of key aspects of using biofeedback, QEEG, and neurofeedback, and addresses why administrators and clinicians should incorporate the use of peripheral biofeedback and neurofeedback into the array of services used to address sexual perpetrators with the inference of its direct application to treat the majority of forensic populations in both public and private settings.

Introduction Until the past decade, the majority of programs treating perpetrators of sexual abuse did not incorporate the use of self-regulation treatments and interventions outside of traditional talk therapies. In fact, national surveys conducted by the Safer Society Foundation in the United States revealed that the majority of treatments, interventions, and methods were sex offender specific, i.e., empathy training, arousal reconditioning, victim impact issues, etc. (Bengis et al., 1999; Burton, Smith-Darden, Levins, Fiske, & Freeman-Longo, 2000; Freeman-Longo, Bird, Stevenson, & Fiske, 1995; Knopp, FreemanLongo, & Stevenson, 1993). Sexual offenders and substance abusers are two of the most likely populations to receive treatment in prison systems, community corrections, and in the community when placed on probation or parole. With forensic clients, few if any interventions have been geared towards self-regulation skills except for traditional talk therapies and limited and often misguided relaxation techniques. For example, interventions using biofeedback and neurofeedback were not being used in sex offender programing until

92

Working with Forensic Populations

the early 2000s. Most sex offender specific treatment programs did not incorporate biofeedback; and with one or two exceptions, to this date only a small handful of programs utilize neurofeedback (Bengis et al., 1999; Burton et al., 2000; Freeman-Longo et al., 1995; Knopp et al., 1993; Longo & Prescott, 2006; Longo, Prescott, Bergman, & Creeden, 2013; Prescott & Longo, 2010). This began to change in 2001 when the field became more informed about trauma and its impact on the brain (Longo, 2010, 2011). One organization that has recognized and implemented updates based on this information is The Council for Accreditation of Counseling and Related Educational Programs (CACREP). In 2009 CACREP mandated that accredited programs include material that facilitates knowledge pertaining to neurobiological development. With this development in the counseling education standards comes a unique opportunity for the inclusion of neurofeedback. Based on these standards, some universities have opted to include additional coursework that focuses specifically on neurofeedback and satisfies The Biofeedback Certification International Alliance didactic course requirements for certification. However, the 2009 standards are not intended to add courses, but rather to include multiple presentations of neurobiological material in current courses as educational opportunities for the students and, in turn, their future clientele (Ivey, Ivey, Zalaquett, & Quirk, 2009). Those counselors who implement neurofeedback as a tool in their practices will likely realize that a vital function of neurofeedback that positively contributes to its success is the sense of empowerment that individuals experience by understanding, recognizing, and altering mental states in order to promote growth and wellness (Myers & Young, 2012). With the turn of the century and the newfound understanding that the brain in fact does have plasticity, mental health treatments and therapies began to make a major shift. Neuroplasticity became a focal point in understanding mental health and brain function. We now understand that the brain has the ability to change and adapt to new and different experiences as well as the ability to grow new brain cells; a theory that was often rejected in the field of neuroscience and neurobiology just a few years before (Begley, 2007). Given the research findings supporting the use of both peripheral biofeedback and neurofeedback for the treatment of a variety of mental and physical health problems, and the advancement of knowledge regarding brain function and mind-body treatment, it becomes more difficult to turn a blind eye towards the use of these treatments. One might argue that individual practitioners, clinics, and programs could be challenged on a variety of levels regarding the quality of care they offer patients and clients if they are not providing these services. For example, The American Academy of Pediatrics1 has determined that biofeedback, including neurofeedback, is an evidenced-based treatment for ADHD, and is considered a Level 1 Best Support intervention. For those parents not wanting their children on medications for ADHD, biofeedback and neurofeedback become viable options. This article argues that treatment for forensic clientele should add biofeedback and neurofeedback to the existing treatment methods and modalities used with these populations. Biofeedback, neurofeedback, and QEEG are not substitutes for traditional mental health counseling and therapies, but rather should be considered as adjunct to a comprehensive program.

Self-Regulation Self-regulation is simply regulating oneself or itself. Self-Regulation Theory or SRT is a system of conscious and purposeful personal health management. Self-regulation is one of the focal points involved with the treatment of individuals in forensic populations due to its ability to address impulse control issues and focus on short-term desires. Often times individuals with low impulse control act on immediate desires resulting in an increased likelihood of partaking in deviant or criminal

93

Robert E. Longo and G. Michael Russo

behaviors. One such example of this deviant behavior is sexually abusive behaviors, which often occurs in the heat of the moment. For non-violent people, lack of self-regulation skills and impulsivity can lead to personal problems (i.e., addictions), financial problems (i.e., impulsive buying, gambling, etc.), interpersonal problems and loss of relationships, and health-related problems including but not limited to stress, anxiety, and sleep problems. So, a question that some readers might be asking is: What exactly is biofeedback? Dr. DeLee Lantz, Ph.D., a Senior Fellow and Board Certified in Biofeedback, describes biofeedback as: A modality that assists our mind and body to interact more effectively. Body signals (e.g. Heart Rate Variability, breathing, skin temperature, etc.) are often very subtle and often overlooked. With training we can learn to regulate these signals thus contributing to an increased sense of wellness.2 Both biofeedback and neurofeedback are valuable treatment modalities for helping patients and clients enhance their self-regulation skills. Research suggests that trauma treatment is more efficacious when biofeedback, specifically somatic modalities such as heart rate variability, and neurofeedback are used in conjunction as opposed to individually (Gevirtz & Dalenberg, 2008). Furthermore, Longo (2010, 2011) has demonstrated positive pre- and post-treatment outcomes and enhanced self-regulation skills using both biofeedback and neurofeedback with juveniles with sexual behavior problems. In the primary author’s clinical experience, the use of biofeedback and neurofeedback as an adjunctive therapy in a secure hospital facility has led to treatment successes with difficult cases that otherwise would have been discharged from the programs they participated in due to poor progress or a lack of satisfactory progress.

What Is Neuroplasticity? Neuroplasticity is the term used to describe a change in the connections of neurons as a result of a systematic reinforcement of stimuli over a period of time.3 However, not all persons experience neuroplasticity all of the time. This is often the mistaken belief and message given by uninformed persons. Neuroplasticity occurs when the following is present (PBS, 2009): 1) 2) 3) 4) 5)

6) 7)

Change occurs only when the brain is in the mood (attention is critical including behavioral circumstances/neurotransmitters are released). Change strengthens connections between neurons engaged at the same time. Neurons that fire together wire together. This process is strengthened when events/stimulus reliably occurs. Initial changes are just temporary. If the brain judges the experience to be novel (good or bad) they are more likely to become permanent. Brain plasticity is not a one-way street, meaning that the brain can “wire” so that neuronal firing occurs more frequently or less frequently. This possibility helps to explain behaviors such as addiction or chronic pain management techniques/treatments/theories. Memory is crucial for learning. The brain recalls previous situations as we develop a skill. Motivation is a key factor for brain growth and plasticity. When changes occur new networks are developed. The brain can repair itself and reorganize itself. New skills acquisition is important for plasticity and change.

In the majority of cases, patient and clients will make progress and improve as a result of the treatment process. However, there are some patients/clients who will not make enough progress to justify 94

Working with Forensic Populations

continued treatment. In some cases the absence of progress may be related to brain function based upon a history of psychological trauma or traumatic head injury. In these cases it is not that the patient/client is unmotivated but rather incapable of adequate participation.

Trauma and Its Impact on the Brain Despite age and/or gender, those who sexually abuse others have often times been victims of sexual abuse and/or neglect (Freeman-Longo, 1986, 1989; Longo & Prescott, 2006). Forensic populations in general are no different. In fact even non-forensic populations have histories of trauma that ultimately affect their overall health (more of this concept will be discussed in following sections regarding The Adverse Childhood Experiences (ACE) study). Both physical and psychological trauma can have a direct impact on the brain and brain development (Longo et al., 2013). The Training & Research Institute, Inc. (2004) in Albuquerque, New Mexico, notes that antisocial behavior can result from excessive activation of the limbic system (which is theorized to regulate memory and emotionality) and the prefrontal cortex (associated with judgment, moral rational, and insight) through perceived traumatic events such as: childhood neglect, physical, sexual, or emotional abuse. They also report that trauma, abuse, and neglect can affect the development of the amygdala, hippocampus, corpus collosum, prefrontal cortex, temporal lobes, cerebellar vermis, and left hemisphere as noted in more detail below. The amygdala is known as the “seat of emotion” due to its involvement with: charging memories with emotions, the mediation of sadness/depression and irritability/aggression, and determining the magnitude and risk associated with fearful situations. Childhood abuse or neglect leads to a significantly smaller or atrophied amygdala resulting in the increased risk for clinical depression, anger disorders, inaccurately charged emotional memories, difficulties or absence of fear conditioning, and psychopathic tendencies (Training & Research Institute, Inc., 2004). An overactive amygdala in those coping with Post-Traumatic Stress Disorder (PTSD) may result in generalization of the fear response, leading to an overall increase in fearful behavior (Ogden, Minton, & Pain, 2006). Some individuals may develop “limbic irritability” with a tendency toward overactive amygdalic responses to traumatic stimuli (Perry, Pollard, Blakley, Baker, & Vigilante, 1995). The hippocampus is known for the role that it plays in the creation and recall of verbal and emotionally based memories. Abuse or neglect in childhood often results in lower scores on verbal memory tests and mental health concerns during adulthood. Like the hippocampus, the temporal lobe is associated with the regulation of emotions and verbal memory. Additionally, childhood abuse or neglect impacts both the hippocampus and the temporal lobe in terms of decreased modulation of emotionality. However, the marked difference is that the impact of childhood abuse or neglect increases the chance for temporal lobe epilepsy. The largest neuronal mass bridging both the left and right hemispheres is known as the corpus collosum. It is due to this connectivity component that the corpus collosum plays a crucial role in coherence between the two hemispheres of the brain. Childhood abuse or neglect results in the atrophy (shrinkage) of the corpus collosum, which can negatively affect responses to everyday situations due to dilemmas with integration. As noted previously, the prefrontal cortex is associated with judgment, moral rational, and insight and acts as an editor of internal emotional states and defining crises. Childhood neglect and abuse impact the prefrontal cortex resulting in the increased likelihood of the development of clinical depression and/or unlawful behaviors. The cerebellar vermis regulates the creation and use of neurotransmitters. This structure has a large number of receptor sites for the stress-related hormones. Childhood abuse or neglect results in an increased chance for the development of depression, hyperactivity, attention deficits, and in some cases psychotic symptoms are possible. 95

Robert E. Longo and G. Michael Russo

In general, the left hemisphere is viewed as more “logical” and contributes to rectilinear or rational thought while providing a sense of balance to the “emotional” right hemisphere of the brain. The impact of childhood abuse or neglect results in a decreased control over emotionality resulting in ineffective sociability. Irritability, paranoia, psychosis, a tendency to pursue toxic relationships, selfsabotaging, or suicidality, are some of the inadequate social responses that those who have histories of childhood trauma could suffer from. Additionally, Ogden et al. (2006) note that the Anterior Cingulated Gyrus is responsible for emotional awareness, the experiential aspects of emotion, and integration of emotion and cognition, and it orchestrates the behavioral expression of emotion. When an individual experiences abuse and/or trauma, these experiences can impact all of the above functions. Leading trauma researcher, Bessel van der Kolk states, “Neurofeedback is a powerful treatment for traumatic stress.”4 His trauma center goes on to report, “Our treatment outcome data show that 70%–80% of neurofeedback clients show significant improvement after their first twenty sessions. . . . In 20 neurofeedback sessions, with feedback every half second, you get 72,000 chances to learn. That’s a lot of repetition and practice. Brain science has shown that repetitive exercise of brain networks reshapes the brain. Neurofeedback allows you to reshape your brain.” Regarding biofeedback, Gevirtz and Dalenberg (2008, p. 22) state: Recent research in the neurobiology of trauma supports the likelihood of more effective treatment with the inclusion of somatic techniques such as heart rate variability biofeedback. . . . Recent work in the trauma field has pointed to the need to integrate somatic techniques into the empirically based cognitive techniques such as cognitive behavioral therapy (CBT), acceptance commitment therapy (ACT), and dialectical behavioral therapy.

The ACE Study One of the most notable studies regarding the impact that childhood experiences have on health was The Adverse Childhood Experiences (ACE) Study. The study took place between The Centers for Disease Control and Prevention and The Kaiser Permanente’s Health Appraisal Clinic in San Diego from 1995–1997.5 One aspect that made The ACE study worth notation was the sheer size, which consisted of 17,337 Health Maintenance Organization (HMO) members. The members underwent a comprehensive physical examination, and provided details regarding family discord, abuse, and neglect. Through the findings of this study a few major risk factors were identified which were listed as leading causes of poor quality of life, sickness, and death in the United States. These include chemical dependency; liver disease; smoking; depression; suicidality; chronic obstructive pulmonary disease (COPD); ischemic heart disease (IHD); and relational difficulties such as: increased risk of intimate partner violence, multiple sexual partners, sexually transmitted diseases (STDs), unintended pregnancies, fetal death, adolescent pregnancy, and premature involvement with sexual activity.6 The ACE Study illustrated the above-mentioned factors and prevalence percentages in Table 6.1. Although the ACE study was conducted on a non-forensic population, one might infer that forensic populations would share similar statistics and thus similar health-related problems as noted above, of which chemical addiction/use, clinical depression, partner or relationship violence, and suicidality could benefit from either peripheral biofeedback, neurofeedback, or both interventions.

96

Working with Forensic Populations Table 6.1 ACE study. ACE Category

Women (N = 9,367)

Men (N = 7,970)

Total (N = 17,337)

Abuse Emotional Abuse

13.1

7.6

10.6

Physical Abuse

27.0

29.9

28.3

Sexual Abuse

24.7

16.0

20.7

Emotional Neglect

16.7

12.4

14.8

9.2

10.7

9.9

Mother Treated Violently

13.7

11.5

12.7

Household Substance Abuse

29.5

23.8

26.9

Household Mental Illness

23.3

14.8

19.4

Parental Separation or Divorce

24.5

21.8

23.3

5.2

4.1

4.7

Neglect Physical Neglect Household Dysfunction

Incarcerated Household Member

Practical and Ethical Considerations The majority of treatment providers and programs working with sexually abusive persons, and who are members of the Association for the Treatment of Sexual Abusers (ATSA; an international organization dedicated to the prevention and treatment of sexual abuse), adhere to the Code of Ethics and standards of practice as outlined by that organization. Most providers use “sex offender specific treatments” and follow the ATSA Code of Ethics (2001). The ATSA Code of Ethics requires members to participate in continuing education and professional growth, follow the regulations and expectations of his/her discipline/training, and attend an adequate number of training sessions and supervision in order to be deemed competent to accurately and reliably administer treatment modalities (Association for the Treatment of Sexual Abusers, 2001). Most professionals assessing and treating sexually abusive persons have relied on the Diagnostic and Statistical Manual of Mental Disorders (DSM), in its various versions as the “divine text” of diagnosing our patients and clients with sexual disorders as well as other mental health problems, i.e., depression, anxiety, ADHD, etc. The DSM, published by the American Psychiatric Association, allows clinicians and others a uniform basis for language and criteria when describing and diagnosing mental disorders. The current version, DSM-V, was met with disapproval and apprehension regarding its restrictions. Belluck and Carey (2013) noted in a New York Times article that the DSM suffers from a lack of scientific validity, resulting in a level of concern from the public, patient groups, and notable senior members from within the field of psychiatry. However, despite the discord, Dr. Thomas R. Insel, the Director of The National Institute of Mental Health (NIMH), describes “that one notable shift is occurring within DSM-V; the shift likely aims to facilitate a new focal point in the research from symptom description towards causal identification through additional research in biology, genetics imaging, cognitive and neurosciences. . . . Much of the previous research has guided us to the understanding that mental disorders often involve neural circuits which implicate specific areas of cognition, emotion, or behaviors.”7

97

Robert E. Longo and G. Michael Russo

Given the above, it is no surprise that individuals have looked to alternatives to medications; oftentimes the medications do not improve mental health problems and may have side effects whose consequences can negatively impact the individual’s overall quality of life (Longo, 2013). As professionals we are required to keep up our education on new and emerging topics and issues related to our work through the reading of books and journals and attendance at workshops and conferences. When faced with science or professional findings that challenge our existing practices and belief systems, then we have an obligation to move with the science in order to provide our patients and clients with optimal care. The denouncing and rejection of the DSM-V is one such instance. One now must question whether the criteria in the DSM that we have used to diagnose a patient or client with a paraphilic disorder are reliable, as we have been led to believe. The field’s inability to clearly establish an etiological pathway or pathways for sexual offending is one such example. Longo and Prescott (2013) note that ethical practice requires proper training and recognition of scope of practice. They go on to say that before one begins to practice treatment techniques involving neurobiological practices, professionals must engage in proper supervision and training. On the other hand, these authors would throw out a slightly different challenge. For example, while it might be considered unethical to operate outside of one’s scope of practice, would it not be equally unethical to ignore the science and technology that can presumably provide our patients and clients with better and maybe more optimal assessment and care? If we invest in equipment, training, certification, and services to measure sexual interest and arousal such as the Abel Screen and Penile Plethysmography; the assessment of psychopathy with the Hare Psychopathy Checklist; and the measurement of truth versus deception regarding sexual history and behavior with Polygraphy, then are we not equally as obligated to utilize QEEG for assessing brain function and biofeedback and neurofeedback to treat disorders that might better respond to these treatment tools and modalities? If we pay professionals upwards of $1000 or more to provide a psychosexual evaluation, should we not spend the same amount for assessing one’s brainwave function? The authors strongly advocate that programs and practitioners who work with both perpetrators and victims of sexual abuse should incorporate biofeedback, QEEG, and neurofeedback into their assessment and treatment methods and modalities. The remainder of this chapter provides a brief overview and description of biofeedback, neurofeedback, and QEEG assessment, and briefly defines or covers related areas of interest.

What Is Biofeedback? Biofeedback has been around for over five decades. In recent years biofeedback has become a more sophisticated area of health and its definition has evolved. The BCIA,8 AAPB,9 and ISNR10 view biofeedback as a non-invasive treatment modality where the therapist attaches small sensors to the body. These sensors record specific bodily functions that are fed back to the client. Often times the feedback is provided either visually or by way of a sound/tone. Some of the common bodily functions that are recorded are skin temperature, muscle tension, and/or neuronal (brainwave) activity. With this technologically facilitated guidance, clients are able to make small changes that are not consciously perceived at first but can lead to symptom relief for a variety of disorders over a period of time. Generally, the term peripheral biofeedback refers to biofeedback techniques used to regulate heart rate, breathing, and the areas described below, and neurofeedback is the term used to specifically describe brainwave biofeedback.

The Impact of Breathing on Heart Rate The basis of all self-regulation skills involving both peripheral biofeedback and neurofeedback is proper breathing. Proper breathing skills are grounded in diaphragmatic breathing, or belly breathing. 98

Working with Forensic Populations

The client can visualize this process when they place their hands on their belly button and watch their hands rise and fall with each breath. This is contrasted to the significant rise and fall of the shoulders in non-diaphragmatic breathing. Breathing is oftentimes one of the first steps in the selfregulation process. This is due to the impact that breathing has on processes like heart rate. Faster and shallower breaths (shoulder breathing) can be observed in those who are experiencing a higher or a more aroused physiological state such as an acute stress response. This is correlated with a rapid heart rate. Conversely, a slower breathing rate is also correlated with a slower and more relaxed heart rate. Heart coherence occurs when your heart rhythms are smooth and balanced resulting in the optimization of all bodily systems.11 By adopting a pattern of 10-second rhythmic breathing techniques (5-second inhalation and 5-second exhalation), we are able to modulate the heart’s rhythm resulting in increased relaxation and increased heart coherence.12

What Measures Are Used in Peripheral Biofeedback? Peripheral biofeedback includes physical responses. The most common physical responses for which peripheral biofeedback is used include: EMG—Muscular reactivity and tension (Electromyography) Thermal—Hand and foot temperature (Thermofeedback) HRV—Heart rate and blood pressure (Heart Rate Variability) SCL/GSR—Sweat gland activity (Galvanic Skin Response) Respiration—Respiratory function through breathing patterns and rate RSA Feedback—Heart rate variability in synchrony with respiration (Respiratory Sinus Arrhythmia)

What Is Brainwave Biofeedback? Brainwave biofeedback is most commonly referred to as neurofeedback but is also known as EEG biofeedback, and neurotherapy. For the sake of simplicity we will refer to brainwave biofeedback (etc.) as neurofeedback. Neurofeedback is a type of biofeedback that allows the client to train his or her own brainwaves in live-time using operant conditioning techniques. However, neurofeedback is different from other biofeedback techniques due to the focus on the central nervous system, most often the brain. Neurofeedback involves attaching very sensitive, non-invasive sensors on the surface of the scalp. These sensors are able to record various brainwaves, which differ based on the frequency and power that they create. Delta is often described as the slowest but most powerful brainwave. Theta is slightly weaker but occurs more rapidly and is followed by Alpha and Beta. Gamma is the weakest but most rapidly occurring brainwave. By identifying the types of brainwaves, licensed and trained clinicians are able to create treatment protocols to assist the client with coping with specific symptomologies. When brain activity changes and falls within the neurofeedback protocol, a positive “reward,” typically in the form of a visual display and/or sound, is given to the patient (Soutar & Longo, 2011). Rewards/reinforcements can be as simple as a change or onset of an auditory pitch or occur visually as in the character moves on the TV/computer screen in a game. At first this will seem like a randomly occurring process to the client; however, their brain will begin to associate the reward with various patterns of firing. Based on the previously mentioned concept of neuroplasticity neuronal changes can occur. Over the course of treatment the client will begin to associate brainwave activity with specific mental states and begin to automatically apply these mental states to their day-to-day lives. Treatment sessions often range from 10–30 minutes in length and must occur minimally one time per week. Often times sessions will occur two times per week but in some cases this can occur more frequently for a minimum of 30–40 sessions. Conditions such as Traumatic Brain Injury (TBI) can 99

Robert E. Longo and G. Michael Russo

require 50–60 sessions while Autism Spectrum Disorder (ASD) can require 60–100 or more sessions in order for a lasting change to be made. Some clinicians report that clients can notice a change in as little as 5–10 sessions, depending on the symptomology; however, without the reinforcement of these neuronal pathways the client will likely experience a regression towards their initial baseline. Like most mental health concerns, session frequency differs based on the severity of the illness. However, some of the most common diagnoses/mental disorders where neurofeedback has been shown to be effective include: ADHD, chemical use/dependency, depression, anxiety, Post-Traumatic Stress Disorder (PTSD), epilepsy and seizures, Mild Traumatic Brain Injury (MTBI), sleep regulation, cognitive impairment, migraines, headaches, and chronic pain. It is important to note that in most states and countries treatment of mental disorders/illnesses fall within the scope of practice of specific licensures and professions. Those who wish to treat these clients should first consult their regional governing body to determine their treatment abilities. Additionally, before beginning neurofeedback, patients should have practice with basic biofeedback and diaphragmatic breathing which often helps to reduce anxiety and therefore enhance the neurofeedback experience. Skin temperature training, for example, could be beneficial with high anxiety clients due to the notion that it is associated with the “theta state” (Hall, 1977). However, it is the belief and practice of both of the authors of this chapter to conduct a QEEG Brain Map before beginning neurofeedback.

What Is QEEG? A QEEG (quantitative electroencephalography) brain map usually includes a complete 19-channel recording based on the international 10–20 system of electrode placement where an individual’s brain activity is compared to a standardized database. Once compared and computed, the clinician can see a range of differences that are depicted in terms of z-scores between the individual’s neuronal activity and the database as seen in the example figure 6.1.

Figure 6.1

An example of a QEEG.

100

Working with Forensic Populations

The picture displayed above is intended as an example only. The brain map indicates areas of abnormality and is used in conjunction with client concerns in the development of treatment protocols for neurofeedback training. EEG (electroencephalography) records the changes in electrical potentials from sensors that are placed on the surface of the scalp and displays these changes as “brainwaves.” EEG measures electrical activity originating from cortical structures (neocortex, cortex). The frontal lobes and temporal lobes are the largest connection to the neocortex. Hans Berger is identified as the first individual to record human brain activity in 1929; however, similar studies had been carried out on animals as early as 1870.

Understanding QEEG Taking an exam or providing a professional presentation: this mental state is often associated with high levels of anxiety. On a QEEG for an individual suffering from an anxiety-related disorder, such as PTSD, this similar state of mind might be noticed and identified as excessive Beta/HiBeta wave activity. This example is given to illustrate the point that our current brainwaves have a significant impact on our thoughts, feelings, and behaviors. Diagnostic brainwave patterns/disorders vary widely; and while QEEG is not currently used to make a diagnosis, it is commonly used for purposes of differential diagnosis. Another such example would be that of excessive Delta waves, which are slow but powerful waves and often range 1–3 cycles per second. When reviewing the QEEG of an individual who is suffering from a traumatic brain injury, this waveform might be quite profound around the area of the marked neurological damage. With this said, it is important that the trained neurotherapist recognize that there are numerous etiologies for these brainwave activities and the QEEG should be examined within the context of the patient’s medical history, goals/reasons for seeking treatment, and additional assessment techniques.

Some Common Findings with QEEG/Brainwaves Delta Waves The brain stem and cerebellum generate Delta waves (a slow wave). Delta generally does not give us clear indications for differential diagnostics. Arrhythmic Delta is normal, while rhythmic Delta may indicate pathology. Extremely slow Delta is significant of TBI, LD, and Dementia. Increased Delta in posterior area is indicative of learning disorder (LD). Increased Delta may also be indicative of head injury (TBI). Parietal lobe Delta affects association and cortex/processing. A Delta deficit is indicative of problems with working memory. Increased global Delta may indicate cognitive decline with age (Delta, Theta, and Alpha start to slow). Delta is usually a measure of white matter and can reflect white matter damage.

Theta Waves Theta emerges from the hippocampal loop (the septal hippocampal circuits in the limbic system), and is involved with memory searching, network linking, and emotional valence. Generally when there is increased Theta, there may be increases in Delta and Alpha (all slowing waves). High amplitude non-rhythmic Theta bursts are often seen with migraine headaches. Increased frontal region Theta is indicative of being overwhelmed, and emotions will often shut down. Elevated Theta may be indicative of a person not being able to grasp concepts, ideas, information, etc., and may also be indicative

101

Robert E. Longo and G. Michael Russo

of PTSD and/or depression. Frontal Theta and increased Theta in the front (and is higher than in the posterior) is also indicative of ADHD.

Alpha Waves Alpha represents the brain in a resting state or neutral state but prepared for action. It is often referred to as the idling frequency. Alpha is generated from resonance between the thalamus and the cortex, and should be higher on the right. The thalamus is thought of as the pacemaker of the brain. The brain idles in Alpha, and constantly shifts up into Beta and down into Theta. The traumatized brain idles too fast in the Beta direction or too slow in the Theta direction. Low Alpha may be indicative of anxiety, PTSD, and short-term memory impairment. Alpha should be higher in the right hemisphere than in the left hemisphere. Alpha asymmetry and locally increased Alpha are indicative of depression (too much Alpha in the left hemisphere). Slow (or low) Alpha can be indicative of: metabolic problems, toxin related issues, bipolar/depression, and substance abuse (i.e., marijuana use/abuse). Increased fast Alpha in the posterior may indicate emotional rumination. All sensory information coming into the body goes through the thalamus, which is divided up into regions that correspond to different areas of the brain. The thalamus acts as a sensory relay station due to its role in passing information to surrounding cortices. It is due to this function that the thalamus plays a crucial role in regulating cognitive and physiological arousal levels. The thalamus is also responsible for regulating sleep states and wakefulness. Decreased Alpha and increased Beta in frontals is indicative of impulsivity, controlled by anxiety, feeling overwhelmed, and impulsivity with explosiveness. Elevated Alpha is indicative of the brain locking up/overworking. Low Alpha is indicative of the brain not activating normally.

Beta Waves Beta is generated from resonances within the cortex. Beta should be higher on the left than on the right. Increased Beta asymmetry (global elevated Beta in the right hemisphere) is indicative of anxiety. Increased Beta in the left frontal area has been associated with blocking amygdala input. Beta hypercoherence may indicate anxiety, panic attacks, and test anxiety (panic attacks can look like a full body seizure—especially when there are sensory integration problems). Increased Beta alone is often indicative of withdrawal, that is the Alpha and Theta wave activity are lower. Increased Beta in the frontal lobes can be indicative of the person hiding feelings and emotions (flat affect may be seen). Increased Beta and decreased Alpha in the frontal region is indicative of impulsivity, controlled by anxiety, feeling overwhelmed, and impulsivity with explosiveness. Low Beta is often indicative of information/cognitive processing difficulties.

Summary and Recommendations In the United States of America, The Food and Drug Administration (FDA) is the governing body that oversees and regulates the usage of medical equipment. They identify the process of biofeedback as a form of relaxation therapy. Both biofeedback and neurofeedback work based on the principles of operant conditioning and reinforcement techniques of naturally occurring biological activity. With guidance, the client is able to apply these theories in order to reinforce specific neurological activity (in neurofeedback). This process usually results in permanent changes between 30–40 sessions, depending on treatment goals; the average noticeable change can occur in as little as 15–30 sessions. Neurofeedback has been identified as the most applicable and accessible form of technology (based in neuroscience) that counselors might employ in either private practice or academic settings (Myers & Young, 2012). 102

Working with Forensic Populations

In order to be effective when administering biofeedback and/or neurofeedback to clients and patients, it is essential that the profession undergo proper training. There is more to using biofeedback with patients and clients than buying software and hardware and using it with a particular population. Basic education in the use of biofeedback and neurofeedback is important, and a solid understanding of human anatomy and physiology is essential. The authors of this text and other professionals practicing biofeedback and neurofeedback encourage those interested in using these treatments in their organizations or practices to become board certified. To become board certified, one could do so through the Biofeedback Certification International Alliance (BCIA).13 The Biofeedback Certification International Alliance (BCIA) was originally founded in 1981 under the name of “The Biofeedback Institute of America” with the goal to provide a common certification source for individuals who meet educational and training standards necessary to accurately and reliably apply biofeedback techniques.14 BCIA is a registered nonprofit corporation whose board of directors consists of an alternating group of distinguished professionals from various scholarly backgrounds. BCIA certification requires recertification through continuing education every four years for both biofeedback and neurofeedback. The Association of Applied Psychophysiology and Biofeedback (AAPB), the Biofeedback Foundation of Europe (BFE), and the International Society for Neurofeedback and Research (ISNR) have identified BCIA certification as the standard for professional identity and practice; however, this certification does not override or take the place of clinical and regionally/ nationally mandated licensure to practice. BCIA offers a clinical certification for Biofeedback, Neurofeedback, and Pelvic Muscle Dysfunction Biofeedback as well as technician-level certification for Biofeedback and Neurofeedback. Clinical Certification is designed for individuals who are primary treatment providers in clinical populations and carry appropriate licensure for such. Technician Certification is intended for individuals who use biofeedback and neurofeedback modalities under the licensure and direct supervision of a BCIA Certified individual. Often times the technicians do not have their own clinical degree/licensure. Those who are certified in biofeedback may use all biofeedback modalities as mentioned previously (Electromyography, Thermofeedback, Heart Rate Variability, Galvanic Skin Response, Respiration, Respiratory Sinus Arrhythmia), where those who are certified in neurofeedback specialize in EEG biofeedback. All certification programs through BCIA follow a strict set of regularly updated professional and ethical standards that can be located on their website. All BCIA coursework is based on their Blueprint of Knowledge, which covers specific information that provides a comprehensive understanding of the history, science, and application of biofeedbackspecific areas.15 Additionally, those who wish to pursue certification require a clinical degree in a health-related field (with the exception of technician-level certification); completion of anatomy/ physiology coursework; clinical supervision and mentorship; and the successful completion of a certification exam that signifies competency.16 The authors would also encourage that any professional considering the incorporation of biofeedback and/or neurofeedback into their private practice, agency, or organization visit a biofeedback/ neurofeedback practice to see how other professionals use these highly specialized treatments and to have a trained and board certified professional mentor you. There are several levels and brands of equipment that can be purchased for biofeedback and/or neurofeedback, and some brands provide the option to do both. It is advised that you do not buy a brand based upon a single recommendation. Take the time to look at various brands of equipment and watch it demonstrated. Ask other users what they consider to be the pros and cons of any particular brand. Do your homework before you invest your money. Finally, ethics is an important aspect of incorporating new treatment techniques with a specific or specialized population of clients and/or patients (Longo & Prescott, 2013). One must be sure to 103

Robert E. Longo and G. Michael Russo

keep within one’s scope of practice based upon one’s licensure and/or state-driven standards and guidelines. Join local, national, and/or international organizations that are founded in this specialized treatment such as the International Society for Neurofeedback and Research (ISNR)17 and/or the Association for Applied Psychophysiology and Biofeedback (AAPB),18 each of which has by-laws, a code of ethics, and provide ongoing education and training.

Notes 1 http://www.isnr.org/catalog-1/if5zb3hx69/American-Academy-of-Pediatrics-lists-Neurofeedback-as-Level1-Research-Best-supported-Interventions 2 http://dlantzphd.com/what-is-biofeedback/ 3 http://www.medterms.com/script/main/art.asp?articlekey=40362 4 http://neurodevelopmentcenter.com/psychological-disorders/ptsd/neurofeedback-for-ptsd/ 5 http://www.cdc.gov/ace/. Modification of table from Centers for Disease Control and Prevention, “Percentage of adults aged ≥ 18 years reporting adverse childhood experiences (ACEs), by ACE category and selected characteristics,” in Behavioral Risk Factor Surveillance System (BRFSS), Five States, 2009. December 17th, 2010. Link: http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5949a1.htm 6 Ibid. 7 http://www.nimh.nih.gov/about/director/2013/transforming-diagnosis.shtml 8 Biofeedback Certification Institute Alliance 9 Association for Applied Psychophysiology and Biofeedback 10 International Society for Neurofeedback Research 11 www.heartmath.org/support/faqs/research/ 12 http://www.emwavepc.com/emwave_pc_science_research.html 13 http://www.bcia.org/ 14 Ibid. 15 http://www.bcia.org/i4a/pages/index.cfm?pageid=3636 16 Ibid. 17 http://www.isnr.org/ 18 http://www.aapb.org/

References Association for the Treatment of Sexual Abusers. (2001). Association for the treatment of sexual abusers professional code of ethics. Beaverton, OR: ATSA Press. Begley, S. (2007). Train your mind, change your brain: How a new science reveals our extraordinary potential to transform ourselves. New York: Ballantine Books. Belluck, P., & Carey, B. (2013, May 6). Psychiatry’s guide is out of touch with science, experts say. The New York Times, Retrieved from http://www.nytimes.com/2013/05/07/health/psychiatrys-new-guide-falls-shortexperts-say.html?hp&_r=0 Bengis, S., Brown, A., Freeman-Longo, R. E., Matsuda, B., Ross, J., Singer, K., & Thomas, J. (1999). Standards of care for youth in sex offense-specific residential programs: National offense-specific residential standards task force. Holyoke, MA: NEARI Press. Burton, D. L., Smith-Darden, J. P., Levins, J., Fiske, J. A., & Freeman-Longo, R. E. (2000). 1996 Nationwide survey: A survey of treatment programs & models serving children with sexual behavior problems, adolescent sex offenders, and adult sex offenders. Brandon, VT: Safer Society Press. Freeman-Longo, R. E. (1986). The impact of sexual victimization on males. International Journal on Child Abuse & Neglect, 10, 411–414. Freeman-Longo, R. E. (1989). The sexual victimization of males—Victim to victimizer: Clinical observations and case studies. In E. C. Viano (Ed.), Crime and its victims (pp. 193–204). Washington: Hemisphere Publishing Company. Freeman-Longo, R. E., Bird, S., Stevenson, W. F., & Fiske, J. A. (1995). 1994 Nationwide survey of treatment programs & models: Serving abuse reactive children and adolescent & adult sexual offenders. Brandon, VT: Safer Society Press.

104

Working with Forensic Populations Gevirtz, R., & Dalenberg, C. (2008). Heart rate variability biofeedback in the treatment of trauma symptoms: Biofeedback. Association for Applied Psychophysiology & Biofeedback, 36(1), 22–23. Hall, M. P. (1977). Theta training: Imagery and creativity. In E. E. Green & A. M. Green (Eds.), Beyond biofeedback. San Francisco, CA: Delacorts. Ivey, A., Ivey, M. B., Zalaquett, C., & Quirk, K. (2009). Counseling and neuroscience: The cutting edge of the coming decade. Counseling Today, Retrieved from http://ct.counseling.org/2009/12/reader-viewpointcounseling-and-neuroscience-the-cutting-edge-of-the-coming-decade/ Knopp, F. H., Freeman-Longo, R. E., & Stevenson, W. F. (1993). Nationwide survey of juvenile & adult sex offender treatment programs & models, 1992. Orwell, VT: Safer Society Press. Longo, R. E. (2010). Helping body & mind: The use of biofeedback, neurofeedback, and QEEG brain mapping with young people who sexually abuse. In D. S. Prescott & R. E. Longo (Eds.), Current applications: Strategies for working with sexually aggressive youth and youth with sexual behavior problems (pp. 257–270). Holyoke, MA: NEARI Press. Longo, R. E. (2011). The use of biofeedback, CES, brain mapping and neurofeedback with youth who have sexual behavior problems. In M. C. Calder (Ed.), Young people who sexually abuse: Assessment and treatment considerations (pp. 292–305). Dorset: Russell House Publishing. Longo, R. E. (2013). Medications and the brain. In R. E. Longo, J. Bergman, K. Creeden, & D. S. Prescott (Eds.), Current perspectives & applications in neurobiology: Working with young persons who are victims and perpetrators of sexual abuse (pp. 291–304). Holyoke, MA: NEARI Press. Longo, R. E., & Prescott, D. S. (2013). Ethical responsibilities in neuroscience and neurobiology. In R. E. Longo, J. Bergman, K. Creeden, & D. S. Prescott (Eds.), Current perspectives & applications in neurobiology: Working with young persons who are victims and perpetrators of sexual abuse (pp. 213–226). Holyoke, MA: NEARI Press. Longo, R. E., & Prescott, D. S. (Eds.) (2006). Current perspectives: Working with sexually aggressive youth and youth with sexual behavior problems. Holyoke, MA: NEARI Press. Longo, R. E., Prescott, D. S., Bergman, J., & Creeden, K. (Eds.) (2013). Current perspectives & applications in neurobiology: Working with young persons who are victims and perpetrators of sexual abuse. Holyoke, MA: NEARI Press. Myers, J. E., & Young, J. S. (2012, January). Brain wave biofeedback: Benefits of integrating neurofeedback in counseling. Journal of Counseling & Development, 90, 20–28. Ogden, P., Minton, K., & Pain, C. (2006). Trauma and the body: A sensorimotor approach to psychotherapy. New York: Norton. PBS DVD. (2009). Brain fitness frontiers: Exploring the brain’s ability to change throughout life. Santé Fe Productions, Inc. Perry, B. D., Pollard, R. A., Blakley, T. L., Baker, W. L., & Vigilante, D. (1995). Childhood trauma, the neurobiology of adaptation and use-dependent development of the brain: How states become traits. Infant Mental Health Journal, 16, 271–291. Prescott, D. S., & Longo, R. E. (Eds.) (2010). Current applications: Strategies for working with sexually aggressive youth and youth with sexual behavior problems. Holyoke, MA: NEARI Press. Soutar, R., & Longo, R. E. (2011). Doing neurofeedback: An introduction. San Rafael, CA: ISN. Training & Research Institute, Inc. (2004). The neurobiology of child abuse (Poster). Albuquerque, NM: Research Foundation.

105

PART II

Pediatric Neurofeedback

7 TRAINING CHILDREN YOUNGER THAN 6 YEARS OF AGE Merlyn Hurd

Abstract This chapter focuses on the treatment of young children below the age of 6. There have been considerable studies on the work with children from age 6 and up but virtually none below that age. The present article examines single case trainings and the outcomes for children with epilepsy. The process for the diagnosis, EEG analysis, protocol decisions, training and outcomes are presented in a stepwise manner with documents to aid the clinician in working with the children and parents. Parents, pediatricians and interested parties such as teachers, relatives of the parents and other clinicians will ask you to train a very young child. Most of the time the request will be for assisting in addressing seizure disorders, brain injuries, language development, autism and/or Asperger syndrome. Obviously those are not the only issues that will arrive at your doorstep. When you conduct a literature search regarding training very young children, the information will induce conflict. Most neurofeedback studies indicate the youngest being 5 years of age, yet virtually all of the above mentioned disorders are recognizable at age 2 as can be verified through looking at any chart of child development. It would seem the child and parent must wait 3 years before neurofeedback training can be instituted even though medication, surgery, physical occupational training, speech therapy and social interaction behaviors training may be being provided from the age of 18 months or younger. The very heart of the disorder, the brain, is ignored or relegated to the wait section of services. The reasons often verbally stated to this clinician, though not in the literature, are that the brain is developing and neurofeedback might interfere with the development. This view seems to ignore that the services sanctioned and provided are also “interfering” with the development of the brain, hopefully in the correct developmental direction. Without studies on the effect of training the very, very young brain with neurofeedback, the most one can do is to address each case as an experimental treatment with careful observation, consultation with experts and healthy working relationships with the parents and the child’s medical team. Thus the first step in the process of providing neurofeedback training to children under the age of 6 years of age: Obtain all medical documents available and study them. If possible, obtain and review them before even meeting with the parents and child. With children this young, first meet with the parents alone. Assess their commitment to long-term work, since usually these children will need more than 30–40 sessions. Review the economic issues since experimental work is usually not paid for by insurance companies. Have informed consent forms signed spelling out the issues. Besides the medical history

109

Merlyn Hurd

of the child assess the behaviors and symptoms of the child. Even though the following symptom assessment form was created by J. D. Elder for teenagers and adults, parents find it helpful to use it to assign the symptoms to very young children. Table 7.1 Neurotherapy Institute. Copyright © 2001 J.D. Elder, Neurotherapy Institute. All Rights Reserved. Name of Client: ______________________________ Name of Rater: __________________________ Date: _____________

Never/Rarely Monthly Weekly Daily

Symptoms

Never/Rarely Monthly Weekly Daily

Please rate yourself, or the person you are assessing, on each of the symptoms below. Check one item on the rating scale for each symptom. Leave symptom blank if you don’t know how to rate it.

0 1 2 3 Symptoms

0 1 2 3

Anxiety, uneasiness, worry, concern

Racing thoughts, many thoughts

Inattention, daydreaming, hard to stay on task Depressed (guilt, helpless, hopeless), sad, blue Dull, slow to learn, not sharp Forgetful, failure to recall or remember Spaciness, fogginess, not tuned in

Agitation, upset, disturbed Hyperactive, excessive movement Difficulty falling asleep Impulsive, spontaneous urge Physical tension in body, taut, nervous Pressure in chest, discomfort in chest Aggressive, hostile, overly assertive & bold Teeth grinding (Bruxism), jaw clenching Headaches, feeling discomfort in head Crawling sensation on skin, leg twitches Sensitivity to touch (hands, feet, or face) Pain awareness, unpleasant sensation Hyper focused, overly attentive

Disrupted sleep, wakes often, difficulty waking Cries easily, sheds tears, weeps easily Feelings easily hurt, vulnerable Low self-esteem, poor selfconfidence Lack of motivation, discouraged Confused thinking, mixed up, baffled Nausea, sickness, upset stomach Loss of emotional control, rage, wrath Lethargic, lazy, drowsy, sluggish, fatigue

Grand Total Left Total

Depressed (agitated, angry)

Subtotals

Right Total

110

Subtotals

Training Children Younger Than 6 Years of Age

Questions

Yes

No

Comments

Have you changed medication? Have you changed herbs or supplements? Have you had any changes at home? Have you had any changes at school? Have you had any changes at work? Have you had any changes in your personal relationships? If yes to any of the above, please explain.

List other changes you have noticed.

A good strategy is to have both parents and any other significant caregiver separately fill out the form. Reviewing the separate assessments and comparing them can help to identify the dynamics that are helping and/or impeding the development of this child. This assessment form can be used as a progress form by administering it every tenth session or on a periodic basis that gives you and the parents an understanding of the progress. The next step is probably the most difficult: conduct a QEEG. A QEEG is a quantitative electroencephalogram. As Dr. Daniel Amen is noted for stating, the only rotation in his medical training in which the organ being treated was not examined was in psychiatry and he thought that rather odd (Amen, 1998). The same is true for neurofeedback clinicians. Although symptom-based treatment has been found to be effective, clinicians have noted that a QEEG shortens the number of training sessions and provides more information to the parents and clinicians. Conducting the QEEG on very small children can be a test of your and the parents’ creativity and patience. The two case studies to be discussed in this chapter happened to be children who were able to be attentive with parents who could assist in the acquisition of raw data. This brings up an issue that is very troublesome to psychologists. How do I refuse to provide service, when an evaluation of the parents, caregivers, and child points to a small possibility of success? The answer is right there. Your goal and, hopefully, the parents’ is success at the highest level. Should the parents and caregivers be unable or undesirable of assisting in the program, limited success is probable. Will you be promoting the child’s development or fighting the impediments to the child’s successful development? If the latter, again, success will be limited. A referral to a family therapist may be the better route with the understanding that neurofeedback training may be useful later. For example, recently a 12-year-old was referred by the parents due to poor sleep issues. She yawned throughout the intake and during the acquisition of raw EEG data, which was on a different day. Both intake and conducting of the acquisition were in the morning. The father attended her training sessions and was helpful in identifying that his daughter rarely ate breakfast or lunch and consumed very little protein, all, to which, the daughter agreed. Changing her nutrition habits showed some change in the quality and length of sleeping. Then the daughter noted on a follow-up assessment that she was particularly sensitive to smells, especially a bunny rabbit in the house, body odor and dirty clothes. She had strategies for the latter two irritants, but the bunny rabbit resided directly below her bedroom and the smell penetrated her room continually. What was to be a simple change in this clinician’s eyes turned out 111

Merlyn Hurd

to be a very formidable task for the father. He resisted any change and focused instead on his view that she needed to reduce her sensitivity. This dynamic underpins another important part of your work: you will need to have a very well honed understanding of the workings of the brain, psychological issues and family relationships needs.

The Acquisition of the Raw Data for QEEG Analysis A form which enables you to gather the relevant material and make notes during the acquisition of the raw EEG is based on two forms, developed by Cory Hammond, Ph.D., and Jolene Ross, Ph.D., separately, which I then combined with my own additions. The form is here provided: INTAKE INFORMATION NAME_________________________________ DATE_____________ BIRTHDATE___________________________ HANDEDNESS: L/R CIRCUMFERENCE ______________INON-NASION__________ MEDICATIONS TAKEN RECENTLY: _____________________________________________ CURRENT SUPPLEMENTS: _____________________________________________________ History of head injuries, including sutures, concussions, and lacerations: _______________________________________________________________________________________________________________________ _______________________________________ Caffeine Use: ______________ Last Cigarette_____________________ Marijuana Use (frequency and/or last use): ____________________________________________ Date&TimeofLastMeal: _____________________________________________________ Hours of Sleep Last Night: _________________________________________________________ Usual Amount of Sleep/Night: ______________________________________________________ Level of Alertness (10= extreme fatigue; 1= well rested, alert, and full of energy) _____________ PMS? _______ Point in Menstrual Cycle:_________ Head Surgeries? ___________________ MEDICAL HISTORY (check all that apply): Strokes ___ Heart Attack ___ Endocrine ____ Recurrent Pulmonary ____ Chronic Pain ____ GI ____ Polio ____ HIV ____ Vascular ____ Chronic Ear Infections or Ear Tubes ____ Recurring Infections or High Fevers ____ Visual disturbances ____ Metabolic Disorders (e.g., diabetes) ____ Chemical Sensitivities ____ Thyroid ____ Allergies ___ Tinnitus__ Viral illnesses ____ Balance problems ____ Incontinence ____ Swallowing Problems _____ Do you eat fish, meat, or fowl?____________ Do you drink artificial sweeteners/diet drinks?_____ Menopausal?_____ Cravings?(sweet, sour, spicy, hot) ________________________________________ Explain any of the above that have been checked: ______________________________________________________________________________________________________________________________ ________________________________________ Exposure to Toxic Agents (e.g., significant exposure to heavy metals, insecticides, carbon monoxide, solvents, drug overdose, chemotherapy or radiation, etc.):___________________________________ _________________________________________________________________________________ Neurological: Neurological Disease ____ Memory Difficulties ____ Seizures ____ Confusion ____ Restless Leg ___ Sleep Apnea or daytime drowsiness ____ Fatigue ____ Headaches or Migraines ____ Accidents ______ Coordination difficulties ____ Difficulty with balance ____ Tics, Twitches, Tremor, or Parkinson’s ____ Sensory Impairments (smell, hearing, seeing) ____ Fibromyalgia ____ # of times under Anesthetics ___ Complicated Birth ( i.e.: forceps; fetal distress; complicated/prolonged labor, anoxia) _______ Premature birth? (Wt: ) _____ Prenatal drug/alcohol exposure _____ History of Physical Abuse ________ Blows to the Head or Head injuries: _____ Loss of consciousness/Concussion? _________________________ Athletics (Football, boxing, soccer, lacrosse, skiing, hockey, horseback riding, martial arts) _________________ Sensitivity to light & sound? ________ Total Number of Head Injuries (incl.date of) ____________________ Development: Slow motor ____ Slow speech _____ Developmental delay _____ Reading/Math/Speech Problems? ______ Co-ordination Problems? _______ Academic Strengths _______________________ School: Below grade____ Special classes ____ Disciplinary Problem ____ Concentration/Distractibility Problems ______________ Previous Psychiatric Diagnoses & TX: Diagnosis: _________________ When? _____________ Were you hospitalized? _________________ How Long? ________________________ Who made the diagnosis? _______________________________________________ Current & Family History (include when & who if possible): Depression (Rating 0-10: ): __________________________________________________________ Anxiety (Rate 0-10 & what provokes): ___________________________________________________ Panic attacks (circumstances that provoke & frequency)?______________________________________ Obsessive Rumination (what, when): _____________________________________________________ Delusions or Hallucinations (when, how?): ____________ Fatigue (Chronic?): ___________________ Bipolar/Mood Swings: ___ Hx of Psychosis _______ OCD ________ Arrests ____ NAME_______ Insomnia (date of onset) _________ Frequent Awakening (#/night: ) ______ Early Morning Awakening ____ Alcoholism ________ Substance Abuse ____ Learning Disability: ______ Anger/Irritability _____ Explosiveness ____ Impulsivity: ____________ Eating Disorder: _____ Sexual Abuse: ____ PTSD (When? Who & when diagnosed?): _________________ ADD (# of Criteria met) or ADHD (# of criteria met ) _________________________

Figure 7.1 Intake information

112

Training Children Younger Than 6 Years of Age Explain any of the above checked items _______________________________________________________________________________________________________________________ ______________________________________________________________ ______________________________________________________________________________________ Family History of (Identify Who) Depression and/or Suicide ___________ Bipolar or Manic Depression: ____________ Epilepsy: ____________ Migraine: ____________ Alcoholism or Drug Abuse: ____________ Anxiety or Panic Attacks: ____________ Tourette's (Motor or Vocal Tics): ____________ ADD/ADHD: ____________ Learning Disability: ____________ Speech Problems: ____________ Autism: ____________ Schizophrenia: ____________ OCD: ____________ PMS: _________ Chronic Fatigue: ____________ Fibromyalgia: ____________ Criminal Behavior: ____________ Thyroid Problems: ____________ Did your mother smoke during her pregnancy with you? ____________

_________________________________ _____________________________ Signature and Print name also Date ALL IMPEDANCES BETWEEN: EAR IMPEDANCE LEVELS: Look Up: Look Down: Look Right: Look Left: Epoch H.V Begins: HV Ends: Were there problems with drowsiness during the recording? Was Alpha Reactive to Eye Opening? Background: _ Normal; _ Slow; _Asymmetrical Condition (comment on quality or any unusual circumstance)

Designation

Time

1. Eyes Closed Rest

EC1

3’ _______

2. Eyes Open Rest

EO1

3’ ________

3. Silent Reading Task

READ

3’ ________

4. Listening Task

T2-1

3’ ________

5. Math Task

MATH

3’ ________

6. Social Attribution Task

T3-1

3’ ________

7. Eyes Open Rest

lexicor

EC2

3’ ________

8. Eyes Closed Rest lexicor

EC3

3’ ________

9. . Eyes Closed Rest (nose reference lead)

EC4

3’ _________

Figure 7.1 (Continued)

This form works very well as a first intake form also. I find asking the clients the questions on the form gives me more useful information than having the clients fill it out and then reviewing it with them. Getting back to the conducting of the acquisition of raw EEG, sometimes the most you can obtain is eyes open. Having the parent, usually the mother, hold the child can facilitate the process. Often the child can play a game of hold their eyes closed; however, be aware that there will be a lot of artifact that will have to be eliminated from the final analysis. Invariably, the child will often open their eyes during this process. Another possibility is to use a soft padded blindfold that can be found at various manufacturers’ websites. Do assess whether the child has any fears of the dark, in fact, what fears have the parents been able to discern. Once you have obtained the raw EEG, 113

Merlyn Hurd

examine for the artifacts, spike and waves and focal issues. Should the child have been referred by a neurologist, a sharing of their and your acquisitions can be beneficial to both of you and definitely lead to better training of the child. Although the raw EEG obtained by the neurologist is sometimes provided, so far the use of the data for QEEG analysis has been limited either because the records cannot be read by the databases or the artifacting leaves too small a number of seconds to provide valid information. You have obtained the raw EEG data for eyes closed and eyes open either by yourself or from another clinician. Now the hard work of analyzing the data begins. Selecting the normative database is the next step. Three databases are used in my office: Neuroguide, LORETA and SKIL. Neuroguide is a normative database with eyes closed and eyes open conditions that is validated for ages 2  months to 82  years. This database continues to be updated by Dr. Robert Thatcher and his associates. Dr. Thatcher conducts training and consultation on the analysis and protocol selection using Neuroguide and attending these trainings is well worth the effort to understand QEEG findings. When I first started using QEEG in 1994, I traveled throughout the USA to wherever he was training to gain as deep an understanding of the interpretation of the data and subsequent protocol selection as possible. His website has a large number of articles that help a clinician to understand the QEEG database interpretations (www.appliedneuroscience.com). He also has an important book ( Handbook of Quantitative EEG and EEG Biofeedback ), that is a must read by anyone conducting analysis regarding the brain. SKIL is another normative database that is validated for ages 5 years to 55 years and includes task normative data besides eyes closed and eyes open conditions. SKIL was created and continues to be updated by Dr. Barry Sterman and Dr. David Kaiser. Dr. Sterman is credited with discovering the techniques for training the brain to reduce and often eliminate seizures over 40 years ago. Sterman and Kaiser’s consultation and training are invaluable and are highly recommended for any person entering the field of QEEG analysis and neurofeedback training. The SKIL database also has time of day as one of the parameters that is examined since research has noted differences in EEG activity depending on the time of day the acquisition took place. The SKIL website has articles and tools to assist in the acquisition of raw data with math and reading graded tasks material a clinician can use. At this point you may be noting that there are other types of brain mapping that clients have mentioned to you and have asked: couldn’t those be used instead of QEEG? For a clinician new to the world of neurofeedback and brain mapping, the number of brain imaging techniques can be intimidating, so let’s take a walk through a few of the various kinds and maybe dispel the mystery. MRI is the magnetic resonance imaging of the brain. The MRI is non-invasive. The imaging is examining the white and gray matter density for disturbances. fMRI is functional magnetic resonance imaging of the brain, usually while conducting some type of task. PET is a positron emission tomography which utilizes radioactive isotopes to perform radioassays. Both the local and distributed functions are monitored (Cherry, Sorenson, and Phelps, 2012). SPECT is a single photon emission computed tomography which uses radioactive isotopes to map the functional organization of the brain. The scan measures cerebral blood flow and metabolic activity patterns (see Dr. Daniel Amen’s book, Change Your Brain, Change Your Life, for examples of various disorders and the SPECT scans of those disorders). Remember SPECT, PET, MRI, etc. are inferring activity from patterns of metabolic uptake while QEEG analysis is looking at the actual electrical activity.

114

Training Children Younger Than 6 Years of Age

Although all of these imaging techniques give patterns according to their specific criteria, the QEEG is measuring the nerve’s actual electrical activities and is excellent in giving more clear information regarding the connectivity of the regions, amplitude asymmetry of the sites and dominant frequencies of the brain and sites, which are the basis for neurofeedback training. Seldom do young children have MRI, PET, SPECT, and fMRI types of imaging performed unless there is severe brain injury. Discussing your QEEG findings with the neurologist can be very helpful to your work. It is worth noting that clinicians have found that the QEEG may pick up seizure disorders and brain injury that these other brain imaging techniques may not “see.” Each imaging method has its benefits and limitations, so being able to help the parents to understand what they have already been informed of and what you are finding is essential for fostering and maintaining their trust in their medical team (you included).

Before You Begin the Data Analysis Pull Out the Top Ten Symptoms Checklist That You Have Developed from the Assessments: These Are Your Guideposts Do you see any spike and wave forms, any drowsiness or high amplitudes in one region/site in either Beta or Delta, as you look at the raw EEG? If yes, first look to be sure that the high amplitudes are not artifact. If the high amplitude in Beta is primarily located at either Fp1, Fp2, F7, F8, T3 and/or T4, examine your notes during acquisition to see if you noted any artifacts at those sites, examine the raw data and see if artifact is at the same sites, and then look at the Neuroguide normative data analysis and see if in the single bin absolute power analysis the high Beta starts somewhere about 15 Hz and extends through to the high 30s Hz in one particular site or region. If the examination yields these high amplitudes as artifact, re-artifact and then examine the results. Should you end up with less than 60 seconds of data, the most you can say is your analysis is an impression and must be treated with caution. If there is not any artifact, or it is minimal, look at the following table developed by Jonathan Walker, MD (Walker, Kozlowski & Lawson, 2007) for the sites that have either high amplitudes in Delta and Beta. Most disturbances/disorders are in the slow frequency areas, i.e. Delta (0–3.5 Hz) and Theta (3.5–4 Hz). Do the regions correlate with the symptoms? When the patterns are different, ask more questions of the clients to ascertain what might be the reason for the differences. Take brain injury for instance. Many times, the client will remember a bump on the head that they forgot or relegated to the view that nothing happened. For instance, one client, when questioned regarding a clear indication of possible brain injury in the parietal area, answered by saying, “Does a window falling on my head qualify as a brain injury?”

Cortical Modules Table 7.2 An analysis of the individual sites with the principal functions and other functions associated with the sites. 10/20 TerritoryModules

Principal Function

Some Other Functions Involving This Area

Fp1

Logical attention

Orchestrate network interactions Planning Decision making Task completion Working memory (Continued)

115

Table 7.2 (Continued) 10/20 TerritoryModules

Principal Function

Some Other Functions Involving This Area

Fp2

Emotional attention

Judgment Sense of self Self-control Restraint of impulses

F7

Verbal expression

Speech fluency Mood regulation (cognitive)

F8

Emotional expression

Drawing (right hand) Mood regulation (endogenous)

F3

Motor planning of right upper extremity (RUE)

Fine motor coordination Mood elevation

F4

Motor planning of left upper extremity (LUE)

Fine motor coordination (left hand)

FZ

Motor planning of both lower extremities (BLE) and midline

Running Walking Kicking

T3

Logical (verbal) memory formation and storage

Phonologic processing Hearing (bilateral) Suppression of tinnitus

T4

Emotional (non-verbal) memory formation and storage

Hearing (bilateral) Suppression of tinnitus Autobiographical memory storage

C3

Sensorimotor integration right upper extremity (RUE)

Alerting responses Handwriting (right hand)

C4

Sensorimotor integration left upper extremity (LUE)

Calming Handwriting (left hand)

CZ

Sensorimotor integration both lower extremities (BLE) and midline

Ambulation

T5

Logical (verbal) understanding

Word recognition Auditory processing

T6

Emotional understanding

Facial recognition Symbol recognition Auditory processing

P3

Perception (cognitive processing) right half of space

Spatial relations Sensations Multimodal sensations Calculations Praxis Reasoning (verbal)

P4

Perception (cognitive processing) left half of space

Spatial relations Multimodal interactions Praxis Reasoning (non-verbal)

PZ

Perception midline

Spatial relations Praxis Route finding

10/20 TerritoryModules

Principal Function

Some Other Functions Involving This Area

O1

Visual processing right half of space

Pattern recognition Color perception Movement perception Black/white perception Edge perception

O2

Visual processing left half of space

Pattern recognition Color perception Movement perception Black/white perception Edge perception

Source: Collura (2014)

Figure 7.2 Eyes closed Dynamic FFT analysis of absolute power and Z score of absolute power. Note the dominant frequency is in the Theta frequencies. This is of a child 2.86 years of age with severe epilepsy and developmental delay.

Merlyn Hurd

Figures 7.3 The picture on the left is one that has been artifacted and the one on the right uses all data without artifacting. The findings still give the same information as to deviation.

In my practice I conduct a SKIL analysis first and then a Neuroguide analysis; if the client is old enough I will use the LORETA as another form of analysis. With children below the age of 5, Neuroguide is recommended as the first step since the database is valid for ages 2 months to 82 years. Using eyes closed first, start the analysis. The eyes closed condition, at this point in time, has the most studies regarding its reliability. The first step is to obtain a Dynamic FFT of Absolute Power Spectrum employing Z score Spectrum data. This can give you the dominant frequency and the deviant scores as a first level of analysis. Why use Z score? Statistically, these scores give you the most informative differences from the norm and you will want to use data that has scientific support. Whether you use the picture in your report or not, take a picture of this information using a program called Snagit or print screen and paste to Paint and save as an image file in your word processing program. Next, select the parameters you will want to investigate, such as amplitude asymmetry, coherence, phase lag, ratios and peak frequencies in a referential analysis. The list of parameters is extensive and I refer you to the Neuroguide website for more detailed explanation of the analysis you can employ (www. appliedneuroscience.com). When the report is generated (this is a one-button task once the artifacting is completed), examine the reliability of the average for the test and for each site. Should you find sites with poor reliability you should go back and re-artifact to increase reliability. Speaking from experience this has happened only once in 14 years of conducting QEEG analysis. There is also another technique that Dr. Thatcher has recommended which can often validate the deviant findings, which is: use all of the data, do not artifact and see if the deviant scores and sites still show up. So far in my experience, they always seem to. So much for artifacting being a factor that confounds the findings. Apparently, when there is damage, it will always be revealed. Using the same artifacted raw data, conduct a Laplacian analysis, which is looking at the connections to the immediate sites. See figure 7.4.

118

Figures 7.4a and 7.4b These are the summaries of the child being discussed. The picture above is a referential summary of absolute power, relative power, amplitude asymmetry, coherence and phase lag. The picture on the next page is a Laplacian summary of the absolute power, relative power and amplitude asymmetry. Coherence and phase will not be displayed since those mathematical formulas were used in the Laplacian method. Note the changes in the absolute power in Beta and high Beta with more localization. In relative power the Delta, Theta and Alpha show more areas that are functioning better than in the referential, whereas the functioning in Beta and high Beta shows more deviation.

Merlyn Hurd

Figures 7.4

(Continued)

Conduct the Next Analysis on the Eyes Open Condition Using the Same Steps Still keeping the symptoms in front of you and the indications of what the particular connections/ sites/amplitudes could indicate, you may now be able to select training. Rather than suggest all possible training that the maps may indicate, you will probably only have 1 to 5 neurofeedback protocols as most advantageous. Now the real fun begins. Carefully look at the information you, as the detective, have gathered, and see if there is one action that seems to be most necessary to be addressed. As an example, let us take a case and examine the findings. You have already seen some of the data of the first case I will discuss. The subject was a 2.83-year-old male child who had suffered brain injury at birth. Documented reports placed his seizure activity at 30 per hour. He could not walk or speak coherent words and was “floppy.” However, he was very animated and displayed flashes of humor. The parents were very supportive of his development and had consulted various medical professionals and programs to assist the child. They requested neurofeedback training, having found the studies of Dr. Sterman via the internet. They had rejected medication and surgery, and instead were willing to try neurofeedback. I was frank with them that I had never trained a child this young and if they were willing to 120

Training Children Younger Than 6 Years of Age

accept the process as experimental, we would, together, address the seizures and his development. A QEEG was conducted with two conditions being able to be analyzed, eyes open and parent reading to the child. Fortunately, I was weekly studying with Dr. Barry Sterman the correct means of QEEG analysis and interpretation. Thus, this child’s data was able to be examined on a very minute basis with him. The findings indicated that the most appropriate training would be increasing amplitude at C4 in 11–14 Hz. The analysis used was the SKIL database plus his vast knowledge of brain development and operation. In the same time frame, I had purchased the Neuroguide database and scheduled two days of private training with Dr. Robert Thatcher. Again, the raw data was carefully examined against the Neuroguide database. The same conclusions were obtained. Both of these brilliant and wise scientists concluded the most appropriate training would be at C4 enhancing 11–14 Hz. Training was conducted on Thought Technology’s Biograph with the turning multicolored fan as the reward. Only the multicolored fan worked as a reinforcer; all other displays did not interest him. The child quickly figured out the process and trained for at least 12 minutes per session and on rare occasions for 22 minutes. During the first 5 months the child was trained at C4 increasing 11–14 Hz for as long as he could comfortably work. This was 3 times a week. A day was in between each session to allow for as much consolidation as could be achieved. He would tire easily and give clear non-verbal indications when he was finished. At the same time I urged the parents to purchase the Thought Technology Pro 2 and train him at home for 10 to 15 minutes a day or whatever time he could tolerate, if possible. Within 3 months the seizures were down to intermittent occurrences and at last count in 2005 he had infrequent seizures. Over the 16-month-period of services he was either on vacation with his parents and/or out of the country with his relatives for 5 months. During those times he did not receive any training either at home or in my office. Thus, the total number of months of training were 11 months, using only the one protocol of increasing amplitude at C4 in 11–14 Hz while inhibiting 3–5 Hz and 25–35 Hz. At the most recent educational evaluation in 2005 he was judged to be at age level in receptive language, was standing in school at appropriate activities and was attempting to walk. The floppiness had been replaced with sturdiness and his verbal communication was improving. In writing this article I called the parents and learned the child has not had a seizure in over a year and is “doing great,” as his mother reported. As can be seen, the changes towards more normal QEEG is obvious. Even though the training was at C4, it is still most out of the norm whereas most of the other frequencies across the cortex seem to have been stabilized.

Figure 7.5 This is a comparison of the pre and post QEEG on the child after 11 months of C4 SMR training. The picture on the left is the pre (age 2.83 years) and the one on the right is post (age 5.06 years).

121

Merlyn Hurd

Case Study # 2 The second young child was aged 2.86 years. The child was the first born with a uncomplicated pregnancy except for the birth which was an emergency C-section after 14 hours of labor. His Apgar was 10. Two days after delivery he was readmitted with jaundice and was hospitalized with his mother in continuous attendance. His father was also present through most of the hospitalization. She breastfed him from birth until he was 18 months old. He was a picky eater, rejecting most solid food except for yogurt, cheese and bread. His pediatrician expressed concern at 12 months when the child did not differentiate between any of the grownups, calling all of them the same name or using only physical pulling or motions to obtain what he wanted. He walked on his toes, but had superior gross motor control, imitating intricate actions precisely. He loved music, singing and dancing. At 2.5 years of age he was enrolled into a preschool half day program and the teachers expressed concern for his lack of language development and immature interactions with other children his age. He was evaluated at age 3 by the NYC Public School Child Study Group. The findings placed him at a 1.4-year-old level in receptive and expressive language with some delay in fine motor control. His emotional development was placed at 2.5 years of age. For example, he would become frustrated when requested to make a transition and often would cry and resist the change quite strenuously and for several minutes, often 30 minutes in length. No medications were used with this child for any of the findings. His Raw EEG with eyes closed follows.

Figure 7.6 A sample of eyes closed showing the dominant frequency is in the Theta range.

122

Training Children Younger Than 6 Years of Age

He was helped to keep his eyes closed with a soft padded blindfold over his eyes. As can be seen in the occipital and parietal areas the dominant frequency is in the Theta/Delta range. One can then conduct a single bin analysis of the data, which confirms the same findings. Looking at the Neuroguide Absolute Power shows that the Z Score for the absolute power is 2–3 Standard Deviations from the norm starting at 20 Hz to 30 Hz in the frontal, central and parietal areas. Next is to look at the eyes open condition. The raw EEG for eyes opened is below (see Figure 7.7). The similarities are striking between the eyes closed and eyes open conditions. The dominant frequency is in the Theta/Delta range and the Neuroguide Absolute Power shows a similar 2–3 standard deviation from the norm in the high Beta especially at CZ. Now one could look at the topometric in SKIL to see the differences between the two conditions and both are indicating the presence of the eyes open being at a higher amplitude than the eyes closed with very little differences between the two. In SKIL, look for the suppression of the dominant frequency across the cortex when the eyes are opened, then when the client is reading and finally when the client is working math problems. Should there not be this suppression or one task is actually higher in amplitude than eyes open or eyes closed, the indications are (1) lack of Alpha suppression and (2) interference of Alpha amplitude during task activities. This client showed the reverse of expectation and points to developmental delay issues.

Figure 7.7 A sample of the eyes open condition showing similar patterns as in the eyes closed condition.

123

Figure 7.8 An analysis of the eyes closed (dotted light grey line) and eyes open (dotted dark grey line) in the child’s dominant frequency of 4–8 Hz. Note the eyes open condition shows more than 2 standard deviation from the mean than even the eyes closed condition. Note also the difference in the eyes closed and eyes open conditions in the parietal sites.

Training Children Younger Than 6 Years of Age

Neuroguide’s summary showed the following for eyes closed (left picture) and eyes open (right picture):

Figure 7.9 Summary of eyes closed and eyes opened of child being discussed at age 2.86 months. The Absolute Power analysis finds globally there are more than 3 standard deviations from the mean in Delta, Theta, Beta and High Beta. In Alpha the 3 standard deviation from the mean is primarily at T3 and T4 in eyes closed while the rest of the cortex is also out of sync. The Amplitude Asymmetry is primarily in Alpha between Fp1-T3, T4 and between P3-T3 and T4. In eyes open the Delta and Theta are also involved. The hypocoherence is primarily in the Theta and Alpha ranges between the prefrontal, frontal, central and PZ sites in eyes closed and is similar in eyes open with Delta also showing similar hypocoherence.

125

Merlyn Hurd

All of these findings support the symptoms of receptive and expressive language delay, concept development delay and immaturity in social development. The raw EEG data was sent also to Dr. Jonathan Walker, a neurologist who is skilled in neurofeedback and QEEG analysis, for analysis and recommendations. His recommendations were as follows: 1. 2. 3. 4. 5. 6. 7.

Decrease 1 to 6 Hz at O1 plus O2 to improve visual processing. Decrease 1 to 5 Hz at T6 plus FZ to improve emotional understanding and working memory. Decrease 4 to 5 Hz at F8 plus F2 to improve emotional expression and judgment, to decrease hyperactivity and impulsivity, and to improve verbal expression. Decrease 2 to 3 Hz at C3 plus FZ to improve sensorimotor integration on the right and working memory. Decrease 3 to 4 Hz at T3 plus CZ to improve verbal memory and sensorimotor integration in the midline and legs. Decrease 21 to 24 Hz and increase 10 Hz at P3 plus P4 to decrease anxiety and irritability and to improve cognitive processing of language and spatiotemporal information. Increase coherence of Delta at F3/C3 to integrate motor function on the right with sensorimotor integration on the right.

The child was first provided the following treatments. From 2/20/07 the child received neurofeedback training. In order to address the language problems the Wernicke and Broca areas were targeted first. On 2/20/07, using BioExplorer software he trained for 19 minutes at P3 decreasing 18–15 Hz. The next 2 sessions on 2/27 and 3/9 were increasing 15–18 Hz at F7 to activate the working visual and auditory memory and increase attention activity. The next session on 3/13/07 again addressed the P3 decrease of 18–24 Hz and then on 3/20/07 and 3/22/07 increasing 15–18 Hz at F7. He was responding and language was beginning to become a little clearer. Not satisfied with the progress and knowing the frontal lobe needed to be activated more—plus reports by Jeff Carmen, Ph.D., on the changes in learning disabled children using HEG—the next sessions included HEG as a part of the process. The HEG was placed at FPZ. He was able to bring the readings up to 85. The next 9 sessions consisted of F7 15–18 Hz increase for 10 minutes and HEG for 20 minutes. The reinforcement for the HEG was keeping the movie he was watching running. By the third session he was reaching 92.69 on the HEG. Again his language was increasing and his ability to soothe himself was becoming more evident. In May he received training at T5 for 2 sessions with the HEG to address his concept development, which was at a low functioning level. From 5/25/07 to 6/5/07 he received 15 minutes of decreasing 1–6 Hz at O1 and O2 to improve visual processing with HEG for 15 minutes at FPZ. From 6/7/07 to 7/3/07 the child was on vacation so training was suspended until 7/3/07. On 7/3/07 HEG was conducted at T3 to help with the sensory regulation of the child. A photon stimulator developed by Len Ochs, Ph.D., was used over the cortex to increase activation of the cortex globally. This use was for only a few seconds. On 7/10/07, decreasing of 1–6 Hz at the O1 and O2 sites was conducted and the photon stimulator was used. On 7/17, 20, 24, 27 and 31/07, T3 with HEG was used and the photon stimulator finished the session as had been conducted in the former 2 sessions.

126

Training Children Younger Than 6 Years of Age

On 8/13/07 the first LENS map was conducted. See below:

Figure 7.10

Note the suppression in each of the frequencies. Look at row 5, which is labeled Dominant Frequencies. The map shows in the dominant frequency distribution considerable suppression in all except Fp2, T4, F7 and Fp1, and the dominant frequency per most sites was Theta. The Beta maps (last 3 rows) show suppression in 95% of the sites.

127

Merlyn Hurd

His ability to develop language; concepts and communicate was severely compromised. The decision was to continue with HEG and add LENS stimulation to the mix. From 8/21/07 to 11/8/07 LENS was conducted using the regular LENS feedback and the new protocols developed by Nick Drogis, Ph.D. Special attention was focused on the areas revealed in the LENS map and the QEEG. O1, O2, T6, FZ, F8, F7, C3, T3, P3, P4 were provided more targeted LENS treatment using the protocols of Rocking the Spectrum, 1–8 Hz, 4 Hz, 5 Hz and Rocking the Brain protocols. The parents were reporting major changes after each session. Language was increasing, sensory issues were decreasing and concept development was clearly taking place. His creativity in dancing, singing and creating games was increasing. A follow up QEEG was conducted in November of the same 2007 year with the following findings by myself and confirmed by Dr. Walker. Dr. Walker noted “vast improvements.”

Figure 7.11 When compared to the first map the changes are obvious. The areas which remain outside the norm are in the Beta area in the central, parietal and left posterior. Amplitude Asymmetry has changed to being mainly in the Delta frequency; the coherence is between the prefrontal and central regions in Theta and the Phase Lag shows Theta, Beta and high Beta essentially in the right hemisphere as being mixed. Speed of processing is too fast for recruitment of resources.

128

Training Children Younger Than 6 Years of Age

The LENS map showed the following:

Figure 7.12 The map shows more activation and flexibility across the cortex with the exceptions being the Beta ranges. The child’s concept development was still below average.

129

Figure 7.12

(Continued)

Training Children Younger Than 6 Years of Age

The recommendations were: 1. 2. 3. 4. 5. 6. 7.

Decrease 5 Hz at O1 plus O2 to improve visual processing. Decrease 4 Hz at C3 to improve sensorimotor integration on the right. Decrease 21 to 30 Hz and increase 10 Hz at F3 plus F1 to decrease anxiety, irritability and ADHD, and to improve motor planning on the right and on the left. Decrease 29 to 30 Hz and increase 10 Hz at P3 plus P4 to decrease anxiety and irritability, and to improve cognitive processing of language and spatiotemporal information. Decrease 29 to 30 Hz and increase 10 Hz at F7 plus F8 to decrease anxiety and irritability, and to improve verbal expression and emotional expression. Increase Alpha coherence at F3/C3 to integrate motor planning on the right with sensorimotor integration on the right. Increase Theta coherence at F2/C4 to integrate judgment and sensorimotor function on the left.

From 11/10/07 to 12/22/07 the child was treated with LENS using Dr. Nickolas Dogris’s protocols and focusing on the decreasing of 5 Hz at O1 and O2 and 4 Hz at C3. The typical LENS training of 7 sites per session was used. The sorting according to the LENS map was the guide for the training. HEG was conducted after the LENS treatment in each session at FPZ. In January of 2008 Z score training was introduced to increase the Alpha and Theta coherence between F3/C3 and F2/C4 to integrate motor planning on the right with sensorimotor integration on the right and to integrate judgment and sensorimotor functions on the left. By 1/26/08 amplitude training of decreasing 29 to 30 Hz and increasing 10 Hz at F7 plus F8 to decrease anxiety and irritability, and to improve verbal expression and emotional expression, was instituted on the Bioexplorer. This training was continued for 7 sessions. Then from 3/15/08 to 4/17/08 Z score training was conducted at F7-P3 and F8-P4 to continue to address the attention, memory and verbal expression issues. From 5/8/08 to 6/26/08 Z score training with every third session HEG training and LENS was conducted to address the emotional issues. For the month of August the child was in a full day camp and no training was conducted. In August, the instrument Neurofield was purchased and the first session was conducted with the child on 9/13/08. The protocols were Brain Fog at 400 microvolt and DTF-1–45 400 microvolt. Table 7.3 Patient treatment history.

131

Merlyn Hurd

The parents reported a large jump in language, ease of transitions and reduction in anxiety. Below is a depiction of the training with Neurofield that had been conducted up to 9/27/2008. The parents comment that the changes are more rapid and greater than they have seen before. He also was receiving HEG and/or LENS with these Neurofield treatments. The child was now 4.5 years of age and was closer to being age appropriate with receptive and expressive language, his fine motor skills had improved and he did not have the “meltdowns,” and instead had smooth transitions. He was still a “picky” eater, but his palate had expanded considerably and he would partake of new foods as long as he was encouraged with a game regarding the food. For example, calling broccoli “trees” and eating the trees. He continued to be very creative with a fine sense of humor. When the Brainmaster Discovery was developed and Avatar training was provided, the full training of 19 channel Z score was begun. This was very profound since one could now see the changes taking place during the training and the targeting of particular areas, and Brodmann areas became the norm. Below is a comparison of the eyes opened QEEG analysis and the results 7 years later after trainings and especially 19 channel and Neurofield trainings. The training over the 7 years was not on a weekly basis; sometimes only 9 sessions were conducted in a year. Even so, the changes are clear and the greatest changes came about after the 19 channel and Neurofield trainings began. The other issue is the finding that he has a very serious auditory processing disorder which has only recently been being addressed. Still this type of disorder is difficult to obtain services for and to address directly with training.

Figure 7.13 Both pictures are of eyes opened. Eyes opened 1st QEEG age 2.86 years (left picture) and at age 9 (right picture) after 19 channel and Neurofield trainings. Neuroguide analysis of child with language delay, sensory issues and eventual discovery of auditory processing disorder. As can be seen the global overactivation in all frequencies has been reduced to more normal operation in the low frequencies of Delta, Theta and Alpha while overactivation remains in the Beta and high Beta ranges. Also of note is the slowed phase lag (timing) which is directly related to his continued difficulty with language and possibly with the auditory processing disorder.

132

Training Children Younger Than 6 Years of Age

In conclusion, working with children below the age of 6 means using the information from the QEEG as an indication of the connectivity, timing and amplitude issues. The eyes open and eyes closed conditions may not show too much differences, which may be explained by the fact that young children do not have the differentiation of the frequencies as is seen in even a 6-year-old. The predominance of Delta, Theta and some Alpha has been noted in the literature as characteristic of the youngest children. Since the neurofeedback work will be eyes open it may be the most practical to have only eyes open data. Ascertain the positive involvement of the parents, caregivers and school personnel. Obtain as much information of the medical, developmental and educational assessments conducted to guide the neurofeedback training. Have parents send e-mails to you after the sessions to maintain a clear progress of the training.

References Amen, D. (1998). Change your brain, change your life. New York: Random House. Cherry, S., Sorenson, J., & Phelps, M. (2012). Physics in nuclear medicine, 4th edition. Saunders. Collura, T. F. (2014). Technical foundations of neurofeedback. New York: Routledge/Taylor & Francis. Thatcher, R. (2012). Handbook of quantitative EEG and EEG biofeedback. (Vol.1). Fort Lauderdale, FL: AniPublishing. Walker, J. E., Kozlowski, G. P., & Lawson, R. (2007). A modular activation/coherence approach to evaluating clinical/QEEG correlations and for guiding neurofeedback training: Modular insufficiencies, modular excesses, disconnections, and hyperconnections. Journal of Neurotherapy, 11(1), 25–27.

133

8 QEEG AND 19 CHANNEL NEUROFEEDBACK AS A CLINICAL EVALUATION TOOL FOR CHILDREN WITH ATTENTION, LEARNING AND EMOTIONAL PROBLEMS Theresia Stöckl-Drax A version of this chapter has been previously published, and this chapter is included with the permission of the copyright holder, Dr. Stöckl-Drax. The previous citation is: Stöckl-Drax, T. (2014) QEEG and 19-Channel Neurofeedback as a Clinical Evaluation Tool for Children with Attention, Learning and Emotional Problems. NeuroRegulation 1(2): 173–182. Retrieved from: http://www.neuroregulation.org/article/view/14294

Abstract Attention, learning and emotional problems can have different causes that cannot be easily and clearly distinguished by clinical testing methods. However, QEEG and, even more so, live 19 channel Z-score training under different task conditions can both give very detailed insights about the specific functioning and dysregulations of an individual’s brain. The clinical intake evaluation of the child is optimized by including a quantitative, neurometric analysis of an eyes open (EO) and eyes closed (EC) EEG acquisition combined with a real-time analysis of the child’s (in vivo) brain functioning during a specific set of conditions. This method was developed and refined with more than 300 children who were tested between June 2012 and April 2014. The goal is to get as much information as possible in only one session lasting 45 to 60 minutes. The different parts of the evaluation consist of: eyes open (EO) and eyes closed (EC) collection of data, display of the actual brainwaves, listing of the Z-score values (also presented as plots or instant brain maps with different task conditions), followed by games to play with a challenge condition. In addition, current source density (CSD) sLORETA of the different wave frequencies (usually delta, theta, alpha, beta and gamma bands), distribution and velocity are shown as they change, as well as when the brain evaluates emotions. The session ends with a brief, individual 19 channel training with video feedback. Because of the usefulness of the information obtained from using this QEEG method, the author recommends that QEEG and an interactive neurofeedback session be included as a standard component in the diagnosis of and treatment planning for children with attention, learning and emotional problems. 134

Dynamic brain evaluation in children

Introduction In the author’s developmental clinic the children and young adults display developmental delays in certain areas; they suffer from ADD and ADHD, processing disorders, failures in school performance. Some display emotional problems and family issues come with them. In some cases it is a mix of these disorders. Clinical testing methods including a thorough patient history, questionnaires, pediatric neurologic exam and neuropsychological testing often do not clearly distinguish the different causes of these clinical conditions and are not precise enough in predicting which therapeutic approach will be the most promising in the individual child. In addition to a quantitative analysis of the EO and EC acquired EEG (QEEG), a 19 channel interactive neurofeedback evaluation session has also proven a strong diagnostic tool and a guide for therapy. Through gathering these data more criteria for choosing the most beneficial therapeutic options and predicting their outcome for the individual patient can be obtained. The need for a more personalized treatment and the possibility to achieve this has already been expressed and studied (Arns 2012). The suggested approach is also in concordance with a recently published Springer Brief with the title “ADHD as a Model of Brain-Behavior Relationship” (Koziol, Budding & Chidekel 2013). Herein the need for integration of tests to investigate the brain function into the evaluation process in ADD and ADHD is strongly recommended. There have been recent studies on QEEG for characterizing the autistic brain (Billeci et al. 2013). A newly released paper (Zhang et al. 2016) on brain network dynamics in disorders like schizophrenia, ADHD and autism points also towards the need to evaluate the brain’s adaptiveness and flexibility, because ultimately brain disorders are dynamic deficiency disorders. The goal of this approach of intake evaluation however is less to characterize the patients according to QEEG findings in certain clusters, but to provide the most individualized therapeutic approach.

Methods The clinically optimized approach adopted in the author’s clinic, using 19 channel EEG data for quantitative analysis in combination with real-time evaluation of how the child brain responds to various challenge conditions, is described below. It was developed and refined with more than 300 children tested between June 2012 and April 2014. The goal is to get as much information as possible in only one session of 45–60 minutes. The data was collected with the Brainmaster 24E, a 24 channel EEG and DC amplifier with BrainAvatar software and an EEG cap (Comby EEG caps, different sizes, Pamel). The real-time analysis of the data and the further evaluation is performed through comparing the patient’s obtained scores to an FDA 510K compliant normative database (Neuroguide, Brain DX). This combined QEEG and 19 channel neurofeedback session is scheduled after a verbal patient history, questionnaires, pediatric neurologic exam and neuropsychological testing for most patients ages 3–21, with usually at least one parent present.

Step 1: Familiarizing the Patient with the Setting and Their Brain Activity, Data Collection The evaluation starts with a brief explanation of what will be done. It leads immediately into the practical process of putting on the EEG cap. The children are included in the process of checking the impedances and most children/teenagers like to become active in turning the positions on an impedance testing meter. Some patients get even interested in the abbreviations displayed (Fz, P3 . . .), which can lead to an explanation of the different parts of the brain. 135

Theresia Stöckl-Drax

Before the actual EEG collection starts the patient’s brain activity is shown on a second screen with the different wave forms briefly explained. Then artifact is demonstrated through eye opening and closure, teeth clenching and swallowing. During this process the children also realize that the activity displayed on the screen is activity of both their brain and muscles. This “experiment” is followed by the explanation about the difference between muscle and brain activity and that we are most interested in the brain activity during the EEG collection. The children also learn how to do diaphragmatic breathing when the situation gets stressful for them. The following eyes open EEG collection lasts for 3–5 minutes. The eyes closed data collection is added immediately. During this process the children get informed every 30 seconds about the elapsed and remaining time.

Step 2: Neurofeedback Training: Different Challenge Conditions The training is performed as a 19 channel Z-score training (Collura, Thatcher, Smith, Lambos & Stark 2008) (Z Scores are the normalized transformations of the various EEG measures taken on the patient compared in real time to a normative database). The start is a game on the patient screen with a moving object. There is no instruction provided besides to watch how fast the object is moving. The training is adjusted in order to give plenty of success to the client. Some children already get an idea in this early investigational stage how to let the object move faster, but for most it is still not perceivable what this movement has to do with their brain activity. The next step is a challenge condition. The application is through a race game. In the beginning the threshold is set to let the child win. In a second step they are asked to allow it to be harder on each race. During this exercise the children usually get an idea how they can get faster or work harder. Some children adjust easily to the more difficult condition, some adjust only for a short period. Other children are easily irritated when only hearing that it might be harder and are discouraged. The evaluator/physician gains insights in how the individual’s brain deals with increasing difficulty through observing the child’s behavior and through the wave pattern displayed: e.g. more slow activity, more alpha activity or more fast activity, or less or more disconnection through the actual coherence values. These activation patterns in conjunction with the child’s experience are integrated in the instructions to the child: either to try harder or to just observe, in case of overactivation. Others need to learn to not be concerned about winning, instead to let go and just allow the brain to do the work. When the child is not winning for the first time then there is a chance to explore how the child deals with failure. The instruction is to give the brain a second chance at the same level of difficulty and often the brain has already accomplished the job and the race is won. Other children get very frustrated or unsure when losing and cannot adjust easily with the difficulty level. In this case the feedback is adjusted in order for the child to accomplish the task and end with a win.

Figure 8.1 Z-scores and display of racing game as a challenge condition.

136

Dynamic brain evaluation in children

Step 3: Overview of Brain Power, Coherence and Phase and Evaluation of Stressors The next step in assessment is to look at the brainwaves again, then explain the transformation into Z-score values for power, coherence and phase, displayed as numbers or plots or instant maps. Here there is another opportunity to bring the client in contact with his brain. The first evaluation step here is to ask the client to make their values/plots more white (normal), if there is dysregulation. When this is instantly possible—in about half of the clients—then there can be challenges applied through the parents who are usually observing the process. They can talk about what they consider stressful—school itself, reading, writing, math, other subjects, the teacher, homework—and topics which they would consider easy. Here usually stress displays through the values/ plots becoming/higher/more abnormal/less white/more reddish on certain topics. Before bringing up a new topic it is important however that the client can normalize the values/plots again. In this part of the evaluation stressors can be identified and also how fast the brain can normalize again is assessed. In some children there are strong hyper- or hypoactivations already, which cannot be regulated instantly or easily. This finding leads to the information that the dysregulation may be more longstanding and fixed or the accompanying parent is a strong stressor himself/herself.

Figure 8.2 Z-scores displayed as numbers.

Figure 8.3 Z-scores displayed as instant brain maps.

137

Figure 8.4a Example for actual activation without stimulus (slight hyperactivation).

Figure 8.4b Activation mentioning “reading” in a dyslexic child (pronounced hyperactivation and hypercoherence). Figure 8.4 Z-scores displayed as plots (power, coherence, phase).

Figure 8.4c Activation mentioning “reading with mom” in the same child (more pronounced hyperactivation esp. in the higher frequencies).

Figure 8.4d Activation “let the brain make it as white as possible” (shows how easily the brain can regulate itself—this is a powerful experience for many children). Figure 8.4 (Continued)

Theresia Stöckl-Drax

Step 4: Brainwave Distributions and Emotional Evaluation The next step is to show the client where the different brainwaves originate and how they spread. This is done by sLORETA current source density display through BrainAvatar. The voxels can be seen as small cubes; the colors show the amount of activity, red being the most and blue the least. It begins by looking at the delta waves, their distribution and their movement. This is followed by the theta waves and the alpha waves. If there is not very much alpha in the posterior area of the brain, the patients are asked to try to let more of those waves happen by allowing the posterior area of the head display to become red. Often clients can do this instantly. Then they are asked to do this for a short period and they usually describe the feeling that comes with it as relaxing. Regarding beta activity we look for symmetry especially in the frontal areas. When much beta activity shows up in the back of the brain then there may be muscle tension in the neck that needs to be worked on. In order to evaluate the emotional life of the brain the gamma waves are displayed (Bonnstetter, Collura, Hebets & Bonnstetter 2012). There is usually a frontal spreading going from right to left and vice versa being symmetrical most of the time. To introduce how the brain evaluates emotions the study of babies’ brains knowing what feels pleasant to them is provided. Water with lemon produces more gamma waves on the right side and water with sugar more on the left side frontally (see Davidson & Begley 2012). Then the children can evaluate how certain things like food or situations or people feel more or less pleasant/comfortable to their brain. The parents also usually like to try out certain subjects. Normally there is a very brief response, then the brain normalizes again. In some children there is a pronounced lateralized difference, mostly more activation on the right side. These are the children who often also display a more negatively focused view.

Figure 8.5a

Neutral without any stimulus, gamma being equally distributed rt/lt.

Figure 8.5 BrainAvatar voxels with left and right shifting of gamma activation.

140

Dynamic brain evaluation in children

Figure 8.5b Thinking about favorite dish. Gamma more to the left.

Figure 8.5c Thinking about boy in class who is bullying him. Gamma more to the right. Figure 8.5 (Continued)

Step 5: Neurofeedback Training This last step is to let the children/teenagers experience neurofeedback while watching a movie for 5–10 minutes, so they can try out another part of real training and see that it can be fun to do so. The training reflects the individual dysregulation/pathology if present—usually as 19 channel surface Z-score training of power, coherence and phase measures move beyond the normal thresholds set by the evaluator. As a result the movie becomes dark or the picture becomes smaller when they don’t meet feedback criteria. As it is the first session, the reward is usually on the higher end (70–100%

141

(a)

(b) Figure 8.6 Z-scores and movie feedback: the different movie sizes reflect the amount of Z-scores being within the chosen limits, and of course the children want to see it big.

Figure 8.7 Session trend over a training period with video feedback of 13 minutes at the end of the evaluative session, showing how the slow waves regulate.

Dynamic brain evaluation in children

of the time) depending on their personal ability to deal with difficulty. During this period the investigator can observe the Z-score values and/or sLORETA display again and see how the child deals with more or less feedback or observe how emotional scenes in the movie impact activation.

Aftermath Here the diagnostic and therapeutic session ends. The parents and the patients will be encouraged to watch for reactions and effects and communicate those to the evaluator/physician through an email the next day. They learn that there can be some tiredness (often), but that there can also be small short-lasting effects like homework or learning becoming easier or something becoming more pleasant. Sometimes strong effects are reported after this single session like a teenager cleaning up his messy room and starting to organize his learning utensils all by himself. Or the teacher reporting very positively about the student the next day or a child is starting to read by himself for the first time. It is important to ask for the email the next day to elicit these effects.

Evaluation Process and Therapeutic Consequences Afterwards the more in-depth evaluation of the collected data takes place. Searching for paroxysmal activity is the first step, followed by surface and connectivity maps, peak frequencies, sLORETA, as well as TBI and learning disability indices when such problems have been noted in the verbal history taken. These results, in combination with the findings from the live Z-score training with the challenge condition, the dealing with failure, the identification of stressors and the emotional situation, lead to suggestions about the most promising therapeutic approach. There is much information now gathered to take into account in developing the individual child’s treatment plan. Here only a rough estimate and some examples of how this can affect the therapeutic approach are provided: The recommendation of medication is more likely given when immediate change is needed as well as slowing in the frontal areas, a typical QEEG pattern of ADD is shown and little endurance in the task condition is displayed. Family therapy is more likely to benefit the child when there are no typical ADD patterns or a good adaption to challenge, but signs of stress, even provoked through the parent present at the investigation. A psychiatric referral in addition to neurofeedback training is considered with signs of depression (activation asymmetries) in the brain maps; or for instance, a pronounced fixed gamma activity at the right frontal area in the sLORETA display. In a lot of cases there are findings that warrant the suggestion of neurofeedback therapy (Arns, de Ridder, Strehl, Breteler, & Coenen 2009; Grevensleben, Rothenberg, Moll & Heinrich 2012), usually performed as 19 channel surface and/or Region of Interest (ROI) LORETA training as a standalone procedure or in combination with other therapies. These findings can include pronounced power elevations in theta or other bands—even stronger under task condition—disconnections displayed as low coherences in the dorsal attention network, alpha abnormalities, hyperactivation and hyperconnectivity, to name just the most prominent. Disconnections are also often found in the author’s patients during puberty especially when there were many failures in school or personal disappointments. These usually display in general low coherences in the delta and theta (and alpha) band in combination with a negative outlook. Here often only a few neurofeedback sessions with coherence training and the experience that their brain is still graciously working can lead to huge improvements. Test anxiety also responds well to neurofeedback training by learning to relax/normalize the values/plots while imagining the testing situation. 143

Theresia Stöckl-Drax

Important information for parents, teachers and the therapist on how distressed the individual brain is, is displayed through elevations in the beta and high beta band in combination with hypercoherences and fast giving-up in the challenge or failure situation as well as low endurance. Longer-standing stress is usually accompanied by a similar activation pattern in the eyes closed condition and no instant ability to change the pattern through the display of Z-score values or plots. There it is most important to identify and reduce the stressors and to provide the child again with the ability to relax through neurofeedback or biofeedback training. The sensitive children have often similar activation patterns like the stressed ones but usually less fixed, or this pattern evolves only even looking at a video like a Tom and Jerry cartoon. In such cases, recommendations have to provide the child with good attentive care as to be careful about too emotionally challenging conditions/movies/situations. The parents and clients get informed about the findings and the possible therapeutic options at a second meeting, when the why and how is thoroughly discussed.

Discussion and Outlook Here only an approximation of all the invaluable information gained through this investigational process can be demonstrated. The value of this process is that a more personalized treatment plan can be chosen and applied. According to the experience of the author, this leads to faster and more pronounced results of therapy. As this has been developed as a clinical approach it can be utilized in full or in parts immediately by clinicians. When there is 19 channel neurofeedback equipment available, it is only a short step to use it also in an investigational way. The Z-scores during the tasks likely reflect the physiological activity required for the task to be performed, and as such do not reflect pathology. It can still be an attempt to evaluate and optimize the children’s efficiency in performing a challenging task. Because the less energy is used to perform the challenging task the lower their physiological Z score will be and as such the resting state Z scores do in a certain indirect way express brain efficiency in challenging tasks. To make it a standard procedure in the diagnosis of attention, learning and emotional disorders, however, there should be a systematic evaluation process in order to find the most powerful diagnostic procedures and integrate them into a general evaluation process. What children, teenagers and parents express is that this is a unique event for them and they understand a lot of their brain’s functioning and often start to admire their brain’s abilities at the end of only one diagnostic and at the same time investigational session of only about 45 minutes all together. This can be an excellent starting point for any neuro treatment. The author declares that the investigation was conducted in the absence of any commercial or financial relationship that could be construed as a potential conflict of interest. Special thanks to Dick Genardi, Ph.D., BCN, for his invaluable advice at any time and for writing the assessment settings file based on the network literature, using cross frequency coupling in the training files.

References Arns, M. (2012). EEG-based personalized medicine in ADHD: Individual alpha peak frequency as an endophenotype associated with nonresponse. Journal of Neurotherapy, 16, 123–141. Arns, M., de Ridder, S., Strehl, U., Breteler, M., & Coenen, A. (2009). Efficacy of neurofeedback treatment in ADHS: The effects on inattention, impulsivity and hyperactivity: A meta-analysis. Clinical EEG and Neuroscience, 40, 180–189. Billeci, L., Sicca, F., Maharatna, K., Apicella, F., Narzisi, A., Campatelli, G., & Muratori, F. (2013). On the application of quantitative EEG for characterizing autistic brain: A systematic review. Frontiers in Human Neuroscience, doi:10.3389/fnhum.2013.00442

144

Dynamic brain evaluation in children Bonnstetter, R. J., Collura, T., Hebets, D., & Bonnstetter, B. J. (2012). Uncovering the belief behind the action. Neuroconnections, Winter, 20–23. Collura, T., Thatcher, R., Smith, M., Lambos, W., & Stark, C. (2008). EEG biofeedback training using live Z-Scores and a normative data base. In H. Budzinsky, J. Evans & A. Abarbanel (Eds.), Introduction to QEEG and neurofeedback (pp. 103–140). Amsterdam: Elsevier. Davidson, R. J., & Begley, S. (2012). The emotional life of your brain. New York: Hudson Street Press. Grevensleben, H., Rothenberg, A., Moll, G., & Heinrich, H. (2012). Neurofeedback in children with ADHD: Validation and challenges, Expert Rev. Neurotherapy, 12(4), 447–460. Koziol, L., Budding, D. E., & Chidekel, D. (2013). ADHD as a model of brain-behavior-relationships, the vertically organized brain in theory and practice. Springer Briefs in Neuroscience. New York: Springer. Zhang, J., Cheng, W., Liu, Z., Zhang, K., Lei, X., Yao, Y., Becker, B., Liu, Y., Kendrick, K., Lu, G., & Feng. J. (2016). Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders. Brain, doi: http://dx.doi.org/10.1093/brain/aww143

145

PART III

The Neurologist’s Perspective

9 QEEG-GUIDED NEUROFEEDBACK TO NORMALIZE BRAIN FUNCTION IN VARIOUS DISORDERS Jonathan E. Walker

Abstract QEEG-guided neurofeedback for various disorders is reviewed. In the first section, specific disorders treated in our clinic are reviewed (anger control disorder, dysgraphia, dyslexia, and enuresis). The second part is a literature review of the other successfully treated disorders using QEEG-guided neurofeedback with a bibliography of published articles.

Introduction In our clinic, we have had success in QEEG-guided neurofeedback in several disorders using specific abnormalities in the QEEG to guide the training. Patients with a given disorder are evaluated with a QEEG. QEEG abnormalities which are present in the subjects are then trained with neurofeedback to decrease abnormally elevated values or increase abnormally decreased values. This is usually accomplished in five to ten sessions per abnormality. The database we have used is the Thatcher Neuroguide, eyes open, for all disorders. Pre and post scores were obtained if a scale existed. If resolution of the problem occurred, that was noted and the results for the group were then determined.

I. Specific Disorders Trained in Our Clinic A. Anger and Anger Control Disorders1 The QEEG correlates for anger were excessive 21–30 Hz activity at one or more cortical areas. The QEEG correlates for anger control were excessive 1–10 Hz at central sites (C4, Cz). When these abnormalities were normalized, anger was decreased, and anger control was increased.

B. Dysgraphia (in Right-Handed Individuals)2 The QEEG correlate was excessive 1–10 Hz at C3. Remediation was accomplished by normalizing 1–10 Hz at C3.

149

Jonathan E. Walker

C. Dyslexia3 QEEG correlates included one or more of the following: 1. 2. 3. 4. 5. 6.

Excess 1–10 Hz at FZ and/or F1, producing attention difficulty. Excess 1–10 Hz at O1 and/or O2, producing visual processing difficulty. Excess 1–10 Hz at T5 and/or T6, producing auditory processing difficulty. Excess 1–10 Hz at T3 and/or T4, producing short-term memory difficulty. Excess 1–10 Hz at F1, producing expressive language difficulty. One or more decreases in coherence (i.e., disconnection) patterns between the areas noted above.

Remediation was accomplished by downtraining of excessive 1–10 Hz in areas where it was increased and by increasing coherence where it was decreased.

D. Enuresis4 No specific QEEG abnormalities. Single band magnitude topography revealed 1–3 microvolts at Oz (not a QEEG site), vs. 0–1 microvolts at Oz in persons with good bladder control. Remediation of anxiety was obtained with 5–10 sessions to decrease 1–7 Hz and increase 15–18 Hz at Oz in 18 of 20 cases. Two cases that failed with that protocol were later remediated by five sessions of C4 training to decrease 2–7 Hz and increase 12–15 Hz (unpublished).

E. Epilepsy (Drug Resistant)5, 6, 7 QEEG correlated with excessive 1–10 Hz in multiple areas, with or without decreases in coherence of delta and theta in one or more coherence pairs. Neurofeedback remediation was carried out by normalizing 1–10 Hz in areas where it was increased and normalizing coherence in affected pairs.

F. Migraines8 QEEG correlates were excessive 21–30 Hz in 1–4 cortical areas (most commonly P3 and/or P4) Neurofeedback remediation was carried out by normalizing 21–30 Hz in the affected areas.

G. Post-Traumatic Stress Disorder9 QEEG correlates were excessive 21–30 Hz and reduced alpha power at parietal sites (P3 and P4). Remediation was accomplished by decreasing 21–30 Hz and increasing 10 Hz at P3 and P4.

List of References (Part I) 1. Walker, J. E. (2012). QEEG correlates of anger/anger control disorder and remediation by downtraining the abnormalities with neurofeedback. Journal of Neurotherapy, 17, 88–92. 2. Walker, J. E. (2012). QEEG-guided neurofeedback for dysgraphia. Biofeedback, 40, 113–114. 3. Walker, J. E., & Norman, C. (2006). The neurophysiology of dyslexia: A selective review with implications for neurofeedback remediation and results of treatment in twelve consecutive patients. Journal of Neurotherapy, 10, 45–56. 4. Walker, J. E. (2012). Remediation of enuresis using QEEG-guided neurofeedback training. Biofeedback, 40, 109–112. 5. Walker, J. E., & Kozlowski, J. (2005). Neurofeedback treatment of epilepsy. Child and Adolescent Psychiatric Clinics of North America, 14, 163–176.

150

QEEG-Guided Neurofeedback 6. Walker, J. E. (2010). Using QEEG-guided neurofeedback for epilepsy versus standardized protocols: Enhanced effectiveness? AAPB, 35, 29–30. 7. Walker, J. E. (2008). Power spectral frequency and coherence abnormalities in patients with intractable epilepsy and their usefulness in long-term remediation of seizures using neurofeedback. Clinical EEG and Neuroscience, 39, 203–204. 8. Walker, J. E. (2011). QEEG-guided neurofeedback for recurrent migraine headaches. Clinical EEG and Neuroscience, 42, 59–61. 9. Walker, J. E. (2009). Anxiety associated with post-traumatic stress disorder—the role of quantitative EEG in diagnosis and in guiding neurofeedback training to remediate the anxiety. Biofeedback, 37, 67–70.

II. Literature Review

Table 9.1 Published series by other investigators of cases successfully treated with QEEG-guided neurofeedback for various disorders. Condition

Characteristic QEEG Abnormalities

Effective Neurofeedback Protocols

Key Ref

ADD/ADHD

Excessive slow (1–10 Hz) or fast (11–30 Hz) activity at F1/F2/F3/ F4 (absolute or relative)

Downtrain all excessive slow or fast activity at F1/F2/F3/F4

1

Abuse/ Neglect

Depression correlated with increased relative power left frontal sites

Decrease excessive 1–10 Hz at frontal and central sites

2

Age-Related Cognitive Decline

Increased theta power and decreased mean frequency, in several areas

Decrease excessive 1–10 Hz in all abnormal areas

3, 4

Alcoholism

Excessive 21–30 Hz at F3 and F4 correlates with craving, anxiety, and irritability

Decrease excessive 21–30 Hz at F3 and F4

5, 6

Anorexia/ Bulimia

Increased 8–10 Hz in central, parietal, and limbic areas. Alpha 1 was increased in bulimia

Decrease excessive alpha and excessive 21–30 Hz in central areas

7, 8

Allergies

Decreased beta 1 power in temporal and right frontal regions

Increase beta 1 power in temporal and right frontal areas

9

Amblyopia

Excessive 1–10 Hz at O1 and/ or O2

Decrease 1–10 Hz and increase 15–18 Hz at O1 and O2 (5–10 sessions)

10

Amphetamine Dependence, Addiction

Increased delta and theta power in frontal and central regions

Decrease elevated delta and theta in affected areas and increase 12–15 Hz in those areas (5–10 sessions, each area)

6

Cerebral Palsy/ Spastic Diplegia

Increased delta and theta in most leads, reduced alpha O1/O2, and decreased interhemispheric coherence of alpha and theta

Decrease 21–30 Hz and increase 10 Hz in affected areas (especially C3/C4), and increase coherence in affected pairs

11

Hemiparesis

Asymmetries in all frequencies between hemispheres with increase of delta and theta interhemispheric

Normalize coherence between hemispheres

12, 13

(Continued )

151

Table 9.1 (Continued) Condition

Characteristic QEEG Abnormalities

Effective Neurofeedback Protocols

Key Ref

Chronic Fatigue Syndrome

Excessive high-frequency beta at C3/Cz/C4

Decrease 21–30 Hz and increase 10 Hz at C3/Cz/C4

14, 15

Closed Head Injury

Increased power at 1–10 Hz and decreased power in low delta frequencies (11–20 Hz) at several sites, especially frontal and temporal

Decrease power in slow frequencies (1–10 Hz) and increase power in beta frequencies (15–20 Hz) in affected areas

16, 17

Cocaine Addiction

Decreased absolute and relative power of delta and increased relative power of alpha

Decrease relative power of alpha in affected areas

6, 18, 19, 20

Coma (Level 2) (25/32 Patients Regained Consciousness)

Poor outcome after trauma correlated with reduced left hemispheric beta power in frontal, parietal, and centrotemporal regions and reduced alpha power in the centrotemporal regions

Bipolar training (T3/C3 and T4/C4) to decrease 2–7 Hz and increase 15–18 Hz (5 sessions each pair of sites)

21, 22

Complex Regional Pain Syndrome

Delta and/or theta activity localized to the somatosensory cortex and the orbital frontal cortex

Decrease 2–7 Hz and 22–30 Hz at C3 for right-sided pain decrease, at C4 for left-sided pain decrease

23, 24

Conduct Disorder (Juvenile Offenders)

Excessive frontal/central 1–10 Hz activity

Wide-band amplitude reduction (2–30) at F3 and F4 followed by wide-band reduction (2–30 Hz) at C3 and C4, followed by theta (2–7 Hz) and right beta (20–26 Hz) reduction and SMR (11–15 Hz) enhancement (20 sessions)

25

Dissociative Identity and Host Disorder

Decreased coherence in alter personalities with excessive frontal alpha activity

Decrease excessive 1–10 Hz in the frontal areas (plus cognitive behavioral therapy)

27, 28

Down’s Syndrome

Excessive 1–10 Hz in several brain areas

Decrease 1–10 Hz and increase 15–18 Hz in affected areas

29, 30

Dyscalculia

Excessive 1–10 Hz at F7 and F3 (calculations). Excessive 1–10 Hz at P4 (word problems)

Decrease excessive 1–10 Hz in affected areas

39

Fetal Alcohol Syndrome

Excessive frontocentral, parietal, and posterior temporal 1–10 Hz activity

Decrease excessive 1–10 Hz in all affected areas

31, 32

Fibromyalgia

Excessive 1–10 Hz in frontal areas, with increased 21–30 Hz in frontal and central areas, widespread coherence decreases in low to medium frequencies frontally

Decrease 1–10 Hz and 21–30 Hz in the affected areas

33, 34

Hallucinations (Auditory)

Increased beta-1, beta-2, and gamma power in left inferior parietal lobe and left medial frontal area

Decrease excessive beta and highfrequency beta in the affected areas

35, 36

Hyperactivity/ Impulsivity

Excessive 2–7 Hz and/or 21–30 Hz at F4 and/or C4

Decrease 2–7 Hz and 20–30 Hz at F4 and C4

37, 38

Condition

Characteristic QEEG Abnormalities

Effective Neurofeedback Protocols

Key Ref

Marijuana Addiction, Chronic

Increased absolute and relative power of alpha (“hyperfrontality”) and decreased relative power of delta and theta

Decrease 8–12 Hz and increase 15–18 Hz at C3 and C4 (5–10 sessions)

40, 41

Mental Retardation

Increased theta, alpha, and coherence abnormalities

Decrease excess theta, alpha, and coherence abnormalities in all areas (80–160 sessions)

42

Mild Cognitive Impairment

Excessive absolute power of theta

Decrease 2–7 Hz and increase 15–16 Hz at the affected areas

43

Obsessive Compulsive Disorder

Decreased alpha and beta power and increased theta power in frontotemporal regions with increased 21–30 Hz at C3 and C4

Decrease 21–30 Hz, 2–7 Hz, and increase 10 Hz at C3 and C4 (5–10 sessions)

44

Opiate Addiction

Excessive frontal slowing (2–7 Hz) and increased highfrequency beta (21–30 Hz)

Decrease 2–7 and 21–30 Hz, and increase 10 Hz in the affected areas (5–10 sessions each)

6

Pain, Neurogenic

Excessive power at 7–9 Hz maximal in the frontal regions

Decrease elevated 2–25 Hz in affected areas

45, 46

Parkinsonism

Excess frontal theta (4–6 Hz), beta (12–18 Hz), and gamma (30–45 Hz)

Decrease frontocentral theta and beta, and decrease 21–30 Hz to decrease tremor, and decrease parietal 21–50 Hz to reduce akinesia and rigidity

47, 48

Panic Disorder/ Phobias

Increased frontal beta (13–26 Hz) with increased right frontal alpha and decreased left temporal theta power

Decrease excessive beta and/or alpha wherever it is found in the cortex

50

Periodic Leg Movements of Sleep/Restless Legs Syndrome

Excessive absolute power of alpha (9–12 Hz) centrally, and excessive beta power (13–30 Hz) along the midline (Cz/Pz)

LENS training (15 sessions) focused on 9–12 Hz and 7–12 Hz activity.

49

Premenstrual Syndrome/ Dysphoric Disorder

Increased delta, theta, and fast alpha (11–12 Hz) in the late luteal phase relative to the follicular phase

Decrease 4–7 Hz and 22–30 Hz where elevated, and increase 12– 15 Hz at C4, 15–18 Hz at Cz

51, 52

Psychopathy/ Sociopathy

Excessive frontal alpha, theta, and beta power as well as alpha, theta, and beta coherence abnormalities

Normalize excessive alpha, theta, and beta power as well as coherence abnormalities (80–120 sessions)

53

Reactive Attachment Disorder

Excessive frontal and central excessive 20–32 Hz at FZ and Cz, and excessive 8–12 Hz in parietal and posterior temporal regions

Decrease excessive frontal and central 1–10 Hz (10–15 sessions), decrease excessive 20–32 Hz at FZ and Cz (5–10 sessions), and 8–12 Hz in parietal and posterior temporal regions

54–55

Restless Legs Syndrome

Absolute power excess in alpha (central) and beta (C7 and P7)

LENS training (3 patients) (20 sessions) focused on F7/Cz/C4/ Pz

56

(Continued )

Jonathan E. Walker Table 9.1 (Continued) Condition

Characteristic QEEG Abnormalities

Effective Neurofeedback Protocols

Key Ref

Schizophrenia

Excessive frontal delta (1–3 Hz) and high-frequency beta (21–30 Hz), and increased 1–10 Hz in right parietal region

Decrease right parietal and left anterior temporal activity, frontal delta, and fast beta activity, reward 8–12 Hz and inhibit 2–7 Hz at FPO2 to treat depression, inhibit alpha, theta, and beta at F7/T3 (bipolar) for paranoia, 19/50 subjects responded, i.e., no longer schizophrenic. 27 subjects were able to discontinue their medication

57, 58

Spelling Difficulties

Excess slow activity (1–10 Hz) at T6

Decrease 1–10 Hz at T6 (5–20 sessions)

59

Stroke

Focal excess of 1–7 Hz in affected areas

Decrease 1–7 Hz and increase 15–21 Hz in affected areas (7–10 sessions each)

60, 61, 62, 63

Stuttering

Excess focal slowing (1–10 Hz) at F7 or F8. Reduced coherence between F7 and other areas

Decrease 1–10 Hz at F7/F8 and normalize coherence in abnormal pairs

64

Tic Disorder/ Tourette Syndrome

Reduced SMR plus excess theta at C3/C4

Decrease 1–10 Hz and 21–30 Hz and increase 12–15 Hz at C3/C4

65, 66

Tinnitus

Excess 21–30 Hz and 40–80 Hz with reduced alpha power in temporal regions (T3, T4, T5, T6) and frontal regions (F3/F4)

Decrease elevated 21–30 Hz and increase 10 Hz in affected temporal areas and frontal areas

67, 68

Vertigo

Increased relative theta in the centrotemporal region with peripheral vestibular dysfunction

Decrease elevated 2–8 Hz at FZ, decrease elevated 21–30 Hz at C3 and C4

69

Violence

Bilateral frontocentral slowing excess 2–10 Hz

Decrease 2–10 Hz and increase 12–15 Hz in frontocentral areas

72, 73

Bibliography of QEEG—Based on Neurofeedback (by Other Authors) 1. Kirk, L. (2007). Protocols for subtypes of attention deficit/hyperactivity disorder. In J. Evans (Ed.), Handbook of neurofeedback (pp.1–16), New York: Haworth Press. 2. Huang-Storms, L., Bodenhamer-Davis, E., Davis, R., & Dunna, J. (2006). QEEG-guided neurofeedback for children with histories of abuse and neglect: Developmental rationale and pilot study. Journal of Neurotherapy, 10, 3–6. 3. Budzynski, T. H. (1996). Brain brightening: Can neurofeedback improve cognitive processing. Biofeedback, 24(2), 14–17. 4. Prichep, L. S. (2007). Quantitative EEG and electromagnetic brain imaging in aging and in the evolution of dementia. Annals of the New York Academy of Sciences, 1097, 150–157. 5. Contin-Churchman, P., Moreno, R., Añez, Y., & Vergara, F. (2006). Clinical correlates of quantitative EEG alterations in alcoholic patients. Clinical Neurophysiology, 117, 740–751. 6. Sokhadze, T. M., Cannon, R. L., & Trudeau, D. L. (2008). EEG biofeedback as a treatment for substance use disorders: Review, rating of efficacy, and recommendations for further research. Applied Psychophysiology and Biofeedback, 33, 1–28. 7. Gurnee, R. (2009). QEEG in anorexia & bulimia: Futurehealth, WinterBrain Plenary presentation.

154

QEEG-Guided Neurofeedback 8. Smith, P. N., Sams, M. W., & Sherlin, L. (2003). Neurological basis of eating disorders. I: EEG findings and the clinical outcome of adding symptom-based, QEEG-based, and analog/QEEG-based remedial neurofeedback training to traditional treatment olans. ISNR Annual Conference (abstract). 9. Montgomery, P. S. (2006). Allergy pattern in the EEG. Journal of Neurotherapy, 10, 89–92. 10. Ordmandy, J. O. (2003). Increase in visual acuity in a male with amblyopia: A single case. Journal of Neurotherapy, 7, 70–71. 11. Kułak, W. (2003). Spectral analysis and EEG coherence in children with cerebral palsy: Spastic diplegia. Przeglad Lekarski, 60(Suppl) 1, 23–27. 12. Kułak, W. (2005). Quantitative EEG analysis in children with hemiparetic cerebral palsy. NeuroRehabilitation, 20, 75–84. 13. Ayers, M. E. (2004). Neurofeedback for cerebral palsy. Journal of Neurotherapy, 8, 96–96. 14. Flor-Henry, P., Lind, J. C., & Koles Z. J. (2010). EEG source analysis of chronic fatigue syndrome. Psychiatry Research, 181, 155–164. 15. James, L. C., & Folen, R. A. (1996). EEG biofeedback as a treatment for chronic fatigue syndrome: A controlled case report. Behavioral Medicine, 22, 77–81. 16. Reddy, R. P., et al. (2013). Neurofeedback training as an intervention in a silent epidemic: An Indian scenario. Journal of Neurotherapy, 17, 213–225. 17. Thatcher, R. W. (2008). EEG evaluation of traumatic brain injury and EEG biofeedback treatment. In T. H. Budzinsky, J. R. Evans, H. K. Budzynski, & A. Abarbanel (Eds.), Introduction to quantitative EEG and neurofeedback (pp. 269–294). New York: Academic Press. 18. Costa, L., & Bauer, L. (1997). Quantitative electroencephalographic differences associated with alcohol, cocaine, heroin and dual-substance dependence. Drug and Alcohol Dependence, 46, 87–93. 19. Alper, K. R., Prichep, L. S., Kowalik, S., Rosenthal, M. S., & John, E. R. (1998). Persistent QEEG abnormality in crack cocaine users at 6 months of drug abstinence. Neuropsychopharmacology, 19, 1–9. 20. Burkett, V. S., Cummins, J. M., Dickson, R. M., & Skolnick, M. (2005). An open clinical trial utilizing realtime EEG operant conditioning as an adjunctive therapy in the treatment of crack cocaine dependence. Journal of Neurotherapy, 9, 27–47. 21. Kane, N. M., Moss, T. H., Curry, S. H., Butler, S. R. (1998). Quantitative electroencephalographic evaluation of non-fatal and fatal traumatic coma. EEG, 106, 244–50. 22. Ayers, M. E. (1995). EEG neurofeedback to bring individuals out of level 2 coma. Biofeedback & SelfRegulation, 20, 304–305. 23. Walton, K. D., Dubois, M., & Llinás, R. R. (2010). Abnormal thalamocortical activity in patients with Complex Regional Pain Syndrome (CRPS) type I, Pain, 150, 41–55. 24. Jensen, M. P., Grierson, C., Tracy-Smith, V., Bacigalupi, S. C., & Othmer, S. (2007). Neurofeedback treatment for pain associated with complex regional pain syndrome type I. Journal of Neurotherapy, 11, 45–53. 25. Gordon E., Palmer, D. M., & Cooper, N. (2010). EEG alpha asymmetry in schizophrenia, depression, PTSD, panic disorder, ADHD and conduct disorder. 26. Martin, G., & Johnson, C. L. (2006). The boys totem town neurofeedback project. Journal of Neurotherapy, 9, 71–86. 27. Hopper, A. (2002). EEG coherence and dissociative identity disorder comparing EEG coherence in DID hosts, alters, controls and acted alters. Journal of Trauma & Dissociation, 1, 75–88. 28. Mason, L. A., & Brownback, T. S. (2001). Optimal functioning training with EEG biofeedback for clinical populations. Journal of Neurotherapy, 5, 33–44. 29. Sürmeli, T. (2007). EEG neurofeedback treatment of patients with down syndrome. Journal of Neurotherapy, 11, 1–7. 30. D’Angiulli, A., Grunau, P., Maggi, S., & Herderman, A. (2006). Electroencephalographic correlates of prenatal exposure to alcohol. Alcohol, 40, 127–133. 31. Hallman, D. W. (2012). 19-channel neurofeedback in an adolescent with FASD. Journal of Neurotherapy, 16, 150–154. 32. Donaldson, M., Mueller, H., Donaldson, S., & Sella, G. (2010). QEEG patterns, psychological status and pain reports of fibromyalgia sufferers. Clinical EEG Neuroscience, 41, 132–139. 33. Kayiran, S., Dursun, E., Ermutlu, N., Dursun, N., & Karamürsel, S. (2007). Neurofeedback in fibromyalgia syndrome. Journal of the Turkish Society of Algology, 19, 47–53. 34. Lee, S. H., Wynn, J. K., Green, M. F., Kim, H., Lee, K. J., Nam, M., Park, J. K., & Chung, Y. C. (2006). Quantitative EEG and LORETA tomography imaging of patients with persistent auditory hallucinations. Schizophrenia Research, 83, 111–119. 35. Walker, J. E. (2010). Recent advances in quantitative EEG as an aid to diagnosis and as a guide to neurofeedback training. AAPB, 35, 25–27.

155

Jonathan E. Walker 36. Snyder, S. M., & Hall, J. R. (2006). A meta-analysis of quantitative EEG power associated with attentiondeficit hyperactivity disorder. Journal of Clinical Neurophysiology, 23, 440–455. 37. Arns, M., de Ridder, S., Strehl, U., Breteler, M., & Coenen, A. (2009). Efficacy of neurofeedback treatment in ADHD: The effects on inattention, impulsivity and hyperactivity: A meta-analysis. Clinical EEG and Neuroscience, 40, 180–189. 38. Gurne, R. (2004). Assessment & QEEG based neurofeedback of dyscalculia, dyslexia and auditory processing disorders. iSNR Workshop, 2004. 39. Struve, F. A. (2003). Acute marihuana (THC) exposure produces a “transient” topographic quantitative EEG profile identical to the “persistent” profile seen in chronic heavy users. Clinical EEG, 34, 75–83. 40. Scott, W. C., Kaiser, D., Othmer, S., & Sideroff, S. I. (2005). Effects of an EEG biofeedback protocol on a mixed substance abusing population. American Journal of Drug Alcohol Abuse, 31, 455–469. 41. Sürmeli, T., & Ertem, A. (2010). Post WISC-R and TOVA improvement with QEEG-guided neurofeedback training in mentally retarded. Clinical EEG and Neuroscience, 41, 32–41. 42. Becerra, J., Fernández, T., Roca-Stappung, M., Díaz-Comas, L., Galán, L., Bosch, J., Espino, M., Moreno, A. J., & Harmony, T. (2012). Neurofeedback in healthy elderly humans with electroencephalographic risk for cognitive disorder. Journal of Alzheimer’s Disease, 28, 357–367. 43. Sürmeli, T., & Ertem, A. (2011). Obsessive compulsive disorder and the efficacy of qEEG-guided neurofeedback treatment: A case series. Clinical EEG and Neuroscience, 42, 195–201. 44. Saratheim, J., Stern, J., Aufenberg, C., Rousson, V., & Jeanmonod, D. (2006). Increased EEG power and slowed dominant frequency in patients with neurogenic pain. Brain, 129, 55–64. 45. Vuckovic, A., Hasan, M. A., Fraser, M., & Allan, D. B. (2012). Effects of neurofeedback treatment on neuropathic pain following spinal cord injury. 14th World Congress on Pain, 27–31, Milan, Italy. 46. Moazami-Goudarzi, M., Sarnthein, J., Michels, L., Moukhtieva, R., & Jeanmonoda, D. (2008). Enhanced frontal slow and high frequency power and synchronization in the resting EEG of parkinsonian patients. NeuroImage, 41, 985–997. 47. Thompson, M., & Thompson, L. (2002). Biofeedback for movement disorders: Theory and preliminary results. Journal of Neurotherapy, 6, 51–70. 48. Hammond, D. C. (2012). Neurofeedback treatment of restless legs syndrome and periodic leg movements of sleep. Journal of Neurotherapy, 16, 155–163. 49. Gurnee, R. (2004). QEEG subtype of generalized anxiety disorder, obsessive compulsive disorder and insomnia. Future Health.org. 50. Baker, F. C., & Colrain, I. M. (2010). Daytime sleepiness, psychomotor performance, waking EEG spectra and evoked potentials in women with severe premenstrual syndrome. Journal of Sleep Research, 19, 214–227. 51. Othmer, S., & Othmer, S. (2007). EEG biofeedback training for PMS. EEG Info. 52. Sürmeli, T., & Ertem, A. (2009). QEEG-guided neurofeedback therapy in personality disorders. Clinical EEG and Neuroscience, 40, 5–9. 53. VanBloom, L. L. (2000). Quantitative encephalogram (qEEG) in children with reactive attachment disorder. Journal of Neurotherapy, 4, 106–207. 54. Fisher, B. S., et al. (2005). Healing the attachment disorder before and after behavioral and neurological functioning. iSNR, Workshop, Denver, CO. 55. Hammond, D. C. (2012). Neurofeedback treatment of restless legs syndrome and periodic leg movements in sleep. Journal of Neurotherapy, 16, 155–163. 56. Sürmeli, T., Ertem, A., Eralp, E., & Kos, I. H. (2012). Schizophrenia and the efficacy of qEEG-guided neurofeedback treatment: A clinical case series. Clinical EEG and Neuroscience, 43, 133–144. 57. Bolea, A. S. (2010). Neurofeedback treatment of chronic inpatient schizophrenia. Journal of Neurotherapy, 14, 47–54. 58. Breteler, M.H.M., Arns, M., Peters, S., Giepmans, I., & Ludo Verhoeven, L. (2009). Improvement in spelling after QEEG-based neurofeedback in dyslexia. AAPB Journal, 35, 5–11. 59. Forman, B., & Claassen, J. (2012). Quantitative EEG for the detection of brain ischemia. Critical Care, 16, 216. 60. Rozelle, G., & Budzynski, T. (1995). Neurotherapy for stroke rehabilitation: A single case study. AAPB Journal, 20, 211–228. 61. Bearden, T. S, Cassisi, J. E., & Pineda, M. (2003). Neurofeedback for a patient with thalamic and cortical infarctions. AAPB Journal, 28, 241–253. 62. Cannon, K. B., Sherlin, L., & Randall, R. L. (2010). Neurofeedback efficacy in the treatment of a 43-year-old female stroke victim: A case study. Journal of Neurotherapy, 14, 107–121. 63. Mascaro, L. (2013). Healing stuttering with Z-score neurofeedback. NeuroConnections, winter issue. 64. Tansy, M. A. (1986). A simple and a complex tic (Gilles de la Tourette’s syndrome). International Journal of Psychophysiology, 4, 91–97.

156

QEEG-Guided Neurofeedback 65. Messerotti, B. S., Buodo, G., Leone, V., & Palomba, D. (2011). Neurofeedback training for Tourette’s syndrome: An uncontrolled single-case study. AAPB Journal, 36, 281–288. 66. Shulman, A., Avitable, M. J., & Goldstein, B. (2006). Quantitative electroencephalography power analysis in subjective idiopathic tinnitus patients: A clinical paradigm shift in the understanding of tinnitus, an electrophysiological correlate. International Tinnitus Journal, 12, 121–131. 67. Ashton, H., Reid, K., Marsh, R., Johnson, I., Alter, K., & Griffiths, T. (2007). High-frequency localized “hot spots” in temporal lobes of patients with intractable tinnitus. NeuroSci Letters, 426, 23–28. 68. Dietzen, T., Balkenhol, T., & Delb, W. (2012). Neurofeedback treatment in tinnitus patients in a comparative study of different treatment strategies. 6th International Congress on Tinnitus, Abstract #9. 69. Pollak, L., Schiffer, J., Klein, C., Giladi, R., & Rabey, J. M. (1998). Quantified EEG in patients with vertigo of peripheral or central origin. International Journal of Neuroscience, 93, 35–41. 70. Weiler, E. W., Brill, K., Tachiki, K. H., & Schneider, D. (2012). Neurofeedback and quantitative electroencephalography. International Tinnitus Journal, 8, 87–93. 71. Evans, J. (1999). Abnormal QEEG patterns associated with dissociation and violence. Journal of Neurotherapy, 3, 21–27. 72. Martin, G. (2002). EEG biofeedback with incarcerated adult felons. iSNR abstract, Scottsdale, AZ. 73. Smith, P. N., & Sams, M. W. (2005). Neurofeedback with juvenile offenders: A pilot study in the use of QEEG-based and analog-based remedial neurofeedback training. Neurotherapy, 9(3), 87–99.

157

10 QEEG (BRAIN MAPPING) AND LORETA Z-SCORE NEUROFEEDBACK IN NEUROPSYCHIATRIC PRACTICE J. Lucas Koberda Abstract This chapter presents my clinical approach to neuropsychiatric patients including application of QEEG/LORETA for the confirmation of diagnosis and use of LORETA Z-score neurofeedback (NFB) in therapy. Introduction of QEEG/LORETA brain imaging has improved our diagnostic ability in neuropsychiatric practice by identification of dysregulated cortical areas implicated in patients’ symptoms. Additional use of LORETA Z-score NFB enables us to directly target these areas of dysregulation in order to improve symptoms associated with them. Based on approximately 260 patients treated in our clinic with Z-score LORETA NFB and suffering from different neuropsychiatric conditions, detailed analyses of representative cases are presented. Specific areas of cortical dysregulation identified by QEEG/LORETA are described, and results of computerized cognitive testing are presented. Follow-up findings of QEEG/LORETA electrical imaging after NFB and computerized cognitive testing indicating NFB mediated cognitive enhancement will be shown. Several major groups will be discussed including patients diagnosed with TBI, cerebrovascular disease (CVA), chronic pain/headaches, seizures/epilepsy, a static (non-progressing) cognitive dysfunction, progressing cognitive dysfunction including Alzheimer’s disease (AD), anxiety/depression, ADD/ADHD, as well as autistic spectrum disorder (ASD).

Introduction Since the introduction of QEEG analysis to the neurofeedback (NFB) community, QEEG-brain mapping has become increasingly utilized for the confirmation of diagnosis and targeted neurotherapy. The initial phase of NFB therapy (which utilized one or two channel technology) relied mostly upon on a symptoms approach with no definite guidance as to whether or not the assumed treatment was accurate. It was not unusual to see patients who were subjected to more than 100 NFB sessions with only minor or modest improvement of their symptoms. Therefore, the introduction of the QEEG was one of the most important milestones in NFB development. By performing a periodic analysis of brain maps between the neurotherapy sessions, every therapist is able to see if a desired progress in brain wave neuromodulation was achieved. Another game changer was the subsequent introduction of LORETA imaging and LORETA mediated NFB. LORETA

158

QEEG and LORETA Z-Score Neurofeedback

imaging has added value of three dimensional visualization of the brain’s electrical activity and enables us to see electrical dysregulations in deeper structures of the brain, which are not visible with two dimensional electrical imaging. Several important structures of the brain including the insula, amygdala, hippocampus and cingulate gyrus are able to be visualized with this type of electrical imaging. Many neuropsychiatric symptoms are associated with electrical dysregulation in these structures, as has been shown in my prior papers (Koberda, Koberda, Bienkiewicz, Moses & Koberda, 2013; Koberda et al., 2014). Therefore, our ability to self-regulate these electrically dysregulated structures with LORETA NFB gave us an additional therapeutic tool in neurotherapy armamentarium. An additional benefit of QEEG/LORETA electrical imaging is the low cost associated with this technology. Many insurance companies reimburse approximately $300 for this testing, which is just a fraction of the cost when compared to other types of functional brain imaging (functional MRI, PET, SPECT). QEEG/LORETA is based on portable electronics and computer software; it can be administered in any type of clinical outpatient setting. In this chapter, I would like to share my approach to evaluating patients suffering from common neuropsychiatric disorders and present several cases as an illustration of this model. The majority of patients referred to my clinic have already been subjected to conventional therapy with no major clinical benefits. Some patients have seen multiple specialists, including pain management/psychiatrists, and were subjected to brutal therapies such as electroconvulsive therapy (ECT) in order to improve symptoms of depression. A selection of referred patients endured previously administered failed surgeries.

Initial Evaluation During the initial evaluation (which usually takes around 60 minutes in addition to general neuropsychiatric examination) an extensive history is taken from the patient and his/her family. In order to deliver correct diagnosis and effective therapy, I have to be well familiarized with all my patients. This allows me to know my patient almost as well as I know my family members when they come back for a follow-up visit. For that purpose I collect extensive family and social history, and document as many details as possible, since they may have influence on the patient symptoms. Individuals who complain of cognitive problems are usually scheduled for computerized cognitive testing (NeuroTrax, Inc., Bellaire, TX). This testing takes approximately 60 minutes to complete, and evaluates seven different cognitive domains including memory, information processing speed, attention, executive function, motor function, verbal function and visual-spatial function. The individual is compared to education and age-matched controls. Standard deviations (SD) are used to show whether the patient’s cognition is close to the expected one. For those who elect to be subjected to NFB or other forms of neuromodulation, cognitive testing may be repeated in order to see if any benefits of this therapy was accomplished.

Z-Score LORETA Neurofeedback In Z-score NFB, a real-time comparison to an age-matched population of healthy subjects is used for data acquisition, simplifying protocol generation and allowing clinicians to target modules and hubs that indicate dysregulation and instability in networks related to the symptoms (Thatcher, 2013). Z-score NFB increases specificity in operant conditioning, providing a guide that links extreme Z-score outliers to the symptoms, and then reinforcing Z-score shifts toward states of greater homeostasis and stability. The goal is increased efficiency of information processing in brain networks related to the patient’s symptoms (Thatcher, 2013).

159

J. Lucas Koberda

A recently introduced method called Low Resolution Electromagnetic Tomography (LORETA) Z-score NFB is capable of targeting specific dysregulated anatomical structures, many of which are in deeper cortical locations (Koberda et al., 2013; Thatcher, 2013). For example, the insula and anterior cingulate have been identified as potential NFB target sites to improve pain control in patients who display electrical dysregulation of these areas (Koberda et al., 2013). In this chapter, I would like to focus on the results of Z-score LORETA NFB therapy from my clinic. At the time of this writing, we have completed over 260 neuropsychiatric cases using this method. Depending on the diagnosis, the subjective response to Z-score LORETA NFB within 10 NFB sessions ranges between 70–100%. The most responsive group to NFB therapy is the traumatic brain injury (TBI) population. One of the most challenging populations to see benefits seems to be a group of progressing neurodegenerative disorders including Alzheimer’s disease (AD). This may not be a big surprise to many of us since the natural history of TBI is gradual regeneration, whereas in AD we would expect a gradual decline. Despite the fact that NFB has been shown to be neuroplastic (able to create new neurons and connections) (Ghaziri et al., 2013), the relatively rapidly progressing neuronal degeneration which is seen in AD may be occurring faster than benefits coming from NFB-based neuromodulation. Therefore, we should have lower expectations with neurodegenerative disorders and see that the preservation of cognition (no definitive decline over time) may be good enough as a clinical outcome. Since some of the diagnoses were more frequent than others, I was able to summarize and stratify the data in order to reach more conclusive findings. I will also present some statistics from these groups as well as some representative cases. Several major groups will be discussed including patients diagnosed with TBI, cerebrovascular disease (CVA), chronic pain/headaches, seizures/epilepsy, a static (non-progressing) cognitive dysfunction, progressing cognitive dysfunction including AD, anxiety/depression, ADD/ADHD, as well as autistic spectrum disorder (ASD).

Traumatic Brain Injury The TBI group, which includes 40 patients, represents one of the most responsive NFB populations. Practically all TBI patients subjected to neurotherapy reported benefits of this treatment. Most of the patients were diagnosed with mild TBI (mTBI) and were treated within the first year after brain injury. Few patients were diagnosed with more severe TBI and were subjected to delayed outpatient NFB therapy (more than one year since the TBI occurrence). Most patients complained of headaches and cognitive problems, with a few of them also suffering from dizziness and overlapping depression. Those who complained of cognitive problems were subjected to analysis with computerized cognitive testing before and after 10 sessions of NFB. During NFB therapy, a subjective response from patients was collected to discern whether or not there was an improvement of symptoms. In addition, QEEG maps were obtained before each NFB session initiation in order to see an objective improvement of QEEG abnormalities. Subsequent analysis revealed that all patients (100%) noticed subjective improvement of their symptoms during 10 sessions of NFB therapy, out of which most of them reported initial improvement after only 1–3 sessions. Thirty-five patients also had an objective improvement (87%) in QEEG maps manifesting as a reduction of excessive beta activity and/or normalization of delta or theta power. Fifteen patients completed pre- and post-cognitive testing with 14 patients (93%) having significant cognitive enhancement (Global Cognitive Score increased 3–23 points with 12 points on average) after 10 sessions of NFB therapy. When compared to

160

QEEG and LORETA Z-Score Neurofeedback

another cognitive enhancement group from our practice (consisting mostly of ADD patients), the TBI group resulted in even greater cognitive recovery. These results are very encouraging and indicate the high potential of Z-score LORETA NFB in rehabilitation of patients suffering from TBI. When programming NFB sessions, we have focused on a symptoms check list (SCL), and selection was made based on up to five of the most severe problems identified by either the patient or cognitive testing. The following case illustrates my approach to a patient with mTBI with mild cognitive problems.

Case 1 A 17-year-old male with a history of two mild concussions (last one two years ago) and a possible overlapping attention deficit disorder was seen for consultation. The patient came with his mom from Atlanta for consideration of NFB due to poor high school performance and possible overlapping depression and behavioral problems. His initial computerized cognitive testing (NeuroTrax) showed lower than expected (expected score, 100) global cognitive score (GCS), 90.9 (Table 10.1) with low memory score, 89.3, as well as executive function, 85.6. After initiation of Z-score LORETA NFB, a subjective improvement of his cognition (feeling sharper) as well as mood improvement was noted. After 10 sessions of NFB, marked improvement of cognitive score was recorded (GCS-101.5). An additional 10 sessions (total of 20 sessions) of NFB increased his GCS to 108.8, which was almost 18 points higher than the initial one. The initial QEEG showed an elevated frontal and temporal theta power, as well as frontal alpha and beta power (Figure 10.1). An increased frontal and temporal theta power may be seen in patients with cognitive difficulties including ADD. Elevated frontal alpha is frequently seen in individuals with depression. High frontal beta may be encountered in patients with anxiety. Also, abnormalities in amplitude asymmetry, coherence and phase lag were noted. Additional TBI discriminant analysis showed TBI probability index of 97.5% and TBI severity index of 5.23, confirming evidence of prior concussions. LORETA imaging showed electrical dysregulation of the anterior cingulate and right temporal region—including several Brodmann’s areas (BA): BA 20, 24 and 37 (Figures 10.2a and 10.2b). In addition to cognitive and behavioral improvements, a correction of previously identified QEEG abnormalities was achieved.

Table 10.1 A 17-year-old with prior concussions and ADD. NeuroTrax computerized cognitive testing was completed before NFB initiation (first vertical column from left) and after completion of 10 and 20 NFB sessions (second and third column). Number of NFB Sessions

0

10

20 108.8

Global Cognitive Score

90.9

101.5

Memory

89.3

86.2

108

Executive Function

85.6

98.5

113.6

Attention

93.7

102.7

112.7

Information Processing Speed

91.7

99.7

105.8

107.3

113.9

107.3

Verbal Function

83.2

114.2

112.1

Motor Skills

85.8

95.1

Visual Spatial

161

102

Figure 10.1 A 17-year-old with prior concussions and overlapping ADD and behavioral problems—QEEG showed elevated frontal and temporal theta and alpha power (in red), increased frontal beta power and right temporal delta power. Green color shows absolute and relative power within one standard deviation (SD), yellow color within two standard deviations and red color within three SD.

QEEG and LORETA Z-Score Neurofeedback

(a)

(b) Figures 10.2a and 10.2b LORETA of 17-year-old male with prior concussions, and ADD-area of electrical dysregulation of the right temporal lobe is shown in red (a) and anterior cingulate in blue (b).

Cerebrovascular Disease We have completed Z-score LORETA therapy with more than 260 patients with different neuropsychiatric conditions including five patients suffering from stroke. One of the patients who was diagnosed with occipital cerebrovascular accident (CVA) and complained of visual problems due to homonymous hemianopia completed only three NFB sessions and reported subjective improvement of his vision. However, no follow-up visual fields study was completed. All patients suffering from CVA, regardless of the origin (ischemic or hemorrhagic), were found to have an improved performance after completion of NFB (Koberda & Stodolska-Koberda, 2014). In this chapter, I will present one of our representative patients who completed Z-score LORETA NFB due to ischemic stroke.

Case 2 A 69-year-old male came for rehabilitation with LORETA Z-score NFB after suffering from CVA (four months earlier). During CVA he complained of confusion with memory problems, left-sided numbness associated with pain and visual and spatial problems. MRI of the brain showed evidence of the right thalamic and temporal/hippocampal CVA and occlusion of occipital cerebral artery.

163

J. Lucas Koberda

QEEG showed increased right temporal delta and theta power (Figure 10.3). LORETA showed several areas of electrical dysregulation including the right temporal lobe including BA-36 and the cingulate gyrus BA-24 (Figures 10.4a and 10.4b).

Figure 10.3 A 69-year-old with prior CVA. QEEG showed increased right temporal delta and theta power (in yellow).

164

QEEG and LORETA Z-Score Neurofeedback

(a)

(b) Figures 10.4a and 10.4b LORETA showed several areas of electrical dysregulation, including right temporal lobe BA-36 and cingulate gyrus BA-24 (in red). Table 10.2 Computerized cognitive testing (NeuroTrax) of a 69-year-old with prior CVA before and after 10 sessions of NFB. Number of NFB Sessions Global Cognitive Score Memory Executive Function Attention Information Processing Speed Visual Spatial Verbal Function Motor Skills

0 83.6 85 87.5 80.2 74.4 85.8 86.1 86

10 94.8 99.7 103.9 94.2 84.3 99.3 83.7 98.4

Initial (before NFB) NeuroTrax showed low cognitive score GCS, 83.6, with a low memory score, 85.0, and information processing speed (IPS), 74.4. After four sessions of NFB, the patient reported some improvement of his cognitive symptoms. After 10 NFB sessions, repeated cognitive testing showed an improvement in cognitive functions (GCS-94.8) including memory-99.7, IPS-84.3, as well as improvement in other cognitive domains. In addition, a correction of QEEG/LORETA abnormalities was noted after NFB therapy completion. Overall, this patient has been able to come back to his accounting duties and perform at his pre-stroke baseline level. 165

J. Lucas Koberda

Chronic Pain and Headaches Most pain conditions produce an elevation of beta power on QEEG as a response to painful stimulation (Stern, Jeanmonod & Sarnthein, 2006; Walker, 2011). NFB is becoming an increasingly popular modality of therapy utilized for reduction of this beta activity and potential pain amelioration (Walker, 2011). In addition to localized beta activity reduction by a standard one to two electrode NFB, a more specific LORETA NFB may be even more effective and capable of targeting specific pain matrix anatomical structures that may help in diminishing pain (Thatcher, 2013). For example, the insular cortex and anterior cingulate have been identified as potential targets of NFB for pain control (Moisset & Bouhassira, 2007). An additional enhancement in specificity is the use of a comparison to an age-matched normative database using “live” or real-time Z-scores (Thatcher, 1999, 2000). The recent introduction of 19-electrode Z-score LORETA NFB equipment has generated hopes for an improvement of NFB efficiency (Thatcher, 2010, 2013). Previous reports from my clinic described high effectiveness of LORETA Z-score NFB in the treatment of many neuropsychiatric disorders (Koberda, 2012; Koberda, Moses, & Koberda, 2012). In Z-score NFB, a real-time comparison to an age-matched population of healthy subjects simplifies protocol generation and allows clinicians to target modules and hubs that indicate dysregulation and instability in networks related to symptoms (Thatcher, 2013). Z-score neurofeedback increases specificity in operant conditioning, and provides a guide by which extreme Z-score outliers are linked to symptoms, and then reinforced toward states of greater homeostasis and stability. The goal is increased efficiency of information processing in brain networks related to the patient’s symptoms (Thatcher, 2013). Table 10.3 contains the summary of 23 patients who completed Z-score LORETA NFB in my clinic due to headaches. As we can see, most of our patients (22 out of 23) responded very well to NFB therapy with 95% reporting headache reduction (subjective response). The majority of the patients (17 out of 23) were also found to have an objective improvement (74% improvement of beta power overexpression) on brain mapping reanalysis. Most headache patients noticed an improvement by the fifth NFB session. However, many of them felt pain improvement just after 2–3 sessions. In this chapter, I would like to present one case of chronic pain of a patient seen in my clinic with a good response to surface/LORETA 19-electrode Z-score NFB therapy (patient # 15 from the above table). Protocol selection was based on the patient’s symptoms using a “symptom check list,” as well as QEEG/LORETA characteristics. Patients with defined pain matrix LORETA dysregulations were frequently subjected to specifically targeted LORETA NFB therapy if they were resistant to the “symptom check list” treatment. Mostly anterior cingulate and insular cortex were targeted. The most frequently applied symptoms for programming from the “symptom check list” were chronic pain, fibromyalgia, headache, anxiety and depression. Neuropathic pain, musculoskeletal pain and intrinsic connectivity network 4 (ICN 4) were also used for achieving the best results (Thatcher, 2010). Patients’ sessions were frequently alternated between surface NFB and LORETA NFB, depending on the degree of elevated beta power or the degree of LORETA matrix dysregulation before a particular session. A commercially available computerized neurocognitive testing was used for the initial assessment of two patients (NeuroTrax Corp., Bellaire, TX). NeuroTrax Corp. cognitive testing is a computerized neuropsychological assessment where a patient is compared to aged- and education-matched healthy controls where mean = 100 with one standard deviation = 15. QEEG analysis was completed using commercially available Neuroguide software (Applied Neuroscience, Inc., Largo, FL) and previously recorded 19 channel digital EEG. Approximately 1–3 minutes of artifact free (eyes closed) EEG segments were selected after previously recording EEG with the Deymed, Truscan 32 (Deymed Diagnostic, Payette, ID) and subjected to further QEEG analysis. NFB therapy consisted of 30-minute sessions once or twice a week using auditory feedback.

166

QEEG and LORETA Z-Score Neurofeedback Table 10.3 Summary of headache patients: TN-Trigeminal Neuralgia. Patient

Subjective Perception

Objective Reduction of Beta Power

Number of Sessions Needed to Notice Subjective Improvement

Total # NFB Sessions

1. 35 F

Improved

−

2

5

2. 20 F

Improved

+

3

15

3. 53 F

Improved

−

6

7

4. 22 M

Improved

−

2

4

5. 58 F

Improved

+

5

21

6. 50 F

Improved

−

4

10

7. 20 F

Improved

+

3

8

8. 20 F

Improved

+

3

6

9. 44 F

No Improvement

−

-

10

10. 24 F

Improved

+

3

26+

11. 52 F

Improved

+

3

10

12. 43 F

Improved

−

4

8

13. 23 F

Improved

+

2

11+

14. 38 F

Improved

+

5

14

15. 36 F TN

Improved

+

2

10

16. 31 M

Improved

+

2

10

17. 49 F

Improved

+

6

7

18. 27 F

Improved

+

3

3

19. 50 F

Improved

+

2

10

20. 57 F

Improved

+

4

26+

21. 38 F

Improved

+

4

10

22. 65 M

Improved

+

1

10

23. 51 M

Improved

+

4

15

Case 3 This is a 36-year-old female with an 18-month history of the right trigeminal neuralgia. The patient had been taking pregabalin (Lyrica) 100 mg PO BID for neuralgic pain and duloxetine (Cymbalta) for depression and pain. The patient reported that despite taking increasing doses of Lyrica (she previously was on 50 mg BID), the pain was not improving and she gained a lot of weight (her weight exceeding 300 lbs.). The patient was interested in trying an alternative modality of therapy, exhibiting interest with starting Z-score LORETA NFB. Her initial QEEG showed elevation of fronto-temporal delta and theta power, as well as frontal beta power (Figure 10.5). In addition, LORETA analysis showed an area of dysregulation in the left insular cortex (Figure 10.6). She reported marked pain improvement after the first session of NFB, and was able to reduce the dose of Lyrica after the second session and then discontinue Lyrica completely after the fourth session. The patient completed 10 sessions of NFB and has been in remission for over an 8-month period. The QEEG, completed after total pain resolution, showed an improvement in frontal delta, theta and beta power, as well as a resolution of insular cortex electrical dysregulation (Figures 10.7 and 10.8).

167

Figure 10.5 A 36-year-old female with trigeminal neuralgia. Pre-treatment QEEG showed marked frontal and temporal increase in delta and theta power (in red) and frontal beta power (in yellow).

Figure 10.6 A 36-year-old female with trigeminal neuralgia. Pre-treatment LORETA showing left insular cortex electrical dysregulation (in red).

Figure 10.7 A 36-year-old female with trigeminal neuralgia. Post-NFB treatment QEEG showed partial correction of previously identified abnormalities.

Figure 10.8 A 36-year-old female with trigeminal neuralgia. Post-NFB treatment LORETA showed resolution of previously identified insular electric dysregulation.

J. Lucas Koberda

Cognitive Dysfunction We have conducted a larger case series report where 35 consecutive patients complaining of cognitive dysfunction were subjected to LORETA Z-score NFB therapy (Koberda, Moses, Koberda & Winslow, 2014). Before NFB initiation, these patients were evaluated with computerized neurocognitive testing in order to document and confirm any cognitive dysfunction reported in chief complaint. Many patients underwent brain imaging and laboratory testing to rule out any treatable condition which could have contributed to the patient’s symptoms. Electrical imaging with QEEG/LORETA localization was also completed in order to visualize any area of cortical electrical dysregulation which could have been potentially responsible for the patient’s symptoms (with specific Brodmann areas). The NFB protocol was based either on the patient’s symptoms, the area of cortical dysregulation, or both. After 10 sessions of surface/LORETA Z-score NFB, the computerized cognitive testing and QEEG were repeated in order to see if any increase in cognitive score and/or reduction in QEEG abnormalities were achieved. In addition, the patient’s subjective response was recorded as to whether or not they felt that the therapy was beneficial. Twenty-five patients (71%) were identified as having a significant objective improvement (on average 10 points) in cognitive testing. In addition, subjective cognitive improvement and an objective reduction of QEEG abnormalities with NFB were also achieved in most of the patients. These results are very promising and indicate good effectiveness of LORETA Z-score NFB in cognitive enhancement. This clinical data illustrates high effectiveness of Z-score LORETA NFB therapy in complex neuropsychiatric patients, where an improvement of depression/anxiety and other associated cognitive domains can be achieved in most of the patients within just 10 treatment sessions. However, many patients requested to continue NFB for a longer period of time in order to continue benefits of this therapy. The following case will illustrate my approach to patients with static cognitive dysfunction.

Case 4 A 19-year-old male was brought for appointment by his adoptive parents indicating problems with cognition, mostly executive function and information processing speed dysfunction. After completing a QEEG and before initiation of NFB, neurocognitive testing was completed in March 2013. Results showed low cognitive score—Global Cognitive Score 80.2—with low executive function, 87.2, and a very low information processing speed of 52.1. His LORETA and QEEG showed evidence of anterior cingulate electrical dysregulation and increased frontal delta power (Figures 10.9a and 10.10). After the first 10 sessions of NFB, his executive function improved to 89.9, and with

Figure 10.9a A LORETA imaging of 19-year-old patient showing area of electrical dysregulation of anterior cingulate (AC) region BA-32 (in red).

170

Figure 10.9b

Figure 10.10a

Resolution of AC dysregulation after 20 sessions of NFB.

QEEG before NFB—noticeable area of frontal increase in delta power.

Figure 10.10 QEEG of 19-year-old with cognitive problems before initiation of NFB (a) and after a course of NFB sessions (b).

Figure 10.10b QEEG after NFB—an improvement in previously overexpressed frontal delta power is recorded.

QEEG and LORETA Z-Score Neurofeedback

Figure 10.11 Executive testing (NeuroTrax) before NFB (March 2013) and after each round of 10 sessions of NFB (in May 2013, October 2013 and January 2014)—gradual increase in executive function was recorded.

each round of NFB, repeated cognitive testing was giving better results with executive function (subsequently 92 and 97; see Figure 10.11). Also previously electrically dysregulated AC area and frontal delta power overexpression became normalized (Figures 10.9b and 10.10b). In addition to QEEG and cognitive testing, his parents noted a tremendous improvement in social and cognitive performance at home and work. This clinical data illustrates high effectiveness of Z-score LORETA NFB therapy in complex neuropsychiatric patients, where an improvement of depression/anxiety and other associated cognitive domains can be achieved in most of the patients within just 10 treatment sessions. However, many patients requested to continue NFB for a longer period of time in order to continue benefits of this therapy. Some patients were also treated with additional NFB (5–10 sessions) in case of reoccurrence of symptoms. Based on our encouraging data, I recommend implementation of QEEG/LORETA brain mapping testing in all patients suffering from depression, anxiety and cognitive dysfunction.

Alzheimer Disease It is important to note that NFB seems to not only be effective in patients with static (non-progressive) cognitive dysfunction, but also with progressive neurodegenerative disorders like AD. We have completed therapy of six AD patients so far. The NFB protocol included surface and LORETA (NF1/NF2-Neuroguide, Inc.) feedback, in an alternating protocol while focusing on a symptom check list including: attention deficits, concentration problems, executive function problems and short-term memory. NFB sessions were conducted with a frequency of once, twice or three times per week, using auditory or auditory and visual feedback combined. The most frequent finding on QEEG of AD patients was increase in slow frequencies (mostly theta and delta) in frontal and temporal locations. LORETA frequently indicated

173

J. Lucas Koberda

electrical dysregulation in temporal areas. Out of six AD patients who completed at least 10 sessions of Z-score LORETA NFB, four achieved cognitive enhancement on cognitive testing and one achieved a cognitive stabilization. One AD who deteriorated despite NFB therapy was not fully compliant with treatment (not able to come for NFB sessions at least weekly). Unfortunately, after NFB discontinuation, these patients usually deteriorate quickly. It is recommended to at least continue NFB therapy on a weekly basis. My recommendation is to have NFB therapy five times a week in order to counteract progressive neuronal degeneration due to disease progression. Larger studies with prolonged administration of NFB may be beneficial to determine optimal timing and duration of NFB for this group of patients.

Case 5 My representative case of AD is a 64-year-old who recently retired (dentist) who started having short-term memory problems approximately five years before seeing me for consultation. He was started (by another neurologist) on medications for memory improvement—initially on Exelon (Acetylcholine esterase inhibitor) and subsequently Namenda (NMDA receptor modulator) was added. Unfortunately, Namenda was causing major side effects including constipation, requiring frequent use of laxatives and enemas. MRI of the brain was reported as normal. Initial QEEG revealed central increase of delta and beta power with coherence abnormalities (Figure 10.12); LORETA detected several areas of electrical dysregulation including anterior cingulate (BA-24) and precuneus (BA-7) and BA-6 (Figure 10.13). This patient QEEG did not show any major area of frontal or temporal slowing, which is typical for individuals suffering from AD. My assumption is that Exelon, which has a cholinergic action, most likely was responsible for QEEG findings of delta and theta reduction. This effect of Exelon was previously reported in QEEG literature (Fogelson et al., 2003; Gianotti et al., 2008). His initial NeuroTrax testing showed marked reduction of GCS78.1 (Table 10.4) and low memory score-44.6 (expected score 100). After completion of six NFB sessions NeuroTrax testing was repeated and marked cognitive enhancement was recorded (Table 10.4) with increased GCS-86.4 and memory score-53.7 as well as other cognitive domains. Due to chronic constipation caused by Namenda, this medication was gradually discontinued based on the patient’s request. Repeated NeuroTrax testing showed further increase in GCS-90.3 despite recent discontinuation of Namenda. In addition, his chronic constipation was resolved which contributed to marked improvement of his quality of daily living and reduction of other medication usage (laxatives). At the same time, a partial correction of previously identified QEEG/LORETA abnormalities was found. This patient will continue to be monitored with periodic cognitive testing in order to see whether LORETA Z-score NFB may be able to hold its cognitive benefits. Table 10.4 A 64-year-old with AD. NeuroTrax testing before NFB initiation (left column), after 6 NFB sessions (middle column) and after 12 NFB sessions (right column). Number of NFB Sessions

0

6

12

Global Cognitive Score

78.1

86.4

90.3

Memory

44.6

53.7

48.6

Executive Function

66.7

83

80.3

Attention

67.8

78.8

91.6

Information Processing Speed

88.7

79.3

94.1

Visual Spatial

94.8

109.1

113.6

Verbal Function

88.5

101.6

99.2

Motor Skills

95.5

99.2

104.8

174

Figure 10.12 A 64-year-old male with AD. Initial QEEG showed central increase of delta and beta power (in yellow) as well as coherence abnormalities.

Figure 10.13 A 64-year-old with AD. LORETA detected several areas of electrical dysregulation (in red) including anterior cingulate and other BA (not shown).

J. Lucas Koberda

Depression and Anxiety Our neurology center conducted Z-score LORETA NFB therapy for 31 patients with depression and/or associated anxiety. In addition to depression and anxiety, these patients frequently reported other coexisting problems including cognitive dysfunction, OCD and chronic pain. Most patients were found to have QEEG abnormalities including alpha power increase, asymmetry or LORETA electrical dysregulation in frontal areas (Figures 10.14 and 10.15).

Figure 10.14 Summary of frequently identified QEEG/LORETA abnormalities in patients suffering from depression and anxiety.

Figure 10.15 Orbitofrontal electrical (area in red) dysregulation (BA-11) seen in one of the patients with depression and anxiety.

176

QEEG and LORETA Z-Score Neurofeedback

Detailed analysis of our patients diagnosed with depression and/or anxiety showed that out of 31 included in the study, 24 (77%) were found to have both subjective and objective (improvement of QEEG abnormalities) improvement of the symptoms within 10 sessions of LORETA Z-score NFB. I would like to present one of our patients who successfully completed Z-score LORETA NFB with marked improvement in both depression and cognitive function. Cognitive function, which is often impaired in patients with depression, usually improves after NFB therapy.

Case 6 A 58-year-old university professor was seen in my office due to major depression associated with anxiety and memory problems. She noticed problems with teaching due to possible cognitive dysfunction. Figure 10.16 shows her LORETA imaging before NFB, which identified several areas of electrical dysregulation, including the cingulate cortex and left temporal region. The computerized cognitive testing completed before NFB identified deficiency of memory and executive function. Blood work was negative for B12 deficiency. After 10 sessions of NFB, major improvement in memory (from 42.8 to 107.1) was recorded on neurocognitive NeuroTrax testing (Table 10.5). In addition, marked improvement in the patient’s mood and anxiety was reported. Post-NFB LORETA imaging confirmed an improvement in electrical dysregulation of previously identified regions (picture not shown). Patient also reported better performance in her academic settings.

Figure 10.16 A 58-year-old female: LORETA imaging before NFB shows two areas of electrical dysregulation, (in red) including the cingulate cortex and left temporal region.

177

J. Lucas Koberda Table 10.5 A 58-year-old female cognitive testing results before (left column) and after (right column) 10 sessions of NFB. Expected score is 100 with 1 standard deviation = 15. Global CS:

86.7

Memory:

42.8

107.1

Executive Function:

81.7

122.9

Attention: Info Processing Speed:

112.1

110.5

108.6

96.2

108.6

Visual Spatial:

93.8

113.6

Verbal Function:

93.6

110.0

Motor Skills:

88.6

114.1

Autistic Spectrum Disorder Out of approximately 260 patients who have completed LORETA Z-score NFB therapy due to variety of neuropsychiatric disorders, 10 patients were diagnosed with ASD (see Table 10.6 for details). All patients reported subjective improvement of their symptoms during or after NFB therapy. Most of the patients were found to have objective improvements of QEEG abnormalities identified before NFB therapy initiation. The most frequent finding on QEEG testing was frontal increase in beta power with frequently associated increase in frontal or temporal elevation of delta or theta power. Only a few patients were able to complete computerized cognitive testing before and after NFB. After completion of NFB the cognitive testing revealed marked cognitive enhancement in most of the patients tested (Table 10.6). More detailed analysis of one of these individuals will be presented below.

Table 10.6 ASD patients’ demographics and other clinical information. M-male, F-female. Age and Diagnosis

Subjective Improvement

Objective Improvement QEEG

Cognitive Testing NeuroTrax

Number of NFB Sessions

1.

12 yo M

Yes

Yes

N/A

5

2.

15 yo M

Yes

Yes

N/A

10

3.

5 yo M

Yes

Yes

N/A

56

4.

6 yo M

Yes

Yes

YesImproved

87

5.

23 yo M

Yes

Yes

N/A

47

6.

18 yo M

Yes

Yes

N/A

51

7.

14 yo F

Yes

No

N/A

7

8.

12 yo M

Yes

Yes

N/A

8

9.

18 yo M

Yes

Yes

Yesimproved

25

10.

14 yo M

Yes

Yes

Yesimproved

35

178

QEEG and LORETA Z-Score Neurofeedback

Case 7 This is a 12-year-old autistic boy whose family was interested in trying NFB as therapeutic modality. He had practically no speech output except occasional single words, was restless and fidgety. MRI of the brain was normal. His NeuroTrax cognitive testing was grossly abnormal with low memory score of 25.7 and attention score of 39.7. QEEG showed marked increase of central and temporal beta power (Figure 10.17) and LORETA demonstrated anterior cingulate (BA-24) electrical dysregulation (Figure 10.18).

Figure 10.17 This is a 12-year-old boy with ASD. Pre-NFB QEEG shows increased central and temporal beta power (in red).

179

J. Lucas Koberda

Figure 10.18 Pre-NFB LORETA showed area (in blue) of anterior cingulate electrical dysregulation in theta frequency.

This patient completed only five NFB sessions (due to insurance termination) after which his family noticed some improvement in his behavior, mostly noticing him being less restless. In addition, after five NFB sessions, reduction of excessive beta activity on QEEG was noted.

ADD and ADHD We have previously reported effectiveness of LORETA Z-score NFB in the patient with cognitive and attention deficit (Koberda et al., 2012). We have completed LORETA NFB therapy in 20 patients with symptoms of ADD/ADHD at the Tallahassee Neurobalance Center. Subjective improvement of 90% (18 patients) was reported, and objective improvement of QEEG abnormalities of 80% (16 patients) was noted. Many of the patients completed pre- and post-NFB (after 10 NFB sessions) NeuroTrax testing with cognitive enhancement rate of 75%. One representative ADHD case will be presented here in more detail.

Case 8 This is a 15-year-old boy with history of Asperger Syndrome (AS) associated with ADHD and behavioral problems including anger control. His cognitive profile showed Global Cognitive Score (GCS) of 90.5 with weakness in memory (87.8) and verbal function (68.7). LORETA demonstrated (Figure 10.19) area of electrical dysregulation in the right frontal lobe Brodmann area 9 (BA-9),

Figure 10.19 A 15-year-old male with AS/ADHD. LORETA showed electrical dysregulation of BA-9 (in red) in delta frequency.

180

QEEG and LORETA Z-Score Neurofeedback

which has a role in memory and other cognitive functions. QEEG showed mild increase in frontal beta and delta power. Patient completed 10 NFB sessions; however, no follow-up cognitive testing was accomplished due to insurance termination. Overall, his family and the patient noticed some subjective improvement in his cognition. QEEG recorded after NFB also showed some improvement of previously identified abnormalities.

Seizures and Epilepsy A preliminary data coming from my office indicate successful primary and secondary generalized seizure response to LORETA Z-score NFB therapy. Table 10.7 presents summary of 10 patients who were treated with LORETA and/or surface Z-score NFB in my office. We have not had any seizure patients who didn’t at least partially respond to this type of NFB; this category can be viewed as having a 100% effectiveness with this therapy. I will give more information on patient #1 from the above table in the following pages. Table 10.7 Summary of patients suffering from seizures and subjected to NFB therapy. 1.

18 F with intractable epilepsy with secondary generalization.

2.

17 F with intractable primary idiopathic epilepsy with generalization.

3.

44 M with frontal epilepsy with generalization-S/P tumor resection.

4.

46 F with focal epilepsy and intermittent generalization and cognitive dysfunction-S/P tumor resection.

5.

16 F with intractable absence epilepsy with cognitive problems.

6.

59 M with intractable generalized epilepsy with cognitive problems.

7.

18 M with Autistic spectrum and intractable partial complex epilepsy.

8.

23 M with Autism and frequent generalized seizures.

9.

28 F pharmacist with frequent intractable generalized seizures.

10.

19 M with well controlled generalized epilepsy on valproate desiring discontinuation of medication.

Case 9 An 18-year-old female high school senior whose seizures started at age 11, sometimes proceeded by eye blinking episodes. Her current medications included Keppra XR 1500 mg daily and Lamictal 150 mg daily. Prior MRI of the brain was reported to be normal. EEG showed multiple sharps in several locations including P3, P4, T5, T6, O1, O2. Cognitive testing showed low information processing speed and memory (Figure 10.20). Despite extensive neurological evaluations at UCLA and multiple medications, no seizure reduction was observed. Her mother even reported an increase in seizure numbers with use of anti-epileptic mediations. Dr. Barry Sterman was also consulted, and sensory-motor rhythm (SMR) NFB was initiated with some seizure reduction noted. However, despite 90–100 sessions of NFB completion, seizures were still occurring, but with lower frequency. This patient came to my office for one week of intensive therapy with Z-score LORETA NFB (twice a day). LORETA imaging showed multiple areas of cortical electrical dysregulations (see Figure 10.21 below). After completion of eight NFB sessions, a long period of remission was achieved (approximately four months seizure free). Further NFB therapy was continued at another location but with much lower frequency. Since the initiation of Z-score LORETA NFB therapy (approximately 14 months) only three seizures were noted. In addition, marked improvement of cognitive status was found with resultant admission to college and very good grades during the freshmen year (mostly As—on the dean’s list). 181

Figure 10.20 Illustrates lower than expected cognitive score, especially memory and attention, as well as information processing speed (too low to be scored).

Figure 10.21 An 18-year-old female with medication resistant epilepsy. LORETA showed several areas of electrical dysregulation including left temporal region (in red).

QEEG and LORETA Z-Score Neurofeedback

Conclusion As we can see from the reviewed cases, Z-score LORETA NFB has proven to be a very effective therapeutic modality in many neuropsychiatric disorders. Some cases, such as the one pertaining to intractable epilepsy, were left with practically no medical options except possible neurosurgical interventions. Many progressive neurodegenerative disorders have no effective medical alternatives to slow the progression of AD. Assuming that NFB may have some neuroplastic properties (Ghaziri et al., 2013), we can hope that this treatment modality may slow relatively rapid cognitive decline of neurodegenerative disorders. Longer studies with five-year follow-ups will be needed to see if NFB is able to hold cognitive benefits in those AD patients (and for how long).

References Fogelson, N., Kogan, E., Korczyn, A. D., Giladi, N., Shabtai, H., & Neufeld, M. Y. (2003, April). Effects of rivastigmine on the quantitative EEG in demented Parkinsonian patients. Acta Neurologica Scandinavica, 107(4), 252–255. Ghaziri, J., Tucholka, A., Larue, V., Blanchette-Sylvestre, M., Reyburn, G., Gilbert, G., . . . Beauregard, M. (2013). Neurofeedback training induces changes in White and Gray matter. Clinical EEG Neuroscience, 44(4):265–72, published online 26 March 2013. Gianotti, L. R., Künig, G., Faber, P. L., Lehmann, D., Pascual-Marqui, R. D., Kochi, K., & Schreiter-Gasser, U. (2008, June). Rivastigmine effects on EEG spectra and three-dimensional LORETA functional imaging in Alzheimer’s disease. Psychopharmacology (Berl), 198(3), 323–332. Koberda, J. L. (2012). Autistic Spectrum Disorder (ASD) as a potential target of Z-score LORETA neurofeedback. The Neuroconnection, Winter Edition (ISNR), 24–25. Koberda, J. L., Koberda, P., Bienkiewicz, A., Moses, A., & Koberda, L. (2013). Pain management using 19-electrode Z-Score LORETA neurofeedback. Journal of Neurotherapy, 17, 179–190. Koberda, J. L., Koberda, P., Moses, A., Winslow, J., Bienkiewicz, A., & Koberda, L. (2014). Z-score LORETA neurofeedback as a potential therapy in depression and anxiety. Neuroconnection, Spring, 52–55. Koberda, J. L., Moses, A., & Koberda, P. (2012). Cognitive enhancement using 19-electrode Z-score neurofeedback. Journal of Neurotherapy, 3, 224–230. Koberda, J. L., Moses, A., Koberda, L., & Koberda, P. (2012). Cognitive enhancement using 19-Electrode Z-Score neurofeedback. Journal of Neurotherapy, 16(3), 224–230. Koberda, J. L., Moses, A., Koberda, P., & Winslow, J. (2014). Cognitive enhancement with LORETA Z-score neurofeedback. Presented during the AAPB meeting in Savannah, GA-March, 2014. Koberda, J. L., & Stodolska-Koberda, U. (2014). Z-score LORETA neurofeedback as a potential rehabilitation modality in patients with CVA. Journal of Neurology and Stroke, 1(5): 00029. Moisset, X., & Bouhassira, D. (2007). Brain imaging of neuropathic pain. Neuroimage, 37(Suppl 1), S80–S88. Stern, J., Jeanmonod, D., & Sarnthein, J. (2006, June). Persistent EEG overactivation in the cortical pain matrix of neurogenic pain patients. Neuroimage, 31(2), 721–731. Thatcher, R. W. (1999). EEG database guided neurotherapy. In J. R. Evans & A. Abarbanel (Eds.), Introduction to quantitative EEG and neurofeedback (pp. 29–64). San Diego: Academic Press. Thatcher, R. W. (2000). EEG operant conditioning (Biofeedback) and traumatic brain injury. Clinical EEG, 31(1), 38–44. Thatcher, R. W. (2010). LORETA Z-score biofeedback. Neuroconnections, December, 9–13. Thatcher, R. W. (2013). Latest developments in live z-score training: Symptom checklist, phase reset, and LORETA z-score biofeedback. Journal of Neurotherapy, 17, 69–87. Walker, J. E. (2011, January). QEEG-guided neurofeedback for recurrent migraine headaches. Clinical EEG and Neuroscience, 42(1), 59–61.

183

11 CONCUSSIONOLOGY Sport Concussion Management Harry Kerasidis

Abstract There is a growing awareness of the repercussions of a concussion and, increasingly, clinicians are called on to treat post-concussion syndromes. This review covers the elemental basics of concussions in terms that will not only inform clinicians but patients themselves. Standard neurological concussion treatment is described. Advanced treatment techniques of neurofeedback and neuromodulation are presented. Case studies illustrate treatment protocols using QEEG-driven selection of targets for training. The brain is beautiful, the new frontier of science. However, the brain is also vulnerable. Its fragile, gellike consistency floats unattached inside the skull. When force is applied, as it does many times in sports collisions, military injuries, work accidents and motor vehicle accidents, the brain sloshes from side to side, end to end, almost like scrambling the yolk of an egg as it floats inside, without breaking its shell. The consequences from concussions and undiagnosed brain injuries can be seen immediately or take several hours or even days for symptoms to materialize. However, their effects can be life altering. It is a mystery that medical science is unraveling. For example, the Wall Street Journal reported on the front page in 2008 that undiagnosed brain injuries are a major cause of: • • • • • •

Homelessness Psychiatric illness Depression and anxiety Alcoholism and drug abuse Suicide Learning problems

To understand concussions and mild traumatic brain injury, first we need a better understanding of the brain itself. It is a wonderfully complex organ, and “command central,” literally responsible for regulating or executing every move we make, every word we say, every emotion we feel and every thought we think. Brain health is so critical to proper life functioning that maybe protective helmets should include a label: “WARNING: Contents Are Fragile.”

184

Concussionology

Brain Matters The consistency of the brain is between slightly set gelatin and peanut butter, almost like impressionable memory-foam pillows. Despite being encased within the stone-like skull, the brain can be easily affected from outside trauma. Its soft texture is impressionable, like a banana. Therefore, when the brain bounces inside the skull, it does not immediately spring back into shape. The brain is in continual use, so when it experiences trauma—like a hard fall or direct hit to the head—normal functioning can be interrupted momentarily or for longer periods of time. Since we cannot see the brain, unless we use a form of scanning, we must assess for potential damage from a variety of visible or felt symptoms, and measures of brain performance. The brain directs our body to digest food we eat, signals the heart when and how to react, it administers healing when it perceives trouble, and so many other functions. When the brain experiences a form of trauma, whether physical, mental or emotional, it has the power to begin a selfhealing process. However, it is important to note that our conscious behaviors can influence the healing process as well. For example, the kind of food we eat either helps or hurts our brain. Getting enough sleep and drinking enough water influence the brain. In addition, toxic chemicals from tobacco smoke, drug use, alcohol and even fumes from paint or hair and nail salons can drastically reduce the brain’s effectiveness.

Brain Basics The brain is not a uniform mass of tissue. It is made of several sections. The brain is also composed of billions of specialized cells, called neurons and glia. • •

•

The brain floats within fluid inside the skull that has ridges and shelf-like areas, which is why it is vulnerable to jarring hits or whiplash-type action. The brain requires significant blood flow and oxygen to operate. The brain makes up about 2 percent of our body weight, but consumes 20 percent of the oxygen we breathe and 20 percent of the energy we consume. This enormous consumption of oxygen and energy fuels billions of chemical reactions in the brain every second. This is perhaps one of the most motivating reasons to provide the right kind of fuel, with a healthy diet and regular exercise.

Brain Anatomy Regions The brain is organized into three main sections: the cerebrum, cerebellum and the brainstem. Trauma received in any of these areas tends to cause problems associated with that area’s functions. Any sudden movement with force can result in the brain sliding back and forth which can cause temporary or permanent cognitive damage. The most vulnerable areas of the brain are the frontal and temporal sides (lobes) due to their anatomy and proximity to the skull. • •

Frontal: Responsible for executive functioning, forethought, planning, organizing, complex thinking, focus and concentration, and emotional self-regulation. Temporal: Responsible for auditory processing, short-term memory and mood regulation.

185

Harry Kerasidis

Below is a brief overview of some of the key areas of the brain, and associated behaviors which can be affected by concussions and more severe brain trauma. 1.

Cerebrum—The “forebrain” is the largest area of the brain, and is divided into left and right sections called hemispheres. Prefrontal Cortex (PFC)—Damage to the PFC can result in poor decision-making, impulsivity, short attention span, lack of goal setting and procrastination. Anterior Cingulate Gyrus (ACG)—Running lengthwise under the PFC, the ACG regulates our ability to shift attention when needed, adapt to change and be flexible in thought and reasoning. When this area of the brain is not working properly, people can get stuck on negative thoughts or actions, become overly worrisome, hold grudges and be oppositional or argumentative. Temporal Lobes—The temporal lobes are involved in auditory processing, language, short-term memory, mood and temper stability. They also help interpret and name what things are. These lobes often experience trauma from jarring hits from contact and collision sports that can lead to problems with memory, mood and temper. Parietal Lobes—The parietal lobes are involved with sensory processing, spatial relations and direction sense. Typically, Alzheimer’s disease will impact this area, giving people with this condition a hard time with finding their way and getting lost. Other problems with parietal lobes can lead to inaccurate interpretations of body perception. Occipital Lobes—The occipital lobes are involved with vision and visual processing. Information taken through the eyes are sorted out in the occipital lobes and dispersed to the various regions of the brain for action. Limbic System (LS)—Arching deep inside the brain, from the frontal lobes through the parietal lobes to the temporal lobes, the LS helps set emotional tone, either positive and hopeful or negative and desperate. Problems with the LS have been linked to low motivation, poor self-esteem and feelings of depression, helplessness and hopelessness.

2.

3.

Cerebellum—The cerebellum is the “coordinator,” involved with voluntary physical (motor) movements, posture, balance, motoric coordination, processing speed and facilitating the PFC’s role of helping with judgment and impulse control. Trouble with the cerebellum leads to motoric coordination problems and ability to learn. Brainstem—The portion of the brain that is continuous with the spinal cord at the base of the brain. The brainstem relays signals from the brain throughout the body via this section, controlling and regulating vital body functions including respiration, heart rate and blood pressure.

If there is trauma experienced in any of these processes, the brain’s ability to process the behavior can be affected.

Brain Talk Features of brain physiology make the brain an electro-chemical organ. The electrical and chemical physiology of the brain are inseparable. Yet, medical science has largely focused on the chemical side of the brain, attempting to modulate function, or restore physiologic balance using pharmacological agents. Only recently has medical science turned to technology that allows clinicians to modulate the electrical physiology of the brain such as neurofeedback, transcranial direct current stimulation and transcranial magnetic stimulation. In my clinical practice, I have found that a balanced approach using neuromodulatory technology, nutritional support, pharmacologic interventions and physical/ occupational therapy results in optimal outcomes in helping those with mild traumatic head injury return to normal daily activity. 186

Concussionology

Brain Gain The brain develops rapidly throughout early childhood. Neurons are making billions of connections during the first few years of a human’s life. The brain continues developing until the age of 25 years for most. Even the adult brain generates new neurons within a region important for learning and memory. The brain’s ability to change and reorganize in response to some input is known as plasticity. Learning is a form of plasticity, since it leads to structural changes in the brain. While brain plasticity can be gained, it can also be drained. Plasticity is highly dependent on the health of the brain, which is largely dependent on lifestyle factors, brain injuries and other brain-related training. When the brain is traumatized, there can be lapses within the typical brain function, which is revealed through symptoms. Moreover, these lapses can lead to future cognitive and emotional impairment.

Concussion Definition So what is a concussion? Does every hit to the head cause one? How severe is a concussion? Does a loss of consciousness qualify as a concussion? Concussion is a brain injury. Think of shaking an egg yolk inside its shell or a bruised banana. One may think of a concussion like a brain sprain, because it is usually disabling, but typically can recover with a lot of rest. Like an ankle sprain, a concussion has temporary consequences, and if not healed properly, can result in residual, and degenerative, difficulties. You may be able to continue playing on an ankle sprain, and brain sprain, but if you do, the risk of greater injury increases. As with ankle sprains, the reported severity of a concussion is often minimized and that is something you do not want to do. Here are other medical association definitions: 1)

2)

3)

“Concussion is recognized as a clinical syndrome of biomechanically induced alteration of brain function, typically affecting memory and orientation, which may involve loss of consciousness (LOC)” (Giza et al., 2013, p. 8); “The formal medical definition of concussion is a clinical syndrome characterized by immediate and transient alteration in brain function, including alteration of mental status and level of consciousness, resulting from mechanical force or trauma. People with concussions often cannot remember what happened immediately before or after the injury and may act confused” (American Association of Neurological Surgeons, 2011, p. 1); Here is a consensus statement from an international conference report: Definition of Concussion Panel discussion regarding the definition of concussion and its separation from mild traumatic brain injury (mTBI) was held. There was acknowledgement by the Concussion in Sport Group (CISG) that although the terms mTBI and concussion are often used interchangeably in the sporting context and particularly in the US literature, others use the term to refer to different injury constructs. Concussion is the historical term representing low-velocity injuries that cause brain “shaking” resulting in clinical symptoms and that are not necessarily related to a pathological injury. Concussion is a subset of TBI and will be the term used in this document. It was also noted that the term commotio cerebri is often used in European and other countries. Minor revisions were made to the definition of concussion, which is defined as follows: •

Concussion is a brain injury and is defined as a complex pathophysiological process affecting the brain, induced by biomechanical forces. Several common features that incorporate clinical, pathologic and biomechanical injury constructs that may be utilized in defining the nature of a concussive head injury include: 187

Harry Kerasidis

• •

•

•

Concussion may be caused either by a direct blow to the head, face, neck or elsewhere on the body with an “impulsive” force transmitted to the head. Concussion typically results in the rapid onset of short-lived impairment of neurological function that resolves spontaneously. However, in some cases, symptoms and signs may evolve over a number of minutes to hours. Concussion may result in neuropathological changes, but the acute clinical symptoms largely reflect a functional disturbance rather than a structural injury and, as such, no abnormality is seen on standard structural neuroimaging studies. Concussion results in a graded set of clinical symptoms that may or may not involve loss of consciousness. Resolution of the clinical and cognitive symptoms typically follows a sequential course. However, it is important to note that in some cases symptoms may be prolonged. (McCrory et al., 2013, paragraphs 6–7)

Severity The severity of brain injuries range on a spectrum from mild to moderate to severe. Concussion is widely considered a mild traumatic brain injury. Concussion is an injury that can last hours, days, weeks or even months, with the persistent symptom complex referred to as Post-Concussion Syndrome or mild traumatic brain injury (mTBI).

Concussion Signs and Symptoms The most important factor about signs and symptoms to know is that they may not materialize until several minutes, hours or days after the injury occurs. This makes concussion detection a tricky business, because the victim may not make the association of delayed symptoms such as headache with the collision injury. Below are explanations of common signs and symptoms. These symptoms result in temporary lapses according to the areas of the brain that have been affected. A variety of signs accompany concussion including somatic (such as headache and vertigo), cognitive (such as difficulty with memory or concentration), emotional (such as worry, mood or anger problems), and physical signs (such as loss of consciousness, numbness, weakness and loss of balance). It is also important to note that any these symptoms often result in combination with the others. •

•

•

Loss of Consciousness—This is the most obvious, and scariest, sign of a concussion. However, only 10 percent of concussions result in loss of consciousness, according to a 2010 Pediatrics review article (Halstead, Walter & The Council on Sports Medicine and Fitness, 2010). When a player gets “knocked out” temporarily, the brain continues serving its involuntary functions, but its conscious and voluntary functions discontinue. During this period of time, it is important not to move the individual, and allow the brain to “re-boot” for a few seconds. Confusion—The most common symptom is temporary confusion, often associated with a “dazed” look or vacant stare. A confused, concussed individual probably will not talk much, because the brain is trying to restore order and understand the circumstances. If the individual does talk, the words may be jumbled, rapid or generally nonsensical and irrelevant. Amnesia—Amnesia is temporary memory loss that can be divided into two types: • Retrograde: Forgetting things that happened before the incident. • Anterograde: Inability to remember facts after the concussion. 188

Concussionology

•

• • • • • • •

•

Disorientation—Related to spatial relations, a concussion can affect the individual’s ability to know where they are, what day it is and what they were doing at the time of the injury. The athlete may get up and go to the wrong huddle or sideline. Delayed Verbal/Motor Response—Slow, slurred or incoherent speech as well as inability to move or walk normally can be associated with concussion. Inability to Focus—A concussion may be evident if the individual has trouble paying attention, or focusing on the conversation or game situation. Headache—When due to concussion, headaches are very similar to migraines and may be accompanied by nausea, vomiting and sensory sensitivity. Disequilibrium—A problem with balance, and feelings of dizziness are common signs. Visual Disturbances—The vision may become blurred, doubled or overly sensitive to light. Nausea/Vomiting—May occur in the absence of headache. Emotional Lability (mood swings)—When hits occur to the sides of the head, or temporal lobes, you may notice anger outbursts, inappropriate laughing, extreme sadness or overt stubbornness not typical of the individual. Sleep Disruption—Excessive drowsiness or inability to sleep are usually delayed symptoms of a concussion presumably due to disruption of the sleep pathways rising up through the brainstem, and imbalance of the neurotransmitters.

The majority of our understanding of the complex changes in brain physiology due to traumatic injury comes from studies of mild traumatic brain injury occurring in military and athletic settings.

The Physics of Concussion Injury When outside force is applied to the supple and most important organ in the body, problems can occur. Memory can be forgotten. Emotions can be triggered. Impulsive decisions can be unleashed. Headaches can invade. Motivation can fade. Disabilities may develop. Some injuries have the capacity to alter a person’s sense of self, while others affect abilities, such as speech or vision, but do not affect a person’s sense of who they are. The resulting “brain drain” is definitely another force to be reckoned with. However, it is important to emphasize that I do not intend my term—brain drain—to be derogatory. Neither do I want to confuse drain with brain sprain, a phrase I use to describe concussion and mild traumatic brain trauma, which can lead to permanent neurological impairment, headache disorders and Chronic Traumatic Encephalopathy (CTE) that have much more dire consequences than a musculoskeletal sprain. Instead, “drain” refers to reactions resulting in the brain when concussion occurs, due to increased struggle with supplying the metabolic demand for healing.

Mechanisms of Concussion Injury “Mechanisms” refers to the ways the brain may be injured. You might think of a brain injury, or concussion, like a bruise to a banana. The exterior peel can only sustain so much pressure before the inside meat of the banana is bruised. The same goes for the brain and skull. Although the skull can sustain more forceful blows, the brain inside can become bruised, limiting the oxygen and blood flow in that particular area resulting in various symptoms. Unlike the banana, however, the brain can heal itself with proper health and lifestyle changes, or with certain medication interventions. However, the skull does not have to suffer a blow for the brain to be injured. A concussion may occur in any or all of the following scenarios: • •

Head to head contact Head to object contact 189

Harry Kerasidis

• • •

Head to ground contact Head to body part contact Non-head contact due to sudden change in direction, i.e. whiplash.

Types of Contact There are different types of contact that affect the brain. The following terms explain the differences, which also give insight to what symptoms may result. • •

• •

•

Focal Impact—The brain injury is located where the head was hit, point of contact. Linear (or Translational)—Brain injury that occurs as the brain moves within the skull. The brain sits inside the skull buoyed by fluid, and can slide back and forth. With enough force, like with a whiplash, or focal impact, the brain can be injured on both the “coup” side and the “contracoup” side: Coup—The location where impact is received. Contra-coup—The opposite side where the brain is damaged resulting from a recoil or counter movement. The contra-coup injury occurs when the brain “bounces back” from the focal location, injuring the opposite side. An example would be a hard fall that lands a person on their back. The back of the head slams the ground (coup, focal), and the brain’s momentum immediately bounces forward to collide with the front area of the skull, affecting the prefrontal cortex. Angular (or Rotational)—Concussion resulting from a sudden head twist, causes shearing injury to the deeper tissues of the brain and to the brainstem.

* Important Note: The rotational injury is the worst type of concussion injury, associated with the most serious neurological injuries. In my experience, rarely do concussions occur from only one mechanism of injury. Typically, they are realized in combination. The most common type of contact that results in a concussion is when all three of these mechanisms are involved, focal, linear and rotational.

Pathophysiology Pathophysiology explains the abnormal effects of a concussion or brain injury to the physiology of the brain. This is my particular area of specialty as a cognitive neurologist. Getting to the root-cause of concussions helps to understand the treatment options and ways to enhance the overall health of the brain. We look at traumatic injuries from macroscopic, microscopic and molecular views: •

Macroscopic—This refers to the tissue changes that occur with brain injury such as a concussion from a “naked eye” perspective: • Direct Trauma—Tissue changes resulting from direct trauma, which leads to traumatic brain injury. This is analogous to the fracture of a bone, the crush of a muscle or the laceration of skin. • Cerebral Blood Flow—Changes to the volume of blood flow to brain tissue resulting from the trauma. This may lead to secondary ischemic injury or stroke, which are problems resulting from insufficient blood supply in more severe injuries. • Hemorrhage (or Bleeding)—Hemorrhage can occur in various regions. There is a leathery cover of the brain and spinal cord called the “dura.” Trauma can cause bleeding outside the dura called an “epidural hematoma.” Torn arteries usually cause these, and so under high pressure, this kind of hemorrhage can lead to rapid and severe neurological deterioration and even death. Bleeding can occur under the dura, known as a “sub-dural hematoma.” 190

Concussionology

•

•

These are usually due to tears of the veins, under much less pressure, with a slower deterioration, but could still lead to severe neurological impairment or even death. When bleeding occurs in the space immediately outside the surface of the brain, it is called a “subarachnoid hemorrhage.” When caused by trauma, a subarachnoid hemorrhage is not usually fatal, but can be associated with neurological impairment, severe headache and, rarely, a delayed spasm of the blood vessels that may lead to stroke. Finally, bleeding can occur within the tissue of the brain itself. This usually is like a bruise in the brain tissue associated with the direct tissue injury, or can be under pressure due to a torn blood vessel, where the pressure itself can cause further injury, and become life threatening. Microscopic—The microscopic view is much more important because many of the changes due to brain injury happen on a cellular level. When the brain sustains an injury, the membranes of the brain cells stretch, and lose their ability to regulate the environment of the cell. The membranes get “leaky,” preventing the brain cells from working properly, leading to dysfunction which can affect behavior, concentration, memory and other cognitive function. Additionally, brain trauma increases the metabolic demand—or energy—necessary to repair the brain and regain the equilibrium inside the cell or neuron. Molecular—Brain trauma causes changes in the neurons, preventing the brain from working normally, including: 1. Inability to regulate electrolytes, which prevents the brain cells from operating properly. In the brain’s natural or healthy state, the brain cell maintains the balance of salts and electrolytes inside and outside the cell. It takes energy to maintain that balance. When the brain is damaged, the membrane of the cell leaks out potassium while sodium leaks in. Therefore, the cell has to expend more energy to maintain this balance than before. The effect is that the brain cell does not work properly in that particular region. For example, if the brain damage and leaky electrolyte balance occur in the area responsible for memory, then the result is short- and possibly long-term memory loss. 2. Releasing of toxic excitatory neurotransmitters such as “glutamate” which are toxic to the cell. Although glutamate naturally occurs for proper brain function, when there is too much released as a result of brain trauma, then it results in dysfunction and further injury to the brain cells. 3. The concussion energy crisis: These injured neurons have a harder time getting enough glucose into the cells as they struggle to repair themselves and regain their equilibrium. The entry of glucose into the cell depends on the proper functioning of a protein embedded in the cell membrane called the glucose transporter protein. After the stretch injury to the neuronal cell membranes, the glucose transporter protein does not function properly. The markedly increased demand for energy coupled with the reduction of available fuel leads to an energy crisis in the affected brain cells associated with brain dysfunction. Furthermore, with reduced blood flow, energy metabolism shifts from aerobic (using oxygen) to anaerobic metabolism resulting in a release of lactic acid. Lactic acid also can build up because of the trauma, which is further toxic to the brain cells (local lactic acidosis).

* Important Note: This is the main reason the brain needs rest immediately following a suspected concussion. You do not want to add more metabolic demand to the brain during recovery.

Brain Mapping Here is an overview of the various types of “brain scans” and what you can expect, should red flags of brain injury present after head trauma. I believe the best technique to measure the brain’s function is a QEEG. 191

Harry Kerasidis

•

•

•

•

•

•

•

CT—Computed Tomography is typically what the Emergency Room doctor will order for someone suspected of having a head injury. A CT scan is a static imaging method, featuring a computer-assisted method of assembling a “cross-section” X-Ray image of the brain. While the CT scan will reveal hemorrhaging, skull fractures and other structural abnormalities, it will not be able to distinguish the subtle changes resulting from a concussion. MRI—Magnetic resonance imaging reveals the anatomy of the brain in greater detail than the CT and depicts a black-and-white virtual “section” of the brain. Using specialized techniques, an MRI may detect the shearing force injury and micro hemorrhages associated with concussion injury. fMRI—Functional magnetic resonance imaging is similar to that of MRI imaging. However, fMRI imaging takes advantage of a special property of tissue chemistry associated with metabolic activity. fMRI images provide scientists with both functional and anatomical information about brain tissue. PET—Positron emission tomography allows scientists to view metabolic brain activity. PET works by measuring the distribution and movement of radioactively labeled molecules in the tissues of living subjects. The technique can be used to investigate changes in brain activity while the subject performs assigned tasks. Computers reconstruct PET scan data to produce twodimensional or three-dimensional images. While MRI scans are used for research and in clinical settings for patient diagnosis, PET scans are used exclusively for research. SPECT—Similar to PET, single-photon emission computed tomography (SPECT) provides functional brain imaging, showing a three-dimensional view of the brain and measuring the blood flow and activity in the brain. It is cheaper than PET scans and does not require a cyclotron nearby to produce the necessary radioactive dyes. QEEG—In my practice, I prefer using quantitative electroencephalography (QEEG) to study brain physiology. This inexpensive technology measures electrical patterns at the surface of the scalp that reflect cortical electrical activity in the brain. This real-time measurement records various electrical rhythms of the brain that we can measure accurately, and map with statistical comparisons to normative populations, and therefore localize areas of the brain that may be traumatized. Advanced computer technology now allows for two-dimensional and threedimensional mapping of the electrical activity of the brain, along with statistical comparison of an individual’s brain activity to a normative database. Furthermore, newer database technology allows for discriminant analysis for comparison of an individual’s EEG to a database consisting of individuals having suffered mild traumatic brain injury to determine if there is a statistical match to the EEGs of brain-injured individuals. Cognitive Event Related Potentials—In the same way that PET, SPECT and fMRI scans can be done while the subject is performing cognitive, thinking and memory tasks, the electrical responses of the brain can also be measured and mapped to identify regions of dysfunction related to a specific cognitive task. Although this mapping lacks the spatial resolution that imaging studies such as MRI have, EEG analysis makes up for this with very high temporal resolution, with ability to record events measured in milliseconds, rather than the several minutes required to do other imaging scans.

However, it is important to note that none of these imaging techniques serves as the ultimate, or final answer to detecting a concussion. They may, however, serve critical roles in the assessment process to gather information for a specialist to make a diagnosis and a prognosis of recovery. It is important to recognize that, currently, there is no test that is diagnostic of concussion/mTBI. On the horizon, the medical world is beginning to see the concept of biomarkers to help with detecting concussions. Using blood and saliva tests, we are continuing to learn that changes occurring in the brain may be apparent in these fluids. 192

Concussionology

According to a study by Pastun Shahim et al. (2014), blood levels of total-tau—a protein signaling axonal damage in the brain—could be used as a biomarker to gauge severity of concussions in athletes and to assess when it is safe to return to play. The study showed that the plasma levels of T-tau increased in ice hockey players with sports-related concussions. The highest concentrations of T-tau were measured immediately after the injury, and the levels declined during the first 12 hours, followed by a second peak between 12 and 36 hours. Importantly, T-tau concentrations at one hour after concussion predicted the number of days it took for the concussion symptoms to resolve and the players to return to play safely. Researchers at George Mason University are comparing preseason samples of saliva to the samples from kids who suffered head injuries. They think the change in saliva proteins after a concussion may become a non-invasive way to identify the presence of a concussion (Bradley, 2013) As can be seen, diagnosing a concussion and traumatic brain injuries is not a cut-and-dry process.

Repercussion Factors Unfortunately, when the brain is injured, the injury causes malfunction. While concussions are a milder form of brain injury, a number of factors influence how well the brain heals and what functions may be altered. As the brain grapples to return to normalcy, it often builds alternate paths for the neural signaling to occur. This can result in a change in performance that can appear to be a change in someone’s personality. Some factors that influence the lasting impression concussions may have include: •

•

•

• •

•

•

Heredity—Genetic history of neurological disorders can be passed down. Although these genetic profiles do not automatically guarantee a future of disorder, they can have an influence. Heredity can also influence the vulnerability or susceptibility to develop neurological conditions. Gender—A number of studies have identified that women are more vulnerable to concussion injury than men (Fakhran, Yaeger, Collins & Alhilali, 2014; Guerriero, Proctor, Mannix & Meehan, 2012; Hootman, Dick & Agel, 2007; Kerasidis, 2014; Marar, McIlvain, Fields & Comstock, 2012). In any sport (such as soccer) where women compete as well as men with the same rules, the incidence of concussion injury is greater in women (Lincoln et al., 2011). A variety of factors has been postulated as contributors to this phenomenon. Women have smaller neck sizes, making the head more likely to “whiplash”; about 20 percent of women suffer from migraines; and women are more likely to express concerns about their health than men. Pre-Natal Health—The health of your brain directly relates to health of your mother during pregnancy, particularly during the first trimester. During this phase, the human brain development is largely based on the level of nutrition, exercise and psychological condition of the mother. Alcohol, smoking tobacco and drug use during pregnancy can inhibit brain development, which can have negative effects on the whole neurological system in the future. History of migraines—Individuals with a history of migraines are more vulnerable to postconcussion symptoms than those without. Previous Brain Injury—One concussion offers enough risk for the future. However, when they add up, then the brain is forced to work around the problem area. Any previous brain injury, whether it was diagnosed, perceptible or recognized, can lead to future problems. Lifestyle—The future health of your brain is made of the past—heredity, pre-natal health and previous brain injury—but also the present. The lifestyle, or in other words, how well someone takes care of their health, can affect future risk of impairment. Nutritious Foods—Diets void of healthy carbohydrates, protein and water can leave the brain gasping for nutrition critical for proper cellular development and operation. 193

Harry Kerasidis

•

•

•

•

•

•

Exercise—With sports, we often assume athletes lead a life with regular exercise. However, it is not always the case, particularly after the playing years. Low levels of exercise reduce blood flow to the brain, creating vulnerability to injury. Sleep—Rejuvenating restful, deep sleep is required for the brain to work optimally. Low levels of sleep force the brain to work harder than necessary to perform normal functions. When brain injury occurs, the brain may be at a deficit already due to lack of sleep which can extend the healing period, and risk of impairment. Alcohol—Drinking alcohol alters the brain chemistry, affects memory and leaves a toxic—or poisonous—presence that reduces overall brain function. Alcohol is a neurotoxin. However, perhaps most concerning is that the brain cannot heal as well from injury when there are residual effects of too much alcohol. Drug Use—Illegal drug use is not only highly addictive, and leads to bad decisions, but drugs also leave toxic elements in the brain that “weaken” the brain’s ability to operate properly. The more drugs are present, the easier a brain can be damaged from blunt trauma such as a concussion. Smoking Tobacco—Not only does nicotine affect memory negatively, but also tobacco is loaded with many additives and carcinogens, which are also toxic to the brain. Like alcohol and drug use, a toxic brain is more susceptible to the effects of concussion injury. Environment—The air we breathe, the water we drink and other toxins found in foods can affect our brain health. For example, fumes from paint, nail and hair salons, if absorbed frequently, can reduce your brain’s ability to heal quickly.

Cognitive and Emotional Impairments Other than dying or paralysis from complications suffered from brain trauma, the most serious concussion repercussion is a decrease in overall quality of life due to headaches and a variety of cognitive and emotional impairments that may develop as the post-concussion syndrome. Memory loss, attention span, and poor decision-making are warning signs of brain-related trouble. An estimated 50 million Americans suffer from disorders of the brain or nervous system. Some brain disorders are influenced by genetics, some are environmental, others from spinal cord or brain injury; and some result from a combination of any or all of these factors. Traumatic brain injury (TBI) refers to damage resulting from trauma to the brain. TBI, like spinal cord injuries, may result in impaired physical function. Injuries to the brain can affect cognitive abilities or disturb behavioral and emotional functioning. In addition, brain trauma has the potential to alter personality, and the sense of self.

TBI by Region Trauma to different regions of the brain results in different types of disabilities. Some injuries have the capacity to alter a person’s sense of self, while others affect abilities, such as speech or vision, but do not affect a person’s sense of who they are. Functional imaging has greatly aided the ability to locate the region of the brain responsible for behavior. In my practice, I avoid using “labels” for various conditions, preferring to understand the physiological issue causing the symptoms. Nevertheless, I have grouped a number of cognitive and emotional impairments that may occur should a concussion, or repeated sub-concussive hits, lead to degenerative brain or neurological damage (in alphabetical order): Alzheimer’s Disease—Researchers believe Alzheimer’s and other forms of dementia actually start decades before people experience their first symptoms. This may, in part, have to do with related brain trauma. Alzheimer’s is a progressive, degenerative brain disease that 194

Concussionology

leads to loss of cognitive function and short- and long-term memory, behavioral changes, personality changes, and impaired judgment. Typically affecting the temporal and parietal lobes, the brains of Alzheimer’s patients also contain tangled masses of abnormal protein in the cerebrum. A recent study (Mielke et al., 2014) reported evidence of a link between concussions and Alzheimer’s disease-related neuropathology. The conclusion stated that self-reported head trauma by individuals with cognitive impairment was associated with greater amyloid deposition. Amyloid is associated with the brain plaques and tangles seen in Alzheimer’s disease. Additionally, risk factors for chronic neurobehavioral impairment include repeated concussion exposure and APOE e4 genotype (Giza et al., 2013). Anxiety—Anxiety is an emotional response from over-anticipation of a real or perceived future threat, according to the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-V). People can experience anxiety when the cingulate gyrus, an area of the brain surrounding the deep limbic system, is damaged. The basal ganglia help to integrate thoughts, feelings and movements, as well as help set and interpret anxiety levels. This area is also involved with experiencing feelings of pleasure. Problems associated with the cingulate can be related to struggles with stress, anxiety and related symptoms such as insomnia, stomachaches and muscle tension. Attention Deficit—New research suggests brain injury may also be a cause of attention deficit disorder (Subhulakshmi, 2015). This can come about following exposure to toxins or physical injury. Head injuries can cause ADD-like symptoms in previously unaffected people, perhaps due to frontal lobe dysfunction or damage, resulting in impairment of executive functioning. However, further research connecting brain trauma with attention deficit disorder is needed. The DSM-V describes attention deficit disorder as a persistent pattern of inattention that interferes with functioning and development, characterized by inability to give close attention to details, difficulty sustaining attention in tasks, inability to listen when spoken to directly, inability to follow through on instructions, failing to finish work and difficulty organizing tasks and activities, especially sequential tasks. More succinctly put, it is the inability to focus your thoughts and attention. Balance—Sometimes traumatic brain injury can cause balance and equilibrium problems. Balance problems after head trauma can fall under either peripheral or central injuries. Central injuries refer to injury of the central nervous system structures having to do with balance and coordination including the cerebellum and the brainstem. Peripheral injuries refer to injury to the inner ear structure or the VIIIth Cranial Nerve, which is the cranial nerve that carries information from the ear (including the inner ear) to the brain. There are also issues, which are considered sensory problems, as they are associated with the parts of the brain that govern vision and hearing. Midline shift syndrome, in which balance and equilibrium are affected, generally goes hand-in-hand with post-trauma vision syndrome. Symptoms of midline shift syndrome and other balance disorders include: • Continual sense of disequilibrium • Difficulty maintaining balance • Incorrect weight distribution and posture • Inappropriate gait • Trouble walking in a straight line Patients of midline shift syndrome frequently also complain that the walls seem to be moving in on them, or the horizon is oddly tilted. Behavior Problems—Symptoms of brain injury can appear in a wide range of previously rare behavior. Examples include: • Aggression toward others • Aggression toward self 195

Harry Kerasidis

• Tantrums and crying • Yelling and cursing • Explosive anger • Non-compliance • Property destruction Bipolar—A mood disorder somewhere on the spectrum between depression and schizophrenia and marked by severe fluctuation of manic episodes. Several studies have shown a link between people with bipolar and other psychiatric disorders with head trauma (Silver, Kramer, Greenwald, & Weissman, 2001). Brain Fog—Brain fog refers to a low degree of delirium where the brain is sluggish with its function. Sometime called “clouding of consciousness,” it can be manifested in a wide range of general daily activities, affecting short-term memory, ability to focus and calculate decisions. It is similar to the feeling of being sleep deprived for a few days, or experiencing a severe hangover. Chronic Fatigue—When the brain is injured, it requires a lot of fuel and energy to heal and find ways to return to normal operation. The unfortunate byproduct can be a sense of fatigue during typical daily activities. Long-term studies are few with this condition, but logic would attribute poor brain health with lack of overall energy, and susceptibility to decline unless medical or lifestyle changes intervene. Cognitive Difficulty—Cognitive issues are any issues related to thinking. These problems can be relatively mild and can improve over time, or they can be more severe, long-term issues that make it difficult to live independently. Cognitive thinking includes being aware of one’s surroundings, being able to pay attention, concentrate, short-term memory, reasoning, problem solving and executive skills such as goal setting, planning, initiating, self-awareness and self-monitoring and evaluation. Typically, cognitive difficulty arises from trauma to the frontal lobes, or prefrontal cortex. Comorbidity—In psychiatry, psychology and mental health counseling, comorbidity refers to the presence of more than one diagnosis occurring in an individual at the same time. However, in psychiatric classification, comorbidity does not necessarily imply the presence of multiple diseases, but instead can reflect our current inability to supply a single diagnosis that accounts for all symptoms. Depression—Prolonged sadness may be a symptom of any number of circumstances in life, brain health included. A brain diminished in functioning either from concussion, being severely shaken, other trauma or degenerative disease may not have normal aptitude for resilience to deal with events of life. Language and Speech—Language-related difficulties can develop from traumatic brain injury. Problems can be the result of damage to areas that govern communication in the brain, or they can be the result of motor problems or weaknesses. Examples include various forms of aphasia, which affects both comprehension and production of speech, as well as the ability to read and write. Language difficulties relating to motor problems include: apraxia, exhibited with difficulty coordinating mouth and speech movements; and dysarthria, the individual can think of the right words to use but neurological damage prevents him or her from using the muscles needed to form the words. Memory and Learning—Immediate recall, otherwise known as “short-term” memory, as well as “long-term” memory, which refers to information stored for extended durations, may be affected by brain trauma. Thought to be like a super-computer with files of information, the brain stores memories much differently, typically encoding neural connections giving the brain the ability to recall information and experiences. Memory and learning are related, and remain fascinating subjects of research. Still, we know memory fades over time, and memory loss may be accelerated by brain trauma and lifestyle. 196

Concussionology

Parkinson’s Disease—Another human brain disorder that may be connected to brain trauma is Parkinson’s disease, a motor system disorder affecting more than 500,000 Americans. A study conducted in 2011 by UCLA researchers found that while a traumatic brain injury does not cause Parkinson’s, it can make the brain more susceptible to the neurodegenerative disorder (Hutson et al., 2011). Parkinson’s affects roughly 1 percent to 2 percent of the population over the age of 65. The disease is characterized by tremor, rigidity, slowness of movement and impaired balance and coordination. It occurs when neurons in certain sections of the midbrain die or become impaired. The neuronal loss causes a decrease in the level of an excitatory neurotransmitter, which causes the neurons in another part of the brain to initiate aberrant neural impulses. Genetic factors may play a stronger role in some forms of the disease, while environmental factors play a prime role in other forms. Sleep Disorders—Brain trauma may interfere with the sleep process resulting in fatigue felt in the brain and body. My practice has taken sleep very seriously, treating patients with sleep apnea, fatigue, restless leg syndrome, chronic insomnia and narcolepsy. Without deep sleep, the brain and body malfunctions, like an engine without fuel. Concussions can affect sleep negatively, which is ironically the best way to heal from the trauma. The repercussions of concussions can be deadly, dangerous and can start or exacerbate any number of pre-existing conditions with long-term impairments. The next time you hear someone say, “You got your bell rung,” it will not illicit casual banter about being tough and getting back in the game. Instead, take it as serious as a heart attack.

Post-Concussion Treatment: Healing from Concussion during the First 30 Days Immediately after a concussion occurs, the brain begins the healing process. Over the course of the next few days and up to about 30 days, it is critical to accept my treatment strategy and do . . . Nothing. The brain physiology, blood flow and neuronal pathways all need to rest. Particularly during the first 24–72 hours, I recommend minimizing any activity that provokes the symptoms of concussion. This includes physical and mental stimulation that may interrupt the healing process by forcing the brain to work. I use the phrase “brain sprain” because like an ankle sprain, you have to limit movement so the muscles, tendons and ligaments get a chance to return to normal before adding any more pressure to the wound. During the acute phase, meaning the period of time immediately following a concussion injury, the brain requires rest while dealing with a metabolic demands of repairing the affected brain cell membranes that have been stretched. PET scan studies show that glucose, the primary energy source, is not able to freely get into the brain cells as usual, preventing the cell’s ability to get the fuel to supply the demand for repair or proper functioning into the cell. In other words, the damaged brain cells are grasping for energy, but they cannot get the fuel. Thus the need for rest, as well as healthy foods, plenty of water and perhaps nutritional supplements—all support the healing process. Immediate recommendations include: within a day or two of the concussion, while symptomatic at rest, do not jog, run, lift weights or do any kind of physical exercise because it pumps more blood into the “leaky” brain cells that are trying to heal. Also, avoid any mental activity like reading, writing, texting, learning, even talking. Avoid the sunlight or well-lit rooms when sensitive, because the eyes and nervous system pathways that take in visual stimuli may also be affected. Even watching movies, playing video games, loud music, working on the computer or trying to fix something may all exasperate the concussion healing. The brain is involved with everything we do, so for the first day or two, just rest. 197

Harry Kerasidis

When the initial symptoms have dissipated, that does not mean the concussion has healed and the athlete is ready for action or the classroom. Now we move into a phase I refer to as “relative rest,” minimizing mental and physical stimulation until the athlete is symptom free during activity. “Relative rest” refers to gradually liberalizing mental or physical activity, still avoiding those that provoke the athlete’s concussion-related symptoms. Once the injured player is free of symptoms at rest, I begin a 5-Step Progressive Exertion recovery guide which is built into XLNTbrain Sport™ that monitors symptoms, and guides the timeline for a return to practice and game play. During this recovery phase, each day presents tasks with increased levels of difficulty. Should any of the concussion symptoms reappear, then it is back one day, to the previous level of activity which did not provoke symptoms. However, if the athlete progresses each day through recovery without provoking any symptoms, then the brain is healing from the trauma. When the athlete can complete all the “5 Steps” of the Progressive Exertion plan and their cognitive performance remains at baseline, then they can seek medical permission to return to play. Typically, this allows for a seven-day cycle. It could vary, and I recommend erring on the side of caution, without rushing the return to normal activity and game play. One of the reasons recovery time can vary is because athletes with a history of previous concussions may require longer periods of time to heal. A study reported in Neurosurgery (Slobounov, Slobounov, Sebastianelli, Cao & Newell, 2007) indicated the presence of long-term residual visual-motor disintegration in concussed individuals with normal neuropsychological measures. Most importantly, athletes with a history of previous concussion demonstrate significantly slower rates of recovery of neurological functions after the second episode of mTBI (Slobounov et al., 2007). Many other factors influence the concussion recovery time in addition to previous history of concussion, including the severity of the concussion, level of pain and personal lifestyle factors such as history of drug and alcohol use, exposure to toxic environments, previous brain-related impairments, even genetic history. Every brain is different, every brain injury varies. Most healthy athletes, however, will see significant improvement within 7 to 10 days following their concussion, with 93–97 percent recovered by day 30.

Relieving Pain In the first 24 hours after sustaining a concussion, the person should not take any pain medications. A pain medication can “mask” the symptoms, which could allow someone to return to activities with a concussion. As stated, many concussion symptoms will take several minutes, hours or days to arise. After this 24-hour period, should the athlete experience a severe headache, I recommend taking an anti-inflammatory (over the counter) acetaminophen. Naproxen, aspirin and ibuprofen (NSAIDtype medications) should not be used at first, as they may increase the risk of bleeding. Beyond this, ask your doctor for help with addressing any other pain.

Sleep Insomnia is a common post-injury symptom. I recommend a temporary sleep aid. Over the counter remedies are usually made of antihistamines which are sedating for most people and help improve sleep quality. However, it is not uncommon for some people to become more alert with antihistamines, worsening the insomnia. Short-term use of traditional sleep aids is appropriate in this setting. A doctor may even recommend taking imipramine, a tricyclic antidepressant, which not only helps with sleep but also can help protect against headaches and improve cognitive performance. 198

Concussionology

Post-Concussion Recovery Recap Post-concussion symptoms typically last about 7 to 10 days, depending on how severe the concussion is and other factors. Most people get better within a week. However, that varies based on how well they adhere to the recovery protocol. General advice for treating a concussion includes the following: •

•

• •

• • • • • • •

• •

Sleep—Contrary to some common belief, sleep is the first and best thing to do to help recover from a concussion. Try to get at least 7–8 hours of sleep per night within the first week of sustaining a concussion. Mental Rest—The brain needs to rest while it is recovering. Avoid strenuous mental activities during the first few days after sustaining a concussion to avoid provoking symptoms. Limit reading, writing, texting, using computer and playing video games. Also, avoid other visual and auditory stimulus like bright lights and loud music. Physical Rest—Engage in no physical exercise until symptom free. Exercise adds strain on the brain, delaying the healing process. Eat Healthy—The brain needs a nutritional diet and perhaps some nutritional supplements, such as Omega 3 fatty acids, medium-chain fatty acids, Vitamin B Complex, Vitamin E, CoQ10 and other brain healthy supplements to enhance the healing process. Drink Water—The brain needs water to facilitate returning to proper balance. Aim for 100 oz per day. Avoid toxins, such as drinking alcohol, and smoking. Ease into normal activities slowly, not all at once. Follow my Recovery Protocol for guidance about when to return to the sport or school. Make sure to let employers or teachers know that you had a concussion. Avoid activities that could lead to another concussion, not only sports, but also certain amusement park rides or (for children) playground activities. Avoid driving, operating machinery or riding a bike (since a concussion can slow one’s reflexes). If necessary, discuss whether it is possible to return to work gradually (for example, starting with half-days at first). Students may need to spend fewer hours at school, have frequent rest periods, or more time to complete tests. Take only those drugs approved by the doctor. For some people, an airplane flight shortly after a concussion can make symptoms worse.

Fortunately, 50 percent of all concussions will resolve within 10 days, and 93 percent will resolve within 30 days. The small minority whose symptoms are prolonged need added support and care to assist them in returning to normal life activities. It is in this group that I feel that neuromodulatory technology has a particularly helpful therapeutic role.

Rewarding the Brain for Peak Performance Neurofeedback refers to an operant conditioning technique in which an individual’s EEG is monitored and analyzed in real time, and certain patterns of electrical brain activity are rewarded using visual, auditory and sometimes tactile feedback. Rewarding these patterns makes them more likely to occur. Changing, or modulating, the electrical activity of the brain has been shown to have therapeutic and performance benefits. Neurofeedback has been applied in a variety of clinical conditions including attention deficit disorder, anxiety, insomnia and traumatic brain injury. Over the last 40 years, different strategies of neurofeedback have evolved as computer hardware and software technology advanced in the real-time analysis of the EEG activity. In describing the strategies of 199

Harry Kerasidis

developing a neuromodulation treatment protocol, I like to use the analogy of planning a trip to the beach. If you have never been there, there are three ways you can find your way. The first is that you can ask someone who has been there before. Over 40 years, neurofeedback providers and clinicians have published their anecdotal and evidence-based findings describing protocols that have therapeutic and performance enhancing benefits. Protocols which reward faster rhythms and inhibit slower rhythms, for example, are shown to improve focus and attention functions. Other protocols rewarding slower EEG patterns have a relaxation effect. By assessing the condition of the individual, the neurofeedback provider can choose the protocol including the region of the brain to be trained to suit the needs of the individual. The second way you can plan a trip to the beach is to buy a map. As the technology of EEG analysis developed in parallel to neurofeedback, it became possible to map EEG patterns in twodimensional and three-dimensional space and compare an individual’s EEG statistically to a normative database. By identifying regions of brain dysregulation and correlating these regions to symptoms, the neurofeedback provider is able to design a protocol specific to the needs of the individual, implement the reward and inhibit protocols as guided by the maps. The third way you can plan a trip to the beach is to buy a GPS. The GPS will give real-time feedback and provide corrections should one make wrong turns on the way to the destination. Recent advancements in neurofeedback have integrated the mapping technology of QEEG analysis with the real-time reward feedback of neurofeedback. The neurofeedback practitioner again matches the regions of brain dysregulation to the symptoms of the client. After selecting brain regions of interest, neurofeedback software provides feedback rewarding more normal brain activity by real-time comparison of the EEG to the normative database. This type of training is often referred to as “live Z-score training.”

Guiding the Brain to Peak Performance An exciting new technology aimed at guiding the brain towards improved performance is emerging. Transcranial magnetic stimulation is a technique in which pulsing magnetic energy is applied to selected regions of the brain. This magnetic energy induces electrical currents that activate or inhibit neuronal activity where the magnetic pulses are applied. The frequency patterns and brain locations are guided by QEEG mapping and the correlation of the regions of dysregulation with symptoms.

Case Examples The following cases illustrate how neuromodulatory technology can be applied to assist in the resolution of symptoms related to mTBI and prolonged post-concussion syndrome.

A Motor Vehicle Accident TW was 43 years old at the time of his motor vehicle accident. He was the belted driver of a car that was hit from the driver’s side by another car that had run a red light. His head struck and broke the side window. He briefly lost consciousness at the time of the accident, and upon regaining consciousness he was confused, disoriented and exhibited both anterograde and retrograde memory loss. He immediately complained of headache and nausea. Imaging of the brain in the emergency room was normal. TW developed prolonged post-concussion symptoms that consisted of cognitive fogginess, memory problems, chronic headaches, personality changes and emotional dysregulation including depression and marked anxiety. He had been to several physicians and had tried several medications including antidepressants, headache treatments and attention deficit disorder medications with limited benefits. 200

Figure 11.1 The high resolution frequency spectra are shown below at each scalp location for QEEG Z-Score Log Power Spectra. The cursor is at 7.42 Hz.

Figure 11.2 The high resolution frequency spectra are shown below at each scalp location for QEEG Z-Score Log Power Spectra. The cursor is at 14.06 Hz.

Figure 11.3 sLORETA pre-treatment excess frontal alpha following motor vehicle accident (MVA).

Figure 11.4

sLORETA pre-treatment excess beta in parietal and precuneus following MVA.

Concussionology

TW came to our clinic about six years after his motor vehicle accident. Initial QEEG analysis demonstrated theta and beta dysregulation in the frontal regions correlating with his symptoms of dysregulation of executive function described above. sLORETA current source density analysis demonstrates lateralization of the increased theta activity to the left frontal region. Increased gamma activity is noted on sLORETA current source density analysis in the midline frontal and parietal regions, including the cingulate and precuneus regions corresponding to his symptoms of marked anxiety. His alpha peak frequency was borderline slow at 8.2 Hz. Based on TW’s symptom correlations with QEEG dysregulation, he was treated with sessions of sLORETA live Z-score training for frontal lobe regions of interest that generates rewards when the real-time EEG analysis detects more normal brain patterns. Transcranial magnetic stimulation at 10 Hz over the left frontal and temporal regions was included to address his anxiety and depression. After 20 sessions, TW reported a dramatic resolution of his symptoms. He no longer complained of headaches. His work performance improved dramatically. His cognitive fogginess cleared, and his depression and anxiety improved markedly. His wife was nearly in tears as she described that she had gotten her husband back after six years. QEEG analysis after treatment demonstrated a normalization of the peak alpha frequency to 9.4 Hz, improvement of the theta and beta frontal dysregulation and complete resolution of the increased gamma activity noted in the midline frontal and parietal regions.

Figure 11.5 The high resolution frequency spectra are shown at each scalp location for QEEG Magnitude Spectra. The cursor is at 8.20 Hz.

203

Figure 11.6 The high resolution frequency spectra are shown at each scalp location for QEEG Magnitude Spectra. The cursor is at 9.38 Hz.

Figure 11.7 The high resolution frequency spectra are shown at each scalp location for QEEG Z-Score Log Power Spectra. The cursor is at 7.81 Hz.

Figure 11.8 The high resolution frequency spectra are shown at each scalp location for QEEG Z-Score Log Power Spectra. The cursor is at 13.67 Hz.

Figure 11.9

sLORETA post-treatment reduced frontal alpha after live Z-score training following MVA.

Harry Kerasidis

Case of Emotional Incontinence BR was 57 at the time he sustained a falling injury at work. He fell backwards off a two-foot ledge striking his hardhat protected head against concrete. He did not lose consciousness, but immediately experienced headache and neck pain. In the days that followed the accident, BR complained of sensitivity to light and sound, headache, nausea, sleeplessness and marked personality changes. He was short tempered, and unable to control his emotional outflow. In casual conversation, if the subject turned to one of even the most minimal sadness, his eyes would often well up with tears and he would become overwhelmed with crying. Less often, he would blurt out with inappropriate laughing at things that were only marginally funny. Neurologists term this emotional incontinence “pseudobulbar affect,” referring to the fact that emotional outflow is no longer being regulated by frontal lobe inhibition. BR also complained of being easily overwhelmed by complex environments, social settings and conversations in which several people were speaking at the same time. When he came to our clinic several months later, BR’s post-concussion symptoms largely resolved, with the exception of the emotional incontinence and intolerance of complex environments. He had become increasingly depressed and socially withdrawn. Initial QEEG analysis demonstrated increased theta activity in the frontal regions, lateralized to the right, along with generalized hypercoherence patterns in the theta, alpha and beta bands. BR was treated with sLORETA live Z-score training with the frontal and temporal lobes targeted as regions of interest along with transcranial magnetic stimulation. After 20 sessions, BR enjoyed a

Figure 11.10 A summary of the QEEG results for this patient is provided by these topographic images, displaying the Z-scored features computed from 19 standardized electrode positions, as viewed from above with the nose at top, and left on the left. The scale is set at +/− 3.0 Z.

206

Concussionology

Figure 11.11 A summary of the QEEG results for this patient is provided by these topographic images, displaying the Z-scored features computed from 19 standardized electrode positions, as viewed from above with the nose at top, and left on the left. The scale is set at +/− 3.0 Z.

marked improvement in his symptoms of emotional incontinence. He was able to carry on entire conversations without uncontrolled emotional outflow. He was able to tolerate complex social environments much better, and was less reclusive. Follow up QEEG analysis demonstrated a dramatic improvement in the frontal theta activity and the broad-spectrum coherence.

The Train Wreck AC is a porter for a railroad company. One day he was sitting in a seat in an empty passenger car that was being coupled to the train. The car and train collided at medium velocity and AC was thrown forward and recoiled back striking his head against the headrest. He had immediate global headache, followed by fatigue and cognitive impairment. He was amnestic for the event. In the weeks that followed, he suffered from excessive daytime sleepiness and intermittent insomnia. He had difficulty concentrating and memory problems. Crowds and bright light overwhelmed him. A CT scan of the head done at the ER was normal. Analysis of AC’s QEEG performed six months after his injury demonstrated increased delta band activity in the occipital and parietal regions with a faster than expected alpha rhythm of 11.7 Hz. This likely represents disruption of the physiology of the default mode network (DMN), the regions of the brain that are most active when cognitive activity is least active. Neurofeedback 207

Figure 11.12

sLORETA pre-treatment excess posterior delta following train accident.

Figure 11.13

sLORETA post-treatment showing reduced posterior delta following train accident.

Concussionology

training focused on bringing the default mode network back online using sLORETA live Z-score training with the DMN and precuneus as training regions. After 20 sessions, AC reported a marked improvement in his focus, daytime alertness and headaches. Post-neurofeedback QEEG analysis demonstrated improvement in the posterior delta activity and the alpha rhythm frequency coming down to 9.4 Hz.

Summary Although generally self-limited, concussion/mTBI can have long-term and quite serious consequences in a small minority of victims. These consequences include prolonged post-concussion symptoms such as headaches, cognitive disturbances, balance problems, sleep-related complaints and mood dysregulation. Repeated concussions are typically slower to heal and carry along the more serious risks of early Alzheimer’s Disease, Chronic Traumatic Encephalopathy, Parkinson’s and, rarely, Amyotrophic Lateral Sclerosis. Initial post-injury treatment consists of physical and cognitive rest, combined with symptomatic medication treatment. For those individuals with prolonged post-concussion symptoms, neuromodulatory interventions such as neurofeedback and transcranial magnetic stimulation can be effective interventions, even in cases that fail medication management.

References American Association of Neurological Surgeons. (2011). Concussion. Retrieved Dec. 2, 2015 from http:// www.aans.org/Patient%20Information/Conditions%20and%20Treatments/Concussion.aspx Bradley, B. (2013, October 29). George Mason researchers think saliva might unlock concussions. Retrieved Dec. 3, 2015 from http://www.nfl.com/news/story/0ap2000000271940/article/george-mason-researchersthink-saliva-might-unlock-concussions Fakhran, S., Yaeger, K., Collins, M., & Alhilali, L. (2014). Sex differences in white matter abnormalities after mild traumatic brain injury: Localization and correlation with outcome. Radiology, 272(3), 815–823. Giza, C., Kutcher, J., Ashwal, S., Barth, J., Getchius, T., Gioia, G., . . . Zafonte, R. (2013). Evidence-based guideline update: Evaluation and management of concussion in sports. Neurology, 80(24), 2250–2257. Guerriero, R., Proctor, M., Mannix, R., & Meehan, W. (2012). Epidemiology, trends, assessment and management of sport-related concussion in United States high schools. Current Opinions in Pediatrics, 24(6), 696–701. doi:10.1097/MOP.0b013e3283595175 Halstead, M., Walter, K., & The Council on Sports Medicine and Fitness. (2010). Sport-related concussion in children and adolescents. Pediatrics, 126(3), 597–615. doi:10.1542/peds.2010–2005. Retrieved Dec. 3, 2015 from http://pediatrics.aappublications.org/content/126/3/597 Hootman, J., Dick, R., & Agel, J. (2007). Epidemiology of collegiate injuries for 15 sports: Summary and recommendations for injury prevention initiatives. Journal of Athletic Training, 42(2), 311–319. Hutson, C., Lazo, C., Mortazavi, F., Giza, C., Hovda, D., & Chesselet, M. (2011). Traumatic brain injury in adult rats causes progressive nigrostriatal dopaminergic cell loss and enhanced vulnerability to the pesticide paraquat. Journal of Neurotrauma, 28(9), 1783–1801. doi:10.1089/neu.2010.1723 Kerasidis, H. (2014, August 19). Gender differences complicate concussion care. [Web log post]. Retrieved Dec. 2, 2015, from www.psychologytoday.com/blog/brain-trauma/201408/gender-differences-complicateconcussion-care Lincoln, A., Caswell, S., Almquist, J., Dunn, R., Norris, J., & Hinton, R. (2011). Trends in concussion incidence in high school sports: A prospective 11-year study. American Journal of Sports Medicine, 39(5), 958–963. doi:10.1177/036354651039232 Marar, M., McIlvain, N., Fields, S., & Comstock, R. (2012). Epidemiology of concussions among United States high school athletes in 20 sports. American Journal of Sports Medicine, 40(4):747–755. doi:10.1177/ 0363546511435626. Epub 2012 Jan 27 McCrory, P., Meeuwisse, W., Aubry, M., Cantu, B., Dvorák, J., Echemendia, R., . . . Turner, M. (2013). Consensus statement on concussion in sport: The 4th International Conference on Concussion in Sport held in Zurich, November 2012. Journal of Science and Medicine in Sport, 16(3), 178–189. Mielke, M., Savica, R., MD, Wiste, H., Weigand, S., Vemuri, V., Knopman, D., . . . Jack, C. (2014). Head trauma and in vivo measures of amyloid and neurodegeneration in a population-based study. Neurology, 7, 82(1), 70–76. doi:10.1212/01.wnl.0000438229.56094.54

209

Harry Kerasidis Shahim, P., Tegner, Y., Wilson, D., Randall, J., Skillback, T., Pazooki, D., . . . Zetterberg, H. (2014). Blood biomarkers for brain injury in concussed professional ice hockey players. The Journal of the American Medical Association Neurology, 71(6), 684–692. doi:10.1001/jamaneurol.2014.367. Retrieved Dec. 3, 2015 from http:// archneur.jamanetwork.com/article.aspx?articleid=1846623 Silver, J., Kramer, R., Greenwald, S., & Weissman, M. (2001). The association between head injuries and psychiatric disorders: Findings from the New Haven NIMH Epidemiologic Catchment Area Study. Brain Injury, 15(11), 935–945. Slobounov, S., Slobounov, E., Sebastianelli, W., Cao, C., & Newell, K. (2007). Differential rate of recovery in athletes after first and second concussion episodes. Neurosurgery, 61(2), 338–344. Subhulakshmi, R. (2015). Diagnosis of ADHD using brain imaging technique: A survey. International Journal of Science and Research, 4(6), 1920–1924. Retrieved Dec. 2, 2015 from http://www.ijsr.net/archive/v4i6/ SUB155772.pdf

210

PART IV

Dyslexia and Reading

12 RHYTHMS OF DYSLEXIA EEG, ERP and Neurofeedback Tony Steffert and Beverly Steffert

Abstract We present electroencephalogram and event related potentials of dyslexics, which support evidence of abnormality in the rapid visual processing of the magnocellular pathway and then show how this impairment can be ameliorated with a combination of qEEG guided neurofeedback and conventional dyslexia therapies. Previous research shows deficits in one of two sensory processing systems, the magnocellular ‘where pathway’ which processes rapid stimuli such as motion so affects reading continuously when the eyes track text across a page. The parvocellular ‘what pathway’ deals with fine spatial, static sensory information affecting accurate letter identification.

Introduction Summary of Neurological Research into Dyslexia and Current Treatment Dyslexia is generally understood as a discrepancy between one or more literacy skills on the one hand and cognitive, numerical and/or other academic skills on the other that is not due to lack of educational opportunity or socio/emotional hindrances. There are of course degrees and differences in the exact type of reading difficulty which are now better encapsulated by the new DSV-5 (American Psychiatric Association, 2014), which allows categories such as “word reading accuracy,” “reading rate or fluency” and “reading comprehension” to be distinguished under a specific learning disorder (code 315.00 or F81.0 for IC10 classification (World Health Organization (WHO), 1990). As an addon the DSM code 315.2 (ICD 10 F81.81) allows “with impairment in written expression,” which of course is the old name, dysgraphia, and refers to spelling accuracy, grammar and punctuation accuracy and clarity or organisation of written expression. It chimes with the common sense view of dyslexia as a “difficulty or incompletely developing accurate and fluent word reading/spelling,” which most schools espouse. This ignores the older “compensated” dyslexics who get on very well using their coping strategies, but of course coping strategies are just that that, coping. They can’t be maintained under stress, time demands, illness, fatigue or major distractions. Because they have not developed an automaticity in literacy, all literacy tasks demand more cortical resources than the non-dyslexic who has their literacy skills backed up in the cerebellum, as are all linguistic, motor and cognitive skills that are well practised. The consequence is slowness, often inaccuracy and fatigue during literacy tasks (Nicolson & Fawcett, 1995). 213

Tony Steffert and Beverly Steffert

Reading is probably the most skilled activity humans have developed. Even in reading a simple consonant–vowel–consonant word, like “cat,” the brain must recognise three distinct sounds represented by each letter—“cah”–“ah”–“tuh”—and in the right order, blend then into the whole word and mentally map the sounds onto the letters printed on the page. No other species has developed a written language, so it is not surprising that around 10% to 15% of humans, at least in the English speaking world, have not developed reading, despite an adequate education and socioeconomic and emotional background (Shaywitz, Shaywitz, Fletcher & Escobar, 1990). The causes of dyslexia have a very high genetic correlation, and when that is absent the cause can usually be found in the medical history of the dyslexic. This will include early ear infections at the biologically sensitive time for discriminating the sounds of the language, before two to three years old (Peer & Watson, 2005). Prematurity has its correlation, especially if the baby is put in an incubator and therefore cut off from speech sounds around them; this also includes the last trimester of pregnancy when the auditory nerve is active and registering speech sounds. Full-term babies can recognise acoustic boundaries that separate phonemes, and so can some animals. We know this because of language studies (Aitchison, 1996) that show that babies copy the frequencies of the speech sounds in their native language in their “babbling”; also, newborns will pay more attention to the sounds of their mother’s voice than others. We make it easier for them with exaggerated intonation, increased pitch and duration of speech sounds, i.e. “motherese.” What makes it harder is the exposure to the sounds of another language before one has mastered one’s own—and in this multi-cultural/lingual world, this is an increasing problem. Of course, visual problems, such as astigmatism, affect how the child sees words and are a frequent barrier to accurate reading. There are many subtle visual problems that are unrelated to the dorsal/magnocellular deficits (Stein & Walsh, 1997), increasingly suspected to underlie dyslexia, some unrelated to reading too. Others that do inhibit reading are, for example, shortsightedness, which is not claimed to be a cause of dyslexia, since physical remediation such as spectacles generally immediately restores the ability to read. Fouche (2012), among others, suggests that sometimes head injuries, even when concussion was not present, can also inhibit literacy skills. A recent large survey (Fuller-Thomson & Hooper, 2014) claimed a correlation between physical abuse and dyslexia, although this needs to be replicated and examined as this is the first time this has been raised as a contributor to dyslexia. The strongest correlation of dyslexia is the genetic contribution and there are several genes identified which are involved in neuronal migration and axonal growth which affect neuronal membrane efficiency (Shaywitz et al., 1998). Prior research (Spironelli, Penolazzi & Angrilli, 2008) had already shown that the integrity and timing of the connections between functional areas of the reading circuit in the brain differed between poor and fluent readers. The decoding of a word requires breaking down the sound sequence of the word into phonemes, from which all words are composed, whilst simultaneously ordering the graphemes (letters and letter clusters) from left to right in exact orthographic sequence. A slowness in the auditory circuit was first identified by Tallal (1980) and has given rise to the “rapid temporal processing deficit” theory. Other research asking dyslexics to identify tones, sounds and speech sounds has shown that their auditory discrimination is compromised. They often can’t tell the pitch of the sound, will perceive two sounds that are milliseconds apart as one sound, and even mix up the sequence of the sound. This suggests an auditory cortex “discriminator” impairment to explain the difficulty of dyslexics in attaining sufficient phonological awareness to match sound to symbol in reading (Baldeweg, Richardson, Watkins, Foale & Gruzelier, 1999). In fact, Goswami, Gerson and Astruc (2010) looked at the effect of this auditory deficit on word structure, finding that the elements of a word, such as the rise time of the first sound and the perception of the stressed syllable in the word (which is denoted as the amplitude envelope) were the critical hurdles to efficient reading for dyslexics. Thus both timing and amplitude are correlates of efficient reading and the difficulty in processing amplitude onsets accurately, in Goswami’s view, may constitute the primary deficit in developmental dyslexia. 214

Rhythms of Dyslexia

This suggests that the simple view of a deficiency in phonological awareness caused by difficulty in discriminating fast sounds (identified by Tallal (1980) as t, k, b, d, g) is insufficient. Rather it is the supra-segmental cues that contribute to the rhythm and stressed syllables that are important in developing phonological representation. The rhythm of the language is marked by the duration of the speech sound, its intensity, depth of amplitude modulation, the rate of change of amplitude modulation and changes in the fundamental frequency—or pitch. The rise time is an aspect of syllable production and is the most important supra-segmental cue that partially describes the structure of the amplitude envelope and plays an important role in development of phonological awareness and the acquisition of literacy. Of course, the importance of rhythmic cues to the development of phonological representation may differ depending on the language. Nevertheless, a consistent finding in dyslexics, especially those who have an adequate phonological awareness, is a difficulty in rapidly naming a list of letters, numbers, pictures and colours put in front of them, i.e. matching the visual aspect to the verbal. This is called rapid automatised naming, and it is particularly associated with reading fluency, so it may well reflect a separate aspect of the whole reading circuit (Denckla & Rudel, 1976) to phonological awareness. Although phonics-based remedial literacy programmes are usually successful in helping dyslexic children develop their reading, there is some question over whether phonological awareness is a better predictor of reading ability in general rather than reading disability (Scarborough, 1998). Certainly auditory discrimination is a key factor. It appears to be genetic and can be identified even in babies with a familial history of dyslexia (Guttorm et al., 2005) and can lead to different types of difficulty affecting reading, with poor phonological awareness the major, but not only, consequence.

Visual Processing Problems As if these subtle auditory discrimination problems weren’t enough of a barrier, Margaret Livingstone et al. (1991) showed that dyslexics also had visual processing problems and developed the magnocellular hypothesis of dyslexia. This was followed up by Stein and Walsh (1997) who showed

Figure 12.1 Magnocellular and parvocellular are two separate visual pathways going from the eyes to the Lateral Geniculate Nucleus (LGN) of the thalamus and on to the visual cortex. The magnocellular pathway from LGN feed into the dorsal visual stream in the parietal cortices and is involved in resolving motion and coarse outline detection and estimating where things are in space; whereas the parvocellular pathway goes to the ventral visual stream and has a higher spatial resolution but lower temporal resolution and is also sensitive to colour.

215

Tony Steffert and Beverly Steffert

that dyslexics had similar orthographic deficits to their phonological ones. Orthography refers to the visual letter sequence of words so it is also important in word recognition. Studies of contrast sensitivity (Borsting et al., 1996) and rapid motion detection (Demb, Boynton, Best & Heeger, 1998) all suggested visual perceptual deficits which provided further evidence for deficits (a slowness) in the visual magnocellular pathway in the dorsal visual stream of dyslexics. This contributes to the rapid integration of visual information in reading, including the control, direction and organisation of saccadic eye movements, and consistent with the magnocellular hypothesis, dyslexics have smaller and less organised cellular structure in the retinal–cortical visual pathways, which discriminate rapid changes. Galaburda, Menard and Rosen (1994) and Pammer (2012) go so far as to suggest that the primary cause is visual with the phonological deficits consequent on them. Visuo-attentional tasks that use the dorsal/magno pathway, such as coherent motion, frequency doubling, visual search and attentional shifting, all correlate with reading efficiency, predict normal reading variation, exist in children at genetically based risk for dyslexia, and have a neurophysiological basis: the observed deficit in the dorsal visual pathway. When pathways of reading are compromised by patchy myelination, as Klingberg et al. (2000) has shown, there is a slower, more inaccurate, visual processing which causes unfocussed visual attention and unstable eye control as well as letter order confusion. Since tiny details of the visual pattern of print have to be accurately segmented into separate letters in order to match them with the phonemes they represent, mistakes are more likely. Additionally, slower, inaccurate auditory processing means difficulty in tracking subtle changes in sound frequency or amplitude with time. This is necessary for phoneme detection in words, and the dyslexic can confuse sound order in phonemes and words (b/d/p and was/saw, for example). This is the fundamental cause of phonological problems. The language can make a difference. For example, Spanish and Italian are more regular languages than English or French. Regularity means words look like what they sound like—so the same set of letters gives rise to the same sound. Not so with English: some similar letter clusters have different sounds—consider: through, bough, cough, bought, tough and so on. Children hear the syllable pattern of words in spoken language (3 syllables in that word—syll-a-ble) but not the phonemic pattern (5 phonemes in that word—ph-o-n-e-m), so when decoding many words there is not a structure they can count on. Since these magnocellular pathways have projections to the cerebellum, Nicolson and Fawcett (1995) suggest that dyslexics may have coordination and balance problems. Their dyslexics showed less cerebellar activity compared to controls, which compromise the development of automaticity in cognitive, literacy and motor skills. This would explain rapid automatised naming being a good correlate of word production and slowness in reading even when few phonological deficits exist. Recently a pathway linking the dorsal and ventral stream, the vertical occipital fascicus, has been identified, although this was described by Wernicke over a hundred years ago. The vertical occipital fasciculus is a flat sheet of white matter tracts that extend up through the brain for a distance of 5.5 cm, connecting the “lower” and “upper” streams of the visual pathway (Yeatman et al., 2014). Yeatman et al. (2014) quote examples of reading loss with damage to this pathway. It would certainly account for reading comprehension difficulties which occur in the absence of phonological deficits. Our species has not evolved a “Reading gene.” Instead, reading has exploited attentional mechanisms to select and process a small fraction of sensory information, relevant at that moment from the vast array of sensory inputs impinging on us. Vidyasagar (2005) suggests that the cortical region that orchestrates such selective attention is the posterior parietal cortex, which directs a “spotlight” of attention onto earlier visual areas. Such gating of sensory inputs rapidly and at a fine spatial scale by the feedback pathways from the dorsal stream is a fundamental process in reading. So the observed 216

Rhythms of Dyslexia

deficits in this pathway in dyslexic brains may be showing us that the also commonly observed phonological deficits are a simple downstream consequence of the dyslexic’s original deficit in visuo/ spatial attention. Other suggestions are that there may be an “auditory” magnocellular pathway, equivalent to the visual magnocellular pathway, to account for the commonly observed auditory and phonological difficulties of dyslexics (Galaburda et al., 1994). Others (Roach & Hogben, 2007) take issue with the magnocellular hypothesis of causation. Not only are there magnocellular deficiencies in other conditions besides dyslexia, but Sperling et al. (2005), on the basis of their research, interpret the magnocellular findings as a “perceptual noise exclusion deficit.” They support this by showing that dyslexic adults and children experience difficulty in targeting visual information in the presence of visual perceptual distractions, but not when the distracting factors are removed, at least in an experimental setting. Thus, some dyslexic symptoms appear to arise because of an impaired ability to filter out distractions. Further, exposure to external visual “noise” produced the same level of impairment in dyslexic subjects regardless of the speed of the task being tested, so this difficulty is related to the difficulty with filtering ambient or extraneous data. Focusing on relevant factors while disregarding irrelevant distracters would make it difficult to judge the relative importance and relevancy of perceived details. This idea is supported by a study showing dyslexic subjects—in comparison to non-dyslexic subjects—were less responsive to cueing in a visual discrimination task, suggesting that the dyslexics had greater difficulty than controls with prioritising certain visual information based on previous exposure. The researchers suggest that performance on the cuing task could be a more accurate means of discerning dyslexic from normal readers in comparison to the range of other psychophysical tasks typically used in dyslexia research. This deficit is not purely visual, it is more of a basic problem in sensory perception that gives rise to poor filtering ability, which can distort speech perception in infancy and thus later their ability to discriminate phonemes and letter/spelling to sound links. This would certainly fit the common observation of teachers and parents, that dyslexics can better read text when it is larger and widely spaced. When the same text in small print on a cluttered page is presented, the dyslexic cannot focus on and read the words (Facoetti et al., 2010). Witton et al. (1998) demonstrated that 93% of the variance in reading could be accounted for by identifying speed of discrimination in both visual and auditory changes in the stimulus, which in their case was a screen of moving dots. The dyslexics needed to see up to 30% more dots move before they perceived any movement and were less sensitive to auditory frequency modulation at 2 Hz and 40 Hz. This was interpreted by them as dyslexics not having sufficiently rapid visual and auditory perceptual processing to track rapid changes in either modality. This would affect the ability to determine frequency changes of phonemes, which occur in milliseconds, and/or identifying differences in letters and letter positions. However, this interpretation is equally open to the “perceptual noise” theory. Clearly, however, both orthographic and phonological/auditory sensitivity independently contribute to different aspects of reading. Even in non-dyslexic children, these sensory abilities correlate with reading and spelling (Lovegrove, 1996) and, what is more, exist pre-school (Simos et al., 2002) and have a 40% to 50% hereditary component. Both need rapid and accurate discrimination of visual and auditory stimuli. Hopefully the question of which comes first, the orthographic or the phonological component, will be resolved eventually. However, we can say that parsing fine optical detail is not usually required to perceive the visual field, but tiny details of the visual pattern of print have to be accurately segmented into separate letters in order to match them to the phonemes they stand for. So inefficient visual sequencing means the reader cannot track rapid changes in visual signals, so letters appear blurry or to move because they cannot achieve stable binocular control. 217

Tony Steffert and Beverly Steffert

Thus dyslexics don’t have the level playing field they need to segment the word they perceive (inaccurately) into syllables or phonemes, often getting the letters out of sequence. The most relevant label to describe their problems is simply slow processing of visual and auditory sensory information. At the biochemical level it is interesting to speculate on dyslexia as a Phospholipid Spectrum Disorder. Phospholipids are the building blocks of all cell membranes. In addition to phospholipids, cell membranes contain numerous proteins, including ion channels, receptors and enzymes, which have essential roles in cell signaling. Phospholipids are mostly composed of fatty acids which determine the structure and function of the membrane. Phospholipids form the white matter or myelin sheath around nerve cells that allows the signal to travel faster and further through the brain. These membranes are subject to wear and tear, making them “leaky,” and this then affects the function of the proteins embedded in the membrane. This then affects the post-synaptic potential and signal transduction needed for the signal transduction that links receptor occupancy and neuronal response. This is a G-protein mediated post-synaptic response which leads to phosphorylation and onward transmission of the neural message. The non-biochemist reader can think of this as the neurons not passing the message on to their neighbors like they should! The take-home message here is that it is the magno cells, which are big cells, that are the most vulnerable to malfunction (Stein & Walsh, 1997). Given that reading is one of the most rapid things humans do, there is a causal link here. Omega 3 makes up part of the fatty acid molecules attached to phospholipid, and supplementation of Omega 3 has been shown to help reading. In fact these fatty acid molecules are particularly rich in the retina, which of course is where fast action is most necessary. The fatty acid system is developmentally regulated but can be affected by maternal diet, prematurity and toxins (Richardson, 2006).

Conventional Treatment Ever since Bradley and Bryant (1983) found that phonics-based teaching helped most dyslexics learn to read, phonics has been the standard remedy for dyslexics. Many organisations, from OrtonGillingham and Lindamood Bell in the U.S. to the Hornsby Centre and Dyslexia Action in the U.K., have developed phonics-based programmes through which many hundreds of thousands of children have benefited. Most have developed training programmes for teachers who can then understand and work with the weaknesses of a dyslexic while still promoting their strengths. There are book-based programmes such as “Letterland” with narratives and colourful pictures to help children remember letter sounds. There are also computer-based programmes which dyslexics can work through by themselves, if a dyslexia-trained teacher is not available. Some programmes elongate the sounds, in particular, the stop-consonants which are too rapid for the sluggish auditory system of most dyslexics. All these programmes work on a fundamentally similar principle. They are all carefully structured and go through all the letter and word families of the language in a cumulative and hierarchical way, using touch, image, colour, sound and any other sense that may be usefully invoked to strengthen memory, i.e. multi-sensory teaching. Goswami’s (2005) research on the rhythmic deficits in dyslexia could lead to more tailored programmes that strengthens the ability to perceive the rhythmic and prosodic aspect of the language which may include music entrainment programmes. At the visual level, distortions of text complained of by children range from blurriness, moving letters, fading of letters and words, sore or watery eyes on reading, lines waving and disappearing into each other, letters reversing and reading more comfortably with dim rather than bright lighting, among other complaints. Opticians were sceptical about these complaints when they found ophthalmic lenses didn’t help. The current treatment for such visual symptoms is to supply spectrum-specific overlays or lenses, and there are several organisations devoted to screening for and providing tinted lenses and overlays. How these work is the subject of much controversy. Colour is both simple and complex, with 218

Rhythms of Dyslexia

characteristics that change perception in quality and quantity of many optometric measures. Even in some medical and neurological conditions, the timing and mapping of aspects of the sensory systems affect arousal levels and many cognitive processes (Jordan, 2005). Mechanisms are not currently well understood, although theories abound which explain some of the symptoms dyslexic children experience but tend to assume that there is an underlying similar mechanism. Certainly, some children improve their reading by the simple provision of these lenses, but only some—far less than those who improve their reading through phonics-based programmes. Part of the visual system interprets colour, based on the wavelengths that strike the cone cells in the retina and this is held to have an effect on the visual system and therefore on reading, the fastest action humans undertake. The most established ideas of how colour works ranges from Irlen’s (1994) simple trial and error (is reading faster/more accurate with different overlays?) to more scientific studies of wavelength, rod and cone distribution, hyper-excitability of visual cells, among other possibilities (Evans, Holden & Nicholls, 2001; Wilkins, 2003). In her book The Light Barrier, Rhonda Stone (2002) has produced a chart of common traits that attempts to identify the ways dyslexic reading and spelling problems differ from other visual disorders. Based on the knowledge that the non-image forming visual system is an important regulator of attention and eye movements, Taylor (2013) hypothesised that the effects of colour (in this case blue and yellow) on attention are mediated via the locus coeruleus, which is influenced by a subcortical pathway linking the hypothalamic nuclei, which themselves are activated by the retinal ganglion cells. An oculometric measure is proposed to determine which colour filter would be of most benefit. There are also computer-based programmes asking the dyslexic to detect changes in rapidly turning bars or lines as well as exercises in visual tracking that behavioural optometrists prescribe, such as focusing on a particular stimulus while moving, threading beads on moving strings and other hand/ eye coordination exercises. Schools use books in which the child has to detect various letters, lines or dots and undoubtedly there are many more therapies that various practitioners have found that suit them, including ball sports to improve had/eye coordination, balancing boards to promote cerebellar function and computer games based on tracking rapidly moving objects. Franceschini et al., (2013) has demonstrated improvement in the reading of dyslexics who played an action video game, promoting visual spatial attention instead of direct phonological or orthographic training of their other dyslexic subjects. It would certainly be more fun and could be combined with neurofeedback.

Brief Summary of QEEG in Dyslexia Early QEEG studies of dyslexics were hampered by confusion over dyslexia itself, so often comorbidities were not screened out, age/stage of reading not accounted for, as well as different recording measures. Thus, from the 1970s and ’80s, many studies (Duffy, Denckla, McAnulty & Holmes, 1988; Kaye, John, Ahn & Prichep, 1981; Lubar et al., 1985; Sklar, Hanley & Simmons, 1972, to mention a few) did not produce replicable results. Galin et al. (1992) realised that background EEG, or spectra, may not show a difference between dyslexics and non-dyslexics until the system was loaded. Galin et al. conducted a well-controlled study on the difference in EEG when reading, both silently and aloud. Even here, differences were hard to tease out usefully, but they did interpret the strongest effect: theta in the temporal lobe, as reflecting difficulty in changing reading strategies from the sound of the word to the meaning of it. Meanwhile, brain-based studies of dyslexia used imaging techniques (fMRI) (Shaywitz & Shaywitz, 2008; Temple et al., 2001) and others to identify three important systems in reading, all primarily in the left hemisphere. These are the inferior frontal gyrus (the articulatory rehearsal system BA44, ~F3), the left parieto-temporal (phonemic analysis, acoustic short-term memory BA40, P3/T3) and the temporo-occipital (automatic word recognition, T5/O1). Activation of the left temporal and parietal areas by normal readers was different to dyslexics who tend to activate right-sided homologous 219

Tony Steffert and Beverly Steffert

areas. Clearly this is a compensatory strategy which leaves open the question of remediation—does the neurofeedback therapist strengthen their strategy by stimulating the right temporo/parietal areas, or try to boost their weakness by stimulating the normal areas for reading? This is an issue that faces therapists in treating stroke patients as well. Evans and Park (1996) confirmed these three sites with QEEG measures of phase, symmetry and coherence, one or other of which were abnormal at P3, T3, T5 and O1 in their small sample of dyslexics. Walker and Norman (2006) reported improvements in reading speed and comprehension with QEEG guided neurofeedback, which was mostly training 16 to 18 Hz at T3. Thus, as QEEG studies became more specific in their measures, the QEEG seemed to offer some promise in guiding neurofeedback for dyslexia. However, Breteler et al. (2010) failed to replicate any reading improvement and the only change in pre and post QEEGs were alpha changes, which they speculated were more due to the extra attention the dyslexics had available to concentrate on their spelling, which, unlike reading, did increase. Here again, lack of specificity (sub-types of dyslexia) was suggested by the authors to be the reason for the failure to observe the expected differences in the QEEG spectra. Thornton and Carmody (2005) use QEEG guided gamma neurofeedback, but since until recently much neurofeedback equipment did not provide the ability to use gamma because it is a high frequency, low amplitude wave that needs accurate, and therefore expensive, equipment. Therefore many practitioners have been unfamiliar with it and should refer to any of Kirtley Thornton’s excellent works on gamma frequency. Again specificity returns results. In a QEEG study we conducted, we recruited visual dyslexics on the basis of visual difficulties in reading such as those listed above. Our study showed that the excess occipital alpha seen in visual dyslexics during reading was significantly decreased when they were reading with the tinted lenses. The colours were chosen by the optician on the basis of increased discrimination of text. Professor Kropotov analysed the results, shown below, and we reported this at The International Dyslexia Conference at Warwick University in 2004 (Steffert, Steffert & Kropotov, 2004). When the dyslexics with visual problems were reading they showed an excess alpha in the occipital lobes which decreased when reading with their prescribed coloured glasses (Figure  12.2).

Figure 12.2 Shows two superimposed EEG spectra of 35 subjects who had been diagnosed with visual dyslexia. The grey shaded area shows the difference in alpha power at O2 (right occipital) between reading with and without coloured glasses. There is a clear excess of alpha when the visual dyslexic child is reading without their prescribed coloured glasses that reduces when reading with their coloured glasses.

Figure 12.3 Shows the topographic maps of alpha power (9 to 11 Hz) of 35 visual dyslexia subjects with visual dyslexia reading with and without coloured glasses.

220

Rhythms of Dyslexia

Aspects of their reading did improve but not overall. Many were quicker in timed reading; some were more accurate in their word recognition, although all found reading easier. Given this latter finding was a subjective judgement, the idea of sub-types of reading difficulties is strengthened. One of the most intriguing findings of the research was that the children improved their balance. They had been asked to stand upright on cushioned pads while their sway (a measure of balance stability) was measured. In all cases when tinted lenses were worn the dyslexics improved their balance. The pattern of alpha seen in the occipital cortex when the visual dyslexics were reading looks very much like someone with their eyes closed, implying a hypoactivation of this area. Unfortunately, at that time we did not do Event-Related Potentials (ERPs), which have since shown repeatedly that the major deficit in dyslexics is the visual dorsal stream.

Evoked Potentials and Event-Related Potentials in Dyslexia Evoked Potentials (EPs) refer to the electrical activity of the nervous system that is elicited by a stimulus and whose pattern of response is dependent on the physical properties of the stimuli such as the brightness or loudness. EPs tend to occur within the first 50 milliseconds of a trial and are evoked by external (exogenous) events. Event-Related Potentials (ERPs) are the electrical brain potentials prompted by the cognitive processing of a stimulus. So ERPs tend to arise after the EPs and reflect internal (endogenous) mental processes. A stimulus will induce both EPs and ERPs. So, for example, a well-known ERP is called the Mismatch Negativity (MMN). This is a negative shift in electrical brain potentials in response to an odd or rare stimulus in a long sequence of “typical” stimuli and is therefore not dependent on the nature of the stimuli per se, but its meaning or higher order properties, such as novelty. Both EPs and ERPs are elicited by the same method of the presentation of hundreds of short stimuli. These stimuli are then averaged to cancel out any random background activity that is not synchronised with the onset of the stimuli or the response of the person. EPs

Figure 12.4a Shows a stereotypical Evoked and Event-Related response. The shaded area is the 100 ms when the stimulus was presented and the line shows the positive and negative voltage fluctuations at different time points, elicited by the stimulus and the cognitive processing of the stimulus. The lower half shows the topological scalp distribution of the peak amplitude at 340 ms after the stimulus onset.

221

Tony Steffert and Beverly Steffert

Figure 12.4b Evoked and Event-Related Potentials can reflect the time course of sensory and cognitive processing and can help to distinguish between sensory and cognitive deficits even when there is no external behavioural response such as a button push. EPs and ERPs have excellent temporal resolution, as quick as 1 millisecond, but the amplitude is very small, so they require hundreds of trials to be averaged. Therefore, a typical task can take around 20 minutes.

and ERPs have both positive and negative voltage fluctuations in comparison to a baseline directly preceding the stimulus presentation. These fluctuations are characterised by the timing and amplitude of the peaks and troughs of their activity, as well as the scalp topology. So, for example, a positive deflection at 300 milliseconds is called the P300 and a negative-going trough at around 100 milliseconds after the presentation of the stimulus is termed the N100 or N1 for short.

Auditory Evoked Potential An auditory evoked potential (AEP) is elicited by an auditory stimulus. So, for example, in a study by Baldeweg et al. (1999), pure tones of 50 milliseconds long were used. In this auditory mismatch negativity task 80% of the trials played a 1,000 Hz tone and four “deviants” stimuli at 1,015, 1,030, 1,060 and 1,090 Hz for 5% of the trials each. Baldeweg et al. found that dyslexics had difficulty in monitoring the frequency of incoming sound and concluded that this sensory deficit may impair the feedback control necessary for the normal development of phonological skills. If you can’t discriminate between subtle changes in frequency, you can’t use them to decode phonemes in words. In 2005, Giraud et al. surveyed the literature on the dysfunction in low level auditory processing of dyslexics and concluded on the basis of their research with dyslexics and auditory ERPs that there was a central auditory deficit in dyslexics in the language and auditory areas of the brain, although not all dyslexics were impaired in the same way. This is not surprising if the underlying cause is lack of adequate connections between only some of the functional aspects of the literacy circuit, which according Diffusion Tensor Imaging (DTI) studies (Klingberg et al., 2000) and others is the case. Leppänen et al. (2002), using auditory ERPs, buttressed the auditory school of thought when they found that babies who had genetically related dyslexic relatives could not discriminate subtle changes of speech sounds of short duration. At the very least, we can say that patterns of brain activation, measured by Evoked and EventRelated Potentials, are consistent in showing that there is a different time course of reading activation through the brain between dyslexics and normal readers.

222

Rhythms of Dyslexia

Visual Evoked Potentials Evoked Potentials elicited by visual stimuli, Visual Evoked Potentials (VEPs), have differentiated normal readers from dyslexics since Margaret Livingstone showed lower amplitude or longer latency in dyslexics in response to the rapid changes in checkerboards or grating patterns of light and dark lines (Livingstone et al., 1991). VEP with non-linguistic stimuli show that the visual deficits in dyslexia are most likely explained by “sluggishness” in the fast processing transient visual pathway, thought to be the magnocellular system (Stein & Walsh, 1997) that gives readers fast temporal resolution at low contrast and spatial frequencies. Livingstone further demonstrated the magnocellular cells in the Lateral Geniculate Nucleus were smaller, fewer and more disordered in dyslexics than in normal readers. Eden et al. (1996) then showed that in dyslexics, movement sensitivity to visual stimuli was slower in V5 MT, a visual area where integrating local motion signals into global perception and guided eye movements occurs. This was not observed in static visual perception. Fawcett et al. (1993) showed a similar pattern in a small scale and age range of dyslexics. VEPs of longer latencies and attenuated amplitudes suggest that rapid motion and contrast perception are compromised enough to affect reading. Reading itself is a rapid scanning of grapheme/ phoneme/meaning conversion from letter order, line tracking as well as mapping sound to symbol. These results uphold the magnocellular hypothesis. Magnocellular cells are big myelinated cells and are the most vulnerable to disruption. If the magnocellular system is compromised it would affect the signalling transduction of glutamate and catecholamines, the second messenger system. Dopamine, in particular, underlies the timing of sequences (Wiener, Lohoff & Coslett, 2011) while glutamate mediates fast action. Thus faulty membranes could lead to hypofunctioning of aspects of both dopamine and glutamate functioning, affecting speed of signalling and memory (Takahashi et al., 1997), already mentioned as the two major problems in dyslexia.

Motor Evoked Potentials Still, one aspect of dyslexia that a lack of phonological and/or orthographic sensitivity doesn’t account for is the weak motor coordination observed in many dyslexic children, their frequent difficulty in keeping in time (clapping to a metronome) or learning a musical instrument. Further, dyslexics can’t perceive speech rhythms well and Muneaux et al. (2004) and others have suggested an underlying dysregulation of rhythmic oscillatory activity in tracking and segmenting syllables that affects syllable stress and phonemic perception. This deficit in the rhythmic timing of speech correlates with poor beat perception as well as not being able to tell which the odd sound out was, or which word rhymes with other words. This suggests that reading has a rhythm which is out of sync in dyslexia. Motor Evoked Potentials (MEP) have mostly been used to monitor the integrity of motor pathways in Multiple Sclerosis and other demyelinating diseases as well as strokes, sleep irregularities and other pathologies where it is useful to locate lesions of the peripheral and central nervous system. Many years ago Kuhlman and Schweinhart (1999) measured motor rhythms by timing tapping a foot and hand to a beat to get a “Beat competence” score that correlated with school achievement, suggesting a link between language and motor systems, but this has not been explored at an electrophysiological level. It is suggested that MEPs may well reveal the underlying deficit in dyslexia. Our study (Steffert & Steffert, 2013) reported at The British Dyslexia Association Conference 2013, showed a deficit in the P300 no-go component. This is a dorsal visual component and indicates a deficit in visual rise time. This deficit shown in the ERPs seems to correlate with the rise time in the onset of speech and also the stressed syllable, shown by Goswami, Gerson and Astruc (2010) to be important predictors of reading difficulties. The rise time in reading is an acoustic property, while rise time in sensory ERPs reflects the rate at which sensory information enters the brain—but these seem

223

Tony Steffert and Beverly Steffert

to be correlated. Whether one predicts the other or whether there is an underlying factor predicting both slow auditory and visual rise time remains to be determined. To date the research suggests that ERPs are better predictors of dyslexia than the QEEG or psychometric measures. In any case, converging evidence from many sources suggests that this disruption happens before dyslexic children learn to read, and is related to under development of white matter fibres in the temporal/parietal/occipital link and is best reflected in ERPs.

QEEG and ERPs Retrospective Case Series of Dyslexics A retrospective case series of 41 dyslexics aged between 7 and 40 years old. The EEG/ERP data and full I.Q. was collected as part of a diagnostic or intervention plan by the authors. The dyslexic participants were all U.K. educated with English as a first language. The subjects fulfilled the definition of dyslexia as given in the Rose report (Rose 2009), commissioned by the Secretary of State for Education in the U.K.: “Dyslexia is a learning difficulty that primarily affects the skills involved in accurate and fluent word reading and spelling. Characteristic features of dyslexia are difficulties in phonological awareness, verbal memory and verbal processing speed.” In fact most countries define dyslexia similarly. We measured the children’s I.Q. with the Wechsler Intelligence Scale for Children (IV) and the Wechsler Individual Achievement Tests (II). The children all had an I.Q. level significantly higher than their literacy levels. This was due to a phonological or visual processing deficit, affecting literacy but NOT due to lack of educational opportunity or socio/emotional inhibitions. All major comorbidities were excluded. A “Full Cap” 19 channel EEG with a linked ears reference montage was recorded with an ECI Electro cap (Electro-Cap International, Inc.) on a Mitsar-201 EEG amplifier (Mitsar, Ltd.), with a sampling rate 250 Hz WinEEG software used to record and analyse the data and the ERP task was presented using the PsyTask software. Data was recorded in three conditions: (1) eyes closed for 5 minutes, (2) eyes opened for 5 minutes, and (3) an Event-Related Potential task that was a modified GO/NOGO Visual Continual Performance Task (VCPT) for 20 minutes. The VCPT is an executive function task and is the main ERP used in our clinic on all clients undergoing neurofeedback. If dyslexia or any auditory processing issues are suspected, an auditory ERP will usually be carried out, but as this adds an extra 20 minutes to the assessment, some children will not tolerate it. Therefore at this stage the ERPs presented are from the VCPT. The EEG and ERPs of healthy control subjects were taken from the HBI reference database (http://www.hbimed.com/). Comparison between dyslexic patients and healthy controls were made in four separate age ranges (Table 12.1). By using the independent component analysis (ICA) method on the ERPs of the VCPT, (Kropotov & Ponomarev, 2009) it is possible to decompose scalp data into functionally unique activity based on both the temporal and spatial characteristics. The ERPs components are associated with different psychological operations on the basis of correlation with behavior and other imaging techniques such as PET and fMRI. This method yields seven independent components with different topographies, time courses and psychological operations from the VCPT (Mueller, Candrian, Kropotov, Ponomarev & Baschera, 2010). There was a statistically significant difference between the dyslexics and corresponding healthy age matched control groups, in the Independent Component of the ERP generated in the parietal cortex (Figure 12.6). This component appears to be associated with activation of the dorsal visual stream, known as the P3 GO posterior. It could be also considered as the visual aspect of the conventional P3b (or target P3) wave.

224

Figure 12.5 This Event-Related Potential is a GO/NOGO, Visual Continuous Performance Task (VCPT). There are three types of visual stimuli, presented in four different pairs of 100 trials each: The “Animal followed by an Animal” is the “GO” trial where the subject must press a button as quickly as possible when the second stimulus is an Animal that matches the first stimulus. The “AnimalPlant” is the “NOGO” trial where the motor response of pushing the button is prepared when the first stimuli is “Animal” but must be inhibited when the second stimuli is a “Plant.” The other two trials are “Plant-Plant”—this is an ignore trial—and “Plant-Human” presented combined with an artificial beep sound—this is a novel, mismatch trial. Table 12.1 Shows the number of participants in the dyslexic and control groups for each age range. Age

7–10

11–13

14–16

17–40

Dyslexic

10

11

10

10

Healthy

58

85

97

432

Figure 12.6 The Independent Component (IC) of the P3b posterior of the GO trial, in the Visual Continuous Performance Task (VCPT), Event-Related Potentials. On the right, the light grey line shows the IC of the ERP of the dyslexics and the black line of the age matched healthy control at Pz. The grey shaded area shows the difference between the two and the small grey horizontal lines indicate the time interval with statistically significant (p < 0.05 by t-test) differences between the dyslexics and healthy control groups. On the Y-axis: averaged potential, each mark corresponds to 2 μV. The X-axis is time after the onset of the second GO stimulus. The whole time interval corresponds to 400 ms. Numbers (such as 7–10, 11–13, etc.) indicate age range for a group. On the left: topography and sLORETA image of the independent component. The independent component is back projected to the electrodes.

Tony Steffert and Beverly Steffert

This deficit in the dorsal stream could affect timing throughout the brain and particularly in matching symbol to sound—i.e. speech sounds (phonemes) to the syllables and words on the page, which is our interpretation of our findings.

A Case Study of a Pre and Post QEEG/ERP and Neurofeedback Treatment Background A 20-year-old male who had been sent to a good school by his aspirational parents had, due to his reading and attendant attentional difficulties, left after gaining minimal qualifications despite being “quite bright” according to his school reports. This was the case, at least, in his Biology and Physics classes. His English reports had quite a different view! Andrew never read and got all his information from videos, illustrations in his Biology books and listening in classes. School had generally been a daily frustration and once he could, he left. But he found that most jobs he was interested in required a literacy standard that he couldn’t achieve. After a few tries in starting a business, working for friends, etc., he decided to go to college and take a Biology course. His parents contacted us as a local neurofeedback practise to try to help him develop his literacy and study skills before start of term, three months hence.

Pre-Neurofeedback QEEG A usual “Full Cap” EEG and ERP in eyes closed, eyes opened and the VCPT ERP, as above, was recorded to establish the neurofeedback protocol. His QEEG revealed an excess of alpha at P3, the temporal/parietal areas. This was interpreted by Professor Kropotov (Kropotov, 2010) as a hypoactivation of the parietal cortex leading to a hypersensitivity of neurons in the left parietal/temporal area.

Figure 12.7 Raw EEG bipolar P3-Cz, shows the excess of alpha.

(a)

(b)

Figures 12.8a and 12.8b Show the difference between the client and age matched norms. Left: topographic map of 9.77 Hz activity. Right: Shows the age matched norms data subtracted from the client data. The central horizontal line is zero difference between them and when the difference wave goes up this indicates more activity in the client than in the norms and vise versa. The grey shaded area shows excess alpha in comparison to age matched norms at P3.

226

Rhythms of Dyslexia

(a)

(b)

(c)

Figures 12.9a, 12.9b and 12.9c Shows the deviation from norms. Left: topographic map. Middle: spectra, the grey shaded area shows the excess alpha activity at P3. Right: sLORETA image of Independent Component (IC).

(a)

(b)

(c)

Figures 12.10a, 12.10b and 12.10c ERPs. Left: topographic map of the largest deviations from normality at 456 ms. Middle: time course of ERP; the light grey line is the dyslexic client, the dark grey line shows the age matched normative data and the black line shows the difference between the two, with p-values shown by the small blue bars below the ERP traces. Small bars show time points where P < 0.05, middle bars equal P < 0.01 and large bars show P < 0.001 by t-test. Right: sLORETA image of Independent Component (IC) of the ERP.

The ERPs reflected a slowness in the dorsal stream, which is part of the magnocellular system. Analysis of the QEEG and ERPs by Professor Kropotov concluded: 1)

2) 3)

From the point of view of clinical EEG no signs of neurological abnormality (such as spikes, spike/slow wave complexes, paroxysms of slow activity) were observed in the raw EEG trace. The EEG is characterised by occipital (10 Hz) and parietal (7.5 Hz) rhythms in the eyes closed condition. EEG spectra deviations in reference to the normative database indicate hypoactivation (decrease of metabolic activity) in the parietal cortical areas. This pattern is often seen in dyslexic patients. 227

Tony Steffert and Beverly Steffert

4)

The ERPs deviations in reference to the normative database indicate no significant executive abnormalities: the deviations in the sensory components indicate dysfunction of neurons in the left parietal-temporal area.

Neurofeedback We commenced on 24 sessions of neurofeedback, with the electrodes placed at P3 to Cz and downtraining (i.e. inhibiting) 7 to 12 Hz activity. This was intended to activate the left parietal cortex. Training for 30 to 40 minutes each session was followed by reading aloud from a text, for 5 to 10 minutes which was timed and recorded. He was asked to read newspapers and/or magazines at home as much as he could, which he did, albeit reluctantly. After the fourth session the family went on holiday and was amazed and delighted when Andrew bought a novel at the airport and read it all the way through during the two-week holiday in Spain, the first book he had ever read, according to his joyful mother. The neurofeedback continued with alternating sessions at Fz to Cz (beta up) to address the attention problem which was elicited when Andrew had to do any reading or writing. As his reading improved he moved onto well illustrated Biology texts. He found these hard going and may not have preserved as much as he could, although his mother certainly reminded him periodically. After 24 sessions it was time for college and a post QEEG was performed. After the 24 sessions of neurofeedback there was a statistically significant reduction of the alpha activity at P3 and P4. There were also statistically significant changes in the ERP shown in the P3 GO component which had a shorter latency, i.e. the information flow in the dorsal stream was faster. This was despite the fact that the neurofeedback sessions had been normal one channel training, since we do not have a neurofeedback system that can train ERPs. An important caveat here is that if the dyslexic has either phonological or visual deficits that cause the reading problem, these must be addressed simultaneously. If Andrew had been younger, our sessions would have been interspersed with a phonics-based remedial literacy programme. Any visual perceptual problem would have been remediated with specialist lenses or exercises. However, it is important

(a)

(b)

Figure 12.11 Spectra: pre and post. Figure 12.11a: The top graph shows the spectrogram at P3. The X-axis is 0 to 15 Hz with each mark being 5 Hz. The Y-axis is amplitude. The black line is the pre EEG showing an excess of slow (around 8 Hz) activity over the temporal-parietal (P3, P4) areas. The light grey line is the post EEG after the 24 sessions on neurofeedback. The grey shaded area shows the reduction in the alpha band activity. The middle graph shows the post minus pre EEG difference wave. The yellow shaded area shows reduction in the alpha band activity in the post. The little black boxes on the botten show the statistically significant difference from pre to post. Figure 12.11b: The graph on the right shows the topographic map of the activity at 7.81 Hz. The dark areas show the reduction in the post EEG in comparison to the pre EEG after 24 neurofeedback sessions.

228

Rhythms of Dyslexia

(a)

(b)

(c)

Figures 12.12a, 12.12b and 12.12c Pre and post ERP for GO trial of the VCPT. The left graph shows the topographic maps of the largest pre and post difference at 240 ms. The middle graph shows ERP for GO trial of the VCPT, the grey trace is the pre, the light grey trace post and the black is the difference wave. The black bars on the bottom show the statistically significant changes in GO ERPs. The right graph is an sLORETA image with the dark grey in the occipital area showing the location of the difference.

to note that visual problems plague the dyslexic far less than phonological deficits, despite the fact that the original sensory deficit is in the visual dorsal stream. Any neurofeedback therapist aiming to treat dyslexics should ask for a screening from a dyslexia specialist first and take advice about the literacy programmes that would be normally recommended by the organisations that exist to help dyslexics. As far as Andrew is concerned, he is coping well at college, and is able to produce at least pass level written assignments. The college is helping him with efficient revision and written expression techniques and we await optimistically to hear about his final success.

Conclusions A deficit in timing of the sensory systems could affect all sensory information processing, so a “Dyschronia” of dyslexia may well be the next step in research. Our study (Steffert & Steffert, 2011) provides further support of the many prior studies showing that the basic problem in dyslexia is the reduced activation in the visual dorsal stream, which supports the magnocellular hypothesis. Karl Lashley (1930) many years ago proposed that cerebral activity is organised in a series of hierarchies: the order of thoughts, images, words and how they are expressed and ordered in language— right down to the timing of muscular contractions of speech rhythms and vocal apparatus that reflects rapidly shifting emotional sates. The repetition of brain events needs a “coordinator.” Like an orchestra, individual components have their own locality and frequency characteristics but each has to fit into an overall pattern of timing and rhythmicity. Thus there seems to be an overall harmonious structure, based in part on thalamo-cortical reciprocal loops—although there is some theoretical case for concomitant gamma band oscillatory involvement (Sheer, 1989). There are known dysrhythmias in many pathological conditions from schizophrenia and Parkinson’s to slight dysrhythmias in normal aging, so the search for a rhythmic “g” analogous to the general “g” of I.Q. seems worth pursuing. Neurofeedback is well placed to regulate timing in the brain, through multi-channel feedback over several sites. Presently P3, T3, T5 and O1 are suggested sites for trying to encourage synchronisation. 229

Tony Steffert and Beverly Steffert

A neurofeedback system that could train ERPs could hold much promise in regulating the specific timing deficits that are probably at the root of dyslexia. But whatever the protocol, it is important to realise that while neurofeedback can help dyslexics, it should not be considered as a stand-alone therapy. Success is more likely if a phonics-based programme is delivered by a dyslexia-trained teacher in conjunction with neurofeedback. Even if this is unavailable the neurofeedback practitioner can use a computer programme that requires the dyslexic to listen to and repeat the phonemes of the language after each session, or even between neurofeedback games. A good understanding of the phonological basis of the language and what aspects of it dyslexics currently find difficult is crucial, for example, understanding the levels of language that exist from the basic units of phonology and morphology, up to the lexicon, semantics and discourse levels. This would help the practitioner understand that it is only the lowest brainbased levels—phonology and morphology—that dyslexics have difficulty with. A phoneme is the smallest unit of speech sound that changes a word. Maybe 30% of children do not have a good enough phonological awareness to hear phonemic differences so they have to be taught explicitly. This difficulty is what makes an intelligent, articulate child one who can’t read the same words he/ she speaks so fluently. A puzzle, until one realises that words are a series of phonemes and to be read have to be broken down into these units to be decoded while at the same time matching them to the whole word. Children who have a phonological difficulty may overly rely on the visual aspect of the word so will easily mistake similar words (house for horse, smiling for similar, etc.). If they were using their phonological awareness and sounding out the word they would not make these errors. Given that there are only 44 to 48 phonemes (depending on which linguistic theory one accepts) from which all English words are made up, it is an easier task to decode words using phonemic decoding than to remember by sight the thousands of words that make up the English language. Using phonological awareness, any word can be decoded by the individual who knows the phonological code, even unfamiliar words. But the reader relying on only the visual pattern of words has to see the word several times to commit it to memory, and there is a limit to human sight word memory. The visual perceptual problems of dyslexia also need to be accommodated if they exist, which will require screening by a visual stress specialist. Any attempt to help visual perceptual problems with neurofeedback would target the dorsal stream and occipital cortex. We await a neurofeedback programme that targets ERPs, although note that Andrew’s ERP slowness improved with only one channel training at P3 and CZ. If neurofeedback needs to be done without the benefit of QEEG guided protocols, then the left temporo-parietal junction (P3) seems the most likely to produce good results by training up the low beta activity. In conclusion, like with many difficulties, neurofeedback cannot be considered a total therapy for dyslexia. Presently, it can only put dyslexics on a level playing field so that their auditory, visual and literacy circuits are activated enough to benefit from remedial literacy programmes. For academic success, working memory and speed of processing—both key elements that are often compromised in dyslexia—need strengthening. A further progression in dyslexia research would be to find an endophenotype which would encompass the visual, auditory and motor symptoms that dyslexics complain of. This would unify the bewildering differences that still rest on the same underlying and brain-based deficit. Actually, dyslexia may not even be considered as a deficit when one takes account of the visuo/ spatial talents of many dyslexics, which must arise as a consequence. We have presented evidence (Steffert & Steffert, 2014) to uphold the better Block Design ability, a measure of 3-D perception, of dyslexics compared to norms on the Wechsler Intelligence Scale. We also refer to the results of our study at an Art and Design College (Padgett & Steffert, 1999) in which we found a much higher rate of writing problems, reading reluctance and weak verbal 230

Rhythms of Dyslexia

working memory among the Art students, many of whom had taken Art as a subject to avoid writing essays. Although many of these students had not been previously diagnosed as dyslexic and not all had phonological problems as measured by the standard tests at the time, there was a significant difference between these Art students and a control sample made up of teachers, who had good working memory and good reading and writing but only average visuo-spatial ability, as measured by the Wechsler Intelligence scale. We label them as “Sign minds” versus “Design minds.” The Design minds, who will include most dyslexics, will be the creative minds so needed in our uncertain future, since computers can do almost everything Sign minds can do. An ambition we have is to develop a visuo/spatial test battery for 6- to 7-year-olds. The aim would be to find the visuo/spatial “design minds” before they or their classmates find out that they can’t read. Thus teachers would have a different view of their poor readers, as would these children themselves. They would not be tempted to label themselves as slow or stupid or teachers to underrate their intelligence, thus avoiding the stressful school experience that many bright dyslexic school children face, the loss of self-esteem and motivation to learn. Furthermore, society would have a better chance of keeping those design minds on designing a better future for us all.

References Aitchison, J. (1996). The seeds of speech: Language origin and evolution. Cambridge University Press. Retrieved from http://books.google.co.uk/books?hl=en&lr=&id=68Y5gUavbzwC&oi=fnd&pg=PR7&dq=The+Se eds+of+Speech:+Language+Origin+and+Evolution&ots=PTYhuAomDE&sig=869xqrmjdac_2CNxJLw8 pmnc72I#v=onepage&q=The Seeds of Speech%3A Language Origin and Evolution&f=false American Psychiatric Association. (2014). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Association. Baldeweg, T., Richardson, A., Watkins, S., Foale, C., & Gruzelier, J. (1999). Impaired auditory frequency discrimination in dyslexia detected with mismatch evoked potentials. Annals of Neurology, 45(4), 495–503. Retrieved from http://doi.wiley.com/10.1002/1531–8249%28199904%2945%3A4%3C495%3A%3AAIDANA11%3E3.0.CO%3B2-M Borsting, E., Ridder, W. H., Dudeck, K., Kelley, C., Matsui, L., & Motoyama, J. (1996). The presence of a magnocellular defect depends on the type of dyslexia. Vision Research, 36(7), 1047–1053. Retrieved from http:// www.sciencedirect.com/science/article/pii/0042698995001999 [Accessed November 30, 2014]. Bradley, L., & Bryant, P. E. (1983). Categorizing sounds and learning to read—a causal connection. Nature, 301(5899), 419–421. Retrieved from http://dx.doi.org/10.1038/301419a0 [Accessed October 22, 2014]. Breteler, M.H.M., Arns, M., Peters, S., Giepmans, I., & Verhoeven, L. (2010). Improvements in spelling after QEEG-based neurofeedback in dyslexia: A randomized controlled treatment study. Applied Psychophysiology Biofeedback, 35(1), 5–11. Demb, J. B., Boynton, G. M., Best, M., & Heeger, D. J. (1998). Psychophysical evidence for a magnocellular pathway deficit in dyslexia. Vision Research, 38(11), 1555–1559. Retrieved from http://linkinghub.elsevier. com/retrieve/pii/S0042698998000753 Denckla, M. B., & Rudel, R. G. (1976). Rapid “automatized” naming (R.A.N): Dyslexia differentiated from other learning disabilities. Neuropsychologia, 14(4), 471–479. Retrieved from http://www.ncbi.nlm.nih.gov/ pubmed/995240 [Accessed November 30, 2014]. Duffy, F. H., Denckla, M. B., McAnulty, G. B., & Holmes, J. A. (1988). Neurophysiological studies in dyslexia. Research Publications—Association for Research in Nervous and Mental Disease, 66, 149–170. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/3358008 [Accessed November 29, 2014]. Eden, G. F., VanMeter, J. W., Rumsey, J. M., Maisog, J. M., Woods, R. P., & Zeffiro, T. A. (1996). Abnormal processing of visual motion in dyslexia revealed by functional brain imaging. Nature, 382(6586), 66–69. Retrieved from http://dx.doi.org/10.1038/382066a0 [Accessed November 27, 2014]. Evans, J. R., & Park, N.-S. (1996). Quantitative EEG abnormalities in a sample of dyslexic persons. Journal of Neurotherapy, 2(1), 1–5. Evans, K., Holden, B., & Nicholls, C. (2001). Vision 2020: The right to sight—Australia. New South Wales Public Health Bulletin, 12(1), 14–15. Retrieved from www.ncbi.nlm.nih.gov/pubmed/12105446 Facoetti, A., Trussardi, A. N., Ruffino, M., Lorusso, M. L., Cattaneo, C., Galli, R., Molteni, M., & Zorzi, M. (2010). Multisensory spatial attention deficits are predictive of phonological decoding skills in developmental dyslexia. Journal of Cognitive Neuroscience, 22(5), 1011–1025.

231

Tony Steffert and Beverly Steffert Fawcett, A. J., Chattopadhyay, A. K., Kandler, R. H., Jarratt, J. A., Nicolson, R. I., & Proctor, M. (1993). Eventrelated potentials and dyslexia. Annals of the New York Academy of Sciences, 682, 342–345. Retrieved from http://www.hubmed.org/display.cgi?uids=8323131 Fouche, M. (2012). When your brain is bumped, bruised, injured or traumatized, what do you do?. Retrieved from http://www.healthyoulifestyle.com/melindafouchesblog/2012/07/12/when-your-brain-is-bumpedbruised-injured-or-traumatized-what-do-you-do/ Franceschini, S., Gori, S., Ruffino, M., Viola, S., Molteni, M., & Facoetti, A. (2013). Action video games make dyslexic children read better. Current Biology: CB, 23(6), 462–466. Retrieved from http://www.sciencedirect. com/science/article/pii/S0960982213000791 [Accessed November 10, 2014]. Fuller-Thomson, E., & Hooper, S. R. (2014). The association between childhood physical abuse and dyslexia: Findings from a population-based study. Journal of Interpersonal Violence. Retrieved from http://www.ncbi. nlm.nih.gov/pubmed/24981005 [Accessed November 27, 2014]. Galaburda, A. M., Menard, M. T., & Rosen, G. D. (1994). Evidence for aberrant auditory anatomy in developmental dyslexia. Proceedings of the National Academy of Sciences of the United States of America, 91(17), 8010– 8013. Retrieved from http://www.hubmed.org/display.cgi?uids=8058748 Galin, D., Raz, J., Fein, G., Johnstone, J., Herron, J., & Yingling, C. (1992). EEG spectra in dyslexic and normal readers during oral and silent reading. Electroencephalography and Clinical Neurophysiology, 82(2), 87–101. Retrieved from http://linkinghub.elsevier.com/retrieve/pii/0013469492901517 Giraud, K., Démonet, J. F., Habib, M., Marquis, P., Chauvel, P., & Liégeois-Chauvel, C. (2005). Auditory evoked potential patterns to voiced and voiceless speech sounds in adult developmental dyslexics with persistent deficits. Cerebral Cortex, 15(10), 1524–1534. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/15689520 [Accessed November 27, 2014]. Goswami, U. (2005). Rhythm, rhyme, reading and dyslexia: Getting the beat. In Summer Institute on Mind, Brain and Education. Erice, Italia. Retrieved from http://www.mbe-erice.org/2005-abstract-goswami.php [Accessed November 30, 2014]. Goswami, U., Gerson, D., & Astruc, L. (2010). Amplitude envelope perception, phonology and prosodic sensitivity in children with developmental dyslexia. Reading & Writing, 23(8), 995–1019. Guttorm, T. K., Leppänen, P.H.T., Poikkeus, A.-M., Eklund, K. M., Lyytinen, P., & Lyytinen, H. (2005). Brain event-related potentials (ERPs) measured at birth predict later language development in children with and without familial risk for dyslexia. Cortex, 41(3), 291–303. Retrieved from http://www. ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&DbFrom=pubmed&Cmd=Link&LinkName=pubmed_ pubmed&LinkReadableName=Related Articles&IdsFromResult=15871595&ordinalpos=3&itool=EntrezSy stem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum Irlen, H. (1994). Scotopic sensitivity/Irlen syndrome: Hypotheses and explanation of the syndrome. Journal of Behavioural Optometry, 5, 62–65. Jordan, I. (2005). Restoration of visual fields and acuity by changing retinal stimulus. In International Congress Series. Elsevier, p. Vol. 1282, pp. 604-607). Elsevier. April 2005 in London, doi:10.1016/j.ics.2005.04.012 http://www.sciencedirect.com/science/article/pii/S0531513105007314 Kaye, H., John, E. R., Ahn, H., & Prichep, L. (1981). Neurometric evaluation of learning disabled children. International Journal of Neuroscience, 13(1), 15–25. Retrieved from https://www-swetswise-com.libezproxy.open. ac.uk/swoc-web/filingCabinet.html [Accessed November 30, 2014]. Klingberg, T., Hedehus, M., Temple, E., Salz, T., Gabrieli, J. D., Moseley, M. E., & Poldrack, R. A. (2000). Microstructure of temporo-parietal white matter as a basis for reading ability: Evidence from diffusion tensor magnetic resonance imaging. Neuron, 25(February), 493–500. Retrieved from http://www. ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&DbFrom=pubmed&Cmd=Link&LinkName=pubmed_ pubmed&LinkReadableName=Related Articles&IdsFromResult=10719902&ordinalpos=3&itool=EntrezSy stem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum Kropotov, J. D. (2010). Quantitative EEG, event-related potentials and neurotherapy. San Diego, CA: Academic Press Inc. Kropotov, J. D., & Ponomarev, V. A. (2009). Decomposing N2 NOGO wave of event-related potentials into independent components. Neuroreport, 20(18), 1592–1596. Retrieved from http://www.hubmed.org/display. cgi?uids=19904220 Kuhlman, K., & Schweinhart, L. J. (1999). Timing in child development. Ypsilanti, MI: High/Scope Educational Research Foundation. Lashley, K. S. (1930). Basic neural mechanisms in behavior. Psychological Review, 37(1), 1–24. Leppänen, P.H.T., Richardson, U., Pihko, E., Eklund, K. M., Guttorm, T. K., Aro, M., & Lyytinen, H. (2002). Brain responses to changes in speech sound durations differ between infants with and without familial risk for dyslexia. Developmental Neuropsychology, 22(1), 407–422. Retrieved from http://www.ncbi.nlm.nih.gov/ pubmed/12405511

232

Rhythms of Dyslexia Livingstone, M. S., Rosen, G. D., Drislane, F. W., & Galaburda, A. M. (1991). Physiological and anatomical evidence for a magnocellular defect in developmental dyslexia. Proceedings of the National Academy of Sciences, 88(18), 7943–7947. Lovegrove, B. (1996). Dyslexia and a transient/magnocellular pathway deficit: The current situation and future directions. Australian Journal of Psychology, 48, 167–171. Lubar, J. F., Bianchini, K. J., Calhoun, W. H., Lambert, E. W., Brody, Z. H., & Shabsin, H. S. (1985). Spectral analysis of EEG differences between children with and without learning disabilities. Journal of Learning Disabilities, 18(7), 403–408. Mueller, A., Candrian, G., Kropotov, J. D., Ponomarev, V. A., & Baschera, G. M. (2010). Classification of ADHD patients on the basis of independent ERP components using a machine learning system. Nonlinear Biomedical Physics, 4(suppl 1), S1. Retrieved from http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=288079 5&tool=pmcentrez&rendertype=abstract Muneaux, M., Ziegler, J. C., Truc, C., Thomson, J., & Goswami, U. (2004). Deficits in beat perception and dyslexia: Evidence from French. NeuroReport, 15(8), 1255–1259. Retrieved from http://content.wkhealth.com/ linkback/openurl?sid=WKPTLP:landingpage&an=00001756–200406070–00007 [Accessed November 27, 2014]. Nicolson, R. I., & Fawcett, A. J. (1995). Dyslexia is more than a phonological disability. Dyslexia, 1(1), 19–36, 18p. Padgett, I., & Steffert, B. (1999). Visual spatial ability and dyslexia: A research project. London: London Institute. Pammer, K. (2012). The role of the dorsal pathway in word recognition. In J. Stein & Z. Kapoula (Eds.), Visual aspects of dyslexia (pp. 137–149). Oxford: Oxford University Press. Peer, L., & Watson, T. (2005). Glue ear: An essential guide for teachers, parents and health professionals. Abingdon: Routledge. Richardson, A. J. (2006). Omega-3 fatty acids in ADHD and related neurodevelopmental disorders. International Review of Psychiatry, 18(2), 155–172. Roach, N. W., & Hogben, J. H. (2007). Impaired filtering of behaviourally irrelevant visual information in dyslexia. Brain: A Journal of Neurology, 130(Pt. 3), 771–785. Retrieved from http://brain.oxfordjournals.org/ content/130/3/771 [Accessed November 30, 2014]. Rose, Sir J. (2009). Identifying and teaching children and young people with dyslexia and literacy difficulties. Nottingham: DCSF Publications. Retrieved from http://publications.dcsf.gov.uk/eOrderingDownload/006592009DOM-EN.pdf Scarborough, H. S. (1998). Predicting the future achievement of second graders with reading disabilities: Contributions of phonemic awareness, verbal memory, rapid naming, and IQ. Annals of Dyslexia, 48(1), 115–136. Retrieved from http://link.springer.com/article/10.1007/s11881-998-0006-5 [Accessed November 30, 2014]. Shaywitz, S. E., & Shaywitz, B. A. (2008). Paying attention to reading: The neurobiology of reading and dyslexia. Dev Psychopathology, 20(4), 1329–1349. Retrieved from http://www.hubmed.org/display.cgi?uids=18838044 Shaywitz, S. E., Shaywitz, B. A., Fletcher, J. M., & Escobar, M. D. (1990). Prevalence of reading disability in boys and girls: Results of the Connecticut Longitudinal Study.  The Journal of the American Medical Association, 264(8), 998–1002. Shaywitz, S. E., Shaywitz, B. A., Pugh, K. R., Fulbright, R. K., Constable, R. T., Mencl, W. E., .  .  . Gore, J. C. (1998). Functional disruption in the organization of the brain for reading in dyslexia. In Proceedings of the National Academy of Sciences of the United States of America. Department of Pediatrics, Yale University School of Medicine, New Haven, CT 06520, USA. [email protected] SRC—Pubmed ID2–9482939 FG- 0, pp. 2636–2641. Retrieved from www.ncbi.nlm.nih.gov/pubmed/?term=9482939 Sheer, D. E. (1989). Sensory and cognitive 40-Hz event-related potentials: Behavioral correlates, brain function, and clinical application E. Başar & T. H. Bullock, eds. Brain Dynamics, 2, 339–374. Retrieved from http:// link.springer.com/chapter/10.1007/978-3-642-74557-7_29 [Accessed November 26, 2014]. Simos, P. G., Fletcher, J. M., Foorman, B. R., Francis, D. J., Castillo, E. M., Davis, R. N., Papanicolaou, A. C. (2002). Brain activation profiles during the early stages of reading acquisition. Journal of Child Neurology, 17, 159–163. Sklar, B., Hanley, J., & Simmons, W. W. (1972). An EEG experiment aimed toward identifying dyslexic children. Nature, 240(5381), 414–416. Retrieved from http://www.nature.com/nature/journal/v240/n5381/ pdf/240414a0.pdf [Accessed November 27, 2014]. Sperling, A. J., Lu, Z.-L., Manis, F. R., & Seidenberg, M. S. (2005). Deficits in perceptual noise exclusion in developmental dyslexia. Nature Neuroscience, 8(7), 862–863. Retrieved from http://www.ncbi.nlm.nih.gov/ pubmed/15924138 [Accessed November 12, 2014]. Spironelli, C., Penolazzi, B., & Angrilli, A. (2008). Dysfunctional hemispheric asymmetry of theta and beta EEG activity during linguistic tasks in developmental dyslexia. Biological Psychology, 77(2), 123–131. Retrieved from http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&DbFrom=pubmed&Cmd=Link&LinkNam

233

Tony Steffert and Beverly Steffert e=pubmed_pubmed&LinkReadableName=Related Articles&IdsFromResult=17997211&ordinalpos=3&ito ol=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum Steffert, B., & Steffert, T. (2011). Deficits in the Dorsal component of the P3b Executive ERP in Dyslexia. The Society of Applied Neuroscience, Thessaloniki. Steffert, B., & Steffert, T. (2013). Do Dyslexics really have better visuo/spatial ability than non-Dyslexics: A psychometric analysis of 10 years of assessments? In The British Dyslexia Association conference, Harrowgate. Steffert, B., & Steffert, T. (2014). Rhythms of Dyslexia: EEG, ERP & Neurofeedback. IEEE Conference on Biomedical Engineering and Sciences, Miri. Steffert, B., Steffert, T., & Kropotov, J. D. (2004). Coloured lenses for dyslexics: QEEG, balance and psychometric evaluation. The British Dyslexia Association, Warwick. Stein, J., & Walsh, V. (1997). To see but not to read; the magnocellular theory of dyslexia. Trends in Neurosciences, 20(4), 147–152. Retrieved from http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&DbFrom=pubm ed&Cmd=Link&LinkName=pubmed_pubmed&LinkReadableName=Related Articles&IdsFromResult=91 06353&ordinalpos=3&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum Stone, R. (2002). The light barrier: A color solution to your child’s light-based reading difficulties. St. Martin’s Press, p.240. Retrieved from http://www.amazon.co.uk/The-Light-Barrier-Light-Based-Difficulties/ dp/0312304056 [Accessed November 27, 2014]. Takahashi, M., Billups, B., Rossi, D., Sarantis, M., Hamann, M., & Attwell, D. (1997). The role of glutamate transporters in glutamate homeostasis in the brain. The Journal of Experimental Biology, 200(Pt. 2), 401–409. Retrieved from www.ncbi.nlm.nih.gov/pubmed/9050249 Tallal, P. (1980). Auditory temporal perception, phonics, and reading disabilities in children. Brain Lang, 9(2), 182–198. Retrieved from http://www.hubmed.org/display.cgi?uids=7363063 Taylor, J. (2013). The physiological effects of coloured filters on attention. Third Oxford-Kobe Symposium, Oxford, UK. Temple, E., Poldrack, R. A., Salidis, J., Deutsch, G. K., Tallal, P., Merzenich, M. M., & Gabrieli, J.D.E. (2001). Disrupted neural responses to phonological and orthographic processing in dyslexic children: An fMRI study. Neuroreport, 12(2), 299–307. Retrieved from http://content.wkhealth.com/linkback/openurl?sid=WKPTLP :landingpage&an=00001756–200102120–00024 Thornton, K. E., & Carmody, D. P. (2005). Electroencephalogram biofeedback for reading disability and traumatic brain injury. Child and Adolescent Psychiatric Clinics of North America, 14(1), 137–162, vii. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/15564056 [Accessed October 30, 2014]. Vidyasagar, T. R. (2005). Attentional gating in primary visual cortex: A physiological basis for dyslexia. Perception, 34(8), 903–911. Retrieved from http://www.perceptionweb.com/abstract.cgi?id=p5332 [Accessed November 30, 2014]. Walker, J. E., & Norman, Charles A. (2006). The neurophysiology of dyslexia: A selective review with implications for neurofeedback remediation and results of treatment in twelve consecutive patients. Journal of Neurotherapy. Retrieved from http://www.tandfonline.com.libezproxy.open.ac.uk/doi/abs/10.1300/ J184v10n01_04#.VHkOn9KsU1I [Accessed November 29, 2014]. Wiener, M., Lohoff, F. W., & Coslett, H. B. (2011). Double dissociation of dopamine genes and timing in humans. Journal of Cognitive Neuroscience, 23(10): 2811–2821. Wilkins, A. (2003). Reading through colour: How coloured filters can reduce reading difficulty, eye strain, and headaches. Chichester: John Wiley & Sons. Witton, C., Talcott, J. B., Hansen, P. C., Richardson, A. J., Griffiths, T. D., Rees, A., Green, G. G. (1998). Sensitivity to dynamic auditory and visual stimuli predicts nonword reading ability in both dyslexic and normal readers. Current Biology, 8(14), 791–797. Retrieved from http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubm ed&DbFrom=pubmed&Cmd=Link&LinkName=pubmed_pubmed&LinkReadableName=Related Article s&IdsFromResult=9663387&ordinalpos=3&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel. Pubmed_RVDocSum World Health Organization (WHO). (1990). International Statistical Classification of Diseases and Related Health Problems (ICD). Retrieved from http://www.who.int/classifications/icd/en/ Yeatman, J. D., Weiner, K. S., Pestilli, F., Rokem, A., Mezer, A., & Wandell, B. A. (2014). The vertical occipital fasciculus: A century of controversy resolved by in vivo measurements. Proceedings of the National Academy of Sciences of the United States of America, p.1418503111. Retrieved from http://www.pnas.org/content/early/2014/11/13/1418503111. abstract?sid=334be987–52d7–46d7–91bc-46dd5bc9131d [Accessed November 19, 2014].

234

13 NORMAL AND ABNORMAL READING PROCESSES IN CHILDREN Neuropsychophysiological Studies Giuseppe A. Chiarenza Abstract Early identification of dyslexia would be fundamental to prevent the negative consequences of delayed treatment in the social, psychological and occupational domains. Movement-related potentials of dyslexic children are characterized by inadequate ability to program movements and reduced capacity to evaluate their performance and to correct their errors. Reading-related potentials recorded during different reading conditions elicit a series of positive and negative components with specific functional meaning and with a characteristic spatial-temporal pattern. These reading-related potentials, when analyzed with sLORETA, show significantly different patterns of activation when comparing self-paced reading aloud to passive viewing of single letters. Comparison of fMRI and sLORETA during both tasks showed that the cortical region with the widest inter-modality similarities is the middle-superior temporal lobe during self-paced reading aloud. Neuropsychological studies have shown the existence of clinical subtypes of dyslexia; these studies have been confirmed by the results of ICA applied to the EEG. Dyslexia can be defined as a disorder of programming and integrating ideokinetic elements, associated with a deficiency in the fast processing and integration of sensory information, with reduced efficiency of error systems analysis. Each of these phenomena occurs at different levels of the central nervous system and at different times.

Introduction Developmental dyslexia is a neuropsychological disorder that affects reading and writing skills: subjects who are affected generally have intelligence within normal limits, normal hearing and visual acuity and have received adequate education. At the social, psychological and occupational level, dyslexia has a significant impact. In general, the level of education of dyslexic individuals is less than what they could potentially reach on the basis of their intellectual ability, with significant side effects on their emotional and relational abilities. The efficacy of a therapy is greater the earlier it is done. For this reason it is important to identify the presence of disorders of reading beginning with the first grade of primary school; moreover it is important to design rehabilitation treatments based on a precise knowledge of the clinical 235

Giuseppe A. Chiarenza

manifestations of dyslexia. The most obvious symptoms of the presence of dyslexia are lack of reading fluency and reading/writing errors.

Neuropsychology of Dyslexia Several methods have been used to diagnose dyslexia. For many years the most widely used method has been diagnosis based on exclusion criteria. This method, while providing objective criteria for a correct diagnosis, does not allow the identification of clinical subtypes of dyslexia. In an attempt to overcome the limitations of diagnosis by exclusion, many researchers have attempted to identify the psychological processes underlying learning disabilities through a diagnostic approach termed “indirect” (Boder, 1973) or “extrinsic” (Ellis, 1985). This approach relies on eliciting the typical neurological or psychometric and psycholinguistic concomitants. Though useful, this approach is insufficient in itself for the diagnosis, since most of the concomitants can also exist without developmental dyslexia. An important feature of dyslexia is the reduced ability to carry out a phonological analysis of individual words. This difficulty is indicative of an altered auditory perception and memory. A dyslexic child may guess a word from minimal clues, for example from the first or last letter and the length of a word. He also tends to read words better in context, although he may substitute a word similar in meaning but dissimilar phonetically. Omission or substitutions of different syllables while reading multisyllabic words are also very frequent. This is referred to as a “gestalt” or “global” approach to reading. A further important aspect of reading deficits associated with dyslexia concerns a weak perception and lack of visual memory; the child thus has difficulty learning what the letters look like. This difficulty is reflected in misplacements of accents and confusion of reversible letters and mirror reading and writing. Therefore Myklebust (1968) confirmed the existence of a visual and an auditory dyslexia, and Kinsbourne and Warrington (1963) have outlined two syndromes, “language retardation group” and “Gerstman group.” Bakker (1992) has described “Language type” and “Perceptual type” dyslexia. Dyslexia can, therefore, manifest as a wide and varied spectrum of errors. Empirical demonstrations have shown that dyslexia is not a homogeneous syndrome, but comprises different subtypes, each one with its own characteristics and features (Castles & Coltheart, 1992).

Dyslexia Subtypes Boder (1973), inspired by the studies of Bannatyne (1966), Benton (1962), Birch and Belmont (1964) and Myklebust (1968), and observing the variety of reading errors of dyslexic subjects, introduced a “direct” diagnostic approach that involved the observation of the performance observed in the course of reading and writing to differentiate subtypes of dyslexia (Boder, 1968). The operating assumptions were as follows: 1)

2) 3)

Reading requires visual perception and discrimination, visual sequential memory and recall, and directional orientation (Benton, 1962; Birch, 1962). It also requires cross-modal integration, including the translation of visual symbols into meaningful auditory equivalents (Birch & Belmont, 1964). Writing requires, in addition, fine motor and visuo-motor coordination and tactile-kinesthetic memory. Reading and writing are viewed as two interdependent functions and therefore must be analyzed “jointly.” A normal reader recognizes the familiar words that constitute his or her sight vocabulary through the visual channel as instantaneous visual gestalts of whole words, without having to discriminate individual letters or component syllables. He or she reads familiar words on sight or “visualizes” 236

Normal and Abnormal Reading Processes

4)

them (Myklebust, 1965). In contrast, a normal reader reads unfamiliar words through the auditory channel by a process of phonetic analysis and synthesis. In the dyslexic child, normal reading is dissociated (Boder, 1971). The normal automatic interplay of gestalt and analytic-synthetic processes is disrupted. The dyslexic child reads and spells differently from the normal reader both qualitatively and quantitatively.

On the basis of these assumptions, Boder (1973) has postulated the existence of three subtypes of dyslexia: dysphonetic, dyseidetic and mixed. These have been also found in the Italian language by applying the Direct Test of Reading and Writing (TDLS), the translated version of the Boder test to the Italian language (Bindelli et al., 2001; Chiarenza, Barzi, Coati & Cucci, 1992; Chiarenza & Bindelli, 2001; Chiarenza & Cucci, 1989; Chiarenza, Cucci & Coati, 1990; Chiarenza et al., 2004). Readers with dysphonetic dyslexia show good skills in visual-gestalt function and disability in auditory function analysis. They have difficulty in making the phoneme-grapheme association and consequently do not develop phonetic skills to decode if not re-educated. Readers with dyseidetic dyslexia show good skills in analytic-synthetic auditory function and difficulties in the visual-gestalt function. They show a deficit in visual memory and perception of letters and whole words with important negative consequences in the development of an adequate internal vocabulary. Readers with mixed dyslexia have a global deficit, even in difficulties in the organization of visual and auditory perception. These difficulties prevent the formation of an internal vocabulary and the acquisition of phonetic skills. The words that can be recognized on sight and written properly are just those of the first school level or very simple words. In summary, we can say that reading and writing are two interdependent functions and must be analyzed “jointly.” It follows that the integrity and the automatic integration of auditory, visual and kinesthetic-motor processes are essential prerequisites for fluent reading and writing. Another clinical aspect of dyslexia that has been little explored is the lack of fluency and prosody during reading, namely aspects related to the organization of movement. Various difficulties in the execution of neuromotor acts, such as simple repetitive movements or alternating complex movements such as bimanual coordination have long been observed in dyslexic children (Abercrombie, Lindon & Tyson, 1964; Bruininks & Bruininks, 1977; Connolly & Stratton, 1968; Denhoff, Siqueland, Konich & Hainsworth, 1968; Fog & Fog, 1963; Klicpera, Wolff & Drake, 1981; Lewis, Bell & Anderson, 1970; Pyfer & Carlson, 1972). Furthermore, clinical signs such as dysrhythmia and the presence of synkinetic movements have often been described in dyslexic individuals (Adams, Kocsis & Estes, 1974; Denckla, 1973; Kennard, 1960; Rutter, Graham & Birch, 1966; Stine, Saratsiotis & Mosser, 1975; Wolff & Hurwitz, 1973). These difficulties were interpreted as a disorder of the temporal organization of motor skills (Denckla, 1973; Klicpera et al., 1981). These observations were also recently confirmed by Punt, Jong, Groot and Hadders-Algra (2010), who reported that 87% of dyslexics exhibit minor neurological dysfunction, especially in fine manipulative skills, the regulation of muscle tone and the excessive presence of associated movements. All of these observations support the hypothesis of an important involvement of cerebellar function in reading and writing. It is therefore possible to maintain that we are facing a considerable heterogeneity in the dysfunction of skills in dyslexic children, not only visual and auditory, but also motor: Nicolson and Fawcett (2005) stated that children with dyslexia show difficulties when they have to acquire new skills quickly and fluently, and when they have to assemble two or more actions. In our opinion, the reason for the neglect of the motor component of dyslexia lies in the fact that all experimental designs, both neurophysiological and behavioral, were built on the stimulus-response model. This is able to describe only phenomena that occur in the interval between the stimulus and the response of the subject, without being able to observe the phenomena before the onset of the stimulus and after the onset of the response. In this way, only phenomena related to the processing of auditory and visual stimuli have been described. 237

Giuseppe A. Chiarenza

To study in detail the organization of a motor act, both simple and complex, such as reading and writing, it is necessary to devise other experimental models that take into account not only what happens during the processing of a stimulus, but also phenomena that take place before and after it. This is the fundamental and unique contribution of cognitive psychophysiology.

Psychophysiology of Dyslexia Belmont (1980) reported that reading and writing are processes that require high skills and complexity that comprise a set of serially and hierarchically organized modular routines. Children who develop a reading disorder are lacking in the control of perceptual and motor behaviors (Belmont, 1980). Therefore, the performance of a complex perceptual-motor task appears to be particularly well suited to provide information on those systems and subsystems that regulate and organize the functions of reading and writing (Chiarenza, Papakostopoulos, Guareschi Cazzullo, Giordana & Giammari Aldè, 1982a). In addition, since the assumptions in dyslexia predict poor reading skills, a test of perceptual-motor skills, which lies outside the domain of reading, would be particularly suitable to test this hypothesis. The task we used was self-paced, voluntary, goal-directed and interactive. To perform adequately, it requires the following skills: bimanual coordination, bimanual ballistic movements, adaptive programming, learning a proper timing and performance improvement. The task provides online knowledge of results and feedback (Chiarenza et al., 1982a, 1982b). In particular, the subject sat in an armchair 70 cm in front of an oscilloscope and held a joystick-type push button in each hand. The excursion of the button was 5 mm. The task consisted in starting the sweep of the oscilloscope trace with the left thumb and stopping it in a predetermined area of the oscilloscope by pushing the other button with the right thumb. The sweep velocity was 1 mm per ms and the target area corresponded to a time interval between 40 and 60 ms. The brain electrical activity associated with this task is called movement-related brain potentials. Using this task, we have shown that dyslexic children, besides being slow and not very accurate from a behavioral point of view, present a deficit of programming movements, a deficit of visual and kinesthetic sensory processes and a deficit and a reduced capacity to evaluate their performance and correct their errors (Chiarenza, 1990; Chiarenza et al., 1986; Chiarenza et al., 1982a, 1982b). Dyslexic subjects showed a reduced BP amplitude of very short duration, indicating a nonadequate preparation; MCP reduced amplitude, indicating a lack of kinesthetic processing; N100 and reduced P200 amplitude, indicating a deficit of visual perception and reafferent activity, respectively; and SPP reduced amplitude on the parietal regions and the presence of PAN on the central and frontal regions, suggesting a reduced ability to evaluate target performance and non-target performance, respectively (for more details see Chiarenza, 1990). These studies clearly demonstrate that dyslexia is not only a phonological or a gestalt deficit, but also a praxic disorder in which praxic abilities, such as motor programming, sequential and sensorymotor integration and evaluation processes, are required and somehow defective in dyslexia. Subsequently, Chiarenza, Olgiati, Trevisan, Marchi and Casarotto (2013), by recording the electrical activity of the brain associated with reading aloud letters visually presented for 5 ms, described the specific brain responses related to this event, called reading-related potentials (RRPs). The recording of these potentials has been extremely helpful in understanding the dynamics of the activation of cortical areas involved in reading. In designing a research protocol of this type, we must take into account the fact that reading is a voluntary and conscious process that needs specific attention mechanisms. To investigate the interactions among these processes, it is appropriate to give the subject the possibility to indicate, for example by pressing a button, when starting to read. It is also necessary to record, in addition to the potential of the brain, the activity of thumb muscles associated with button presses and lip muscles associated with 238

Figure 13.1 Average movement-related brain potentials elicited during the execution of a complex motorperceptual task, in healthy subjects (dashed line) and dyslexic subjects (continuous line), recorded at Fpz (Fpz = central prefrontal), Fz (Fz = central frontal) and Pz (Pz = central parietal), along with (bottom) arm electromyographic activity (EMG). BP = Bereitschaftspotential; MCP = Motor Cortex Potential, SPP = Skilled Performance Positivity; PAN = Post Action Negativity. In this figure and in Figures 13.2 and 13.3, the vertical bar is the trigger point that corresponds to the pressure of left button and the appearance of the sweep trace on the oscilloscope screen. From this point the N100 latency of P200, SPP and PAN have been measured.

Figure 13.2 Average of movement-related brain potentials associated with target performance in healthy subjects (dashed line) and dyslexic subjects (continuous line). The potential associated with knowledge of results (SPP) is present in all areas of the brain but is reduced in amplitude.

Figure 13.3 Average of movement-related brain potentials associated with non-target performance in healthy subjects (dashed line) and dyslexic subjects (continuous line). The potential associated with the assessment (SPP) is only present at the parietal areas (perceptual activity), whereas at central and frontal areas, a negative potential (PAN) is evident, which reflects failure to process the error (Chiarenza, 1990).

Giuseppe A. Chiarenza

speech. For these reasons, we developed two different tasks to differentiate among the sub-processes involved in reading and evaluated the corresponding RRPs modifications. The first task, defined passive, consists of simply “looking” at visually presented letters. The passive task was useful to evaluate the role of perceptual processes only and the potentials were considered as a baseline in the comparison with those obtained during active task. The active task consists of reading aloud self-paced letters. The self-paced condition is the most similar to spontaneous reading and allows therefore investigating not only perception, attention and articulation, but also programming and intention altogether involved in reading processes. This task allows the subject to intentionally and autonomously decide when reading. In fact, reading is essentially a voluntary phenomenon. Moreover, in this task we assumed that the subject maintained a constant level of attention that could be more variable during passive condition. We decided to use isolated characters instead of entire words for overcoming the complexities of letters association and fusion into syllables. The presentation of isolated letters simply involves the perception of a letter’s graphic shape, the association of a single character to a sound and its production, and therefore allowed us to evaluate the simplest unit of grapheme-phoneme association. The short duration of the stimuli (5 ms) was chosen for testing the ability of subjects to perceive the graphic shape of letters as a gestalt that is supposed to be already completely acquired by 6-year-old children at the end of the first grade. Five millisecond greatly recruited subjects’ attention, cooperation and motivation, while longer persistence would have reduced task difficulty. Furthermore, the perception of characters with very short duration elicits foveal vision and limits eye saccadic movements that are critical sources of artifacts. Furthermore, the use of words would have inevitably introduced a semantic interference, thus additionally complicating RRPs morphology. Neuro-pathological studies have shown that identification of letters is an early predictor of later reading success and distinguishes adult dyslexics (Flowers et al., 2004). The use of this pre-lexical skill is expected to be helpful for highlighting the neurobiological and functional basis of reading in both healthy and impaired readers. The mean interval between appearance of the letters and onset of speech during self-paced letter recognition was 719.37 ± 125.9 ms. Moreover, accuracy of letter reading (percentage of correctly read letters) was 95.9% ± 4.2% during self-paced letter recognition. Following this approach, it is possible to identify and describe a series of waves and positive and negative peaks that appear before, during and after reading aloud (Chiarenza et al., 2013). These components can be divided into different periods as is shown graphically in Figure 13.4. The preparatory period includes the electrical activity of the brain that precedes the phasic electromyography (EMG) linked to the button press: this brain activity is represented by the Bereitschaftspotential (BP), which slowly increases its amplitude for a duration of about 500 ms over frontal, central and precentral regions. It has been proposed as an index of the subject’s intention to start reading. The brain potentials that appear in the interval between the EMG activity of the right thumb and the EMG activity of the lips are the Motor Cortex Potential (MCP), P0, N1 and P1. These potentials are related to the appearance of the letter of the alphabet on the screen for 5 ms, and belong to the pre-lexical period. In particular, the MCP recorded over the precentral cortex is related to proprioceptive sensory information of the movement coming from skin joints and muscles. Instead, the potentials P0, P1 and N1 are the expression of the early stages of visual perceptual processing of letters that occur mainly in the occipital and parietal areas. The N2, P2, N3, P4, N4 and N areas that appear during the ascending limb of the EMG lip activity, belong to the period of reading aloud, which is called the lexical period. They are mostly recorded in the frontal, precentral and central areas. Because these components are elicited during the explicit verbal production of the subject, they are thought to be related to the activation of reafferent activity and control mechanisms. The components that are located along the descending limb of the EMG lip activity (P600, and L area), when the subject has completed reading, belong to the post-lexical period: they are most represented on posterior parietal regions. It is assumed that these components are related to mnesic 242

Figure 13.4

Chronology of reading-related potentials. Outline of the chronology of (top) reading-related potentials during self-paced letter reading, recorded at Fz (thick line) and Oz (thin line), associated with (bottom) the electromyographic activity of lips (EMG-lips, thin line) and forearm (EMG-arm, thick line). Reading-related potentials were classified into four periods: (a) preparatory, (b) prelexical, (c) lexical and (d) post-lexical period. Bereitschaftspotential = BP; MCP = motor cortex potential; LNA = late negative area. Subject’s right thumb press, which triggers letter appearance, was bipolarly recorded from the right forearm flexor muscles (EMG-Arm). Lip movements were bipolarly recorded by two electrodes placed on the superior and inferior orbicularis oris muscles (EMG-lips).

Giuseppe A. Chiarenza

and feedback mechanisms which help in teaching the subject to read. The activation of attention mechanisms is definitely present in each of the periods described above. This is manifested mainly during the pre-lexical and lexical period through an increase in the amplitude of P1and P2, and a reduction in the latency of P2. These reading-related potentials recorded from healthy children have been analyzed with sLORETA (Pascual-Marqui, 2002), in order to obtain distributed source images of neural activity from scalp recordings (Casarotto et al., 2007a). In particular, significantly different patterns of activation were observed when comparing self-paced reading aloud to passive viewing of single letters (Figure 13.5). During self-paced reading aloud, functional brain activity was significantly higher in the left supramarginal gyrus and middle-inferior parietal lobule before 150 ms; in the right angular gyrus and middle-inferior temporal lobe between 150 and 250 ms; in the middle-inferior frontal gyrus bilaterally, between 300 and 400 ms; and in the left ventral inferior temporal gyrus after 600 ms. In contrast, during passive letter viewing, the activation in the right middle-inferior temporal-occipital cortex before 150 ms; in the superior frontal gyrus bilaterally between 150 and 200 ms; in the left middle frontal gyrus between 200 and 250 ms; and in the left occipital gyrus between 300 and 400 ms was significantly greater than during self-paced reading aloud. These results indicate that voluntary reading aloud requires greater attentive, motor and cognitive effort compared to passive observation of letters. The significantly greater activation of the left supramarginal gyrus at short latencies during self-paced reading aloud may be interpreted as a facilitatory effect produced in regions specifically related to grapheme-to-phoneme association mechanisms in order to improve reading performances. The observed task-related differences at middle and long latencies may suggest that self-paced reading aloud additionally engages some regions in the right hemisphere, homologous to left temporal-parietal cortices related to phonological analysis, and in the bilateral frontal regions, related to higher order cognitive processes that are not required for looking passively at letters. In order to explore reading-related brain activity with high spatial and temporal resolution at the same time, reading-related potentials and functional magnetic resonance images (fMRI) were recorded during the same experimental protocol in healthy adults (Casarotto et al., 2008). fMRI results showed that the left inferior parietal lobule (BA 7/40) and medial frontal gyrus (BA 6) were specifically activated by alphabetic letters; bilateral pre- and post-central gyri (BA 3/4), left middle frontal gyrus (BA 46) and left superior temporal gyrus (BA 22/47) were additionally engaged during

Figure 13.5 Significant differences (paired t-test: P < 0.01) between sLORETA maps estimated from readingrelated potentials recorded during self-paced reading aloud and during passive viewing of letters. Warm/cold colors indicate that brain activity is significantly higher/lower during self-paced reading aloud as compared to passive observation of letters. L = left hemisphere; R = right hemisphere.

244

Normal and Abnormal Reading Processes

reading aloud. LORETA results showed that neural sources of middle-latency components were observed in the medial frontal areas and middle-superior temporal gyrus bilaterally (BA 22/39) during all tasks. Late components had partially common sources in the middle-superior temporal gyrus bilaterally (BA 21/22/37/39/42). During both tasks (i.e., passive letter viewing and reading aloud), the cortical region with the widest inter-modality similarities was the middle-superior temporal lobe (BA 19/22/37/39) and the greatest similarities between fMRI and LORETA results were observed during reading aloud of letters. These findings indicate that the role played by the middle-superior temporal gyrus is crucial and multifunctional for linguistic and reading processes. The reason may be related to the fact that it receives inputs from the visual system and strongly interacts with temporal auditory areas. Therefore, its spatial location and its high interconnection with the main sensory system may have favored its specialization in phoneme-grapheme matching (Figure 13.6).

Figure 13.6 Superimposition of functional activations on inflated anatomical cortical surface: lateral and medial views of both hemispheres. LORETA maps (green) of the N2, P2b, P2a, and LNA potentials and fMRI activation maps (red) are reported for the letter and letter reading aloud tasks. L = left hemisphere; R = right hemisphere.

245

Giuseppe A. Chiarenza

The analysis of reading-related brain potentials has demonstrated that children with dyslexia show an increased latency and a reduction in the amplitude of some components as compared to normal subjects both in the pre- and post-lexical period (Chiarenza, Olgiati, Trevisan & Casarotto, 2006). The differences in the preparatory period represent a non-optimal coordination between motor processes and the intention to read. The anomalies in the pre-lexical components are due to a reduced level of attention and a delay of processing visual sensory information. The difficulties in phonological decoding and verbal articulation of letters can be explained as a failure of phonological and phono-articulatory feedback during the lexical period. The anomalies of the post-lexical components likely generate an additional malfunction of feedback processes, which are essential to monitor performance and promote learning. In fact, when a person hears himself or herself while reading aloud, it reinforces the elaboration of the visual-verbal stimulus at a cognitive level and reinforces the map of the correspondence between phonemes and graphemes. These abnormalities in latency and amplitude of the cognitive potential associated with reading reflect at a neurophysiological level the behavioral observations made on dyslexic children, which reveal reduced fluency and increased reading time. The comparison between reading-related potentials recorded during single-letter self-paced reading aloud from healthy children and children with dysphonetic dyslexia (Figure 13.7) shows that abnormal activation in the subjects with dyslexia was present at short latencies in the left temporal polar area, at middle latencies involving temporal polar and inferior frontal regions bilaterally and at long latencies clusters in fronto-temporal regions of the right hemisphere (Casarotto et al., 2007b). This result is consistent with previous findings of greater recruitment of regions in the right hemisphere in dyslexic children in comparison with controls. It suggests further that early involvement of frontal regions and significantly higher activation of the right hemisphere are likely related to compensatory mechanisms adopted by reading-impaired children in order to improve their performance. Impaired neural activation of the dyslexic group was located in left and medial parietal regions: at short-middle latencies, impaired activation was present in the angular and then in the supramarginal gyrus; whereas at long latencies, impaired activation moved in the middle precuneus and occipital lobe. Behavioral signs of reading impairment can be related to reduced activation in the left dorsal parieto-occipital regions that have been shown to be specifically involved in reading processes and particularly in the storage and processing of the visual and auditory representations of alphabetic characters.

Figure 13.7 Significant differences (unpaired t-test: P < 0.05) between sLORETA maps estimated from reading-related potentials recorded during self-paced reading aloud in healthy and dyslexic children. Warm/cold colors indicate that brain activity is significantly higher/smaller in controls as compared to dyslexics. L = left hemisphere; R = right hemisphere.

246

Normal and Abnormal Reading Processes

These findings suggest that dyslexia is not simply caused by a perceptual deficit, but is a more complex disorder associated with a malfunction of higher cognitive functions, including attention, phonological analysis, verbal-motor coordination, control mechanisms and feedback, and memory. These malfunctions seem to be closely and causally related to each other. Independent component analysis (ICA) of 2–5 minutes of resting EEG obtained for the classical frequency bands and applied to a customized child MRI template and separately calculated for each clinical subtype showed that two types of ICA-based cluster centroids were observed. One was common to different clinical subtypes and could be attributed to a default mode network; the second was cluster centroids linked only with a certain subtype of developmental dyslexia. A cluster centroid in anterior cingulate cortex (ACC) for theta band was present only in the dysphonetic group. Cluster centroids located in the left lingual gyrus, in the beta band and in the thalamus in the theta band were present only in dyseidetic group. In the “mixed” group, cluster centroids distribution overlapped partially with distribution of both dysphonetic and dyseidetic. The “mixed” group additionally had cluster centroids in the right precentral gyrus (in the theta and beta bands) and in the left superior temporal gyrus in the beta band (Velikova & Chiarenza, 2012). These findings provide additional neurophysiologic evidence for the existence of subtypes of developmental dyslexia and pave the way for targeted-made approaches to rehabilitation.

Conclusions Dyslexia can be defined from a psychophysiological point of view, as a disorder of programming and integrating ideokinetic elements, associated with a deficiency in the fast processing and integration of sensory information, with a reduced efficiency of error systems analysis. All these phenomena occur at different levels of the central nervous system and at different times during reading (Chiarenza, 1990; Chiarenza et al., 1982a, 1982b, 1986). The combination of these disorders leads individuals with dyslexia to read more slowly than healthy subjects, and to commit more errors. The specific characteristics allow us to establish a close correspondence with the Boder (1973) model of reading and writing, and to emphasize the great importance of taking into account the models of different subtypes of dyslexia.

References Abercrombie, M. L. J., Lindon, R. L., & Tyson, M. C. (1964). Associated movements in normal and physically handicapped children. Developmental Medicine and Child Neurology, 6, 573–580. Adams, R. M., Kocsis, J. J., & Estes, R. F. (1974). Soft neurological signs in learning disabled children and control. American Journal of Diseases of Children, 128, 614–618. Bakker, D. J. (1992). Neuropsychological classification and treatment of dyslexia. Journal of Learning Disabilities, 25, 102–109. Bannatyne, A. (1966). The etiology of dyslexia and the color phonics system. In J. Money & G. Schiffman (Eds.), The disabled reader: Education of the dyslexic child (pp. 193–204). Baltimore: Johns Hopkins Press. Belmont, I. (1980). Perceptual organization and minimal brain dysfunctions. In H. E. Rie & E. D. Rie (Eds.), Handbook of minimal brain dysfunction, Wiley series on personality processes (pp. 253–271). New York: John Wiley & Sons. Benton, A. L. (1962). Dyslexia in relation to form perception and directional sense. In J. Money (Ed.), Reading disability: Progress and research needs in dyslexia (pp. 81–102). Baltimore: John Hopkins University Press. Bindelli, D., Colombo, P., Magugliani, P., Martini, P., Pacagnella, S., & Chiarenza, G. A. (2001). Individuazione e validazione dei sottotipi della dislessia evolutiva. Giornale di Neuropsichiatria dell’età evolutiva, 21(suppl. 1), 55. Birch, H. G. (1962). Dyslexia and the maturation of visual function. In J. Money (Ed.), Reading disability: Progress and research needs in dyslexia (pp. 161–175). Baltimore: John Hopkins University Press. Birch, H., & Belmont, L. (1964). Auditory-visual integration in normal and retarded readers. American Journal of Orthopsychiatry, 34, 852. Boder, E. (1968). Developmental dyslexia: A diagnostic screening procedure based on three characteristic patterns of reading and spelling. In M. Douglass (Ed.), Claremont reading conference 32nd yearbook (pp. 1–133). Claremont, CA: Claremont University Center.

247

Giuseppe A. Chiarenza Boder, E. (1971). Developmental dyslexia: A diagnostic approach based on three characteristic patterns of reading and spelling. In B. D. Bateman (Ed.), Learning disorders (Vol. 4, pp. 268–275). Seattle, WA: Seattle Special Child Publications. Boder, E. (1973). Developmental dyslexia: A diagnostic approach based on three atypical reading-spelling patterns. Developmental Medicine and Child Neurology, 15, 663–687. Bruininks, V. I., & Bruininks, R. H. (1977). Motor proficiency of learning disabled and non disabled students. Perceptual Motor Skills, 44, 1131–1138. Casarotto, S., Bianchi, A. M., Ricciardi, E., Gentili, C., Vanello, N., Guazzelli, M., . . . Cerutti, S. (2008). Spatiotemporal dynamics of single-letter reading: A combined ERP-fMRI study. Archives Italiennes de Biologie, 146(2), 83–105. Casarotto, S., Ricciardi, E., Sani, L., Guazzelli, M., Pietrini, P., & Chiarenza, G. A. (2007a). ERPs disclose the specificity of spatio-temporal patterns of brain activity during self-paced reading aloud. Neuroimage, 36(suppl. 1), 169, T-AM. Casarotto, S., Ricciardi, E., Sani, L., Guazzelli, M., Pietrini, P., & Chiarenza, G. A. (2007b). Single-letter reading elicits a different spatio-temporal modulation of brain activity in dyslexic children as compared to healthy controls. Neuroimage, 36(suppl. 1), 171, T-PM. Castles, A., & Coltheart, M. (1992). Varieties of developmental dyslexia. Cognition, 47(2), 149–180. Chiarenza, G. A. (1990). Motor-perceptual function in children with developmental reading disorders: Neuropsychophysiological analysis. Journal of Learning Disabilities, 23, 375–385. Chiarenza, G. A., Barzi, E., Coati, P., & Cucci, M. (1992). Identification of dyslexia sub-types among Italian readers. Proceedings Vth European Conference on Developmental Psychophysiology Seville, 164. Chiarenza, G. A., & Bindelli, D. (2001). Il test diretto di lettura e scrittura (TDLS): versione computerizzata e dati normativi. Giornale di neuropsichiatria dell’età evolutiva, 21(2), 163–179. Chiarenza, G. A., & Cucci, M. (1989). Test diretto di lettura e scrittura (TDLS). Adattamento italiano del Boder test, Saggi, 15, 7–19. Chiarenza, G. A., Cucci, M., & Coati, P. (1990). Diagnosi diretta di dislessia ed identificazione del sottotipo disfonetico mediante TDLS. Neuroni, mente e corpo, 2(suppl. 1–2), 377–378. Chiarenza, G. A., Mascherpa, A. C., Casarotto, S., Martini, P., Broggi, V., & Colombo, L. (2004). The identification of clinical subtypes of dyslexia in native language Italian children. International Journal of Psychophysiology, 65(1–2), 84. Chiarenza, G. A., Olgiati, P., Trevisan, C., & Casarotto, S. (2006). Preparatory and pre-lexical periods in dyslexic children: A reading-related potential study. Developmental Medicine and Child Neurology, 58(2–3), 303. Chiarenza, G. A., Olgiati, P., Trevisan, C., Marchi, I. D., & Casarotto, S. (2013). Reading aloud: A psychophysiological investigation in children. Neuropsychologia, 51, 425–436. Chiarenza, G. A., Papakostopoulos, D., Grioni, A. G., Tengattini, M. B., Mascellani, P., & Guareschi Cazzullo, A. (1986). Movement-related brain macropotentials during a motor perceptual task in dyslexic-dysgraphic children. In W. C. McCallum, R. Zappoli, & F. Denoth (Eds.), Cerebral psychophysiology: Studies in event-related potentials (EEG suppl. 38, pp. 489–491). Amsterdam: Elsevier Science Publisher B.V. Chiarenza, G. A., Papakostopoulos, D., Guareschi Cazzullo, A., Giordana, F., & Giammari Aldè, G. (1982a). Movement related brain macropotentials during skilled performance task in children with learning disabilities. In G. A. Chiarenza & D. Papakostopoulos (Eds.), Clinical application of cerebral evoked potentials in pediatric medicine (pp. 259–292). Amsterdam: Excerpta Medica. Chiarenza, G. A., Papakostopoulos, D., Guareschi Cazzullo, A., Giordana, F., & Giammari Aldè, G. (1982b). Movement related brain macropotentials (MRBMs) and their relationship with the accuracy of skilled performance in normal and learning disabled children. In A. Rothenberger (Ed.), Event related potentials in children: Basic concepts and clinical applications (pp. 243–256). Amsterdam: Elsevier Biomedical Press. Connolly, K., & Stratton, P. (1968). Developmental changes in associated movements. Developmental Medicine and Child Neurology, 10, 49–56. Denckla, H. B. (1973). Development of speed in repetitive and successive finger-movements in normal children. Developmental Medicine and Child Neurology, 15, 635–645. Denhoff, E., Siqueland, M., Konich, M., & Hainsworth, P. (1968). Developmental and predictive characteristics of items from the meeting school screening test. Developmental Medicine and Child Neurology, 10, 220–232. Ellis, A. W. (1985). The cognitive neuropsychology of developmental and acquired dyslexia: A critical survey. Cognitive Neuropsychology., 2, 169–205. Flowers, D. L., Jones, K., Noble, K., VanMeter, J., Zeffiro, T. A., Wood, F. B., & Eden, G. F. (2004). Attention to single letters activates left extrastriate cortex. Neuroimage, 21(3), 829–839. Fog, E., & Fog, M. (1963). Cerebral inhibition examined by associated movements. In M. Bax, R. C. Mac Keith (Eds.), Minimal cerebral dysfunction: Clinics in developmental medicine, N. 10 (pp. 25–35). London: London,

248

Normal and Abnormal Reading Processes Spastics Society Medical Education and Information Unit in association with William Heinemannn Medical Books Ltd.; Philadelphia, PA: J.B. Lippincott Co. Kennard, M. (1960). Value of equivocal signs in neurological diagnosis. Neurology, 10, 753–764. Kinsbourne, M., & Warrington, E. (1963). Developmental factors in reading and writing backwardness. British Journal of Psychology, 54, 154–156. Klicpera, C., Wolff, P. M., & Drake, C. (1981). Bimanual co-ordination in adolescent boys with reading retardation. Developmental Medicine and Child Neurology, 23, 617–625. Lewis, F. D., Bell, D. B., & Anderson, R. P. (1970). Relationship of motor proficiency and reading retardation. Perceptual and Motor Skills, 31, 395–401. Myklebust, H. (1965). Development and disorders of written language: Picture story language test. New York: Grune and Stratton. Myklebust, H. (1968). Learning disabilities: Definition and overview. In H. Myklebust (Ed.), Progress in learning disabilities (Vol. 1, pp. 12–25). New York: Grune & Stratton. Nicolson, R. I., & Fawcett, A. J. (2005). Developmental dyslexia, learning and the cerebellum. Journal of Neural Transmission, 69, 19–36. Pascual-Marqui, R. D. (2002). Standardized low-resolution brain electromagnetic tomography (sLORETA): Technical details, methods find. Clinical & Experimental Pharmacology, 24D, 5–12. Punt, M., Jong, D. E. M., Groot, D. E., Hadders-Algra, E. (2010). Minor neurological dysfunction in children with dyslexia. Developmental Medicine and Child Neurology, 52(12), 1127–1132. Pyfer, M. L., & Carlson, B. R. (1972). Characteristic motor development of children with learning disabilities. Perceptual and Motor Skills, 35, 291–296. Rutter, M., Graham, P., & Birch, M. G. (1966). Interrelations between choreiform syndrome, reading disability and psychiatric disorder in children of 8–11 years. Developmental Medicine and Child Neurology, 8, 149–159. Stine, D. C., Saratsiotis, J. M., & Mosser, R. S. (1975). Relationship between neurological findings and classroom behaviour. American Journal of Diseases of Children, 129, 1036–1040. Velikova, S., & Chiarenza, G. A. (2012). EEG correlates of clinical subtypes of developmental dyslexia: Independent component analysis study. In P. Pietrini, G. A. Chiarenza, A. Gemignani, & E. Ricciardi (Eds.), Proceedings of the 16th world congress of psychophysiology of the international organization of psychophysiology (IOP). International Journal of Psychophysiology, 85(3) 322. Wolff, P. N., & Hurwitz, J. (1973). Functional implications of the mentally brain damaged syndrome. Seminar Psychiatry, 5, 105–115.

249

14 THE ELECTROPHYSIOLOGICAL COORDINATED ALLOCATION OF RESOURCE (CAR) MODEL OF EFFECTIVE READING IN CHILDREN, ADOLESCENTS AND ADULTS Kirtley E. Thornton and Dennis P. Carmody Abstract This chapter examines the QEEG correlates of reading memory across six groups ranging from children to adults (ages 6–62) and clinical and non-clinical. The analysis involves the individual QEEG correlates as well as employing the Coordinated Allocation of Resource model (CAR), and the flashlight and processing unit concepts to organize the data theoretically and for purposes of statistical reduction of the many variables involved in the analysis. The QEEG data is organized according to arousal (magnitudes, relative power, peak amplitude, peak frequency) and communication concepts (coherence, phase). There are three tasks involved in reading—the initial encoding task (silent reading), immediate silent recall of the reading material, and delayed (15 minutes later) silent recall of the material. The results indicate that the brain employs almost all the frequencies (arousal and communication patterns) to accomplish successful memory. However, there are negative influences on memory functioning involving the different frequencies (arousal and communication patterns) which present an interesting and heretofore not fully recognized factor in QEEG. Perhaps the most interesting findings involve the processing unit concept, which points to a dominant role of the frontal lobe and frontal central processing unit in reading memory. In addition, there is a strong pattern of left hemisphere involvement in reading memory as well as flashlight activity from T5, T3, and left frontal locations with the dominant frequency involving alpha (coherence and phase). The results offer a new viewpoint on how to construct EEG biofeedback protocols to improve reading.

Background The relations between brain activity and reading ability have studied for more than a half century. Attempts have been made to use electroencephalography (EEG) to identify individuals who are at risk for poor reading ability in anticipation that interventions might be applied to remedy the reading disorder. Often clinical studies have compared individuals with a specific reading disorder, such as dyslexia, and typical readers to identify the EEG measures that discriminated the disorder. Much of 250

CAR Model of Effective Reading

the earlier scientific reports have failed to show a distinction in EEG recordings between those with and without dyslexia (Kennard & Levy, 1952; Kenny et al., 1972). Reasons for the lack of differentiation in brain activation between poor and good readers included heterogeneity of comorbid disorders in the groups with dyslexia, passive subjects, and clinical interpretation of EEG recordings that relies on visual inspection (Johnstone et al., 1984). There is wide variability in the presentation of EEG on recordings in the non-clinical population for both children (Henry, 1944) and adults (Kennard, 1953). With the introduction of digital EEG (Duffy, Burchfiel, & Lombroso, 1979), concerns about the clinical usefulness were raised (Binnie & MacGillivray, 1992) as well as the advantages over conventional EEG (Swartz, 1998). Early use of digital EEG continued the same analysis of the measures of power and relative power in reading disorders. However, the measures of activity were restricted to power analyses at different scalp locations and were restricted to analyses of frequencies lower than 32 Hz (K. E. Thornton & Carmody, 2005). Looking at the continued advances in QEEG analysis, the identification of brain activation in the task of reading has moved from local power analyses to measures of brain connectivity. The measures of coherence and phase have identified many of the complicated relations between brain activation and reading ability. In addition, while a majority of reports have restricted their findings to frequencies below 32 Hz (Arns, Peters, Breteler, & Verhoeven, 2007), some investigators have identified discriminating measures in frequencies above the 32 Hz threshold (Babiloni et al., 2012; K. E. Thornton, 1999). While many investigators continue to assess QEEG at rest (Babiloni et al., 2012), others assess brain activation in a task related to reading ability and relate the activity to task performance (K. E. Thornton, 1999; K. Thornton, 2002a, 2002c; K. E. Thornton & Carmody, 2009a, 2012). Early evaluations of a normative database, neurometrics (John et al., 1977), did not support a discrimination of brain activation between subjects with and without dyslexia (Fein et al., 1986; Yingling, Galin, Fein, Peltzman, & Davenport, 1986). However, the early version of the neurometrics database (John et al., 1977) assessed coherence between homologous pairs between hemispheres, and not all combinations of pairs, including within hemisphere, which is an important analysis that emerged in later research. While there has been mixed evidence for markers of dyslexia at the group level, there are many reports of large variability in functioning within both groups with dyslexia and those without. Perhaps looking at the reading process across large groups of individuals will serve to identify the measures of brain activation that are involved in reading level. Later research focused on multiple aspects (auditory, visual input) of language (verbs, abstract and concrete nouns, sentence processing, etc.) and how variations in stimulus affect the coherence variables (Weiss & Mueller, 2003). Beta coherence (13–18 Hz) was associated with semantic information (concrete vs. abstract nouns) in the left frontal locations and between left frontal and right posterior, while differences between concrete nouns and verbs were most evident in terms of increases of beta coherences (13–31 Hz) within the frontal lobes (Weiss & Mueller, 2003). Additionally, a pilot study on text processing by interpreters found increased EEG coherence involvement of the left temporal regions in the higher beta band (24–32 Hz; Petsche, Etlinger, & Filz, 1993). Weiss and Rappelsberger (2000) noted that “recalled nouns exhibited overall enhanced synchronization but showed typical patterns, especially between anterior and posterior brain regions in all frequency bands except the alpha-1 band 8–10 Hz.” This chapter employs the Coordinated Allocation of Resource (CAR) model, which asserts that cognitive effectiveness is dependent upon the successful employment of specific sets of resources, albeit overlapping in some situations (K. E. Thornton & Carmody, 2009a). Concomitant with this model is the use of the flashlight metaphor in understanding the coherence and phase relations between locations. The metaphor states that each location can function as a flashlight which sends out a “beam” to the other locations within a frequency and thus is the source of the signal. The “beam” can involve all the other locations or be a mini-flashlight which will involve only selected locations. 251

Kirtley E. Thornton and Dennis P. Carmody

The flashlight metaphor has some similarities to the network concept in neuroscience theory (Wig, Schlaggar, & Petersen, 2011). The sources of the different frequency bands vary and have been conceptualized as the thalamus for the delta (δ) and alpha (α) frequency bands, the hippocampus and neocortex for the theta (θ) frequency band, and the cortex for the beta (β) frequency band (Al-Kadi, Reaz, & Ali, 2013). The current network viewpoint is that cognitive processes are a result of interactions between distributed information processing locations (Park & Friston, 2013). The CAR model takes the theory several steps forward as it asserts the concept of effective versus ineffective interactions and discusses specific definitions of frequencies and locations involved for specific cognitive tasks. To ascertain the effective and ineffective patterns, the CAR model (1) employs a cognitive activation evaluation; (2) assesses the high gamma frequency (32–64 Hz); and (3) examines the relation between cognitive performance and the QEEG variables. This approach represents an advance over the standard eyes closed procedure or the simple attention tasks (K. E. Thornton & Carmody, 2009a), as the QEEG variables related to successful performance during the task are not the ones that are related to eyes closed data. For example, relative power of theta values (during eyes closed data) predict subsequent good auditory memory performance but theta values are not related to good performance during the task. The flashlight approach was first delineated in the K. Thornton (2002c) article on adult reading memory performance, followed by the Coordinated Allocation of Resource model (K. E. Thornton & Carmody, 2009b). The clinical effectiveness of the approach and related understanding of different cognitive tasks has been explored in articles published between 1999 and 2013. These publications have addressed issues such as brain injury patterns (K. E. Thornton, 1999; K. Thornton, 2000b, 2000c, 2003), rehabilitation interventions for brain injury (K. Thornton, 2000c, 2002c; K. E. Thornton, 2002; K. E. Thornton & Carmody, 2005, 2008, 2009b, 2010), the rehabilitation of the learning disabled child (K. E. Thornton & Carmody, 2005, 2013; K. Thornton, Carroll, & Cea-Aravena, 2007), and discriminant analysis of QEEG data in the identification of brain injury (K. E. Thornton, 2014a). Previous investigations with non-clinical participants into the CAR networks of different cognitive abilities as expressed with the flashlight metaphor have been published in the area of auditory memory (K. Thornton, 2000a, 2000c, 2002c; K. E. Thornton, 2006), memory for Korean figures (K. Thornton, 2002b), and a clinical sample for the Symbol Digit task (K. E. Thornton & Carmody, 2012).

Research Goals The goals of the research were to (1) determine the QEEG measures which relate to the spontaneous free recall measure of reading material (children and adults); (2) determine developmental trends in the variables which are positively and negatively correlated with age and performance; and (3) to understand the interrelationships between QEEG variables. The scientific goal is to better understand the relation between cognitive development / performance and electrophysiological measures. An additional goal is to provide an empirical basis for the operant conditioning of the EEG signal to aid the child in obtaining better educational performance. To obtain these goals with reading material a correlation analysis was conducted between the QEEG variables and the cognitive measure of spontaneous free recall, a more difficult cognitive task than a recognition task.

Methods The technical background and methodology of the research can be obtained via the website chp-neurotherapy.com. On the opening screen there is a link to the Technical Foundations statement.

252

CAR Model of Effective Reading

Quantitative EEG (QEEG) Measures The cognitive activation research reports employed the following frequency definitions: delta (0–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta1 (13–32 Hz), beta2 (32–64 Hz). QEEG research variables can be conceptualized as involving two sets of data. One set concerns “activation/arousal” variables which involve specific cortical locations and the different frequencies with reference to magnitude (M), relative power (RP), peak frequency (PF), and peak amplitude (PKA). The second set quantifies the association between locations with concepts of phase (P) and Spectral Correlation Coefficient (SCC; Lexicor Medical Technology) which are defined by amplitude correlation coefficients.

Activation/Arousal Measures RP: Relative Magnitude/Microvolt or Relative Power: the relative magnitude of a band defined as the absolute microvolt of the particular band divided by the total microvolts generated at a particular location across all bands. M: Absolute Magnitude: the average absolute magnitude (as defined in microvolts) of a band over the entire epoch (one second). PA: Peak Amplitude: the peak amplitude of a band during an epoch (defined in microvolts). PF: Peak Frequency: the peak frequency of a band during an epoch (defined in frequency).

Connectivity Measures C: Coherence or (SCC) Spectral Correlation Coefficients: the average similarity between the waveforms of a particular band in two locations over the epoch (one second). The SCC variable is conceptualized as the strength or number of connections between two locations and is a correlation of the magnitudes. P: Phase: the time lag between two locations of a particular band as defined by how soon after the beginning of an epoch a particular waveform at location #1 is matched in location #2. References will employ a combination of letters. For example, CA refers to coherence (SCC) alpha and RPA refers to relative power of alpha (K. E. Thornton, 2014b).

Cognitive Performance Assessment The reading task was part of a larger assessment which involved an eyes closed condition, visual and auditory attention, auditory memory, and problem solving tasks. The reading task was administered approximately 20 minutes into the assessment and required the participant to read a story (542 words, Flesch grade level = 6.3, Flesch reading ease = 74.9; presented on a laminated sheet) silently for 100 seconds while their EEG was recorded. The story contained names, dates, numbers, and a story line. The story was of such length that very infrequently a participant completed the story. After the 100 seconds the participant closed their eyes and recalled silently the story for 40–60 seconds while their EEG was being recorded. The participant opened their eyes (recording paused) and told the examiner all the information they recalled during the quiet recall period. Approximately 15 minutes later, after the problem solving tasks, participants were asked to silently recall (delayed recall task) the story with eyes closed for 40–50 seconds while EEG was recorded. The participant then verbalized the recalled content.

253

Kirtley E. Thornton and Dennis P. Carmody

Scoring the Cognitive Performance Assessment Both the immediate and delayed recall tasks where scored by the “gist” method that scored the response according to whether it was a conceptual match to the original information. For example, if the original story said “he lived on an old dirty barge” and the participant’s statement was “he slept on a boat,” the participant received ½ point.

Participants Six groups were analyzed: 1) 2) 3) 4) 5) 6)

all participants, all ages (6–62); children, ages 6 to 13 consisting of non-clinical and clinical participants; adult and adolescent group of non-clinical and clinical participants (ages 14–62); non-clinical group of children, adolescents, and adults (ages 6–62); non-clinical group of children (ages 6–13); non-clinical group of adolescents and adults (ages 14–62).

Table 14.1 presents descriptive information on the six groups. The grouping of participants according to the different age and clinical criteria allows the reader to understand the brain’s complexity in more detail. In addition, the inclusion of only a normative reference group does not address possible problems in a clinical population that are presenting memory problems. The inclusion of the clinical groups allows for greater applicability of results. There are patterns in the results which will be discussed in the summary. Table 14.2 presents the information on what tasks and data were analyzed according to the respective groups. All the participants were either recruited to develop a normative database or undertook the evaluation as part of a treatment program that they enrolled in. The clinical sample consisted of traumatic brain injured participants (N = 78), attention deficit disorder, memory problems, and a specific learning disability or nonclinical participants.

Table 14.1 Participant characteristics. Group

Sample Size

Age Mean (SD) and Range

Male

Female

1) Combined group (all nonclinical, all clinical, all ages)

268

336.6 (218)/28 yrs. Range: 74–869 mos.

153

115

2) Children (non-clinical & clinical)

102

127.5 (26.2)/10.6 yrs. Range: 74–167 mos.

68

34

3) Adolescents & adults (nonclinical & clinical)

180

455.3 (191)/38 yrs. Range: 169–869 mos.

92

88

4) Children, adolescents, & adults (non-clinical)

129

332.8 (222.8)/27.7 yrs. Range: 74–857 mos.

65

64

5) Children (non-clinical)

46

132.4 (25.5)/11 yrs. Range: 74–166 mos.

24

22

6) Adolescents & adults (nonclinical)

82

447.3 (204)/37 yrs. Range: 169–857 mos.

40

42

254

CAR Model of Effective Reading Table 14.2 Tasks presented and analyzed by groups. Group

a) Input Patterns b) Developmental Patterns

a) Immediate Recall Patterns b) Developmental Patterns

a) Delayed Recall Patterns b) Developmental Patterns

1) All combined

a) Yes

a) Yes

a) Yes

2) Children (non-clinical & clinical)

a) Yes b) Yes

a) Yes b) Yes

a) Yes b) No

3) Adolescents & adult (non-clinical & clinical)

a) Yes b) Yes

a) Yes b) No

a) No b) No

4) Children, adolescents & adults (non-clinical)

a) Yes b) Yes

a) Yes b) No

a) Yes b) No

5) Children (non-clinical)

a) Yes b) Yes

a) No b) No

a) No b) No

6) Adolescents & adults (non-clinical)

a) Yes b) Yes

a) Yes b) No

a) Yes b) No

Table 14.3 Age and memory scores. Group

Age and Memory Score Alpha Set at 0.05

1) All ages, all groups

0.13 (sig.)

2) Children (non-clinical & clinical)

0.41 (sig.)

3) Adolescents & adults (non-clinical & clinical)

–0.06 (ns)

4) Children, adolescents & adults (non-clinical)

0.25 (sig.)

5) Children (non-clinical)

0.38 (sig.)

6) Adolescents & adults (non-clinical)

0.0 (ns)

Memory Performance and Age Table 14.3 presents the Pearson product correlations between age and memory scores for the six groups. The results do not present a strong pattern of improving memory with age, even in the child group where it would be most expected.

Developmental Changes in QEEG As expected there are age related changes in QEEG measures during the reading task. An example is shown in Figure 14.1 for the relative power values of the frequencies during the reading task. For relative power delta in frontal locations (Fp1, Fp2, F3, F4, F7, F8, Fz) there were no significant differences between age groups 6–13 years and 14–20 years; however, significant relative power reductions were found in young adults (21–50 years) and reductions continued in older adults. In a similar age related change, relative power beta1 in frontal locations remained unchanged until young adulthood when it increased in value and continued to increase into older adulthood. In general, coherence and phase measures increased with age for all frequencies, except for frontally / centrally located delta and frontal theta connection patterns, which showed decreases with age. Similar trends of changes in relative power with age were found in central (C3, C4, Cz, T3, T4, T5, T6) and posterior (P3, P4, Pz, O1, O2) regions.

255

Kirtley E. Thornton and Dennis P. Carmody

Figure 14.1

Participant characteristics.

Group 1 Results: All Participants A correlation analysis was conducted between the QEEG variables and the total reading memory score with alpha set at 0.05. If a frequency at a location had 3 significant relations with memory performance, the emanating circle was blackened. Only frequencies which had at least one location with 3 significant relations were presented in the figures. There were many examples of a location involved in multiple connections. A location that has more significant relations than another could logically assumed to be the “source” of a signal. However, there are many examples which have an equal number of significant relations and thus determination of the “source” is problematic. The location would be similar to the “node” concept in modern network theory. Park and Friston (2013, p. 581) employ the following definition of networks and nodes: “A network is composed of nodes and their links, called edges. A node, defined as an interacting unit of a network, is itself a network composed of smaller nodes interacting at a lower hierarchical level.” The + sign indicates a positive relation between the QEEG variable and memory performance. The – sign indicates a negative relation. The individual significant groupings were arranged according to frequencies to provide a clearer presentation of the results. The value for the positive correlations ranged from 0.19 to 0.69 across the analysis. Figure 14.2 shows the significant relations when all participants are included in the analysis. The analysis assumes a bottom up processing model and focuses on the projections from the posterior locations. As the figure indicates there are broad posterior location-based increases mostly involving

256

CAR Model of Effective Reading

Figure 14.2 Group 1—reading condition all participants—all ages. CD = Coherence Delta; CT = Coherence Theta; CA = Coherence Alpha; CB1 = Coherence Beta1; CB2 = Coherence Beta2; RPD = Relative Power Delta; RPT = Relative Power Theta; PD = Phase Delta; PT = Phase Theta; PA = Phase Alpha; PB1 = Phase Beta1; PB2 = Phase Beta2; PKAD = Peak Amplitude Delta; PKAT = Peak Amplitude Theta; MD = Microvolts Delta; MT = Microvolts Theta; MB1 = Microvolts Beta1; MB2 = Microvolts Beta2

CA, PT, and PA but also involving central and frontal locations. There is a tendency for frontal and central locations to show increases in SCC and phase alpha and beta activity and decreases in SCC and phase activity in the lower frequencies (delta, theta). While the frontal locations show decreases in the connection patterns of the lower frequencies, the posterior locations show increases in these values (CT, CD, PD). The PB1 activity from F7 shows a unique pattern. Additionally, the beta1 (SCC

257

Kirtley E. Thornton and Dennis P. Carmody

Figure 14.3 Group 1—immediate recall reading condition all participants—all ages. CT = Coherence Theta; CA = Coherence Alpha; CB1 = Coherence Beta1; PD = Phase Delta; PT = Phase Theta; PA = Phase Alpha; PB1 = Phase Beta1; PB2 = Phase Beta2; RPT = Relative Power Theta; RPA = Relative Power Alpha; RPB2 = Relative Power Beta2; PKAD = Peak Amplitude Delta; PKAT = Peak Amplitude Theta; PKAB1 = Peak Amplitude Beta1; PKAB2 = Peak Amplitude Beta2; MD = Microvolts Delta; MT = Microvolts Theta; MB1 = Microvolts Beta1; MB2 = Microvolts Beta2

and phase) relations between F3, Fz, F4 and C3, Cz offers a unique pattern, which is also evident in some of the other figures. The results strongly argue for a systems approach to understanding brain functioning with a focus on connection patterns. Figure 14.3 presents the results of the analysis for all participants and all ages for the immediate recall condition. The results indicate frontal and central involvement of SCC and phase alpha and beta1, RPA and LP, and central left hemisphere (LH) PT. The negative influences include the variables involved in the lower frequencies, orbital frontal (Fp1, Fp2) phase beta activity, right hemisphere (RH) PA activity, and RH and frontal beta activity. The increases in microvolts in the right hemisphere related to poorer memory is similar to Figure 14.11 in which variables involving beta levels in predominantly the right hemisphere are negatively related to memory. What is also clear in this figure, as well as others, is that memory is predominantly a connection-related issue which is negatively affected by higher values of the lower frequencies but not involving increased beta values, except in a negative direction in the right hemisphere. Figure 14.4 presents the delayed recall relations between the QEEG variables and memory performance for all the participants. The results are very similar to the immediate recall condition. As the figure indicates there was a frontal focus for the CB1, PA, and PB1 and diffuse relations involved for CD, PD, PT and CA relations. Negative relations involve diffusely located variables involving the lower frequencies (delta, alpha), frontal/central beta activity, T6 (CB1, PB2), and Fp1 (PB2). Negatively related arousal variables involve frontal beta activity as well as increases in values of delta and theta variables (diffuse locations). 258

CAR Model of Effective Reading

Figure 14.4 Group 1—delayed recall reading condition all participants—all ages. CD = Coherence Delta; CT = Coherence Theta; CA = Coherence Alpha; CB1 = Coherence Beta1; PD = Phase Delta; PT = Phase Theta; PA = Phase Alpha; PB1 = Phase Beta1; PB2 = Phase Beta2; RPT = Relative Power Theta; RPA; Relative Power Alpha; PKAD = Peak Amplitude Delta; PKAT = Peak Amplitude Theta; PKAB1 = Peak Amplitude Beta1; PKAB2 = Peak Amplitude Beta2; MD = Microvolts Delta; MT = Microvolts Theta; MB1 = Microvolts Beta1; MB2 = Microvolts Beta2

Group 2 Results: Children—Combined Non-Clinical and Clinical Group Developmental Changes Figure 14.5 shows the changes in variable values during development for group 2 while engaged in the reading task. The figure indicates 5 significant increases (involving 3 or more connections) emanating from T5 which include PA, CB1, CB2, PB1, and PB2. When the alpha level was lowered to 0.10 there were 8 (of a possible 10) CAR patterns emerging from the T5 location. The relevance of the T5 location is also evident in: group 3 QEEG data during reading (Figure 14.5—T5PA, T5PB1, T5CB1); group 2 delayed recall (Figure 14.8—T5CA, T5PT, T5PA); group 3 (adults combined) (Figure 14.10—T5CA); group 4 reading (combined non-clinical group) (Figure 14.13—T5CA, T5PT, T5PA) and immediate recall (Figure 14.14—T5PA); group 5 developmental trends (non-clinical 259

Kirtley E. Thornton and Dennis P. Carmody

Figure 14.5 Group 2 (children—non-clinical and clinical): Developmental changes. CD = Coherence Delta; CT = Coherence Theta; CA = Coherence Alpha; CB1 = Coherence Beta1; CB2 = Coherence Beta2; PD = Phase Delta; PT = Phase Theta; PA = Phase Alpha; PB1 = Phase Beta1; PB2 = Phase Beta; RPB1 = Relative Power Beta1; PKFA = Peak Frequency Alpha; MD = Microvolts Delta; PKAD = Peak Amplitude Delta; RPT = Relative Power Theta; MT = Microvolts Theta; PKAT = Peak Amplitude Theta; PKAB1 = Peak Amplitude Beta1; PKAB2 = Peak Amplitude Beta2; MB1 = Microvolts Beta1; MB2 = Microvolts Beta2

children) (Figure 16—T5CD, T5CB1, T5CB2, T5PD, T5PA, T5PB2); group 5 (non-clinical children) reading condition (Figure 14.17—T5PA, T5PB1); group 6 (adult non-clinical group) QEEG reading task (Figure 14.19—T5CA): group 1 (all subjects) reading condition (Figure 14.1—T5CA, T5PT, T5PA, T5PB1); group 1 immediate recall condition (Figure 14.2—T5PT); and group 1 delayed recall (Figure 14.3—T5CD, T5CA, T5PT, T5PA). The T5 location appears to be a critical factor in both development and memory performance. Kaiser (2008) analyzed eyes closed resting EEG (101 children and adults between ages of 5 and 35 years) and found the strongest age related developmental increases from T5 in terms of alpha (8–12 Hz) coherence. However, in the child group (ages 5–16) he found no significant relations between age and connectivity measures. Kaiser employed the same Lexicor equipment and algorithms that were employed in this research. The main difference was Kaiser’s use of eyes closed data (Kaiser, 2008). 260

CAR Model of Effective Reading

Figure 14.5 also indicates there are 14 positive increases in PA (8 / 8 in right hemisphere, RH) values and 10 in CA values. Most of the SCC and phase changes are in the alpha (24) and beta1 (17) frequencies (5/8 PB1 in left hemisphere, LH). The major negative developmental trend, related to improved performance, are decreases in values of the delta and theta variables. The bold italic larger font size label is the indicator that variables within in the head figure below the label are significantly and positively related to memory performance (Figure 14.5). For the CA variable the overlapping critical origin location is F3 and T3. For the CB1 and PB1 variables the overlapping critical origin location is T5. For the PA variable the overlapping critical origin locations are F7, C3, T5, Fz, Cz, Pz, P4, and O2. Thus, with development these variables improve concomitant with memory performance. The other overlapping variables (development and performance) involve posterior RPB1 and diffuse PKFA. From an intuitive point of view, it might be expected that the variables which improve with age would be the ones related to effective functioning. This does not appear to be generally the case, albeit with some significant exceptions. For example, PA appears to be predominantly a RH developmental pattern but memory scores are related to LH PA. Figure 14.6 presents the results for group 2 (children—non-clinical and clinical sample) between the QEEG variables and memory performance. The most dominant variable is phase alpha (evident

Figure 14.6 Group 2 (children—non-clinical and clinical): Positive and negative relations between QEEG variables and memory performance during reading task. CD = Coherence Delta; CT = Coherence Theta; CA = Coherence Alpha; CB1 = Coherence Beta1; CB2 = Coherence Beta2; PD = Phase Delta; PT = Phase Theta; PA = Phase Alpha; PB1 = Phase Beta1; RPB1 = Relative Power of Beta1; RPB2 = Relative Power Beta2; PKFA = Peak Frequency Alpha; PKFB2 = Peak Frequency Beta2; MD = Microvolts Delta; PKAD = Peak Amplitude Delta; PKAT = Peak Amplitude Theta; MT = Microvolts Theta; RPT = Relative Power Theta; MA = Microvolts Alpha

261

Kirtley E. Thornton and Dennis P. Carmody Table 14.4 Group 2 (children—non-clinical and clinical): Intercorrelations between variables (significant relations are bolded in larger font; alpha level at 0.05). MD

MT

MA

MB1

MB2

RPD

0.75

0.07

–0.18

–0.35

–0.38

RPT

–0.22

0.48

0.09

–0.31

–0.38

RPA

–0.38

–0.05

0.55

–0.18

–0.47

RPB1

–0.5

–0.22

0.15

0.56

0.23

RPB2

–0.3

–0.16

–0.22

0.33

0.8

0.43

0.2

0.19

0.14

PKAD

0.99

PKAT

0.54

0.99

0.6

0.46

0.31

PKAA

0.15

0.56

0.77

0.47

0.27

PKAB1

0.21

0.47

0.62

0.97

0.58

PKAB2

0.15

0.31

0.21

0.73

0.98

in 10 of the 19 CAR patterns) almost entirely in the left hemisphere. In addition, phase and SCC (alpha and beta1) from T5, posterior RPB1 and LH PKFA, and right frontal PKFB2 contribute to success. Eight of the eleven (excluding Fz, Cz, Pz) significant CAR patterns are in the left hemisphere. There is no positive patterns involving the beta2 frequency. The dominant negative pattern is right frontal CB2 (FP2, F4), PT (Fp1, F8, T4), PB1 (F7), PD (F7, F8), and higher values in the lower frequencies (delta, theta). Six of the nine negative CAR patterns are in the right hemisphere. The most dominant negative arousal pattern is theta levels in parietal locations (P3, Pz, P4; 9 variables). Table 14.4 presents the interrelationships between the different arousal variables for group 2. These variables consistently involve negative relations results, although their interrelationships vary by group. As the table indicates there is a high correlation between the magnitude and peak amplitude value of a specific frequency. Table 14.4 presents the intercorrelations between some of the relevant arousal variables. As the table indicates there are high (r > 0.76) intercorrelations between the magnitude of a frequency and its corresponding peak amplitude.

Recall Stage Figure 14.7 presents the development changes evident during the immediate recall task. The dominant developmental trend is one of increases in frontal alpha and beta1 (SCC and phase) and increases in frontal RPA and posterior PKFA, RPB1, and central/parietal RPB2. The only two variables which relate to increased recall are F8 PA and Fp2 CA. An interesting developmental pattern is the increase in frontal RPA and increases in posterior beta and PKFA activity. This might be interpreted as a developmental relaxation of the frontal lobes and increased arousal levels of posterior locations. The negative developmental pattern involves decreases mostly in the lower frequencies (delta and theta). The overall pattern is one of decreases in the lower frequencies (delta, theta), decreases in CAR pattern values involving the lower frequencies, and decreases in phase alpha from 4 posterior locations. The negative related overlapping variables (development and effectiveness) include the T6PA variable and delta and theta variables decreases. It is an interesting theoretical problem why frontal SCC and phase relations would relate to effective recall. Is the information

262

CAR Model of Effective Reading

Figure 14.7 Group 2 (children—non-clinical and clinical): Recall developmental trends. CD = Coherence Delta; CT = Coherence Theta; CB1 = Coherence Beta1; PD = Phase Delta; PT = Phase Theta; PA = Phase Alpha; PB1 = Phase Beta1; RPA = Relative Power Alpha; RPB1 = Relative Power Beta1; RPB2 = Relative Power Beta2; PKFA = Peak Frequency Alpha; RPD = Relative Power Delta; RPT = Relative Power Theta; MD = Microvolts Delta; MT = Microvolts Theta; PKAT = Peak Amplitude Theta

held in the frontal lobes and/or the frontal lobes related to conscious awareness? These data do not answer these questions. Figure 14.8 presents the variables that were correlated with total recall during the silent recall period. The major predictors during the recall stage are the T3 phase CAR pattern (PD, PA, PB1), right frontal SCC (alpha and theta), and RPA (T3, F3, F8). The major negative CAR patterns during recall involve Fp1 and Fp2 (PT, PB1, PB2, CB2), T6 (PA, PB1), and increases in values of the variables involving the lower frequencies (delta, theta) mostly in the central regions (C3, Cz, C4). The only variable which overlaps with negative variables during the input task is Fp2 CB2.

263

Kirtley E. Thornton and Dennis P. Carmody

Figure 14.8 Group 2 (children—non-clinical and clinical): Reading immediate recall relations. CT = Coherence Theta; CA = Coherence Alpha; CB2 = Coherence Beta2; PD = Phase Delta; PT = Phase Theta; PA = Phase Alpha; PB1 = Phase Beta1; PB2 = Phase Beta2; RPA = Relative Power Alpha; MD = Microvolts Delta; PKAD = Peak Amplitude Delta; PKAT = Peak Amplitude Theta; MT = Microvolts Theta; PKAB2 = Peak Amplitude Beta2

Delayed Recall Figure 14.9 presents the positive and negative CAR patterns associated with the participant’s delayed recall score (not total memory) during the task. The participant’s eyes were closed during the data collection. The dominant positive relations involve CA (8 locations; mostly F7 and T5) and PD (8 locations). The negative arousal variables involve the lower frequencies (9 in central locations of delta, theta), CAR activity in the theta and alpha frequency (SCC and phase), and decreases in right frontal MB1 activity. The dominant developmental pattern (delayed recall—not presented in a figure) is one of increases in PA, CA, PB1, and CB1 from frontal locations with the Fp1, Fp2, F7, F8, F3, and F4 locations involving the most relations. There are also positive increases in frontal / central RPA, occipital RPB1 and diffuse PKFA. The negative developmental trends are large decreases in phase alpha from all posterior locations and decreases in the lower frequencies (delta, theta) as well as decreases in frontal beta variables. The overall pattern is very similar to the immediate recall results. The overall pattern, across all 3 conditions, are decreases in delta and theta variables, and increases in alpha (PKF, RPA) and beta (RP) variables in different locations and different tasks, many relating to better memory performance. In reference to SCC and phase values, the correlates of effective

264

CAR Model of Effective Reading

Figure 14.9 Group 2 (children—non-clinical and clinical): CAR patterns of delayed recall of reading material. CD = Coherence Delta; CT = Coherence Theta; CA = Coherence Alpha; PD = Phase Delta; PT = Phase Theta; PA = Phase Alpha; Relative Power Alpha; RPT = Relative Power Theta; MT = Microvolts Theta; PKAT = Peak Amplitude Theta; PKAB1 = Peak Amplitude Beta1; MB1 = Microvolts Beta1

functioning predominantly involve LH locations and the alpha and beta1 frequencies. There are strong developmental trends of decreases in phase alpha in the recall conditions (immediate and delayed).

Group 3 Results: Adolescent and Adult Performance—Combined Clinical and Normative Sample Figure 14.10 presents the developmental patterns of the combined clinical and normative sample of adults. The figure indicates significant increases in values from the frontal polar locations (Fp1, Fp2) mostly in the PA frequency but also evident in CB1, PB1, and PB2. The CA effect is focused on the frontal and C3 locations. Additionally, there are significant increases in beta frequency activity (MB1, MB2, RPB1, RPB2). The SCC and phase variables which decrease with age involve variables in the lower frequencies (delta, theta), posterior SCC and phase beta1 and beta2 connection patterns (T5 and T6 predominantly), CA from frontal locations (Fp1, Fp2, F3), PD (diffuse locations), PT (O1, O2, F8, T4), and CD from right frontal and left posterior locations. Figure 14.11 presents the analysis of effectiveness for the combined adult sample (group 3). As the figure indicates there is a dominant positive pattern of CB2 involving both hemispheres, posterior PT (O1, O2, Pz) and CA (T5), and frontal and C3 PB2 in addition to central and posterior RPA. Frontal activations involve the lower frequencies (delta, theta), indicating a relaxation of the frontal lobes in this reading task. Part of the CB2 effect could reside in the high number of head injured in the sample (N = 78). However, if CB2 does not have an effect on the memory score, then it would not have shown up as related. Negative relations involve frontal SCC and phase in the lower frequencies (CD, CT, PD, PT, and PA) and diffusely located RPB2 values. The negative effect of RP beta values is opposite to the results of the children. However, the reading material was at the 6th grade level for the adults, whose average age was 38. Thus, the task was not a very challenging reading task. There are no overlapping developmental and effectiveness variables.

265

Kirtley E. Thornton and Dennis P. Carmody

Figure 14.10 Group 3 (adolescents and adults—non-clinical and clinical): Developmental trends. CD = Coherence Delta; CA = Coherence Alpha; CB1 = Coherence Beta1; CB2 = Coherence Beta2; PD = Phase Delta; PT = Phase Theta; PA = Phase Alpha; PB1 = Phase Beta1; PB2 = Phase Beta2; MD = Microvolts Delta; MT = Microvolts Theta; MA = Microvolts Alpha; MB1 = Microvolts Beta1; MB2 = Microvolts Beta2; RPD = Relative Power Delta; RPT = Relative Power Theta; RPB1 = Relative Power Beta1; RPB2 = Relative Power Beta2; PKFA = Peak Frequency Alpha; PKAD = Peak Amplitude Delta; PKAT = Peak Amplitude Theta

The F4PA relations, problematic in many of the following figures, present a unique pattern. While most effectiveness and / or developmental relations consistently present either (1) increased short connections, (2) increased long distance connections, or (3) a combination, the F4PA projection system can simultaneously be involved in negative and positive relations. Figure 14.12 presents the immediate recall correlates. The figure indicates a positive frontal locus (alpha to beta2) of the correlates with a negative effect of right posterior PB1 and PA relations (central and right hemisphere locations) with a right hemisphere (RH) negative effect of MB2 and PKAB2. This RH negative effect could be interpreted as the participants looking in the wrong location (RH) for verbal information. The enigmatic F4PA correlates present a conflicting pattern of positive relations (F3, C3, T5, O1, and Pz) and negative correlates (Fz, Cz, P3, P4).

266

Figure 14.11 Group 3 (adolescents and adults—non-clinical and clinical): Correlates of memory during reading. CT = Coherence Theta; CA = Coherence Alpha; CB2 = Coherence Beta2; PD = Phase Delta; PT = Phase Theta; PA = Phase Alpha; PB1 = Phase Beta1; PB2 = Phase Beta2; RPD = Relative Power Delta; RPA = Relative Power Alpha; RPB1 = Relative Power Beta1; RPB2 = Relative Power Beta2; MD = Microvolts Delta; MT = Microvolts Theta; PKAD = Peak Amplitude Delta

Figure 14.12 Group 3 (adolescents and adults—non-clinical and clinical): Immediate recall correlates. CA = Coherence Alpha; CB1 = Coherence Beta1; CB2 = Coherence Beta2; PA = Phase Alpha; PB1 = Phase Beta1; PB2 = Phase Beta2; MB2 = Microvolts Beta2; PKAB2 = Peak Amplitude Beta2

Kirtley E. Thornton and Dennis P. Carmody

Group 4 Results: Normative Group—Combined Non-Clinical Children, Adolescents, and Adults—Developmental Trend in Normative Group Figure 14.13 presents the developmental patterns of group 4 (non-clinical—children and adults) during the reading task. As there were broad changes with development, the figure presents the developmental patterns which are unique. The developmental pattern for the non-clinical child and adult sample is marked by diffuse decreases in RPD, PKAD, MD, RPT, PKAT, MT, RPA, PKAA, MA, and MB1, and increases in RPB1 (all locations except F7, F8) and RPB2 (all locations). There are also decreases in CD, frontal locations for PD (from F7, Fp1, Fp2) but posterior and central increases for PD (from T5, T6, T3, T4, P3, P4, O1, O2, Pz, Cz, Fz), frontal decreases in PT (F7, F8, Fp2), diffusely located increases in CT, CA, CB1, and PT (diffuse central and posterior locations), and diffuse locations for PB1. From a location point of view there are no increases in CA from F7, T3, and minimal (5) from T5. The locations for CB1 which minimally increased relations are F7 (3), T3 (1), and T5 (1). Thus, the most important left hemisphere locations did not show developmental increases. Figure 14.14 presents the analysis of the correlates between the QEEG variables and performance for the normative sample. The figure indicates a strong influence of CA (T5, O1), frontal phase (B1, B2), PA (frontal and posterior), posterior PD and PT as well as RPB1 in central and posterior locations, RPB2 in right posterior locations, and frontal RPA and RH PKFA. Negative effects are mostly evident in frontal and central diffuse PD and right frontal (F8) PT, T3 PA and higher values of the variables involving the lower frequencies (delta, theta). The predominant effects involve the lower frequencies (delta, theta). However, almost all of this negative effect is confined to the under age 14 group. The analysis of the sample over age 14 does not yield similar effects of the lower frequencies. The difference between the child and adult group on the RPT value is about 3 points (14.9 vs. 11.8) or about 1 SD (SD = 2.5) difference. It appears that the negative influence of RPT becomes irrelevant once it obtains a certain value (11.8). Similarly, the positive influence of RPB1 diminishes significantly after the age of 14. The mean values for the child group is 23.2 while for the adult group is 26.9. The SD value for the children is 3.6 and for adults 4.0. Thus, a 1 SD increase in value appears to negate the positive influence of RPB1. Figure 14.15 presents the relations during the quiet recall task. The results present a complex patterning of interrelationship between locations and frequencies, particularly with reference to coherence and phase values. The dominant pattern is one of positive relations of frontal SCC and phase relations (alpha, beta1), posterior and central RPB1, and diffuse RPA, and negative effects of RPD and RPT, as well as predominantly right hemisphere activity in PA, PB1, and PB2. Three projection systems involving T6 demonstrated negative effects (PA, PB1, and PB2). Other negative

Figure 14.13 Group 4 (normative group—children and adults): Unique developmental patterns during reading. CB2 = Coherence Beta2; PB2 = Phase Beta2; PKFB1 = Peak Frequency Beta1; PKFA = Peak Frequency Alpha; MB1 = Microvolts Beta1; MB2 = Microvolts Beta2; PKAB2 = Peak Amplitude Beta2

268

Figure 14.14 Group 4 (normative group—children, adolescents, and adults): Reading memory correlates during input task. CD = Coherence Delta; CA = Coherence Alpha; CB1 = Coherence Beta1; CB2 = Coherence Beta2; PD = Phase Delta; PT = Phase Theta; PA = Phase Alpha; PB1 = Phase Beta1; PB2 = Phase Beta2; RPA = Relative Power Alpha; RPB1 = Relative Power Beta1; RPB2 = Relative Power Beta2; PKFA = Peak Frequency Alpha; MD = Microvolts Delta; MT = Microvolts Theta; RPD = Relative Power Delta; RPT = Relative Power Theta; PKAD = Peak Amplitude Delta; PKAT = Peak Amplitude Theta

Figure 14.15 Group 4 (normative group—children, adolescents, and adults): Immediate recall. CA = Coherence Alpha; CB1 = Coherence Beta1; PT = Phase Theta; PA = Phase Alpha; PB1 = Phase Beta1; PB2 = Phase Beta2; RPD = Relative Power Delta; RPT = Relative Power Theta; RPA = Relative Power Alpha; RPB1 = Relative Power Beta1

Kirtley E. Thornton and Dennis P. Carmody

Figure 14.16 Group 4 (normative group—children, adolescents, and adults): Delayed recall. CD = Coherence Delta; CT = Coherence Theta; CB2 = Coherence Beta2; CA = Coherence Alpha; CB1 = Coherence Beta1; PT = Phase Theta; PA = Phase Alpha; PB1 = Phase Beta; PB2 = Phase Beta2; RPA = Relative Power Alpha; RPB1 = Relative Power Beta1

patterns (not in figure) indicate diffuse negative effects of PKAD, PKAT, MD, MT, and right frontal MB1. Positive influences of PKFD are evident at T3, C3, T6, O1, O2, and Cz, and PKFT at F4, C4, and T6. Figure 14.16 presents the variables during the delayed recall task which are positively related to the delayed recall score (not the total memory score). The variables are very similar to the immediate recall variables (frontal CA, CB1, PA, PB1) but include PT (left hemisphere—frontal, central, and posterior). The other variables which are negatively related to performance include diffuse locations for RPD, RPT, PKAD, MT, and MD (not presented in figure).

Group 5: Non-Clinical Children Figure 14.17 shows the developmental patterns within the child normative group. As the figure indicates there is broad increases in posterior CD, PD, PA, CB1, and CB2. The T5 location showed increases in CD, CB1, CB2, PD, PA, and PB2. The overlapping (with performance) involves the T5 PA relations but not the T5 PB1 relations. There is also positive increases in posterior RPB1 and PKFA and frontal RPA. The negative developmental patterns involve the delta and theta frequency variables and large decreases in frontal PT and PD relations. The F4 location shows positive relations for PA with FP1, FP2, Fz, and P3, and negative relations with O1 and O2. Figure 14.18 shows the positive and negative relations for the child normative sample (N = 41). The figure indicates a T5 focus (PA, PB1) and increases in occipital beta activity (PKAB1, PKAB2, MB1, MB2) and posterior RPB1 as well as right frontal PKFB2. Negative relations focus on right frontal SCC and phase activity (PD, PT, and CB2), and parietal CT and diffuse RPD activity. The similarity with the combined child group indicate a replication of the T5 (PA, PB1), T3 (CA), and posterior relative power of beta values. The similarity with the negative relations involve right frontal PD, PT, and CB2.

270

Figure 14.17 Group 5 (non-clinical child): Shows the developmental relations. CD = Coherence Delta; CT = Coherence Theta; CA = Coherence Alpha; CB1 = Coherence Beta1; CB2 = Coherence Beta2; PD = Phase Delta; PT = Phase Theta; PA = Phase Alpha; PB2 = Phase Beta2; RPD = Relative Power Delta; RPT = Relative Power Theta; RPA = Relative Power Alpha; RPB1 = Relative Power Beta1; PKFA = Peak Frequency Alpha; PKFB1 = Peak Frequency Beta1; PKFB2 = Peak Frequency Beta2; PKAD = Peak Amplitude Delta; PKAT = Peak Amplitude Theta; MD = Microvolts Delta; MT = Microvolts Theta

Figure 14.18 Group 5 (child normative group): Relations during the reading task. CT = Coherence Theta; CA = Coherence Alpha; CB2 = Coherence Beta2; PD = Phase Delta; PT = Phase Theta; PA = Phase Alpha; PB1 = Phase Beta; RPB1 = Relative Power Beta1; RPB2 = Relative Power Beta2; PKAB1 = Peak Amplitude Beta1; PKAB2 = Peak Amplitude Beta2; MB1 = Microvolts Beta1; MB2 = Microvolts Beta2; PKFB2 = Peak Frequency Beta2

Kirtley E. Thornton and Dennis P. Carmody

Group 6: Non-Clinical Adolescents and Adults Figure 14.19 presents the developmental trends for the normative adolescent and adult group. The results indicate a broad increase in RPB1 and RPB2 and deceases in variables involving the delta and theta variables. The SCC and phase relations show broad increases from frontal connections (Fp1, Fp2), central (Fz, Cz, Pz), and occipital locations in the theta to beta1 frequencies. Figure 14.20 presents the results for the adolescent and adult non-clinical group. The positive patterns involve F7 (CA, CB1, CB2, PB2) and T5 (CA) and increases in RPA and MA, while the negative patterns involve right hemisphere flashlight origins in 11 patterns (out of 13 patterns—excluding central locations) and RPB2. The overlap with the positive variables reported in the combined adult sample (Figure 14.7) include T5 (CA) and RPA. The F7 projections for CB1, CB2, and PB2 are connected to the same locations (F4, O1, and O2). The F4PA variable shows positive relations to Fp2, T5, and Pz, and negative relations with Fz, Cz, C4, and P3.

Figure 14.19 Group 6 (adolescent and adult normative group): Developmental trends during reading task. CT = Coherence Theta; CA = Coherence Alpha; CB1 = Coherence Beta1; PT = Phase Theta; PA = Phase Alpha; PB1  = Phase Beta1; RPD = Relative Power Delta; RPT = Relative Power Theta; RPB1 = Relative Power Beta1; RPB2 = Relative Power Beta2; MD = Microvolts Delta; MT = Microvolts Theta; MA = Microvolts Alpha; MB1 = Microvolts Beta1; MB2 = Microvolts Beta2; PKAD = Peak Amplitude Delta; PKAT = Peak Amplitude Theta; PKAB2 = Peak Amplitude Beta2

272

CAR Model of Effective Reading

Figure 14.20 Group 6 (adolescent and adult normative group): Relations during reading task. CT = Coherence Theta; CA = Coherence Alpha; CB1 = Coherence Beta1; CB2 = Coherence Beta2; PT = Phase Theta; PA = Phase Alpha; PB1 = Phase Beta1; PB2 = Phase Beta2; RPT = Relative Power Theta; RPA: Relative Power Alpha; RPB2 = Relative Power Beta2; MT = Microvolts Theta; MA = Microvolts Alpha; PKAB2 = Peak Amplitude Beta2

Summary of Patterns The figures present a complex pattern of positive and negative relations between QEEG and reading memory performance. The results point to a dominant role of the SCC and phase interrelationships. Despite the complexity, however, there are patterns evident throughout the data. The most dominant pattern throughout all of the different analyses is the developmentally decreasing values of the delta and theta variables and their negative influence on performance and positive relationship between beta relative power variables with development and intermittently with performance. The positive involvement of RPB1 is evident in 11 of the figures. Six figures relate to performance issues (Figures 14.6, 14.14, 14.15, 14.16, and 14.18) and five figures relate to development (Figures 14.5, 14.7, 14.10, 14.17, and 14.19). Positive relations for MB1 and MB2 variables relate to only one figure (Figure 14.10—group 3—adolescent and adult combined group—developmental trends—F7). Figure 14.12 (group 3) shows negative influences of RH MB2 during the immediate recall task. Figure 14.18 (group 5—child non-clinical) shows positive relations of occipital MB1 and MB2 to reading memory. Thus, rewarding the magnitude beta variables is not a productive focus for EEG biofeedback sessions, while rewarding relative power variables is likely to be more effective if the goal is to improve memory functioning. The SCC and phase relations present a more difficult and interesting problem for the analysis due to the complexity of the data. There is evident a strong pattern of SCC and phase projections from the T5 location. However, there are also indications that the LH is dominant for effective memory, RH involvement relates to ineffective memory, and frontal lobe connection patterns relate to recall performance. There are several problematic findings in the data. Generally, the flashlight patterns involve a specific location and (a) neighboring locations, (b) long distance connections, or (c) both. The patterns are almost always either all positive or all negative influences from a specific location. However, there is an enigmatic relationship of F4 PA to cognition. In several figures the F4 location (PA) operates as a negative and positive correlate. Figure 14.7 (group 2) shows positive developmental relations to frontal locations and negative to T5,O1, and O2.; Figure 14.11 (group 3) shows negative relations to Cz, P3, and P4; Figure 14.12 (group 3) shows positive relations to left posterior locations (T5, O1, Pz) and negative relations to Fz, Cz, P3, and P4.; Figure 14.14 (group 4) shows positive relations to left posterior locations as well as negative relations to Fz, Cz, and P4.; Figure 14.15 (group 4) shows 273

Kirtley E. Thornton and Dennis P. Carmody

positive relations to left frontal locations and T5 and Pz; Figure 14.17 (group 5) shows positive relations to frontal locations and CZ, P3, and T5; and Figure 14.20 (group 6) shows positive relations to Fp2, T5, and Pz, and negative relations to Fz, CZ, P3, and C4 locations. The second major problematic finding begs the question: “How is it possible that a location can have non-significant relations to memory for short connections and positive/negative relations for long distance connections in the same frequency?” The problem occurs throughout the figures. The problem exists both from a conceptual level and electrophysiological level and implicates possible subcortical influences, which can be further explored with LORETA analysis (Pascual-Marqui, Michel, & Lehmann, 1994).

Alternate Viewpoints on the Data The data can be explored through different methods of analysis. The previous analysis focused on significant individual correlations between the QEEG and performance variable. The results indicate memory performance is not frequency specific. Memory processing involves all the SCC and phase relations and arousal variables in all locations, to varying degrees and varying effects on performance levels. The SCC and phase relations offer a unique and complex set of data to understand. The analysis of individual locations and respective significant SCC and phase relations emanating from those locations could provide a valuable different perspective on the issue. Figure 14.21 shows the results of the analysis which examines each location and the number of significant SCC and phase relations to other locations for all the subjects available (clinical, non-clinical, all ages). The number within the circle represents the total number of significant relations. The number adjacent to the line connecting two locations represents the number of significant relations between those two respective locations

Figure 14.21 Significant relations between locations across frequencies.

274

CAR Model of Effective Reading

with alpha set to 0.01. As there are 5 frequencies and 2 variables (SCC and phase), the maximum number of significant relations is 10 per connection pair. As the figure indicates, the location with the highest number of significant relations is C3 (30), followed by T3 (28), T5 (23), and Pz and O2 (20). Thus, it appears the sensorimotor strip is significantly involved in reading memory, contrary to the usual conception of the locations. In the process of reworking the data analysis the concept of a Central Processing Unit began to become a viable alternative view, due to developmental patterns which were occurring and the data results. The developmental pattern across numerous tasks appear to be one of the lateral locations (F7, T3, T5, etc.), locations increasing their values (across all frequencies) to the central locations (F3, Fz, F4, etc.) with development. However, the connection activity between lateral locations (F7-T3, etc.) was not increasing with age as much as the connections to the central locations. In addition, many of the correlates of success involve relations between the central locations. These observations prompted the heuristic concept of a Central Processing Unit (CPU) composed of the F3-Fz-F4, C3-Cz-C4, and P3-Pz-P4 locations. In continuing the logic, a Frontal Processing Unit (FPU; Fp1, FP2, F7, F3, Fz, F4, F8), Posterior Processing Unit (PPU; T5, P3, P4, Pz, T6, O1, O2) were developed. A frontal processing unit (fCPU; F3-Fz-F4, C3-Cz-C4) was also added due to the appearance of the significant results emanating from this subdivision of the CPU. To address the activity of the lateral positions, the flashlight metaphor is employed as well as a hemispheric analysis of the SCC and phase values in the reanalysis. Arousal variables were re-organized according to all locations, central/posterior, hemisphere and quadrants. Employing these concepts the data was reanalyzed for the entire participant population. Figure 14.22 shows the results of the analysis for the encoding phase of the reading

Figure 14.22 All participants—encoding stage—correlates with reading memory. CD = Coherence Delta; CT = Coherence Theta; CA = Coherence Alpha; CB1 = Coherence Beta1; PD = Phase Delta; PT = Phase Theta; PA = Phase Alpha; PB1 = Phase Beta1; PB2 = Phase Beta2; RPT = Relative Power Theta; RPA = Relative Power Alpha; RPB1 = Relative Power Beta1; MD = Microvolts Delta; MT = Microvolts Theta; MB1 = Microvolts Beta1; MB2 = Microvolts Beta2

275

Kirtley E. Thornton and Dennis P. Carmody

task. As the figure indicates, there are significant relations between the processing units and memory performance with a tendency for the beta SCC and phase connection patterns to be more frontally and LH located, while increased values of the SCC and phase in delta and theta are negatively related to performance. The dominant flashlight variable is CA (all 10 locations), followed by PA (5 locations, 4 in LH), posterior PT (4 locations) and left frontal/temporal PB1 and CB1. Arousal variables positively related to performance are diffuse locations for RPA and central/posterior RPB1. The variables which were positively correlated with performance across all conditions (encoding, immediate and delayed recall) are the FPU (CA, CB1), CPU (CA), and fCPU (CA, PB1), and flashlight activity involves CA (F7, F8). The RPA variable was positively related to performance across all tasks. The variables which were consistently negatively related involve RPT, PKAT, and MT. The variable which is consistently related to performance in both the immediate and delayed recall task is F8 (PB1). Figure 14.23 shows the results for the immediate recall stage. The figure shows the importance of the FPU (CA, CB1, PA, PB1) and fCPU (CA, PB1) in memory processing as well as frontal flashlights involving CA (F7, F8) and F8 (CB1, PB1). The RPA variable retained its positive relevance. Variables negatively related to performance involve the theta and beta2 (RH in particular) frequencies as well as T6PA. Figure 14.24 shows the relations during the delayed recall task. The QEEG variables are correlated with the delayed recall score, not the total memory score. The figure supports the previous figures with the positive influence of the processing unit’s involvement in memory while the CA flashlight

Figure 14.23 All participants—immediate recall stage—correlates with reading memory. CA = Coherence Alpha; CB1 = Coherence Beta1; PA = Phase Alpha; PB1 = Phase Beta1; RPT = Relative Power Theta; RPA = Relative Power Alpha; RPB2 = Relative Power Beta2; MT = Microvolts Theta; MB2 = Microvolts Beta2; PKAT = Peak Amplitude Theta; PKAB2 = Peak Amplitude Beta2

276

CAR Model of Effective Reading

Figure 14.24 All participants—delayed recall stage—QEEG correlates with delayed recall reading memory. CD = Coherence Delta; CT = Coherence Theta; CA = Coherence Alpha; CB1 = Coherence Beta1; PD = Phase Delta; PT = Phase Theta; PA = Phase Alpha; PB1 = Phase Beta1; RPD = Relative Power Delta; RPT = Relative Power Theta; RPA = Relative Power Alpha; RPB2 = Relative Power Beta2; MD = Microvolts Delta; MT = Microvolts Theta; MB1 = Microvolts Beta1; MB2 = Microvolts Beta2; PKAD = Peak Amplitude Delta; PKAT = Peak Amplitude Theta; PKAB1 = Peak Amplitude Beta1; PKAB2 = Peak Amplitude Beta2

activity dominates the flashlight results. The LH CB1 and PB1 effect was also evident in the encoding task. As in the encoding task, frontal beta activity was negatively related to performance.

Discussion The presence of the right frontal F8PB1 during immediate and delayed recall provides a basis to Tulving’s (1972, 1983; Tulving et al. 1994) HERA hypothesis of right frontal activation patterns involved in recall tasks. The reanalysis of the data from the processing unit point of view opens up a new viewpoint on the understanding of brain functioning. Pribram’s approach to the holographic theory of brain functioning was summarized by Prideaux who states the following in presenting Pribram’s hypothesis: “dendritic processes function to take a ‘spectral’ transformation of the “episodes of perception.” . . . This transformed “spectral” information is stored distributed over large numbers of neurons. When the episode is remembered, an inverse transformation occurs that is also a result of dendritic 277

Kirtley E. Thornton and Dennis P. Carmody

processes. It is the process of transformation that gives us conscious awareness. . . . The idea is simply that each part contains some information of the whole. Or stated another way, the information (or features) are not localized, but distributed. . . . The holonomic brain theory claims that the act of “re-membering” or thinking is concurrent with the taking of the inverse of something like the Fourier transform. The action of the inverse transform (like in the laser shining on the optical hologram) allows us to re-experience to some degree a previous perception. This is what constitutes a memory. . . . Memory is a form of re-experiencing or re-constructing the initial sensory sensation. (K.E. Thornton, 2014b, p. 2) The involvement of similar variables during the input, immediate, and delayed recall tasks gives some empirical credence to the Pribram hypothesis.

Relationship to Intervention Efforts This type of information has proven useful in obtaining average memory (auditory and reading) improvements of 1.78 standard deviations in a sample of 86 brain injured, learning disabled, and non-clinical participants (K. E. Thornton & Carmody, 2013). In this research, there were 15 cases of child specific learning disability who had an average memory score of 0.94 (Standard Deviation (SD) = 0.95) on the initial reading task. Following the interventions the children’s memory score was averaging 3.06 (SD = 1.87), a SD improvement of 1.38 or 225% on the reading task. The normative value was 2.47 (SD = 2.1). The dominant intervention (over 95% of the sessions) involved the SCC and phase relations, a finding deserving more attention in the EEG biofeedback scientific field. Present educational interventions average about a 0.50 standard deviation value in improvements on employed measures (K. Thornton, 2004; K. E. Thornton, 2006). A government supported website (WhatWorks.com) reported lower effectiveness values for presently employed intervention models. Popular interventions programs such as Orton-Gillingham and FastForWord have not been able to produce justifiable results to continue employment. The Orton-Gillingham individual tutoring method research has documented 0.32 SD improvements in employed measures which were replicated with the Orton-Gillingham video tape approach (Oakland, Black, Stanford, Nussbaum, & Balise, 1998). These results were not documented to improve 2 of the 4 measures when confidence intervals and sample size statistical analysis were applied to the published data (K. Thornton et al., 2007). FastForWord interventions effectiveness results have not been replicated in three independent studies (K. E. Thornton, 2006).

Conclusion The analyses of QEEG data presented in this chapter indicate that there are significant positive and negative CAR patterns involved in reading memory performance for children, adolescents, and adults. The change in the QEEG signals with age does not solely explain the improvement in memory functioning. It also does not appear that the human brain increases the critical variables as it matures, except in some cases. The complexity of the system for the reading task is readily apparent when examining these results. However, there were some common patterns evident in the data. This report does not attempt to explain why these differences are occurring or what are the associated mental processes involved in the locations or frequencies. What the results indicate is that the brain is a complex system which must be approached as such in terms of rehabilitation. The value for these findings is that they can potentially lead to more effective educational interventions with operant conditioning EEG biofeedback approach. This type of information has been able to successfully improve cognitive abilities to a significant degree in children with reading problems. 278

CAR Model of Effective Reading

Future developments in the field need to focus on the continued development of discovery of the CAR patterns involved in different cognitive tasks and application of this information to the clinical populations which have reading problems. This information is of vital importance in the field of traumatic brain injury and the special education population and should be actively employed in these rehabilitation areas.

References Al-Kadi, M. I., Reaz, M. B., & Ali, M. A. (2013). Evolution of electroencephalogram signal analysis techniques during anesthesia. Sensors, 13(5), 6605–6635. doi:10.3390/s130506605 Arns, M., Peters, S., Breteler, R., & Verhoeven, L. (2007). Different brain activation patterns in dyslexic children: Evidence from EEG power and coherence patterns for the double-deficit theory of dyslexia. Journal of Integrative Neuroscience, 6(01), 175–190. Babiloni, C., Stella, G., Buffo, P., Vecchio, F., Onorati, P., Muratori, C., . . . Rossini, P. M. (2012). Cortical sources of resting state EEG rhythms are abnormal in dyslexic children. Clinical Neurophysiology, 123(12), 2384–2391. doi:10.1016/j.clinph.2012.05.002 Binnie, C. D., & MacGillivray, B. B. (1992). Brain mapping—a useful tool or a dangerous toy? Journal of Neurology, Neurosurgery, and Psychiatry, 55(7), 527–529. Duffy, F. H., Burchfiel, J. L., & Lombroso, C. T. (1979). Brain electrical activity mapping (BEAM): A method for extending the clinical utility of EEG and evoked potential data. Annals of Neurology, 5(4), 309–321. doi:10.1002/ana.410050402 Fein, G., Galin, D., Yingling, C. D., Johnstone, J., Davenport, L., & Herron, J. (1986). EEG spectra in dyslexic and control boys during resting conditions. Electroencephalography and Clinical Neurophysiology, 63(2), 87–97. Henry, C. E. (1944). Electroencephalograms of normal children. Monographs of the Society for Research in Child Development, 9(3), i, iii, v–xi, 1–71. John, E. R., Karmel, B. Z., Corning, W. C., Easton, P., Brown, D., Ahn, H., . . . Schwartz, E. (1977). Numerical taxonomy identifies different profiles of brain functions within groups of behaviorally similar people. Neurometrics Science, 196(4297), 1393–1410. Johnstone, J., Galin, D., Fein, G., Yingling, C., Herron, J., & Marcus, M. (1984). Regional brain activity in dyslexic and control children during reading tasks: Visual probe event-related potentials. Brain and Language, 21(2), 233–254. Kaiser, D. A. (2008). Functional connectivity and aging: Comodulation and coherence differences. Journal of Neurotherapy, 12(2–3), 123–137. Kennard, M. A. (1953). The electroencephalogram in psychological disorders; a review. Psychosomatic Medicine, 15(2), 95–115. Kennard, M. A., & Levy, S. (1952). The meaning of the abnormal electro-encephalogram in schizophrenia. The Journal of Nervous and Mental Disease, 116(5), 413–425. Kenny, T. J., Clemmens, R. L., Cicci, R., Lentz, G. A., Jr., Nair, P., & Hudson, B. W. (1972). The medical evaluation of children with reading problems (dyslexia). Pediatrics, 49(3), 438–442. Oakland, T., Black, J. L., Stanford, G., Nussbaum, N. L., & Balise, R. R. (1998). An evaluation of the dyslexia training program: A multisensory method for promoting reading in students with reading disabilities. Journal of Learning Disabilities, 31(2), 140–150. Park, H. J., & Friston, K. (2013). Structural and functional brain networks: From connections to cognition. Science, 342(6158), 1238411. doi:10.1126/science.1238411 Pascual-Marqui, R. D., Michel, C. M., & Lehmann, D. (1994). Low resolution electromagnetic tomography: A new method for localizing electrical activity in the brain. International Journal of Psychophysiology, 18(1), 49–65. Petsche, H., Etlinger, S. C., & Filz, O. (1993). Brain electrical mechanisms of bilingual speech management: An initial investigation. Electroencephalography and Clinical Neurophysiology, 86(6), 385–394. Swartz, B. E. (1998). The advantages of digital over analog recording techniques. Electroencephalography and Clinical Neurophysiology, 106(2), 113–117. Thornton, K. (2000a). Electrophysiology of auditory memory of paragraphs. Journal of Neurotherapy, 4(3), 45–73. Thornton, K. (2000b). Exploratory analysis: Mild head injury, discriminant analysis with high frequency bands (32–64 Hz) under attentional activation conditions & does time heal? Journal of Neurotherapy, 3(3–4), 1–10. Thornton, K. (2000c). Improvement/rehabilitation of memory functioning with neurotherapy/QEEG biofeedback. Journal of Head Trauma Rehabilitation, 15(6), 1285–1296.

279

Kirtley E. Thornton and Dennis P. Carmody Thornton, K. (2002a). Electrophysiology (QEEG) of effective reading memory: Towards a generator/activation theory of the mind. Journal of Neurotherapy, 6(3), 7–66. Thornton, K. (2002b). Electrophysiology of visual memory for Korean characters. Current Psychology, 21(1), 85–108. Thornton, K. (2002c). The improvement/rehabilitation of auditory memory functioning with EEG Biofeedback. Neurorehabilitation, 17(1), 69–81. Thornton, K. (2003). Electrophysiology of the reasons the brain damaged subject can’t recall what they hear. Archives of Clinical Neuropsychology, 17, 1–17. Thornton, K. (2004). A cost/benefit analysis of different intervention models for the LD/special education student. Biofeedback, Winter, 9–13. Thornton, K., Carroll, C., & Cea-Aravena, J. (2007). Remediation of reading disability: Contributions of an activation database to effective treatment planning. Neuroconnections, July, 9–11. Thornton, K. E. (1999). Exploratory investigation into mild brain injury and discriminant analysis with high frequency bands (32–64 Hz). Brain Injury, 13(7), 477–488. Thornton, K. E. (2002). The improvement/rehabilitation of auditory memory functioning with EEG biofeedback. NeuroRehabilitation, 17(1), 69–80. Thornton, K. E. (2006). No child left behind goals (and more) are attainable with neurocognitive interventions (Vol. 1). North Charleston, SC: Booksurge Press. Thornton, K. E. (2014a). A QEEG activation methodology that obtained 100% accuracy in the discrimination of traumatic brain injured from normal and does the learning disabled show the brain injury pattern? NeuroRegulation, 1(3–4):209–218. doi:10.15540/nr.1.3-4.209 Thornton, K. E. (2014b). Technical foundations of the activation quantitative EEG evaluation. Retrieved from chp-neurotherapy.com Thornton, K. E., & Carmody, D. P. (2005). Electroencephalogram biofeedback for reading disability and traumatic brain injury. Child and Adolescent Psychiatric Clinics of North America, 14(1), 137–162, vii. doi:10.1016/j. chc.2004.07.001 Thornton, K. E., & Carmody, D. P. (2008). Efficacy of traumatic brain injury rehabilitation: Interventions of QEEG-guided biofeedback, computers, strategies, and medications. Applied Psychophysiology and Biofeedback, 33(2), 101–124. doi:10.1007/s10484–008–9056-z Thornton, K. E., & Carmody, D. P. (2009a). Eyes-closed and activation QEEG databases in predicting cognitive effectiveness and the inefficiency hypothesis. Journal of Neurotherapy, 13(1), 1–21. Thornton, K. E., & Carmody, D. P. (2009b). Traumatic brain injury rehabilitation: QEEG biofeedback treatment protocols. Applied Psychophysiology and Biofeedback, 34(1), 59–68. doi:10.1007/s10484–009–9075–4 Thornton, K. E., & Carmody, D. P. (2010). Quantitative electroencephalography in the assessment and rehabilitation of traumatic brain injury. In R. A. Carlstedt (Ed.), Handbook of integrative clinical psychology, psychiatry, and behavioral medicine (pp. 463–508). New York: Springer Publishing Company. Thornton, K. E., & Carmody, D. P. (2012). Symbol digit and the quantitative EEG. Journal of Neurotherapy, 16(3), 210–222. Thornton, K. E., & Carmody, D. P. (2013). The relation between memory improvement and QEEG changes in three clinical groups as a result of EEG biofeedback treatment. Journal of Neurotherapy, 17(2), 116–131. doi:1 0.1080/10874208.2013.785183 Tulving, E. (1972). Episodic and semantic memory. In E. Tulving & W. Donaldson (Eds.), Organization of memory. New York: Academic Press. Tulving, E. (1983). Elements of episodic memory. Oxford: Clarendon Press. Tulving, E., Kapur, S., Craik, F. I., Moscovitch, M., & Houle, S. (1994). Hemispheric encoding/retrieval asymmetry in episodic memory: Positron emission tomography findings. Proceedings of the National Academy of Sciences USA, 91(6), 2016–2020. Weiss, S., & Mueller, H. M. (2003). The contribution of EEG coherence to the investigation of language. Brain and Language, 85(2), 325–343. Weiss, S., & Rappelsberger, P. (2000). Long-range EEG synchronization during word encoding correlates with successful memory performance. Cognitive Brain Research, 9, 299–312. Wig, G. S., Schlaggar, B. L., & Petersen, S. E. (2011). Concepts and principles in the analysis of brain networks. Annals of the New York Academy of Sciences, 1224, 126–146. doi:10.1111/j.1749–6632.2010.05947.x Yingling, C. D., Galin, D., Fein, G., Peltzman, D., & Davenport, L. (1986). Neurometrics does not detect “pure” dyslexics. Electroencephalography and Clinical Neurophysiology, 63(5), 426–430.

280

PART V

sLORETA/LORETA and Z-Score Training

15 sLORETA IN CLINICAL PRACTICE Not All ROIs Are Created Equal Mark Llewellyn Smith

Abstract QEEG driven neurofeedback relies on a statistical threshold of significance to define pathology. Many, if not most, clinicians would describe abnormal brain electrical activity as two standard deviations from the mean value being assessed. This threshold is not always achieved in networks of function in a clinical setting. When this occurs it becomes necessary to analyze brain structures most often related to pathological behavior with a lower statistical threshold. Clinical experience with sLORETA/ LORETA training has led to the establishment of a hierarchical set of regions of interest (ROI) that most often lead to clinical success. The field of QEEG driven neurofeedback training has promoted the process of identifying pathology through a statistical analysis of brain indices that link deviance to locations within networks of function. The definition of pathology relies on a statistical definition of deviance: two standard deviations removed from the mean value being measured. This process has been successful in clinical practice. There are clients whose brain maps do not conform to this standard of deviance within functional networks. A clinical approach that associates brain areas with behavior and lowers the statistical standard has been successful in providing targets for neurofeedback training. Advancements in neurofeedback software, specifically sLORETA training and real-time analytics, have allowed for a powerful means of intervention beyond the traditional database driven protocols. LORETA imaging was reported by Cannon and Lubar as early as 2004 with work on a nonclinical population that set the foundation for the addition of this technology in clinical practice (R. Cannon, Lubar, Thornton, Wilson, & Congedo, 2004). Later work by this group suggested that it was possible to change coherence and EEG spectral power, leading to changes in behavior through LORETA neurofeedback training (R. Cannon et al., 2006). This imaging technology has recently moved out of the academy and into clinical practice. Initially useful as a method to move “stuck” clients, currently sLORETA is a first order intervention in neurofeedback training utilized to address a host of clinical presentations. With the addition of a normative database it is possible to make clinical decisions and adjust training “on the fly.” This is made possible by combining analytic tools that include the LIVE LORETA Projector, an imaging tool developed by Brainmaster Technologies, Inc., real-time surface brain maps, and real-time Z-scores. QEEG brain mapping is used to assess brain function and plan treatment strategy. In addition, other tools including LORETA/sLORETA can be employed to determine the location of deviance within functional networks. Post hoc identification of the most extreme deviant region in cortex is 283

Mark Llewellyn Smith

helpful for clinical decision making particularly when it is correlated with functional location and client complaint. The customary definition of pathology in QEEG analysis is a Z-score that is a minimum of two standard deviations from the mean value being assessed whether it is coherence, phase, absolute power, or one of several other QEEG measurements. However, brain maps of clients with acute pathology do not always reflect this standard in networks of function. When this occurs, it becomes necessary to lower the statistical standard of pathology or to train a functional network that is related to the client’s chief complaint despite its “normative” expression. Selecting regions of interest (ROIs) to train in these circumstances may rely on real-time analytics that focus on Z-scored moments in time during training. Very often these circumstances must take into account clinical wisdom, as well as functional neuroanatomy, client complaint, and the results of software innovations that provide immediate information through advanced imaging technologies. The early days of Z-score training reflected an analytic versus clinical wisdom conundrum. While much successful clinical work was accomplished training deviant neuronal patterns based on brain mapping, early reports suggested that four channel Z-score training across the midline for many presentations was equally as successful (Collura, Thatcher, Smith, Iambos, & Stark, 2009; Hammer, Colbert, Brown, & Ilioi, 2011). The treatment strategy was later validated in imaging research that identified a structural core at the posterior/anterior midline. Research argued that the structural core in this region contained hubs that linked all major modules of cortex and suggested a reason for theses clinical successes (Hagmann et al., 2008). In many cases it is as or more effective to begin training in this manner regardless of client complaint. sLORETA neurofeedback may have a similar hierarchy in ROIs related to successful neurofeedback training. Regions of interest that may lie at the apex of a behavioral hierarchy include the Default Mode Network (DMN), the Cingulate Gyrus, the right Insula, and the right Prefrontal Gyrus. These regions have been identified in research as critical to behavior often found compromised in a clinical setting. Assessing and training these regions as a first order intervention even when they have not met the pathological watershed of two standard deviations has proved clinically successful. The Anterior Cingulate Gyrus (ACC) has been associated with the hierarchical control of cognitive signals. As part of the Salience Network with the right Insula, it acts as a brain switch with respect to the activation and deactivation of the central executive network and the DMN (Sridharan, Levitin, & Menon, 2008). It identifies subjective salience whether cognitive, homeostatic, or emotional and recruits appropriate neuronal networks to process the stimuli. A recent case study that trained increased current source density (CSD) in the cognitive division of the ACC produced positive changes in working memory and processing speed (R. Cannon & Lubar, 2011). In clinical practice we address the cognitive distortions of our clients as a matter of course. The Salience Network is implicated in this distortion in many presentations including those clients with somatic complaints. Pain experts often refer to the “perception of pain” making the distinction between chronic and acute pain that acknowledges that chronic pain may not have well defined physiological causes. The brain regions involved in the processing of pain cited in many studies include the Anterior Cingulate Gyrus and Frontoinsular Cortices (FIC) (Apkarian, Bushnell, Treede, & Zubieta, 2005; Brown, Seymour, El-Deredy, & Jones, 2008). The ACC and FIC, structures of the limbic system, have been implicated in the affective processing of pain (Apkarian et al., 2005; Morrison, Lloyd, Di Pellegrino, & Roberts, 2004; Price, 2000; Singer et al., 2004). Apkarian identified the ACC, as cited across many studies, for having a particularly robust activation pattern in both chronic and acute pain. The referenced studies have divided the ACC into as few as four and as many as six components with affective reactions to pain localized to the perigenual (rostral) ACC. The alpha band, nominally 8–12 Hertz, is a particularly useful one in neurofeedback training for a variety of clinical presentations. One primary physiological function of synchronized alpha band power may be as a control mechanism particularly with respect to memory storage. It has also been hypothesized to direct the flow of information to task relevant brain structures while inhibiting 284

sLORETA in Clinical Practice

regions that are task irrelevant (Klimesch, Fellinger, & Freunberger, 2011; Knyazev, 2007). More generally, it may have a preventative, inhibitory role in sparing brain regions from continuous excitatory impulses (Simonov, 1968). This quieting function of the alpha band may have been helpful with a recent chronic pain client when the ACC was trained with sLORETA neurofeedback, substantially reducing the intensity of pain.

The Client (1) The client was a 72-year-old male who suffered a “stroke like event” that resulted in reduced functionality of his right forearm and hand. The impairment in the right arm and hand hindered his ability to play the piano, a much prized avocation. The MRI analysis was normal. The MRI made clear that the insult to the client’s brain did not result in tissue damage. The client was not naive to neurofeedback training, having trained in several modalities prior to the introduction of sLORETA training. QEEG analysis revealed insufficient power in the central, bilateral posterior temporal, and parietal areas in the delta band. Mildly insufficient power was evident in posterior regions from 21 to 30 Hertz. The Z-Scored LORETA Viewer analysis revealed reduced current sources in the Superior Frontal and Anterior Cingulate Gyri (Figures 15.2 and 15.3). Additionally, hypocoherence was

Figure 15.1

Pre-treatment QEEG.

285

Figure 15.2 Insufficient amplitude of Current Source Density (CSD) at 1 Hertz in the right Superior Frontal Gyrus.

Figure 15.3 Pre-treatment LORETA analysis revealing insufficient Current Source Density (CSD) in the Anterior Cingulate Gyrus at 11 Hertz. The Z-score is 1.6 standard deviations less than the mean.

sLORETA in Clinical Practice

discovered in all bands but the beta band, with most deviance in the delta band between visual and motor areas, temporal and motor areas, speech and motor regions, and sensory and temporal areas of cortex. Phase lag or the speed of information sharing was deviant in all bands affecting most areas of cortex with most deviance in the delta band.

sLORETA Training Training was executed with BrainMaster Inc.’s Discovery amplifier and the BrainAvatar sLORETA software. The trained regions of interest (ROI) were imaged with BrainMaster’s Live sLORETA Projector. Z-score training was employed using Applied Neuroscience Inc.’s Neuroguide Normative Database with Brainmaster BrainAvatar software. A simultaneous combination of sLORETA and surface four channel Z-score training was implemented targeting the deeper structures that included the Superior Frontal Gyrus, Post Central Gyrus, Anterior Cingulate Gyrus, sensory, and motor areas in posterior regions. Bilateral central and parietal 10/20 sites were trained with Z-scores while simultaneously training to increase either 1–3 or 1–5  Hertz in the aforementioned structures with sLORETA. Nine sessions were completed with minor improvement in the mobility and dexterity of the right arm and hand. However, the client did report an unusually vivid “clean windshield” effect after the first session: Wanted to report back to you on the treatment of this morning, as you had requested. What took place holds ENORMOUS promise for healing the weaknesses in the brain we discussed today, as of this moment, 9 pm. When I left your office and walked down the hall toward the elevator, I could tell that something significant had taken place. I felt wider and larger and clearer in some unknown way. I felt, and still feel, that my right side was working better than when I woke up this morning. All this is talk but something did happen. My right foot landed cleaner on the ground at least for a time. It may have regressed later in the day somewhat. Is that possible? That’s how it felt. That the client was not naive to neurofeedback argues that the report may reflect a genuine treatment effect rather than placebo. Additionally, this kind of first response, reflecting a profound sensorial clarity, has been the reaction of several patients trained with sLORETA. After the initial training session, the client began to complain of increased pain in both knees, a chronic arthritic condition which he attributed to an additional intervention related to his presenting problem. As the training proceeded, the pain steadily increased to the point where the patient’s mobility was severely limited. A review of the pain literature suggested several brain structures on which to focus treatment. The client’s QEEGs, collected every training session, continually revealed insufficient power in Anterior Cingulate Gyrus. This structure is involved in pain and motor processes. The ACC became the focus of training with very little improvement in either complaint. Training in the ACC had been focused on the delta and theta bands. A fresh look at the Neuroguide Z-scored LORETA analysis revealed that the alpha band power was deficient in the ACC (see Figure 15.5a). It was less than the two standard deviations away from the population mean, the generally accepted standard in the field as a marker for pathology. Nonetheless, it was hypothesized that the lack of alpha was contributing to an overactive ACC, producing a sensitivity to the painful condition. At the eleventh session, the client was trained with a combination of surface Z-score training and sLORETA CSD to increase 8 to 12 Hertz in the ACC. The training was 17 minutes in duration. The next day via email the client stated: Felt blissful yesterday afternoon. Was walking extremely well. Felt on top of the world. A profound clarity. A seldom-felt calmness of the emotional system. Very “in the moment”, if that makes sense. 287

Mark Llewellyn Smith

I had a wonderful full feeling in my chest and back, way out of the ordinary. Would not mind feeling like that again (oh, for the rest of my life). 1.

2. 3.

Right hand. Two things mixed in. The soreness in my middle finger which runs up into my right forearm and into my right elbow. On top of this, the lack of dexterity and strength in the right hand, especially the pointing finger and the middle finger. Not sure if I’ve mentioned this to you before, but I often drop things from my right hand, silverware, tablets, pens and the like. Sometimes dishes. Strength is way down from normal. Am reluctant to shake hands often because of a weak grip and soreness in the middle finger. Dexterity in the right hand is still compromised. Knees felt very good yesterday afternoon. Was able to go up and down stairs close to normally.

This report is striking for several reasons. The euthymic response is clear. The training produced a significant reduction in pain when focused on the ACC, a limbic structure that is consonant with the pain literature. At the same time, there was no improvement in the right arm and hand. The mixed response argues against placebo and for a training response. Moreover, at the next session, to address the client’s concern with his chief complaint, the right hand and arm, the training moved away from the ACC to train other brain regions. After that session the client complained that the knee pain had returned and via email stated: Still experiencing profound clarity pretty much continuously. Knees still sore. At the following session we returned to training an alpha increase in the ACC in combination with Z-score training of bilateral Central Parietal 10/20 sites. The training session was 20 minutes in length. The next day via email the client stated: I experienced immediate relief in the knees after the session yesterday. Up and down stairs was significantly better. Remains so today. 50% better. Mathematically though, 50% still remains. I would argue that the client’s training is a de facto A-B-A research design. Observe the reduction in pain with the initial sLORETA training of the ACC with alpha, the A condition. Then the return of painful stimuli when the training is focused on other brain structures, the B condition. Finally, the reduction in pain with the reimplementation of the A condition: sLORETA training of the ACC with alpha. Three more sessions of sLORETA training of an alpha increase in the ACC were performed. The level of pain continued to improve, although more slowly, without the dramatic reductions of the earlier sessions. At the next session: I am experiencing a slow but steady improvement in my knees and ankles and hips and right elbow. And after the last session: Am experiencing incremental but perceptive lessening of the knee and other joint pain. I just walked up the stairs in our house and could feel the improvement in my steps. Have experienced this pretty much since the session yesterday. The elbow pain has improved. The forearm pain has improved. The ankle and hip pain has improved. If this is a placebo effect, then that would be fine also. I understand the placebo effect is a VERY powerful healing phenomenon. There are several factors that may have contributed to the client’s positive response that should be considered. The literature points to placebo being effective for more than a third of patients presenting with certain conditions, pain among them (Beecher, 1955). Although not naive to neurofeedback, sLORETA training requires a full cap rather than the few electrodes that characterized his early 288

sLORETA in Clinical Practice

neurofeedback training, increasing the potential for a subject-expectancy effect. This definition of placebo happens when a client expects a certain result and unconsciously affects the outcome. The most salient argument against a treatment effect would be the misattribution of the effect to another agent. The client discontinued an herbal intervention at the approximate time of his pain reduction. The increase in pain may have been attributable to the remedy and the decrease due to its removal. I would make several arguments for a training effect as the cause for the client’s experience of pain reduction. The client did not submit to neurofeedback for the pain condition and so had no preconceived notion that the training might improve the condition. The client’s comments make it clear that he was aware of the potential for a placebo effect. This awareness may reduce the unconscious power of placebo in the intervention. His swift positive response to increasing alpha in the ACC argues against the expected slower resolution of discontinuing the herbal remedy. Finally, the client was not made aware of the changes in cortical location and frequency during the course of training. The increase in pain after moving away from Anterior Cingulate training and the reduction in pain following the return to ACC alpha training are strongly suggestive of a treatment effect. Post-treatment analysis revealed improvements in information sharing as demonstrated by the complete resolution of deviance in coherence in the delta, theta, and alpha bands (Figure 15.4). Moreover, phase lag revealed extensive remediation in all bands. Most importantly, Z-scored alpha current source density improved across the band with an increase of 0.8 STD at 11 Hertz in the Anterior Cingulate Gyrus (Figure 15.5).

Figure 15.4 Post-treatment QEEG reveals delta band resolution and improvements in network information sharing.

289

(a)

(b)

Figures 15.5a and 15.5b Pre- and post-treatment LORETA analysis reveals an increase of 0.8 STD in alpha power at 11 Hertz. Direct your attention to the rectangular box at the right bottom corner. Note the same Taliarach coordinates: X = 3, Y = 30, Z = 15, denoting the exact area being assessed. The activity in this area of pre-treatment is expressed as a negative Z-score = –1.61932. The activity in the post-treatment analysis is observed to be –0.87358, an increase in 11 Hertz by almost a standard deviation.

sLORETA in Clinical Practice

The Default Mode Network has been observed to be active during self-referential, introspectively oriented mental activity and has been hypothesized to play a central role in the maintenance of a sense of self (Andrews-Hanna, Reidler, Sepulcre, Poulin, & Buckner, 2010; Fransson, 2005; van den Heuvel, Mandl, Kahn, & Hulshoff Pol, 2009). The Precuneus, one of the last areas of the brain to be myelinated, is a core region of the Default Mode Network. It becomes less active in externally driven tasks but increases in activation in response to rest and certain self-referential tasks such as autobiographical memory. It has the highest resting metabolic rate within the DMN, utilizing 35% more glucose than any other region in the human cortex. Moreover, the region’s extensive connectivity to higher association regions suggests a direct involvement in integrating internally and externally driven stimuli (Utevsky, Smith, & Huettel, 2014). Recent research suggests that a core function of the DMN is cognitive integration and constraint. That is, the brain works through the Default Mode Network to limit an individual’s experience of the world to prevent over-stimulation (Carhart-Harris et al., 2012). The authors hypothesized that in pathology, DMN regions are too rigid thus preventing new information from being processed and assimilated. For instance, activity in the Medial Prefontal Cortex is known to be elevated in depression. Trait pessimism and pathological brooding have been linked to this same region in cortex (Bhagwagar et al., 2007; Farb, Anderson, Bloch, & Segal, 2011). In the literature, depression has been characterized as an “over stable” state of rigid pessimism (Holtzheimer & Mayberg, 2011) So it follows, then, that desychronized EEG activity leading to the overactivation of DMN may play a role in the maintenance of pathology. New behavior may be integrated more easily through training to “relax” these areas, leading to a more flexible sense of self. Alpha band power has been offered by researchers as a means of cortical inhibition (Klimesch, Sauseng, & Hanslmayr, 2007). They postulated that high amplitude, synchronized alpha activity in the scalp EEG reflects a state of cortical inhibition or low cortical excitability. This EEG band, nominally 8 to 12 Hertz, was used in sLORETA training of DMN regions in a clinical population facilitating positive behavioral change.

The Client (2) A women in her mid-30s diagnosed with Borderline Personality Disorder. She suffered with extreme affective dysregulation and Bulimia. She was unable to maintain an intimate relationship. She selfmedicated with alcohol. The client submitted to 35 sessions of Infra-slow Frequency training targeting temporal, parietal, and frontal regions. The training substantially reduced the incidence of purging, promoted better affective regulation, and reduced her acting out behaviors. The client reported continued bouts of anxiety, agitation and frustration in maintaining a satisfying intimate relationship. It is interesting to note the substantial remediation in the amount of network information sharing as measured by the Coherence metric. The speed of information transfer was improved in the high beta band. Absolute power changed less dramatically but did reveal shifts toward more normal expression in posterior regions in the beta band and the right prefrontal regions showed less absolute power in the delta band. Despite these changes, her frustration in maintaining an intimate relation remained unabated. Moreover, she reported dreading being alone for any length of time. sLORETA training of the DMN was proposed as a means to address the core issue of Borderline Personality Disorder: a pervasive pattern of instability in self-image. Despite the normal alpha band power globally revealed her in brain maps, an examination of the eyes closed raw EEG revealed many moments that lacked alpha band activity (Figure 15.7)—again making the point that two standard deviations from the statistical mean as the measure of pathology needs to be tempered by other forms of examination.

291

Figures 15.6

Pre/Post ISF training brain maps.

Figures 15.6

(Continued)

Mark Llewellyn Smith

Figure 15.7

Raw traces of EEG during training. Note the complete lack of alpha morphology.

Figures 15.8 and 15.9 are real-time analytics that confirmed deficient slow wave activity. These analytic tools suggested frequent moments of low alpha, particularly in posterior regions. Figure 15.8 is a real-time Z-scored absolute power map of the theta and alpha band revealing low power. Figure 15.9 is the Z-scored sLORETA Projector identifying insufficient power in alpha band in the Precuneus. Both analytical tools are depictions of transient moments in time and are not quantified Z-scores. sLORETA training included using Z-scores and uptraining current source density. We explored the impact of the training on several regions of interest. The goal was to have the most immediately positive impact on agitation and anxiety. ROIs trained included the Medial Frontal Gyrus; Brodmann areas 7, 39, 40; the Posterior Cingulate Gyrus; and the Precuneus. The Precuneus provided the best response in terms of measurable autonomic regulation. The client’s peripheral body temperature, normally in the mid/low eighties, climbed into the low 90s during Precuneus training, indicating relaxation and parasympathetic response. Moreover, the client preferred this region as a target of training over others, reporting a profound existential experience. She completed 25 sessions of eyes closed sLORETA training. The majority of those sessions trained to increase alpha band power in the Precuneus with Current Source Density. During the training her real-time Z-scored maps revealed that she used a “deviant” information sharing strategy to promote a “normal” level of alpha power. Figure 15.10 depicts the client during a moment of Z-scored low alpha power. Note the alpha coherence map directly below the alpha absolute power map (fourth from left). Also observe alpha 1 and alpha 2 at the far right with similar information sharing/absolute power maps. Note that there is very little deviation and primarily hypocoherence telescoping from the right lateral frontal region. The value of real-time Z-scored analysis is revealed in these screen shots. The client uses elevated connectivity (hypercoherence) to promote elevated alpha band power. Based on this analysis, coherence was eliminated as a target of Z-score training promoting elevated alpha, alpha 1, and alpha 2 band power. Reinforcing “normal” coherence in this instance may have inhibited the client’s ability to increase absolute power. 294

sLORETA in Clinical Practice

Figure 15.8 BrainAvatar real-time analytics revealing Z-scored global insufficient absolute power in the alpha band during training.

Figure 15.9

Z-scored sLORETA Projector identifying insufficient power in alpha band in the Precuneus.

The prognosis for BPD clients is dismal at best. BPD is a serious mental illness marked by unstable moods, behavior, and relationships. According to the National Institute of Mental Health, nearly 80% of BPD clients will attempt suicide. Co-occurring disorders include depression, anxiety, substance abuse, eating disorders, and self-harm behaviors. The client exhibited all of the above during 295

Figure 15.10

Depicts a client during a moment of Z-scored low alpha power with right lateral frontal region hypocoherence.

Figure 15.11

Depicts the client during a moment of “normal” alpha power. Note the globally deviant hypercoherence. At the far right, alpha 1 and alpha 2 reveal normal absolute power levels with the same deviant coherence expression.

Mark Llewellyn Smith

the course of treatment. She has made remarkable progress despite her formidable obstacles. Chief among her gains, two stand out as emblematic of her overall progress. After the termination of treatment she maintained an intimate relationship for over a year and when that relationship ended she was able to remain alone for months without becoming symptomatic.

Conclusion Neurofeedback training has continued to evolve from simple one channel training in its infancy to more mature clinical approaches with multiple channels that include normative databases and now utilize the inverse solutions of sLORETA/LORETA. QEEG driven neurofeedback has relied on a statistical definition of pathology that does not always prove useful in a clinical setting. In those circumstances, an analysis of brain structures most related to behavior with a lower statistical threshold is indicated. Real-time analytics should be utilized in protocol choice, assessment of clinical efficacy, and protocol modification.

References Andrews-Hanna, J. R., Reidler, J. S., Sepulcre, J., Poulin, R., & Buckner, R. L. (2010). Functional-anatomic fractionation of the brain’s default network. Neuron, 65(4), 550–562. Apkarian, A. V., Bushnell, M. C., Treede, R.-D., & Zubieta, J.-K. (2005). Human brain mechanisms of pain perception and regulation in health and disease. European Journal of Pain, 9(4), 463–463. doi:10.1016/j. ejpain.2004.11.001 Beecher, H. K. (1955). The powerful placebo. JAMA: The Journal of the American Medical Association, 159(17), 1602–1606. Bhagwagar, Z., Murthy, N., Selvaraj, S., Hinz, R., Taylor, M., Fancy, S., . . . Cowen, P. (2007). 5-HTT binding in recovered depressed patients and healthy volunteers: A positron emission tomography study with [11C]DASB. American Journal of Psychiatry, 164(12), 1858–1865. doi:164/12/1858 [pii] 10.1176/appi.ajp.2007.06111933 Brown, C. A., Seymour, B., El-Deredy, W., & Jones, A. K. P. (2008). Confidence in beliefs about pain predicts expectancy effects on pain perception and anticipatory processing in right anterior insula. Pain, 139(2), 324–332. Cannon, R., & Lubar, J. (2011). Long-term effects of neurofeedback training in anterior cingulate cortex: A short follow-up report. Journal of Neurotherapy, 15(2), 130–150. doi:10.1080/10874208.2011.570688 Cannon, R., Lubar, J., Gerke, A., Thornton, K., Hutchens, T., & McCammon, V. (2006). EEG spectral-power and coherence: LORETA neurofeedback training in the anterior cingulate gyrus. Journal of Neurotherapy, 10(1), 5–31. Cannon, R., Lubar, J., Thornton, K., Wilson, S., & Congedo, M. (2004). Limbic beta activation and LORETA: Can hippocampal and related limbic activity be recorded and changes visualized using LORETA in an affective memory condition? Journal of Neurotherapy, 8(4), 4–24. Carhart-Harris, R. L., Erritzoe, D., Williams, T., Stone, J. M., Reed, L. J., Colasanti, A., . . . Nutt, D. J. (2012). Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin. Proceedings of the National Academy of Sciences, 109(6), 2138–2143. doi:10.1073/pnas.1119598109 Collura, T. F., Thatcher, R. W., Smith, M. L., Lambos, W. A., & Stark, C. R. (Eds.). (2009). EEG biofeedback training using Z-scores and a normative database (2nd ed.). New York: Elsevier. Farb, N. A. S., Anderson, A. K., Bloch, R. T., & Segal, Z. V. (2011). Mood-linked responses in medial prefrontal cortex predict relapse in patients with recurrent unipolar depression. Biological Psychiatry, 70(4), 366–372. doi:10.1016/j.biopsych.2011.03.009 Fransson, P. (2005). Spontaneous low-frequency BOLD signal fluctuations: An fMRI investigation of the resting-state default mode of brain function hypothesis. Human Brain Mapping, 26(1), 15–29. doi:10.1002/ hbm.20113 Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008). Mapping the structural core of human cerebral cortex. Public Library of Science Biology, 6(7), e159. doi:07-PLBIRA-4028 [pii] 10.1371/journal.pbio.0060159 Hammer, B., Colbert, A., Brown, K., & Ilioi, E. (2011). Neurofeedback for Insomnia: A pilot study of Z-score SMR and individualized protocols. Applied Psychophysiology and Biofeedback, 36(4), 251–264. doi:10.1007/ s10484–011–9165-y

298

sLORETA in Clinical Practice Holtzheimer, P. E., & Mayberg, H. S. (2011). Stuck in a rut: Rethinking depression and its treatment. Trends in Neurosciences, 34(1), 1–9. doi:10.1016/j.tins.2010.10.004 Klimesch, W., Fellinger, R., & Freunberger, R. (2011). Alpha oscillations and early stages of visual encoding. Frontiers in Psychology, 2, 1–11. doi:10.3389/fpsyg.2011.00118 Klimesch, W., Sauseng, P., & Hanslmayr, S. (2007). EEG alpha oscillations: The inhibition–timing hypothesis. Brain Research Reviews, 53(1), 63–88. Knyazev, G. G. (2007). Motivation, emotion, and their inhibitory control mirrored in brain oscillations. Neuroscience & Biobehavioral Reviews, 31(3), 377–395. Morrison, I., Lloyd, D., Di Pellegrino, G., & Roberts, N. (2004). Vicarious responses to pain in anterior cingulate cortex: Is empathy a multisensory issue? Cognitive, Affective, & Behavioral Neuroscience, 4(2), 270–278. doi:10.3758/cabn.4.2.270 Price, D. D. (2000). Psychological and neural mechanisms of the affective dimension of pain. Science, 288(5472), 1769–1772. doi:10.1126/science.288.5472.1769 Simonov, P. V. (1968). Basic (alpha) EEG rhythm as electrographic manifestation of preventive inhibition of brain structures. In E. A. Asratyan (Ed.), Progress in brain research (Vol. 22, pp. 138–147). Cambridge, MA: Elsevier. Singer, T., Seymour, B., O’Doherty, J., Kaube, H., Dolan, R. J., & Frith, C. D. (2004). Empathy for pain involves the affective but not sensory components of pain. Science, 303(5661), 1157–1162. doi:10.1126/ science.1093535 Sridharan, D., Levitin, D. J., & Menon, V. (2008). A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proceedings of the National Academy of Science, 105(34), 12569–12574. doi:0800005105 [pii] 10.1073/pnas.0800005105 Utevsky, A. V., Smith, D. V., & Huettel, S. A. (2014). Precuneus is a functiional core of the default-mode network. The Journal of Neuroscience, 34(3), 932–940. van den Heuvel, M. P., Mandl, R. C., Kahn, R. S., & Hulshoff Pol, H. E. (2009). Functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain. Human Brain Mapping, 30(10), 3127–3141. doi:10.1002/hbm.20737

299

16 sLORETA NEUROFEEDBACK AS A TREATMENT FOR PTSD Nir Getter, Zeev Kaplan and Doron Todder

Abstract In this chapter we will discuss the localized approach for neurofeedback practice using the standardized low resolution tomography algorithm as applied to patients suffering from post-traumatic syndrome. PTSD is a chronic, severe and disabling mental disorder resulting from the exposure to specific, prolonged or a series of threatening events in which the individual experiences intense anxiety due to immediate danger to one’s self or exposure to other people’s injury, suffering and death. Providing a relief for these people’s suffering is a major concern in most of the modern citizen and military healthcare systems. In this chapter we will use the neuropsychological theoretical framework for the understanding of PTSD suggesting that these symptoms are the cognitive and behavioral consequences of post-traumatic altered functioning of the fronto-temporal limbic network. In our discussion we put an emphasis on the amygdale and the ventromedial prefrontal cortex (vmPFC) and their connectivity properties. We will suggest an improved approach to the traditional alpha-theta neurofeedback practice by a localized tomographic neurofeedback that is based on the advanced EEG standardized low resolution tomographic method (sLORETA). Having the patient practice the localized brain activity in the vmPFC at the theta band power we expected the resetting of the frontotemporal limbic network to a more healthy state providing the patient with anticipated symptoms relief. We present the case of one patient suffering from PTSD resistant to other treatments who was practicing sLORETA neurofeedback treatment. This patient’s intrusion symptoms were improved after 22 sLORETA neurofeedback meetings. Furthermore, we demonstrated executive function and memory tasks performance improvement after the last session compared to baseline.

Post-Traumatic Stress Disorder Clinical Perspective The Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-5) define PTSD as a pathological response to an event in which one is exposed to a serious threat of injury, death and then experiences extreme fear, helplessness or horror. This disorder is characterized by three symptom clusters: (1) The re-experiencing symptom cluster is characterized by recurrent and intrusive recollections, dreams of the trauma and flashbacks. (2) The hyper arousal symptom cluster is characterized by an enhanced startle response, sleep and concentration difficulties, problems with anger management, 300

sLORETA Neurofeedback as a Treatment for PTSD

hyper-vigilance for danger and a sense of a foreshortened future. (3) The avoidance symptom cluster includes symptoms such as an inability to remember aspects of the event, extreme distress and avoidance of cues even remotely related to the actual trauma and emotional numbing with difficulties feeling positive emotions.

Neuronal Network Implicated in PTSD Fear Conditioning Fear conditioning is the process by which a previously neutral conditioned stimulus (such as a tone or visual stimulus) is presented immediately before an aversive stimulus (such as a shock) and predicts its onset. After repeated presentations, the conditioned stimulus can elicit a fear response, such as freezing, increased startle or increased skin conductance (SCR). For instance, an anxious individual may learn that a stimulus or situation is threatening, which may then lead to pathological reactions (e.g., phobias and post-traumatic stress disorder). Support for this view has emerged from both animal models for PTSD (H. Cohen & Richter-Levin, 2009) and neuroimaging studies in humans (Phan, Wager, Taylor & Liberzon, 2002). The amygdala, previously found to be implicated in emotional behavior (Phelps & LeDoux, 2005), shows altered activity in animal PTSD models in comparison to controls. Different studies have shown that PTSD patients show exaggerated activation in the amygdala in response to both traumatic reminders (e.g., pictures from the traumatic scene) and more general predictors of threat (e.g., fearful facial expressions). In addition, a positive relationship between symptom severity and amygdala activation have been reported (Pissiota, Orjan, Fernandez, Fischer & Fredrikson, 2000; Shin et al., 2004). The amygdala’s activity is modulated by input from the ventromedial prefrontal cortex (vmPFC), which is thought to inhibit expression of conditioned fear following extinction training (Phelps & LeDoux, 2005). Activation in the vmPFC demonstrates a role for this region in the automatic regulation of fear prior to fear extinction. Increased vmPFC activity was observed primarily in low trait anxious individuals and was inversely correlated with SCRs (Indovina, Robbins, Núñez-Elizalde, Dunn & Bishop, 2011). Results from a prospective functional magnetic resonance imaging (fMRI) study have established the formation of a physiological coupling between the right ventromedial prefrontal cortex (vmPFC) and the right amygdala after exposure to highly stressful events (Admon et al., 2009). The strength of this coupling had a positive correlation with the magnitude of the reported post-traumatic symptoms. Interestingly, a unique lesion study (Koenigs et al., 2008) compared the size of the vmPFC lesion in brain injured trauma survivors and the risk for PTSD. Koenigs et al. concluded that the vmPFC plays a significant role in contributing to the development of chronic PTSD. Low activity or omission of this area due to physical damage might serve as a protective factor against the development of PTSD (Koenigs et al., 2008). These two studies present contradictory evidence regarding vmPFC’s role in the formation and maintenance of PTSD. One suggests that the absence or a reduction in vmPFC activity might protect from the symptomatology. The other suggests that a reduction in vmPFC activity might contribute to symptom severity (Admon et al., 2009; Koenigs & Grafman, 2009; Koenigs et al., 2008). A model suggested by Rainnie and Ressler (2009) can explain this contradiction. The model emphasizes the different roles of the vmPFC in various processes of fear conditioning (i.e., acquisition and retaining of fear responses). Neural activity in the amygdala and the vmPFC may usually be under mutual inhibitory control. These physiological connections may cognitively reflect executive “top down” control of the vmPFC on the amygdala. A breakdown of such process could contribute to the psychopathology of PTSD by means of retention of a hyper-vigilant state. Therefore, hypoactive vmPFC may contribute to the retention of PTSD symptoms. 301

Nir Getter, Zeev Kaplan and Doron Todder

Alternatively, the absence of vmPFC activity immediately following a traumatic event may prevent an individual from developing PTSD by means of interfering with the process of fear conditioning. Fear conditioning is normally formed at the time of the traumatic stressor and requires both the amygdala and the vmPFC to form the fear acquisition. Therefore, damage to the vmPFC will protect the individual from developing PTSD for no acquired fear conditioning is formed. VmPFC’s role in the modulation of fear is further emphasized by Etkin, Egner, and Kalisch (2011). This line of research strongly suggests that dorsal mPFC structures are implicated in threat appraisal and the expression of fear whereas mPFC structures are involved in the inhibition of conditioned fear (Etkin et al., 2011). Failure to activate the appropriate ventromedial prefrontal cortex structures among PTSD individuals may contribute to the retention of hyper-vigilance by blocking extinction. Further data reviewed by Etkin et al. (2011) suggest a controlled conscious top down regulation, like emotional conflict regulation, uses structures at the ventral PFC (such as vmPFC) to inhibit negative emotional processing in the amygdala. The vmPFC might thus perform a generic negative emotional inhibitory function that can be triggered/elicited by other regions (e.g. dorsal ACC and mPFC and lateral PFC) when there is a need to suppress limbic reactivity. One might speculate that a dysfunction in this circuit might contribute to the PTSD dysfunction in regulating negative emotional conflict. Unable to regulate and suppress the emotional arousal associated to trauma relevant information, the PTSD affected individual might choose to avoid trauma related arousal eliciting stimuli altogether. The limbic reactivity might contribute to hyper-vigilant responses, and affect memory processes leading to an intrusion, as well. Dysfunction in the ventromedial PFC structures and their connections may contribute to all symptom clusters. To summarize, according to the fear acquisition theory in PTSD, the hyper-responsiveness of the amygdala combined with hypoactivity observed in the vmPFC can explain the individual’s hypersensitivity to threatening stimuli. The question of whether increased activity in the vmPFC will result in down-regulation of amygdala activity or PTSD symptoms is still to be answered. This perspective focuses on the hyper-vigilance cluster of symptoms and therefore covers only part of the PTSD phenomenology.

Neurocognitive Perspective In a neurocognitive perspective, it is commonly assumed that the symptom clusters of re-experiencing and avoidance are in effect a manifestation of memory impairments. These impairments can be the result of the exposure to trauma but can also present prior to the traumatic event and therefore serve as a pre-traumatic risk factor. Specifically, the autobiographical memory is hypothesized to be compromised among PTSD diagnosed population, therefore eliciting the above symptoms. Autobiographical memory is a general term describing an array of cognitive mechanisms dedicated for the encoding, retention and retrieval of information from personally experienced events. The data processed by these mechanisms includes sensory and perceptual data from the event together with feelings and thoughts activated at the time of the event. According to the encoding specificity principle suggested by Tulving and Thomson (1973), a key property of these data is their contextual relevance at the time of encoding and the time of retrieval. Therefore, perceptual and higher cognitive information that does not fit correctly with the context of the experienced event will have lesser probability to be encoded. Moreover, recollection of details from a past event will be more probable when the context of the situation is congruent with the context of the retrieved event. Recent findings from animal studies (H. Cohen, Liberzon & Richter-Levin, 2009; Ježek et al., 2010) support a relationship between stress exposure and the utilization of contextual cues in both the encoding 302

sLORETA Neurofeedback as a Treatment for PTSD

phase and retrieval phase. Failing to use context while retrieving episodic memory had been further supported by the results obtained from a study comparing memory performance of PTSD patients with controls. Guez et al. (2011) compared performance in a context sensitive recall task (a pair association recall) and a free, non-context sensitive recall task (free recall). The results have indicated that memory for the paired items remained intact while memory for the pair’s arrangement was compromised. These findings were interpreted as representing the PTSD group’s difficulties in using context for retrieving an existing memory (Guez et al., 2011). Together with other findings from neuroimaging studies (Acheson, Gresack & Risbrough, 2012; Werner et al., 2009), it is compelling to attribute intrusion and avoidance symptoms of PTSD to the difficulties of this population in retrieving memory by using its context. In PTSD, paradoxically the alterations in memory for the traumatic event can take the form of both intrusive recollections and difficulties with intentionally retrieving aspects of the traumatic event. Furthermore, individuals with PTSD are often described as showing fear responses to trauma reminders outside of contexts in which these cues would reasonably predict danger (Acheson et al., 2012). The structure most attributed to autobiographical memory dysfunction is the hippocampus (Cipolotti & Bird, 2006). It has been suggested that emotion plays a key role in the quality of memory integration preformed at the hippocampus (Dere, Pause & Pietrowsky, 2010). It has been shown that the strength of neuronal activity in the hippocampus and amygdala is correlated both during the encoding and the retrieval of emotional information (Kensinger & Corkin, 2004). Imaging studies in PTSD suggested compromised network of limbic structures including the hippocampus, the vmPFC and the amygdala (Nutt & Malizia, 2004). Hippocampal volume decreases after psychological stress (Woon, Sood & Hedges, 2010) and recent studies in animal models implicate a key role to the connectivity between the hippocampus and vmPFC specifically in the integration of context into the stored and retrieved memory (Ježek et al., 2010; van Kesteren, Fernández, Norris & Hermans, 2010). Together with the pairing of amygdala and vmPFC, an fMRI study in healthy medicine corps cadets by Admon et al. (2009) also documented a relationship between the hippocampus and the vmPFC formed after stressful events. There seems to be a correlation between the magnitude of post-traumatic symptoms as reported by the subjects and strength of the relationship between the amygdala and vmPFC. In another longitude study PTSD patients underwent two fMRI scans, 6–9 months apart, while viewing fearful and neutral faces in preparation for a memory test (administered outside the scanner). At the end of the protocol, symptom levels correlated positively with memory-related fMRI activity in the amygdala and ventromedial prefrontal cortex (vmPFC) (Dickie, Brunet, Akerib, & Armony, 2011). Taken together, findings from neuroimaging studies suggest a specific network compromised in PTSD with the amygdala, the vmPFC and the hippocampus as key neural structures. We believe that the vmPFC has a significant role in the functions of contextual encoding and retrieving of the autobiographical memory.

Intermediate Summary The fear acquisition theory and the autobiographical theory are two proposed complementary perspectives for the symptomatology of PTSD. Fear acquisition is attributed to hyperactivation of the amygdala and to the exaggerated sensitivity of PTSD individuals to threatening signals. Intrusion symptoms such as flashbacks need no threatening signals to appear and might be explained by the autobiographical model for PTSD as a failure in fitting the retrieved memory to the current context. Investigating both perspectives using different approaches has pointed to the same functional network with the medial prefrontal cortex, the amygdala and the hippocampus as its central interconnected structures. This implies that an intervention in the activity of one structure can affect the whole network with a reduction in symptoms outcome. 303

Nir Getter, Zeev Kaplan and Doron Todder

Neurofeedback Treatments for PTSD Traditionally PTSD was considered to be a type of anxiety disorder. Neurofeedback treatments for anxiety aimed primarily at increasing brain activity in the alpha band. Hardt and Kamiya (1978) assigned 16 students to high and low trait anxiety groups by means of MMPI scores and trained them to increase and decrease their alpha band activity. The results suggested a link between alpha changes and anxiety rating changes in the high anxiety group but not in the low anxiety group. Since this original study other studies have also demonstrated the efficacy of neurofeedback, specifically alpha upregulation, as an anxiety treatment. The most documented approach for the treatment of PTSD via neurofeedback training is the protocol named alpha-theta. This neurofeedback protocol was initially developed for the treatment of alcoholism (Peniston & Kulkosky, 1989) and later adopted for the treatment of PTSD (Peniston & Kulkosky, 1991). Patients participating in this protocol had a surface electrode attached to their scalp at a posterior midline location (Pz in the 10/20 electrode positioning system). Both alpha band (8–12Hz) and theta band (4–7Hz) are filtered from the EEG signal, and each signal band power is marked by a different tone. The patient task is twofold: their main task is to upregulate both alpha and theta activity by increasing both types of tone prevalence. The secondary goal is to maintain a relatively equal frequency of “alpha tone” compared to “theta tone.” To achieve the second goal a “cross-over” pattern has to emerge in which theta waves gradually increase, and the alpha waves gradually decrease. This pattern is a marker for a state of consciousness believed to increase the probability for the release of repressed imagery content. PTSD treatment by means of alpha-theta protocol was documented in a hallmark study published by Peniston and Kulkosky (1991). Twenty-nine male Vietnam combat veterans with a comorbid diagnosis of PTSD and alcohol abuse were assigned to either neurofeedback protocol or a control group. The neurofeedback protocol combined the alpha-theta practice with a relaxation program. The participants in the control group received psychotropic and behavioral therapy. All participants continued their regimen of psychotropic drugs during this study. Results indicated a reduction in the consumption of psychoactive medication by the end of the treatment compared to control. All participants from the experimental group reduced their consumption of drugs compared to only one participant from the control group. Also, by comparing MMPI questionnaire profiles of participants before and after treatment, both groups showed decreases on the schizophrenia scale, but only the experimental group showed reductions in hypochondriasis, depression, hysteria, psychopathic deviation, paranoia, psychasthenia, hypomania, introversion and the PTSD subscales. A follow-up study indicated low relapse rates in the study group compared to control. Only three of the 20 original cohorts had relapsed to alcohol by 26 months after (Peniston & Kulkosky, 1991). Graap and Freides (1998) raised two questions regarding the work of Peniston. First, are different published articles reporting independent samples? Second, what was the clinical status of the patients prior to treatment and what is the mechanism underlying the alpha-theta protocol? At least the last question can be, for some extent, answered by the work of Egner and Gruzelier (2009) on healthy high performing musicians, actors and people suffering social anxiety disorder. According to these authors, while the main benefit of alpha training is a relaxation, the alpha-theta protocol helps the participant to induce a “hypnogogic conscious state.” This served as a state for re-experiencing and reprocessing past traumatic events. “It is as though the patient was capable of integrating past traumatic experiences by coping with previously unresolved conflicts represented in the essential anxiety-free images and memories generated during the theta state of consciousness” (Gruzelier, 2009, p. 103). Neuroanatomically, these authors propose enhanced circuit connectivity, especially of frontal structures to more limbic and meso-limbic structures after alpha-theta training. Taken together that alpha-theta enhance frontal-limbic networks connectivity and the biological approach to PTSD symptomatology described above, it is reasonable to view the mechanism behind alpha-theta therapy

304

sLORETA Neurofeedback as a Treatment for PTSD

for PTSD as a reactivation of the broken inhibitory connection between the frontal structures such as the ACC or vmPFC and the amygdala and hippocampus.

Low Resolution Tomography Neurofeedback for the Treatment of PTSD A major shortcoming of traditional neurofeedback, such as the alpha-theta protocol described, relates to the limited information provided by a single electrode placed on the scalp. In conventional neurofeedback, electroencephalographic (EEG) activity is recorded at a particular scalp location. This electrical activity recorded by a single electrode represents not only below the cortical area but the sum of all neuronal activity that is detectable by any given electrode. Therefore, at the single scalp site of recording there is a weighted accumulation of all electrical signals in the brain sphere that cannot be separated. This limitation can be overcome by using more than a single electrode recording and a low resolution electromagnetic tomography (LORETA), which is a mathematical process to extract the source of the recorded data. LORETA is widespread linear, discrete, instantaneous, full-volume family of an inverse solution proximity for brain electromagnetic measurements (Pascual-Marqui, Esslen, Kochi & Lehmann, 2002). Whereas EEG is a measure of electric potential variations on a two dimensional surface, LORETA estimates the current density in a three dimensional space that results in the potential divergence on the scalp. sLORETA is an evolutionary development of the original LORETA algorithm. This novel algorithm suggests more reliable and zero localization errors compared to the old one (Pascual-Marqui, 2002). EEG tomography directed biofeedback correlates the physiological signal with a constant feedback signal; however, the physiological signal is defined as the current density in a specified region of interest (ROI) calculated by means of sLORETA algorithm. This allows the continuous feedback signal to become a function of the intracranial current density and to co-vary with it. Congedo, Lubar and Joffe (2004) established a method for extracting and providing feedback on intracranial current density, and carried out an experimental study to ascertain the ability of the participant to drive their own EEG power in a desired direction by means of this tomographic EEG biofeedback. The authors demonstrated that healthy participants have the ability to drive the current density of their own Anterior Cingulate Cortex (ACC) (a subregion of the medial prefrontal cortex; mPFC) in a desired direction using LORETA directed biofeedback. Other studies used tomographic EEG biofeedback in clinical populations such as antisocial personality disorder (Surmeli & Ertem, 2009) and chronic pain (Ozier, 2010). These studies reported successful alteration of the brain activity in the targeted anatomical location with no side effects. A neural network that includes the amygdala, the hippocampus and the medial prefrontal cortex is responsible for emotion regulation and emotional behavior among healthy population. A deficit in this network serves as a general neurophysiological account to both behavioral and cognitive deficits in PTSD. This research proposal focuses on the activity of the ventral structures of the medial prefrontal cortex and its relation to amygdala activity. We propose that, among PTSD patients, the activity of this network is shifted to a novel, inflexible homeostatic state in which the medial prefrontal cortex is mainly hypoactive, and the amygdala is hyperactive. The intrusions can be explained by a failure to control the memory retrieval. We will adopt the autobiographical memory retrieval failure hypothesis for explaining the intrusion symptoms cluster. The medial prefrontal cortex is thought to be implicated in executive functions and as such the initiator of a “top down” control over lower cognitive mechanisms. Using a context for the appropriate selection of memory representation for retrieval is considered one of the “top down” processes affected in PTSD. We will hypothesize that alleviating activity in the vmPFC will improve the subjects’ functioning in contextual retrieving tasks (Guez et al., 2011) together with mitigation in the intrusion symptoms. 305

Nir Getter, Zeev Kaplan and Doron Todder

sLORETA Neurofeedback in PTSD A compromised neural network responsible for emotional behavior serves as a general neurophysiological explanation for both behavioral and cognitive deficits. This network includes the amygdala, the hippocampus and the medial prefrontal cortex. According to the classical fear conditioning model for PTSD, amygdala hyperactivity can explain the symptoms from the hyper-vigilance and the avoidance cluster. Following this model, increasing vmPFC activity will inhibit the activity of the amygdala and will result in symptom reduction together with an observable reduction in the sensitivity to threatening stimuli. Changes in the vmPFC following treatment and symptom reduction have been documented (Dickie et al., 2011). Following this, LORETA neurofeedback (LNF) can be attempted to alter neural activity of vmPFC regions in a chronic PTSD population. In the suggested protocol, our objective was to practice an increase in brain activity at the theta (4–8Hz) band. An increase in theta activity is related to an increase in the fronto-limbic network connectivity (Denham & Borisyuk, 2000). Specifically, increase in theta activity reflects an enhanced ability to encode new information (Klimesch, 1999), the conscious feeling of knowing and the later accurate retrieval of these memories (Klimesch et al., 2001). Enhancement in anterior theta activity also correlates with an increase in hypnotic susceptibility (Brady & Stevens, 2000). In another study, differences in theta activity were observed when comparing a normal sample and a PTSD sample in response to watching emotional pictures or neutral pictures (J. E. Cohen et al., 2013). As mentioned previously, PTSD symptoms can be explained both by the fear acquisition theory and the neurocognitive model. Briefly summarizing, according to the neurocognitive model, exposure to a traumatic event form a recurrently retrieval of its memory causing an emotional distress. According to the fear acquisition theory in PTSD, the hyper-responsiveness of the amygdala combined with vmPFC hypoactivity may explain the individual’s hyper-sensitivity to threatening stimuli. Given the possible therapeutic effects of increasing theta in PTSD patients, we elected to train these patients via neurofeedback to increase theta band activity in vmPFC.

Participants Five PTSD diagnosed patients (4 male) from the PTSD clinic at the Mental Health Institute in Beer Sheva were referred to the study by their attending physician. Participants with closed or open brain injury were excluded. Four patients had above high school education. Traumatic event varied between the patients (car accidents, military actions and terrorist attack).

Procedure Participants watched a sitcom episode while wearing a 19 electrode EEG cap attached to an EEG-200 amplifier. The signal was fed into a computer running the software Brain Tuner that online calculated the sLORETA source distribution of the participant’s brain electrical activity. The software then isolated the current density in the bilateral vmPFC, at the theta band. The vmPFC defined by the sLORETA software (Pascual-Marqui, 2002) was the combination of voxels from the “medial frontal gyrus” (Brodmann area 11; 30 voxels) and Orbital gyrus (Brodmann areas 18, 13; 31 voxels); therefore our definition for vmPFC includes 61 sLORETA voxels. The LNF practice had two phases. In the first period, the treatment goal was determined by recording 3 minutes of the vmPFC current density fluctuations and taking the 80th percentile of the current density distribution of this baseline as the participant’s goal for this session. When the goal had been fixed, the practice phase immediately begun. The video quality of the television display was manipulated so that for current density higher than the goal, the video quality was sharp. But when the activity dropped under the goal, the video quality was degraded in correlation with the amount of discrepancy of the theta activity from its goal. 306

sLORETA Neurofeedback as a Treatment for PTSD

Results Three minutes of open-eye EEG (sampled at 256 Hz and digitally bandpass-filtered to between 1 and 40 Hz) was recorded before and after every LNF session. Using the paired nonparametric voxel-wise statistics implemented in the LORETA-KEY statistical software package, we analyzed the average difference of the source distribution at baseline, before the beginning of the LNF sessions and after the last LNF session. A statistically significant increase in vmPFC activity was recorded (t = 3.89, p < 0.05) at resting time with eyes open. Therefore, we could demonstrate a specific effect of LNF on the targeted brain structure. When analyzing differences in beta band activity we found that the most significant difference in the source distribution is in Brodmann area 40, but other less significant differences were distributed on other Brodmann areas. This result can be interpreted as a nonspecific effect of the LNF procedure. We also investigated the effect of LNF on connectivity between the frontal region that the participants trained and other regions, specifically limbic regions. Using the Neuroguide statistical package we set the orbitofrontal cortex (an overlap region with vmPFC) as a seed and calculated its current density correlations to other regions. In a paired t-test for the difference in correlation coefficients before compared to after NBF, we found a significant increase in correlation between the orbitofrontal cortex and other brain regions only in the theta band and not in other frequencies. This correlation increase can be interpreted as an increase in connectivity after one session of LNF, specifically in the theta activity that was the target of this protocol. This finding represents the first attempt to use LNF as treatment for severe chronic PTSD patients. We demonstrated the validity of LNF and presented proof of concept for the ability to alter the network underlying PTSD symptoms as described previously. The effect of this LNF protocol on patients’ PTSD symptoms still needs to be evaluated. In the next section, we present a case study that will demonstrate the effect of LNF treatment on one of this study participants.

Case Study—Alona Alona, age 55, married with 2 children, lives in the southern city Beer Sheva. On August 31, 2004, Alona was on her way to work when a suicide bomber blew up the bus she was on. Alona was saved from serious injury only because she bent down to pick up her bag at the exact moment the explosion occurred. Seven years later at the time of the research (2013), she reports a severe re-experiencing of the moments after the explosion both in frequent nightmares and during waking hours. She cannot get on a bus and specifically cannot get on the bus line that was bombed. When she tries to face her fears by attempting to go on this bus line, she suffers a severe physiological reaction when the bus approaches the location where the bomb exploded and has to leave the bus. She suffers from concentration problems, mood swings, feeling always alert and easily startled, always on the edge of outburst. She avoids going to any social encounters and had decreased her participation in family gatherings. Given her intense reaction to the event, Alona was diagnosed with PTSD and started treatment at the Post Trauma Clinic in the Mental Health Center of Beer Sheva. In the seven years following the event Alona received a behavioral cognitive therapy (Prolonged Exposure protocols) as well as insight-oriented psychotherapy and was pharmacologically treated with a variety of antidepressant. In spite of all the efforts, the post-traumatic symptoms continued to impair Alona’s life, and her PTSD was estimated to be resistant and not responsive to currently practiced therapies. We invited Alona to participate in a clinical study aimed at reducing her symptoms by teaching her to control specific brain activity in the vmPFC. Alona was seated in a comfortable armchair in a noise attenuated room while her brain’s electrical activity was recorded by means of a 19 electrode EEG cap. Data attained from the recording was processed online, and the current density (CD) in the bilateral vmPFC was calculated by means of the sLORETA algorithm. A video and audio clarity was altered by means of a device connected to the 307

Nir Getter, Zeev Kaplan and Doron Todder

video and audio input of a television set. Alona watched a 30-minute video with natural emotional valence. Reward for fulfilling the current density goals of the vmPFC was applied by means of a clearer picture (less distorted) and clearer sound (with less white noise). This session protocol was repeated twice a week for a total of 22 neurofeedback sessions. To measure the clinical treatment outcome, a trained psychiatrist completed with Alona the CAPS interview (Blake et al., 1990) before the first neurofeedback session and after the last neurofeedback session. Alona also completed a neurocognitive test battery that included memory, visual perception, problem solving and executive function tests, before and after the neurofeedback set. Figure 16.1 shows neuropsychological measurements before compared to after the neurofeedback set. Alona’s executive function, attention and verbal performance improved dramatically while no change was found in other cognitive domains such as memory functions, visual spatial and motor functions. Furthermore, Table 16.1 compares Alona’s baseline CAPS score to her score at the end of the treatment protocol. Alona reported a marked reduction in the frequency of intrusion symptoms (from score 14 to 4) as well as a reduction in avoidance scores (from 12 to 9). These changes are demonstrated by two anecdotal reports from Alona. First, after approximately 15 neurofeedback sessions, Alona was able to get to the hospital by the bus line that was the target for the terrorist attack. Prior to the neurofeedback treatment, she was unable to travel on this line past the junction where the terrorist detonated the bomb. Second, Alona reported a marked reduction in agitation at a fireworks display on the Israeli Independence Day. Previously she could not stay near the square where the fireworks display took place. We believe that these results show that elevation of vmPFC activity by means of sLORETA neurofeedback training resulted in a reduction of some of Alona’s PTSD symptoms.

Figure 16.1 Alona’s neurocognitive normalized score at baseline compared to follow-up assessments. Higher scores reflects better performance. Normal population score is 100.

308

sLORETA Neurofeedback as a Treatment for PTSD Table 16.1 Alona’s CAPS score at baseline (Pre) compared to follow-up assessment (Post). Higher score mean more severe and frequent PTSD symptoms. Symptoms Group Intrusion Hyper-vigilance Avoidance

Frequency/Severity

Pre

Post

F

14

4

S

10

10

F

3

2

S

3

2

F

12

9

S

10

9

Summary In this chapter, we discussed two converging models for the explanation of PTSD phenomena. Fear conditioning serves as a behavioral explanation of PTSD while the autobiographical deficit model is a cognitive related account. Both describe the same anatomical network of connected frontal and limbic structures. The dysregulation and decreased connectivity in the fronto-limbic network is, therefore, an appropriate target for intervention by means of neurofeedback techniques. The alphatheta protocol is discussed as the first protocol tested specifically on PTSD population and gained a good support for its effectiveness. This efficacy on reducing PTSD symptoms is explained as the result of an integration of fronto-limbic network and the release of repressed memories in a secure environment. Nevertheless, we suggested LORETA neurofeedback as a more specific protocol for PTSD intervention. This protocol is based on findings supporting both vmPFC and theta activity dysregulation among patients suffering from PTSD compared to normal samples. Our discussed results suggest both a specific and unspecific effect of this treatment on theta and beta activity together with an improvement in CAPS scores as described in the presented case report.

References Acheson, D. T., Gresack, J. E., & Risbrough, V. B. (2012, February). Hippocampal dysfunction effects on context memory: Possible etiology for posttraumatic stress disorder. Neuropharmacology, 62(2), 674–685. doi:10.1016/j. neuropharm.2011.04.029 Admon, R., Lubin, G., Stern, O., Rosenberg, K., Sela, L., Ben-Ami, H., & Hendler, T. F. (2009). Human vulnerability to stress depends on amygdala’s predisposition and hippocampal plasticity. Proceedings of the National Academy of Sciences, 106(33), 14120–14125. doi:10.1073/pnas.0903183106 Blake, D. D., Weathers, F. W., Nagy, L. M., Kaloupek, D. G., Klauminzer, G., Charney, D. S., & Keane, T. M. (1990). A clinician rating scale for assessing current and lifetime PTSD: The CAPS-1. Behavior Therapist, 18, 187–188. Brady, B., & Stevens, L. (2000). Binaural-beat induced theta EEG activity and hypnotic susceptibility. American Journal of Clinical Hypnosis, 43(1), 53–69. doi:10.1080/00029157.2000.10404255 Cipolotti, L., & Bird, C. M. (2006). Amnesia and the hippocampus. Current Opinion in Neurology, 19(6), 593–598. doi:10.1097/01.wco.0000247608.42320.f9 Cohen, H., Liberzon, I., & Richter-Levin, G. (2009). Exposure to extreme stress impairs contextual odour discrimination in an animal model of PTSD. International Journal of Neuropsychopharmacol, 12, 291–303. Cohen, H., & Richter-Levin, G. (2009). Toward animal models of post-traumatic stress disorder. In J. E. LeDoux, T. Keane, & P. Shiromani (Eds.), Post-Traumatic Stress Disorder (pp. 133–149). New York, NY: Humana Press. Retrieved from http://link.springer.com/chapter/10.1007/978-1-60327-329-9_6 Cohen, J. E., Shalev, H., Admon, R., Hefetz, S., Gasho, C. J., Shachar, L. J., . . . Friedman, A. (2013). Emotional brain rhythms and their impairment in post-traumatic patients. Human Brain Mapping, 34(6), 1344–1356. doi:10.1002/hbm.21516 Congedo, M., Lubar, J. F., & Joffe, D. (2004). Low-resolution electromagnetic tomography neurofeedback. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 12(4), 387–397. doi:10.1109/TNSRE.2004.840492

309

Nir Getter, Zeev Kaplan and Doron Todder Denham, M. J., & Borisyuk, R. M. (2000). A model of theta rhythm production in the septal-hippocampal system and its modulation by ascending brain stem pathways. Hippocampus, 10(6), 698–716. doi: 10.1002/1098–1063(2000)10:63.0.CO;2-Z Dere, E., Pause, B. M., & Pietrowsky, R. (2010). Emotion and episodic memory in neuropsychiatric disorders. Behavioural Brain Research, 215(2), 162–171. doi:10.1016/j.bbr.2010.03.017 Dickie, E. W., Brunet, A., Akerib, V., & Armony, J. L. (2011). Neural correlates of recovery from post-traumatic stress disorder: A longitudinal fMRI investigation of memory encoding. Neuropsychologia, 49(7), 1771–1778. doi:10.1016/j.neuropsychologia.2011.02.055 Etkin, A., Egner, T., & Kalisch, R. (2011). Emotional processing in anterior cingulate and medial prefrontal cortex. Trends in Cognitive Sciences, 15(2), 85–93. doi:10.1016/j.tics.2010.11.004 Graap, K., & Freides, D. (1998). Regarding the database for the Peniston alpha-theta EEG biofeedback protocol. Applied Psychophysiology and Biofeedback, 23(4), 265–272; 273–275. Gruzelier, J. (2009). A theory of alpha/theta neurofeedback, creative performance enhancement, long distance functional connectivity and psychological integration. Cognitive Processing, 10, 101–109. doi:10.1007/ s10339–008–0248–5 Guez, J., Naveh-Benjamin, M.,Yankovsky,Y., Cohen, J., Shiber, A., & Shalev, H. (2011). Traumatic stress is linked to a deficit in associative episodic memory. Journal of Traumatic Stress, 24(3), 260–267. doi:10.1002/jts.20635 Hardt, J. V., & Kamiya, J. (1978). Anxiety change through electroencephalographic alpha feedback seen only in high anxiety subjects. Science, 201(4350), 79–81. doi:10.1126/science.663641 Indovina, I., Robbins, T. W., Núñez-Elizalde, A. O., Dunn, B. D., & Bishop, S. J. (2011). Fear-conditioning mechanisms associated with trait vulnerability to anxiety in humans. Neuron, 69(3), 563–571. doi:10.1016/j. neuron.2010.12.034 Ježek, K., Lee, B. B., Kelemen, E., McCarthy, K. M., McEwen, B. S., & Fenton, A. A. (2010). Stress-induced outof-context activation of memory. PLoS Biology, 8(12). doi:10.1371/journal.pbio.1000570 Kensinger, E. A., & Corkin, S. (2004). Two routes to emotional memory: Distinct neural processes for valence and arousal. Proceedings of the National Academy of Sciences of the United States of America, 101(9), 3310–3315. doi:10.1073/pnas.0306408101 Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Research Reviews, 29(2–3), 169–195. doi:10.1016/S0165–0173(98)00056–3 Klimesch, W., Doppelmayr, M., Yonelinas, A., Kroll, N. E. A., Lazzara, M., Röhm, D., & Gruber, W. (2001). Theta synchronization during episodic retrieval: Neural correlates of conscious awareness. Cognitive Brain Research, 12(1), 33–38. doi:10.1016/S0926–6410(01)00024–6 Koenigs, M., & Grafman, J. (2009). Posttraumatic stress disorder: The role of medial prefrontal cortex and amygdala. The Neuroscientist: A Review Journal Bringing Neurobiology, Neurology and Psychiatry, 15(5), 540–548. doi:10.1177/1073858409333072 Koenigs, M., Huey, E. D., Raymont, V., Cheon, B., Solomon, J., Wassermann, E. M., & Grafman, J. (2008). Focal brain damage protects against post-traumatic stress disorder in combat veterans. Nature Neuroscience, 11(2), 232–237. doi:10.1038/nn2032 Nutt, D. J., & Malizia, A. L. (2004). Structural and functional brain changes in posttraumatic stress disorder. The Journal of Clinical Psychiatry, 65(Suppl 1), 11–17. Ozier, D. (2010). LORETA neurotherapy for chronic pain related suffering. NeuroConnections, Winter, 11. Pascual-Marqui, R. D. (2002). Standardized low-resolution brain electromagnetic tomography (sLORETA): Technical details. Methods and Findings in Experimental and Clinical Pharmacology, 24(Suppl D), 5–12. Pascual-Marqui, R. D., Esslen, M., Kochi, K., & Lehmann, D. (2002). Functional imaging with low resolution brain electromagnetic tomography (LORETA): Review, new comparisons, and new validation. Japanese Journal of Clinical Neurophysiology, 30, 81–94. Peniston, E. G., & Kulkosky, P. J. (1989). α-θ Brainwave training and β-Endorphin levels in alcoholics. Alcoholism: Clinical and Experimental Research, 13(2), 271–279. doi:10.1111/j.1530–0277.1989.tb00325.x Peniston, E. G., & Kulkosky, P. J. (1991). Alpha-theta brainwave neurofeedback for Vietnam veterans with combat-related post-traumatic stress disorder. Medical Psychotherapy, 4(1), 47–60. Phan, K. L., Wager, T., Taylor, S. F., & Liberzon, I. (2002). Functional neuroanatomy of emotion: A meta-analysis of emotion activation studies in PET and fMRI* 1. Neuroimage, 16(2), 331–348. Phelps, E. A., & LeDoux, J. E. (2005). Contributions of the amygdala to emotion processing: From animal models to human behavior. Neuron, 48(2), 175–187. Pissiota, A., Orjan, F., Fernandez, M., Fischer, H., & Fredrikson, M. (2000). Neurofunctional correlates of posttraumatic stress disorder: A PET symptom provocation study. NeuroImage, 11(5), S192–S192. Rainnie, D. G., & Ressler, K. J. (2009). Physiology of the Amygdala: Implications for PTSD. In J. E. LeDoux, T. Keane, & P. Shiromani (Eds.), Post-Traumatic Stress Disorder (pp. 39–78). New York, NY: Humana Press. Retrieved from http://link.springer.com/chapter/10.1007/978-1-60327-329-9_3

310

sLORETA Neurofeedback as a Treatment for PTSD Shin, L. M., Orr, S. P., Carson, M. A., Rauch, S. L., Macklin, M. L., Lasko, N. B., . . . Pitman, R. K. (2004). Regional cerebral blood flow in the amygdala and medial prefrontal cortex during traumatic imagery in male and female Vietnam veterans with PTSD. Archives of General Psychiatry, 61(2), 168–176. Surmeli, T., & Ertem, A. (2009). QEEG guided neurofeedback therapy in personality disorders: 13 case studies. Clinical EEG and Neuroscience: Official Journal of the EEG and Clinical Neuroscience Society (ENCS), 40(1), 5–10. Tulving, E., & Thomson, D. M. (1973). Encoding specificity and retrieval processes in episodic memory. Psychological Review, 80(5), 352. van Kesteren, M. T. R., Fernández, G., Norris, D. G., & Hermans, E. J. (2010). Persistent schema-dependent hippocampal-neocortical connectivity during memory encoding and postencoding rest in humans. Proceedings of the National Academy of Sciences of the United States of America, 107(16), 7550–7555. doi:10.1073/ pnas.0914892107 Werner, N. S., Meindl, T., Engel, R. R., Rosner, R., Riedel, M., Reiser, M., & Fast, K. (2009). Hippocampal function during associative learning in patients with posttraumatic stress disorder. Journal of Psychiatric Research, 43(3), 309–318. doi:10.1016/j.jpsychires.2008.03.011 Woon, F. L., Sood, S., & Hedges, D. W. (2010). Hippocampal volume deficits associated with exposure to psychological trauma and posttraumatic stress disorder in adults: A meta-analysis. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 34(7), 1181–1188. doi:10.1016/j.pnpbp.2010.06.016

311

17 THE EFFICACY OF Z-SCORE NEUROFEEDBACK TRAINING Joseph Guan

Abstract This chapter traces the use of Z-score neurofeedback training in diverse clinical settings utilized by Brain Enhancement Centre Private Limited. All the clients used a form of live Z-score training (LZT) that produces audio and visual feedback. Over multiple sessions, QEEG brain maps indicate positive changes toward overall normalization of brainwaves. Other assessment tools were used, for example psychological reports, assessments from speech and occupational therapists and interviews with parents/guardians were compiled to provide a comprehensive and composite picture of the issues of the clients. From these multiple sources of data, treatment protocols were designed for the respective clients. Z-score training is a scientifically validated approach and this new intervention significantly reduces guesswork, particularly with brain connectivity training. In this book chapter, a variety of clients with different issues are presented to give the reader a flavor of how Z-score neurofeedback training can effectively treat clients with many different clinical issues. Since 2008, Z-score training has been the most common neurofeedback intervention at the Brain Enhancement Centre Private Limited. It has demonstrated a remarkable efficacy with a wide and diverse range of clients. Issues such as global developmental delay, autism, dyslexia, ADD, ADHD, slow learners, sleep disorders, migraine headaches, Parkinson, stroke recovery, dementia patients, tinnitus, bipolar and vertigo have been treated. The majority of the time, an initial assessment is done with a QEEG (Quantitative Electroencephalogram) brain map which gives a pictorial view of the areas of the brain and assists in identifying regions that have specific problems. The results provide targets for relevant interventions (Thatcher, 2012). Research suggests that QEEG has a high level of reliability. A comprehensive literature review (Hughes & John, 1999, p. 191) in the Journal of Neuropsychiatry and Clinical Neurosciences reported, “Of all the imaging modalities, the greatest body of replicated evidence regarding pathophysiological concomitants of psychiatric and developmental disorders has been provided by EEG and QEEG studies.” Besides the QEEG brain map, psychological reports, assessments from speech and occupational therapists, and interviews with the clients and the parents of the clients were employed. From the multiple sources of data, a treatment protocol was designed for the respective clients. The interventions utilized were Z-score training, sLORETA and Infra Slow Fluctuation training protocols. BrainDx and Neuroguide normative databases were used for assessment and training. For

312

Z-Score Neurofeedback Training

Z-score training, the Atlantis 4x4 amplifier and 4 channel training was utilized. In addition, the Discovery 24 channel amplifier was employed for 19 channel and sLORETA training. Z-score neurofeedback training is a scientifically validated approach that analyzes selected training sites, compares those sites with the Neuroguide and BrainDx normative databases and automatically trains those sites using a chosen targeted protocol. Protocols automatically train a client’s brainwave patterns towards a more normative expression. This new approach significantly reduces guesswork, particularly with brain connectivity training. It is most effective when preceded by a full QEEG assessment. Training consists of the following estimators: absolute power, relative power, power ratios, asymmetry, coherence and phase. Advanced built-in functions and variable definitions facilitate simple design of complex targeting strategies with intuitive trainee feedback. Any combination of targeted Z-scores may be included in the protocol design, which may train toward normative values, or can be biased for peak performance, self-awareness, mental fitness, healing or other neurofeedback applications. In a position paper entitled “Standards for the Use of Quantitative Electroencephalography (QEEG) in Neurofeedback: A Position Paper of the International Society for Neuronal Regulation” (Hammond et al., 2004), the panel of authors have this to say about Z-score training: The most important thing about live Z Score training is that it is scientific. It is based upon published research and a well-documented normative database. It uses concepts that have been proven in clinical research to lead to beneficial outcomes. It eliminates guesswork, and reduces the risk of over- or under-training key parameters including coherence, phase and asymmetry. These parameters are known to have optimal values, and it is important in neurofeedback training to seek training targets that are beneficial. Z-score training can address the whole head, normalizing activation and connectivity. It promotes relaxation, concentration, focus and affective regulation. For a detailed discussion, readers are directed to a white paper written by Tom Collura, Ph.D., and Robert Thatcher, Ph.D., entitled “Real-Time EEG Z-Score Training—Realities and Prospects” (April 2006). In the authors’ own words, this paper discusses the realities and possibilities raised by the implementation of “real-time Z-score training” as an emerging neurofeedback paradigm (p. 1). Both authors describe the positive and promising method of training the brain utilizing live Z-scores. For additional information, please see a paper entitled “EEG Biofeedback training using Live Z-Scores and a Normative Database” (Collura, Thatcher, Smith, Lambos & Stark) published in 2009, which expounds on the technical background and the clinical results using Z-score training together with some case studies. A more recent article published in the Journal of Neurotherapy in 2010, “EEG Biofeedback Case Studies Using Live Z-score Training and a Normative Database” (Collura, Guan, Tarrant, Bailey & Starr) further elaborates the efficacy of Z-score training. This form of neurofeedback makes it possible to compute, view and process normative Z-scores in real time. Towards the end of the article, there are summarized case study details, including clinical, behavioral, psychometric and QEEG changes. This table is very helpful in giving the reader easy comparison of the case studies. In the Discussion and Conclusion section of this article, the authors strongly suggest that the Z-score training “is capable of inducing brain changes that are specific and profound, particularly with regard to whole-brain activation and connectivity” (p. 45). A recent paper from Clinical EEG and Neuroscience Society, “Neurofeedback Training Induces Changes in White and Gray Matter” (Ghaziri et al., 2013), is a defining moment for us in the field of neurofeedback. The above authors utilized structural magnetic resonance imaging (MRI) 313

Joseph Guan

to investigate whether neurofeedback training could induce structural changes in gray and white matter in the brain. This article describes how their findings, using diffusion tensor imaging (DTI), demonstrates that neurofeedback can induce measurable changes in white matter architecture. The authors emphatically conclude that “after 50 years of research in the field of neurofeedback, their study constitutes the first empirical demonstration that NFT can lead to microstructural changes in white and gray matter” (p. 1). The purpose of the following case studies is to demonstrate the efficacy of Z-score training using appropriate 4 channel Z-score neurofeedback training.

Case Study 1: 5-Year-Old Boy with Pervasive Developmental Delay (February 2009) before Purchasing Discovery Amplifier Timothy and his mother came to see me when I visited Surabaya in February 2009. I did not have my Discovery amplifier at that point in time so I depended on the medical reports given by the pediatrician and also a description of Timothy’s condition given to me by his mother as my initial assessment. Due to the client’s diagnosis of pervasive developmental delay, the intervention strategy started with a Z-score training program that would address most of his issues by training all 19 electrode sites. I was only in Suraybaya for a week and treated him for 10 sessions at two sessions a day for a period of five days. I returned to Surabaya in four weeks and initiated another 10 sessions of Z-score training for Timothy. On my third visit to Surabaya, a final round of 10 sessions of Z-score training was implemented. In the meantime, I trained a local practitioner to continue the treatment under my supervision. In all Timothy had 60 sessions of neurofeedback. His training protocol is supplied below: T3 T4/C3 C4 (5 sessions)—For emotional and physiological stability, to impact the client’s sensory motor cortex to address fine and gross motor skills. In addition, attention and mental processing speed, manual dexterity, sensory and motor integration were addressed with these placements. It was hoped that this training combination would help with the initiation, activation and performance of motor activity and normalization of affective response. T3 T4/Fp2 P4 (5 sessions)—Addresses emotional and physiological stability, promoting positive emotions, attachment issues and helps to promoter deep physiological calming by addressing sensory processing regions. T3 T4/Fp1 Fp2 (5 sessions)—Impacts attention and impulse control, planning and organization, mental clarity, compulsive behaviors and tics, obsessive and impulsive thoughts, behavior contentment or equanimity. T3 T4/F7 F8 (5 sessions)—F7, which lies in the Broca’s area, and its homologous site F8 are involved with speech production, speech initiation, language fluency and word finding. These areas are central hubs of language processing and comprehension. F7 F8/C3 C4 (5 sessions)—This montage coordinates the motor component of speech production with the primary language areas involved with productive speech. The goal of this electrode array was to promote verbal fluency and clarity of speech. T3 F7/P3 P4 (5 sessions)—This electrode array addresses the combination of language pathways and sensory processing. T3 T4/F3 F4 (5 sessions)—Besides treating emotional and physiological stability, this treatment protocol engages in fine motor coordination, initiation and sequencing of movements, increases motivation and self-confidence, and reduces depression and anxiety. T3 T4/P3 P4 (5 sessions)—Deals with primary somatosensory processing (awareness of body-somesthetic, kinesthetic and proprioceptive information, sensory integration), especially the visual and somatosensory including body position, body movement and awareness of movement through space.

314

Z-Score Neurofeedback Training

After 40 sessions, Timothy’s mother gave the following testimonial: My son is 5 years old and was diagnosed with pervasive developmental disorder. He was speech delayed, kept to himself most of the time and did not express his emotions and could not interact with others. He had difficulties in two way communication. He also exhibted a lack of understanding of instructions given to him. I was so distressed that I had to take antidepressant medication. However I started neurofeedback with Dr. Guan in February 2009, and my son has made tremendous progress ever since. He talks appropriately and in context, interacts well with others, and expresses his emotions. I am so hopeful that I have stopped my medication and I am no longer depressed. His concentration and focus has also improved a lot. I am confident that my son will make greater progress in the coming months. His father has come closer to him and he plays and interacts with his elder brother. T3 T4/T5 T6 (5 sessions)—This treatment combination entails the deeper structures of the amygdala, hippocampus, thalamus and tail of the caudate nucleus. T5 is aimed at issues involving linguistic shortand long-term memory (both auditory and visual). The functions of T6 include facial recognition and spatial awareness. This region is involved with social skills. The processing of auditory stimuli occurs here especially with regard to discerning the location of sounds, identification and recognition of nonverbal environmental sounds, and music and sounds conveying emotional meaning. T3 T4/O1 O2 (5 sessions)—The occipital lobe contains neurons that, although predominantly concerned with the analysis of visual stimuli, respond to vestibular, acoustic or somesthetic input as well as processing of primary visual input and visual perception. This combination is very good for clients with visual processing issues like dyslexia. T3 T4/Cz Fz (5 sessions) and T3 T4/Cz Pz (5 sessions)—Fz and Cz are located in the medial cortex. Directly below the medial cortex is the cingulate gyrus, an integral part of the limbic system. The cingulate is responsible for emotional processing, learning and memory. It is highly influential in linking motivation and behavior. The cingulate has a role in attention. The anterior cingulate specifically is central in shifting one’s attention from one subject to another, adapting with changing circumstances, or seeing options and promoting flexibility. Pz—The parietal lobe is commonly thought to be concerned predominantly with processing of somesthetic, kinesthetic and proprioceptive information. However, in addition, the networks of this region are responsive to a variety of divergent stimuli, including movement, hand position, objects within grasping distance, audition, eye movement, as well as complex and motivationally significant visual stimuli. After 60 sessions of Z-score training, Timothy finished his neurofeedback sessions and his mother was very pleased with the outcome of the program.

Case Study 2: Vertigo A 64-year-old woman suffered from vertigo for many years. When the attack comes, she has the sensation that objects in the environment are moving and she has to remain in bed for several days. Occasionally, this attack is accompanied with vomiting. During an attack, she would take medication to stabilize the dizzy feeling and rest in bed for a few days. She came to me for help because when the attack comes, she feels miserable and helpless. Nineteen channel Z-score training was implemented for her. After 12 sessions spanning a period of six weeks, the incidence of vertigo disappeared. In addition to the 19 channel Z-score training, I also treated her using 4 channel Z-score training at T3 T4/C3 C4 since the probable cause of her vertigo was due to the disturbance in the balance organs of the inner ear.

315

Joseph Guan

Case Study 3: Dementia A 77-year-old woman with dementia came to my clinic. Her daughter-in-law provided me with a history of memory problems. She misplaced objects very frequently and often repeated instructions to her son and maid several times, not realizing that she had already given the instructions. This frequent occurrence annoyed members of her family. She also had bouts of depression for the last few years. She has been taking medication for her dementia. She has been suffering from this ailment since 2008. After taking a full history of her condition, I started treatment. For this case study, I did not do a QEEG brain map. She resisted having the electro-cap placed on her head. The symptoms of her condition determined the placements of the Z-score training protocol. I employed the 4 channel Z-score training with electrode placements at T3 T4/T5 T6. This targets the temporal lobe which includes the Hippocampal Gyrus. The Hippocampal Gyrus plays a central role in information processing, including memory, new learning, attention, behavioral arousal and orienting reactions in social interactions. She came for treatment twice a week for her Z-score neurofeedback sessions. After 20 sessions of the above training, I changed the sites to T3 T4/F3 F4 to reduce anxiety and depression and increase her self-confidence. After a total of 40 sessions of 4 channel Z-score training, her condition improved significantly. Her symptoms of depression have resolved. Her short-term memory has improved. She continues once a week of neurofeedback training for maintenance. She continues to make steady progress with attention and memory.

Case Study 4: Parkinson A 64 year-old man was referred to address the symptoms for Parkinson disorder. He had been suffering from Parkinson for nine years. He had been prescribed three kinds of medication. He was undergoing physical therapy twice a week. His condition was considered quite severe. He needed help from his wife or nursing aide in order to walk. He had difficulty eating, chewing and swallowing his food. His right upper arm was very stiff. If he needed to walk more than 10 yards, he would use a wheel chair. As a result of his immobility, neurofeedback treatment was performed in his residence three times a week. Four channel Z-score training was used for his treatment. The training sites were T3 T4/C3 C4 targeting the sensory motor strip. These sites are concerned with the initiation, activation and performance of motor activity. It was hoped this would help with his fine and gross motor skills including chewing and swallowing food. C3 C4/F3 F4 (central strip and frontal region) for motor planning and sequencing of movements was also employed. The target range of Z-score training was from -1 (standard deviation) to +1.5 (standard deviation). After the first 10 sessions of Z-score training at T3 T4/C3 C4, there was marked improvement in his condition. His sleep improved. His ability to maintain restful sleep improved from one to six hours. His wife reported that he was able to speak with greater clarity. He was able to eat, chew and swallow his food more easily. His right arm was less rigid and the range of movements had improved. He is now able to walk with a steadier gait and he made the remark, “I feel that I can now get better.” When his wife heard this, she interjected and said, “This is the first positive comment he had uttered in the last three to four years.” After 20 sessions of neurofeedback, there is further progress in his condition. His wife reports that occasionally, John has seven hours of uninterrupted sleep. His movements are more flexible and his speech has improved with less slurring and hesitation in expressing his views. He now manages to walk with a cane. At the time of writing this article, this client finished 30 sessions of Z-score training. 316

Z-Score Neurofeedback Training

Case Study 5: Bipolar Client A 26-year-old woman with bipolar disorder presented in treatment recently. She was under the care of a psychiatrist and was taking four different kinds of medication. I performed a QEEG. The brain map revealed excessive theta waves in the pre-frontal and frontal areas. The excessive theta at F3 F4 represented an electrophysiological signature, consistent with her reports of having depression. In addition, she reported anxiety and difficulty maintaining affective equilibrium. She had 4 channel Z-score training on: T3 T4/C3 C4—for emotional stability. The temporal lobe is central in the regulation of emotional processing. T3 T4/F3 F4—targets depression and anxiety and motivation and self-confidence. T3 T4/Fp2 P4—helps in deep physiological calming and promoting of positive emotions and attachment issues.

Figure 17.1

Pre-treatment QEEG.

Excessive theta waves in the frontal region suggesting anxiety and depression. From April 2013 to August 2013, she had 40 sessions of 4 channel Z-score training.

317

Joseph Guan

Figure 17.2

Bipolar client post-test August 2013.

The post-treatment QEEG demonstrated that the excessive theta waves have been reduced globally. She is emotionally stable now with reduced severity and frequency of mood swings. She smiles more and is more expressive in her speech. She is more postively oriented. This client’s medication regimen currently includes only one medication. She is now planning to pursue an overseas educational program, a long-time goal that had been deferred due to illness. 318

Z-Score Neurofeedback Training

Case Study 5: ADHD A 12-year-old girl with ADHD was referred for treatment. She had been taking Ritalin for the last three years. The first session included a QEEG brain map and a consultation with her parents. After collecting five minutes of raw data (Eyes Open), the Neuroguide software was used to generate a QEEG report. We discussed the findings of the brain map and implemented a 4 channel Z-score treatment.

Figure 17.3

ADHD client pre-treatment QEEG brain map February 2012.

319

Joseph Guan

The pre-treatment QEEG revealed excessive delta in the pre-frontal and frontal areas. This finding may be influenced by eye movement artifact. However, elevated theta waves were observed in similar frontal regions. The excessive delta and theta waves contributed to difficulty with attention and focus. Excess power was evident in the beta and high beta bands in bilateral frontal regions. Either hyper- or hypo-coherence was discovered in all bands with most deviance in the delta band in hyper-coherence. Abnormal coherence was observed in mixtures of hyper- and hypo-coherence in the beta and high beta bands, predominantly in the left hemisphere. Coherence is a measure of the amount of information

Figure 17.4

ADHD client interim QEEG brain map December 2012.

320

Z-Score Neurofeedback Training

sharing within functional networks. The disturbances in network sharing in the faster frequencies in the left hemisphere interfered with the client’s skilled-based abilities especially with regard to language. After 40 sessions of 4 channel Z-score training spanning a period of eight months, her ability to focus and sustain attention increased substantially. In addition, she demonstrated increased affective regulation and arousal reduction. Her teachers reported that she was less fidgety and able to remain on task for a longer period of time. Her grades in school have improved and she was able to reduce the dosage of her medication.

Figure 17.5

A QEEG brain map was done in March 2013.

321

Joseph Guan

Occasionally, the client continued to demonstrate episodes of emotional distress. A post-treatment QEEG was performed to determine the targets for further neurofeedback training. The QEEG revealed a reduction of excess delta in the frontal and pre-frontal regions. However, excess delta and theta waves were present in the occipital region. Over-activation in F8 in the beta and high beta bands may be the result of muscle artifact. From the results of the brain map, another 30 sessions of 4 channel Z-score training was applied. In March 2013, another QEEG was performed in order to observe progress and identify areas of concern. After much discussion with her parents, another 30 sessions spanning a period of three months were carried. At the completion of 70 sessions of 4 channel Z-score training, the client had discontinued her methylphenidate and had demonstrated significant academic improvement. Her attention is now much longer and she has reduced her level of distraction. Her motivation to study has increased and her mother no longer has to nag at her to perform academic tasks. With regard to coherence, all the hyper-coherence in the delta band has disappeared. With regard to absolute power, there is slight over-activation in the pre-frontal and frontal region in the delta band. In the alpha band, there is insufficient absolute power in the parietal region suggesting that there should be more neurofeedback sessions at the P3 P4 sites to improve understanding and comprehension. It was used to address logical and sequential thinking as well. The high beta band reveal excess absolute power at right inferior frontal region. In the post-treatment QEEG above, network information sharing has improved in all frequency bands. In addition, the timing of information delivery within function networks has improved substantially.

Case Study 6: Autism This autistic boy was diagnosed with Autism at the age of four by his pediatrician. He was very delayed in productive language despite two years of speech therapy. At six years old, he was not capable of conversation but could only label objects. His ability to understand instruction was also very poor. After attending seven sessions of Z-score neurofeedback training, he began to speak in four-word sentences. He became capable of having short conversations with his elder brother. He was able to recall and describe where he had been the previous day. In addition, he was able to articulate his desires for the following day. The client began treatment in the first week of October 2013. He was hyper-kinetic during application of the electro-cap. It was determined that a QEEG was not possible under the circumstances. As a result, a 4 channel Z-score training at T3 T4/ C3 C4 was implemented for five sessions. Subsequently, the electrode placements were changed to T3 T4/ F7 F8 to address speech issues. Finally, a 19 channel Z-score training of all indices was utilized for 10 sessions. This autistic boy’s QEEG after 20 sessions of 4 and 19 channel Z-score. In absolute power, there is slight over-activation at F7 and F8 which seems to suggest that more sessions are required to further improve his productive speech and prosody. In the alpha band, there is slight under-activation in the parietal region which implies that this autistic boy still requires more neurofeedback sessions to enhance his level of understanding and comprehension and spatial awareness. In coherence, there is some hyper-coherence among T3 T4 T5 T6 O1 and O2. It indicates that the affected networks are locked in with each other, thus preventing optimal communication between functional areas. More training was recommended to reduce this hyper-coherence. Despite the non-optimal QEEG, this autistic boy is now speaking in full sentences and beginning to have conversations with his parents and his peers at school. He is exhibiting a high level of curiosity and has deepened his understanding and comprehension of social interaction. His awareness of his environment and his ability to navigate physical space has improved such that he does not collide with objects in his surroundings. 322

Z-Score Neurofeedback Training

Figure 17.6 Autistic boy’s QEEG (post-treatment).

Conclusion The above case studies demonstrate the effectiveness of Z-score neurofeedback training. The pre/post QEEGs demonstrated the normalization of outlier Z-scores. More importantly, clients have revealed symptom reduction. This kind of change truly reflects what one paper commented, that Z-score neurofeedback training “is capable of inducing brain changes that are specific and profound, particularly with regard to whole-brain activation and connectivity.” 323

Joseph Guan

Dr. Collura’s excellent scientific paper, “Toward a Coherent View of Brain Connectivity,” which was published in the Journal of Neurotherapy (2008), suggests that training to the norm or average like what we do in Z-score training is the most logical and intuitive approach to Neurofeedback training or connectivity measures. In this paper, he writes, When Z-scores are used for EEG training, a variety of targeting options are available. The most obvious is to train toward the norm or average range in which the protocol is designed to guide the trainee into the normal range. There are various options available when using this approach including the number of z scores available and the type of reinforcement feedback. (Collura, 2008, p. 108) With the advent of BrainAvatar and sLORETA, we are entering into a more exciting phase in the evolution of neurofeedback both in the assessment and training protocols. These developments will herald an exponential and accelerated body of knowledge with regard to symptoms of diseases and how to effectively treat them with real-time images as featured in the BrainAvatar software. On its website, BrainMaster Technologies Inc. has this to say about BrainAvatar: It provides a new standard of excellence and will become the new standard for comparison for the future of the field. It combines all existing BrainMaster capabilities with new features incorporating quantitative EEG (QEEG), peripheral modalities, and integrating assessment with training in a seamless system. In NeuroConnections, Gracefire and Durgin (2012, p. 30) reiterate the power of BrainAvatar in the following words: For the first time, practitioners and researchers could observe a three-dimensional display of the current source density in specified areas of the brain in particular frequency ranges in real time. Not only is the activity observable, but it can be utilized as the basis of a feedback paradigm that provides information to the brain, effecting responses in the targeted area. In essence, the BrainAvatar software is rewarding the client when targeted regions of the brain increase or decrease activity in chosen frequency bands. The technology behind BrainAvatar allows it to measure in real time actual brain electrical activity, whereas other imaging techniques offer only structural or metabolic images. In turn, the system “has significant new value for neurology, psychiatry, mental health assessment and treatment, consumer research, sports, art, peak performance and optimal functioning.” Dr. Collura foresees BrainAvatar in his own visionary words: “We see this invention opening new markets and uses, and changing the face of mental health care due to its extraordinary speed and accuracy, economy and potential importance (Collura, 2012b). A comprehensive description of BrainAvatar can be found in NeuroConnections in the Summer 2012 issue where Dr. Collura features BrainAvatar in an article entitled, “BrainAvatar, Integrated Brain Imaging, Neurofeedback and Reference Database System” (2012a).

References Collura, T. (2008). Toward a coherent view of brain connectivity. Journal of Neurotherapy, 12(2–3), 99–110. Collura, T. (2012a). BrainAvatar, integrated brain imaging, neurofeedback and reference database system. NeuroConnections, Summer, 31–36. Collura, T. (2012b, March 5). Speech at Nortech Innovation Award 2012, Crain’s Cleveland Business.

324

Z-Score Neurofeedback Training Collura, T. Guan, J., Tarrant, J., Bailey, J., & Starr, F. (2010). EEG biofeedback case studies using live Z-score training and a normative databases. Journal of Neurotherapy, 14(1), 22–46. Collura, T., & Thatcher, R. (2006). Real-time EEG Z-Score training—realities and prospects. Bedford, OH: Brainmaster Technologies, Inc., and Applied Neurosciences, Inc. Collura, T., Thatcher, R., Smith, M., Lambos, W., & Stark, C. (2009). EEG biofeedback training using live Z-scores and a normative database: Introduction to QEEG and neurofeedback. In T.H. Budzynski, H. Kogan Budzynski, J.R. Evans, & A. Abarbanel (Eds.) Introduction to Quantitative EEG and Neurofeedback: Advanced Theory and Applications (2nd ed., Ch. 5, pp. 103–140). New York, NY: Academic Press. Ghaziri, J., Tucholka, A., Larue, V., Blanchette-Sylvestre, M., Reyburn, G., Gilbert, G., Levesque, J., & Beauregard, M. (2013). Neurofeedback training induces changes in white and gray matter, clinical EEG and neuroscience. Clinical EEG and Neuroscience, 44(4), 265–72. Gracefire, P., & Durgin, G. (2012). Combining sLORETA and 19-channel live Z-score training. NeuroConnections, Winter, 30–33. Hughes, J. & John, R. (1999). Standards for the use of quantitative electroencephalography (QEEG) in neurofeedback: A position paper of the international society for neuronal regulation. Journal of Neuropsychiatry and Clinical Neurosciences, 11(2), 190–208. Thatcher, R. (2012). Handbook of quantitative electroencephalography and EEG biofeedback. St. Petersburg, FL: Anipublishing Co.

325

18 INTRODUCTION TO THE CONCEPTS AND CLINICAL APPLICATIONS OF MULTIVARIATE LIVE Z-SCORE TRAINING, PZOK AND sLORETA FEEDBACK Penijean A. Gracefire Abstract Within the last decade, evolutions in neurofeedback technology have established a groundbreaking new clinical paradigm for neurotherapy. This overview is a brief, chronological account of these industry altering developments, providing practical context through which to understand how the principles underlying Z-scored and sLORETA feedback have emerged and integrated to form an unprecedented window into both the operational and the healing mechanisms of the brain. The intent is to offer examples and educational elements from which clinicians at all levels of skill can derive value, through a combination of case studies, clarification of procedural concepts and considerations for protocol designs drawn from firsthand experience and observation. Section 1 Surface Amplitude Feedback Section 2 sLORETA Region of Interest Amperage Feedback Section 3.1 Surface Live Z-Score Feedback (PZOK) Section 3.2 4 Channel Surface PZOK: The Origin of Live Z-Score Training Section 3.3 9 Channel Surface PZOK: The Intermediate Solution Section 3.4 19 Channel Surface PZOK: Live Z-Score Training on a Global Scale Section 4 sLORETA Region of Interest Live Z-Score Feedback Section 5 Z-Plus: No Z-score Left Behind The ability to quantify electro neurological activity representative of both cognitive function and emotional response has initiated unprecedented opportunities for deeper insight into the mechanisms of human experience. As the resolution and the quality of imaging methods have evolved, so has the complexity and dimension with which neurotherapists can provide feedback to a brain regarding the nature of its strategies for prioritizing and allocating resources, thereby facilitating increased cortical integration and performance. This brief overview will examine key developments in the field of neurotherapy through the lens of a progression of case studies which have each contributed to the current body of clinical understanding in its own unique way. Every case described herein is drawn from the firsthand experience of the author.

326

Concepts and Clinical Applications Table 18.1 Summary of characteristics primary to each neurofeedback technique discussed in Chapter 18. Informational Paradigm

Feedback Based on EEG Measurement of

Required EEG Response to Meet Criteria

Number of Active Electrodes

External Database Needed?

Surface Amplitude Feedback

Surface microvoltage at, and/ or between, selected site(s)

Unidirectional increase or decrease of selected metric(s)

Conventionally 1-4 channels, but can be more

No normative database referenced

sLORETA Region Of Interest (ROI) Amperage Feedback

Nanoamperage of current source density probability in selected ROI(s)

Unidirectional increase or decrease of selected metric(s)

19 channels for true sLORETA source localization

No normative database referenced

Surface Live Z-score Feedback (PZOKUL)

Surface microvoltage at, and between, selected sites

Percentage of variables within SD range

2-19 channels

Yes, uses normative database

sLORETA ROI Live Z-score Feedback (ZBRAUL)

Nanoamperage of current source density probability in selected ROI(s)

Percentage of variables within SD range

19 channels for true sLORETA source localization

Yes, uses normative database

Additional Z-Plus paradigms: PZMO and PZME

Surface microvoltage at, and between, selected sites

Percentage of variables within SD range

2-19 channels

Yes, uses normative database

Due to the range of training methods discussed, the chart in Table 18.1 is intended to provide a summary of the characteristics primary to each technique for convenient reference.

Section 1: Surface Amplitude Feedback Surface amplitude neurofeedback occurs when electrodes are placed on the scalp to measure EEG activity, and then sensory input (visual, auditory, vibro-tactile, etc.) is provided to the individual when their observed EEG activity alters to meets predetermined criteria for change. Two elements of the EEG signal are evaluated to determine change: amplitude and frequency. Amplitude is the size or “amount of power” contained in the signal, and frequency is the speed, or number of times the signal cycles up and down in a second of time (Figure 18.1). The frequency with which the signal moves allows an estimation of the tasking state being observed at the measured location, while the amplitude observed in each frequency band contains information related to how tasking states are being resourced and prioritized. This principle can be observed by how the brain tends to oscillate at slower frequencies for more internally oriented tasks, such as nutritional support, consolidation and repair, while it typically generates faster frequencies when engaging in more externally oriented tasking states requiring complex cognitive processing and integration of information being received from the external environment. Because the central nervous system modulates its response mechanisms by integrating and processing feedback from its environment, a healthy brain exhibits the ability to shift flexibly back and forth between slower and faster frequency production in different cortical regions simultaneously, depending on environmental demands. When there is a chronic excess or deficit of amplitude in a particular frequency band, and this excess or deficit of amplitude does not shift when the environmental demand alters, this is an indication the central nervous system is experiencing difficulty regulating arousal responses.

327

Penijean A. Gracefire

Figure 18.1

Diagram of EEG sine wave: amplitude and frequency.

To illustrate, when a person lives for a period of time in an unpredictable and chronically stressful environment, possibly even suffering harm, mechanisms which govern arousal can develop a range of sustained hyper vigilant responses which prioritize safety and survival, but require higher amounts of energetic resources to maintain. While there are a variety of ways these mechanisms can be reflected in a quantitative EEG analysis, one commonly observed signature manifests as atypically high amplitudes of beta (a faster frequency reflecting a more externally oriented complex tasking state) in regions of the brain associated with scanning, integrating and processing somatosensory information. The presence of an atypical amount of activity (amplitude) in beta (frequency) which persists even when there is no immediate discernible threat, indicates a prioritization of resources toward maintaining vigilance, and can offer insight into not only the physical state of the individual, but also their potential cognitive and emotional experience (Gracefire, 2015). Amplitude training is the delivery of sensory feedback to the client when their surface EEG activity meets a desired set of behavioral parameters programmed into the software, usually a combination of simultaneous increases and decreases of amplitude in the various frequency bands at each electrode site, measured by increases and decreases in microvoltage. For example, if a person with a history of trauma and chronic stress is exhibiting higher than typical amplitudes of beta (a frequency band which reflects a tasking state attentive to, and engaged with, the immediate external environment) and lower than typical amplitudes of alpha (a frequency band which indicates a more relaxed and flexible state of awareness, but less engagement with the immediate external environment), then a feedback strategy might consist of delivering a musical tone when there is an increase in alpha amplitude above a certain microvoltage threshold, and a simultaneous decrease in beta. This shift in amplitude distribution commonly correlates with the client experiencing a more relaxed state. Surface amplitude training is the simplest and least labor intensive feedback task for clients due to its small number of interacting variables. Typical protocols track and provide feedback on as few as two or three EEG metrics at a single electrode site, while even the more complex designs usually do not isolate out more than a handful of frequency bands and their corresponding amplitudes relevant to a small number of electrodes placed on the head. It is an excellent option for introductory sessions when the clinical intake process reveals the client may experience sensitivity or reactivity to informational inputs from the surrounding environment. 328

Concepts and Clinical Applications

Chronic stress or illness, injury and systemic inflammation are variables which are likely to reduce an individual’s systemic resilience and capacity for integrating incoming information, producing a constellation of symptoms which can show up as exhaustion, irritability, impatience, emotional lability, anger, avoidance, anxiety, depression and so forth, which nearly always affect work or school performance, and impact the quality of interpersonal relationships. Estimating the cumulative load the client is already bringing into sessions facilitates the clinician’s ability to determine whether the first few sessions might be most helpful to the client if the feedback tasks were simple, demanding fewer resources from an already depleted central nervous system, to successfully complete. In cases such as these, beginning with 1 or 2 channel feedback, with criterion such as reinforcing alpha amplitude at Pz, or perhaps increasing 12–15 Hz at C4 for a few sessions can sometimes support the client’s experience of relaxation or systemic stability, while allowing for a gentle introduction to the process of change. It is important to treat all human systems as potentially compromised, exhausted, fragile and easily overwhelmed until the first few sessions of feedback indicate otherwise. Regardless of the clinician’s skill and the client’s cooperation in the early reporting stage, the clinician will be unable to accurately estimate the appropriate learning pace for each individual client until there has been opportunity to observe responses to feedback sessions over a reasonable period of time. Optimal results with surface amplitude training are achieved when the feedback strategies are formed by drawing from thorough and appropriate clinical evaluation methods prior to beginning treatment, including QEEG analysis, and are then regularly updated through timely reevaluation (minimum every 20–30 training sessions) and consideration of feedback from the client regarding their ongoing subjective experience. Advantages of Surface Amplitude Training • • • •

Ease of setup, typically 3–6 electrodes on the scalp Less initial financial investment in software and hardware Introduces simpler tasks for the brain to engage in until resilience and stability is increased Can result in subjective client experience of state shift within the first few sessions, if optimal protocols are chosen by provider

Additional Considerations When Surface Amplitude Training • • • •

Can monitor only limited EEG activity while training Typically will have to move electrode placements, as often can only treat a few sites and a few bands at a time Can require more sessions to achieve overall treatment goals Feedback information received by the cortex is isolated to the selected metrics and lacks spectral context from which to develop more complex cortical regulation strategies

Section 2: sLORETA Region of Interest Amperage Feedback Standardized Low Resolution Electromagnetic Tomography (sLORETA) is an inverse mathematical algorithm used to triangulate the source of EEG activity deeper within the cortex by referencing 19 active electrodes placed on the scalp surface. sLORETA compartmentalizes its estimated model of the cortex into 6,239 voxels, or 5 millimeter cubes of space. The current source density (CSD) of 1–45 Hz are estimated in each individual voxel using amperage, and the BrainAvatar Live sLORETA Projector™ (LLP) software can turn these estimates into a three dimensional image which portrays real-time changes in a variety of regions of interest (ROIs). In Figure 18.2, the BrainAvatar Live sLORETA Projector software is imaging the nanoamperage of the estimated current source density of theta in every individual voxel which comprises both the left and right parahippocampal gyrus. 329

Figure 18.2

BrainAvatar live sLORETA projector software displaying CSD estimation of theta amperage in the parahippocampal gyrus.

Concepts and Clinical Applications

Figure 18.3 Illustration of power measurements: surface EEG estimated in amplitude (height) at each electrode location, sLORETA EEG estimated in amperage (volume) using a composite of 19 electrodes across the entire scalp.

If one were to measure the amount of water in a glass (Figure 18.3), one could use the surface level as an indicator and say the glass has more water or less water as the surface level increases or decreases. This would be analogous to the increase or decrease of microvolts in surface power or amplitude training. Another way to measure the water would be to estimate its volume, and when the volume increases or decreases, so does the amount of water. sLORETA amperage training occurs when the volume of a given frequency band, for example theta, increases or decreases in the selected ROI, as indicated by an increase or decrease in the nanoamperage of the current source density estimate (Gracefire, 2013b). When doing surface amplitude training, as discussed in Section 1, the feedback is based the on amplitude of the frequency band being measured, and when it decreases or increases in “height,” the feedback occurs to indicate the direction of the brain movement. For example, if the goal is to reduce fast beta waves in the parietal region, then electrodes can be placed at parietal sites and when the surface amplitude of high beta drops below the desired microvolt threshold, the client receives the feedback, alerting their system to the change in EEG activity. If the clinician wishes to increase or decrease the amperage (or volume) of theta in the parahippocampal gyrus, they (1) select the parahippocampal gyrus as the region of interest, then (2) select the theta band as the desired band, and then the software estimates the amperage of the current source density of theta in every individual voxel which comprises both hemispheres of the parahippocampal gyrus. All of the individual voxel theta estimates are combined into two numbers, one for the left and one for the right hemisphere, which represent the theta current source density amperage average for each half of the ROI. These averages update in real time, and represent the theta amperage being observed throughout the entirety of the ROI. The client receives feedback when the overall running composite average of theta decreases, representing a decrease of the volume of theta in the three dimensional space in the cortex estimated to correlate with the position of the parahippocampal gyrus. Choosing regions of interest in which to increase or decrease amperage can be done by matching symptomatic reports from the client with cortical areas that appear relevant based on functional 331

Penijean A. Gracefire

Figure 18.4 17 yr old male with 11 Hz alpha in excess of 5.93 standard deviations in Brodmann Area 9.

imaging literature, but this may not take into account each client’s developmental idiosyncrasies. Ideally, the clinician would do a 19 channel EEG recording prior to training, and analyze the acquired data using software which offers both surface and source referential database values for comparison, allowing for greater efficacy of region and frequency band selection. The sLORETA feedback approach allows for protocol designs which target larger or smaller cortical regions and reinforce or inhibit amperage activity in chosen frequency bands, a type of training that can result in rapid changes within the prioritization dynamics affecting cortical resourcing, and should be used with discretion. It is recommended to not single out and reinforce or inhibit amperage in the same region for more than five to ten minutes at a time for the first few sessions, and not to exceed ten sessions of training before doing another full QEEG and sLORETA analysis to compare against the data gathered prior to beginning training. Any observed changes in both the QEEG analysis and the client’s identified symptom set should be considered before deciding whether to continue with the same feedback selections, or to adjust the selections (Rutter-Gracefire & Durgin, 2012b). Figure 18.4 shows a LORETA image generated from the analysis of a 17-year-old male who reported difficulties with concentration, motivation, slowed verbal comprehension and processing speed, and memory issues. This image is pre-training, and indicates 5.93 standard deviations of excess 11 Hz alpha activity in his superior frontal gyrus, more particularly Brodmann area 9. This was considered significant, as 99.7 percent of individuals fall within 3 standard deviations of the norm, and his alpha amperage score was nearly 6 standard deviations above the normed reference point. The inclusion of Brodmann areas within the list of available regions of interest permits even more tailored selections of cortical areas for training, and opens up possibilities for creating functional changes by providing feedback based on the increase or decrease of power in a Brodmann area. Brodmann areas are a breakdown of the cortex based on the structure and organization of cells, and can be useful when attempting to identify regions of the brain involved in particular functions. After eight sessions of inhibiting alpha activity in Brodmann area 9, a region implicated by the available literature as involved in all of the symptoms listed by this client, he reported a noticeable improvement in the reported categories, and the 11 Hz activity observed in his Brodmann area 9 had reduced to 1.16 standard deviations above the normed reference point (Figure 18.5). 332

Concepts and Clinical Applications

Figure 18.5 17 yr old male from Figure 18.4—post 8 sessions of sLORETA amperage training with alpha in Brodmann area 9 now at 1.61 standard deviations.

Figure 18.6

39 yr old female with 4 Hz theta in excess of 5.97 standard deviations in Broadmann area 6.

Figure 18.6 is an image is from the pre-training LORETA analysis of a 39-year-old woman who had suffered a stroke two years previous to evaluation and reported persistent coordination and motor issues which left her anxious and depressed and afraid to leave her house for fear she would suffer an injury from loss of balance. Figure 18.6 indicates 5.97 standard deviations of excess 4 Hz in her right hemisphere Brodmann area 6, a region indicated by the literature as involved in a number of motor related functions. 333

Penijean A. Gracefire

Figure 18.7 39 yr old female from Figure 18.6—post 11 sessions of sLORETA amperage training with alpha in Brodmann area 9 now at 1.86 standard deviations.

Figure 18.7 shows her LORETA analysis after 11 sessions of theta inhibition in Brodmann area 6, indicating a decrease of 4 Hz in Brodmann area 6 to 1.86 standard deviations. She reported improved motor control and increased confidence in her movements, and after 16 sessions she was able to leave her house regularly, and worked up to a handful of brief excursions alone, something she had not done since her stroke. Advantages of sLORETA Amperage Training • • • •

More precise selection of EEG activity localized to a cortical region on which to base feedback Potential increases in how rapidly EEG changes are learned due to improved accuracy of feedback criterion Possibilities for more sophisticated protocol development with increased nuance in brain area feedback potentially leading to greater efficacy of training Live monitoring of EEG changes in three dimensions with the facility to select multiple ROIs at a time or during a session to create a sequence of power recruitment training strategies which provide support to the cortex at a customized pace and reflects the innate priorities of the central nervous system

Additional Considerations When sLORETA Amperage Training • • •

Requires a minimum of 22 EEG electrodes placed on the head to utilize the sLORETA algorithm More initial financial investment in hardware and software Attentive assessment and monitoring of the client’s EEG during sessions is always strongly recommended, however in the case of sLORETA power training without live z-score database references built into the feedback paradigm, it is particularly emphasized, due to the rapidity with which cortical resources shifts can occur, requiring more care on the part of the clinical provider to determine when it is time to adjust the training protocols

334

Concepts and Clinical Applications

Section 3.1: Surface Live Z-Score Feedback (PZOK™) The addition of Live Z-score Training™ to the field of neurofeedback opened up an entirely new way of thinking about brain training. Previously, clinicians were limited to unidirectional feedback, alerting the brain to when the amplitude of a frequency band moved “up” or “down,” but in 2004 the groundbreaking design team at BrainMaster Technologies, Inc., created software which made it possible to provide feedback on EEG activity relative to a standard mean. Although the idea of monitoring z-scores during a live training session had been around for a while, no one anticipated how the combination of Bill Mrklas’ original suggestion to incorporate live z-scores directly into the feedback paradigm, and Tom Collura’s immediate support and considerable engineering skill which brought the idea to life, would result in a range of applications which have expanded not only the ways in which neurofeedback providers can provide efficacious services, but have also challenged a variety of previously accepted principles regarding dynamic brain function. The use of QEEG database software for evaluation and analysis prior to designing and implementing clinically-oriented neurofeedback strategies began gaining traction in the late 1990s, as clinicians recognized the utility of an age-normed reference to which they could compare a client’s EEG. Quantitative analysis of EEG produces metrics on EEG signal recordings and provides z-scored images indicating where the client falls on the bell curve in a number of categories. In Figure 18.8 there are two EEG recordings, the left one from an 11-year-old boy independently diagnosed with features of profound autism, and the right one from a 12-year-old boy who tested within all functional ranges typical to his chronological age. With red indicating activity more than 3 standard deviations above the mean, and dark blue indicating activity less than 3 standard deviations below the mean, even a novice clinician referring to these images can begin to build a working hypothesis regarding which brain regions and frequency bands could correlate to client symptom reports of functional irregularities. When the BrainMaster team devised a way to compare live EEG recordings to a normative database so power and connectivity z-scores could be generated in real time, they tapped into one of the fundamental laws of feedback: if it can be quantified, then it can be incorporated into a feedback paradigm as a contingency factor. However, the conceptual model of informing the brain about its change on a linear spectrum (“up or down”) was insufficient to convey the more dimensional feedback generated by this new method, and so the BrainMaster team developed PZOK, a thoroughly unique approach to neurofeedback. PZOK means “percentage of z-scores which are OK” or the percentage of the total z-scores being measured which are currently within the standard deviation parameters helping to stabilize the desired range of EEG movement. In amplitude or amperage training, there is a single threshold (reference point) and two points in space for the brain to use the feedback to locate itself: above or below the threshold. When using PZOK training, there are three points of reference: the upper standard deviation threshold, the mean itself and the lower standard deviation threshold, which creates four locations in space, or quadrants, in which the z-scores can register at any given time as the brain dynamically engages with the feedback: Quadrant 1 — Above the mean, above the upper threshold (outside the PZOK range) ————————Upper Standard Deviation Threshold———————— Quadrant 2 — Above the mean, below the upper threshold (within the PZOK range) —————————Zero Standard Deviation Mean————————— Quadrant 3 — Below the mean, above the lower threshold (within the PZOK range) ————————Lower Standard Deviation Threshold———————— Quadrant 4 — Below the mean, below the lower threshold (outside the PZOK range) Each single z-score represents an element of brain engagement, and the summary movement of them together in real time is a live updating picture of cortical resourcing strategies. The trend reflecting

335

Figure 18.8

Comparison of two QEEG maps to illustrate potential clinical applications when considering neurofeedback strategies.

Concepts and Clinical Applications

Figure 18.9

Illustration of the four locations in which live z-scores are observed during PZOK feedback.

how much percentage of which types of z-scores fall most frequently in which designated “place,” communicates to the brain information necessary to alter resource allocation trends based on its history of behavioral responses. This increase in paradigm dimensionality facilitates more complex information delivery to the brain in an unprecedentedly organic manner. Instead of being limited to feedback based on increases or decreases of activity within the amplitude spectrum at each electrode site, now clinicians could provide feedback on measures of connectivity between electrode sites. Introducing feedback directly contingent on connectivity metrics resulted in the first direct “network training” capabilities in neurotherapy. Within the PZOK community the terms “PZOK” and “PZOKUL” can be used somewhat interchangeably when referring to the overall clinical training paradigm of using two standard deviation parameters and a third variable percentage threshold to produce the specific feedback effects; however, the terms do contain some technical differences which can impact the nuances of protocol design. “PZOK” is an acronym for “percentage of z-scores which are OK,” or the percentage of z-scores out of the total set of z-scores designated by the clinician which are currently within the upper and lower standard parameters. The original PZOK protocol design only allowed the clinician to adjust the upper and lower standard deviation parameters as a single expanding or shrinking threshold range. The clinician would push a designated command key on their training computer, and the upper and lower parameters would both either expand out away from the central mean, thereby widening the z-score training window to potentially include more z-scores and make the training task easier, or the clinician would push the designated command key and the “Shift” key simultaneously, and both of the standard deviation parameters would shrink back closer to the mean, narrowing the window to capture z-score movement and making the training task more challenging. At the request of Mark Smith, one of the earliest adopters and advocates of PZOK training, the BrainMaster team coded the “PZOKUL” feature, the “UL” meaning “Upper” and “Lower.” This gave the clinician the ability to adjust the upper or lower standard deviation threshold independently 337

Penijean A. Gracefire

from each other, so for example, if the client being trained had a very low power distribution across his frequency spectrum, the clinician could raise the lower standard deviation while leaving the upper standard deviation stationary. This meant that the brain being trained was receiving feedback based on raising power and increasing activation because it had to increase those power z-scores to get within the feedback range due to the “floor” now being higher, but at the same time the stationary upper standard deviation maintained a safety “ceiling” so that the brain did not increase activity past the point of balance. The PZOKUL feature was so useful that it became the standard for most of the PZOK designs created after its release, and PZOK is commonly used as a catch-all phrase among trainers and educators, implying the inclusion of the “UL” feature even if the letters are not explicitly stated. When used in this chapter, PZOK implies the PZOKUL feature, as there are no extant reasons to deploy the older designs in clinical practice. However, the history behind the use of these two terms in earlier publications and in educational materials is important, as the differences can be mildly confusing for practitioners in the early stages of learning neurotherapy.

Section 3.2: 4 Channel Surface PZOK: The Origin of Live Z-Score Training The first developmental stage of live z-score training consisted of four active channels, two ear references and a ground electrode, adding up to seven total electrodes placed on the scalp. The two separate ear references are necessary when providing z-scored feedback to replicate the linked ears condition under which the original EEG data was recorded for inclusion in the QEEG databases being referenced for training purposes. Electrode placement choices are made on the 10–20 spectrum, limited to the 19 locations at which the reference EEG was originally recorded. This is necessary so the z-scores generated when the client’s EEG is compared in real time to the database norms are an accurate representation of the client’s cortical activation patterns. Figure 18.10 represents a possible placement strategy a clinician could choose to maximize integration of the frontal executive center (F3, F4) with the sensorimotor strip (C3, C4) in a client who might be experiencing attentional difficulties combined with hyperactivity. This F3-F4-C3-C4 placement was utilized in the neurotherapy regimen responsible for the changes between the pre and post EEG images displayed in Figure 18.11. This was one of the earliest

Figure 18.10 Example of a 4 channel PZOK electrode placement strategy which potentially targets improved focus and attention.

338

Figure 18.11

Pre and post treatment maps of 7 yr old male with ADHD.

Penijean A. Gracefire

cases in which the PZOK training method was used in a clinical setting, and was a contributing factor to the current working hypothesis regarding the rapidity with which EEG biofeedback appears to affect younger clients with greater neuroplasticity. The case in Figure 18.11 is a 7-year-old male diagnosed with severe hyperactivity and attention deficit disorder. His pre-training QEEG evaluation on the left indicates frontal slowing consistent with the conventional literature available regarding EEG signatures associated with ADHD. The vertical rows of topographical head maps from left to right are intended to summarize activity in the delta, theta, alpha, beta, high beta bands respectively, meaning the two heads toward the top left corner of both maps are demonstrating excess delta and theta activity in the frontal lobe. The sites F3-F4-C3-C4 were chosen with the intent to address his excess frontal power in delta and theta first, and then the second stage was going to be F3-F4-P3-P4 to address the irregularities observed in his connectivity measures (the pink heads indicating hypercoherence), in addition to the amplitude asymmetry reflecting the excess power in the green heads above. However, after 21 sessions of PZOK, not only had his ADHD symptoms dramatically abated, but a second evaluation indicated that his connectivity issues had also resolved. This was an unexpected outcome after so few sessions, and was one of the earliest indicators of how the ability to provide feedback directly based on connectivity metrics was to permanently alter the course of the future of neurotherapy (Gracefire, 2014b). BrainMaster created a dynamic display screen for their PZOK training software (Figure 18.12), giving clinicians the ability to monitor the live changes in the client’s EEG patterns relative to a normative reference. This display updates in real time, and the changing numbers are also color coded, shades of blue and green to indicated descending minus standard deviations, and yellow, orange and red to show ascending plus standard deviations. While the absolute power (Abs) and relative power (Rel) columns tracking the z-scored activity in each frequency band at the individual sites was a level of innovation unto itself, the connectivity z-scores at the bottom of the display contained the feedback paradigm shift which represented the historical point at which neurofeedback entered a new matrix of complexity. Live observation of activity in amplitude asymmetry (ASY), coherence (COH) and phase (PHA) represents how a brain allocates resources between regions in real time. Now clinicians could not only watch cortical networking mechanisms shift states and demonstrate prioritization strategies in real time as the trainee engaged with the neurotherapy tasks, but the feedback program could incorporate this information into the training task, creating the conditions for the monitored brain itself to learn new prioritization strategies. Figure 18.13 contains the QEEG maps of a 19-year-old male with a history of brain injury and long-term heavy substance abuse, as well as severe emotional and physical trauma. He was trained with 4 channel PZOK and his electrodes were placed at F3-F4-P3-P4, a choice which has evolved into a common initial treatment step when clients present with diffuse power and connectivity irregularities. The F3-F4-P3-P4 placements position an active monitoring and feedback channel in each quadrant of the brain, taking advantage of the increase in connectivity fibers between homologous sites, and leveraging the innate executive function network between the frontal and parietal regions which plays a critical role in the determination of cortical resourcing strategies. This client (Figure 18.13) required 30 sessions to ameliorate his excess beta and sort out a connectivity strategy which supported his particular difficulties with self-regulation. A follow-up interview four years after his treatment regimen found him off probation, enrolled in community college, gainfully employed and happily married. The pre and post QEEG maps in Figure 18.14 are from a 44-year-old female who presented with a history of trauma, and reported a series of failed personal and professional relationships, saying she felt “unable to tolerate intimacy.” She stated that, after a personal relationship progressed 340

Figure 18.12

Screenshot of BrainMaster live z-score monitoring display.

Figure 18.13

19 yr old male, hx brain injury and trauma, progression with 4 channel PZOK neurofeedback at F3-F4-P3-P4.

Figure 18.14

44 yr old female, hx of trauma, before and after PZOK training, F3-F4-P3-P4.

Penijean A. Gracefire

to a certain point, she felt compelled to engage in behaviors she knew would ultimately sabotage the connection. She additionally expressed concern at how the perception of her by her work colleagues as cold and unapproachable was affecting her ability to advance professionally. Similar to the previous case, she was also trained with a 4 channel PZOK approach using F3-F4-P3-P4, and after 40 sessions reported substantial improvements in the original presenting symptom sets (Rutter, 2012c). This case helps to highlight the difference between treating the map and treating the person, a distinction beginning neurotherapy providers occasionally struggle to establish. After 40 sessions this client was very pleased with her progress, felt she had accomplished her treatment goals and decided her training was complete. It can be observed that several atypical activation patterns remained in her post 40 sessions QEEG map, most notably the excess high beta power at Cz, and the centralized hypocoherence and slow phase present in her beta bands. In the early stages of education, neurotherapy providers will commonly equate a “clear” or “normal” QEEG map with “better” or “improved,” and while many atypical EEG signatures do correlate with functional concerns, statistically deviant EEG patterns are representative of the strategies developed by each individual cortex as it works to optimize the accessible resources within the informational context available up until the point the initial EEG was recorded. Not all atypical activation patterns are problematic, and many clients come in with specific goals that do not include falling within a database average of quantitative metrics. Ethical practitioners will support the goals established by the client, and will not exert influence or pressure to persuade a client to continue neurotherapy simply to reduce the visible standard deviations on their QEEG maps. Neurotherapy is the process by which a clinician is able to evaluate how the brain is allocating and prioritizing its available resources, assess whether the current cortical strategies are aligned with the client’s functional priorities, and if they are not, draw the brain’s attention to particular facets of its own function with intent to influence cortical resourcing and prioritization strategies. Attention is a function of resource prioritization, and resources are prioritized toward the things to which we most frequently attend. Biofeedback occurs when the clinician selects metrics which they believe most represent the aspect of the system requiring increased prioritization, isolating out specific measurable characteristics to indicate change, and providing informational inputs (visual, auditory, tactile, etc.) contingent upon observation of the desired changes in activity. This results in long-term change because the brain prioritizes resources based on whichever aspects of its function its attention is being drawn toward most frequently. Neurofeedback consists of repeatedly drawing the brain’s attention to a particular metric, or network of metrics, until changes in global resourcing strategies have occurred. The profoundly unique characteristic of the PZOK methodology, which differentiates it from any other training software utilizing the term “z-score training,” is that it provides the informational context which draws the brain’s attention to particular aspects of its function in a manner which leaves space for the brain to decide how to engage with the informational inputs on its own terms. PZOK does not “train brains to be more normal,” it informs each individual brain of how it operates in relationship to typical parameters of activity across a spectrum of variables, and then provides feedback to support more efficient and flexible cortical activation patterns anywhere on this spectrum. Every brain has structural principles in common, but each individual’s cortical resourcing strategies have been influenced developmentally by their own unique set of circumstances. PZOK helps the brain to identify areas within its operational strategies where poor integration and connectivity between regions are affecting the efficient modulation of cortical activity and the allocation of resources. Once the brain’s attention has been drawn to these mechanisms, and consistent feedback has supported a shift in functional priorities, the new strategies will remain as part of the brain’s cumulative learning, allowing future models for response and regulation to build on these gains. This expands the 344

Concepts and Clinical Applications

Figure 18.15

55 yr old female with 28 Hz beta in excess of 3.28 standard deviations in her cingulate gyrus.

repertoire of strategies the brain has to select from when encountering future stressful circumstances, and gives the client their optimal chance to sustain functional improvement. While exercise, attention to cognitive and emotional well-being, a healthy diet and regular training sessions contribute significantly toward maximizing the degree of improvement a client can experience, it is possible for clients to still report positive benefits from PZOK even under less than ideal circumstances. In Figure 18.15, we have the pre-neurotherapy LORETA image of a 55-year-old woman who was seeking treatment for severe trichotillomania, a condition characterized by compulsive removal of body hair. She begin pulling out the hair on her head when she was about nine years old, and when she presented for neurotherapy was nearly completely bald. She had only a tiny fringe of hair at the base of her scalp, which she would trim closely with scissors when it reached more than a quarter inch in length. In Figure 18.15, you can see she has plus 3.2 standard deviations of activity at 28 Hz in her cingulate gyrus. This image was generated analyzing a 19 channel EEG recording with the original LORETA algorithm, estimating the source of the surface activity to be originating from her cingulate gyrus. Excess beta activity in the cingulate is consistent with findings reported by other clinicians who have published and presented cases correlating high beta activity in central and parietal regions with client diagnoses of various types of obsessive compulsive disorders. This is also a case from the first year of clinical implementation of PZOK training. These pre and post neurotherapy LORETA images demonstrate that feedback based on only a few active surface electrodes can impact brain activity deep within the cortex. Figure 18.16 is an image of at the same frequency (28 Hz) taken after she had received 40 sessions of PZOK training at Cz-Pz-P3-P4. The client only came 3–4 times a month, maintained a poor diet, drank between half to one and a half bottles of wine most nights, and struggled with a stressful and hostile work environment throughout treatment. Her circumstances were not ideal. Yet not only did her symptoms reduce dramatically over the course of her neurotherapy, but the beta activity in her cingulate gyrus decreased from 3.28 standard deviations to 1.39, well within typical ranges. 345

Penijean A. Gracefire

Figure 18.16 55 yr old female from Figure 18.15—post 40 sessions of 4 channel PZOK training Cz-PzP3-P4—28 Hz beta in cingulate gyrus reduced to 1.39 standard deviations.

By the end of her course of neurotherapy, this client’s hair had grown in to such a degree that her scalp was barely visible, and she came in one afternoon gleefully relating how stunned her pessimistic dermatologist had been earlier that day when she had shown up for her quarterly appointment with a full head of hair (Gracefire, 2015). This final 4 channel PZOK case is another early client whose recovery arc contained aspects which influenced the evolution of clinical comprehension regarding the principles underlying how the PZOK feedback paradigm supports cortical reorganization. Figure 18.17 shows the pre and post training surface coherence maps of a 12-year-old male with autistic spectrum features. The presenting issues of most immediate concern were aggression, behavioral meltdowns in response to changes in routine, perseverations, and sensory sensitivities to environmental stimuli such as sounds and lights and social interactions. The left image in Figure 18.17 shows not only atypically low amounts of global alpha coherence, but also the lack of integration between his anterior and posterior cortices. Within 20 sessions of 4 channel PZOK feedback (F3-F4-C3-C4), his mother was reporting noticeable reductions in aggression and rigidity, as well more rapid cognitive processing (Rutter, 2009b). The image on the right shows the changes in his alpha coherence patterns after 40 sessions of training. Figure 18.18 shows a before training and after training image of his resource distribution strategies across a magnitude spectrum from 1–30 Hz. The low power in his alpha band is visible in his pretraining map, as well as some excess beta from 23–27 Hz. After 40 session, there is almost complete balance across his spectrum, with the notable exception of the atypically high activity at 2 Hz. This was initially puzzling and ten minutes of 2–3 Hz inhibition were introduced into his training sessions. After three sessions of inhibiting his 2–3 Hz activity, he begin exhibiting sensitivities to light and sound similar to his pre-training behaviors, so the 2–3 Hz down training was suspended while other cases were reviewed to determine a working hypothesis for what was occurring. After reviewing a number of cases, a cluster of other clients emerged who displayed similar QEEG patterns, but different clinical presentations. A few examples of these cases can be seen in Figure 18.19, which shows three pre-training maps of adults with typical cognitive function and no verbal or learning delays, who all reported anxiety, a history of trauma and at least one medically documented brain injury. 346

Concepts and Clinical Applications

Figure 18.17

12 yr old male, pre and post training alpha coherence maps, 4 channel PZOK, F3-F4-C3-C4.

In these three example cases in Figure 18.19, each individual exhibited some form of alpha power deficit and an excess of 2–3 Hz activity (Gracefire, 2015). A feature similar to each individual, documented in their case notes, was their self-report of how difficult it was for them to feel reassurance or comfort from their immediate environment, describing it as feeling “walled off ” or “distant and detached from people and places and conversations.” They described this experience as undesirable, saying it negatively impacted their interactions with friends and family. In each of these cases, increased connection with their environment was accompanied by a reduction in delta activity and a corresponding increase in alpha. In the case of the boy on the autistic spectrum, it would appear as if his brain used these same principles to form a strategy which helped him differentiate from his immediate environment and

Figure 18.18

12 yr old male, pre and post 40 sessions PZOK, 1 Hz bins, magnitude.

347

Penijean A. Gracefire

Figure 18.19 Three cases with PTSD, TBI and anxiety all exhibiting atypical amounts of 2–3 Hz and low amplitudes in alpha.

experience less distress when exposed to light and sound, by increasing his 2–3 Hz activity, a frequency range Dr. Barry Sterman has referred to informally as the “Cortical Disassociation Rhythm.” According to Dr. Sterman’s firsthand observation in the laboratory, the activity measured in this frequency span appears to exhibit the least amount of response to external stimuli when compared to other frequency ranges (Rutter, 2009a). 348

Concepts and Clinical Applications

In the cases of the adults with trauma and brain injury and anxiety, this adaptation potentially functioned as a defense mechanism which modulated central nervous system response to stress by “disassociating” from environmental stimuli. While this strategy made sense during periods of exposure to trauma, its continued presence post-trauma no longer served the original purpose, and resulted in maladaptive apathy and indifference to caring gestures of loved ones until the delta was reduced and the alpha increased. Observing these similar cortical activation strategies being deployed for different purposes impacted the ongoing conversation regarding the limitations of “phenotyping” QEEG signatures for simplified interpretation, and brought to the forefront how critical it is to consider the environmental and historical context in which cortical strategies develop to formulate more accurate hypothesis regarding their current purpose. These considerations assist the clinician in structuring a treatment plan which takes possibly useful cortical adaptations into account when choosing electrode placements based on atypical activation patterns observed in the QEEG. Advantages of 4 Channel PZOK Training • • • •

•

•

•

•

Cost-effective entry point for new providers Ease of client setup, with a maximum of seven electrodes placed Flexibility to create over 3,800 unique combinations of 4 channel placements drawing from the 19 possible sites Capacity to adjust both the upper and lower standard deviation parameters as well as the percentage of variables threshold, creating a dimensional task with three separate customizable thresholds Ability to select as many or as few of the z-score metrics from which to comprise the feedback task as desired, allowing for increased attention to specific facets of power or connectivity based on the QEEG or live z-score analysis, while still maintaining the critical flexibility of the paradigm to support the brain’s own process for creating effective solutions Permits a gentle introductory step into more complex training tasks, providing additional context and feedback to the brain, including critical connectivity information, while containing a built-in pacing mechanism that doesn’t ask the brain to engage in an overwhelming amount of global change simultaneously Arguably the ideal protocol for beginning neurotherapy providers with minimal experience, as the software delivers an combination of optimal efficacy and safety if trainers meet the required baseline for education and competency in its operation An excellent option for clinicians who are expanding their services, and who choose to purchase a few 4 channel amplifiers to provide 4 channel PZOK training sessions to meet either increasing demand for in-office sessions, or to supervise remote training sessions for clients who have been determined to be good candidates for this alternative

Additional Considerations When 4 Channel PZOK Training •

The inclusion of connectivity metrics in the feedback paradigm has changed the nature of how quickly and effectively neurotherapy supported cortical integration can occur. While in many cases this is considered positive, the increase in both efficacy and rapidity also introduces the necessity for additional care on the part of the neurotherapy provider. A thorough intake is required to ascertain if a history of trauma or injury is likely, as sudden shifts in cortical resourcing strategies or integration can potentially outpace the client’s current coping mechanisms, resulting in the need for additional cognitive and emotional support to assist with processing experiences and formulating updated coping strategies. 349

Penijean A. Gracefire

•

When introduced years ago, four active training channels with both z-scored and connectivity training metrics dramatically impacted the amount of sessions required to see long-term clinical change, sometimes requiring only 30–40 sessions in cases where clients had previously needed 80–100. However, with the advent of 19 channel training options, and the observation of cortical shifts in even briefer training regimens, 4 channel PZOK was briefly overshadowed. Now it is experiencing a well-deserved renaissance, and a renewed appreciation from practitioners for its economy, efficacy and ease of implementation.

Section 3.3: 9 Channel Surface PZOK: The Intermediate Solution When BrainMaster Technologies released their Discovery 24 channel EEG amplifier in 2008, and with it their 19 channel PZOK training software, the neurotherapy industry was still trying to wrap its head around the idea of training with four active electrodes and three interactive thresholds. Many conventional practitioners, accustomed to providing feedback with one or two training channels, balked at the idea of prepping seven site placements every session for 4 channel PZOK, and 19 sites plus two references and a ground seemed initially overwhelming for some clinicians to implement in daily practice. A small scale exploration into the idea of an interim step between 4 channel and 19 channel training was conceived, with the intent to determine if there was sufficient cause to hypothesize that nine channels of PZOK feedback would provide clinical improvements significant enough to warrant the additional setup time from the perspective of a busy practitioner (Rutter, 2011). With these questions in mind, 30 adults between the ages of 21 and 49 presenting with symptoms of anxiety, depression or mixed anxiety and depression, were administered both the Beck Anxiety Inventory (BAI) and the Beck Depression Inventory (BDI). The decision to employ these reporting scales as a pre and post evaluation metric was influenced by the construction of their inquiry matrix referential to the previous week only, making the scale ideal for re-administration within six weeks, as well as reflective of the client’s shifts in self-perception. These inventories are 21-question, multiple-choice self-report scales used to help evaluate the severity of client’s perception of their own condition and are scored as follows: The Beck Anxiety Inventory • • • •

0–7: minimal level of anxiety 8–15: mild anxiety 16–25: moderate anxiety 26–63: severe anxiety

The Beck Depression Inventory • • • •

0–9: minimal depression 10–18: mild depression 19–29: moderate depression 30–63: severe depression

A candidate scoring 20 or higher on both the BAI and the BDI was categorized for the purposes of the evaluation as “mixed anxiety and depression.” Out of the original 30, the ten candidates who reported the most severe scores were selected to continue. An initial QEEG evaluation was performed on each participant, then one 9 channel PZOK session of 30 minutes was administered once a week for five weeks, and the sixth week a follow-up QEEG analysis was done, as well as a second, post training administration of the BAI and the BDI. 350

Concepts and Clinical Applications

The conditions were set to mimic the less-than-ideal circumstances of real-life neurotherapy practice by: • • •

selecting individuals with moderate to severe symptom sets; only training once a week (a common result of busy work and family schedules); and only administering five training sessions total.

Many neurotherapy providers offer pay-by-session services, and clients are less likely to continue training if they do not experience improvements within the first five to ten sessions. While this approach has inherent weaknesses as both a business philosophy and an effective treatment plan design, it is still commonly deployed in the neurotherapy service provision industry, and the intent of this project was to replicate typical conditions and observe the outcomes. The training protocol itself was identical across all participants regardless of QEEG presentation. Each participant was hooked up to the following nine channels: F3, Fz, F4, C3, Cz, C4, P3, Pz, P4 (Figure 18.20), with linked ear references and a ground, adding up to 12 total electrodes with nine active feedback channels. The central surface sites were chosen to: • •

•

reduce potential muscle artifact contamination, more prevalent in peripheral sites exclude the temporal and occipital sites with closest proximity to limbic structures, as some of the participants indicated emotional lability, and as more than five training sessions were not planned, the goal was to introduce feedback with intent to increase potential for emotional stability while minimizing possible therapeutic concerns that might arise from direct impact to temporal and occipital regulatory systems in the absence of additional follow-up sessions to support long-term processing expand on the initial 4 channel design using F3-F4-P3-P4 which had previously produced sufficient anecdotal evidence to indicate global clinical improvements were possibly enhanced by prioritizing a model of quadratic cortical integration (a sensor placed in each quadrant of the brain creating an interactional matrix of both inter- and intra-hemispheric coordination)

Figure 18.20

Screenshot from BrainMaster software of 9 channel PZOK setup.

351

Penijean A. Gracefire

Figure 18.21

Screenshot from BrainMaster software of 9 channel PZOK setup.

Figure 18.21 is a screenshot from the earlier version of the BrainMaster PZOK training software used in this endeavor, showing the training metrics selected for inclusion in the 9 channel protocol: absolute power, relative power, power ratios, amplitude asymmetry, coherence and phase lag. To limit the number of variables, only the delta, theta, alpha, beta and hi beta summary bands were selected. The additional break out bands were not included in the standardized protocol because each participant’s QEEG assessment indicated different areas which could be focused on with more customized selection, and the goal of this effort was to ascertain if there was sufficient flexibility and efficacy in a standardized PZOK setting administered under variable circumstances to result in clinical improvement. Out of the original ten candidates, seven completed the project, and three of those cases were selected for inclusion in this overview because they are useful examples of clinical principles which can be extrapolated across all applications of PZOK feedback. Figure 18.22, labeled as “Case #2,” are the pre and post QEEG maps of a 32-year-old female who scored 42 (severe anxiety) on the BAI, and reported that her fears of speaking in front of groups and 352

Figure 18.22

32 yr old female, severe anxiety, pre and post 5 sessions of 9 channel PZOK.

Penijean A. Gracefire

driving long distances were negatively impacting her work performance. By her third 9 channel PZOK session, she was reporting a feeling of relaxation toward the end of the session, and an experience of reduced anxiety persisting between session visits. After completing five 30-minute sessions, one week apart, she scored 30 on a second administration of the BAI, saying she felt there had been a noticeable reduction in her experience of anxiety and stress related to public speaking and driving, as well an increased sense of relaxation in her general state. A review of her eyes closed pre and post QEEG surface summary maps indicate a few interesting shifts which took place over the six weeks. The coherence in her hi beta frequencies reduced significantly. The skewed power distribution in her posterior cortex appears to have acquired more balance, with an increase in her absolute delta and theta bands, and a corresponding decrease in her relative beta and hi beta bands. This particular individual maintained a healthy diet and a regular exercise regimen at the time of participation, but had struggled through her adolescence with disordered eating, food allergies and migraines. While many of her developmental symptoms had been resolved by her mid-20s, her initial QEEG evaluation indicated ongoing difficulty with effective cortical resource recruitment (low absolute power across the frequency spectrum), and a trend toward maintaining a level of attention and activation relevant to external events (atypically high activity in beta bands) which prevented appropriate allocation of resources toward the slower frequency bands. A deficit of slow wave activity in the eyes closed condition can correlate with difficulties in cortical resourcing, consolidation, regeneration and repair. While her healthy lifestyle helped to support her system’s capacity for adaption and response to feedback, and likely contributed to the rapidity with which her cortex was able to reduce the hypercoherent activity in her beta frequencies, it is easier for the brain to reroute excess activity than it is to develop the necessary infrastructure to address hypocoherence. More than five sessions would be required to see corresponding resolution of the hypocoherence in her posterior connectivity patterns, but her overall response to the five sessions was encouraging, and a follow-up verbal interview three months later found she believed her improvements had held. The pre and post QEEG surface summary images of Case #3 can be seen in Figure 18.23. This participant was a 24-year-old male who presented with obsessive thoughts, poor flexibility in social situations and disproportionate emotional reactivity and volatility under relatively small amounts of stress. He scored 33 (severe anxiety) on the BAI. He had a history of chronic inflammation related to bouts of strep throat, dental infections, systemic candida overgrowth and unfortunate dietary choices, preferring processed foods with high sugar content and energy drinks, and refusing to drink water. Clients who are non-compliant with adjunctive recommendations regarding nutrition, hydration, exercise and medical care typically experience slower rates of improvement and less overall symptom reduction when engaging in neurotherapy. Neurofeedback is just one type of input among the thousands a central nervous system experiences in a day. A few hours of neurotherapy a week isn’t going to override other competitive inputs such as chronic inflammation or illness, regular influxes of processed sugar or chemicals, dehydration, sleep deprivation, daily exposure to environmental stressors or a variety of other potential sources of information competing for attention and resources. Case #3 was selected to see what type of impact five sessions could have when introduced into an environment full of competing inputs and operating under conditions known to minimize potential efficacy of neurofeedback. The participant in this case did report modest improvements, stating that he felt “clearer” and “more relaxed” for several hours immediately following each training session, and his post QEEG surface summary map does demonstrate some visible decreases in beta relative power, hi beta hypercoherence and reduced phase lag in his delta and alpha bands. 354

Figure 18.23

24 yr old male, severe anxiety, pre and post 5 sessions of 9 channel PZOK.

Penijean A. Gracefire

While these mild changes are encouraging, given the substandard conditions of the system into which neurotherapy was being introduced, the most heartening finding required source localization analysis to observe. The images in Figures 18.24 and 18.25 were produced using the earlier LORETA algorithm, and show a reduction of 19 Hz activity from a pre-training value of 2.96 standard deviations above

Figure 18.24

24 yr old male with 19 Hz beta in excess of 2.96 standard deviations in his Brodmann area 24.

Figure 18.25 24 yr old male from Figure 18.24—post 5 sessions of 9 channel PZOK—19 Hz beta in Brodman area 24 reduced to 0.14 standard deviation.

356

Concepts and Clinical Applications

the mean (Figure 18.24) to a post training level of 0.14 in his Brodmann area 24 (Figure 18.25), the ventral anterior cingulate, a region implicated in emotional response and regulation of heart rate and blood pressure. This, combined with his follow-up score of 28 on the BAI, indicated that deeper cortical shifts could occur even in briefer amounts of time, under unfavorable conditions. His three month follow-up verbal report stated he felt the positive effects fade about six weeks after his last session, a predictable outcome considering the small number of sessions and his refusal to make any other adjustments to his daily routine or diet. This case highlights how neurotherapy can support positive momentum toward long-term changes even under adverse conditions, and also how change can occur at the source before the full impact of power or connectivity shifts are visible using surface imaging techniques. This last case, referred to as #7 in Figure 18.26, is a 46-year-old male who presented with ruminating thoughts, attentional difficulties, memory problems, challenges with executive function, social withdrawal, fearful responses to change, habitual negativity and anger, and daily alcohol use. He described himself as “hopeless and bitter,” scored 32 on the BAI (severe anxiety) and 29 on the BDI (severe depression). Unlike case #3, this participant was willing to make changes to his daily routine to optimize potential for positive outcomes, altering his diet, increasing his water consumption and attempting to reduce his alcohol intake. He reported feeling energized and relaxed after the first session, said he noticed an increase in mental clarity, focus and motivation after his sessions each week, and a cumulative reduction in his reflexive negativity and fearfulness at the end of the six weeks of training. His post training administrations yielded a BAI score of 24 and a BDI score of 23. His pre and post QEEG surface summary maps in Figure 18.26 demonstrate power shifts consistent with observations from the other PZOK training cases wherein excess activity is reduced and rerouted more easily than deficits in connectivity, particularly in cases where compounding issues such as poor diet, maladaptive cognitive strategies and substance abuse slow the rate of systemic response. Between the fourth and fifth training session, the participant reported he was experiencing a re-sensitization to the unpleasant effects of regular alcohol use, including feeling sick, “hung over,” mental fogginess, headaches and bouts of depressed mood. This report is consistent with other PZOK training cases in which the client is still a regular user of alcohol, marijuana or other drugs during the initial training stages. It is common for the client to notice changes in how their system processes the substances as the neurotherapy takes effect, typically reporting increased sensitivity and reactivity similar to how their bodies reacted when they first began using the substance, including requiring smaller amounts of the substance to experience effects of its use. This is a notable ramification when considering treatment of individuals with disordered substance use, as it can either be encouraging and motivating to a client who desires to reduce their drug consumption, or it can deter compliance and pursuit of further treatment. In ten years of clinical PZOK training observations by this author, the population with the highest attrition rates have been alcohol users who begin to experience the responses of a reawakening and re-engaging central nervous system to the influx of alcohol, and decided to discontinue treatment. Figure 18.27 is a pre-training LORETA image of case #7, showing a 6 Hz excess of 4.57 standard deviations in his anterior cingulate gyrus, a region implicated in executive function, memory, attention and emotional processing. Figure 18.28 shows a reduction of the 6 Hz activity down to 2.47 standard deviations after the five training sessions. He was sufficiently encouraged by the progress he experienced during this brief endeavor that he went on to engage in further additional neurotherapy sessions after its conclusion. His verbal interview three months post 9 channel PZOK found him further progressed along his recovery arc, maintaining and building on the clinical progress he experienced in his initial sessions. 357

Figure 18.26

47 yr old male, severe depression, pre and post 5 training sessions, 9 channel PZOK.

Figure 18.27

47 yr old male with 6 Hz theta in excess of 4.57 standard deviations in his cingulate gyrus.

Figure 18.28

47 yr old male with 6 Hz theta in excess of 4.57 standard deviations in his cingulate gyrus.

Penijean A. Gracefire

Advantages of 9 Channel PZOK Training •

•

•

•

Provides an increase in complexity and dimensionality of feedback paradigm from the 4 channel training option, but does not require the full 19 channel setup to achieve potential additional therapeutic gains Is an excellent interim training stage for a client who has plateaued with 4 channel training, but may have difficulty consolidating the compounded increase in feedback complexity which accompanies a full 19 channel PZOK session Is easily customized based on the clinician’s assessment of the individual client’s needs regarding pacing of feedback and treatment trajectory: • Elimination of the three center zenith sites reduces the protocol automatically to 6 channels to gently transition clients up from 4 channel training • The addition of other channels to the original nine sites in any desired combination of sites, frequency bands and training metrics (power, connectivity, etc.) provides a wide range of possible protocol designs from 4 to 19 channels All customizations still operate under the paradigm of PZOK, so regardless of how specifically the clinician adapts the final protocol, it will still provide feedback intended to optimize cortical integration, flexibility and efficient resourcing strategies within any metrics the brain is asked to prioritize

Additional Considerations When 9 Channel PZOK Training •

•

At the time of this publication, PZOK training is only supported on BrainMaster Technologies’ EEG amplifiers, and is available for up to two channels, up to four channels and up to 19 channels; any PZOK training consisting of more than four surface feedback sites requires an amplifier and corresponding software which provides training feedback for up to 19 channels, which makes the entry costs one tier higher than 4 channel training The addition of more sites and more training metrics increases the task load of the feedback paradigm, asking for complex attention and resourcing strategies from the participating cortex; clinicians will want to consider adjusting the length and frequency of feedback sessions based on each client’s individual resiliency and the degree of fatigue reported between sessions

Section 3.4: 19 Channel Surface PZOK: Live Z-Score Training on a Global Scale When BrainMaster Technologies released the ability to provide PZOK training with up to 19 combined channels of feedback, creating a full cortical immersion paradigm, clinicians finally possessed a therapeutic tool with both the power and the finesse required to produce clinical results in a subset of cases which had proved slow to respond to previous interventions. Figure 18.29 displays a more recent version of an interactive screen from the BrainAvatar™ software, released by BrainMaster, which allows the clinician to customize protocol designs by selecting training metrics and determining with a few clicks which channels will comprise the feedback paradigm. Some clinicians chose from the already available databases which interfaced with the BrainMaster software so they could provide training feedback relative to a standard population mean, encouraging brains with distinctly atypical behavioral patterns toward more regulated and efficient resource allocation. Other clinicians began to explore the Z Builder option, a feature which performs statistical analysis on an individual EEG recording and then generates a z-scored baseline which can be used as a 360

Concepts and Clinical Applications

Figure 18.29

Screenshot from BrainMaster software of 19 channel PZOK setup.

point of reference to facilitate numerous applications, including preserving a status quo for comparison in case an individual incurs a brain injury and wishes to more accurately assess damage, or even rehabilitating people after brain injuries by providing a feedback paradigm based on their EEG baselines collected prior to injury. The two cases included in this section are both examples of how even the most elementary application of 19 channel PZOK training, with minimal to no customization and employing the pre-structured database normative references, resulted in a feedback environment not only rich with informative context for neural reorganization, but also sufficiently accommodating and sensitive for the individual brain to identify effective internal prioritization strategies resulting in functional improvements. Figure 18.30 is the pre and post QEEG surface summary maps of a 9-year-old boy with developmental delays, learning difficulties, hyper sensitivity to environmental stimuli, chronic candida infections, “leaky gut syndrome” as diagnosed by an alternative medicine practitioner, behavioral and attentional perseverations, and poor emotional regulation. He had a history of proving resistant to a variety of treatments; however, his parents had made a number of alterations to his diet in the several months prior to seeking neurotherapy, and were also experiencing some success with adjunctive treatment regimens for the candida and chronic inflammation. Their commitment to a holistic wellness approach identified him as a good candidate for training, and he became one of the first round of clinical cases to receive 19 channel PZOK feedback. Figure 18.30 shows the pre and post eyes open QEEG surface summary maps documenting the changes after ten training sessions of approximately 20 minutes in length. 361

Figure 18.30

9 yr old male, developmental delays, pre and post 10 sessions of 19 channel PZOK.

Concepts and Clinical Applications

The change in cortical prioritization and resource allocation strategies was dramatic, creating a corresponding temporary downshift across the frequency spectrum as the beta bands reduced in activation. This effect has been observed in the early training stages of some cases, indicating one method the brain may select for regulating power dynamics is to address the excess activity first by reducing power output across the spectrum, and then reorganizing resource distribution in later stages of consolidation. His parents reported improvements within the first two sessions, and by session ten he had gone from about three emotional outbursts a day to about four a week. His school performance also improved, and he less frequently verbalized distress and irritation from sound, light and motion in his immediate environment (Rutter-Gracefire, 2012a). Figure 18.31 is a breakdown of 16 to 30 Hz in 1 Hz bins. The left half of the frame is his pretraining beta activity, and the right half of the frame shows beta activity after ten sessions. Part of the decision to begin with 19 channel PZOK in this child’s case was impacted by the diffuse nature of the high beta activity in power and coherence, as well as the presence of irregularities in nearly every metric under analysis. It seemed logical to provide global feedback so the brain could determine its strategy for beginning the process of improving overall regulation. However, this next case presented with very focal QEEG irregularities, making the choice of a 19 channel approach less intuitive. Figure 18.32 shows the pre and post surface summary maps of a 14-year-old male who had unsuccessfully sought treatment for the last two years for his rumination and severe social anxiety with somatic features. He and his parents reported that when he was at school, if his anxiety regarding social interaction or academic tasks exceeded a certain threshold, then the boy would “lose the ability to use his legs,” describing numbness, loss of circulation, lack of volitional control and feeling as if his legs would not support his body weight. There was a lack of consensus among the parents and the previously consulted professionals regarding the validity of the boy’s complaints, with some parties convinced it was a deliberate ploy of avoidance, and others believing he was experiencing genuine physiological symptoms. The initial QEEG surface assessment did little to clarify the etiology at play, with his eyes open maps appearing almost completely typical, and only a few findings present in his eyes closed recordings: hypercoherence in his high beta band, and a slow wave deficit in his right temporal region. He and his parents both denied a history of head injury or marijuana use, so the best hypothesis at hand came from the LORETA analysis (Figure 18.33), which showed a deficit of 2.43 standard deviations in 5 Hz activity in the vicinity of his superior temporal lobe and supramarginal gyrus, regions directly involved in motor planning, complex movement, social and emotional processing and executive decision making, and then registering in the theta band, a frequency range key to thalamocortical resource recruitment. While these seemed possible contributing variables, at the time of this assessment software to provide database referenced feedback on LORETA regions did not yet exist, and the diffuse global distribution of his excess hypercoherence tipped the scales toward the decision to run a few sessions of 19 channel PZOK instead of a design with fewer channels. The clinical reasoning was the PZOK feedback paradigm should maintain the sensitivity to both identify and draw the brain’s attention to the focal irregularities, while still providing the context for efficient reprioritization within the thousands of power and connectivity metrics being monitored simultaneously and in real time. After five separate 30-minute feedback sessions over the course of about a month, the family reported he had not had an episode of motor control loss in three weeks, and he seemed less stressed about attending school. His post 5 sessions assessment showed a resolution of the hypercoherence in his surface maps, and an increase of 5 Hz in his LORETA images from -2.43 in Figure 18.33 to -1.36 as seen in Figure 18.34. He went on to do additional training sessions for learning difficulties and ruminating thoughts, but the episodes never re-occurred after those first few sessions. 363

Figure 18.31

9 yr old male, developmental delays, pre and post 10 sessions of 19 channel PZOK.

Figure 18.32

14 yr old male, developmental delays, pre and post 5 sessions of 19 channel PZOK.

Figure 18.33 14 yr old male with 5 Hz theta in deficit of − 2.47 standard deviations in his temporal lobe and supramarginal gyrus.

Figure 18.34 14 yr old male from Figure 18.33—post 5 sessions of 9 channel PZOK—6 Hz theta increased to –1.36 standard deviations.

Concepts and Clinical Applications

Advantages of 19 Channel PZOK Training • • •

•

With approximately 5,700 training variables to choose from, this is one of the most sophisticated network training designs available in the field of neurotherapy Nineteen channels create an immersive contextual feedback paradigm which gives the brain unprecedented dimensions of self-observation The ability to monitor activity across the entire scalp simultaneously, referential to a normative database, is essentially the ability to watch a brain respond in real time, a data rich source of information regarding how the observed individual deploys cortical resources and prioritization strategies compared to an average range of typical activity Some individuals need the global feedback perspective for their brains to grasp new behavioral alternatives which can function sustainably in the context of their particular systemic challenges

Additional Considerations When 19 Channel PZOK Training •

•

•

•

The addition of more sites and more training metrics increases the task load of the feedback paradigm, asking for complex attention and resourcing strategies from the participating cortex; clinicians will want to consider adjusting the length and frequency of feedback sessions based on each client’s individual resiliency and the degree of fatigue reported between sessions When deciding whether to start with four channels, nine channels, 19 channels or any other number of training sites, the client’s intake and medical history will be key in helping the practitioner estimate where they might place the client on a spectrum of fragility to resiliency; clients with chronic illness, allergies, sensitivities and low energy are more likely to have compromised systems which need a slower training pace, while some clients may be quite resilient and their systems can process, consolidate and benefit from more intensive feedback paradigms Feedback can be modulated by: • Number of channels (the fewer training sites, the less work) • Number of training variables (these can be selected to draw the brain’s attention to the most immediate clinical concerns correlating to the QEEG evaluation findings) • Length of session (can range from 5 minutes to 55 minutes) • Frequency of sessions (some people can train twice a day with a meal and a nap and a few hours in between; others can maybe do about one session per week because they need longer recovery and consolidation periods; the average tolerance tends to be 2–3 times a week, with 1–2 days in between) The clinician’s decision to increase, maintain or decrease the task load during training sessions can be determined by whether or not the client reports symptoms of fatigue immediately after or between sessions which are severe enough to interfere with quality of life or daily tasks of living

Section 4: sLORETA Region of Interest Live Z-Scored Feedback While the original LORETA algorithm has contributed inestimable value over the years to the field of non-invasive neuroimaging, the arrival of sLORETA, with three times the resolution as the original LORETA equation, as well as normative reference data on each individual voxel, added a gamechanging additional dimension to the PZOK training paradigm. When training sLORETA amperage in a region of interest, as discussed in Section 2, the feedback is based on an increase or decrease of estimated current source density in each voxel in the identified frequency band. The data in each voxel is averaged together and a representative nanoamperage value fluctuates in real time as activity increases or decreases in the selected region. A single nanoamperage threshold is set, and if the desired task is to reduce activity, then feedback occurs when the value of the total average activity in the region drops below the threshold. If the desired outcome is to increase 367

Penijean A. Gracefire

the frequency activity, then the feedback is contingent on the total average nanoamperage of the region exceeding the single threshold value. sLORETA overlays the voxel data with referential z-scores, meaning that now, in addition to the option to deliver feedback based on activity above or below a single threshold, the clinician also has the option to provide PZOK feedback based on the voxel activity within a z-scored range. So, for example, instead of tracking the running average of the alpha nanoamperage in each voxel, the clinician can choose to track the running average of the alpha z-scores in each voxel, and provide feedback when the alpha activity in a selected region registers within a z-scored range. Figure 18.35 displays

Figure 18.35 Screenshot from BrainAvatar software of some of the sLORETA regions of interest available for selection when designing a feedback program.

368

Concepts and Clinical Applications

the interface used to select the cortical regions on which the PZOK feedback can be based. The list of regions includes entire lobes as well as Brodmann areas, and vary widely in size. When deciding how many regions to select for a protocol design, clinicians need to consider the relative sizes of the regions being included, as it requires more resources and effort to modulate activity in larger regions comprised of more voxels. The rationale behind considering the size and number of regions selected when constructing feedback designs derives from the attempt to balance context with efficiency. The fewer z-scored variables included in the training paradigm, the quicker the brain can decipher and manage the tasks required to modulate those variables using more efficient strategies, but the less global context it has within which to adapt its behavior. The larger the number of included variables, the more nuanced and dimensional the operational context of the feedback information, but also the more time and energy required to process and incorporate the additional nuance. There are currently two primary clinical strategies when designing sLORETA z-score protocols. The first is to select a single ROI and provide feedback on the z-scored activity observed there, and the second is to select a group of regions and provide feedback on the z-scored activity reflected by the cooperative modulations observed within the network constructed by the selected areas. In cases where analysis of the QEEG and sLORETA data indicate a focal dysregulated region with functional correlates to the client’s symptoms of concern, basing sLORETA z-scored training on this primary region draws the brain’s attention toward improved regulation of the chosen area, and can result in rapid changes in resourcing and activation (Gracefire, 2014a). Figure 18.36 is the pre-training LORETA image of a 44-year-old adult male who presented with depression, problems with language processing and word retrieval, and attentional difficulties. His analysis shows 14 Hz activity upwards of 5.2 standard deviations above the mean in Brodmann area 6. He received two 30-minute sLORETA training sessions, seven days apart, targeting the z-scored activity

Figure 18.36

44 yr old male with 14 Hz in excess of 5.2 standard deviations in Brodmann area 6.

369

Penijean A. Gracefire

Figure 18.37 44 yr old male from Figure 18.36—post 2 sessions of sLORETA z-score training—14 Hz in Brodmann area 6 decreased to 2.1 standard deviations.

in Brodmann area 6, and providing feedback when his 14 Hz activity registered within the PZOK parameters of +/– 1.8 standard deviations. Figure 18.37 shows the 14 Hz activity in his Brodmann area 6 after the two feedback sessions, indicating a decrease to 2.1 standard deviations. When the client came in the third week for his follow-up QEEG evaluation, he reported experiencing noticeable improvements in motivation and focus, and a reduction in frequency of incidents in which he felt frustrated or embarrassed with his slow verbal processing. In cases where an initial assessment indicates more global irregularities, including atypical connectivity patterns, constructing an individualized network of regions reflecting the client’s areas of concern is a second feedback approach which utilizes the advantage of greater informational context to support changes in more complex cortical regulation strategies. Figure 18.38 is an example of a protocol design with a mixture of smaller and larger ROIs. These are a selection of regions drawn from the brain areas identified from an in-house meta-analysis of fMRI literature as regions most commonly affected by a history of trauma. As sLORETA has been cross-validated with fMRI, neurotherapy providers can construct training designs based on fMRI research reflecting correlations between neural activation patterns and symptom sets, as well as creating custom neural networks specific to each client based on a combination of QEEG evaluation and clinical intake data. Anecdotal case studies across a spectrum of clinical conditions over the last few years have yielded the observation that compiling 1–2 large areas, 3–5 relatively medium sized regions, and 5–10 smaller regions appears to include sufficient variability to support changes in global networking strategies while still being an easily navigable amount of variables for the client during the feedback process (Gracefire 2014a). Decisions regarding which regions and frequency bands to include in the sLORETA PZOK feedback paradigm are facilitated when clinicians use QEEG database products with corresponding 370

Figure 18.38

Screenshot from BrainAvatar software of an sLORETA z-score display.

Penijean A. Gracefire

Figure 18.39

Image from a QEEGPro sLORETA extreme z-score summary report.

sLORETA z-scored evaluation and high frequency resolution. Figure 18.39 is an example from one of the more current referential databases, and exhibits an approach to identifying the most extreme z-score in each frequency band, as well as the corresponding regions from where the activity is originating. This permits clinicians to prioritize the inclusion of regions of interest which have the highest correlation probability to the functional improvement goals established with the client in their treatment plan (Gracefire, 2013b). Effective region selection can result in more rapid cortical integration, and potentially shorter time frames in which therapeutic gains are observed. The images on the left of Figures 18.40–18.42 show the pre-neurotherapy QEEG evaluation of a 47-year-old male who reported chronic anxiety, moodiness, emotional lability and ruminating thoughts. On the right side of Figures 18.40–18.42 are the data collected after five sessions of sLORETA z-score training. These sessions utilized a feedback design constructed from the regions identified in his initial assessment as the most dysregulated, and which also exhibited the highest overlap to areas with functional correlates to his particular symptoms. The post neurotherapy images on the right side of Figures 18.40–18.42 show the global changes in activation after five sessions of sLORETA z-scored feedback based on his custom designed network. The current version of sLORETA z-score training only provides neurofeedback based on power (amperage) z-scores. Connectivity metrics such as coherence and phase z-scores between regions of interest are still in development, and not yet available. So while the reductions in excess power observed between the initial brain map and the second brain map in Figures 18.40 and 18.41 could be considered an expected outcome, the shifts in coherence metrics in Figure 18.42 illustrate how directly cortical resource prioritization mechanisms impact neural connectivity infrastructure. This finding has been observed in numerous other pre and post QEEG maps from individuals who trained with protocols using a selected combination of sLORETA regions and feedback based on power z-scores. These results demonstrate the principles which underlie the concept of constructing “custom networks,” and how it is possible to affect connectivity mechanisms by requiring the selected regions to cooperate together to accomplish the requested tasking. 372

Figure 18.40

47 yr old male, anxiety, rumination; pre and post 5 sessions of sLORETA z-score training.

Figure 18.41

47 yr old male, anxiety, rumination; pre and post 5 sessions of sLORETA z-score training.

Figure 18.42

47 yr old male, anxiety, rumination; pre and post 5 sessions of sLORETA z-score training.

Penijean A. Gracefire

This technique is effective because the design elements of the PZOK paradigm already function by creating a condition under which the chosen variables are required to cooperate together for the feedback to occur. This type of tasking utilizes principles of neural infrastructure and network development which have existed since early Hebbian theory (Hebb, 1949), and in popular vernacular is often simplistically summarized as “what fires together, wires together” (Shatz, 1992). The regions selected to construct the individualized network feedback were the: • • • • • •

“limbic lobe” (a large region comprised of the areas in the cortex with the most physical proximity to the limbic structures) anterior, posterior and central cingulate gyrus insula middle temporal gyrus parahippocampal gyrus Brodmann areas 6, 9, 23 and 24

A screenshot of the live z-score display used to monitor the dynamic changes in real time during training sessions can be observed in Figure 18.43. The intent behind an sLORETA z-scored network training approach is to provide feedback that supports and prioritizes changes in global power and connectivity, but this higher capacity informational context typically results in resolution of focal power abnormalities as well. Figure 18.44 shows a local shift in power z-scores from this same case, indicating the pre-training reading of 3.4 standard deviations in 28 Hz at F7, and then the post 5 sessions measurement exhibiting that, in the beta band, the most extreme z-score had shifted to 2.6 at F3 in 26 Hz. Advantages of sLORETA Z-Score ROI Training • • •

• • • •

Provides the highest resolution real-time EEG imaging of cortical dynamics currently available Offers a variety of analytical displays which allow the neurotherapist to observe complex cortical network dynamics under a variety of conditions, increasing the accuracy and nuance of protocol designs Uses a software interface which permits clinical decision making in real time, allowing fluid alterations during a live feedback session as the neurotherapist monitors cortical activation and includes or excludes training variables on the fly to streamline the feedback design and optimize client response Uses the PZOK method, so the clinician never has to worry about inadvertently increasing or decreasing cortical activity past the point of therapeutic benefit Reinforces cortical efficiency and connectivity while still leaving flexibility for the brain to determine its own unique strategy for reorganization Provides the option to create a fully customized network reflecting the cortical areas most in need of support and regulation relative to each individual client Can support protocol designs ranging from simple, focal tasks such as drawing the brain’s attention to regulating power in the lingual gyrus to address a learning difficulty, all the way to highly specialized networks separating out regions, hemispheres and individual frequency bands; for example, the following recommendations can be combined to optimize healing and recovery from developmental trauma: • Select out and construct a network with the following metrics: • Delta and theta z-scores in the insula and parahippocampal gyrus (thalamo-cortical recruitment of resources) • Alpha and theta z-scores in the posterior cingulate (cognitive processing and integration of data from limbic structures) 376

Figure 18.43

ROIs selected for the sLORETA z-score protocol used to achieve the results observed in the pre-post maps in Figures 18.40–18.42.

Figure 18.44

47 yr old male, anxiety, rumination; pre (left) and post 5 sessions of sLORETA z-score training (right).

Concepts and Clinical Applications

• •

Low beta z-scores in the left medial frontal gyrus (selective attention and prioritization of approach behaviors) Gamma z-scores in the anterior cingulate and the right hemisphere of the precuneus (processing social and emotional experience, self and other differentiation)

Additional Considerations When sLORETA Z-Score ROI Training • •

Requires a minimum of 22 EEG electrodes placed on the head to utilize the sLORETA algorithm Requires feedback software which supports live sLORETA z-score computation and PZOK training paradigms

Section 5: Z-Plus: No Z-Score Left Behind The dominant philosophical principle underlying the clinical success of the PZOK feedback paradigm rests in the idea that each brain is its own best advocate when it comes to self-regulation and repair. As skilled and knowledgeable as a clinician might be, even the best neurotherapist will not be privy to the unique history and experience every brain has accumulated to form its very particular set of strategies for prioritization and resource allocation. With this in mind, PZOK seeks to provide referential information to the brain being trained, so that a collaborative interaction between the connected brain and the database is formed, giving the individual neural system direct access to the collective insight and education compiled through years of EEG recordings and thousands of hours of statistical analysis to identify patterns reflecting typical brain function. Each individual brain uses these patterns as reference points to develop new operational strategies which draw on the incoming information to help with efficacy and integration, while adapting those lessons to the particular concerns and challenges of their specific circumstances. This is a cooperative effort between mind and machine, typically yielding positive functional improvements. However, in some cases the system is sufficiently compromised by injury or illness or chronic stress to degrade its own capacity for learning and adapting and healing. When this occurs, sometimes the innate wisdom of the brain needs a little extra nudge. Z-Plus was conceived to offer two additional z-score training options to enhance the feedback delivered by PZOK. The first option is PZMO (percentage of z-scores in motion). PZMO references the same central mean as PZOK to calculate z-scores, but it only tracks and provides feedback on the variables that fall outside of the PZOK range. For example, the orange arrows in Figure 18.45 identify an upper and lower PZOK standard deviation range of approximately +/- 2. This means that while PZOK is tracking and providing feedback based on how many z-scores are within +/- 2 standard deviations, PZMO is tracking the movement of all the z-scores outside of +/- 2 standard deviations. The yellow arrows indicate that PZMO is based on the aggregate movement of all the outlying z-scores. Feedback is delivered when more of the z-scores outside of +/- 2 standard deviations are moving “inward,” or toward the central mean, than are moving away from it. This is a separate feedback component designed to encourage the movement and flexibility of the outliers and support more efficient cortical integration. The second option is PZME (percentage of z-scores relevant to the mean), indicated by green in Figure 18.45. PZME also tracks the z-scores outside of the PZOK range, but instead of using the same central mean as PZOK and PZMO, PZME generates a running average of only the outlying z-scores, and formulates its own separate mean reflecting the activity of z-scores on the very ends of the spectrum. PZME provides feedback when its separate, secondary mean decreases in value. For this to happen, the most extreme z-scores out on the edges of the spectrum would have to reduce in value, requiring those most stubborn and poorly integrated areas of the brain to cooperate more effectively with their neighboring regions. In Figure 18.46 are the pre and post surface QEEG brain maps of a 6-year-old boy who presented with irritable bowel syndrome, tantrums, learning delays and obsessive compulsive behaviors. His first 379

Penijean A. Gracefire

Figure 18.45 Illustration of Z-Plus metrics: PZMO and PZME.

Figure 18.46 6 yr old male, IBS, learning delays, OCD; pre and post 8 sessions with 19 channel PZOK, PZMO, PZME.

eight sessions of neurotherapy consisted of 19 channel PZOK with PZMO. There was a rapid reduction in both beta power and beta coherence, and a corresponding change in his behavior, with his parents reporting a noticeable decrease in his ruminations and perseverations, as well as fewer tantrums. His concentration and task completion in school also improved, possibly related to his increase in alpha phase lock duration shown in Figure 18.47. Atypically short phase lock duration can impact 380

Figure 18.47

6 yr old male, IBS, learning delays, OCD; pre and post 8 sessions with 19 channel PZOK, PZMO, PZME—z-scored alpha phase lock duration.

Penijean A. Gracefire

focus and reduce capacity for staying on task, and the addition of PZMO or PZME to a PZOK feedback design can support more rapid resolution of network and connectivity inefficiencies. The practical benefits of PZMO and PZME emerge in cases where particular frequency bands or regions are so poorly integrated with the rest of the brain that it takes many sessions to achieve the desired therapeutic effects. PZMO and PZME can be combined with PZOK to create a protocol design which delivers more structured feedback for systems which struggle to form better resourcing strategies due to the severity with which they are compromised. Advantages of PZMO and PZME Training • • •

Additional dimension and complexity for more sophisticated feedback designs More comprehensive context and structure for brains too compromised to reorganize effectively without the additional support Possible reduction in number of sessions to achieve desired therapeutic effects

Additional Considerations When PZMO and PZME Training • •

More complex tasking can require shorter sessions at first Feedback sounds need to be distinct from each other and introduced one at a time until the tasks have been learned separately, and then combined together at a gradual pace All included images are original copyrighted material.

References Gracefire, P. (2013b, March). BrainAvatar for beginners: sLORETA training using BrainDx Z-scores with 3D live projection. PowerPoint presentation at the meeting of Association for Applied Psychophysiology and Biofeedback, Portland, OR. Gracefire, P. (2014a, October). Early cognitive decline and Alzheimer’s disease: Detection and intervention using sLORETA Z-scored imaging. PowerPoint presentation at the meeting of the International Society for Neurofeedback and Research, Denver, CO. Gracefire, P. (2014b, March). Clinical integration of low-resolution EEG imaging with QEEG and neurofeedback. PowerPoint presentation at the meeting of Association for Applied Psychophysiology and Biofeedback, Savannah, GA. Gracefire, P. (2015, August). sLORETA brain imaging: PTSD assessment and intervention in real time. PowerPoint presentation at University of South Florida Counselor Education Program’s 6th Annual Institute on Counseling the Military, Families, and Children, Tampa, FL. Hebb, D. O. (1949). The organization of behavior. New York: Wiley & Sons. Rutter, P. (2009a, April). Reconnecting Our Lost Children to the World: Z-score Training and Autistic Spectrum Disorder. PowerPoint presentation at the meeting of Association for Applied Psychophysiology and Biofeedback, Albuquerque, NM. Rutter, P. (2009b) Z-score training with profound autistic spectrum disorder: A case study. NeuroConnections, Fall Issue, 32–24. http://media.wix.com/ugd/cba323_7f141f21a21a4825a19e36f8fae0a624.pdf Rutter, P. (2011, September). Potential clinical applications for symptom reduction of anxiety, depression, or mixed anxiety/depression using 19 channel live Z-score training using percent ZOK and ZPlus protocols. PowerPoint presentation at the meeting of the International Society for Neurofeedback and Research, Phoenix, AZ. Rutter, P. (2012c). Five case studies using live Z-score training PercentZOK on individuals diagnosed with PTSD. NeuroConnections, Spring Issue, 28–30. http://www.isnr.net/uploads/NeuroConnections/2012/NCSpr12. pdf Rutter-Gracefire, P. (2012a, May). Data driven clinical applications for Z-score training. PowerPoint presentation at the LORETA Z Score Biofeedback Conference, Cancun, Mexico. Rutter-Gracefire, P., & Durgin, G. (2012b). Combining sLORETA and 19 channel live Z-score training: Targeting Hi Beta in Brodmann areas to reduce symptoms of anxiety. Neuroconnections, Winter Issue, 30–33. http:// media.wix.com/ugd/cba323_f1ab1486399c42b6967c22013eb08d30.pdf

382

Concepts and Clinical Applications Shatz, Carla J. (1992). The developing brain. New York: Scientific American. The exact sentence is: “Segregation to form the columns in the visual cortex [. . .] proceeds when the two nerves are stimulated asynchronously. In a sense, then, cells that fire together wire together. The timing of action-potential activity is critical in determining which synaptic connections are strengthened and retained and which are weakened and eliminated” (pp. 60–67.) Also referenced in Doidge, Norman. (2007). The brain that changes itself. New York: Viking Press. p. 427.

383

PART VI

QEEG and Brain Dynamical Approaches

19 PERSPECTIVE AND METHOD FOR A QEEG BASED TWO CHANNEL BI-HEMISPHERIC COMPENSATORY MODEL OF NEUROFEEDBACK TRAINING Richard Soutar Abstract Neurofeedback has recently evolved rapidly into a wide variety of technical methods seeking to enhance efficacy and reduce treatment time. However, more conservative traditional methods employing more sophisticated perspectives, such as the two channel approach, may have been overlooked with respect to their clinical potential. This chapter reviews an exploration of a basic two channel bi-hemispheric methodology, based on QEEG, and grounded in a traditional arousal model of neurofeedback enhanced by recent findings in neuroimaging. This method also calls for a biopsycho-social approach to clinical neurofeedback as well as the recognition and management of metabolic and psychosocial limitations that can confound training outcomes.

Introduction Over the last decade neurofeedback has evolved into a wide variety of effective methods which have developed increasingly complex techniques and rationales. Some methods only bear a faint resemblance to the original single channel approach which is presently still the primary method being investigated at the research level for efficacy. The more complex methods demand a high level of sophistication and technical background in math and science that many clinicians entering the field find alien to their clinical training as well as intimidating. Consequently a large number of practitioners apparently continue to use a one channel training approach. On the one hand, the research literature in the field of neurofeedback has established the efficacy of this paradigm (Arns, de Ridder, Strehl, Breteler, & Coenen, 2009; Monastra, 2005; Rossiter, 2004) and defined the specific protocols that can be used effectively by a conservative practitioner. On the other hand, the newer methods are an effort to enhance the efficacy of neurofeedback through more complex and technologically sophisticated means to reduce the number of sessions required to achieve significant results. Whether this has been accomplished remains to be proven through equivalent research but clinical reports are promising. The shift in the more traditional approach from single to multichannel protocols happened very rapidly, when compared to the reign of the one channel paradigm. A cursory review of journal articles and conference workshops show there was only a limited exploration of an emerging two

387

Richard Soutar

channel method such as proposed by Baehr, Rosenfeld, Baehr, and Earnest (1999), and Valdeen Brown (personal communication, January 15, 1998), or the coherence methods of training such as represented by Horvat (2009). At the same time, QEEG guided neurofeedback in several different forms also expanded assessment methods and protocol derivation rationales and an ISNR position paper recommended it strongly as a future direction of the field (Hammond et al., 2004). Having trained other professionals in neurofeedback methods for over 15 years, I have observed that the complexity of QEEG has been daunting to many practitioners and remains so to this day. As the field leaped forward into complex multichannel methods, as well as adding a host of other neuromodulation technologies, it seemed important to our team of practitioners to fill in the gap between the simple and rapidly emerging complex approaches through a more thorough exploration of the dynamics of two channel bi-hemispheric training. We were further inspired by the emerging findings in neuroimaging literature that were highly pertinent to this approach, especially the work of Alstott, Breakspear, Hagmann, Cammoun, and Sporns (2009), Davidson (1995), Heller, Nitschke, Etienne, and Miller (1997), Pascual-Leon (2005), and others, which appeared to be supportive of this direction. At the same time, the utilization of QEEG to improve outcomes through more effective strategies of sensor placements seemed an important enhancement. The new emerging findings about network theory and an explicit systems approach to explain local and regional activity were also supportive of this perspective (Buzsaki, 2006; Freeman, Ahlfors, & Menon, 2009; Honey, Kotter, Breakspear, & Sporns, 2007; Meehan & Bressler, 2012). However, providing a simplified or streamlined approach to QEEG analysis and protocol derivation would be necessary to increase the accessibility of the technology to clinicians wishing to enter the field. As commercial QEEG databases became available in the mid to late 1990s and pre/post training maps became more common among advanced practitioners, certain puzzling aspects of the technology became apparent. One such feature was the tendency for clients to report and demonstrate very significant changes in symptomology, through measures such as the Test of Variables of Attention (TOVA) and the Beck Inventories, yet display only modest changes in a normative direction in their maps. We had five clinics across the country at the time and recorded hundreds of such cases. Our discussions with other clinicians who were utilizing QEEG at that period confirmed our observations. At that juncture we also employed every reported method of neurofeedback we could find as well as peripheral biofeedback, photic stimulation, HEG, Alpha Stim, and adjunctive clinical methods such as EMDR. Our conclusion was that since QEEG is an enduring signature (John, Prichep, Fridman, & Easton, 1988), any significant change in the QEEG was likely to be positive if it was associated with positive changes in symptoms. Some pre/post maps moved significantly toward a normative pattern and others moved in more complex ways, often with areas moving away from the norm. It is traditional among practitioners to show their best maps and cases when explaining the technology because the displayed changes are tacitly didactic. However, it is not always the norm. Once again, emerging research in the neuroimaging field has begun to explain this conundrum through the theory of compensatory response (Pascual-Leon, 2005). Compensation and plasticity are the two terms that ground our entire methodology. We felt it was important to measure the percent change in a map to more effectively define significant change in brain function with respect to QEEG measures. Another conundrum also emerged over time as our clinical cases increased into the thousands. Some individuals responded robustly to neurofeedback and others did not. Clinicians typically look to their equipment or protocols as the point of failure and yet this ignores other more obvious confounds. Judith Lubar (Lubar & Lubar, 1999) found that events in the family system could profoundly alter the efficacy of neurofeedback. To make this kind of observation requires appropriate clinical skills that many practitioners who are not trained in clinical psychology or counseling, including family systems theory, do not have. In fact, without this training many professionals are likely to be blind to this effect. We determined it was critical to consider these outside confounds and manage them to produce a higher efficacy level of neurofeedback rather than focus on technology alone. 388

Perspective and Method

We found adding this dimension of intervention to our practice enhanced our efficacy far more than just changes in equipment or protocols. To ignore this aspect of training is to disregard decades of research in the behavioral sciences. A cursory reading of Alan Schore’s (1994) work regarding the impact of trauma on the emotional development of the brain and the behavioral consequences that ensue should be sufficient to convince the average clinician. An additional confound we encountered with respect to outcomes was in the biological realm. In reviewing the trend screens from session to session we frequently find that although significant changes are made during the session, sometimes there is considerable backsliding with respect to progress. When no psychosocial confounds are uncovered we are typically inspired to investigate physiological or metabolic confounds. Since the recruitment of metabolic resources is critical to neuro-metabolic processes, any deficit in this area is likely to limit the ability of the brain to respond to challenges. The brain uses a large amount of the body’s resources (Raichle & Gusnard, 2002; Tomasia, Wang, & Volkowa, 2013) and is sensitive to changes in these resources (Kilner, Mattout, Henson, & Friston, 2005). It is reasonable to assume, based on the present research, that every brain has metabolic limitations based on its resources, especially glycogen, glutamate, and lactate, as well as sodium and potassium (Be’langer, Allaman, & Magistretti, 2011). The effect, for instance, of hypothyroid function on the brain is well documented (Niedermeyer & Lopes da Silva, 2005) and levels of Thyroid T3 have been shown to be linked with critical neuronal function including enzyme production, the production of extracellular matrix proteins, glutamate regulation, growth factors controlling neuronal growth, and neuritogenesis (Gilbert & Zoeller, 2010; Trentin, 2006). Hypothyroid clearly manifests as slowed alpha while increasing its power. Attempting to downtrain this metabolic limitation is generally futile, but hormone supplementation can alter a pre/post map rapidly. Unfortunately many clinicians are not trained in this area and do not recognize such limitations, again turning to better equipment technology for a solution. We found that treating these metabolic deficiencies greatly enhances clinical outcomes. As an outcome of these historical findings in our clinical records, we developed an agenda to review every case from a bio-psycho-social perspective. This approach assumes a Diathesis Stress model (Zubin & Spring, 1977) that was introduced into psychology to explain the emerging research findings on schizophrenia, such as the concordance studies being conducted in that period. The assumption is that stressors in any one of these three dimensions can result in the elicitation of genetic weaknesses and related symptomology. It is parallel to Mark Schwartz’s model of “Window of Vulnerability” as outlined in Biofeedback: A Practitioners Guide (Schwartz & Andrasik, 2003). We have developed our own integrated assessment system to process and integrate measures and reports but this approach can be done with any good variety of measures commercially available. It could easily be done with standard inventories and available databases. We prefer a higher level of integration and have developed our own and will be using that for didactic purposes in the case presentation that follows. We have discovered that making these measures easily accessible means they are more likely to be employed by our clinicians for more effective outcomes. In addition we have chosen a difficult case that only partially resolves at the QEEG level but significantly resolves at the symptom level to more effectively demonstrate our points of interest. From our 20 years of clinical experience with neurofeedback we believe that a more comprehensive approach such as follows is a more productive direction to pursue than purely enhancing neurofeedback modalities.

Rationale for the Two Channel Method Arousal Theory, Cognition, and the Vertical Axis In the field of psychology, as well as psychiatry, the arousal model has been well established for decades. Sterman (1996), drawing on his own pioneering research, recruited this perspective specifically with respect to neurofeedback (NFB) and Abarbanel and Evans (1999) explicated it in more 389

Richard Soutar

extensive detail at a later date. Sterman constructed his perspective on neurofeedback based on the experimental observation that repeated changes induced in the sensory motor rhythm (SMR) activity correlate with important permanent changes in the striatum (Sterman & Egner, 2006). In his model, Sterman (1996) proposed that three systems of brain activity influence thalamic generation of EEG at the scalp. The vigilance system involves the reciprocal relationship between specific centers in the brain stem and ascending inputs to thalamic, limbic, and cortical regions. The sensorimotor system involves ascending proprioceptive inputs to the thalamus and sensorimotor cortex and related cortical afferents. The cognitive integration system involves neural centers that process and integrate sensory and motor activity. Abarbanel and Evans (1999) note that Sterman’s theory did not directly include the role played by limbic oscillatory activity in the manifestation of cortical EEG. He does, however, lay out how limbic activity may influence attentional mechanisms. Fortunately, Kirk and MacKay (2003) do outline mechanisms by which low frequency theta activity related to emotional processing is shifted through arousal mechanisms involving the mammillary pathways into higher frequency theta that results in memory processing. The investigation of Morillas-Romero, Tortella-Feliu, Bornas, and Aguayo-Siquier (2013), showing the relationship between attention and emotion, further documents this relationship nicely. Emotion works hand in hand with cognition at the neurophysiological level (Demasio, 1994, 1999) and this critical finding has considerable implications for psychology and neurofeedback. The idea that specific networks become readily available at each unique level of arousal is indirectly well established in the psychology literature. Performance is arousal dependent according to the Yerkes-Dodson Law (Diamond, Campbell, Park, Halonen, & Zoladz, 2007). Sterman’s arousal theory resonates well with this perspective. Various tasks call forth different levels of arousal and recruit different networks. Simple or well learned tasks require less arousal while more complex or new tasks require more arousal. This has recently been verified in the developing research around the Default Mode Network (Buckner, Andrews-Hanna, & Schacter, 2008). Long-Term Potentiation (LTP), the primarily mechanism involved with memory consolidation, is most efficient at moderate glucocorticoid levels and suggests learning consolidation is arousal dependent (Diamond, Campbell, Park, Halonen, & Zoladz, 2007). A more challenging task recruits more networks and increases metabolic load on the cortical system reducing alpha idling of networks and increasing both cell column activity (beta) and hemodynamic response (Kilner et al., 2005). The hemodynamic response is merely an effort of the astrocytes to provide resources, if they are available, to the neurons as they become progressively active to process the task at hand. If the resources are not available then metabolic limitations will clearly hamper the learning process. The shifting of network resources due to the orienting response has been traditionally observed under the term desynchronization and is a well established principle of electrophysiology (Niedermeyer & Lopes da Silva, 2005). A high value stimulus triggers complex or automatic responses that in turn increase arousal and recruit networks associated with higher arousal levels. Task expression becomes state dependent, calling forth different networks from various regions of the brain based on arousal (Fan et al., 2012). Consideration of these findings invites an emergent network systems perspective with respect to the expression and integration of cognitive functions that transcends simple anatomically defined distinctions regarding function such as Brodmann areas (Breakspear, Jirsa, & Deco, 2010; Nakagawa, Jirsa, Spieglar, McIntosh, & Deco, 2013). This in turn suggests that strategic intervention from a systems perspective (Othmer, Othmer, & Kaiser, 1999) is most likely to be effective when implementing neurofeedback. Specificity of training may not be as important as accuracy in selecting a general network to train with upstream and downstream influences considered. Based on our present limited understanding of networks, we may be most effective at attempting to establish optimal attractor states in meta-networks involving Rich Club Hubs using norms as an approximate or fuzzy reference. These Rich Club Hubs have a dominant influence on network activity because they have the most connections with the shortest 390

Perspective and Method

pathways between network nodes in what is now recognized as a Small World scale free network design that appears to define brain networks in general (Meehan & Bressler, 2012). In reviewing Laird et al. (2012), it can readily be seen that a large number of these hubs, by a strange twist of fate, can already be found to closely correlate with the present 10–20 system. It is well understood at this point in psychology that memory is frequently state dependent. Buzsaki’s (2006) work on hippocampal theta has led to a general recognition that theta and gamma are linked in function. Meehan and Bressler (2012), in reviewing the literature, note that gamma in turn triggers beta activity and consequently all three are tied together. Hence the underlying reasons for the value of training beta to theta ratios or enhancing beta frequencies while inhibiting theta frequencies or the relationship between fast waves and slow waves. One group of researchers proposed that this be considered a measure of inhibitory control of the cortex over subcortical domains. Their preferred term for this mechanism was “Brain Rate” (Pop-Jordanova & Pop-Jordanov, 2005). As arousal increases and decreases, sympathetic tone shifts with corresponding shifts in activation between hemispheres as well as shifts in frequency ratios between hemispheres along the Horizontal Axis. The spectral shift from slow wave to more fast wave dominance and beta arousal is tied to the Horizontal Axis (interhemispheric) through its classical reciprocal relationship with alpha.

Arousal Modification, Affect Regulation, and the Horizontal Axis The extremes of approach and avoidant behavior and their correlates of sympathetic tone require more refined ongoing adjustment and modification to implement social behavior than that which the basic ascending reticular arousal mechanisms might offer. This modification of arousal appears to be mediated by the ongoing dynamic relationship between the left and right hemispheres. Davidson’s (1995) research explored the electrophysiological aspects of this consideration and found that higher levels of alpha amplitude in the right hemisphere with respect to the left hemisphere resulted in a propensity towards a reduction in approach type of social behaviors and a tendency for depression. Since that time Choi et al. (2011) has demonstrated the efficacy of asymmetry neurofeedback to reduce features of depression using a random trials design. In addition, Davidson found that this alpha asymmetry correlated with lower arousal levels in the left hemisphere with respect to the right hemisphere. Heller, Nitschke, Etienne, and Miller (1997) reported that beta asymmetry, with higher beta amplitudes in the right hemisphere with respect to the left hemisphere, correlated with anxiety of various kinds. Avram, Baltes, Miclea, and Miu (2010) had similar findings. The research of Heller et al. shows that enhanced right hemisphere beta results in increased sympathetic tone and often corresponding anxiety. It appears that a traumatized network system may become stuck in an overaroused hypercoupled state involving excessive cell column activity and corresponding beta asymmetry (elevated right hemisphere beta). Further research has revealed this can include excessive frontal or posterior beta activity, depending on the level of chronic exposure and whether the response is worry, panic, or rumination (Engels et al., 2007). This increased tone enhances norepinephrine activity and adrenal response as well as upregulating the ACTH system. Over time the body inevitably begins to suffer the consequences of chronic hyperarousal (Sapolsky, 1999) including neuronal death and telomere shortening (Epel et al., 2004). Hans Selye noted that this trend leads to exhaustion of the organism and death. An alternative response available to the organism is withdrawal from stereotypical activities that lead to frustrating situations that may expose it to trauma (Beck, 1979). Since this is correlated in humans with social withdrawal activity (Davidson, 1995), it may be concluded that alpha asymmetry and depression constitute a protective response. It not only includes a reduction in dopamine but reductions in serotonin. In this unique state, the organism is alert to danger and functional but hesitant to engage. This would buy an organism time to incubate novel alternative behaviors and recover some measure of resources. From this perspective depression is a protective moratorium from overarousal levels that can lead to severe excitotoxicity. In viewing depression and anxiety from this 391

Richard Soutar

perspective they provide a unique balancing act at an elevated level of arousal. We define this left/ right dimension of training as the Horizontal Axis. Baehr et al. (1999) introduced an asymmetry protocol developed initially by Peter Rosenfeld based on the research by Richard Davidson, as mentioned above. This protocol involved training homologous sites, F3-F4, using two channels of EEG (one for each side). Variations of this protocol circulated in the neurofeedback community at the time. The goal of this type of protocol was to increase alpha activity in the right hemisphere and decrease it in the left in order to enhance left hemisphere activation. The Horizontal Axis, however, has perhaps a more complex dynamic than appreciated at the time. One recognized aspect of this dynamic is found in Pascual-Leone’s (2005) research on compensatory mechanisms of the brain. TBI in one hemisphere can lead to reductions in transcallosal inhibitory mechanisms that in turn result in one hemisphere co-opting function in the contralateral hemisphere to maintain an allostatic state while the injured region goes offline for repairs. Alstott et al.’s (2009) modeling of lesions predicts this outcome as well. The implication is that training a region of diminished function should take into consideration contralateral inhibitory mechanisms. A two channel protocol to manage the effect of training a specific dysregulated site on a contralateral site and its reciprocal response allows for the monitoring and management of compensatory mechanisms. The reason for opting for homologous sites is based on the reported bilateral nature of major functional networks (Laird et al., 2012). It also suggests that fine tuning of a protocol should consider innate hemispheric norms and dynamics as well as compensatory mechanisms between frequency domains as well as functional domains. Teipal et al. (2009) notes that interhemispheric coherence very efficiently and accurately reflects the integrity of intracortical and subcortical fiber systems. He also argues that interhemispheric coherence is also a proxy measure of intrahemispheric integrity. Consequently a high level of leverage over cortical function should be expected from training in this manner and utilizing amplitude to shift coherence is the most conservative approach. Careful adjustment on the fly in the reinforcement rates of the amplitude enhancement and inhibits of the various component bands based on their interhemispheric response allows for an optimal adjustment of reinforcement in harmony with the unfolding interhemispheric dynamics of the training session. Monitoring a trend or review screen in real time as the training progresses, while using a two channel monopolar montage, allows the clinician to observe emerging trends in symmetry between hemispheres as well as the layering or distribution of component bands that indicate arousal levels. Additionally, coherence measures can be monitored to discern the level of compensatory activity in response to changes in reinforcement rates. Increasing and decreasing coherence rates can be observed to follow amplitude patterns as homologous sites increase and decrease communication in an effort to move in a relatively normative direction. This activity in amplitude and coherence, however, often involves long periods of movement far outside normative ranges of activity and can be easily observed as the brain seeks an optimal autocorrelative solution. By the avoidance of limiting the compensatory activity with arbitrary ranges of norms, the maximum adaptive responses can be encouraged. These homologous sites appear to provide maximum leverage in the system.

Theoretical Conundrums of QEEG Assessment and Application Given the existence of a bewildering number of interacting specific systems in the brain, challenging a system to increase regulation through operant conditioning of brainwaves would seem problematic. Through QEEG we can identify regions of the brain that are dysregulated but it is often a very complex picture with many levels of possibility with respect to training protocols to employ. Meehan and Bressler (2012) and others in the neuroimaging community note that network systems in the brain display a pattern of effective connectivity that is Small World with a Rich Club Hub system that dominates information exchange. Honey et al. (2007), McIntosh and Korostil (2008), and many other key researchers comment that networks are typically situated with upstream modulation and 392

Perspective and Method

downstream feedback loops that can potentially instigate or maintain dysregulation. Identifying the exact source(s) of dysregulation transcends our technical abilities at present. With respect to QEEG analysis and protocol development, this results in a difficult and unclear decision making conundrum. Early NFB practitioners utilized protocols that implicitly employed a systems perspective. One electrode location was strategically selected to generate change in the entire system. In fact, Sterman’s experiments had implicitly proven this to be the case. A careful reading of his research showed that he was able to influence temporal lobe instability by training over the motor strip with a monopolar placement. In fact he reported that training over the seizure location was not as effective as training over the motor strip (Sterman, 2000). Over time, however, clinical experience clearly demonstrated that there were distinct advantages and disadvantages in training in different locations. Unfortunately, the rules were not always clear. Both Demos (2005) and Soutar (1999; Soutar & Longo, 2011) proposed guidelines for what could be done in each quadrant of the brain in terms of positive and negative outcomes; however, the underlying mechanisms that contributed to them were unclear as the research community was still struggling with network theory as it is today. As practitioners moved from an implicit systems theory approach to a more QEEG or location based approach, it appears to us that they began to lose sight of the implicit systems perspective. The focus on location in some instances bordered, and perhaps still does, upon electronic phrenology. The idea of always localizing a functional problem to a small ROI is problematic when considering the level of connectivity of the brain and the diffuse nature of EEG.

The Value of Location To date, the majority of NFB practitioners have developed location based training from decades of clinical experience demonstrating that clients respond differentially to training, both different frequencies and different locations. This is consistent with the above observations in the neuroimaging literature. A unitary vision of timing and timing remediation that could alter the entire system is too simplistic and ignores local systems dynamics and their influence and contribution to an entire complex system such as the brain. Within any system such as the brain, it makes more sense to assume multiple timing events that are autocorrelative (Bassette, Meyer-Lindenberg, Archard, Duke, & Bullmore, 2006; Buzsaki, 2006). Efficient remediation is frequently likely to be local and strategic, often requiring the tuning of several networks. In addition, there is the decision to be made regarding training modality, i.e. amplitude versus coherence. Given the compensatory nature of phase and coherence (Alstott et al., 2009; Pascual-Leon, 2005), the established dangers of undermining these mechanisms, and the documented negative clinical consequences associated with training these dimensions (Horvat, 2009), it seems reasonable to conclude that amplitude training is a very reliable and conservative approach. Training amplitude by definition alters the other dimensions of measurement but at the brain’s discretion. Although these measurement domains are mathematically discrete, they are nonetheless interdependent at the neuronal level with respect to the physics of brainwave production (Nunez & Srinivasan, 2006). Any clinician can easily confirm this by reviewing coherence, phase, symmetry, and dominant frequency while amplitude training. It is simple with today’s technology to train any one of these domains and observe the global as well as specific response. Given the interdependence of these domains and the efficacy, reliability, and safety of training amplitude, it makes sense to continue to develop the one channel amplitude perspective into a multichannel amplitude perspective beginning with a thorough understanding of two channel training dynamics. The work of Baehr, Rosenfeld, and Baeher (1997) and Baehr et al., (1999) originally moved in this direction. Yet to our knowledge, little research or clinical reporting followed.

Applying the Standard Bi-Hemispheric Protocol As mentioned above, the work of Kilner et al. (2005) and others demonstrated that a strong bidirectional link exists between electrophysiology and hemodynamics and consequently between NFB and 393

Richard Soutar

hemodynamics. Training down slow waves and increasing fast waves typically increases demand on astrocytes to increase lactate production for neuronal metabolism, which in turn places demand on local capillaries to provide hemoglobin and glycogen. In view of this relationship it actually makes more sense to increase beta in the LH and reduce slow wave activity (delta, theta, or even alpha) in order to enhance perfusion and generate greater activation than to just train alpha down. The contrary should hold for the right hemisphere. Training alpha or theta up and beta down should more robustly exert pressure to reduce perfusion and metabolic activity. There are neurotransmitter correlates to this as well. Activating the left hemisphere exerts pressure on the neural system to increase dopamine output while reducing activation in the right hemisphere exerts pressure to decrease norepinephrine output (Davidson, 1995). From this perspective, hemispheric balance of activity provides a general improvement in system function but can be focused and modified by shifting to different meta-networks to address regional dysregulation related to more specific functions. Training different homologous sites allows the practitioner to address more specifically symptoms associated with these locations while moving the system globally in a normative direction. This complex dynamic integrates emotional spectrum of variation with cognitive spectrum of variation. Training left–right asymmetry indirectly trains arousal. Typically, then, we enhance beta in the LH while reducing slow wave activity based on the map findings. If we are reducing delta or theta then we are emphasizing what I have termed the Vertical Axis of arousal, and increasing inhibitory control and general signal to noise ratio in the brain. If we are reducing alpha, then we are emphasizing what I have termed the Horizontal Axis of arousal, which further modifies the effect of arousal and integrates limbic and cortical activity as well as cognitive attention and emotional functions. By the same token, in the RH, we are reducing delta or theta to reduce slow waves on the Vertical Axis or we enhance alpha to train the Horizontal Axis. We may also inhibit beta in the RH to train horizontally. In such cases it is important to increase the enhancement of beta in the LH as it will tend to reduce in amplitude. Training a frequency in one direction in either direction tends to train it in the same direction on the contralateral side. Our goal is to maintain or encourage normal asymmetry between alpha and beta. However, we must allow room for compensatory excursions in amplitude and coherence as the system reorganizes itself around successive attractor states in the progressive iterations that lead to optimal functioning. Alpha should tend to be higher in the right hemisphere and beta should tend to be higher in the left hemisphere when reviewing the grand averages on the training screen. At the same time we train horizontally, we are watching for slow to fast wave ratios to normalize along the Vertical Axis. Montgomery, Robb, Dwyer, and Gontkovsky (1998) reported that with eyes open, delta and theta should dominate the spectral distribution with beta being approximately half the amplitude of the slow wave at the vertex. In our analysis of hundreds of normal QEEGs at our clinic we noted that this relationship roughly held across the scalp. This was confirmed more recently by Almurshedi and Ismail (2014). With this in mind, can train along the Vertical Axis of arousal in the LH by inhibiting theta while training along the Horizontal Axis of arousal in the RH by increasing alpha or SMR. Thus both Axes can be trained at the same time through different combinations of enhancements and inhibits. We expect hemispheres to shift symmetry continuously during a session as a reflection of plasticity in the system and healthy mood fluctuation in response to the narrative in the training video. Typically, abnormally high amplitudes in slow waves such as alpha or theta will shift down as symmetry is normalized and vice versa. We have thousands of training records to support this observation. From this discussion it should be clear that we are training regional network dynamics in selected bilateral networks based on statistical determination of the most dysregulated bilateral network. The question then arises regarding which network is most dysregulated.

394

Perspective and Method

Upstream and Downstream Contributions to Dysregulation Through analyzing the abnormal standard deviations of each location in each dimension of magnitude (or power), dominant frequency, asymmetry, coherence, and phase, we can determine which locations are most globally abnormal with respect to dimensions of analysis. Typically, training in the worst bilateral network will generate a positive response. However, Honey et al. (2007) and others have noted that upstream or downstream networks that are dysregulated may be the actual source of dysregulation for a network hub. Typically, the disturbing input is from upstream, which is usually a more posterior location. By training the most dysregulated network posterior to the identified worst network, better responses can be obtained. The only way to determine this is to actually try training both networks to find the optimal response. In either case, the clinician is training the brain toward a more normal distribution based on the QEEG findings. By training each network into a more balanced Vertical and Horizontal relationship, several iterations of training and mapping can be done. Typically each remap shows between 30–40% change, when degree of deviation is tallied, between pre and post maps when things are proceeding well. Fifty percent change is usually the most we usually encounter in our system of analysis. The percent change is not additive, as each map represents a new configuration of the entire system as it cycles through progressive iterations of adjustment. This is difficult for new clinicians to grasp as they tend to expect change in a linear succession. The good news is that at each remapping we characteristically observe fewer compensatory changes away from the mean and more changes toward the mean.

Methods Bio-Psycho-Social Assessment and Tracking The first step in our assessment process is to statistically determine the most deviant locations based on a weighting method that assigns a value for each location based on its z score in magnitude, dominant frequency, phase, coherence, and symmetry. The symmetry value used in the calculation is derived from magnitude difference between left and right hemispheres rather than a z score value. Each location is paired with its homologous site on the contralateral hemisphere. Frequently these rank next to each other in the list. Eight sites are listed in rank order with the top sites selected as the most likely location to generate maximum results. Once a site is selected it is evaluated for frequency deviances and an initial training strategy is generated. A typical example would be downtraining theta in the left hemisphere and uptraining Lo Beta in the right hemisphere. The training strategy is further adjusted by clinical rules considerations based on the Quadrant Rules (Demos, 2005; Soutar, 1999; Soutar & Longo, 2011), i.e. not to train beta up in the right posterior quadrant. A third stage of analysis is also applied in which typical compensatory dynamics are considered within the existing frequency context in each hemisphere under applied protocol conditions as observed in the past. A final complex three-frequency approach results typically involving low, mid, and high frequency ranges for each hemisphere. Training is typically done based on separately referenced ears but using linked ears has also generated favorable results. A monopolar montage is utilized for each hemisphere and either a one or two channel protocol is employed in each hemisphere. Once the first session has been run the training graph is evaluated for significant changes in amplitude in the normative direction. The review screen or trend screen is also evaluated for compensatory patterns such as theta dropping when beta is downtrained in the right hemisphere or alpha decreases in the left hemisphere resulting in beta increases in the right hemisphere. In terms of vertical movement, changes of 2 to 5 μv are significant for delta, theta, and alpha, and changes of 0.5 to 2 μv are significant for beta. These trends are best considered in artifact

395

Richard Soutar

free analysis with regression lines calculated but can be visually estimated by a practiced clinician. In terms of horizontal movement, trends showing increases of alpha on the left and beta on the right are monitored. The convergence of each left and right hemisphere combination for each component band is usually a sign of enhanced plasticity and tends to correspond with normalization of amplitude on the vertical plane. Maximum normalization typically occurs in the first 15 sessions, marking the end of the acquisition period of training. Clients are usually observed to have the most significant symptom changes during this period but degrade or backslide partially between sessions. The consolidation period begins with progressive retention of symptom reduction between sessions until symptoms are permanently diminished. Clients are typically backed off to one session per week to confirm retention.

Symptom Tracking Symptom tracking in our methodology is a critical component of the training process as clients tend to quickly habituate to their new level of function and forget their prior level of distress. The Cognitive Checklist is used to evaluate clients on each dimension for function monitored with EEG map analysis. The checklist provides a series of questions designed to tap constructs utilized in fMRI research and locate regions noted in the research to be hubs and nodes of various networks associated with specific functions such as short-term memory or sequential memory. A projected map of locations associated with client-endorsed items indicating problems is generated and then compared to the EEG generated map for correlations. Correlated items are then sent to an automated symptom tracker, confirmed by therapist and client as significant, and then tracked over time using various graphs. Clients then report level of symptom changes.

Pre/Post Maps The assumption has been typically made that the brain changes that occur have a linear function and move progressively toward the normative pattern in steady increments over the course of training. This has never been found to be the pattern in the majority of cases we have reviewed over the past three years with our new analysis methods used in approximately 300 clinics. We have seen dramatic cases of rapid shifts in the normal direction but they are the exception. This could be considered a consequence of our training protocols but we have a large number of clinicians also using LENS, Infra Low Protocols, z score, and a host of others while conducting pre and post maps with similar outcomes. None of these has consistently produced linear changes toward the norm across sessions and clients. Consequently, in our opinion, claims to the contrary should be met with skepticism. Our pre/post map method reviews the changes toward and away from the norm across all dimensions of analysis and computes a percent change as well as an over percentage of change in any direction. The changes we observe across NFB methods reflect what compensatory theories predict; that there is considerable movement in many locations away from the norm as other areas move toward the norm. It is only reasonable to expect a nonlinear dynamical autocorrelative system with a 1/f power function and over 30 billion neurons with minimal degrees of separation to have a complex solution space. As attractor states shift in each network and allostasis is renegotiated there is considerable compensatory movement away from normative expectations and back again. We observe several cycles of change with considerable retrograde movement during initial acquisition phases of learning and then progressive movement toward the norm in the consolidation phases. These cannot be easily correlated directly with symptom changes and symptom intensity. We typically observe robust changes in QEEG measures in the 35% range within the first 15 sessions and exceptional levels of change in 45–50% range. These changes typically reflect dramatic changes in symptomology. 396

Perspective and Method

A Case Study The subject is a 9-year-old girl diagnosed with anxiety disorder who is afraid to go to school where she frequently has panic attacks. We had her parents fill out the Cognitive Emotional Checklist (CEC) and her symptoms included Worry, Whining, Scary Thoughts, Stuck on Thoughts, Stuck on Behaviors, Argumentative, Bargaining Behavior, Procrastination, Minimal Patience, Disorganized, Careless Mistakes, Lack of Motivation, Poor Follow Through, and Difficulty with Attention and Focus. Some of these features are more anxiety based and some depression based. The lack of motivation, poor follow through, argumentative and bargaining behavior all suggested considerable emerging depression. This was confirmed by a pronounced alpha asymmetry in the headmaps. The Test of Variables of Attention (TOVA) showed normal performance with an increase in omission errors in the beginning of the second half of the test. We find this is common in children with anxiety disorders. The CEC (Figure 19.1) indicated that anxiety features dominated the symptom ranking system with 12 items selected at a high score in the anxiety category but also 7 items ranked high in the depression category. Memory items ranked high in 9 items. Attention ranked lowest with 6 items. This suggested that anxiety was the driving force generating loss of attentional function as a consequence of degraded memory functions due to elevated glucocorticoid and cortisol degradation of hippocampal

Figure 19.1 The Cognitive Emotional Checklist rank orders questions regarding cognitive and emotional problems by categories of anxiety, memory, impulse control, depression and attention based on responses measured with a Likert like scale. The category of anxiety has the higher average response. Dysregulated locations are predicted in the headmap to the left based on response level to questions regarding symptoms and behaviors that correlate with those locations as indicated in fMRI research.

397

Richard Soutar

function. This is a common feature in anxiety and typically shows up as a loss of theta to parietal regions and deficient hippocampal input to Short-Term and Sequential Memory regions. The client received 40 sessions over a five-month period, training approximately twice weekly. She was trained using a two channel BrainMaster Atlantis amplifier and software with a two channel protocol at C3-C4 with alpha 8–12 inhibited, high beta 20–30 inhibited, and beta 15–20 enhanced at C3, and beta 15–20 inhibited, beta 20–30 inhibited, and alpha 8–12 enhanced at C4. The training location was based on a statistical analysis of amplitude, dominant frequency, phase, coherence, and asymmetry to locate the most deviant locations on all measures. At session 9 we shifted to a bipolar montage using C3-Fz and C4-P4 in order to focus more on the left front and right posterior quadrant. We find this often enhances training when working at C3-C4. The choice of frequency inhibits and enhancements was made in order to shift alpha symmetry, reduce depression, and reduce beta elevations in the right hemisphere related to anxiety. Reinforcement rate changes, such as increasing right hemisphere beta inhibits or decreasing left hemisphere beta inhibits and increasing left hemisphere alpha inhibits, were made on the fly from session to session as needed while observing the trend screen in real time. We view this as “riding the ratios” in response to interhemispheric compensatory changes taking place during the session. These efforts were initiated to enhance movement of trend lines in the desired direction. The second map (Figure 19.2) shows a 36% change, which is a good average. Note, however, that 80% of the changes were away from the normal distribution and 20% toward the normal distribution.

Figure 19.2 The Pre-Post assessment compares the first QEEG with those that follow by calculating the total percent change in a terms of standard deviation in five neurometric dimensions including magnitude, dominant frequency, coherence phase and asymmetry in all 10–20 locations. Of the 36% change approximately 25% of that change was toward the norm (anterograde) and approximately 75% was away from the norm (retrograde).

398

Perspective and Method

Figure 19.3 This time series analysis shows client symptom severity based on a 10 point Likert rating scale with higher numbers representing greater symptoms. Clients rate themselves during each visit with a tablet linked to their client folder. This graph indicates significant reductions in symptoms over a period of months with greatest changes occurring in the first few weeks and toward the end of the training.

Figure 19.4 This graph shows the same information as the previous one but in the form of bar graphs with the uniform grey component of each bar representing where they started out and the lighter/darker grey ( or color) part where they finished up with respect to symptom severity. This graph shows symptom reductions of 60% or more on average with each symptom.

This metric is based upon the simple total percentage of change in measures across all dimensions of analysis. Also note the significant and steady improvement in symptomology during this period (Figure 19.3). This demonstrates how reorganization of any kind with positive changes in symptoms is the most important aspect in the short run. Note that in the next remap (Figure 19.4), the majority of the changes were 60% in the normative direction or anterograde direction while 40% were in the abnormal direction or retrograde direction. This constitutes a distinct improvement in QEEG measures from a statistical perspective with continued improvement in symptoms. The client was enjoying school and singing solos in the school choir. Her insomnia had dissipated considerably along with the majority of her other symptoms. I would like to make special note that she had significant symptom changes by session 15 (in fact, the graph shows the global pattern is 6–8 sessions), which is typical, but we consider this the latter part of the acquisition phase and our experience shows that if we do not train at least another 15 sessions the gains may not consolidate, i.e. become permanent. Also note that we achieved further gains in the last sessions which we would have missed if we had discontinued prematurely. While the map still showed a dominant diffuse alpha slightly above one standard deviation which did not diminish, her alpha asymmetry resolved and her beta asymmetry diminished (Figure 19.5). These are the most reliable markers, based on the reviewed research, of anxiety and depression and the changes were consistent with her changes in symptomology. This overall pattern of change confirms our observations that each iteration of training and mapping demonstrates a progression from dominant retrograde changes to dominant anterograde changes. In reviewing the metabolic analysis (Figure 19.6), we concluded that the diffuse elevated alpha that did not respond to NFB was likely due to metabolic issues after reviewing and ruling out problems 399

Figure 19.5 This pre-post QEEG compares the last two maps and shows a consistent 36% overall change (which is in the average range statistically) but this time with reduced retrograde movement away from the norm (now only 40%) and increased anterograde movement toward the norm (now 60%). This pattern of change in later phases of training is common in the clinics we have monitored.

Figure 19.6 The asymmetry headmaps are based on magnitude and not standard deviation and show either red, if the value is highest in that side, or blue if the value is lowest in that side. This map shows improved asymmetry with alpha showing higher on average on the right and beta showing higher on the left. These changes correlate with reductions in depression and anxiety.

Perspective and Method

with the family system. The high number of symptoms relating to blood sugar regulation as well as gastrointestinal distress and food sensitivities further supported the hypothesis that metabolic sources were responsible for the diffuse elevated alpha. The elevated alpha was in the range of one standard deviation as well so it did not constitute an extreme deviation but rather a mild one.

Discussion It should be clear from the preceding material that the efficacy of this method is considerable. This case is representative of thousands of cases from hundreds of clinics using the identical analysis methods. By standardizing assessment and protocol implementation methods we have been able to more carefully observe and control outcomes as well as share them. Allowing the brain to determine its own limits of deviation during repeated trials rather than using a normative constraint model is clearly as effective as other more complex modalities. It does not utilize more intrusive measures involving microcurrent stimulation, microtesla induction, or entrainment, but could be used in conjunction with these approaches. Early trials on a large scale involving thousands of cases from clinics around the country have been encouraging and some vendors are using this approach with photic stimulation and what appears to be greater efficacy. The most important advantage of this methodology is that it is grounded in standard NFB peer reviewed methodology and QEEG research and draws heavily from neuroimaging research. It trains clearly identified meta-networks based on recent research and focuses the training emphasis on a complex nonlinear dynamical process rather than just focusing on an ROI. It does not have the accuracy of LORETA training methods but anatomical specificity may be of limited value with EEG biofeedback. The resources required to engage this technology are considerably less expensive than more complex approaches and provides a good springboard for learning theory and advancing into these more complex approaches should future research demonstrate superiority. Typically, entering clinicians are counselors without technical backgrounds and extensive complexity reduces the potential for the expansion of neurofeedback into these professional domains. Several years of experience with this market segment suggests that this approach is a very effective introductory method that addresses these issues.

References Abarbanel, A., & Evans, J. R. (1999). Introduction to quantitative EEG and neurofeedback. New York: Academic Press. Almurshedi, A., & Ismail, A. K. (2014). Cross coherence independent component analysis in resting and action states EEG discrimination. Journal of Physics: Conference Series, 546. doi:10.1088/1742–6596/546/1/012019 Alstott, J., Breakspear, M., Hagmann, P., Cammoun, L., & Sporns, O. (2009). Modeling the impact of lesions in the human brain. PLoS Computational Biology, 5(6), e1000408. doi:10.1371/journal.pcbi.1000408 Arns, M., de Ridder, S., Strehl, U., Breteler, M., & Coenen, A. (2009). Efficacy of neurofeedback treatment in ADHD: The effects on inattention, impulsivity and hyperactivity: A meta-analysis. Clinical EEG and Neuroscience, 40, 180–189. Avram, J., Baltes, F. R., Miclea, M., & Miu, A. C. (2010). Frontal EEG activation asymmetry reflects cognitive biasesin anxiety: Evidence from an emotional face stroop task. Applied Psychophysiology Biofeedback, 35, 285–292. Baehr, E., Rosenfeld, J. P., & Baeher, R. (1997). The clinical use of an alpha asymmetry protocol in the neurofeedback treatment of depression: Two case studies. Journal of Neurotherapy, 3, 12–23. Baehr, E., Rosenfeld, J. P., Baehr, R., & Earnest, C. (1999). Clinical use of an alpha asymmetry protocol in the treatment of mood disorder. In J. R. Evans & A. Abarbanel (Eds.), Introduction to quantitative EEG and neurofeedback (pp. 181–200). San Diego: Academic Press. Bassette, D. S., Meyer-Lindenberg, A., Archard, S., Duke, T., & Bullmore, E. (2006). Adaptive reconfiguration of fractal small-world human brain functional networks. PNAS, 103(51), 19518–19523. Beck, A. T. (1979). Cognitive therapy and the emotional disorders. Cleveland, OH: Meridian. Be’langer, M., Allaman, I., & Magistretti, P. J. (2011). Brain energy metabolism: Focus on astrocyte-neuron metabolic cooperation. Cell Metabolism, 14, 724–738.

401

Richard Soutar Breakspear, M., Jirsa, V., & Deco, G. (2010). Computational models of the brain: From structure to function. NeuroImage, 52, 727–730. Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008). The brain’s default network: Anatomy, function, and relevance to disease. Annals of the New York Academy of Sciences, 1124, 1–38. Buzsaki, G. (2006). Rhythms of the brain. New York: Oxford University Press. Choi, S. W., Chi, S. E., Chung, S. Y., Kim, J. W., Ahn, C. Y., & Kim, H. T. (2011). Is alpha wave neurofeedback effective with randomized clinical trials in depression? A pilot study. Neuropsychobiology, 63, 43–51. Davidson, R. J. (1995). Cerebral asymmetry, emotion, and affective style. In R. J. Davidson, & K. Hugdahl (Eds.), Brain asymmetry (pp. 361–387). Cambridge: MIT Press. Demasio, Antonio. (1994). Descartes’ error: Emotion, reason, and the human brain. New York: Avon Books. Demasio, Antonio. (1999). The feeling of what happens. New York: Harcourt Brace. Demos, J. M. (2005). Getting started with neurofeedback. New York: W. W. Norton & Company. Diamond, D. M., Campbell, A. M., Park, C. R., Halonen, J., & Zoladz, P. R. (2007). The temporal dynamics model of emotional memory processing: A synthesis on the neurobiological basis of stress-induced amnesia, flashbulb and traumatic memories, and the Yerkes-Dodson Law. Neural Plasticity, 2007(60803). doi:10.1155/2007/60803. Web. 14 June 2016. (Published Online). Engels, A. S., Heller, W., Mohanty, A., Herrington, J. D., Banich, M. T., Webb, A. G., & Miller, G. A. (2007). Specificity of regional brain activity in anxiety types during emotion processing. Psychophysiology, 44, 352–363. Epel, E. S., Blackburn, E. H., Lin, J., Dhabhar, F. S., Adler, N. E., Morrow, J. D., & Cawthon, R. M. (2004). Accelerated telomere shortening in response to life stress. PNAS, 101(49), 17312–17315. Fan, J., Xu, P., Van Dam, N., Eilamstock, T., Gu, X., Luo, Y., & Hof, P. (2012). Spontaneous brain activity relates to autonomic arousal. The Journal of Neuroscience, 32(33), 11176–11186. Freeman, W. J., Ahlfors, S. P., & Menon, V. (2009). Combining fMRI with EEG and MEG in order to relate patterns of brain activity to cognition. International Journal of Psychophysiology, 73(1), 43–52. Gilbert, M. E., & Zoeller, R. T. (2010). Thyroid hormones—impact on the developing brain: Possible mechanisms of neurotoxicity. In G. Jean Harry & Hugh A. Tilson (Eds.), Neurotoxicology (pp. 79–112). New York: Informa Healthcare. Hammond, D., Walker, J., Hoffman, D., Lubar, J., Trudeau, D., Gurnee, R., & Horvat, J. (2004). Standards for the use of quantitative electroencephalography (QEEG) in neurofeedback: A position paper of the international society for neuronal regulation. Journal of Neurotherapy, 8(1), 5–27. Heller, W., Nitschke, J. B., Etienne, M. A., & Miller, G. A. (1997). Patterns or regional brain activity differentiate types of anxiety. Journal of Abnormal Psychology, 106(3), 376–385. Honey, C. J., Kotter, R., Breakspear, M., & Sporns, O. (2007). Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proceedings of the National Academy of Sciences of USA, 104, 10240–10245. Horvat, J. (2009). Coherence and the quirks of coherence/phase training: A clinical perspective. In J. R. Evans (Ed.), Handbook of neurofeedback (pp. 213–227). New York: Informa. John, E. R., Prichep, L. S., Fridman, J., & Easton, P. (1988). Neurometrics: Computer assisted differential diagnosis of brain dysfunctions. Science, 293, 162–169. Kilner, J. M., Mattout, J., Henson, R., & Friston, K. J. (2005). Hemodynamic correlates of EEG: A heuristic. NeuroImage, 28, 280–286. Kirk, I. J., & MacKay, J. C. (2003). The role of theta-range oscillations in synchronizing and integrating activity in distributed mnemonic networks. Cortex, 39, 993–1008. Laird, A. R., Fox, P. M., Eickhoff, S. B., Turner, J. A., Ray, K. L., McKay, D. R., Glahn, D. C., . . . Fox, P. T. (2012). Behavioral interpretations of intrinsic connectivity networks. Journal of Cognitive Neuroscience, 23(12), 1–16. Lubar, J. F., & Lubar, Judith. (1999). Neurofeedback assessment and treatment for attention deficit/hyperactivity disorders. In James R. Evans & Andrew Abarbanel (Eds.), Introduction to quantitative EEG and neurofeedback (pp. 243–310). New York: Academic Press. McIntosh, A. R., & Korostil, M. (2008). Interpretation of neuroimaging data based on network concepts. Brain Imaging and Behavior, 2, 264–269. Meehan, T. P., & Bressler, S. L. (2012). Neurocognitive networks: Findings, models, and theory. Neuroscience and Biobehavioral Reviews, 36(10), 2232–2247. Monastra, V. J. (2005). Electroencephalographic biofeedback (neurotherapy) as a treatment for attention deficit hyperactivity disorder: Rationale and empirical foundation. Child Adolescent Psychiatric Clinics of North America, 14, 55–82. Montgomery, D. D., Robb, J., Dwyer, K. V., & Gontkovsky, S. T. (1998, Spring). Single channel QEEG amplitudes in a bright, normal young adult sample. Journal of Neurotherapy, 2(4), 1–7. Morillas-Romero, A., Tortella-Feliu, M., Bornas, X., & Aguayo-Siquier, B. (2013). Resting parietal electroencephalogram asymmetries and self-reported attentional control. Clinical EEG and Neuroscience, 44(3), 188–192.

402

Perspective and Method Nakagawa, T., Jirsa, V., Spieglar, A., McIntosh, A., & Deco, G. (2013). Bottom up modeling of the connectome: Linking structure and function in the resting brain and their changes in aging. NeuroImage, 80, 318–329. Niedermeyer, E., & Lopes da Silva, F. H. (2005). Electroencephalography: Basic principles, clinical applications, and related fields. New York: Lippincott Williams & Wilkins. Nunez, P. L., & Srinivasan, R. (2006). Electric fields of the brain: The neurophysics of EEG (2nd ed.). New York: Oxford University Press. Othmer, S., Othmer, S. F., & Kaiser, D. A. (1999). EEG biofeedback: An emerging model for its global efficacy. In James R. Evans & Andrew Abarbanel (Eds.), Introduction to quantitative EEG and neurofeedback (pp. 243–310). New York: Academic Press. Pascual-Leone, A., Amedi, A., Fregni, F., & Merabet, L. B. (2005). The plastic human brain cortex. Annual Review of Neuroscience, 28, 377–401. Pop-Jordanova, N., & Pop-Jordanov, J. (2005). Spectrum-weighted EEG frequency (“brain rate”) as a quantitative indicator of mental arousal. Contributions, Macedonian Academy of Sciences and Arts, Section of Biological and Medical Sciences, 26(2), 35–42. Raichle, M. E., & Gusnard, D. A. (2002). Appraising the brain’s energy budget. PNAS, 99(16), 10239. Rossiter, T. (2004). The effectiveness of neurofeedback and stimulant drugs in treating AD/HD: Part II. Replication. Applied Psychophysiology and Biofeedback, 29(4), 233–243. Sapolsky, R. M. (1999). Glucocorticoids, stress, and their adverse neurological effects: Relevance to aging. Experimental Gerontology, 34(6), 721–732. Schore, A. N. (1994). Affect regulation and the origin of the self: The neurobiology of emotional development. Hillsdale, NJ: Lawrence Erlbaum Associates. Schwartz, M., & Andrasik, F. (Eds.) (2003). Biofeedback: A practitioners guide (3rd ed.). New York: Guildford Press. Soutar, R. (1999). Doing neurofeedback: A workshop manual. Roswell, GA: New Mind Publications. Soutar, R., & Longo, R. (2011). Doing neurofeedback: An introduction. SanRafael, CA: ISNR Research Foundation. Sterman, M. B. (2000). Basic concepts and clinical findings in the treatment of seizure disorders with EEG operant conditioning. Clinical Electroencephalography, 31(1), 45–55. Sterman, M. B. (1996). Physiological origins and functional correlates of EEG rhythmic activities: Implications for self-regulation. Biofeedback and Self–Regulation, 21, 3–33. Sterman, M. B., & Egner, T. (2006). Foundation and practice of neurofeedback for the treatment of epilepsy. Applied Psychophysiology and Biofeedback, 31(1). Teipal, S. J., Pogarell, O., Meindl, T., Dietrich, O., Sydykova, D., Hunklinger, U., Georgii, B., et al. (2009). Regional networks underlying interhemispheric connectivity: An EEG and DTI study in healthy ageing and amnestic mild cognitive impairment. Human Brainmapping, 30, 2098–2119. Tomasia, D., Wang, G., & Volkowa, N. D. (2013). Energetic cost of brain functional connectivity. PNAS, 110(33), 13642–13647. Trentin, A. G. (2006). Thyroid hormone and astrocyte morphogenesis. Journal of Endocrinology, 189, 189–197. Zubin, J., & Spring, B. (1977). Vulnerability: A new view of schizophrenia. Journal of Abnormal Psychology, 86, 103–126.

403

20 NEUROTHERAPY FOR CLINICIANS IN THE TRENCHES The ClinicalQ and Braindriving Paul G. Swingle

Abstract Neurotherapy marries perfectly with all health care disciplines by facilitating neurologically based diagnostics and guided treatment for a vast array of disorders. The reasons for the superiority of clinical data base guided diagnostics and treatment for clinical conditions such as the multiple varieties of depression are examined. Braindriving, a more aggressive treatment relative to instrumental conditioning based neurofeedback, has been found to facilitate more efficient neurological change. Practicing clinicians will find sufficient detail to be able to implement the ClinicalQ assessment procedure and to utilize basic braindriving protocols, all of which are guided by the intake clinical QEEG. Conditional probability concepts, relevant to the clinical context of expressivity of neurological predispositions, including differential susceptibility, conditional vulnerability, neurological diathesis and plasticity are reviewed. Neurofeedback and the broader discipline, neurotherapy, are not stand-alone therapies—a misconception commonly made and, in some cases, perpetrated by non-clinicians, pseudo-clinicians and disenfranchised clinicians as well. Neurotherapy marries perfectly with all other therapeutic metaphors, providing no-nonsense, data driven and remarkably efficient treatment for a very wide range of disorders. This chapter is addressed to trained clinicians who have something to bring to the therapeutic context to blend with neurotherapy. There are levels of engagement for the efficient blending of neurotherapy with other therapeutic methodologies. This chapter will focus on very basic levels of EEG intake assessment and treatment methods. These are rapid procedures that can be accomplished with very basic EEG encoders (clinical grade, of course). Basic does not mean compromised or limited, however. These are remarkably powerful procedures that, when combined with the professional health care clinician’s skill set, can handle the majority of the disorders typically seen in practice. Problematic conditions, such as traumatic brain injury or psychoses, require not only enhanced neurotherapy assessment and treatment methods but more knowledgeable and experienced clinicians as well. Clinics that offer these more advanced procedures, however, are most efficient when the basic procedures described in this chapter are utilized for the general client populations. In fact, in many clinical contexts, the ClinicalQ is substantially more accurate than the normative data bases for reasons to be discussed. As will be discussed in detail, the ClinicalQ is an intake assessment QEEG based on measurements at five scalp locations. The obtained QEEG data are compared with a clinical data base to identify clients with

404

Neurotherapy for Clinicians in the Trenches

predispositions for specific clinical conditions. For example, several neurological profiles have been identified for clinical clients reporting “depression.” ClinicalQ data matching one or more of these patterns identifies a client who is predisposed to these conditions and the clinical “probing” of these potentialities is the organizing concept for the clinical intake session. Again, as we will discuss, this process of bottom-up is a marked departure from usual clinical intake procedures and can have a remarkably favorable effect on the client’s confidence in the efficacy of neurotherapy.

Clinical versus Normative Data bases For clinicians, the most accurate data bases are clearly clinical. Normative data bases are far less accurate. The fundamental organizing concept of the normative data base for the clinical practitioner is, simply stated, logically incorrect. The organizing concept for normative data bases is that one can identify a group of individuals who are symptom free and therefore have “normal” functioning neurology. This group of symptom free individuals then serves as the comparative data base to identify those who are statistically deviant. The statistical departures from the normative data base define the anomalous neurological condition that is associated with the client’s clinical condition. This concept is also logically incorrect. The reason that normative data base treatment recommendations are so often incorrect is because the fundamental premise is wrong. Symptom free individuals may well have predispositions to conditions that have not manifested. The data are quite clear and we have definitive evidence for this that spans decades. Let us simply take the example of heritability data for schizophrenia (similar data are available for other conditions as well such as vulnerability to PTSD and Bipolar Disorder). As the data in Table 20.1 (Ginsberg & Cancro, 1985; Gottesman, 1991; Gottesman & Shields, 1972) indicate, if one monozygotic twin has diagnosed schizophrenia, the probability that the second identical twin will have schizophrenia is about 50%. So, the twin with schizophrenia ends up in the ClinicalQ data base. But, the interesting statistic is that 50% will not! Where do we find the 50% without manifested schizophrenia, but obviously with the same genetic load? In the normative data bases! So clearly the organizing concept for normative data bases, at least for clinicians, is incorrect. Normative data bases so constituted ignore basic psychopathology and basic biology. Every person has predispositions. Predispositions to anxiety, depression, emotional volatility and the like. However, many of these predispositions are not manifest at any particular time. In general, clinicians understand that one needs a trigger to “turn-the-key” to manifest a neurological predisposition. These logic considerations are well known and surprisingly, at least to me, ignored by the developers of the normative data bases. If in the normative data base one has subjects with non-manifested Table 20.1 Heritability of schizophrenia. Genetic Predispositions Monozygotic Twins

30–50%

Dizygotic Twins

15%

Siblings

15%

General Population

1%

Adopted-Biological Relatives with Schizophrenia Adoptee with Schizophrenia

13%

Adoptee without Schizophrenia

2%

405

Paul G. Swingle

predispositions, then statistically one can expect very poor discrimination. That is, very poor discrimination between a client with a manifested predisposition compared with a data base containing like individuals with identical but unmanifested predispositions.

Conditional Probability Models There are many conditional probability models associated with the concept of differential susceptibility. In mathematical game theory, the probability of a future event is predicated on present state. In chess, the probability of a specific Queen move is markedly different if Queen Pawn has advanced. This is considered a state conditional probability. In optimal performance contexts, conditional probability theories consider both vulnerability as well as resilience markers. The markers can be direct, or primary, such as the genetic serotonergic system inefficiency affecting stress tolerance. The concept of “preparation for duty” for military and police personnel is premised on reducing vulnerability to work stress (e.g., combat) by increasing the neurological basis for stress tolerance. Secondary markers may be introversion that reduces the probability of development of social relationships that in turn is negatively synergic with the primary marker. Hence, in the latter case the individual who has experienced severe stress may be more vulnerable to negative post-traumatic sequellae if the secondary marker impeded the development of a social support network. Obviously, in the clinical context, individuals who present themselves for treatment have a manifested susceptibility factor. Individuals who do not present for treatment may have the same neurological predisposition but it has not manifested. Hence, the latter individual is a candidate for normative data base whereas his or her cohort with the identical, but manifested, predisposition is in my office and hence in the clinical data base. Also, obviously, the normative data base is going to be statistically blind to many neurological conditions that are predispositions. Where normative data bases have strength are determinant neurological abnormalities such as those associated with epilepsy, autism, structural damage and progressive neurological deterioration. Conditions associated with primary genetic (e.g., dopamine/serotonin) and secondary phenotypic (e.g., autonomic reactivity/sensory processing) are likely to be under the statistical discrimination thresholds for the reasons outlined above. However, most importantly, the normative data bases just simply miss neurological relationships found in brainwave activity for conditions that bring clients into the clinician’s office. The ClinicalQ is a clinical data base. The data base contains more than 1,400 clinical clients. The organizing logic is that clients who report a condition (e.g., depression) have specific neurological representations for the various forms of that condition. Based on the diathesis vulnerability model, the condition reported by the client is one that is associated with a neurological predisposition that has manifested. A normative data base is likely to miss this entirely since this clinical client, before becoming depressed, had the same neurological predisposition but would be considered “normal” (i.e., symptom free) and eligible for the normative data base. The important concepts of the vulnerability and conditional probability models for the clinician include conditional vulnerability (Ingram & Luxon, 2005), diathesis (Belsky & Pluess, 2009; Sigelman & Rider, 2009) and that although neurological predispositions are stable across lifespan they are not unchangeable (Lipton, 2005; Oatley, Keltner & Jenkins, 2006). Although the theoretical concepts associated with predispositions and vulnerabilities are of interest, for the purposes of this chapter the critical issue is that predispositions are just that, predispositions. It is also important to note that predisposition does not mean inevitable. People can have a multitude of predispositions but may be fortunate enough to never have them triggered and therefore be even more fortunate to never need our services. Finally, expressivity of the predisposition in neurology is analogous to severity of a condition in clinical medicine. The severity of the EEG condition is not directly associated with the severity of the 406

Neurotherapy for Clinicians in the Trenches

symptom. In general, the more severe the EEG condition the more pronounced the symptomatology in terms of several parameters including chronicity, intensity, treatment resistance and qualitative manifestation. However, many variations occur so that clinically one uses the ClinicalQ to identify clinical conditions that should be probed/explored with the client. The qualitative features of the symptoms may well be poorly correlated with the magnitude of the ClinicalQ markers. This is especially true of ClinicalQ markers associated with experiential factors as compared to genetic predispositions.

Symptom Checklists The absurdity of this approach is nicely captured by the diagnostic criteria used for applying the labels of ADHD (Inattentive Type) and ADHD (Hyperactive Type) advocated in the DSM-5 (American Psychiatric Association, 2013). As most readers know, the procedure is to rate the child if they have six or more of a list of symptoms such as “makes careless mistakes in schoolwork,” “does not follow through on instructions,” or the one I like the most (actually from the DSM-4; American Psychiatric Association, 1994), “runs/climbs excessively.” Further, the child must be adjudicated to have manifested these symptoms for at least six months. Again, as most readers will recall, there are separate lists for inattentive and hyperactive types of ADHD. So, here are a few questions about these criteria: What if you only have five? Or three? What if the parent notices these behaviors and has observed them for three months—not yet ADHD? Let Johnny fail his semester since we have to wait six months for it to be real ADHD? What if you only observe these behaviors in church? In this regard, it is interesting to note that there has been a marked increase in the NOS (not otherwise specified) diagnoses with children for exactly the reasons stated above. That is, children who do not meet all the criteria for the label are given the subthreshold (shadow) designations. This can be a problem, according to the authors, because of inappropriate off-label prescribing and creation of “vast heterogeneity” that, in turn, confounds intervention research (Safer, Rajakannan, Burcu & Zito, 2015). We also see this problem with misdiagnoses of the attention deficit disorders (Swingle, 2015a). It is important to ask clients about their symptoms, of course. But, we must not base our neurological treatment solely on these self-reports, because they are often incorrect. Later in this chapter, the condition defined generally as “depression” will be reviewed. Clients report they are depressed for all sorts of reasons: poor sleep, loss of a pet, severe anxiety, emotional trauma, as well as neurological predispositions. Each of these conditions implicates different neurology, and to be strictly efficacious treatment must be neurology specific regardless of the self-reports. And given that the normative data bases are likely to miss the relevant neurology, for reasons already discussed, clinicians should be grateful that at least 50% of their effectiveness is placebo and hence canned protocols, either one-sizefits-all or symptom specific, have some efficacy.

The ClinicalQ Definitely NOT business as usual. I do not ask clients why they have come to see me. I tell them why they are seeking treatment. The level of precision of the ClinicalQ is such that, with experience, one can describe the client’s condition based exclusively on the brainwave data. Clients are usually stunned by the accuracy of the description of their condition. The therapeutic value of this method is substantial. This experience is nicely captured by Susan Olding (2008, several paragraphs from pages 169 through 173) in her book Pathologies. In her book, Susan describes the ordeals of trying to find competent treatment for her child. A chapter in this book describes the ClinicalQ process. An excerpt that indicates the diagnostic power of the ClinicalQ is as follows: Desperate, determined, undeterred by cost or lack of insurance coverage, undismayed by the doubts of conventional physicians . . . I switched off my cell phone at the threshold of Dr. Swingle’s office and carried my daughter across. . . . 407

Paul G. Swingle

I had brought a medical and developmental history—the long litany of concerns that had brought us to his door—but Dr. Swingle waved the papers aside without even looking at them. Instead, he ushered Maia toward a computer screen . . . [and] fixed a couple of delicate wires to her ears. . . . Then Dr. Swingle sent Maia to the “treasure chest” in the waiting room. He stared at the printout in his hand. “Here,” he said, and he pointed to an outline of the brain, “these numbers imply trauma.” He shrugged, palms up, waiting for my response. I nodded. “And here,” he continued, “too much Theta. This is the hyperactivity people associate with ADHD. . . .” There was more: extreme stubbornness, a tendency to perseverate, lapses of short-term memory, attachment disorder, inability to read social cues, emotional reactivity, tantrums, explosions. One by one he read the ratios, divining my daughter’s character more quickly, more accurately than any professional I’d yet encountered. The assessment described in this excerpt is based on 6.5 minutes of recording time. The data from the five brain-sites are compared to the clinical data base providing guidance for the clinician to probe the client regarding symptoms. In other words, bottom-up, not top-down. More importantly, the clinical data base gives guidance to location and substance of neurotherapeutic treatment to mitigate the client’s symptoms.

The ClinicalQ Assessment Protocol It is critical that the brainwave ranges be consistent with the clinical data base. Ground is right earlobe; reference is left earlobe. Recording sites are Cz, F3, F4, Fz and O1. The ClinicalQ procedure including the brainwave bandwidths, the administration protocol, the unremarkable and basic remarkable ranges is appended to this chapter. The listed remarkable ranges and related suggested clinical probes are basic. Recognizing that the number of combinations from only the two frontal regions, F3 and F4, is over 100, it requires a bit of practice to fully benefit from the diagnostic potential of the ClinicalQ. But soon, the experienced clinician will find that this procedure provides surprisingly accurate diagnostic data (Swingle, 2015b). To illustrate the superiority of clinical norms, consider the following comparison with a normative data base. Both the ClinicalQ and the 19-point full EEG were obtained simultaneously. The normative report was generated by one of the best known services whereas the ClinicalQ was generated immediately while the client was still hooked up. Many manufacturers of EEG platforms have software available for generating the ClinicalQ data and probes; however, following the outline in the Appendix, one can generate the ClinicalQ data and summary with any EEG platform with the aid of a desk-top hand calculator. It is quite apparent that the ClinicalQ was far more accurate for this client. He reported sleep problems consistent with the low Theta/Beta ratio under eyes closed conditions at location O1. The marked imbalance in Alpha, frontally, with Alpha being considerably higher in amplitude in the right relative to the left, is the marker for emotional volatility. As this client reports: “I get angry easily.” The client’s complaints of problems with focus and attention are reflected in the elevated Theta/ Beta ratios at location Cz, F3 and F4 as well as the elevated Delta and slow Alpha as measured at Fz. We also see another marker that is not reported by the client. Beta is considerably greater in amplitude in the right relative to the left frontal cortex. This is a marker for depression. When probed about this, the client admitted to feeling “low” much more intensely and frequently than he believed was the case with his friends.

408

Figure 20.1

Client’s (M21) self-reported conditions.

Table 20.2 Client M21. Full 19-site QEEG report from independent service using normative data base.

Tables 20.3a, 20.3b, 20.3c and 20.3d ClinicalQ for client M21. (20.3a)

(20.3b)

CZ

Values

O1

Values

EO Alpha

7.1

Alpha EO

EC Alpha

10.2

Alpha EC

14.3

% Change EO to EC Alpha

43.7

% Change in Alpha EO to EC

53.7 15.3

EO Alpha Recovery %

8.3

EO Alpha Recovery %

EO Theta/Beta

2.94

Theta/Beta EO

UT Theta/Beta

3.11

Theta/Beta EC

% Change T/B EO to T/B UT

5.8

% Change T/B EO to T/B EC

Total Amplitude

33.6

Alpha Peak Frequency EC

9.3

Alpha Peak Frequency EO

9.1

Theta/SMR EC

2.45

Alpha Peak Frequency EO

(20.3c)

2.21 1.26 75.4 9.6

(20.3d)

F3 & F4 (All EC)

% Difference F3-F4

Values F3

Alpha Amplitude

9.2

8.9

F4

FZ (All EC)

Values

Delta (2Hz)

17.2

12.3

38.2

HiBeta/Beta

0.48

Beta Amplitude

6.8

8.4

23.5

Sum HiBeta + Beta

Theta Amplitude

22.9

21.4

7.0

LoAlpha/HiAlpha

1.75

Alpha Peak Frequency

9.4

Theta/Beta

3.37

2.55

32.2

14.2

Neurotherapy for Clinicians in the Trenches

The ClinicalQ shows precisely where to treat these conditions and what to treat. Standard Theta/ Beta training at locations Cz, and if necessary later at F3 and F4. Increasing the Theta/Beta ratio at O1, eyes closed, for the sleep problems. Speed up the Alpha Peak Frequency (or decrease the amplitude of low Alpha) and finally balance the frontal regions, F3 and F4 in the Alpha and Beta ranges. Rule of thumb—treat sleep problems first as restored sleep quality is likely to result in other improvements in brain functioning. There are many other general guidelines for how to approach developing a treatment strategy for the client (Swingle, 2015b). It is also apparent that the QEEG report not only did not identify the client’s complaints but the treatment strategy recommended is largely irrelevant to the client’s problems. The possible exception is the recommended 12–15 Hz training at Cz. However, neurofeedback at almost any location is usually associated with client reports of improvement early in treatment. So, it is obvious that the ClinicalQ is not a poor practitioner’s substitution for the full 19-site QEEG. Many mini-Q systems are being marketed on exactly that basis. The purpose of using the ClinicalQ is to make neurotherapy much more efficient; because, again, the ClinicalQ is more accurate for clinical practice than the normative data bases. The intake procedure with the ClinicalQ is the first therapy session. Clients are strongly relieved that their complaints are understood, that there are identifiable neurological causes/corollaries of their condition and there is a precise “game plan” for treatment.

“Depression” A common complaint of clients we all treat is “I’m depressed.” The client has a huge array of options for receiving treatment for this amorphous condition including prescription medications, supplements, exercise, endless psychotherapies, R&R and, of course, an array of neurotherapies. In the array of neurotherapies we have those that are normative data base guided including: neurofeedback, the z-score zapping paradigms (brain-site specific frequency amplitude departures penetrating z threshold evoke an infinitesimal amp/gauss zap), and z-score neurofeedback; sLORETA; canned feedback protocols based on defined condition (i.e., “depression”); and franchises with proprietary symptom checklist driven canned protocol systems. ClinicalQ based treatment is different. A few cases, described below, exemplify how treatment is guided by bottom-up assessment and verification. Neurotherapeutic protocols are then precisely targeted at these verified neurological inefficiencies. Because of space limitations, the ClinicalQs for the following cases will be presented in summary form rather than the full output, as shown above. In addition, only data relevant to the present discussion are included in the summary. The fundamental neurological condition one finds in depression is an imbalance in the frontal cortex with the right (F4) being more active as compared with the left (F3). This imbalance can result from several neurological conditions as measured with the EEG. The Davidson (1995) pattern, identified years ago, is when Alpha has greater amplitude in the left relative to the right. However, there are many other conditions that result in this imbalance. For example, the client shown in Figure 20.2 is what we might call “garden variety” depression. This client has an imbalance where Beta is greater in the right relative to the left. Clinically this appears to be the “genetic” predisposition for depression although it is found in clients who have recently experienced a loss. Figure 20.3 shows the Davidson depression marker of elevated Alpha in the left relative to the right frontal cortex. The client shown in Figure 20.4 is similar in that Theta is greater in the left relative to the right resulting in the right being more active than the left. Clinically the two patterns just described (low frequency amplitude greater in the left) are very frequently associated with reactive depression (exogenous). Finally in Figure 20.5, we see a pattern often found with a person with the predisposition to depression who has experienced a severe emotional stressor that has triggered the predisposition. 411

Paul G. Swingle

Emotional trauma, exposure to a severe emotional stressor or an accumulation of emotional stressors, is associated with a blunting of the Alpha response at locations Cz and O1. We understand that this marker is associated with incompletely processed emotional sequellae of the emotional event(s). Exposure to emotionally negative images (corpses) has been shown to temporarily blunt the Alpha response, and fortuitous exposure to severe emotional stress with clinical clients likewise revealed

Figure 20.2 ‘Genetic’ depression

Figure 20.3

Reactive depression (Alpha)

Figure 20.4

Reactive depression (Theta)

Figure 20.5

Trauma triggered depression

Figure 20.6

Trauma based depression

Figure 20.7

Anxiety based depression

412

Neurotherapy for Clinicians in the Trenches

Alpha blunting. Alpha blunting is seen as restricted elevation of Alpha amplitude when clients close their eyes (Swingle, 2013). (See the parameters for this response in the Appendix to this chapter). The Alpha response is completely ignored in the normative data bases. Occasionally one sees clients who report that they are depressed but there are no depression markers in the ClinicalQ. There are many profiles that are found but two are relatively common. The profile shown in Figure 20.6 shows no depression markers but both trauma markers. There are other details of clinical relevance in this profile but the critical point for this discussion is that unprocessed trauma can be manifested as reports of “depression.” The lack of the reactive depression markers (e.g., Davidson, 1995) may indicate that the client is in the numb phase of post-traumatic exposure. However, although of interest to speculate on these matters, clinically one proceeds to release the Alpha and then utilize whatever therapy the clinician judges relevant to resolve the condition. It is with these trauma clients that the one-size-fits-all franchisers are the most destructive. Often one will hear comments about how to quiet an emotionally abreacted client who has been subjected to one of the canned protocols. Exactly the opposite of good clinical practice. The profile shown in Figure 20.7 is also quite common. These are clients in severe states of anxiety who feel hopeless, frightened and out-of-control. They report being “depressed” because their lives are in shambles, or they feel they are going to decompensate, or they feel just plain helpless. Treating these conditions with antidepressants is a formula for creating a life-long problem. The ClinicalQ identifies the areas for neurotherapeutic treatment quite precisely. Again, there are several other aspects to this EEG profile of clinical relevance such as markers for cognitive perseveration, but for the purposes of the present discussion it is the two markers of deficient Theta/Beta ratio at the occipital location and elevated left frontal Beta that identify the anxiety state. The markers for depression are only part of the profile. To illustrate, consider the child shown in Tables 20.4a and 20.4b. This child was brought for treatment of an attention problem. He was having Tables 20.4a and 20.4b Nine-year-old male child—potential bully victim. Cz

Values

O1

% Change

Values

EO Alpha

8.61

Alpha EO

6.16

EC Alpha

10.23

Alpha EC

12.06

% Change EO to EC Alpha > 30% EO Alpha Recovery

18.78%

% Change in Alpha EO to EC

9.27

% Change EO - Alpha Recovery

EO Alpha Recovery 7.63%

Theta Amplitude EO

15.76

Beta Amplitude EO

6.50

EO Theta/Beta

2.47

Beta Amplitude UT

5.89

UT Theta/Beta

2.32

% Change T/B EO to T/B UT

–6.45%

% UT Beta Increase

–10.29%

Total Amplitude

30.65

Theta Aplitude preceding Omni

14.42

Theta Amplitude with Omnl

13.15

% Change In Theta with Omnl 10.00

Alpha Peak Frequency EO

9.80

Theta/SMR EC

3.15

–8.16%

Theta Amplitude EO

10.09

Beta Amplitude EO

5.17

Theta/Beta EO

1.95

Theta Amplitude EC

10.46

Beta Amplitude EC

6.99

Theta/Beta EC

1.50

% Change T/B EO to T/B EC

–9.68%

Alpha Peak Frequency EC

95.84% 5.70

% Change EO - Alpha Recovery

Theta Amplitude Under Task (UT) 13.69

413

% Change

–30.21%

Alpha Peak Frequency EC

10.00

Alpha Peak Frequency EO

9.90

Paul G. Swingle Tables 20.5a and 20.5b Nine-year-old male child—potential bully victim. F3 & F4 (All EC)

Values F3

Theta Amplitude EC

% Difference F3-F4

F4

10.10 16.93

Beta Amplitude EC

6.37

7.06

EC Theta/Beta

1.59

2.41

% Diff F3T/B - F4TB EC Theta Amplitude EC 10.10 16.93 13.47

50.93%

9.28

0.75 1.83 29.95 33.26

Fz (All EC)

Values

Delta (2Hz)

10.05

HiBeta Amplitude

3.89

Beta Amplitude

6.15

HiBeta/Beta

0.63

Sum HiBeta + Beta

10.04

LoAlpha Amplitude

5.20

HiAlpha Amplitude

3.61

LoAlpha/HiAlpha

1.44

Alpha Peak Frequency

9.40

6.37 7.06 10.74% 13.47 9.28 –45.20% 10.10 16.93 67.56%

significant problems in school and was judged to have many of the symptoms associated with ADHD (inattentive type). The figures are the actual output from the ClinicalQ for this child. Again, there are many features of this profile that are clinically important but we will limit the discussion to those associated with the suspected bullying. This child does show minor marker for ADD. The Theta/Beta ratios at Cz are a bit elevated. However, this child is showing a trauma marker at Cz (Alpha response of 18.78%), a marker for reactive depression at F3/F4 (Alpha is 45.2% greater in the left relative to the right), and a marker for emotional volatility (F4 Theta is considerably greater than at F3). So, the hypotheses are that this child has or is being exposed to significant emotional stressors, that he is experiencing a reactive depression (perhaps related to the emotional stressors) and that he is emotionally volatile (and hence a “sitting-duck” for a bully). This child, if these hypotheses are correct, cannot pay attention or do well in school because he is afraid! Probing the child and the parents revealed that the child was being severely bullied, he was afraid to tell his parents because of the bullies’ threats and he was emotionally volatile (cried frequently over minor issues). The parent corrected the bully issue at school and we did some minor braindriving neurotherapy to improve the minor ADD problem.

Braindriving As mentioned in the last case, braindriving was the neurotherapeutic treatment for the child’s ADD. Braindriving is a very useful procedure that is based on classical rather than instrumental conditioning. Quite simply, it is based on concept of applying Unconditioned Stimuli (UCS) contingent on brainwave activity. For example, when the goal of treatment is to reduce the amplitude of Theta, an UCS for Theta reduction is presented when Theta amplitude exceeds training threshold. Conversely, if Theta enhancement is the goal, negative threshold crossings are contingent on application of a Theta enhancement UCS. Classical conditioning of brainwaves was demonstrated by Herbert Jasper and Charles Shagass (1941). In a series of studies they demonstrated that Alpha blunting can be conditioned to a sound, periodic time intervals and verbal commands. For braindriving, we have found a number of UCS for conditioning brain activity including light at various wavelengths, frequencies and intensities; sounds; acupuncture sites (for electrical stimulation); and brain-sites for magnetic (milligauss) stimulation. 414

Neurotherapy for Clinicians in the Trenches

There are many combinations of UCS that can be used for braindriving. The details for combining UCS can be found in Swingle (2010). Protocols can include a Theta enhancement UCS that is presented when Theta amplitude drops below training threshold, plus an entraining UCS that is presented when the amplitude goes above threshold. For common ADD children, a Theta suppressant can be presented when Theta is above threshold (Swingle, 1996) and a sustaining UCS can occur when the amplitude is below the threshold. Because the lights UCS can be presented on eyeglass frames, around the periphery of the lenses, the child can be engaged in a relevant task such as reading, writing, math, etc. This has proven to be a very effective treatment for children with attention and learning difficulties. Several examples of braindriving protocols are shown in Figures 20.8 through 20.11. Figure 20.8 shows the data associated with braindriving Beta down at location O1 for a client with a deficient Theta/Beta ratio. Common complaints associated with this condition as previously discussed include problems with stress tolerance, sleep quality and self-medicating behavior. Concentration can also be poor because of “brain chatter.” As the data indicate, contingent stimulation of the Heart 6 (palmer ulnar surface slightly above the wrist crease) acupuncture meridian (bilateral) resulted in a decrease in the amplitude of Beta and an increase in the amplitude of Theta. The resulting increase in the Theta/Beta ratio after this 20-minute session was 76.1%. Subjective reports following this session was of profound quiescence. Recall, the efficacy of the UCS for this client is pretested before the braindriving session. Figure 20.9 shows the data from an elderly client who was experiencing problems with memory and cognitive efficiency. Alpha slowing, measured in this case with the Alpha density ratio of loAlpha/hi-Alpha, can be an age related decline. These declines can be effectively treated with brainbrightening protocols such as those developed by the late Tom Budzynski (Budzynski, Budzynski, & Tang, 2007). Braindriving has also been shown to be particularly effective for these clients as the data shown in Figure 20.9 indicate. The protocol was to present both the OMNI harmonic (a blend of sounds that reliably suppresses Theta amplitude) plus 11 Hz visual stimulation whenever 8–9 Hz brainwave amplitude crossed the training threshold. As indicated the pre-treatment L/H Alpha ratio was 2.83, which dropped to 2.76 after the first two minutes of treatment. By the end of treatment the ratio was 1.78, which is a 35.5% decrease. Further sessions, either neurofeedback or braindriving, would be required to bring this ratio into efficient range (below 1.50). Once the L/H Alpha ratio is in an acceptable range, clients with persistent age related decline are assessed and treated between two and four times per year to maintain efficient Alpha Peak Frequency. Figure 20.10 shows a braindriving session with a client with common ADD condition of elevated Theta amplitude as measured at location Cz. By the end of the session the Theta/Beta ratio had decreased by about 30%. One very important use of braindriving is for treatment of emotional trauma. Obviously, this procedure should be used only by licensed providers experienced in dealing with clients affected by post-traumatic stress. The blunted Alpha response, discussed in the first section of this chapter, is a marker for unresolved emotional stress. There are several methods for releasing and processing this emotional state including EMDR, hypnosis, experiential psychotherapies—to mention but a few. Braindriving can markedly accelerate this process in a positively synergic manner. The data shown in Figure 20.11 show emotional release with one of the “Alpha push” protocols. As the data indicate, as the Alpha amplitude starts to increase this client experienced an emotional release, lasting about eight minutes. Many therapists stop the braindriving at this point and continue with a procedure such as EMDR. In this case, the client continued with braindriving and started the recovery phase after about eight minutes. After the session, the client was probed regarding the experience. She reported an emotional episode which she described in some detail. Brief therapeutic intervention resulted in an emotional redefinition of the event, a desired outcome of such therapy. 415

Pre-treatment θ/β 0.85 θ β θ/β

Pre-treatment L/Hα = 2.83 Lα β θ/β

START 14.3

START 14.1

5.0

2.76

END

5.6

1.78

12.4 1.09

END

17.8

9.4 1.92

%

24.5

–24.2 76.1

Figure 20.8 Braindriving Beta down @ O1 with H6 stimulated > T

START

23.5

7.4

3.17

END

18.8

8.4

2.23

–25.0

13.5

–29.1 +12.0 –35.5 Braindriving Low Alpha down @ Fz with OMNI and 11Hz visual > T

Figure 20.9

Trauma Release

Pre-treatment θ/β under cognitive challenge = 3.29 θ β θ/β

%

10.0

%

–29.6

Figure 20.10 Braindriving Theta down @ Cz with 16 Hz and OMNI > T

Trial θ

α

β

1

12.5

11.5

8.2

2

16.0

13.2

9.4

3

13.1

15.5

9.9

4

8.4

12.6

9.3

5

9.7

11.0

8.8

6

9.2

10.9

8.6

7

8.8

10.1

8.1

8

12.9

12.9

9.1

9

15.8

13.4

9.5

10

14.7

15.2

9.5

Figure 20.11

α release

}

emotional release

}

recovery

Braindriving Alpha @ Pz with 11 Hz and Serene < T

Table 20.6 EEG of client going into sleep state. Average Amplitudes: Trial #

Theta Filter 1 uV

Alpha Filter 2 uV

Beta Filter 3 uV

1

14.6

18.1

11.2

2

10.4

18.0

12.1

3

7.3

22.8

12.0

4

9.1

18.8

11.1

5

7.4

17.2

10.5

6

8.5

10.2

7.7

7

9.6

6.9

6.8

8

11.2

8.2

7.6

9

12.7

8.2

7.8

10

14.7

9.7

9.3

Session Avg.

10.5

13.8

9.6

Neurotherapy for Clinicians in the Trenches

Prior to all braindriving sessions the efficacy of the UCS should be assessed. Because braindriving is an aggressive therapy the changes are often sizable but one should expect some after session regression towards pre-treatment levels. The resulting after treatment level is usually above pre-treatment level. Often to stabilize the braindriving gains, the client is shifted to straightforward neurofeedback. Braindriving can also be added to regular neurofeedback protocols by having the UCS sound as the feedback with the client instructed to keep the sound “off.” If the UCS lights are presented on goggles with look-though lenses, the child can be reading, writing, doing math, etc. while the implicated area of the brain is under braindriving treatment. This has been found to be very effective for facilitating skill acquisition (e.g., written output). Finally, quieting braindriving protocols can be extremely soporific so the therapist should be attentive to brainwave activity indicating that the client is falling asleep. Table 20.6 shows just such a client where Alpha and Beta drop substantially while Theta starts to increase. The remarkable increase in the occipital Theta/Beta ratio in this case is attributable to the client’s sleep state even though braindriving induced it!

References American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: American Psychological Association. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: American Psychological Association. Belsky, J., & Pluess, M. (2009). Beyond diathesis stress: Differential susceptibility to environmental influences. Psychological Bulletin, 135, 885–908. Budzynski, T., Budzynski, H. K., & Tang, H.-Y. (2007). Brain brightening: Restoring the aging mind. Chapter. In J. R. Evans (Ed.), Handbook of neurofeedback (pp. 231–265). Binghampton, NY: Haworth Medical Press. Davidson, R. J. (1995). Cerebral asymmetry, emotion, and affective style. In R. J. Davidson & K. Hugdahl (Eds.), Brain asymmetry (pp. 361–388). Cambridge, MA: MIT Press. Ginsberg, G. L., & Cancro, R. (1985). Schizophrenia: The epigenetic puzzle. The Psychoanalytic Quarterly, 54, 305–306. Gottesman, I., & Shields, J. (1972). Schizophrenia and genetics: A twin study vantage point. Boston: Academic Press Gottesman, I. I. (1991). Schizophrenia genesis: The origin of madness. New York: Freeman. Ingram, R. E., & Luxon, D. D. (2005). Vulnerability-stress models. In B. L. Hankin & J. R. Z. Abela (Eds.), Development of psychopathology: A vulnerability stress perspective (pp. 32–46). Thousand Oaks, CA: Sage Publications Inc. Jasper, Herbert, & Shagass, Charles. (1941). Conditioning the alpha rhythm in man. Journal of Experimental Psychology, 28(5), 373–388. Lipton, B. H., (2005). The biology of belief: Unleashing the power of consciousness, matter and miracles. San Rafael, CA: Elite Books. Oatley, K., Keltner, D., & Jenkins, J. M. (2006). Understanding emotions. Oxford, UK: Blackwell Publishing. Olding, S. (2008). Pathologies. Calgary, AB: Freehand Books. Safer, D. J., Rajakannan, T., Burcu, M., & Zito, J. M. (2015). Trends in subthreshold psychiatric diagnoses in youth in community treatment. JAMA Psychiatry, 72(1), 75–83. Sigelman, C. K., & Rider, E. A. (2009). Lifespan human development (6th ed.). Belmont, CA: Wadsworth. Swingle, P. G. (1996). Subthreshold 10Hz sound suppresses EEG theta: Clinical application for the potentiation of neurotherapeutic treatment of ADD/ADHD. Journal of Neurotherapy, 2, 15–22. Swingle, P. G. (2010) Biofeedback for the brain (Revised paperback ed.). New Brunswick, NJ: Rutgers University Press. Swingle, P. G. (2013). The effects of negative emotional stimuli on alpha blunting. Journal of Neurotherapy, 17(2), 133–138. Swingle, P. G. (2015a). When the ADHD diagnosis is wrong. Santa Barbara, CA: Praeger. Swingle, P. G. (2015b). Adding neurotherapy to your practice. New York: Springer.

417

Appendix A ClinicalQ

Procedure Epoch 1

@Cz

@O1

@F4

@F3

@Fz

EO

EO

EC

EC

EC

2

EO

EO

EC

EC

EC

3

EC

EC

EC

EC

EC

EO

EO

EC

EC

EC

4 5 6 7

{

(READ OR COUNT)

8

EO

9

{TEST

10

UCS

(EO = EYES OPEN) (EC = EYES CLOSED)

Technical Notes 1. 2. 3. 4. 6. 7. 8. 9.

Right ear ground and left ear reference Epoch length 15 seconds, shorter if necessary Recording @ Cz is usually one continuous run of 10 epochs Recording @ O1, F3, F4, Fz is one run at each site of 4 epochs Cognitive challenge is either reading or counting UCS Test is testing of stimulus (e.g., sound) Data are mean amplitudes unless artifacts indicate the use of medians Unremarkable ranges, listed below, are normative guidelines; specific ranges may vary somewhat based on equipment, environmental conditions and certainly age of client 10. The ClinicalQ is not appropriate for assessment of stroke, seizure disorders or traumatic brain injury. ClinicalQ is often appropriate for first assessment of autistic spectrum to determine initial treatment protocols to be followed by full QEEG

418

Neurotherapy for Clinicians in the Trenches

Unremarkable Clinical Ranges 1.

2. 3. 4.

@Cz: mean Theta/Beta < 2.2; alpha increase EC > 30%; Theta/Beta ratio cognitive challenge 50% @F4 and F3: F4 = F3 in all bands, Theta/Beta ratios < 2.00; Theta/Alpha ratio 1.25–1.60; TP = and < 60 @Fz: 2Hz < 10.0; 28–40Hz/Beta 0.45–0.55; 28–40Hz and Beta < 15.0; 8–9Hz/11–12Hz < 1.50

Clinical Implications of Remarkable Ranges The following clinical probes should be considered as suggestions for developing a behavioral profile of the client. Remarkable ranges do not validate a clinical diagnosis. Similar remarkable patterns can be associated with different clinical profiles. For example, developmental delay, fetal alcohol syndrome and some autistic spectrum profiles can have very similar remarkable QEEG patterns. It is important to keep in mind, therefore, that the remarkable ranges indicate inefficiencies and not necessarily clinical diagnoses. Unique remarkable patterns are associated with some specific conditions, such as Common Attention Deficit Disorder (CADD) (item 1 under CZ, with no other remarkable ranges). It is the treatment specificity afforded by identifying remarkable ranges rather than diagnostic labeling that makes the ClinicalQ a valuable rapid intake procedure. The following suggested clinical probes are not exhaustive. The experienced clinician will identify many patterns associated with specific client complaints.

@Cz 1. 2. 3. 4. 5.

Mean Theta/Beta > 2.2 and under cognitive challenge > 2.2, probe for CADD Mean Theta/Beta < 2.2, under cognitive challenge >2.2, probe for ADD and/or problem with poor reading, comprehension/retention Mean Theta/Beta > 3.00, probe for AD(H)D Limited or negative EC Alpha increase, probe for visual processing (memory) problem. Probe for exposure to traumatic stress, particularly if also negative @ O1 TA > 60.0, probe for developmental delay, autistic spectrum behavior, marked cognitive deficits

@O1 1. 2. 3. 4.

Theta/Beta EO < 1.80, probe for poor stress tolerance, “racing” thoughts, anxiety. If < 1.00, probe for addictive behavior, GAD and Stress Precipitated Depression If Theta/Beta EC < EO, probe for sleep disturbance particularly sleep onset insomnia. If both EC and EO about = and < 1.50, also probe sleep disturbance If Alpha EC increase minimal or negative probe for exposure to traumatic stress Theta/Beta > 3.00, probe for cognitive inefficiencies. Also found in some Asperger’s patterns

@F4 and F3 1. 2.

Theta/Beta > 2.2, probe for cognitive inefficiencies Theta/Alpha < 1.00, probe for frontal Alpha ADD—problems with organization, sequencing, sustained focus. If Theta/Alpha < 0.80, also probe for fibromyalgia and sleep disturbance

419

Paul G. Swingle

3.

4.

5. 6.

F4 Beta > 15% of F3 Beta; F3 Alpha > 15% F4 Alpha; F3 Theta > 15% F4 Theta; F3 Theta/ Beta > 20% of F4 Theta/Beta, probe for depression particularly in adults; also probe for impulse control problems in children F4 Theta > 15% F3 Theta, probe for emotional volatility or conversely restricted emotional range. F4 Alpha > 15% F3 Alpha, also probe for emotional volatility, oppositional behavior in children, interpersonal problems with adults TA > 60.0, probe for developmental delays, autism spectrum disorders, memory/cognitive deficits in adults F4 Beta > 20% of F3 Beta and F4 Theta > 20% of F3 Theta, probe for fibromyalgia/chronic fatigue, particularly when O1 Theta/Beta < 1.50

@Fz 1. 2. 3.

4.

5. 6.

Delta (2Hz) > 9.0, probe for cognitive deficits 28–40Hz/Beta < 0.45, probe for excessive passiveness 28–40Hz/Beta > 0.55, probe for stubborn behavior, obsessive/compulsive behavior, perseveration in autistic spectrum behaviors; assume hot midline (anterior cingulate gryus) in treatment of autistic spectrum behaviors Implications of ratios in 2 and 3 above apply only if sum of amplitudes of 28–40Hz & Beta < 15 If summated amplitudes > 15, but 28–40Hz/Beta is within normative range, probe for fretting and assume hot midline in treatment of autistic spectrum behaviors 8–9/11–12 > 1.50, probe for cognitive inefficiency, age related deficits in memory and cognitive processing 8–9/11–12 > 1.50, probe for developmental delay, marked cognitive deficits

420

21 THE USE OF SURFACE 19-CHANNEL Z-SCORE TRAINING TO AMELIORATE SYMPTOMS REMAINING OR APPARENTLY CAUSED AFTER WITHDRAWAL FROM THOSE MEDICATIONS Kathy Abbott Abstract Medication influences brainwave amplitude and power (Meckley & Kaiser, 2012). Three cases are presented in which the clients developed symptoms when coming off their medications. In a third case, the client developed worsening symptoms that did not remit when they discontinued the drug. In one case a client developed a tic when trying to discontinue Strattera. In the second, the client developed more severe restless legs syndrome resulting is very poor sleep when coming off her last dose of Hydrocodone. In the third case use of Wellbutrin was associated with worsening of depressive symptoms which remained worse off Wellbutrin. After four to six sessions of surface 19-channel z-score training (19 ZNF), the first two clients were able to discontinue their medications without a resumption of their post withdrawal symptoms. The third client required 20 sessions. Possible reasons for the effectiveness of 19 ZNF are explored. 19 ZNF may be a way of treating post-withdrawal symptoms.

Introduction Medications influence brainwave amplitude and power (Meckley & Kaiser, 2012). For example, Depakote is associated with a decrease in theta and an increase in alpha (Porras-Kattz et al., 2011), and benzodiazepines are associated with an increase in 12–18 Hz (Herrmann & Kubicki, 1981). In addition, it is known that medications sometimes cause side effects and that discontinuing a medication may be associated with physiological withdrawal symptoms. Development of drugs for purposes such as psychiatric disorders can be difficult. According to Greden (1994, p. 33), the brain has neurotransmitters, many of them interacting with receptor sites that have two to ten subtype configurations, each producing different neurobiological actions. The process is further confounded by co-localization and co-release of selected neurotransmitters from certain synaptic junctions,

421

Kathy Abbott

differing duration and extent of neurotransmitter actions at the receptor sites, different intracellular second-messenger systems, different signal transduction mechanisms, and different effects on gene expression and protein synthesis. Thus, it is not surprising that medication problems emerged in these three cases. The purpose of this chapter is to discuss the use of surface 19-channel z-score training (19 ZNF) in cases of clients who have a reaction either to the medication or to physiological withdrawal from the medication. Three cases are presented in which 19 ZNF training was used to decrease or remove these drug-related symptoms. With this in mind, three clients who had a reaction either to the medication or physiological withdrawal from the medication were given 19 ZNF training with the purpose of returning their brainwave pattern closer to normal and probably closer to how it had been before developing their symptoms. One client experienced an increase in depression which remained subsequent to physiological withdrawal from the antidepressant, another had difficulty discontinuing a pain medication because she developed a worsening case of restless legs syndrome, and the third had difficulty discontinuing atomoxetine (Straterra) because she developed a tic when not taking the medication.

Surface 19-Channel Z-Score Training (19 ZNF) Surface 19-channel z-score training (19 ZNF) is based on the following principles. 1. 2.

3.

Z-scores assume a normal distribution (Thatcher, Biver, & North, 2004–2007) with a mean of zero and a standard deviation of one. Normative databases have been developed using z-scores that are corrected for age, location, and frequency range. These databases include Neuroguide (Thatcher et al., 2004–2007) which was used in these cases. The Neuroguide database specifically include measures of power for different frequencies (delta, theta, alpha, etc.) and measures of connectivity, such as coherence, phase, and asymmetry.

Method A Brainmaster Discovery with Brainmaster software, version 1.5.9, was used for feedback and the normative database for the z-scores used was Neuroguide software, version 2.7.2. The Neuroguide database was developed from 625 people ranging from 2 months to 82 years of age (Thatcher et al., 2004–2007). The software includes norms for the following bandwidths: delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–25 Hz), hi-beta (25–30 Hz), beta-1 (12–15 Hz), beta-2 (15– 18 Hz), beta-3 (18–25 Hz), alpha-1 (8–10 Hz), and alpha-2 (10–12 Hz) (Thatcher et al., 2004–2007, pp. 7–8). At each of the 19 sites, the z-scores were trained for Absolute Power and Relative Power for each of the above bandwidths. Also, ratios between delta and theta, delta and alpha, and so on were trained for all 19 sites. In addition, z-scores for Asymmetry, Coherence, and Phase for every possible pair of sites was trained. The 19 channels used were Fp1, Fp2, F3, F4, F7, F8, C3, C4, T3, T4, P3, P4, T5, T6, O1, O1, Fz, Cz, and Pz. These channels represent the 19 basic placements as measured using the 10–20 system (10/20 System Positioning Manual, 2012). The protocol used was Z-Score Percent ZOKUL created by Mark Smith. As indicated above, all 19 channels/placements were used. PZOK is defined as “percentage of all trained z-scores that fall within a given target range” (Collura, 2014, p. 170). Upper and lower limits for z-scores were set around plus or minus 1.0 standard score. Training was done on 5700 z-scores. Clients were 422

The Use of Surface 19-Channel Z-Score Training

challenged to make a Pacman-like image move when a certain percentage of the z-scores were within the z-score range. The percent of these numbers was set so that the client was being rewarded around 50–60 percent of the time.

Case Studies Client G—A Case of Depression Client G, an older adolescent, reported severe depression and ADHD. She had withdrawn to her bedroom and had stopped attending college classes. For her ADHD symptoms, she was given Adderall, which gave her panic attacks and extreme anxiety. Wellbutrin was added but the symptoms did not remit. However, when the Wellbutrin was increased from 75 mg to 150 mg, the depression worsened. The client became more depressed and significantly more socially withdrawn and unmotivated. After discontinuing both drugs, the panic attacks remitted. However, the client continued to report being more depressed. In addition, she remained in her room most of the time and did not communicate with her family. Motivation dropped. Following 20 sessions of 19 ZNF, her affect brightened and she became more talkative, more hopeful, and less depressed. She was able to return to school and hold a part-time job. There are several EEG subtypes of depression (Johnstone, Gunkelman, & Lunt, 2005). One of these is the beta spindling subtype. However, Client G did not have any beta spindling. However, it has been noted that excess frontal beta may be associated with passive personality or avoidant personality, and may be associated with flat affect and hiding one’s feelings (Soutar & Longo, 2011, p. 88). And Kupfer, Reynolds, and Ehlers (1989) found excess beta in people with depression, especially those with delusions. Some antidepressants appear to cause an increase in depression and suicidal ideation in adolescents. Compared to a placebo, Wellbutrin (Buproprion) and other antidepressants may be associated with in increased risk of suicidal ideation and acts in children through young adult ages (Physicians Desk Reference, 2013). Side effects may include agitation, insomnia, allergic reaction, increased blood pressure, confusion, and seizures (Physicians Desk Reference, 2013). Of 1829 people from New Zealand who filled out an on-line questionnaire, 60 percent reported feeling more emotionally numb (Read, Cartwright, & Gibson, 2014). Forty-two percent reported a decrease in positive feelings. Fifty-two percent reported a loss of self-esteem. Five percent reported feeling less motivated. And 39 percent reported “Suicidality,” which was more common in younger respondents. It should be noted that there was no control group. Also, people with negative responses may have been more likely to reply thus producing a self-selection bias. In one study, it was found that a decrease in prefrontal theta cordance, after a one week of daily Buproprion (Wellbutrin), was a predictor of a positive response to four weeks of treatment in people who had not responded to other antidepressants (Bares et al., 2010). A drop in theta probably would result in a decrease in the theta/beta ratio with a relative increase in beta power. But the client had an excess of relative beta to begin with which may help explain how the medication may have contributed to Client G’s depression. Hunter, Leuchter, Cook, and Abrams (2010) studied 72 people with major depressive disorder who were given 20 mg of fluoxetine, 150 mg of venlafaxine, or a placebo. Of those, nine showed emergent suicidal ideation which was associated with a transient drop in right frontal and midline cordance as measured 48 hours after the people started the medication. Why would excess beta along the cingulate be related to poor motivation and depression? The dorsal anterior cingulate and the striatum are involved in cost-benefit decisions (Schouppe, Demanet, Boehler, Ridderinkhof, & Notebaert, 2014). Activation of these areas may be associated with tendency to decide that effort is not worth the reward. 423

Kathy Abbott

Figure 21.1

Client G z-scored FFT summary information showing excess beta at Fz.

Client P—A Case of Restless Leg Syndrome Client P is an elderly woman who is mentally alert. She continued to play bridge, listen to books on tape and socialized until she suffered several compression fractures in her back. The compression fractures resulted in bed rest for three months. She moved to an apartment where she would get out of bed for short times. After the compression fractures had healed, she remained in a great deal of pain but was encouraged to discontinue her pain medications including Hydrocodone. While on the pain medications she had experienced typical side effects including feeling sedated and nausea (Physicians Desk Reference, 2013). She was in favor of discontinuing the medications due to these side effects. The symptoms of feeling sedated and nausea diminished as she went off her medications. Her thinking seemed clearer and she felt more like herself. However, she was unable to discontinue her nightly dose of 15 mg hydrocodone because without this dose she experienced restless legs syndrome (RLS); this resulted in her having difficulty getting to sleep and she would have to get up several times during the night. Client P had a history of RLS from before being on the medication, but she had been able to manage it by using heat and massage. Following six sessions of 19 ZNF, Client P was able to sleep and to manage her restless leg symptoms which had returned to pre-medication levels. Sleep improved dramatically. Five weeks later, she had one night of excessive restless legs but otherwise she was sleeping well. What is Restless Legs Syndrome (RLS)? Restless legs syndrome is characterized by the urge to move one’s legs. The urge grows with rest and becomes worse at night or at evening time. The symptoms may be partially relieved through physical activity (Aurora et al., 2012). According to the National Institute of Neurological Disorders and Stroke (http://www.ninds.nih.gov/disorders/ restless_legs/detail_restless_legs.htm#241543237), restless leg syndrome is a “neurological disorder characterized by throbbing, pulling, creeping, or other unpleasant sensations in the legs.” It is an uncontrollable need to move. RLS disturbs sleep and inhibits falling asleep. It affects 5.5 percent of adults and it occurs more often in women than men (Ohayon & Roth, 2002). Occurrence of RLS is 424

The Use of Surface 19-Channel Z-Score Training

associated with being female, older age, exercise closer to bedtime, smoking, drinking, snoring, shiftwork, and stress (Ohayon & Roth, 2002). The cause of RLS is unknown but may be genetic (National Institute of Neurological Disorders and Stroke). The cause may be related to dysfunction of the circuits of the basal ganglia (http://www. nhlbi.nih.gov/health/health-topics/topics/rls/causes.html). Also, there may a lack of iron or poor use of brain iron. The iron-related causes may be due to kidney failure, rheumatoid arthritis, diabetes, Parkinson’s Disease, or iron deficiency (http://www.nhlbi.nih.gov/health/health-topics/topics/rls/ causes.html; Woimant & Trocello, 2014). Client P does have rheumatoid arthritis but she has none of the other conditions that are related to RLS. There is evidence of the effect of opiates on sleep (Brand, Lehtinen, Hatzinger, & HolsboerTrachsler, 2010). Compared to people with Major Depressive Disorder who also have poor sleep, those with RLS had statistically significantly worse sleep including more difficulty getting to sleep, more waking up during sleep, and smaller amounts of slow-wave sleep.

Non-Drug Treatment for RLS Treatment may include movement itself, heat and leg massage (which Client P was using), and exercise (National Institute of Neurological Disorders and Stroke). “Alerting activities” may also be useful (Silber et al., 2013).

Drug Treatment Dopamine agonists used to treat Parkinson’s Disease (PD) are also used to treat RLS. A study concluded that women with low normal or low ferritin levels showed similar decrease in International Restless Legs Severity Scale (IRLS) scores when treated with iron or with a Parkinson’s drug called pramipexole and the improvement was moderate (Lee, Lee, Kang, Park, & Yoon, 2013). The effects were studied of this dopaminergic drug, pramipexole, on the motor evoked potential (MEP) which was measured through transcranial magnetic stimulation (Scalise, Pittaro-Cadore, Janes, Marinig, & Gigli, 2009). The MEP amplitude increased in those with drug treatment following rest after a motor task. Increases in evoked potential amplitude may be associated with increased excitability (Misulius & Fakhoury, 2001). Other medications used to treat RLS include opioids, anticonvulsants, and benzodiazepines (Aurora et al., 2012).

RLS and EEG/ERD/ERS It has been noted that RLS is “characterized by closely interrelated motor and sensory disorders” (Tyvaert et al., 2009, p. 1090). Event-related beta and mu rhythm (de)synchronization (ERD/ ERS) were measured while thinking about immobilizing their ankle dorsally and while flexing the ankle dorsally at 8:30 pm when symptoms were expected and at 8:30 am when symptoms were not expected in non-symptomatic people and those with primary RLS. The duration and amplitudes of the ERS and ERD were greater during the 8:30 pm (symptomatic) time for voluntary movement for RLS patients than for normal controls. Morning ERDS and ERS were similar for the two groups. It was concluded that RLS symptoms are related to a dysfunction of sensorimotor activity. In addition, beta synchronization was measured after people with PD and a control group actively and passively moved their index fingers and in response to electrical stimulation of the median nerve (Tyvaert et al., 2009). In all three conditions, contralateral beta synchronization was lower in PD people than controls. (ERD can be interpreted as an electrophysiological correlate of activated 425

Kathy Abbott

cortical areas involved in processing of sensory or cognitive information or production of motor behavior (Pfurtscheller, 1992).) Degardin et al. (2009) reached a similar conclusion. They note that when people move, beta synchronizes, which reflects deactivation of the motor cortex. Following movement, people with PD show a decrease in beta synchronization which indicates idling of the motor cortex that is abnormal and may be related to the cause of akinesia. Perhaps this finding is consistent with the use of training up sensory motor rhythm (SMR) in people with PD. And since opiates tend to increase power of slower frequencies and an increase in beta power tends to lower those slower frequencies, it may be that an increase of this frequency was related to the decrease in symptoms of Client P.

Effect of Opiates on EEG Shufman et al. (1996) found that compared to controls, addicts had a higher ratio of low alpha to high alpha (8.0–9.5 Hz to 9.5–12.0 Hz ratio). Those addicts who were abstaining also exhibited alpha slowing, more frequent delta waves, and a higher than average ratio of delta to low alpha which decreased over length of abstinence. Polunina and Davydov (2004) found that a high dose of heroin was associated with changes in alpha-2 and that the change was associated with length of use. Addicts studied had been using for from 6 days to 4.5 months. Phillips, Herning, and London (1994) studied 12 polydrug abusers. Their EEGs were tested following a placebo and two doses of Morphine. The 15 mg dose of Morphine was associated with a global increase in alpha-1, alpha-2, theta, and beta and improved mood. Increases in amplitude were global. According to Greenwald and Roehrs (2005), a high dose of Fentanyl is associated with increases in amplitude of delta and theta. Delta waves are associated with sleep with 20 to 50 percent of the waves being delta in stage III sleep and 50 percent of the brainwaves being delta in Stage 4 sleep (Hughes, 1994). Fingelkurts et al. (2006b) studied 22 opiate addicts and 14 controls. They found that the percentage of beta and fast alpha increased globally and for longer periods of oscillation but were more predominant in the occipital lobe, and in the right side of the frontal, temporal, and parietal lobes. On the other hand, Franken, Stam, Hendricks, and van den Brink (2004) found that compared to controls, chronic heroin users exhibited more beta-2 power and more left gamma coherence intrahemispherically. In the Shufman et al. (1996) study, following 80 days or more of abstinence, the ratio of alpha-1 to alpha-2 gradually returned to match that of controls. However, the length of time of postphysiological withdrawal of RLS symptoms would have contributed to Client P’s difficulty discontinuing her evening dose of the opioid. There is evidence that long-term users of opiates have differences in their QEEGs. And there are differences between users with shorter term dependency of less than 6.5 years from addicts with 6.5  years or more of dependence. Those with less than 6.5 years of dependence exhibited more EEG fast beta power and less alpha than controls, and addicts with a history of 6.5 years or more dependency exhibited an average alpha power greater than controls but average fast beta compared to normal controls (Bauer, 2001). Opiates may also impact connectivity. Fingelkurts et al. (2006a) found that compared to controls, long-term opiate users exhibited more disrupted connectivity. Short-distance connectivity increased and long-distance connectivity was lower. Using fMRIs, Ku et al. (2014) found a relationship between both decreased connectivity and increased connectivity between the thalamus and other structures in the thalamocortical circuit in patients with restless leg syndrome. Diminished connectivity between the thalamus and the right parahippocampal gyrus was most highly connected with severity of restless leg symptoms. 19 ZNF works on connectivity and may normalize these changes.

426

The Use of Surface 19-Channel Z-Score Training

Neurofeedback Treatment of Parkinson’s Disease (PD) Since RLS and Parkinson’s Disease are both movement disorders, neurofeedback that is effective with PD may also be helpful with RLS. Thompson and Thompson (2003) studied 15 patients with PD. All of them had a deficit of SMR—13–15 Hz (p. 120). The Thompsons report using heart rate variability (HRV) and SMR training with PD with dystonia and with people with Tourette’s. Training up SMR also helps decrease hyperactivity in people with ADHD. Training up SMR is consistent with the sensorimotor dysfunction seen in PD and Tourette’s. The presence of SMR is associated with a lessening of motor excitability associated with a decreased transfer of sensory information to the cortex (Sterman, 2000). Training up SMR is also helpful for training down hyperactivity in ADHD. The residual slowing seen in opiate addicts may be ameliorated with SMR training. And 19 ZNF may help decrease the slow activity and increase beta in clients who have developed RLS when trying to discontinue a pain-relieving opioid.

Effect of QEEG-Guided Neurofeedback on Opiate Addicts Evidence that QEEG-guided neurofeedback has positive results in opioid addicts was found by Arani et al. (2010). They followed the Cry Help methodology of Scott, Kaiser, Othmer, and Sideroff (2005) which included 10–20 sessions of SMR or Beta training followed by alpha-theta training. In the Cry Help study, the experimental subjects first trained up either SMR or Beta training until their TOVA scores normalized. In the Arani et al. (2010) study, compared to a control group, the people with addiction improved in SCL-R scores on hypochondriasis (in those with this symptom), obsession, hypersensitivity to others, aggression, and psychotic symptoms. On the Heroin Craving Questionnaire, there were improvements on scores related to craving and expectation of a positive outcome. The post QEEGs showed improvement toward normalization in the following: (1) delta at central, frontal, and parietal areas; (2) theta in frontal and central regions; (3) parietal alpha; and (4) SMR at frontal and central areas. However, perhaps due to lack of symptoms of restlessness or to lack of assessment of this symptom, it is not known if one of the results of this training was a decrease of restlessness in this group. RLS is a movement disorder. Opiates tend to cause slowing of alpha frequency in a patient’s brainwaves and have long-lasting post-physiological withdrawal effects. In the case of Client P, the remaining side effect was worsening of RLS. QEEG-guided neurofeedback has been used to normalize physiological withdrawal effects from opiates. In light of the long-term frequency changes and long-term side effects, it is not surprising that the 19-channel z-score training normalized the client’s brainwaves—assumedly moving them closer to their pre-medication levels, thus causing the RLS to return to pre-medication levels.

Client J—A Case of Tics in Response to Withdrawal from Buproprion Client J had done neurofeedback six years previously for symptoms of Asperger’s disorder. There was an improvement in social skills, empathy, and prosody. Six years later the client returned for neurofeedback for her ADHD symptoms. More recently she had taken atomoxetine (Strattera) to help with her ADHD symptoms. The drug did not help. However, six hours after a missed dose she would develop a tic. The client was given two sessions of 19 channel z-score training and the tic resolved. Six weeks later her mother noted that the tic was returning. One more session of 19-channel training and the symptom resolved. Six months later the tic had not returned.

427

Kathy Abbott

Tourette’s Disorder is sometimes co-morbid with ADHD (Biederman, Newcorn, & Sprich, 1991). Therefore, it is not surprising that the client developed the tic since she may have been prone to developing this disorder. So why did she develop a tic when discontinuing this medication? Atomoxetine has been used to decrease tics but also exacerbates them in some people (Sears & Patel, 2008). Somkuwar et al. (2013) found that in spontaneously hypertensive rats, inhibition of norepinephrine by atomoxetine indirectly decreased dopamine cell surface function in the orbital frontal cortex. This would explain how tics could be caused by this drug but not how they might be exacerbated. It is possible that since atomoxetine is used to treat tic disorders, physiological withdrawal from the medication may cause tics in clients prone to the disorder if the client makes some adjustments to the medication. According to a review on the involvement of norepinephrine in ADHD, atomoxetine modulates the level of dopamine in the prefrontal cortex which may be associated with a rebalancing of the dopamine system (Viggiano, Ruocco, Arcieri, & Sadile, 2004). The effect on the dopamine system may be related to the occurrence of tics in Client J. In Tourette’s Disorder, the motor cortex and lateral orbitofrontal circuits’ activities may be coupled, which differentiates them from controls (Jeffries et al., 2002). PET scans were performed on 18 drug-free clients with Tourette’s’ syndrome and 16 controls matched for age and gender (Jeffries et al., 2002). They found significant differences between those with and without Tourette’s syndrome especially in ventral striatum connectivity but to a lesser degree in the primary motor cortex, insula, and somatosensory association areas. Also, there was a difference between the two groups in the functional connectivity of the motor and lateral orbitofrontal circuits with over coupling in the Tourette’s syndrome people in the motor and lateral orbitofrontal circuits. This is consistent with the hypothesis that there are abnormal interactions between the limbic and motor systems in Tourette’s syndrome. If this interaction were reflected in coherence and/or power, it is likely that z-score training may be helpful when the brain was inadvertently made abnormal by a drug that had an impact on either functional area. It turns out that atomoxetine increases absolute and relative beta (Barry et al., 2009). It is not known how the discontinuation of this drug would cause tics. It is also interesting that the drug can bring on tics or help stop them. If it helps stop tics, perhaps the mechanism of causing them is due to the person adapting to the drug such that physiological withdrawal takes away the person’s natural mechanism for preventing them.

Conclusion Although the reasons are not clear, 19 ZNF neurofeedback appears to help ameliorate symptoms related to side effects from being on medication that do not remit, and improves symptoms that occur when one discontinues medication. Part of the reason may be that the medications alter the relative power of various frequencies. This appears to be the case with Client P and Client G. In these two cases, it is logical to assume that moving their brainwave patterns closer to normal would have returned them to their pre-medication levels. However, the correlation does not appear to be present with Client J even though neurofeedback was effective in ameliorating her tics. However, there may have been some adjustment that was a response to the medication which when the medication was no longer present resulted in her symptoms. Use of 19 ZNF to ameliorate symptoms caused by prescribed drugs, including symptoms following discontinuation of a drug, has not been studied. It may be that use of 19 ZNF has been underutilized for this purpose. Further study is needed.

428

The Use of Surface 19-Channel Z-Score Training

References 10/20 System positional manual. (2012). Wanchai, Hong Kong: Trans Cranial Technologies Ltd. Arani, F. D., Rostami, R., & Nostratabadi, M. (2010). Effectiveness of neurofeedback training as a treatment for opioid-dependent patients. Clinical EEG and Neuroscience, 41(3), 170–177. Aurora, R. N., Kristo, D. A., Bista, S. R., Rowley, J. A., Zak, R. S., Casey, K. R., . . . Rosenberg, R. S. (2012). The treatment of restless legs syndrome and periodic limb movement disorder in adults-an update for 2012: Practice parameters with an evidence-based systematic review and meta-analysis. Sleep, 35(8), 1039–1062. Bares, M., Brunovsky, M., Novak, T., Kopecek, M., Stopkova, P., Sos, P., . . . Hoschl, C. (2010). The change of prefrontal QEEG theta cordance as a predictor of response to bupropion treatment in patients who had failed to respond to previous antidepressant treatments [Abstract]. European Neuropsychopharmacology, 20(7), 459–466. Barry, R. J., Clarke, A. R., Hajos, M., McCarthy, R., Selikowitz, M., & Bruggemann, J. M. (2009). Acute atomoxetine effects on the EEG of children with attention-deficit/hyperactivity disorder. Neuropharmacology, 57(7–8), 702–707. doi:10.1016/j.neuropharm.2009.08.003. Epub 2009 Aug 19. Bauer, L. O. (2001). Electroencephalographic studies of substance use and abuse. In M. Kaufman (Ed.), Brain imaging in substance abuse (pp. 77–112). Totowa, NJ: Human Press. Biederman, J., Newcorn, J., & Sprich, S. (1991). Comorbidity of attention deficit hyperactivity disorder with conduct, depressive, anxiety, and other disorders [Abstract]. American Journal of Psychiatry, 48(5), 564–577. Brand, S., Lehtinen, A., Hatzinger, M., & Holsboer-Trachsler, E. (2010). Comparison of sleep EEG profiles of patients suffering from restless leg syndrome, and depressive symptoms, and major depressive disorders [Abstract]. Neuropsychobiology, 61(1), 41–48. doi:10.1159/000262179. Epub 2009 Nov 26. Collura, T. F. (2014). Technical foundations of neurofeedback. New York: Routledge. Degardin, A., Houdayer, E., Bourriez, J. L., Destee, A., Defebvre, L., Derambure, P., & Devos, D. (2009, March). Deficient “sensory” beta synchronization in Parkinson’s disease. Clinical Neurophysiology, 120(3), 636–642. doi:10.1016/j.clinph.2009.01.001. Epub 2009 Feb 8. Fingelkurts, A. A., Fingelkurts, A. A., Kivisaari, R., Autti, T., Borisov, S., Puuskari, V., . . . Kähkönen, S. (2006a). Reorganization of the composition of brain oscillations and their temporal characteristics in opioid dependent patients. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 30(8), 1453–1465. Epub 2006 Aug 4. Fingelkurts, A. A., Fingelkurts, A. A., Kivisaari, R., Autti, T., Borisov, S., Puuskari, V., . . . Kähkönen, S. (2006b). Increased local and decreased remote functional connectivity at EEG alpha and beta frequency bands in opioid-dependent patients. Psychopharmacology (Berl), 88(1), 42–52. Epub 2006 Jul 19. Franken, I. H., Stam, C. J., Hendriks, V. M., & van den Brink, W. (2004). Electroencephalographic power and coherence analyses suggest altered brain function in abstinent male heroin-dependent patients [Abstract]. Neuropsychobiology, 49(2), 105–110. Greden, J. F. (1994). Part III: New agents for the treatment of depression. Journal of Clinical Psychiatry, 55(2), 32–33. Greenwald, M. K., & Roehrs, T. A. (2005). Mu-opioid self-administration vs passive administration in heroin abusers produces differential EEG activation [Abstract]. Neuropsychopharmacology, 30(1), 212–221. Herrmann, W. M., & Kubicki, S. (1981). The use of electrophysiological techniques to project typical psychotropic drug effects: Some examples [(author’s transl)] [Abstract]. EEG-EMG Zeitschrift Elektroenzephalographie und Electromyographie Verwandte Gebiete, 12(1), 21–32. Hughes, J. (1994). EEG in clinical practice (2nd ed.). Boston: Butterworth-Heinemann. Hunter, A. M., Leuchter, A. F, Cook, I. A., & Abrams, M. (2010). Brain functional changes (QEEG cordance) and worsening suicidal ideation and mood symptoms during antidepressant treatment [Abstract]. Acta Psychiatrica Scandinavica, 122(6), 461–469. doi:10.1111/j.1600–0447.2010.01560.x Jeffries, K. J., Schooler, C., Schoenbach, C., Herscovith, P., Chase, T. N., & Braun, A. R. (2002). The functional neuroanatomy of Tourette’s Syndrome: An FDG PET study III: Functional coupling of regional cerebral metabolic rates. Neuropsychopharmacology, 27, 92–104. doi:10.1016/S0893–133X(01)00428–6 Johnstone, J., Gunkelman, J., & Lunt, J. (2005). Clinical database development: Characterization of EEG phenotypes. Clinical EEG & Neuroscience, 36(2), 99–107. Ku, J., Cho, Y. W., Lee, Y. S., Moon, J. J., Chang, H., Earley, C. J., & Allen, R. P. (2014). Functional connectivity alternation of the thalamus in restless legs syndrome patients during the asymptomatic period: A resting-state connectivity study using functional magnetic resonance imaging [Abstract]. Sleep Medicine, 15(3), 289–294. Kupfer, D. J., Reynolds, C. F, & Ehlers, C. L. (1989). Comparison of EEG sleep measures among depressive subtypes and controls in older individuals. Psychiatry Research, 27, 13–21. Cited by Armitage, R., Roffwarg, H. P., Rush, A. J., Calhoun, J. S., Purdy, D. G., & Giles, D. E. Digital period analysis of sleep EEG in depression. Biological Psychiatry, 31, 52–68.

429

Kathy Abbott Lee, C. S., Lee, S. D., Kang, S. H., Park, H. Y., & Yoon, I. Y. (2013). Comparison of the efficacies of oral iron and pramipexole for the treatment of restless legs syndrome patients with low serum ferritin. European Journal of Neurology, 21(2), 260–266. doi:10.1111/ene.12286 Meckley, A., & Kaiser, D. (2012). EEG Pharmacology. Retrieved from http://www.brodmannarea.info/meds.htm Misulius, K. O., & Fakhoury, T. (2001). Spehlman’s evoked potential primer (3rd ed.). Woburn, MA: Butterworth-Heineman. Ohayon, M. M., & Roth, T. (2002). Prevalence of restless legs syndrome and periodic limb movement disorder in the general population [Abstract]. Journal of Psychosomatic Research, 53, 547–554. PDR Staff (2013). Physicians desk reference, 68 edition 2014. Montvale, NJ: PDR Network LLC. Pfurtscheller, G. (1992). Event-related synchronization (ERS): An electrophysiological correlate of cortical areas at rest. Electroencephalography and Clinical Neurophysiology, 32, 757–760. Phillips, R. L., Herning, R., & London, E. D. (1994). Morphine effects on the spontaneous electroencephalogram in polydrug abusers: Correlations with subjective self-reports. Neuropsychopharmacology, 10(3), 171–181. Polunina, A. G., & Davydov, D. M. (2004). EEG spectral power and mean frequencies in early heroin abstinence. Progress in Neuropsychopharmacology & Biological Neuropsychopharmacology & Biological Psychiatry, 28(1), 73–82. Porras-Kattz, E., Harmony, T., Ricardo-Parcel, J., Galan, L., Fernandez, T., Prado-Alcala, R., . . . Valencia-Solis, E. (2011). Magnesium valproate in learning disabled children with interictal paroxysmal EEG patterns: Preliminary report. Neuroscience Letters, 492(2), 99–104. doi:10.1016/j.neulet.2011.01.065. Epub 2011 Feb 1. Read, R. L., Cartwright, C., & Gibson, K. (2014). Adverse emotional and interpersonal effects reported by 1829 New Zealanders while taking antidepressants. Psychiatry Research, 216(1), 67–73. doi:10.1016/j. psychres.2014.01.042 Scalise, A., Pittaro-Cadore, I., Janes, F., Marinig, R., & Gigli, G. L. (2009). Changes of cortical excitability after dopaminergic treatment in restless leg syndrome. Sleep Medicine, 11(1), 75–81. doi:10.1016/j.sleep.2009.05.003 Schouppe, N., Demanet, J., Boehler, C. N., Ridderinkhof, K. R., & Notebaert, W. (2014). The role of the striatum in effort-based decision-making in the absence of reward. The Journal of Neuroscience, 34(6), 2148–2154. Scott, W. C., Kaiser, D., Othmer, S., & Sideroff, S. (2005). Effects of an EEG biofeedback protocol on a mixed substance abusing population. The American Journal of Drug and Alcohol Abuse, 21, 455–469. ISSN: 0095–2990 print/1097–9891 online. doi:10.1081/ADA-200056807 Sears, J., & Patel, N. C. (2008). Development of tics in thirteen-year-old male following atomexetine use. CNS Spectrum, 13(4), 301–303. Shufman, E., Perl, E., Cohen, M., Dickman, M., Gandako, D., Adler, D., . . . Ginath, Y. (1996). Electroencephalography spectral analysis of heroin addicts compared with abstainers and normal controls [Abstract]. The Israeli Journal of Psychiatry and Related Sciences, 33(3), 196–206. Silber, M. H., Becker, P. M., Earley, C., Garcia-Borreguero, D., Ondo, W. G., & Medical Advisory Board of the Willis-Ekbom Disease Foundation. (2013). Willis-Ekbom Disease Foundation revised consensus statement on the management of restless leg syndrome. Mayo Clinic Proceedings, 88(9), 977–986. doi:10.1016/j. mayocp.2013.06.016 Somkuwar, S. S., Jordan, C. J., Kantak, K. M., & Dwoskin, L. P. (2013). Adolescent atomoxetine treatment in a rodent model of ADHD: Effects on cocaine self-administration and dompamine transporters in frontostriatal regions [Abstract]. Neuropsychopharmacology, 38(13), 2588–2597. doi:10.1038/npp.2013.163. Epub 2013 Jul 3. Soutar, R., & Longo, R. (2011). Normal versus abnormal EEG. In R. Soutar & R. Longo (Eds.), Doing neurofeedback (pp. 63–92). SanRafael, CA: ISNR Research Foundation. Sterman, M. B. (2000). Basic concepts and clinical findings in the treatment of seizure disorders with EEG operant conditioning. Clinical Electroencephalography, 31, 45–55. Thatcher, R. W., Biver, C. J., & North, D. M. (2004–2007). A score EEG biofeedback: Technical foundations. Retrieved from http://www.appliedneuroscience.com/Z%20Score%20Biofeedback.pdf Thompson, M., & Thompson, L. (2003). The neurofeedback book. Ridge, CO: AAPB. Tyvaert, L., Houdayer, E., Devanne, H., Bouriez, J. L., Derambure, P., & Monaca, C. (2009). Cortical involvement in the sensory and motor symptoms of primary restless legs syndrome. Sleep Medicine, 10(10), 1090–1096. Viggiano, D., Ruocco, L. A., Arcieri, S., & Sadile, A. G. (2004). Involvement of norepinephrine in the control of activity and attention processes in animal models of attention deficit hyperactivity disorder. Neural Plasticity, 11(1–2), 133–149. Woimant, F., & Trocello, J. M. (2014). Disorders of heavy metals [Abstract]. Handbook of Clinical Neurology, 120, 851–864. doi:10.1016/B978–0–7020–4087–0.00057–7

430

22 RAW EEG BIOMARKERS, Z-SCORED NORMATIVE ANALYSIS, AND THE DIAGNOSIS OF NEUROBEHAVIORAL DISORDERS Leonardo Mascaro Abstract The EEG offers a wide range of neurological biomarkers for different neurobehavioral disorders. For each disorder to be electroencephalographically diagnosed, its qualitative dimension, represented by its raw EEG signature, and its quantitative dimension, represented by the identified neurological dysregulations observed either on the QEEG, the LORETA z-scored analysis, or both, have to be simultaneously present, therefore turning the electroencephalographic activity into an actual, effective diagnostic tool. The resolution of these dysregulations, through z-scored operant conditioning of both surface and deeper brain structures’ electrical activity, promotes the resolution of the raw EEG signature for any given neurobehavioral condition and, as a result, of its clinical presentation.

Introduction Since its very origin, the field of mental health, initially represented mainly by the medical practice of Psychiatry, has been facing the challenge of diagnosis. Even to present day, the vast majority, if not to say the whole, of diagnoses regarding neurobehavioral disorders, like depression, OCD, anxiety, or ADHD, are established solely on the basis of clinical symptomatology, disregarding the latest scientific developments, like functional neuroimaging techniques, and even well recognized and thoroughly studied tools, like electroencephalography (EEG), that have been around for decades. It may be unnecessary to say that, if a diagnosis is made solely based on clinical symptoms and client complaints, there is a considerably high probability that such a diagnosis may be wrong and, thus, the health professional risks adopting an inadequate course of treatment and compromising the expected outcome (6)—all at the cost of time and financial expenditures by the patient, not to mention the emotional and physical suffering often involved. However, the winds in this regards are changing. The recent decision of the National Institute of Mental Health (NIMH) to abandon the DSM, in its latest revision DSM-V, signals a change in the

431

Leonardo Mascaro

understanding of what the nature of diagnosis should be. In the very words of Dr. Tom Insel, M.D., NIMH Director: Indeed, symptom-based diagnosis, once common in other areas of medicine, has been largely replaced in the past half century as we have understood that symptoms alone rarely indicate the best choice of treatment. Patients with mental disorders deserve better. (1, par. 2 and 3) It is the current view and understanding of the NIMH that diagnosis, if it is to be efficacious and responsible, has to take into account that mental disorders are biological functional dysregulations involving brain circuits and networks that implicate specific domains of cognition, emotion, or behavior. The NIMH is now devoting its efforts aiming at integrating the different and distinct fields of neuroscience towards a body of knowledge that can furnish the foundational basis for a new nosology. Besides the diagnostic issue, not only in psychiatry, but also in the mental health field, the trend has always been to treat neurobehavioral disorders as diseases in themselves, trying to make them fit into the medical paradigm for treatment that has been successful in other areas of medicine. In other words, to validate pharmacologically treating such conditions, the approach has always been to assume that each disorder is itself related to a single etiological factor, thus characterizing each of them as a disease. The disease model has always been the Holy Grail of Psychiatry that has enabled this branch of medicine to achieve scientific validation for its interventions. The implications of the disease model in the mental health field are immense. Consider the two main and more frequently worldwide-diagnosed conditions, depression and ADHD, for example. To treat a given mental condition as a disease implies that such a condition will always relate to the same etiological factor. This means that depression will always emerge out of a chemical imbalance involving serotonin and noradrenalin (norepinephrine) in the brain. ADHD, on the other hand, will be related to a chemical imbalance of dopamine. These are the current accepted theories for these conditions that are used by the pharmaceutical industry to offer a reasonable explanation for why psychiatric medications, such as antidepressants or stimulants, eventually work for these conditions. However, is that so? Medical journalist and Pulitzer Prize nominee Robert Whitaker, in his 2010 book Anatomy of an Epidemic: Magic Bullets, Psychiatric Drugs, and the Astonishing Rise of Mental Illness in America (13) summarizes the facts on this. First, on what relates to depression: . . . NIMH researchers discovered that 5-HIAA levels varied widely in depressed patients. Some had high levels of serotonin metabolites in their cerebrospinal fluid, while others had low levels. The NIMH scientists drew the only possible conclusion: “Elevations or decrements in the functioning of serotonergic systems per se are not likely to be associated with depression.” (p. 82) Then, regarding the biology of ADHD: Although the public often hears that research has shown that ADHD is a “brain disease”, the truth is that its etiology remains unknown. “Attempts to define a biological basis for ADHD have been consistently unsuccessful,” wrote pediatric neurologist Gerald Golden in 1991. “The neuroanatomy of the brain, as demonstrated by imaging studies, is normal. No neuropathology substrate has been demonstrated.” (G. Golden, “Role of attention deficit hyperactivity disorder in learning disabilities,” Seminars in Neurology 11 (1991): 35–41). Seven years later, a panel of experts convened by the National Institutes of Health [2] 432

The Diagnosis of Neurobehavioral Disorders

reiterated this same point: “After years of clinical research and experience with ADHD, our knowledge about the cause or causes of ADHD remains largely speculative.” (p. 241) In short, Monism is a mistake. In regards to neurobehavioral disorders, the truth is there is no single etiological factor that explains one given condition. Be it for diagnosis, be it for treatment, there has been an absolute disregard of biological markers for properly identifying—and treating—each and every one of these conditions. Neurobehavioral disorders are just that, disorders, not diseases. The disease model is a fundamental bias that has been around both in psychiatry and in the field of neurotherapy. This lack of understanding has led professionals in the neurofeedback community to mistakenly persist in utilizing “standard” protocols, both for research and for treatment, as if one given neurobehavioral condition would always be due to the same dysregulations every time, no matter what. All these have compromised the possibility of securely and effectively verifying the validity of neurofeedback treatment in relation to, not just ADHD or depression, but for each and every condition that involves some form of neurological dysregulation in the brain. Thus, when assessing the studies that analyze and attribute levels of efficacy to treatment of specific neurobehavioral conditions using EEG operant conditioning, or neurofeedback, one has to take into account at least the following: a)

b)

c)

How many of these studies conducted an actual screening for the participating subjects based on neurological biomarkers rather than just a clinical diagnosis that only takes into account the participants’ symptoms and complaints? Symptoms can relate to a plethora of conditions. A patient’s symptoms and complaints regarding attentional deficiencies, for example, can be related to anything from dyslexia, anoxia at birth, obsessive compulsive disorder (OCD), head injury, Tourette’s, depression, and, of course, ADHD itself. Moreover, as we will see along this chapter, even when considering a given, well-delimited diagnosed disorder, like depression for example, such a condition is prone to deregulations in neurological circuits that vary from individual to individual. There are no equal patients for any neurobehavioral disorder. Thus, when considering treatment, how many neurofeedback studies on ADHD, anxiety, depression, and other neurobehavioral disorders, took into account the actual neurological condition of each participating subject, based on QEEG and/or LORETA (Low Resolution Tomography Analysis) functional assessments, to design specific protocols that would address the unique neurological deficiencies of each participant, rather than just trying/testing some form of predetermined “supposed to be efficacious” protocols?

It was only recently that two studies (3, 4) addressed such flaws to show the validity of a personalized treatment approach, tailoring neurofeedback protocols for treatment to the individual neurological condition of each participating subject. In the authors’ words (3a, b): .  .  . neurofeedback protocols were tailored to the individual patient. On the basis of patients’ individual quantitative EEG—also called a QEEG—it was determined which wellinvestigated neurofeedback protocol was applied to a specific patient. Sixty-seven percent of patients responded well to this treatment (more than 50% reduction in symptoms). The reported “effect-size” of 1.8 (a measure of the magnitude and clinical relevance of the treatment effect) in this study was found to be almost double the effect-size as compared to previously reported studies. (1, par. 3) 433

Leonardo Mascaro

So, what is the actual feasibility of using the EEG not just as a basis for devising tailor-made personalized protocols that can address one’s actual neurological condition—thus maximizing treatment efficacy—but as a diagnostic tool as well? More than a decade ago, Hughes and John (5) stated that of all the brain assessment modalities available, like PET, SPECT, or fMRI, the greatest volume of replicated evidence for pathophysiological concomitants in psychiatry is provided by EEG and QEEG studies. In 2009, D. Corydon Hammond published a review study (6) where it is very appropriately stated that: . . . diagnostic categories and individual symptoms are not always associated with the same brainwave patterns. . . . Obviously there can be a great deal of guesswork involved when a neurofeedback practitioner seeks to predict how a person’s brain is functioning based merely on the patient having a certain symptom or diagnosis. . . . Similarly, making decisions about changing brain function based on broad generalizations from published QEEG literature (which, as has been shown, displays considerable diversity), rather than an individualized, scientifically acceptable assessment, would seem difficult to defend. (p. 34) In addition, he gives a practical example to that: . . . it can be noted that although alcoholics and children of alcoholics have generally been found to have an excess of beta activity and a deficit in alpha and theta activity (John et al., 1988), another subtype representing 24% of alcoholics has been identified that has a more classic ADD/ADHD pattern of excess slow activity. Therefore, routinely utilizing a “standard protocol” of alpha/theta training with alcoholics, rather than performing individualized assessment, would seem to pose a one in four risk of exacerbating symptoms, such as increasing impulsiveness and cognitive inefficiency, problems with emotional regulation and behavioral self-control, and perhaps even risking seizure activity from reinforcing slow activity that was already excessive. These are matters to be taken seriously. (p. 34) In terms of serving as a diagnostic tool, eye-ball analysis of raw EEG patterns and amplitudes at several locations, as have been demonstrated by Dr. Margaret Ayers (7, 8, and 9), can serve as a first approach towards a differential diagnosis. The work of Dr. Ayers demonstrated that, although brain wave activity is unique for each human being, when a neurobehavioral disorder, like depression or OCD, for example, or a pathological activity, like one resulting from a stroke, is present, each condition would always show up in the raw EEG as a specific pattern of neurological activity, a signature. At that time, the integrated approach of normative databases, quantitative EEG, and LORETA was not yet available. Thus, integrating these quantitative approaches with the qualitative analysis that the raw EEG provides, when trying to confirm a given condition and devising tailor-made protocols for treatment, was unthinkable. This is not the case anymore. Even though, as properly shown by D. Corydon Hammond throughout his 2010 review publication (6), there is considerable heterogeneity in the electrophysiological patterns associated with different symptom complexes, the fact is that the integrated analysis of the qualitative and quantitative dimensions of the EEG can allow not just for the precise detection of objectively well determined neurological patterns—specifically in terms of raw EEG signatures identified in well determined brain regions and frequencies, for one or more conditions that may be simultaneously present—but also for the proper devising of specific, tailor-made, personalized protocols. These protocols are made 434

The Diagnosis of Neurobehavioral Disorders

through the identification of any dysregulation(s) in different brain structures and frequencies that, once confirmed by the comparative analysis given by a normative database, are responsible for the actual surface EEG signature. In other words, if one considers any sample of individuals with a given neurobehavioral disorder, each individual’s condition (even though very similar in terms of its clinical outcome for any member in the given sample) will be due to a different set of dysregulations than the ones found in any other member of such a sample, be it relating to activity found in brain structures, networks, and/or frequencies (19). Thus, any given neurobehavioral disorder, say ADHD, for example, will necessarily show up as a very well determined surface raw EEG signature, but its origin will be tributary, in each individual, to dysregulations present in different brain components and frequencies. That is, there is no single ADHD, OCD, depression, anxiety, dyslexia, or any other disorder, that will be determined by the same dysregulations in the very same brain structures, networks, and frequencies, even though being confirmed by the surface raw EEG signature, that will always be the same for a given condition. Nonetheless, and despite the above explanation, each neurobehavioral disorder, albeit the diversity of possible brain areas, structures, networks, and frequencies involved in each case, such diversity is not, by any means, random. Actually, as shown previously in Hammond’s example for alcoholism (6) and in other well conducted published studies (10, 11, 13, 14, 15, 16, 17, 18, 22, and 23), each neurobehavioral disorder will have a definite group of possible dysregulations. But, then again, if one considers only the identified z-scored quantitative analysis of the dysregulations eventually present, provided by QEEG maps and LORETA, in addition to the patient’s symptoms and complaints, these will not suffice in terms of reaching a differential diagnosis. They will, at most, serve as strong indicators to the presence of a given condition. Therefore, the point to be made is that the EEG can serve as a diagnostic tool if, and only if, the qualitative and the quantitative dimensions of electroencephalographic activity are taken into account. The qualitative dimension, represented by the raw EEG signature, and the quantitative dimension, represented both by the QEEG and the LORETA z-scored identified neurological dysregulations, have to be simultaneously present in order for a given disorder to be diagnosed. Thus, for the purposes of treatment devising, the quantitative, normative determined analysis allows one to objectively and precisely identify and pinpoint such sources of dysregulation in the brain, the sum of which are unequivocally responsible for the identified surface raw EEG signatures, enabling the devising of tailor-made personalized treatment protocols. The resolution of these dysregulations, through z-scored operant conditioning of surface and deeper brain structures, promotes, in turn, the resolution of the raw EEG signature for a given condition. Moreover, this is an amazing thing to testify. Clinical examples speak for themselves.

Case Examples Let us begin by examining how the actual raw EEG signature allows one to reach a differential diagnosis for a given disorder, even when the clinical symptomatology, QEEG maps, and the LORETA won’t give the necessary information in such regards. In addition, after that, we will see, through the analysis of another clinical case, how EEG biofeedback operant conditioning promotes its change towards normalization while, simultaneously, one can observe the resolution, both of the readings in the QEEG maps and tomographic analysis (LORETA), and of the clinical condition. Thus, the first clinical example is a case that perfectly summarizes what this chapter is about. As mentioned previously, if one considers only the identified quantitative analysis of the dysregulations eventually present, furnished by QEEG maps and LORETA, in addition to the patient’s symptoms and complaints, these will not suffice in terms of reaching a diagnosis. 435

Leonardo Mascaro

This case study has to do with a 52-year-old male that arrived at my office with complaints of severe impaired intellectual efficiency, attentional and memory deficiencies, impulsivity, agitation, and anxiety. The interesting thing is that this patient was under medications when his initial evaluation was obtained, but he only mentioned it after I had collected the data. In addition, since all medications have a spatially widespread or global affect on the EEG, we will see why this is important for the present argument as we move on. Therefore, let’s begin with his QEEG map (Figure 22.1).

Figure 22.1 Initial QEEG shows a map that would be considered a normal one except for the fact that the patient was under medication and for the presence of a whole head hypercoherent pattern at Delta, and a hypocoherent one in the midline of the head, at High Beta.

436

The Diagnosis of Neurobehavioral Disorders

The QEEG showed no signs of surface indicators that would account for the severity of his symptoms and complaints, be it for attention deficiencies or the acute anxiety he came in reporting. Even if one considers the focal excessive activity in the parietal, it is no more than 1 to 1.5 standard deviations above the normative mean for this patient’s sex and age. Moreover, this can only be explained by the suppressive action produced by the psychotropic medication, responsible for the normalization of amplitudes in the map. The hypercoherent pattern at Delta is the medication’s footprint in his map. The hypocoherent pattern at the midline, in High Beta, is the reminiscent connectivity indicator to the attentional deficiencies that the patient complained about, albeit one would expect connectivity deficiencies to be also present in other ranges of frequencies, especially in the Beta (15–18Hz) range and, given the severity of his complaints, an overall much more reduced functional connectivity in the midline of the head. Then again, this can be attributed to the medication effect over his neurophysiology. Nevertheless, the LORETA is not as sensitive to medications as is the surface reading, the reason being that both Laplacian montage and LORETA set spatially widespread medication effects to zero, providing very well-delimited localizations for dysregulations in specific networks. Thus, one can see (Figure 22.2) important functional deficiencies in frontal and frontallimbic structures, in the inferior half of the frequency spectrum, confirming the attentional and memory deficiencies, while also identifying a focus of mild excessive fast activity at posterior limbic and parietal structures that could explain this patient’s complaint regarding agitation and anxiety. In such a scenario, how could one reach a diagnostic conclusion to what was the actual condition that this patient had? If we are to take his complaints regarding severe attentional and memory deficiencies, impulsivity, anxiety, and agitation, the LORETA findings could indicate a high probability of

(22.2a) Figure 22.2 LORETA readings indicate, in the lower frequency half of the frequency spectrum (Figure 22.2a, this page, page 438, and top 3 panels on page 439 ), from Delta (0.5–3.5Hz) all through Theta (4–7Hz) and Alpha (8–14Hz), functional deficiencies in the Inferior, Medial, Middle, and Superior Frontal Gyri (Brodmann areas 47, 6, 8, 9, 10, and 11), mainly in the left frontal lobe, and in the Anterior Cingulate (Brodmann areas 24 and 32), in the limbic lobe, with more than 2.5 to 3 standard deviations below the normative mean for this patient’s sex and age, in the Delta/Theta range, and with 1.5 to 2 standard deviations below the normative mean for this patient’s sex and age in the Alpha/Low Beta (15–16Hz) range. On the other hand, in the upper frequency half of the frequency spectrum (Figure 22.2b, bottom panel on page 439), from Beta (17–18Hz) all through Fast Beta (19–22Hz) and High Beta (23–30Hz), there is excessive activity (more than 1.5 standard deviations above the normative mean for this patient’s sex and age) in the Cingulate and Posterior Cingulate (Brodmann areas 23, 31, and 29), in the posterior portion of the limbic lobe, and in the Precuneus (Brodmann area 7), in the parietal lobe of both hemispheres.

437

(22.2a)

(22.2a)

(22.2a) Figure 22.2

(Continued)

(22.2a)

(22.2a)

(22.2b) Figure 22.2

(Continued)

Leonardo Mascaro

this being a case of ADHD (6, 10, 20, and 21). However, it could relate to other conditions, as well. In Hammond’s 2010 review article (6), it is stated that: . . . attentional and memory problems may result from ADD that is genetically based, may be associated with mild head injury or anoxia, originate from anxiety or obsessive-compulsive processes that impair intellectual efficiency and cause the patient to be easily distracted, be associated with medication side effects, stem from developmental or learning disabilities, arise from manic distractibility, derive from attention being diverted by hallucinatory psychotic processes, be traced back to encephalopathies, undiagnosed epilepsy, or even represent early stage dementia in adults. (p. 32) The raw EEG signature in this patient’s electroencephalogram was not for ADHD at all, but for OCD (Figure 22.3). That explained a lot based on the above assertions. This came as an initial, but very impacting, surprise for the patient himself, since he always considered his case to be one of ADHD. In addition, he surely had reason for that, not just for the clinical symptoms and difficulties that he had dealt with during his whole life, but also because of frequent, recurrent, ADHD diagnoses that he had received from different health professionals in trying to find a solution for his cognitive deficiencies. Moreover, even though he hadn’t mentioned the plethora of tics he had, he obviously had never, to that day, linked them to the rest of his cognitive deficiencies. Once I explained what OCD was all about, it all made a lot of sense to him. This is where the raw EEG signature plays a central role in finding the definitive answer for a given symptomatology. The existence of electroencephalographic signatures for different neurobehavioral disorders and pathologies allows for the possibility of reaching, through qualitative raw EEG analysis, a direct, objectively determined confirmation for any given diagnosis, even when medications are a present variable. Let us now check, through the analysis of another clinical case, how one can assess the progress of a given clinical condition by observing the actual process of change towards normalization in the raw EEG signature, something that both the QEEG maps or tomographic analysis (LORETA) won’t show in such clarity and detail. The following is a case of a 14-year-old boy that suffered, since early childhood, from enuresis. Figure 22.4 illustrates the raw EEG signature for enuresis, characterized by the presence, at O1-O2 (10–20 International System of Electrode Placement), of high amplitude Alpha (8–14Hz), obtained from the initial electroencephalographic evaluation of this boy’s brain activity. The condition was eyes-open (EO). Correspondingly, in his initial QEEG map (Figure 22.5), note the excessive Alpha activity, indicated by the z-scored analysis of the neurological activity, mainly from 9Hz through 12Hz. The QEEG map signalizes, at 10Hz and 11Hz, more than 2.5 standard deviations above the normative mean for this patient’s sex and age. His LORETA (Figure 22.6) shows that, at 10Hz, Alpha excessive activity is pervasive throughout the midline at the front and the back of his head, in the parietal and occipital lobes of both hemispheres. The color scale is set to 2.5 z-scores so that only the neurological dysregulations of more than 2.5 standard deviations above the normative mean for this patient’s sex and age will be shown. Sixteen 19-channel z-scored surface neurofeedback training sessions after his first evaluation, there was a significant decrease in the rate of nocturnal enuresis occurrence that, from an historical average of five to seven events per week, decreased to no more than one every two weeks. The QEEG map (Figure 22.7) confirmed the tendency of voltage reduction, at 10Hz and 11Hz, in the Alpha band, at O1-O2, that from an initial value of more than 2.5 standard deviations above the 440

Figure 22.3

Raw EEG signature for OCD at F3-F4 (10–20 System) on and off medication. The suppressive effect of the psychotropic medication promotes an overall reduction of amplitudes ( above in Figure 22.3a), but does not change the electroencephalographic characteristics of the EEG reading. In order for the OCD signature, that is characterized by high amplitude excessive fast Beta at F3-F4, to be perfectly identifiable, in this case, one has to reduce the attenuation at the voltage scale from 20μV to 12μV (Fig. 22.3b on page 442), which, then, compares adequately to this patient’s neurological reality, when off medication (Figure 22.3c on page 443). In addition, the normative database analysis for absolute amplitude confirms the suppressive effect of medication (Figure 22.3d on page 444).

Figure 22.3

(b)

(Continued)

Figure 22.3 (Continued)

(c)

Figure 22.3 (Continued)

Figure 22.4

Raw EEG signature for enuresis. Site is O1-O2. Montage is bipolar. Scale is 20μV.

Figure 22.5 Z-scored FFT Absolute Power Individual head bins confirming the presence of excessive high voltage Alpha at O1-O2. Condition is eyes-open (EO).

Figure 22.6 LORETA localization of dysregulations found in the patient’s brain activity at 10Hz. The color scale is set to 2.5 z-scores above the normative mean for this patient’s sex and age. There is a pervasive excess of Alpha throughout the midline at the front and the back of the head.

Figure 22.7 Z-scored FFT Absolute Power Individual head bins show the significant reduction of almost one standard deviation of Alpha activity, at O1-O2. Condition is eyes-open (EO).

Leonardo Mascaro

(a)

(b) Figures 22.8a and 22.8b Keeping the color scale set to 2.5 standard deviations (Figure 22.8a) does not allow for the remaining brain dysregulations to be seen unless one sets the color scale towards 1.5 standard deviations (Figure 22.8b). Frequency is 10Hz in the Alpha band.

normative mean for this patient’s sex and age, went down to no more than 1.5–2 standard deviations above the normative mean for his sex and age—a decrease of almost one standard deviation in no more than two months of neurofeedback treatment. In addition, the same goes for LORETA results (Figure 22.8). Now, the remaining dysregulations in the Alpha band can only be seen when the scale is adjusted to 1.5 standard deviations, indicating that no dysregulations of more than two standard deviations above the normative mean for this patient’s age and sex are present anymore, thus demonstrating the actual progress of this young man’s treatment. However, what was actually taking place in this boy’s neurological activity? Only the analysis of the raw EEG can give us an answer to that. His second electroencephalographic evaluation showed that the raw EEG signature for enuresis was now coexisting with a closer to normal one. The Alpha activity at the occipitals was now characterized by recurrent periods of significantly lower amplitude Alpha at O1-O2, which alternated with clusters of still persistent high voltage Alpha, indicating that the case was not yet solved at all, but illustrating a perfect snapshot of the process of change (Figure 22.9). It’s never enough to stress that this would never be seen if only QEEGs and LORETA were considered for analysis purposes. In addition, it is, truly, an amazing thing to testify.

448

Figure 22.9

A snapshot of change. Raw EEG signature for enuresis was now cohabitating with a closer to normal one, characterized by recurrent periods of significantly lower amplitude Alpha at O1-O2, which alternated with clusters of still persistent high voltage Alpha.

Leonardo Mascaro

Conclusion The present text will serve as a contribution towards bringing back raw EEG analysis to its rightful place, a central role in the assessment and effective identification of a patient’s neurological condition. Such an approach, which integrates the raw EEG with 19-channel, z-scored, surface, and LORETA distributed inverse solution analyses, can become more and more widespread, serving as a firm basis for devising tailor-made, personalized protocols that can address one’s actual neurological condition, thus maximizing treatment efficacy of specific neurobehavioral conditions. This will certainly have an impact, both in the studies that evaluate and attribute levels of efficacy to neurofeedback interventions, and in the clinical everyday practice of those that, like me, have the privilege of witnessing the true reach of EEG operant conditioning. The EEG, when integrated with 19-channel, z-scored, surface, and LORETA distributed inverse solution analyses is, by far, the most effective, inexpensive, and reliable tool there is to the present day, both in regards to the precise, objective identification of any given neurobehavioral condition, and, as importantly, the treatment of such disorders. No other measurement domain, e.g. Positron Emission Tomography (PET), Magnetic Resonance Tomography (MRI), CT Tomography, Single Photon Tomography (SPECT), or Magnetic Electroencephalography (MEG), can provide such a richness of information and the necessary integration of actual spatial and temporal resolution necessary to take on assessment, diagnosis, and, finally, proper and adequate treatment. As a final remark, I can only say that it is only because the QEEG has proven to be independent from cultural and ethnic biases (12 and 13), that someone living on the other side of the world can perform such remarkable work.

References 1. Insel, Thomas. (2013, April 29). Transforming diagnosis [blog post]. Retrieved from http://www.nimh.nih. gov/about/director/2013/transforming-diagnosis.shtml 2. NIH Consensus Statement Online. (1998, November 16–18). Diagnosis and treatment of attention deficit hyperactivity disorder [online statement]. Retrieved from http://consensus.nih.gov/1998/1998AttentionDe ficitHyperactivityDisorder110html.htm 3a. Arns, M., Drinkenburg, W.H.I.M., & Kenemans, J. L. (2012, September). The effects of QEEG-informed neurofeedback in ADHD: An open label pilot study. Applied Psychophysiological Biofeedback, 37(3), 171–180. 3b. Brainclinics (2012). Doubling of neurofeedback efficacy in ADHD treatment: First study investigating personalized treatment in ADHD. Retrieved from http://www.brainclinics.com/neurofeedback-scientific-results 4. Arns, M., de Ridder, S., Strehl, U., Breteler, M., & Coenen, A. (2009, July). Efficacy of neurofeedback treatment in ADHD: The effects on inattention, impulsivity and hyperactivity: A meta-analysis. Clinical EEG and Neuroscience, 40(3), 180–189. 5. Hughes, J. R., & John, E. R. (1999, May). Conventional and quantitative electroencephalography in psychiatry. Journal of Neuropsychiatry & Clinical Neuroscience, 11, 190–208. 6. Hammond, D. (2010, March). The need for individualization in neurofeedback: Heterogeneity in QEEG patterns associated with diagnoses and symptoms. Applied Psychophysiology and Biofeedback, 35(1), 31–36. 7. Ayers, M. E., & Montgomery, P. S. (2007, August). Whispers from the brain (2nd ed.). Los Angeles, CA: Beverly Hills Press. 8. Ayers, M. E. (1987). Electroencephalic neurofeedback and closed head injury of 250 individuals. National Head Injury Foundation Annual Conference, December 11, 1987. Published in National Head Injury Syllabus, Head Injury Frontiers, 380–392. 9. Ayers, M. E. (1999). Assessing and treating open head trauma, coma, and stroke using real-time digital EEG neurofeedback. In J. R. Evans & A. Abarbanel (Eds.), Introduction to quantitative EEG and neurofeedback (pp. 203–222). New York: Academic Press. 10. Sherlin, L. H. (2008, December). Diagnosing and treating brain function through the use of low resolution brain electromagnetic tomography (LORETA). In T. H. Budzynsky, H. K. Budzynsky, J. R. Evans, & A. Abarbanel (Eds.), Introduction to quantitative EEG and neurofeedback—advanced theory and applications (2nd ed., pp. 83–102). New York: Academic Press.

450

The Diagnosis of Neurobehavioral Disorders 11. Hammond, D. C. (2006, Fall). Quantitative electroencephalography patterns associated with medical conditions. Biofeedback, 34(3), 87–94. 12. Congedo, M., & Lubar, J. F. (2003). Parametric and non-parametric analysis of QEEG: Normative database comparisons in electroencephalography, a simulation study on accuracy. Journal of Neurotherapy, 7(3–4), 1–29. 13. Whitaker, R. (2010). Anatomy of an epidemic: Magic bullets, psychiatric drugs, and the astonishing rise of mental illness in America (Kindle Ed.). New York: Crown Publishing Group. 14. John, E., Prichep, L., & Almas, M. (1991). Toward a quantitative electrophysiological classification system in psychiatry. In G. Racagai, N., Brunello, & T. Fukuda (Eds.), Biological psychiatry (pp. 401–406). Amsterdam / London / New York: Exerpta Medica. 15. Chabot, R., & Serfontein, G. (1996). Quantitative EEG profiles on children with attention deficit disorder. Biological Psychiatry, 40, 951–963. 16. John, E., Prichep, L., & Valdes-Sosa. (1997). Neurometric EEG classification and subtyping of psychiatric patients. Electroencephalography and Clinical Neurophysiology, 103(1), 36–37. 17. John, E., Prichep, L., Fridman, J., & Easton, P. (1988). Neurometrics: Computer assisted differential diagnosis of brain dysfunctions. Science, 293, 162–169. 18. John, E., Prichep, L., Friedman, J., & Easton, P. (1989). Neurometric topographic mapping of EEG and evoked potential features: Application to clinical diagnosis and cognitive evaluation. In K. Maurer (Ed.), Topographic brain mapping of EEG and evoked potentials (pp. 90–111). Berlin: Springer-Verlag. 19. John, E., Karmel, B., Coming, W., Easton, P., Brown, D., Alm, H., . . . Schwartz, E. (1977). Neurometrics: Numerical taxonomy identifies different profiles of brain functions within groups of behaviorally similar people. Science, 196, 1383–1410. 20. Leech, R., Braga, R., & Sharp, D. J. (2012). Echoes of the brain within the posterior cingulate cortex. The Journal of Neuroscience, 32(1), 215–222, 215. 21. Leech, R., & Sharp, D. J. (2013). The role of the posterior cingulate cortex in cognition and disease. Brain, 137(1), 1–21. 22. Thatcher, R. W. (2009). EEG evaluation of traumatic brain injury and EEG biofeedback treatment. In T. H. Budzynsky, H. K. Budzynsky, J. R. Evans, & A. Abarbanel (Eds.), Introduction to quantitative EEG and neurofeedback—advanced theory and applications (2nd ed., pp. 269–294). New York: Academic Press. 23. Price, J., & Budzynski, T. (2009). Anxiety, EEG patterns and neurofeedback. In T. H. Budzynsky, H. K. Budzynsky, J. R. Evans, & A. Abarbanel (Eds.), Introduction to quantitative EEG and neurofeedback—advanced theory and applications (2nd ed., pp. 453–472). New York: Academic Press.

451

PART VII

Traditional Alpha/Theta/ Beta Protocols

23 NEUROFEEDBACK AS A TREATMENT FOR ANXIETY IN ADOLESCENTS AND YOUNG ADULTS Cynthia Kerson Abstract Anxiety and its common comorbidities are disorders that rely upon psychological and medical intervention. Often anxiety is the symptom of underlying dysregulations. These dysregulations, developed over childhood, are both psychological and physiological. Thus, clinical intervention from a psychophysiological perspective may be the best approach and neurofeedback and biofeedback are often used in conjunction with psychological interventions. This chapter discusses the underlying mechanisms of anxiety as well as discusses a case in which both biofeedback and neurofeedback were provided. Across the lifespan, diagnoses of anxiety and depression are on the rise, with the largest effected age group being teenagers and young adults (Blanco et al., 2008). The underlying issues facing this population are their need for excelled school performance, hormonal shifts, social pressures and changing family dynamics such as divorce, death or going off to college. The 2008 study (Blanco et al.) found that close to 50% of all 19–25-year-olds has at least one diagnosed psychiatric disorder, with the highest of them being substance abuse. Depression and anxiety were also ranked high. (Note that these findings do not claim it is at this time in the young adult’s life that the disorders may have begun.) These alarming statistics might well be due to the marketing efforts of big pharma; after all, young adults drink. And they drink to relieve themselves from the pressures of evolving into adulthood. Normal age-related drinking aside, substance abuse is rarely a disorder unto itself; it is the self-medication of underlying psychological mechanisms, such as anxiety and depression as well as underlying physiological EEG mechanisms, which we will later discuss. Medication is treatment as usual (TAU) for this population, who are prescribed SSRIs, anxiolytics and/or anti-seizure medications. However, of the alternative treatment options for them, neurofeedback has been shown to be one with the best outcomes with few adverse reactions or negative side effects. Neurofeedback is usually offered in complement to other treatments, which may be diet, exercise and peripheral biofeedback, such as heart rate variability, EMG or EDA, as well as EMDR and/or CBT. Thus, it is best when accompanied with life-style changes, which can be encouraged from these complementary and adjunct procedures.

455

Cynthia Kerson

Psychophysiological Bases of Anxiety Evolutionarily, anxiety is a healthy behavior. It alerts and prepares for events that threaten safety and survival. Yet, it is not quite as useful when in constant exploit and in response to current, non-lifethreatening stressors. Walter Cannon (1915), Philip Bard (1928), William James (1897), and more currently Robert Post (2007; Post & Weiss, 1998), Joseph LeDoux (1998) and Steven Porges (1995) have discussed the mechanisms underlying fear and subsequent behavioral presentations. During the past 100 or so years, we have learned, first from a behavioral perspective then from a physiological perspective, how chronic overarousal leads to systematic dysregulation. These models look at the progression in the brain from stimulation to reaction. Kindling (Post & Weiss, 1996, 1998; Post, 2007) is a stress phenomenon, which occurs because of brain plasticity. Small appropriate brain responses to threatening stimuli lead to epileptiform spike behaviors if they become more abundant than is normal. On the cellular level, repetitive behavior strengthens neuropathways. When these behaviors are decontrolled they lead to poor regulation in important cortical structures, that then lead to deregulated brain behavior within the subcortex, then in the brain stem, and further to the autonomic nervous system, thus “kindling” regular events of anxiety, stress, depression and other pathologies that become chronic. This kindling action suppresses cognitive influence, making the behavior consistent, overriding normal, healthy reactions in the wake of distress. LeDoux (1998) discussed the simple example of fearing a stick until we know it’s not a snake; he describes a model in which the autonomic nervous system and limbic areas of the brain (specifically the amygdala) react before the cortex gets involved to save precious milliseconds when safety may be breached. This explains the flight/fight mechanism from a physiological perspective, which becomes overaroused (or kindled) the more the system is activated. Porges (1995) described the freeze mechanism, which can replace the flight/fight as the preferred response behavior. He posits that the 10th cranial nerve, the unmyelinated vagal nerve (there are two; one myelinated and one that is not), which has deep connections to the other cranial nerves as well as to the heart and other viscera, will trigger an alternative to flight/fight, which presents as freeze, immobilization, passive avoidance, PTSD and the inability to move forward. These three systems, kindling, flight/fight or immobilization, can be detrimental during the young adult time when life-long coping strategies are set up. How one responds to perceived threats is highly individual and involves any, or many, of the mechanisms described above. Regardless which, one’s psychophysiological style of response/reflex is contingent on prediction of outcome, which is usually perfect at birth. However, with each negative outcome, each outcome that did not match the prediction, the belief structure becomes dysregulated. Enough incongruent outcomes and the system goes awry. Behaviors don’t match expectations; tame situations spur wild and uncontrolled emotions. For example, when a child is hungry the evolutionarily normal behavior is to bring attention to herself—maybe by crying or asking her mother for food. However, when this child brings attention to herself in this manner, she finds herself put into a room in isolation. Her expectation, or prediction, was that when she brought attention to herself, her mother would want to take care of her by bringing her food. However, this prediction failed to present itself and the child’s emotional wellbeing (and possibly her nutritional health) becomes compromised. Now she no longer relies upon the instinctive behavior of attracting her mother to her needs. This cascades into a belief that most, or all, of her needs are not important to her mother. And as she matures, this becomes a deep-rooted dysregulation affecting many aspects of her life. Finally, motivation plays a very important role with this population. The young adult has many obstacles to overcome and can easily get overwhelmed. Many have to fend for themselves for the first time. Life becomes exponentially harder and habits learned during this time will be set for life. Neurofeedback, because it is a long and somewhat slow process, requires a gauge of the level

456

Neurofeedback as a Treatment for Anxiety

of motivation, both during sessions and throughout the treatment process. When motivation is low the clinician must invoke engagement and commitment by creatively providing secondary rewards.

Neurofeedback Treatment Options Adolescents and young adults tend to be very interested in their health care and often experience poor resolution from medication and other traditional treatments. Thus, they increasingly present to the neurofeedback practitioner. They are not quite as skeptical as their predecessors and are more open to new ideas. And over the past few years, the neurofeedback protocol and assessment options have increased, making it a more effective treatment modality than even 10 years ago. The neurofeedback practitioner should look for three important brainwave patterns in the assessment that are shown to be specific to anxiety. These include (but are not necessarily limited to) excessive beta amplitude, which may include spindling; alpha asymmetry in the frontal lobes; and non-attenuating posterior alpha with eyes open based upon eyes-closed measure. These phenotypes are typically found in anxiety, substance abuse, PTSD and other disorders of hypervigilance (Johnstone, Gunkelman & Lunt, 2005). The authors discuss these and other phenotype patterns seen in the EEG, noting that often there is more than one present. While this population will surely present with at least one of these, others may also be present and I defer to Johnstone et al. (2005) for more information and suggest following the training recommendations specific to the pathologies. Simply training the client to published protocols will often be a disservice. This author once worked with a client who did not want a QEEG and we proceeded to train with the alpha theta protocol. The client became more anxious and when finally convinced to do a 19-channel EEG and QEEG assessment, we found that there were alpha dysregulations at sites that were not being trained and that the alpha theta training was exacerbating them. The clinician should also consider that only one training protocol might not be enough. Deciding which to train first depends on their severities and locations. This author has had better success training posterior pathologies first. Often they mitigate the frontal dysregulations simply due to network structures and the flow of neuronal communication. Basing protocol importance on symptoms and goals is also advised.

Missy Recall the many reasons for anxiety in adolescents and young adults from the beginning of this chapter. You’ll find most of them in this case. Missy is a 26-year-old woman who first came to me when she was 22 years old to help with ADD symptoms. She is very creative and was attending the art academy at the time and finding it hard to complete her studies. We did many sessions then to reduce theta frontally and then alpha posteriorly as indicated by her brain map. After finishing neurofeedback for the first time, she almost completed college, but was recruited by a leading movie producer before she finished her last semester. During this time, she found she could not control her alcohol consumption. As she stated, “I did not have an off switch, I would come home from work and drink a glass, then another glass and finally a full bottle of wine ending up too drunk to function.” She had completely stopped drinking but proceeded to become dependent on pain medications for hip pain that she now knows was stress induced. During the last few months, she has been weaning herself off them, and was currently taking a very low dose of Vicodin every four hours. Because it was successful for her in 2010, Missy thought she would revisit neurofeedback after she was laid off from her position, her dog and constant companion of many years died and she was about to be married. She had nearly chronic anxiety and pain. She would wake everyday at least an hour earlier than necessary, prepared to manage the inevitable morning anxiety event. She would listen to relaxation tapes, take her time eating, do her stretching exercises for the hip pain and walk her new

457

Cynthia Kerson Table 23.1 Results of 2-channel pre-assessment. μV **

Frequency

Location*

Theta (5–8)

Frontal

6

Central

6

Parietal

9

Occipital

7

Frontal

6

Central

7

Parietal

7

Occipital

8.5

Frontal

6

Central

6

Parietal

6

Alpha (8–12)

Beta (15–19)

Occipital High Beta (20–30)

5

Frontal

10

Central

10

Parietal

8

Occipital

9

Electrodermal Activity

Left hand

0.66–2.15

Heart Rate

Left middle finger

~ 70

* F3/F4; C4/C4; P3/P4; and O1/O2 ** Magnitude is the average of the homologous sites

pup while weathering her concerns about her future, her upcoming wedding and her aging parents, with whom she is very close. I did not do a full 19-channel EEG because she was concerned about the expense. Therefore, I did a 2-channel assessment looking at frontal, central, parietal and occipital homologous sites. The results of that assessment can be found in Table 23.1. Note that within the 20–30 Hz range the 20–24 Hz range presented as mostly true EEG, often associated with worry and rumination, while the 25–30 Hz range represented muscle artifact as based upon the wave morphology and the presentation of Missy (when she tensed, this EMG was represented in the 25 Hz and higher range). One particular pattern, “low-voltage fast,” as described by Johnstone, Gunkelman and Lunt (2005), was found. I did not observe spindles. In their paper, they recommend posterior alpha suppression to remediate this behavior. However, we also saw that the alpha and theta were lower in amplitude than is considered normal, so we didn’t want to train alpha down any further. I decided to do alpha (8–12 Hz) theta (4–7 Hz) training while suppressing 20–24 Hz at Pz. I monitored electrodermal activity (EDA) and heart rate (HR) to forewarn any shift in state that helped with “tweaking” the thresholds. With alpha theta training, the client has her eyes closed so the sound reward is very important. They are usually water sounds, bells or deep tones that are conducive to relaxation. Feeling this to be intuitive, I usually set the alpha tone to be a bit higher than the theta tone, being sure they are distinct enough from each other. Also, while it’s usually appropriate to make changes during the session and to inform the client of the changes, it is sometimes best to not disturb the client or you might compromise the deeper state that the training incites. It will be apparent whether to inform or not based upon your client’s breathing and EEG. 458

Neurofeedback as a Treatment for Anxiety

To address the muscle artifact seen in the 24–30 Hz range, in the first session we trained trapezius muscle relaxation (electromyography or EMG). The active and reference sensors were placed on the belly of both the left and right trapezius muscles (upper back) (the ground was on the spine at about C3) and Missy was prompted to tense and release at differing intensities to help her differentiate how tense and relaxed muscles feel. She was able to reduce the muscle tension from approximately 8μV to 3μV and remain relaxed for at least three minutes at a time. So, with just this one session of EMG training, we felt she was ready to start the neurofeedback training. For the first three neurofeedback sessions only, I monitored the EMG and Missy was able to maintain a low (approximately 3μV) level for extended periods of time throughout the session. If her HR and/or EDA became aroused, generally the EMG measure also did, however usually delayed (within a minute). HR and EDA levels reduce when relaxed. There is no optimal level for either because body type, age, gender, level of fit, medications and many other factors play a role; a full explanation of this is beyond the scope of this chapter. Mostly, we look for trends. Did the HR and EDA go up? Or did they go down? What if one went up and the other down? If both rise, the body is more aroused. Conversely, if both go down, the body is more relaxed. These trends are easy to follow. However, opposite trending, or fractionating, reflects more complicated issues that are determined as you interact with the client. If you’re not sure, gently ask if your client needs a break, is OK —whatever feels right. As you evaluate your client’s needs based upon physiological measures during an alpha theta training session, the important issue is to determine whether he still feels safe. Because alpha theta training leads to deeper states and thus to exposure to suppressed emotional baggage, the physiological measures will inform you even before your client is aware of any state change. Making appropriate real-time changes to the neurofeedback protocol hinge upon these readings. After nine weekly 20- to 30-minute sessions in which we trained the theta and alpha up and the beta and high beta down at PO3, Missy was having trouble with beta (15–19 Hz) (monitored, but not trained) and theta, which remained low regardless of training parameters. (See Figures 23.1a and 23.1b). At minute 6 the HR was 87, the EDA was 2.02 uSiemens and the alpha magnitude was

Figure 23.1a By session 9, you can see somewhat more typical magnitude levels except for theta and beta. Note: this image shows magnitude at the following frequencies: delta (2–5 Hz), theta (5–8 Hz), alpha (8–10 Hz), beta (15–19 Hz) and high beta (20–24 Hz). Solid lines are magnitude levels averaged per minute; dotted lines are manually changed threshold levels. Y-axis is magnitude and X-axis is time in minutes.

459

Cynthia Kerson

Figure 23.1b The slower frequencies are at what may be considered as too low magnitude, despite the proper proportion to the overall energy. Note: some of these frequencies are changed from the assessment due to training needs. However, the conclusions from them are still relevant. Solid lines are relative power levels averaged per minute; dotted lines are manually changed threshold levels. Y-axis is % power and X-axis is time in minutes.

slipping, so I changed the threshold of the alpha range, as displayed by the dotted lines (from 14 to 11.5). Having checked in with her, and she realized she began thinking about unimportant things, I felt the change in threshold would enable her to transition back to the deeper state. (Interestingly, the beta and high beta increases slightly during this time as well). This is an example of how monitoring the HR and EDA allowed me to observe change in her state that may not have been as obvious by just looking at her or her brainwave patterns alone. Importantly, by this time, the delta (2–5 Hz), theta (5–8 Hz) and alpha (8.5–11.5 Hz) frequency spans are reduced from those during the baseline assessment to accommodate changes in the EEG as the neurofeedback training progressed. I have found that reducing the frequency span increases the chance of success with most neurofeedback protocols, including with alpha theta training. However, this makes reporting cases difficult. So, please bear with me. Reporting on cases such as these tend to be more subjective in nature. To the hard-pressed scientist, I hope I don’t offend. Changing thresholds to make any of the criteria easier or harder depends on what is needed at the time and is very individualized. Recall, I am also monitoring HR and EDA, which help to reveal the client’s internal state, especially when coupled with the EEG measures. For example, when the client is going into a deeper state, I may remove the alpha reward to better distinguish the theta feedback. At this point, the HR would be slower and the EDA level would be reduced from baseline measures (taken at the beginning of each session). This may also provoke the crossover (described below). Adhering to the true operant conditioning model, I do not use auto-thresholding. I do not leave my client alone because I feel these micro on-the-fly changes are extremely useful in coaching and coaxing the client to attain the neurofeedback goals. (Staying with the client during alpha theta training is especially important because of the possibility of abreaction, which may occur if the client is not ready to revisit underlying trauma that is likely to resurface when they achieve the deeper states.) Session 16 was an anomaly, and presented here to show that they can happen—that not all session results are expected. Missy was unable to attend to the training. On that day, she reported feeling concern about a prospective job offer and had a hard time “getting into” the training. The beta and alpha were within expected ranges, yet the delta, theta and high beta were not (see Figures 23.2a 460

Neurofeedback as a Treatment for Anxiety

Figure 23.2a 20-minute eyes-closed session number 16. Peak-to-peak graph (magnitudes). Note high alpha (8–12), beta (15–19) and high beta (20–30 Hz) magnitudes as compared to low theta (5–8 Hz) and delta (2–5 Hz) magnitudes. Solid lines are magnitude levels averaged per minute; dotted lines are manually changed threshold levels. Y-axis is magnitude and X-axis is time in minutes.

Figure 23.2b 20-minute eyes-closed session number 16. FFT graph (relative power). Solid lines are relative power levels averaged per minute; dotted lines are manually changed threshold levels. Y-axis is % power and X-axis is time in minutes.

and 23.2b). At first glance, one might think that since the alpha was out of range with the others, that it was too high. But in fact, it remained within normal range and the delta and theta magnitudes were depressed. So we started by training the theta frequency up and added alpha suppression training just to be sure it wouldn’t rise along with the theta. As you can see, we were not completely successful until the end. HR and EDA were also somewhat elevated, averaging between 72 and 84, and 1.93 and 2.46, respectively. 461

Cynthia Kerson

Starting at session 21, Missy began experiencing intermittent “crossovers,” where the theta magnitude becomes higher than the alpha magnitude for a period of time. In the literature (Kerson & Martins-Mourao, in press) this represents confirmation that the client is experiencing a deeper state— the state that we hope to achieve with alpha theta training. HR averaged approximately 68 and EDA at 1.75 uSiemens during these times, approximately 20% lower than when not in the crossover state. Note in the Figures 23.3 and 23.4 that only theta is being reinforced. At this time, Missy also began working with a psychologist to work through some issues that now began to resurface. Missy had gotten married during this time, which relieved some of her anxiety. However, she still becomes highly anxious whenever an important event emerges. We leave off with her as she attempts to reenter the academic community to complete her Bachelor’s degree and continue to do alpha theta

Figure 23.3 Session 21. Instances of the theta/alpha crossover. Y-axis is magnitude and X-axis is time in minutes.

Figure 23.4 Session 24. Longer instances of the theta/alpha crossover.

462

Neurofeedback as a Treatment for Anxiety

neurofeedback sessions once weekly coupled with the psychotherapy sessions. She finds the neurofeedback sessions very helpful to remain calm for longer and longer periods of time. She states that she can almost always stop and refocus whenever a stressful event presents itself and that she has fewer mornings when she wakes up with unsettling anxiety. Missy still experiences some hip pain, although it is not as severe. She has reduced the dose of Vicodin further, and, along with her prescriber, planned to stop taking it within the next month. While feeling “50% better” she is fearful that the pain will reemerge and that the anxiety will never subside and she discusses these issues with her therapist.

References Bard, P. (1928). A dienecephalic mechanism for the expression of rage with special reference to the sympathetic nervous system. American Journal of Physiology, 84, 490–516. Blanco, C., Okuda, M., Wright, C., Hasin, D. S., Grant, B. F., Liu, S. M., & Olfson, M. (2008). Mental health of college students and their non–college-attending peers: Results from the national epidemiologic study on alcohol and related conditions. Archives of General Psychiatry, 65(12), 1429–1437. doi:10.1001/archpsyc.65.12.1429 Cannon, W. (1915). Bodily changes in pain, hunger, fear and rage: An account of recent researches into the function of emotional excitement. Appleton, MN: Appleton Press. James, W. (1897). The will to believe. Retrieved from http://educ.jmu.edu//~omearawm/ph101willtobelieve. html on 11/6/2014 Johnstone, J., Gunkelman, J., & Lunt, J. (2005). Clinical database development: Characteristics of EEG phenotypes. Clinical EEG and Neuroscience, 36(2), 99–107. Kerson, C., & Martins-Mourao, A. (Eds.) (in press). Alpha theta training in the 21st century: A handbook for clinicians and researchers. San Rafael, CA: ISNR Research Foundation Publication. LeDoux, J. (1998). The emotional brain: The mysterious underpinnings of emotional life. New York: Simon & Shuster. Porges, S. (1995). Orienting in a defensive world: Mammalian modifications of our evolutionary heritage: A polyvagal theory. Psychophysiology, 33, 301–318. Post, R. M. (2007). Kindling and sensitization as models for affective episode recurrence, cyclicity and tolerance phenomena. Neuroscience Behavioral Review, 6(31), 858–873. Post, R. M., & Weiss, S. R. (1996). A speculative model of affective illness cyclicity based upon patterns of drug tolerance observed in amygdala-kindled seizures. Molecular Neurobiology, 1(13), 33–60 Post, R. M., & Weiss, S. R. (1998). Sensitization and kindling phenomena in mood, anxiety, and obsessivecompulsive disorders: The role of serotonergic mechanisms in illness progression. Biological Psychiatry, 44(1), 193–206.

463

24 ALPHA-THETA-BASED CLINICAL OUTREACH Lincoln Stoller

Abstract A modified version of the alpha-theta protocol has been developed for the purpose of demonstrating the therapeutic power of neurofeedback. Neurofeedback demonstration has a different goal from that of therapy in that here the objective is educating the client at a personal level rather than modifying the client. A portable neurofeedback workstation using only audio feedback used in conjunction with this alpha-theta protocol enables an unfamiliar client to experience a single session of neurofeedback and understand what is to them a completely new approach for growth and healing.

Introduction Neuronal regulation is poorly understood in the general population and because of this it is difficult to educate people about of the benefits of neurofeedback. One of the most effective forms of education combines explanation and demonstration. This chapter describes the tools and methods I use to introduce neurofeedback to interested strangers who come to a free clinic that is hosted in an open community space. This program engages both healthcare professionals and the lay public. Neurofeedback lacks recognition in medical and personal growth circles. Traditional Western medicine has yet to accept the role of electricity in the body, and virtually all programs for personal growth ignore neurology. The clinician who wants to educate the public needs to know how neurofeedback can be applied in medicine, and also its role in the areas of personal growth and performance enhancement. By covering both of those bases I find it possible to engage anyone. The Rondout Valley Holistic Health Community offers a free, walk-in clinic as a form of public outreach. The event is held monthly in the Marbletown Community Center in Stone Ridge, New York. Folding tables convert one of the two rooms of the 2,000 square foot space into an intake and registration area. Fabric dividers strung between removable telescoping posts, and folding gym mats used as wall partitions, convert the main room into 17 treatment stations of which my neurofeedback therapy station is one. Clients queue at the door and are given numbers as they enter to the staging area where a list is available showing the treatment modalities available that day. Most of the modalities are considered alternative therapy. Clients submit the names of three practitioners in order of their preference, and sessions with practitioners are arranged on a first come, first served basis. When a practitioner becomes available, a

464

Alpha-Theta-Based Clinical Outreach

volunteer escorts the client to the practitioner’s station. When the session is finished the practitioner escorts the client back to the staging area for sign-out.

Neurofeedback Station My neurofeedback station consists of two small folding tables and a plastic storage box that together provide one-half square meter of desk space. This space is taken up with a computer, EEG amplifier, audio mixing board, and the usual sensor attachment supplies. I sit on a folding metal chair and bring a folding, reclining camping chair with head and arm support for the client. The whole station occupies about three square meters of floor space. All equipment, aside from the two tables and chairs, pack into a computer bag and the storage box. Hardware consists of a laptop computer, a BrainMaster EEG amplifier, an Auvio headphone amplifier, a Mackie 402 VLZ4 4-channel, ultra compact mixer that supports two input channels and a headphone output channel, a Sure PG38 hand-held microphone, PlaneQuiet Platinum noise reduction headphones, a set of earbuds, and a three-wire extension cord providing power to the computer and mixing board. My software consists of BioExplorer running a one-channel protocol, and a sound file that plays a short chime sound through the headphones when the session ends. The neurofeedback protocol is audio-only; the client does not look at the computer screen. The reward and inhibit sounds are played through the computer’s headphone port. Most computers do not have amplified audio output and their audio signal is too weak to be amplified with clarity by the mixing board. The sound level is boosted by the headphone amplifier that is plugged into the computer’s sound port and joined to the mixing board with a stereo cable that has male jacks on both ends. The mixing board’s second input port is connected to the hand-held microphone. Mixing boards are designed to power amplifiers but in this case we are only using the mixing board’s headphone output. Into this output port I plug a two-way splitter to which I connect the noise-reducing headphones that the client wears, and the earbuds that I wear in order to monitor the sound levels and the client’s experience. The mixing board allows me to control audio gain and volume. The most difficult aspect of this enterprise is balancing all the sound levels so that different sounds are equally audible. Balancing sound levels is done by running a prerecorded neurofeedback session stored on the computer’s hard disk. By alternately donning the earbuds and the headphones, I adjust the levels so that the sound is clearly audible through each device. Adjusting sound levels requires attention because there are six volume controls: computer output level, amplifier boost level, mixing board input level, mixing board output level, headphone volume, and earbud volume. The computer output level must be set at its maximum volume because this provides the primary signal. Volume levels on the mixing board are set as low as possible to avoid distortion. The volume levels set on the headphones and the earbuds are set as high as possible since these do not amplify but rather attenuate. I use the mixing board’s headphone output level to adjust and confirm volume levels for each client. It is necessary to listen to the different feedback sounds to ensure that the gain is set for each to be heard clearly and that none are excessively loud. The computer, amplifier, and mixing board all have tunable sound profiles. You can leave them at their default settings or adjust them to improve sound quality.

Neurofeedback Session The session begins with my requesting the client sign a release of liability form, and an explanation of my background and interest. I ask the client why they have come, what they know about biofeedback, and for an overview of their health. I ask them for one issue of personal importance with 465

Lincoln Stoller

which they are struggling. I am looking for basic emotional issues and physical problems between which I hope to discern some correspondence. I use the issue that they present in an affirmation I later read to them. The object of the session is to address whatever interest the client presents by intellectual engagement, establishing an empathic connection, and a session of neurofeedback. The amount of intellectual engagement and empathic connection is determined by the client. My preference is to complete these aspects of the session before conducting neurofeedback but sometimes interaction is more relaxed after the session. The objective of the neurofeedback is to increase sensitivity, control, and relaxation. The basic protocol is a single-channel alpha-theta training. To this I have added two optional audio rewards with independent threshold settings and bandpass filters. One reward is delivered according to a fast response threshold using a 1-second amplitude average that triggers a chime. The other is a 3-second average threshold linked to the volume of a continuously playing musical track. I can selectively turn on or off the alpha-theta reward sounds, the fast reward sound, or the slow reward sound so that these different rewards can be presented alone or in combination. The headphones make it impractical to use the ears as reference sites so I place the reference on the scalp above the ear just above the edge of the headphone pad, which is a little above T3 or T4. This is not much different than an ear placement because the ear is not a neutral site to begin with. The correlation between an ear-to-ear circuit, or an ear to T3 or T4 circuit, is roughly 50% over a broad frequency band.1 Consequently, I expect similar results using a reference near T3 or T4 as from using ear reference sites. In most cases I select an occipital or parietal active site. If I discern in the client a particular openness to their unconscious I will follow Green (1999a) and Urgesi, Aglioti, Skrap, and Fabbro (2010) and select O1 or O2. If their issues seem to relate to issues of orientation or boundaries, then I select P3 or P4 (Berlucchi & Aglioti, 1997). I will use Cz above the sensorimotor strip if their issues are somatic. These choices are driven by our understanding of the functions of these areas and the presentation of the client. It is unclear whether the choice of sites will make much difference if one is only doing a single session of neurofeedback. The prefrontal site at Fp2 is an exception as most people are sensitive to feedback at this site with fear, anxiety, and depression being abreactions. I largely avoid placing contacts at Fp2 in the context of the walk-in clinic.

Alpha-Theta as a Teaching Tool People feel engaged when they sense an aptitude for and a benefit from the training. Introducing neurofeedback through demonstration presents the conflict that “having an aptitude” usually means the client feels in control, yet being in control generally undermines what one might gain from neurofeedback. I tell people that if they could have benefited from exerting conscious control they already would have: they would not need an EEG amplifier. I emphasize our goal is to develop unconscious control, something the Greens’ refer to as “passive volition” (Green & Green, 1977, p. 54). As true as this may be, unconscious control is a foreign concept that is poorly appreciated. In most cases where people do develop unconscious control, as in natural movement, a facade of awareness fosters the illusion of control. Tor Norretrander (1999) argues this illusion extends to consciousness itself, over which, he argues, we have little control. Or to put it more simply, that our sense of self-awareness is largely an illusion. Someone looking for performance enhancement will appreciate this predicament. They understand that developing a skill that one does not have demands that one tolerate frustration. They know that they must develop their own goal and accept responsibility for reaching it. Being asked to accept frustration is more of a problem in a therapeutic context where the client expects clear direction and 466

Alpha-Theta-Based Clinical Outreach

assessment. Someone attracted to an allopathic approach may find my directions unsatisfying but they will benefit from this training as much as anyone else. If it were possible to deliver an obvious and resounding benefit in a single session of neurofeedback with a complete stranger, we could convince clients of neurofeedback’s efficacy by simply improving their condition. However, as it is easy to train a person into an unwanted state, we must introduce neurofeedback gently and avoid dramatic changes. It is better to leave the client unimpressed than to demonstrate the power of neurofeedback by making them uncomfortable.

Demonstrating Alpha and Theta I begin the alpha-theta feedback by first letting the client experience pure theta and then pure alpha reward. This gives them some confidence and introduces them to the skills we are addressing. Pure theta reward is achieved by turning off the alpha-theta reward sounds and replacing this with my slow average threshold linked to music volume. Speaking through my microphone into their headphones I described the theta state (Green & Green, 1977) as one in which one has no thoughts and in which one’s visual field has gone dark and empty. I ask the client to breath slowly, relax, and attend to the pause between the end of their inhalation and the start of their exhalation. In this situation the feedback enables people who generate dominate theta activity for a period of longer than 2 or 3 seconds to hear bursts of theta activity as the reward music becomes audible. Telling the client that this demonstrates their ability to control their brainwaves is somewhat of a ruse because I have control of the amplitude and sensitivity of the feedback. Using automatic thresholding, which I turn on or off at any time, I control the percentage of the time that the client experiences the reward sound. Nevertheless, the client is rewarded for elevating theta amplitude above the threshold regardless of how it’s set so true feedback is occurring. The primary benefit of automatic thresholding is in retaining the attention of a client who can focus on the feedback. In this case I’m simply lowering the threshold to a level where anyone can succeed without trying. The client will usually not know whether they have produced theta as a result of voluntary or involuntary efforts. This does not undermine the success of the session because it does not matter how they produce a change in their EEG. This demonstration helps to satisfy those people focused on control, or who need to feel that they are in control. Once satisfied these people can relax, get out of their own way, and start learning through feedback. After a minute of theta training I shift the frequency filter to the alpha band, and I shift the feedback to the fast reward. I describe the experience of being in an alpha state by instructing the client to open their eyes, clear their mind of thoughts, and focus their attention on “the edges of the sound.” I am trying to guide them to a state of hypervigilant, non-verbal awareness. I want them to distinguish the alpha versus theta enhanced states. Clearing one’s mind is difficult in an alert state. Clients are more likely to succeed in raising their alpha levels intermittently, which makes the use of the fast 1-second amplitude average more appropriate. This illustrates both the client’s intermittency of awareness of their own mental state, and the fragility of their control. The client who can appreciate this is better able to understand the general goal of neurofeedback training. I ask clients to remember these different states of mind and instruct them that the full alpha-theta training involves alternating between these two states. I then turn off the single band rewards and start a 20 minute alpha-theta training session.

Alpha-Theta Background The alpha-theta protocol was created on an inspiration by Eugene Peniston who presented it, fully formed and without argument, as a method of remediating substance addiction (Peniston, 1998). 467

Lincoln Stoller

Because the protocol was the result of a personal inspiration and did not develop from deductive research, and because neurofeedback did not have a place in addiction treatment, Peniston’s protocol and neurofeedback in general has found little acceptance by providers in the addiction treatment industry. This is in spite of Peniston’s approach having one of the highest success rates, if not the highest success rate of any institutional approach, to the remediation of addiction. Peniston’s inspiration came from his experience with theta training at the Menninger clinic, and from Elmer Green’s interpretation of theta and alpha as states that connect one’s conscious ego with deep, trauma-formed behavior patterns (Green, 1999b). While this approach is consonant with transpersonal psychology it has limited correspondence with the currently popular, localized, neuronal model of cognitive function. Variations of alpha-theta training have shown statistically significant improvements in the surgical performance of ophthalmic surgeons (Ros et al., 2009) and the musical expressive ability of conservatory students (Gruzelier et al., 2014). This is in spite of the fact that according to our understanding of localized brain functions these abilities should be handled by areas outside the occipital region: the sensorimotor strip for fine motor control, and the right temporal areas for emotive expression. There is no precise explanation of what is being accomplished by alpha-theta training, although there are several suggestions. The first is that the training develops one’s facility to intertwine a dominantly alpha state with a dominantly theta state. This proposal is built on the unmeasured assumption that the states of theta and alpha brainwave dominance provide access to lower and higher states of self-awareness, and that a new measure of integration is achieved by intertwining the two. A second explanation suggests that equalizing the average amplitudes in the alpha and theta bands leads to a greater connection with one’s unconscious and improved voluntary control over the habitual patterns stored there. According to this understanding the protocol’s advantage comes from training a person to equalize alpha and theta levels at occipital sites, if only for the duration of the training. A third explanation that is rarely discussed is that verbal affirmations have great effect when in the cognitive state precipitated by the alpha-theta training. This explanation is both difficult to quantify and runs contrary to the neuro-mechanical model of mental health. It involves the volitional model of mental health advocated by practitioners on the spiritual side of psychotherapy, such as Louis Hay (1984) and Caroline Myss (Myss & Shealy, 1998). According to this explanation, it is the ability to state and internalize one’s intentions in a way that resonates with one’s deeper self that leads longterm changes in health and behavior. After researching these questions, Mark Johnson concluded that the effectiveness of alpha-theta therapy relies upon “Peniston’s earlier contention [of] emergent emotionally salient imagery” (Johnson, 2011, p. 44). Johnson further resolved the aspect of the training most likely to accompany emergent imagery by defining a “therapeutic crossover.” This is an event identified on the EEG when the theta amplitude rises “at least 1 microvolt in amplitude above alpha and remains dominant over alpha amplitude for 3–10 minutes or longer, as well as contains the 15–20 Hz beta superimposed brainwave frequency components” (Johnson, 2011, p. 31). Here Johnson is adding the 15–20 Hz beta amplitude to the 4–7 Hz theta amplitude when comparing it with the 8–12 Hz alpha amplitude. Johnson did not consider the role of the induction script. Some aspects of each of these explanations are likely at the root of the protocol’s ability to effect change, or perhaps a combination of all of them. None of these mechanisms explain why the protocol is effective over an area of function that is so wide as to include addictive behavior, trauma, fine motor skills, creative ability, and more. Gruzelier (2009) reviews alpha-theta’s wide ranging cognitive effects. Raymond, Varney, Parkinson, and Gruzelier (2005) explore its general effect on mood. Boynton (2001) examines the effect on creativity. Von Stein and Sarnthein (2000) examine the protocol’s neuroregulatory effects. 468

Alpha-Theta-Based Clinical Outreach

Alpha-Theta Configuration Alpha-theta feedback presents alternating sounds depending on whether single-circuit amplitude is dominated by frequencies in the alpha or the theta band. The strength of these signals is determined by the width and location of the filters used to define these bands, the time over which the signals are averaged, the sites chosen, and by each person’s different EEG profile. The objective of alpha-theta training is for the client to alternate between the two sounds. I use the sound of rain alternating with the sound of frogs, 6th order Butterworth filters, and the standard ranges of alpha between 8 and 12 Hz, and theta between 3 and 7.5 Hz. Alpha-theta is done eyes closed both because this boosts the alpha levels as measured over the occipital region, and because it facilitates the targeted thoughtless states. This limits the protocol to rear placements and requires roughly 10 training sessions to develop the ability to balance the levels. Alpha and theta rhythms are generated everywhere on the scalp, although their function and a person’s ability to generate them vary. Training away from the occipital or parietal sites is not part of the original alpha-theta protocol, nor would it be alpha-theta training if we were not focusing on the alpha and theta bands. However, the general structure of the training and the potential for gaining flexibility and control using a protocol that does not entrain suggests that we may obtain benefits from both moving away from sites at the rear of the head, and in moving away from the alpha and theta bands. Making either of these changes requires that we change the assumption that people can or should be trained to generate equal amplitudes in two bands. In fact, quite different results may obtain by training people to establish alternating, unequal levels. Rewarding unequal levels may be beneficial because most people start with average amplitudes that are unequal. In order to enable clients to experience crossing over between the dominance of their alpha and their theta signals, and to facilitate working with crossing over between signals in any two frequency bands, I have added a feature to the protocol that boosts one of the signals. This boost is done in software and provides a crutch of varying strength that moves toward equalizing the amplitudes extracted from any two filter bands. I have designed this boost to apply to the alpha signal, or whatever band I am using in place of the alpha signal, because that is usually the weaker signal. I can vary the boosting factor from 0 to 1. At zero there is no boost. When set to 1 the boost amplifies the associated signal so as to have the same long time average as the complementary signal. This is a multiplicative factor so that it does not distort either signal. Boosting one of the frequencies is much like an automatic threshold in that it allows the clinician to set the level of difficulty that the client will have in generating crossover in the feedback signals. I boost the alpha signal in almost every case as a way to enable the client to experience crossover. Without this it will take a client that much longer to simply find a state that generates the unrewarded, weaker frequency.

Induction Script The induction script is an important but rarely discussed component of the alpha-theta protocol. It is essentially a guided meditation that focuses the client on what they intend to accomplish. The script is either composed for or by the client and read back to them during the first 5 to 10 minutes of the session. Peniston and Kulkosky (1989) allude to this script without detail. Scott, Kaiser, Othmer, and Sideroff (2005, p. 459) mention their 3- to 5-minute script as dealing with “identified essential elements of maintaining abstinence.” The script is not mentioned at all in the work with opthalmic surgeons (Ros et al., 2009) and music students (Gruzelier et al., 2014). 469

Lincoln Stoller

I find the induction script to be important, which is consistent with the experience of others (White, 1999, p. 343). “Visualization seems to be the quickest and surest way of programming the body. . . . Patients’ visualizations of success or failure, sickness or health, and ideas about their body and mind together determine to an important extent what happens to them” (Green & Green, 1977, p. 167ff). The script accomplishes several things: • • •

distracts clients from the cogitation that obstructs both the alpha and theta states; provides a sensory-focused experience aiding the client in relaxing into an psychosomatic experience; and affirms a positive goal that will hopefully remain in focus as they enter a receptive state.

In my private practice I have developed a 10-minute script based on ideas from Louis Hay (1984) and Jacquelyn Aldana (2003). For use in this demonstration protocol I use the following 5-minute script that includes the issue the client has put forward. Imagine you are sitting on a mossy stump beside a beautiful forest lake on a warm spring day. The air is still and heavy with mist. You are wearing a loose parka. You are warm and comfortable. In front of you the surface of the wide lake is dappled with the texture of fine raindrops. You feel the warm air on the backs of your hands. You are relaxed. Rising slowly you stand, face the lake, and then turn left to look along the water’s edge. Smooth water spreads out to your right. The shore rises gently to the tree line. You relax your eyes. Stepping carefully, feeling every placement of your feet, you move along the shore following a smooth earthen path. You move effortlessly. Entering a small bay you see the path turn toward the trees. The path climbs to a tremendous tree and then drops down between two wall-like roots. You descend four large, stone steps and find yourself on a flat, sandy circle with the huge tree arching above you. A wide path leads through a stone arch into a dim passage beneath the tree. You are curious. After a pause you step under this arch and into the cave leaving the grey sky behind you. The cave walls are dry and sparkle with large crystals. The crystals cast a pale glow that is enough to light your way forward, fading to darkness ahead. You follow the path without a sound. After a minute the cave comes to an end at a wide wooden door set on heavy hinges. The door has a round brass knob and a sign hanging above it. The sign says “Grace.” It welcomes you. With hardly a touch the door swings into a carpeted study filled with books. Directly in front of you is a low table and an overstuffed armchair covered in lambswool. Walking to the chair you stop. You turn around and lower yourself into it. The chair fits you like a glove folding you in warm fur. The room is large, its walls covered in bookshelves. Desk lamps cast a peaceful yellow light. Colorful carpets cover the floor. Heavy wooden beams cross the ceiling. You feel secure. A journal is open on the table. Written in a neat handwriting you read this to yourself: Even though I _________________________, I value and love myself. Even though I _________________________, I value and love myself. You feel the warm lambswool. You can still hear the forest sounds alternating between the light rain and the tree frogs. You listen first to one and then the other, back and forth. You are calm. You listen to the sounds. Into the blank areas above I insert a rewording of the client’s statement of their issue of personal importance. It is my intuition that in most cases in the walk-in clinic the clients are not ready to accept this self-affirmation. The intellectual tenor of our interaction and the novelty of the situation work against opening to sensitive personal issues. I include it in spite of this because I want to inject 470

Alpha-Theta-Based Clinical Outreach

a relevant and positive affirmation, and because it may open the otherwise guarded atmosphere surrounding the demonstration.

Session Conclusion After reading the induction script I turn off the microphone and watch the EEG and the client’s body language as the alpha-theta session proceeds. I do not make any further changes to the protocol and I do not speak any further until the 30-minute session ends. A few minutes before the scheduled end of the session I turn the microphone back on and read a short, one-paragraph exit script that walks the client back out of the subterranean study where I’d left them and back to the lake shore where the induction script started. I press a button on the screen to play a sound file of a resonant bell. In an in-office setting I could strike a real bell, but as all audio here comes through the microphones I must play a recording. I then remove the client’s headset and the electrodes. I ask the client about their experience. Everyone is interested in their EEG and most people want to know whether or not it looked normal, by which they usually mean healthy. I explain the great variance of people’s EEG patterns and point out something of note in theirs. I have configured my software to show the session’s amplitude traces over all major frequency bands, not just the alpha and theta bands. I also have a spectrograph, of the sort that is now in common use, that shows time along the horizontal axis, frequency along the vertical axis, with amplitude color-coded such that black is low amplitude and white is high. This makes the relative strength of the frequency bands immediately evident. I usually point out some aspect of the EEG that pertains to or correlates with their personal inventory. The most common distinction I see is between people who show variable EEG patterns and those who do not. I often use these variations, such as sudden bursts of theta activity, to illustrate to clients how their awareness is episodic. Episodes of strong theta correlate with client’s loss of self-awareness, while bursts of beta activity correspond to cognitive activity. If possible I summarize what neurofeedback training might do to address their personal issues in terms of a possible change in their EEG profile. I solicit the client’s email address in order to contact them the next day to further inquire about their experience. I then accompany the client back to the clinic’s sign-out station where they complete their paperwork.

Cases Hyperactivity ADHD is a diagnostic jungle and most parents know little about its many aspects (Stoller, 2014). Most young people diagnosed with ADHD struggle with parental and social issues. When a mother came to my station with an active 8-year-old child and asked if I could help her son calm down, I felt that it was the mother who needed relaxation training. There was little opportunity for me to introduce a solution so I explained what I was doing to the youth and gave him 5 minutes of alpha-theta training, after which he was anxious to run outside and play, followed by his frustrated and apologetic mother. I could hardly get a clean EEG reading for the artifact introduced by his movement, but I’m sure he and I could have a wonderful time exploring further training. If nothing else this would have given him a new level of control.

Mood Improvement I conducted a demonstration at a frontal site for a client who was focused on issues of depression and negativity. Neurofeedback is effective in relieving depression and I showed this client the high 471

Lincoln Stoller

amplitudes of frequencies in the right frontal area postulated to sustain a depressed attitude (Hammond & Baehr, 2009). Not being certain in the effect I will have, I moved the electrode to a less sensitive location between F4 and Fp2. Training a change in frequencies at Fp2 often results in dramatic mood changes, but this version of alpha-theta training, which employs equalized amplitudes, only rewards flexibility. I cautioned this client to be prepared for unusual emotions over the next 24 hours, that such feelings could be ascribed to the neurofeedback, and that such effects will be temporary. Two days after our session this client reported: “[Yesterday] was a rough day for me. Today I felt clear and like a functioning human being. . . . I think I was feeling even more intense than my usual down cycling. I am interested in this process and curious about starting a regular practice.”

Cognitive Flexibility A physically rigid client manifesting emotionally restricted attitudes said they had no knowledge of neurofeedback. This person worked as an accountant, making use of their inclination for organization, but they had difficulty relaxing and sitting still and could not meditate. The client fidgeted throughout most of the session and was only able to relax while I was reading the induction script. I showed them their unusually high beta levels and explained that such a pattern typically correlates with anxiety and cogitation (Hammond, 2003, p. 28). I added a high beta inhibit to the protocol that may have had some effect though the EEG continued to cycle into beta. A few days later this client reported: That was great! I was so anxious to find out what neurofeedback is about. I think it’s really cool and I appreciate the way that you explain everything in detail. I felt wonderful, calm, and relaxed. I also found this to be very helpful in understanding myself better. Everyone should experience this, I think. It can only be positive.

Outcome Neurofeedback demonstration has goals and risks that are different from therapy. Education is a prime objective rather than achieving a change in symptoms or a change in the EEG. Engaging the client’s interest and offering them a useful understanding can provide options for growth and healing outside of traditional medicine and the limited options they may have come to assume. Without much of the client’s history, a normative QEEG or an assessment of their responsiveness, we can only establish a rough correspondence between their personal inventory and EEG. The benign action of the modified alpha-theta described here makes it suitable for a situation where avoiding harm is a prime concern. A basic knowledge of Brodmann areas and behavioral correlations with frequency profiles allows us to create a plausible hypothesis for the neuronal basis of our clients’ issues. This hypothesis may lack precision, it may be wrong, but it makes it possible to argue for what is most likely a completely new approach. In addition to introducing people to neurofeedback, the walk-in clinic introduces neurofeedback to practitioners of other modalities. There is a certain amount of discussion of experiences between practitioners in the kitchen, which is volunteer staffed and stocked with food donations. Also, if client load leaves open slots later in the day, clinicians can sign up to experience each other’s modality. In this manner neurofeedback is integrated into the community of alternative healthcare providers.

472

Alpha-Theta-Based Clinical Outreach

Finally, this sort of personal demonstration of how people can train their own health encourages greater self-control, and presents new options for growth and health. Changes of this sort must occur for neurofeedback’s participatory model of healthcare to find mainstream acceptance.

Note 1. A comparison of the correlation between an ear-to-ear circuit (A2-A1) and either of two ear-to-temporal circuits (A1-T4 or A2-T3) in three subjects yielded correlations between these two signals to range from 25% to 75%, with an average of 54% and standard deviation of 6%.

References Aldana, J. (2003). The 15 minute miracle revealed. Los Gatos, CA: Inner Wisdom Publications. Berlucchi, G., & Aglioti, S. (1997). The body in the brain: Neural bases of corporeal awareness. Trends in Neurosciences, 20, 560–564. Boynton, T. (2001). Applied research using alpha/theta for enhancing creativity and well-being. Journal of Neurotherapy, 5(1–2), 5–18. Green, E. (1999a). Alpha-theta brainwave training: Instrumental vipassana? Subtle Energies and Energy Medicine, 10(1), 221–230. Retrieved from http://journals.sfu.ca/seemj/index.php/seemj/article/view/279 Green, E. (1999b). The work of Eugene Peniston. Subtle Energies and Energy Medicine, 10(1), 231–233. Retrieved from http://journals.sfu.ca/seemj/index.php/seemj/article/view/280/243 Green, E., & Green, A. (1977). Theta training: Imagery and creativity. In E. Green & A. Green (Eds.), Beyond biofeedback (pp. 118–152). New York: Delacorte Press. Gruzelier, J. (2009). A theory of alpha/theta neurofeedback, creative performance enhancement, long distance functional connectivity and psychological integration. Cognitive Processing, 10(1), 101–109. doi:10.1007/ s10339–008–0248–5. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/19082646 Gruzelier, J., van Boxtel, G., Gruzelier, J. H., Holmes, P., Hirst, L., Bulpin, K., . . . Leach, J. (2014). Replication of elite music performance enhancement following alpha/theta neurofeedback and application to novice performance and improvisation with SMR benefits. Biological Psychology, 95, 96–107. Hammond, D. C. (2003). QEEG-guided neurofeedback in the treatment of obsessive compulsive disorder. Journal of Neurotherapy, 7(2), 25–52. Hammond, D. C., & Baehr, E. (2009). Neurofeedback for the treatment of depression, current status of theoretical issues and clinical research. In T. H. Budzynski, H. K. Budsynski, J. R. Evans, & A. Abarbanel (Eds.), Introduction to quantitative EEG and neurofeedback, advanced theory and applications (2nd ed., pp. 295–314). Burlington, MA: Elsevier. Hay, L. L. (1984). You can heal your life. Carlsbad, CA: Hay House. Johnson, M. L. (2011). Relationship of alpha-theta amplitude crossover during neurofeedback to emergence of spontaneous imagery and biographical memory. Dissertation at University of North Texas. Retrieved from http://digital.library.unt.edu/ark:/67531/metadc84227/m2/1/high_res_d/dissertation.pdf Myss, C., & Shealy, C. N. (1998). The creation of health: The emotional, psychological, and spiritual responses that promote health and healing. New York: Three Rivers Press. Norretrander, T. (1999). The user illusion: Cutting consciousness down to size. New York: Penguin Group. Peniston, E. G., & Kulkosky, P. J. (1989). Alpha-theta brainwave training and beta endorphin levels in alcoholics. Alcoholism: Clinical and Experimental Results, 13(2), 271–279. Retrieved from http://www.ncbi.nlm.nih.gov/ pubmed/2524976 Peniston, E. O. (1998). The Peniston-Kulkosky brainwave neurofeedback therapeutic protocol: The future psychotherapy for alcoholism/PTSD/behavioral medicine. The American Academy of Experts in Traumatic Stress. Retrieved from http://www.aaets.org/article47.htm Raymond, J., Varney, C., Parkinson, L. A., & Gruzelier, J. H. (2005). The effects of alpha/theta neurofeedback on personality and mood. Cognitive Brain Research, 23(2–3), 287–292. Retrieved from http://www.ncbi.nlm. nih.gov/pubmed/15820636 Ros, T., Moseley, M. J., Bloom, P. A., Benjamin, L., Parkinson, L. A., & Gruzelier, J. H. (2009). Optimizing microsurgical skills with EEG neurofeedback. BMC Neuroscience, 10(1), 87. Scott, W. C., Kaiser, D., Othmer, S., & Sideroff, S. I. (2005). Effects of an EEG biofeedback protocol on a mixed substance abusing population. The American Journal of Drug and Alcohol Abuse, 31, 455–469. Stoller, L. (2014). ADHD as emergent institutional exploitation. The Journal of Mind and Behavior, 35(1–2), 21–50.

473

Lincoln Stoller Urgesi, C., Aglioti, A. M., Skrap, M., & Fabbro, F. (2010). The spiritual brain: Selective cortical lesions modulate human self-transcendence. Neuron, 65, 309–319. von Stein, A., & Sarnthein, J. (2000). Different frequencies for different scales of cortical integration: From local gamma to long range alpha/theta synchronization. International Journal of Psychophysiology, 38(3), 301–313. White, N. (1999). Theories of the effectiveness of alpha–theta training for multiple disorders. In A. Abarbarnel & J. R. Evans (Eds.), Introduction to quantitative EEG and neurofeedback (pp. 341–370). London: Academic Press.

474

PART VIII

Emerging Paradigms

25 EEG STATE DISCRIMINATION AND THE PHENOMENAL CORRELATES OF BRAINWAVE STATES Jon A. Frederick

Abstract While standard biofeedback training rewards the production or inhibition (or “control”) of certain physiological states, state discrimination training rewards the observation and reporting (or “awareness”) of these states. It is commonly argued that increasing awareness of subtle phenomenological correlates of physiological states is central to the mechanism of action of biofeedback, but the interaction between awareness and control of physiological function is vastly unexplored. Although an EEG alpha state discrimination experiment (Kamiya, 1962) was the first empirical demonstration of operant conditioning of the EEG, relatively few studies have employed this paradigm in the past 50 years. Unlike most discriminative stimulus paradigms, there is a qualitative difference between the physical dimensions of brain activity and our phenomenal experience of them. EEG state discrimination training, by repeatedly training observation of the difference between high and low magnitudes of a given brain state, may provide a novel empirical window for the systematic study of the phenomenal correlates of brainwave states and our efforts to control them. This chapter discusses the similarities and differences between operant discrimination and standard operant conditioning; the importance of observation and awareness in physiological self-regulation; the psychophysics of EEG state discrimination; EEG state discrimination as a sensorimotor process; potential clinical applications; and the possibilities of future research in this paradigm. In 1958 at the University of Chicago, Joe Kamiya began the first experimental study to suggest the possibility of operant conditioning of the EEG (Kamiya, 1962, 1968, 1969, 2011). Kamiya had conducted experiments on the differences in the EEG between sleeping and waking states, and became fascinated with how the alpha rhythm came and went in the waking EEG. Was there a subjective correlate of this variation that people could report? In his experiment, subjects sat quietly with eyes closed in a darkened room, while Kamiya monitored the EEG. Whenever a 2–6 second interval was seen where alpha (8–12 Hz) was clearly present or absent, based on a random order of trials, Kamiya would ring a bell. Subjects responded “A” or “B” and received immediate feedback whether they were right or wrong. Subjects were not told that “A” meant the presence and “B” meant the absence of alpha, and had to figure this out by attending to their subjective state before the prompt and whether their response was correct. Kamiya found that nine out of 12 subjects were able to discriminate their alpha states within seven sessions.

477

Jon A. Frederick

Interestingly, Kamiya also found that those subjects who could learn to discriminate their alpha state were then able to enter and sustain either the alpha or non-alpha state on command. Kamiya wondered if discrimination training was necessary to develop this ability, or if it could be trained in naive subjects by providing feedback whenever alpha waves were present. Eight of 10 subjects were able to learn to control their alpha state by this procedure, and the field of neurofeedback was born. It is remarkable that the entire field of clinical neurofeedback has followed and emulated Kamiya’s second experiment, while his first experiment, although often cited, has developed the status of a historical footnote. It is also of interest that Kamiya’s pursuit of the subjective phenomenology associated with EEG alpha was partly a reaction to the oppressive orthodoxy of the behaviorism of the 1950s, and yet his approach resulted in a powerful and rigorous application of behaviorist methods for studying first person phenomenology.

Operant Conditioning and Operant Discrimination Both standard neurofeedback and EEG discrimination training are forms of operant conditioning, although they differ in the behavior that is reinforced. Discrimination training (or operant discrimination) involves a “three-term contingency” between a discriminative stimulus, a behavior, and a reinforcer. A three-term contingency means that a behavior is rewarded only when a certain discriminative stimulus is present. For instance, answering a phone when it rings is often rewarded— except when it is someone else’s phone. Unlike standard biofeedback where the operant response is the production of some particular physiological state, in discrimination training there are actually two operant behaviors that are rewarded. One operant behavior is to press a key if the discriminative stimulus is present and not press it if absent. The other, and more interesting behavior, is called the observing response. Wyckoff (1952) defined the observing response (or responses; Ro) as any response which results in exposure to the discriminative stimulus (or stimuli; SD) involved. Ro includes behaviors such as attending, orienting, or “sensory organizational activity.” For instance, if stimuli are displayed on a monitor, Ro would be turning the eyes and head toward the monitor. In EEG state discrimination training, Ro is any behavior that increases awareness of one’s EEG state, such as employing or inhibiting attention, self-talk, or imagery. Observing responses are distinguished from “effective responses” (lever presses, choice selection, etc.) upon which reinforcement is based. Most complex behaviors consist of a series of discriminative stimuli and responses linked together in a behavior chain (Cooper, Heron, & Heward, 2007). In a behavior chain, each response produces a new stimulus situation that serves both as a conditioned reinforcer for the response that produced it as well as SD for the next response in the chain. The final response in the chain produces reinforcement that maintains the effectiveness of each conditioned reinforcer in the chain. At each step, the SD (for the next response) acquires secondary reinforcing properties for Ro (to the previous stimulus) upon which it is contingent, based on repeated pairing with reinforcement for the final effective response. Operating a vending machine is an common example (Shapiro & Browder, 1990). Seeing a vending machine is the SD for the Ro of reaching into your pocket to search for coins. Feeling the coins reinforces this Ro, but it also serves as the SD for putting the coins in the machine. Hearing the coins clink in the machine reinforces the coin insertion, but it also serves as SD for the Ro of scanning the array of buttons to find the desired drink. Finding the correct button serves as both a reinforcer and a discriminative stimulus for the next step—leading to the primary reinforcer. In EEG state discrimination training, having sensors attached and being told the session has started is the SD for the Ro of attending to your internal subjective states. This Ro is reinforced by experiencing a subjective state or states associated with previous correct responses. These subjective states (plus the prompt stimulus) serve as SD for the final effective response, the button press. 478

EEG State Discrimination

Wyckoff described how on each discrimination trial there is some probability po of making Ro. If po is small, subjects will rarely be exposed to the discriminative stimuli. In most experiments, such as the famous experiment where pigeons appeared to obey the words “TURN” and “PECK” (Reese, 1966), po is high and the lack of Ro may only be a temporary inconvenience in the early trials. In EEG state discrimination, an effective observing response is never made in some subjects (22%; Frederick, 2012). With external stimuli controlled by the experimenter, there is little or no discussion of the relationship between the stimuli and the animal’s internal subjective phenomenal representation of them. However, in physiological state discrimination training, this relationship is the major phenomenon of interest. The phenomenal representations of, for example, high and low alpha amplitude are very subtle. Indeed, at the onset of training they are generally below the threshold of perception. For these near- or subthreshold stimuli, it seems likely that reinforcement is acting primarily on the quality or structure of the observing response. The consequence is, at least theoretically, that the state discrimination paradigm directly trains and measures observation and awareness of the phenomenal correlates of physiological states.

The Importance of Observation and Awareness in Physiological Self-Regulation Physiological self-regulation skills are largely a form of procedural memory, like sensorimotor skills. Unlike autobiographical or semantic memories that can be reported explicitly, they are carried out largely without attention or awareness. However, awareness does play a role in the learning of procedural memories. It is often argued that increasing awareness of one’s physiological state is important to the mechanism of action of standard biofeedback methods (Brener, 1974; Congedo & Joffe, 2007; Olson, 1987; Plotkin, 1981). However, research on the relationship between awareness and control of physiological responses appears to have reached a peak in the mid-1970s and declined after the mid-1980s. In addition to Kamiya’s (1969) report that subjects trained in alpha discrimination showed evidence of control, facilitation of voluntary control training by prior discrimination training was also seen for heart rate (Brener, 1974, 1977; Brener, Ross, Baker, & Clemens, 1979), and for a cephalic vasomotor response (Fudge & Adams, 1985). The reverse relationship, facilitation of discrimination by voluntary control training, has been reported for the galvanic skin response (Baron, 1966; Lacroix, 1977; Stern, 1972), heart rate (Brener, 1977; Marshall & Epstein, 1978), the sensorimotor rhythm (Cinciripini, 1984), and slow cortical potentials (Kotchoubey, Kubler, Strehl, Flor, & Birbaumer, 2002). My laboratory recently reported a positive correlation between performance in EEG alpha amplitude control and discrimination tasks across seven sessions in four subjects (Frederick, Dunn, & Collura, 2015). My current research focuses on whether these two skills generalize or transfer to each other when the type of training is switched. Black, Cott, and Pavloski (1977) and Lacroix (1981) argued that awareness is not necessary or sufficient for operant conditioning of a physiological response. Self-reported autonomic awareness appeared to correlate inversely (Blanchard,Young, & McLeod, 1972) or not at all (Young & Blanchard, 1984) with self-control of heart rate. There are also abundant examples of learning without awareness (Saltz, 1971). However, Black, Cott, and Pavloski (1977) acknowledged that awareness may facilitate learning even while it may not be necessary or sufficient. For instance, while subliminal stimuli can produce mild emotional and priming effects, conscious awareness is necessary for strong and enduring effects on behavior (Pratkanis & Greenwald, 1988). Facilitation of motor learning by conscious awareness was observed by Boutin, Blandin, Massen, Heuer, and Badets (2014). In this study, participants more effectively learned a sequential finger movement when asked to make a judgment about their performance after each trial. Fitts and Posner (1967) argued that awareness is particularly important for the early stages of learning a skill. During the initial learning of a musical or athletic skill, for instance, careful attention is paid while trial and error is used to identify the correct behavior. Through extensive practice, incorrect behaviors are inhibited and ultimately the skill becomes automated. After extensive practice, awareness can actually impair highly skilled performance (Beilock & Carr, 2001; Heuer & Sülzenbrück, 2012). 479

Jon A. Frederick

Clinical Applications of EEG State Discrimination At this stage, any clinical applications of the state discrimination paradigm are speculative and should only be interpreted as suggestions for research. However, the relationship between awareness and control in biofeedback and other forms of learning points to a prediction that supplementing standard biofeedback methods with state discrimination training, especially in the early stages of learning, could produce more effective clinical results. Taking EEG state discrimination measurements could also be a useful method of assessing client progress in standard clinical neurofeedback. A direct measurement of success is intrinsic to every trial in discrimination learning, and for that reason, discrimination studies could be a more precise and sensitive experimental model of standard neurofeedback training. The study of how individual differences in personality, intelligence, or executive function relate to performance on EEG state discrimination tasks could provide a basis for personalized medicine, suggesting what type of client is better for which type of neurotherapy. Finally, training discrimination could also have therapeutic value in its own right, just as insight-oriented psychotherapy can have value above and beyond behavior-modification psychotherapy.

A related theory of the role of awareness in learning is called chunking (Miller, 1956). Chunking is the association of individual pieces of information or motor actions into meaningful units so they occupy less working memory. Working memory famously has a finite capacity of seven plus or minus two independent items or “chunks.” Chunking, the creation of a meaningful structure that integrates separate chunks, is an effortful process requiring attention and awareness, where the outcome is a structure whose connections are processed implicitly. Subjective awareness manages the relationships and transitions between chunks but not within them (Rushworth, Walton, Kennerley, & Bannerman, 2004). A possible exception may be when it is necessary to unlearn maladaptive associations. Conscious awareness of a stimulus facilitates learning by activating more widely distributed representations in the brain (DeHaene & Changeux, 2011), and mobilizing and integrating brain processes that are otherwise independent (Baars, 2002). These processes include problem solving, decision making, and action planning (Boutin et al., 2014), which allow the solution of conflicts between competing motor plans (Morsella, 2005). In standard biofeedback, a client has the option of not paying attention to the subjective correlates of the physiological signals displayed. Furthermore, the rewarded state differs from the unrewarded state only by an infinitesimal difference at the reward threshold. By contrast, every trial in discrimination training directly measures and trains observation and awareness of differences between extremes of the physiological state. Given the diverse evidence on the importance of awareness for learning, it is remarkable how few physiological state discrimination studies exist in the literature, especially in the past 25 years. EEG state discrimination has been reported for stage 1 and stage 2 sleep (Antrobus & Antrobus, 1967), visual evoked potentials (Rosenfeld & Hetzler, 1973), the sensorimotor rhythm (Cinciripini, 1984), P300 amplitude (Sommer & Matt, 1990), and slow cortical potentials (Kotchoubey et al., 2002). Physiological state discrimination has also been demonstrated outside the central nervous system, for finger temperature (Lombardo & Violani, 1994), galvanic skin response (Dickoff, 1976; Stern, 1972), blood glucose levels (Cox, Carter, Gonder-Frederick, Clarke, & Pohl, 1988), heart rate (Brener & Jones, 1974; Grigg & Ashton, 1986), blood pressure (Greenstadt, Shapiro, & Whitehead, 1986), cardiac R-waves (Violani, Lombardo, de Gennaro, & Devoto, 1996), pulse transit time (Martin, Epstein, & Cinciripini, 1980), and cephalic vasomotor activity (Fudge & Adams, 1985). However, there were at least three studies in the 1970s that failed to replicate alpha state discrimination (Cott, Pavloski, & Black, 1981; Legewie, 1975, 1977; Orne, Evans, Wilson, & Paskewitz, 1975, 480

EEG State Discrimination

cited in Orne & Wilson, 1978). Although most were published with very limited methodological detail, Cott et al. (1981) defined an alpha state as lasting only half a second, where Kamiya had used a much longer interval. In my alpha state discrimination study (Frederick, 2006, 2007, 2012; Viebrock and Frederick, 2006), I compared performance on 1-, 2-, and 4-second intervals.

Psychophysics of EEG Alpha State Discrimination One hundred and six subjects completed 583 sessions consisting of three sets of about 36 trials. A 150-second eyes-closed baseline EEG was recorded at F3 or Pz. Each epoch was ranked among a percentile distribution of alpha amplitudes of the most recent 150 seconds initially derived from the baseline recording. A tone sounded whenever the alpha band power exceeded a critical difference from the median of the baseline. Alpha amplitudes in the first to 30th percentile were defined as “low,” and the 70–99th percentile were defined as “high.” Participants responded “high” or “low,” and received feedback about whether the response was correct or incorrect after each trial. Each session, trials were presented with the following in random order: high vs. low amplitude; 1-, 2-, or 4-second discriminative stimulus durations; and absolute vs. relative amplitude. A successful criterion performance was defined as binomial p < 0.01 for percentage correct. I found that 40/106 participants reached criterion within a median of five sessions. Seventy-six percent of those who completed nine or more sessions achieved criterion, which is very similar to Kamiya’s results. On average, those who achieved criterion did so in 4.8 sessions. Subjects who achieved criterion tended to do significantly better in later sessions than earlier sessions (Figure 25.1). Thus, learning appeared to be cumulative. I observed that very high alpha amplitudes (above the 90th percentile) were discriminated better than moderately high (between the 70th and 80th percentile), and very low (below the 10th percentile) were discriminated better than moderately low (between the 20th and 30th percentile; Figure 25.2). I found that longer (2- and 4-sec) stimulus durations were discriminated better than shorter (1-sec) ones (Figure 25.3). I also found that participants scored significantly higher on absolute rather than relative amplitude trials (Figure 25.4).

Session Percent Correct

0.59 0.57 0.55 0.53

Criterion Ss (N = 40)

0.51

Non-Criterion Ss (N = 66)

0.49 0.47

1

2

3

4

5

6

7

8

9

10

Session Number

Figure 25.1 Learning effect (effect of session number) on session averages in subjects who achieved criterion on the task. Bars indicate standard error. Reprinted from “Psychophysics of EEG State Discrimination” by Jon Frederick, 2012, Consciousness and Cognition, 21, p. 1349. Copyright 2012 by Elsevier, Inc. Reprinted with permission.

481

Percent Correct

0.85 0.8 0.75 0.7 0.65 0.6 0.55 0.5

pct10

pct20 pct30 pct80 pct90 Amplitude Percentile Bin

pct100

Figure 25.2 Effect of percentile amplitude on discrimination task performance. Bars indicate standard error. Reprinted from “Psychophysics of EEG State Discrimination” by Jon Frederick, 2012, Consciousness and Cognition, 21, p. 1349. Copyright 2012 by Elsevier, Inc. Reprinted with permission.

Percent Correct

0.7 0.65 0.6 0.55 0.5

1sec 2sec 4sec Stimulus Duration or EEG Smoothing Average

Figure 25.3 Effect of stimulus duration or EEG smoothing average on discrimination task performance. Bars indicate standard error. Reprinted from “Psychophysics of EEG State Discrimination” by Jon Frederick, 2012, Consciousness and Cognition, 21, p. 1350. Copyright 2012 by Elsevier, Inc. Reprinted with permission.

Percent Correct

0.74 0.72 0.7 0.68 0.66 0.64

rel abs Relative vs. Absolute Amplitude

Figure 25.4 Effect of relative versus absolute amplitude on discrimination task performance. Bars indicate standard error. Reprinted from “Psychophysics of EEG State Discrimination” by Jon Frederick, 2012, Consciousness and Cognition, 21, p. 1351. Copyright 2012 by Elsevier, Inc. Reprinted with permission.

EEG State Discrimination

EEG State Discrimination as a Sensorimotor Process The greater performance seen at extremes of signal intensity, and with longer stimulus durations, is consistent with a signal detection interpretation, or with the conceptualization of EEG state discrimination as a sensory modality. This raises two important questions: what are they sensing, and how are they sensing it? In fact, the alpha state has multiple correlates. We know that it correlates with a relatively quiet and empty state of mind and, in subjects who are good at it, it probably also correlates with the efforts to achieve this state. Kamiya (1968) said subjects in the non-alpha state reported “seeing with the mind’s eye,” where the alpha state was commonly reported as “not thinking,” “letting the mind wander,” or “feeling the heart beat.” My participants were informed prior to the task that alpha involved being alert and relaxed but not drowsy; mentally disconnecting from sensation or imagination; and that it is reduced by mental activity—thinking, problem solving, intending (i.e. thinking about movement), by visual imagination. They were encouraged to guess what state they were in and if they didn’t receive a prompt, see if changing to a different state evoked the prompt. Previous studies raised concerns that subjects may merely be controlling alpha and may not be truly aware of any subjective correlate of spontaneous changes in alpha (Cott et al., 1981; Orne & Wilson, 1978). However, I argue that both perceptual and volitional processes can serve as discriminative stimuli, and that at the level of perceiving cortical activity, it is not entirely clear that there is a difference between the two. This may be easier to understand if we consider the example of EMG biofeedback to train relaxation first. All voluntary muscle activity in the body is accompanied by what is called sensory reafference: kinesthetic and proprioceptive receptors in our muscles and joints give us feedback about their position, tension, and movement. Are people who have chronic muscle tension deficient in their sensory awareness of that muscle tension? Probably. But there is another possibility. The other possibility is that they lack a fine-grained awareness of their own volitional states. So, as a client receives feedback about their muscle tension, they might think, “Am I doing that? Let me try something else. Hey, that worked!” Then, if they go home and they successfully practice their new physiological self-regulation skill without the equipment—are they practicing new sensory skills, or new motor skills? My best guess is that they are doing both. The same question applies to EEG biofeedback, except in the CNS the reafferent loop is much shorter. And if the sensor is right over the premotor cortex, the distinction between sensory and motor, between awareness and volition, may start to lose meaning. Benjamin Libet’s (1985) experiment on the timing of conscious volition illustrates this point. Libet recorded the EMG from a voluntary movement, the premotor potential, and asked subjects to report when they willed the voluntary movement. He reported the surprising finding that the premotor potential occurred about 350 milliseconds before the will to act. This had all sorts of controversial implications for the concept of free will but, for our purposes, it had another important implication. That is, experience of volition in Libet’s experiment is undeniably the subjective correlate of which the premotor potential is the discriminative stimulus. Could alpha be the discriminative stimulus of which the will to produce alpha is the subjective correlate? Possibly. Libet’s finding is one of many in the area of perceptual motor control that suggest the experience of volition is not related in a straightforward way to the voluntary actions themselves (e.g. Castiello, Paulignan, & Jeannerod, 1991; Milner & Goodale, 1995). If a subject becomes aware that he might be in an alpha or non-alpha state, both changing or not changing the state are voluntary choices. It is, thus, not entirely clear whether the maintenance of, or transitions between, these states are voluntary or spontaneous.

483

Jon A. Frederick

It is likely that the will to produce alpha and whatever neurologically precedes it is part of a chain of conditioned responses that includes both the effort and the “reafferent” consequences of that effort. Each effort may be different both in terms of its specific intent (“these thoughts are interesting, but I am going to try to quiet them by focusing on my breathing”) and their relative success. In addition to subtle and variable qualities of volition being a phenomenal correlate, it is also likely that an effort to produce or suppress alpha serves as an observing response that makes the perception the phenomenal correlate(s) possible. For instance, if I attempt to create a state of mental quiet, I will not necessarily succeed, but this effort results in attention to how I may have succeeded or failed.

The Human Phenome Project Kamiya proposed that discrimination training could provide the basis of a first person science, for bridging the gap between physiological measurements and first person phenomenological reports (Kamiya, 2011; Kamiya & Collura, 2011). Even as a graduate student in the 1950s, Kamiya believed that a science of psychology could not be complete without including these essential features of human life: our private thoughts, feelings, images, and dreams (Kamiya, 2011). While the prevailing viewpoint was that introspective reports were an unreliable dead-end for research (Boring, 1953), Stoyva and Kamiya (1968) made a compelling argument that physiological measures provided a renewed validity for hypothesized mental states. For instance, the duration of rapid eye movements (REMs) was observed to correlate with the verbally reported duration of dreams upon awakening (Dement & Kleitman, 1957), and the density of REMs preceding an awakening was related to the amount of physical activity reported in the dream (Berger & Oswald, 1962). In this view, both verbal reports and physiological measures are imperfect operational definitions of subjective experience, which then has the same status as other hypothetical constructs in science like extraversion, genes, and electrons. Kamiya’s initial interest in the operant discrimination of EEG alpha was to determine if transient fluctuations in alpha were associated with changes in subjective experience. I have always argued (1992, 2007, 2012) that neurofeedback is not just a clinical therapy, but a novel empirical method for studying mind–brain interactions. Several recent European studies have agreed with this premise (Bagdasaryan & Le Van Quyen, 2013; Micoulaud-Franchi, Quiles, Fond, Cermolacce, & Vion-Dury, 2014; Petitmengin & LaChaux, 2013). Micoulaud-Franchi et al. contrasted neurofeedback with neuropsychological approaches— which manipulate brain function as an independent variable (IV) and measure cognitive processes as a dependent variable (DV)—and with psychophysiological approaches—which manipulate cognitive processes (IV) and measure brain function (DV). They noted how in neurofeedback, the cognitive and brain processes interact recursively, repeatedly trading places as IV and DV. As a result, the hypothesized relationship between these two variables—rather than being externally specified by the experimenter and tested once per experiment—is internally specified by the subject and can be varied and tested dozens or perhaps hundreds of times per session, until the subject arrives at an optimal solution. State discrimination approaches may leverage this property of neurofeedback with the additional advantage of repeatedly focusing attention on the difference between high and low states rather than always training up or down. Meanwhile, advances in phenomenological interviewing methods (Petitmengin, Remillieux, Cahour, & Carter-Thomas, 2013) have shown that it is possible to elicit more valid, reliable, and precise reports of our cognitive processes than previously thought possible (Nisbett & Wilson, 1977). Kamiya proposed a study (Kamiya & Collura, 2011) where subjects would be extensively trained in the discrimination and control of a large variety of physiological measures, then asked to provide paired comparison ratings on the degree of subjective similarity between physiological states (1 = not

484

EEG State Discrimination

similar; 5 = very similar). A principal components analysis could then identify the major dimensions of the subjective space associated with all of the measures. I proposed doing stimulus generalization studies of how much learning of one psychophysiological modality transfers to another (Frederick, 2007). It would be very interesting to see how the clusters of psychophysiological variables from Kamiya’s proposed study would overlap with the clusters found from mine. If we take the 52 Brodmann areas as the estimate of the number of distinct functional regions of the cortex, across all frequency bands and including coherence measures, not to mention fMRI measures, finding the discriminable phenomenological correlates of all of these variables is enough to keep hundreds of scientists busy for dozens of years. My best guess is that the scientific revolution needed to make this project happen is unlikely to happen in my lifetime. So my second best effort will be for those few of us doing the studies to aim carefully and hope that we get lucky.

Acknowledgements I am grateful to Cynthia Powers, MA, BCBA, for clarifying my thinking about discriminative stimuli and behavioral chains, and to Lincoln Stoller, Ph.D., for his helpful comments on an earlier draft.

References Antrobus, J. S., & Antrobus, S. J. (1967). Discrimination of two sleep stages by human subjects. Psychophysiology, 4, 48–55. Baars, B. J. (2002). The conscious access hypothesis: Origins and recent evidence. Trends in Cognitive Sciences, 6, 47–52. Bagdasaryan, J., & Le Van Quyen, M. (2013). Experiencing your brain: Neurofeedback as a new bridge between neuroscience and phenomenology. Frontiers in Human Neuroscience, 7, 680. Baron, J. (1966). An EEG correlate of autonomic discrimination. Psychonomic Science, 4, 255–256. Beilock, S. L., & Carr, T. H. (2001). On the fragility of skilled performance: What governs choking under pressure? Journal of Experimental Psychology: General, 130(4), 701–725. Berger, R. J., & Oswald, I. (1962). Eye movements during active and passive dreams. Science, 137, 601. Black, A. H., Cott, A., & Pavloski, R. (1977). The operant learning theory approach to biofeedback training. In G. E. Schwartz & J. Beatty (Eds.), Biofeedback: Theory and research (pp. 89–127). New York: Academic Press. Blanchard, E. B., Young, L. D., & McLeod, P. (1972). Awareness of heart activity and self control of heart rate. Psychophysiology, 9(1), 63–68. Boring, E. G. (1953). A history of introspection. Psychological Bulletin, 50, 169–189. Boutin, A., Blandin, Y., Massen, C., Heuer, H., & Badets, A. (2014). Conscious awareness of action potentiates sensorimotor learning. Cognition, 133(1), 1–9. Brener, J. (1974). A general model of voluntary control applied to the phenomena of learned cardiovascular change. In P. Obrist, A. H. Black, J. Brener, & L. V. DiCara (Eds.), Cardiovascular psychophysiology (pp. 365–391). Chicago: Aldine. Brener, J. (1977). Visceral perception. In J. Beatty & H. Legewie (Eds.), Biofeedback and behavior (pp. 235–259). New York: Plenum. Brener, J., & Jones, J. M. (1974). Interoceptive discrimination in intact humans: Detection of cardiac activity. Physiology & Behavior, 13(6), 763–767. Brener, J., Ross, A., Baker, J., & Clemens, W. J. (1979). On the relationship between cardiac discrimination and control. In N. Birbaumer & H. D. Kimmel (Eds.), Biofeedback and self-regulation (pp. 51–70). Hillsdale, NJ: Lawrence Erlbaum. Castiello, U., Paulignan, Y., & Jeannerod, M. (1991). Temporal dissociation of motor responses and subjective awareness: A study in normal subjects. Brain, 114(6), 2639–2655. Cinciripini, P. M. (1984). Discrimination of sensorimotor EEG (12–25 Hz) activity: A comparison of response, production, and no-feedback training conditions. Psychophysiology, 21(1), 54–62. Congedo, M., & Joffe, D. (2007). Multichannel tomographic neurofeedback: Wave of the future? In J. R. Evans (Ed.), Handbook of neurofeedback (pp. 85–108). New York: The Hayworth Medical Press.

485

Jon A. Frederick Cooper, J. O., Heron, T. E., & Heward, W. L. (2007). Applied behavior analysis (2nd ed., p. 436). Upper Saddle River, NJ: Pearson Education, Inc. Cott, A., Pavloski, R. P., & Black, A. H. (1981). Operant conditioning and discrimination of alpha: Some methodological limitations inherent in response-discrimination experiments. Journal of Experimental Psychology: General, 110(3), 398–414. Cox, D. J., Carter, W. R., Gonder-Frederick, L. A., Clarke, W. L., & Pohl, S. L. (1988). Blood glucose discrimination training in insulin-dependent diabetes mellitus (IDDM) patients. Biofeedback and Self-Regulation, 13(3), 201–217. Dehaene, S., & Changeux, J. P. (2011). Experimental and theoretical approaches to conscious processing. Neuron, 70(2), 200–227. Dement, W., & Kleitman, N. (1957). The relation of eye movements during sleep to dream activity: An objective method for the study of dreaming. Journal of Experimental Psychology, 53(5), 339–346. Dickoff, G. M. (1976). Effects of feedback in a forced-choice GSR detection task. Psychophysiology, 13, 22–26. Fitts, P. M., & Posner, M. I. (1967). Human performance. Oxford, England: Brooks and Cole. Frederick, J. A. (1992). A preface to applied psychophysiology. Unpublished senior thesis in philosophy, University of Utah. Frederick, J. A. (2006). Psychophysics of EEG state discrimination. Journal of Neurotherapy, 9(4), 107–108. Frederick, J. A. (2007). The role of mind-body medicine in the mind-body problem. Neuroconnections, Oct 2007, 15, 32–33. Frederick, J. A. (2012). Psychophysics of EEG alpha state discrimination. Consciousness and Cognition, 21(3), 1345–1354. Frederick, J. A., Dunn, K. N., & Collura, T. F. (2015). Interactions between discrimination and control of EEG alpha. NeuroRegulation, 2(3), 126. Fudge, R., & Adams, H. D. (1985). The effects of discrimination training on voluntary control of cephalic vasomotor activity. Psychophysiology, 22(3), 300–306. Greenstadt, L., Shapiro, D. A., & Whitehead, R. (1986). Blood pressure discrimination. Psychophysiology, 23(5), 500–509. Grigg, L., & Ashton, R. (1986). Is an accurate verbal language necessary for heart rate discrimination? Biological Psychology, 22(3), 269–277. Heuer, H., & Sülzenbrück, S. (2012). Mind and movement. Psychological Research, 76(2), 159–170. Kamiya, J. (1962). Conditioned discrimination of the EEG alpha rhythm in humans. Proceedings of the Western Psychological Association, San Francisco, CA. Kamiya, J. (1968). Conscious control of brain waves. Psychology Today, 1, 57–60. Kamiya, J. (1969). Operant control of the EEG alpha rhythm and some of its reported effects on consciousness. In C. Tart (Ed.), Altered states of consciousness (pp. 519–529). New York: Wiley. Kamiya, J. (2011). The first communications about operant conditioning of the EEG. Journal of Neurotherapy, 15(1), 65–73. Kamiya, J., & Collura, T. (2011). Panel: Dimensions of experience and first-person science. Journal of Neurotherapy, 15, 401–402. Kotchoubey, B., Kubler, A., Strehl, U., Flor, H., & Birbaumer, N. (2002). Can humans perceive their brain states? Consciousness and Cognition, 11, 98–113. Lacroix, J. M. (1977). Effects of biofeedback on the discrimination of electrodermal activity. Biofeedback & SelfRegulation, 2, 393–406. Lacroix, J. M. (1981). The acquisition of autonomic control through biofeedback: The case against an afferent process and a two process alternative. Psychophysiology, 18(5), 573–587. Legewie, H. (1975). Subjective correlates of EEG feedback: Discrimination learning or superstition? EEG alpha learning: State of the art. Symposium Presented at the Meeting of the Biofeedback Research Society, Monterey, California. Legewie, H. (1977). Subjective correlates of alpha activity in EEGs: Discrimination learning or superstition? Zeitschrift fur Experimentelle und Angewandte Psychologie, 24(3), 443–454. Libet, B. (1985). Unconscious cerebral initiative and the role of conscious will in voluntary action. The Behavioral and Brain Sciences, 8, 529–539. Lombardo, C., & Violani, C. (1994). Feedback and autonomic awareness: The ability to discriminate the direction of spontaneous changes in peripheral body temperature. International Journal of Psychophysiology, 17, 145–151. Marshall, W. R., & Epstein, L. H. (1978). Effect of heart-rate control training on heart-rate discrimination. Perceptual & Motor Skills, 47, 40–42. Martin, J. E., Epstein, L. H., & Cinciripini, P. M. (1980). Effects of feedback and stimulus control on pulse transit time discrimination. Psychophysiology, 17(5), 431–436.

486

EEG State Discrimination Micoulaud-Franchi, J. A., Quiles, C., Fond, G., Cermolacce, M., & Vion-Dury, J. (2014). The covariation of independent and dependent variables in neurofeedback: A proposal framework to identify cognitive processes and brain activity variables. Consciousness and Cognition, 26, 162–168. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81–97. Milner, A. D., & Goodale, M. A. (1995). The visual brain in action. Oxford: Oxford University Press. Morsella, E. (2005). The function of phenomenal states: Supramodular interaction theory. Psychological Review, 112, 1000–1021. Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we know: Verbal reports on mental processes. Psychological Review, 84, 231–259. doi:10.1037/0033–295X.84.3.231 Olson, P. (1987). Definitions of biofeedback. In M. S. Schwartz (Ed.), Biofeedback: A practitioner’s guide (pp. 33–38). New York: The Guilford Press. Orne, M., Evans, F., Wilson, S., & Paskewitz, D. (1975). The potential effectiveness of autoregulation as a technique to increase performance under stress. Final Summary Report under Contract, (00014), 70-C0350. Orne, M. T., & Wilson, S. K. (1978). On the nature of alpha feedback training. In G. E. Schwartz & D. Shapiro (Eds.), Consciousness and self-regulation: Advances in research and theory (Vol. 2, pp. 349–400). New York: Plenum Press. Petitmengin, C., & Lachaux, J. P. (2013). Microcognitive science: Bridging experiential and neuronal microdynamics. Frontiers in Human Neuroscience, 7, 617. Petitmengin, C., Remillieux, A., Cahour, C., & Carter-Thomas, S. (2013). A gap in Nisbett and Wilson’s findings? A first-person access to our cognitive processes. Consciousness and Cognition, 22, 654–669. doi:10.1016/j. concog.2013.02.004 Plotkin, W. B. (1981). A rapprochement of the operant-conditioning and awareness views of biofeedback training: The role of discrimination in voluntary control. Journal of Experimental Psychology: General, 110(3), 415–428. Pratkanis, A. R., & Greenwald, A. G. (1988). Recent perspectives on unconscious processing: Still no marketing applications. Psychology and Marketing, 5(4), 337–353. Reese, H. W. (1966). The perception of stimulus relations: Discrimination learning and transposition. New York: Academic Press. Rosenfeld, J. P., & Hetzler, B. E. (1973). Operant-conditioned evoked responses: Discrimination of conditioned and normally-occurring components. Science, 181, 767–769. Rushworth, M. F. S., Walton, M. E., Kennerley, S. W., & Bannerman, D. M. (2004). Action sets and decisions in the medial frontal cortex. Trends in Cognitive Sciences, 8, 410–417. Saltz, E. (1971). The cognitive bases of human learning. Homewood, IL: Dorsey Press. Shapiro, E. S., & Browder, D. M. (1990). Behavioral assessment. In J. L. Matson (Ed.), Handbook of behavior modification with the mentally retarded (pp. 93–122). New York: Plenum. Sommer, W., & Matt, J. (1990). Awareness of P300-related cognitive processes: A signal detection approach. Psychophysiology, 27(5), 575–585. Stern, R. M. (1972). Detection of one’s own spontaneous GSRs. Psychonomic Science, 29(6), 354–356. Stoyva, J., & Kamiya, J. (1968). Electrophysiological studies as the prototype of a new strategy in the study of consciousness. Psychological Review, 75, 192–205. Viebrock, S. O., & Frederick, J. A. (2006). Optimizing dimensions for EEG alpha state discrimination. International Society for Neurofeedback & Research Annual Meeting, Atlanta, GA. Violani, C., Lombardo, C., De Gennaro, L., & Devoto, A. (1996). Left movers’ advantage in heartbeat discrimination: A replication and extension. Psychophysiology, 33(3), 234–238. Wyckoff, L. B. (1952). The role of observing responses in discrimination learning: Part I. Psychological Review, 59(6), 431–442. Young, L. D., & Blanchard, E. B. (1984). Awareness of heart activity and self control of heart rate: A failure to replicate. Psychophysiology, 21(3), 361–362.

487

26 INFRA-SLOW FLUCTUATION (ISF) TRAINING FOR AUTISM SPECTRUM DISORDERS Mark Llewellyn Smith, Leonardo M. Leiderman, and Jackie de Vries

Abstract There is growing evidence that the symptoms of Autism Spectrum Disorder have been improved with Infra-slow Fluctuation neurofeedback training. This form of neurofeedback focuses on the slowest oscillations in the human cerebral cortex. These low frequencies have been associated in research with the coordination of neuronal network communication including networks that regulate social, emotional, and sensory processing. Autonomic response was one of the first behaviors linked with ultra-slow frequencies in animal research. The concomitant research and clinical outcomes provide a rationale for the use of ISF training for individuals with Autism Spectrum Disorder. Recent research suggests that the core symptoms of Autism Spectrum Disorder (ASD), which include restricted reciprocal social interaction, deficits in communication, repetitive behaviors, and sensory sensitivity, are associated with abnormalities in neural connectivity (Billeci et al., 2013; Cantor, Thatcher, Hrybyk, & Kaye, 1986; Coben, Clarke, Hudspeth, & Barry, 2008; Duffy & Als, 2012; Khan et al., 2013; Monk et al., 2009). Disordered connectivity interferes with the normal synchronization of neuronal networks and compromises communication within and between networks of function. This produces abnormal processing of sensory inputs necessary for normal behavior. Altered connectivity within the Default Mode Network has been linked to ASD behavioral deficits, with weaker connectivity between posterior and frontal regions correlated to poorer social functioning and stronger connectivity between posterior and right temporal regions associated with repetitive behaviors (Lynch et al., 2013; Monk et al., 2009). Moreover, the functional rigidity that characterizes hyperconnected brain networks may provide an explanation for the sensory entrapment of the sensitive autistic brain in a painfully intense world (K. Markram & Markram, 2010; H. Markram, Rinaldi, & Markram, 2007). Neurofeedback as a therapeutic intervention for autism was first reported in a single case study in 1995 that utilized a theta/beta ratio training (Sichel, Fehmi, & Goldstein, 1995). This was followed by several studies whose main protocol was similar (Jarusiewicz, 2002; Kouijzer, de Moor, Gerrits, Buitelaar, & van Schie, 2009; Kouijzer, van Schie, de Moor, Gerrits, & Buitelaar, 2010). The Mu Rhythm was the object of brain training in two recent studies (Pineda et al., 2008; Pineda, Juavinett, & Datko, 2012). Quantitative Electroencephalogram (QEEG) guided neurofeedback for autism began with a small group of children diagnosed with Asperger’s Syndrome (Scolnick, 2005). A quantitative analysis determined each child’s protocol, a variation of the initial theme of rewarding low beta and

488

Infra-Slow Fluctuation (ISF) Training

suppressing slower activity. QEEG guided training has been employed to address the remediation of abnormal coherence (Coben, 2007; Coben et al., 2008; Coben & Myers, 2008; Coben & Padolsky, 2007). Infra-slow Fluctuation (ISF) training is a form of neurofeedback that focuses on the slowest frequencies in cortex. Research proposes that the infra-slow activity is endogenously driven neuronal activity that is crucial in shaping brain network connectivity (Vanhatalo, Voipio & Kaila, 2005). This low-frequency regime has been postulated to coordinate neuronal activity between cortico-cortical networks (Buzsaki, 2006) and play a central role in the control of gross cortical excitability (Hiltunen et al., 2014). Recent research suggests that infra-slow oscillations (ISO) are embedded in and determinant of the excitability cycle of faster frequencies (Ko, Darvas, Poliakov, Ojemann, & Sorensen, 2011; Vanhatalo et al., 2004). In 1976, Pfurtscheller made the first observations of embedded infra-slow oscillations in the alpha frequency band in the human EEG (Pfurtscheller, 1976). Later studies expanded on this work, identifying fluctuations in the frequencies from 4–30 Hertz that were power-law autocorrelated in time scales from tens to hundreds of seconds. Higher frequencies (100–150 Hz) recorded directly from cortex revealed robust power-law scaling of ISOs in amplitude and coherence (Ko et al., 2011; Linkenkaer-Hansen, Nikouline, Palva, & Ilmoniemi, 2001). The Default Mode Network (DMN), first labeled and defined by Raichle (Raichle et al., 2001), is theorized to be engaged in the maintenance of a sense of self. This functional definition reflected a key insight of early research that associated elements of the DMN with self-referential processing (Buckner, Andrews-Hanna, & Schacter, 2008; Raichle et al., 2001; Raichle & Snyder, 2007). The Medial Prefrontal Cortex (MPC), part of the DMN, becomes active during the interpretation and prediction of others beliefs, intentions, and desires, a central deficit in ASD. Additionally, this region becomes active, along with the right Fusiform Gyrus, during facial recognition. Both processes have been linked to core deficits of social and communication impairments The DMN has been shown to be characterized by high gamma band coherence at infra-slow frequencies. According to Ko, this “spectral” coherence, similar to commodulation, forms the neurophysiological basis of the DMN (Ko et al., 2011). More broadly, the phase of the infra-slow activity appears to be coordinated with the excitability cycle of faster frequencies (Monto, Palva, Voipio, & Palva, 2008). Homologous regions of the human cerebral cortex have been described as coordinated by coherent fast and ultra-slow spontaneous rhythmic activity that reflects the communication between and among large-scale functional networks (Liu, Fukunaga, de Zwart, & Duyn, 2010). Noting a similar correlation to the Blood Oxygen Level Dependent signal, researchers have recently concluded that infra-slow frequencies arise from cellular mechanisms including glia, neurons, and blood. These oscillations reflect the same underlying physiological phenomena: a superstructure of interrelating slow activity that coordinates the integration of active neuronal networks (Palva & Palva, 2012). Poor sensory processing in the autistic brain has a host of consequences. Researchers have theorized that the disorder is characterized by overwhelmingly painful sensory perceptions that are experienced as emotionally charged and traumatic. This painful hyper-perception leads to obsessively detailed information processing of fragments of the world and a defensive decoupling of the autistic individual from a painfully intense world (H. Markram et al., 2007; K. Markram & Markram, 2010). This excruciatingly extreme sensory world is made more so, according to the researchers, due to a hyper-active amygdala. Citing animal research that suggests Valproic Acid exposed rat off-spring exhibit autistic traits, the researchers identify a hyper-active amygdala as the agent responsible for generating enhanced anxiety and fear processing (K. Markram, Rinaldi, Mendola, Sandi, & Markram, 2007). The amygdala is a central part of neuronal networks that mediate emotional and social behavior. Situated in the temporal lobe, this brain region participates in the interpretation of emotional and socially significant cues in the environment. Responsible for the creation, retrieval, and modulation of fear memory, the amygdala regulates anxiety through the modulation of autonomic and hormonal responses (LeDoux, 2003). 489

Mark Llewellyn Smith et al.

The earliest research observed the infra-slow band to increase in amplitude when subjects were exposed to stress producing stimuli (Aladjalova, 1957, 1964). These researchers theorized that the increase in amplitude of the infra-slow oscillations reflected the Hypothalamus’s reparative, parasympathetic response. In addition to organizing neuronal networks, Aladjalova’s research suggests that the efficacy of ISF neurofeedback for autism may lie in its impact on the Autonomic Nervous System. ISF training turns on the selection of an Optimum Frequency for each client. This frequency is chosen through the identification of an autonomic response to the training in session. Peripheral measures of autonomic function such as finger temperature and skin conductance are utilized, as is heart rate variability. These measures and a close scrutiny of emergent state shifts within session guide the clinician through trial and error to a state of autonomic balance. Reductions in anxiety, better affect regulation, improved language skills, and enhanced social interaction are common outcomes for autistic clients treated with ISF training. Infra-slow Fluctuation training has produced remarkable behavioral successes with ASD clients in a manner consistent with the proposed functional significance of these slow oscillations. Addressing the networks involved with a sense of self, productive language, sensory processing, and social interaction has produced profound changes in behavior. These networks demonstrate significantly improved information sharing in post-treatment QEEGs. Normalization of activation within these functional regions has also been demonstrated in post hoc analysis. Two case studies follow. The first is a high-functioning person on the Autism Spectrum who might have carried an Asperger’s diagnosis before the release of DSM-5. The second is a client diagnosed PDD-NOS, a category that has also been subsumed by Autism Spectrum Disorder. These cases are emblematic of ISF training in that both individuals exhibited notable changes in behavior specifically in autonomic arousal, social interaction, and language skills. The brain mapping reveals significant remediation of activation and extraordinary improvements in network information sharing.

Case Study 1 Pt is a 29-year-old single male on the Autism Spectrum with a long history of being non-communicative; limited interpersonal relations; shyness; limited emotional abilities to express self/connect with others; was selectively mute during latency age years and had sensory issues. He was placed in a gifted program in elementary and middle schools, doing well academically in all school settings. In addition he complained of ongoing problems concentrating; confusion; difficulty making decisions; low self-esteem; decreased interest in others; being disorganized; conflict avoidance and being unable to solve personal issues. He has a history of three psychiatric hospitalizations to address increased paranoia and agitation and is being treated with a neuroleptic. In his initial QEEG brain map hypocoherence was noted in the delta, theta, and alpha bands (Figure 26.1). This disordered neural information sharing was observed telescoping from the right hemisphere regions of the parietal and temporal lobes. Excessive power in theta was evident at 4–7 Hertz with most deviance in bilateral central and temporal lobes (Figure 26.2). ISF therapy began three times a week for the first month wherein he was optimized with bipolar placements at the bilateral temporal lobes. He then opted to be seen two times a week and had a combined right temporal, right parietal followed by a bilateral temporal lobe ISF therapy protocol. His third protocol first combined the right prefrontal and right temporal lobe, followed by the right temporal and right parietal areas, and finished by training the bilateral temporal regions. At the time of his second QEEG he had received a few sessions of his fourth protocol: right temporal lobe and right lateral frontal lobe, followed by right temporal and right prefrontal, and finished with the right temporal and right parietal areas. His second QEEG, after less than three months of ISF therapy, revealed a near complete normalization of network information sharing (Figure 26.3). The excessive 490

Figure 26.1 Pre-treatment QEEG summary maps. Excess theta band absolute power. Hypocoherence in all bands but the beta band. Most deviance in the delta and theta bands with telescoping hypocoherence from right hemisphere parietal and temporal areas. (a)

(b)

Figure 26.2 Pre-treatment QEEG single Hertz bins revealing near global excess absolute power in the theta band at 5–7 Hertz.

Figure 26.3 Post-treatment QEEG. Complete regulation of absolute power in the theta band. Complete normalization of coherence in the delta, theta, and alpha bands. Some minor hypocoherence in the beta and high beta bands.

Infra-Slow Fluctuation (ISF) Training

(a)

(b)

Figure 26.4 Post-treatment QEEG. Near total normalization of power in the theta band single Hertz bins at 5–7 Hertz.

power in the theta band revealed in the first QEEG was observed to be normal in the second brain mapping (Figure 26.4). After ISF sessions, the client frequently gave a 24-hour report like the following: “feeling relaxed, mental clarity, energetic, calm, and alert. I am also noticing higher quality information coming through my senses which is useful for music.” Interpersonally he reported feeling less fear relating to others, left his cognitive behaviorist for a therapist who was more relational, began co-ed dance lessons in New York City, and began dating.

Case Study 2 G was six years old when he appeared with his mother for an intake appointment. He came in preoccupied by two miniature robots that he moved around on a tiny pad. In that first meeting there was no greeting, no eye contact, and no communication. The client had retreated to the solace of a 6ʺ × 6ʺ world which held his full attention. G had low physical tone, speech delays with articulation issues, trouble focusing, significant acting out behaviors, explosive anger, anxiety, and carried a PDD-NOS diagnosis. The client was enrolled in a special education school specific to low functioning ASD children whose curriculum included academic subjects. He was one of twelve students with two teachers. In the first year of neurotherapy G was treated with traditional forms of neurofeedback plus the Low Energy Neurofeedback System (LENS). He made little progress. He was assessed for heavy metals and began a lead and mercury chelation detoxification regimen. After approximately a year on this detox protocol, ISF neurofeedback was started. For the first month, the client was trained with ISF neurofeedback once per week. Following the first month we shifted G to a twice per week schedule over the next three months. He was then put on a twice per week supervised home training schedule for the duration of treatment. During this time, and throughout his nearly two years of ISF neurofeedback training, G was treated with a host of interventions by allied health practitioners that included detoxification, reflex integration, and a gluten free diet. G progressed in so many ways: he was able to participate in karate classes and his physical coordination was greatly improved. He would come into the office and head right for the bungee trampoline where he would burn off some energy performing karate moves in the air before starting his neurofeedback training session. As time progressed he was writing his letters, starting to read, and doing math. His articulation improved and his temper began to diminish. He completed approximately eighty sessions of ISF neurofeedback at termination of treatment. Given G’s performance in 493

Figure 26.5 QEEG summary maps at intake. Note the hypercoherence in all bands with a global expression in the beta and high beta bands. This map reveals excess high beta absolute power.

Figure 26.6 2nd QEEG. Mid-treatment summary maps. Note the normalization of delta absolute power, the virtually normalized excess high beta and substantial regulation of coherence abnormalities in the beta and high beta bands.

Figure 26.7 Post-treatment QEEG. Note the normalization of coherence values, particularly in the beta and high beta bands.

Infra-Slow Fluctuation (ISF) Training

school, by mid-term of the second year of ISF training, his school began discussing a mainstreaming strategy with his parents. G moved to a mainstream classroom setting with twenty-five students, one teacher, and one aid, the following fall. G performed at grade level and he has been declassified from PDD-NOS to Learning Disabled.

Conclusion Autism Spectrum Disorder is a neurodevelopmental disorder characterized by impairments in communication, social skills, sensory processing, and behavior. These deficits have been associated in research with abnormalities in neural connectivity. Disordered connectivity interferes with the normal synchronization of neuronal networks negatively impacting communication within and between networks of function. Abnormal network communication may be responsible for the entrapment of the autistic individual in a painfully intense sensory world. In case studies of ISF neurofeedback training for ASD clients, normalization of network connectivity has been demonstrated with QEEG post hoc analysis in this and other case reports (Smith, 2013). Client, parent report, and behavioral scales have demonstrated symptom improvement (Legarda, McMahon, Othmer, & Othmer, 2011; Smith, Collura, Ferrara, & de Vries, 2014), and ISF training and demonstrable academic progress have been recorded (Smith et al., 2014). Documented behavioral progress combined with network sharing normalization revealed in QEEG post hoc analysis suggest that ISF training should be considered a viable neurofeedback intervention for ASD clients.

References Aladjalova, N. A. (1957). Infra-slow rhythmic oscillations of the steady potential of the cerebral cortex. Nature, 179(4567), 957–959. Aladjalova, N. A. (Ed.) (1964). Slow electrical processes in the brain (Vol. 7). Amsterdam: Elsevier. Billeci, L., Sicca, F., Maharatna, K., Apicella, F., Narzisi, A., Campatelli, G., . . . Muratori, F. (2013). On the application of quantitative EEG for characterizing autistic brain: A systematic review. Frontiers in Human Neuroscience, 7, 1–16. doi:10.3389/fnhum.2013.00442 Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008). The brain’s default network. Annals of the New York Academy of Sciences, 1124(1), 1–38. doi:10.1196/annals.1440.011 Buzsaki, G. (2006). Rhythms of the brain. New York: Oxford University Press. Cantor, D. S., Thatcher, R. W., Hrybyk, M., & Kaye, H. (1986). Computerized analysis of autistic children. Journal of Autism and Developmental Disorders, 16(2), 169–187. Coben, R. (2007). Connectivity-guided neurofeedback for autistic spectrum disorder. Appl Psychophysiological Biofeedback, 35(4), 131–135. Coben, R., Clarke, A. R., Hudspeth, W., & Barry, R. J. (2008). EEG power and coherence in autistic spectrum disorder. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology, 119(5), 1002–1009. Coben, R., & Myers, T. E. (2008). Connectivity theory of autism: Use of connectivity measures in assessing and treating autistic disorders. Journal of Neurotherapy, 12(2–3), 161–179. doi:10.1080/10874200802398824 Coben, R., & Padolsky, I. (2007). Assessment-guided neurofeedback for autistic spectrum disorder. Journal of Neurotherapy, 11(1), 5–23. Duffy, F., & Als, H. (2012). A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls—a large case control study. BMC Medicine, 10(1), 64. Hiltunen, T., Kantola, J., Abou Elseoud, A., Lepola, P., Suominen, K., Starck, T., . . . Palva, J. M. (2014). Infra-slow EEG fluctuations are correlated with resting-state network dynamics in fMRI. The Journal of Neuroscience, 34(2), 356–362. doi:10.1523/jneurosci.0276–13.2014 Jarusiewicz, B. (2002). Efficacy of neurofeedback for children in the autistic spectrum: A pilot study. Journal of Neurotherapy, 6(4), 39–49. Khan, S., Gramfort, A., Shetty, N. R., Kitzbichler, M. G., Ganesan, S., Moran, J. M., . . . Kenet, T. (2013). Local and long-range functional connectivity is reduced in concert in autism spectrum disorders. Proceedings of the National Academy of Sciences, 110(8), 3107–3112. doi:10.1073/pnas.1214533110

497

Mark Llewellyn Smith et al. Ko, A. L., Darvas, F., Poliakov, A., Ojemann, J., & Sorensen, L. B. (2011). Quasi-periodic fluctuations in default mode network electrophysiology. The Journal of Neuroscience, 31(32), 11728–11732. doi:10.1523/ jneurosci.5730–10.2011 Kouijzer, M. E. J., de Moor, J. M. H., Gerrits, B. J. L., Buitelaar, J. K., & van Schie, H. T. (2009). Long-term effects of neurofeedback treatment in autism. Research in Autism Spectrum Disorders, 3(2), 496–501. doi:http:// dx.doi.org/10.1016/j.rasd.2008.10.003 Kouijzer, M. E. J., van Schie, H. T., de Moor, J. M. H., Gerrits, B. J. L., & Buitelaar, J. K. (2010). Neurofeedback treatment in autism: Preliminary findings in behavioral, cognitive, and neurophysiological functioning. Research in Autism Spectrum Disorders, 4(3), 386–399. doi:http://dx.doi.org/10.1016/j.rasd.2009.10.007 LeDoux, J. (2003). The emotional brain, fear, and the amygdala. Cellular and Molecular Neurobiology, 23(4–5), 727–738. doi:10.1023/A:1025048802629 Legarda, S. B., McMahon, D., Othmer, S., & Othmer, S. (2011). Clinical neurofeedback: Case studies, proposed mechanism, and implications for pediatric neurology practice. Journal of Child Neurology, 26(8), 1045–1051. doi:10.1177/0883073811405052 Linkenkaer-Hansen, K., Nikouline, V. V., Palva, J. M., & Ilmoniemi, R. J. (2001). Long-range temporal correlations and scaling behavior in human brain oscillations. The Journal of Neuroscience, 21(4), 1370–1377. Liu, Z., Fukunaga, M., de Zwart, J. A., & Duyn, J. H. (2010). Large-scale spontaneous fluctuations and correlations in brain electrical activity observed with magnetoencephalography. Neuroimage, 51(1), 102–111. Lynch, C. J., Uddin, L. Q., Supekar, K., Khouzam, A., Phillips, J., & Menon, V. (2013). Default mode network in childhood autism: Posteromedial cortex heterogeneity and relationship with social deficits. Biological Psychiatry, 74(3), 212–219. Markram, H., Rinaldi, T., & Markram, K. (2007). The intense world syndrome—an alternative hypothesis for autism. Frontiers in Neuroscience, 1(1), 77–96. doi:10.3389/neuro.01.1.1.006.2007 Markram, K., & Markram, H. (2010). The intense world theory—a unifying theory of the neurobiology of autism. Frontiers in Human Neuroscience, 4, 1–29. doi:10.3389/fnhum.2010.00224 Markram, K., Rinaldi, T., Mendola, D. L., Sandi, C., & Markram, H. (2007). Abnormal fear conditioning and amygdala processing in an animal model of autism. Neuropsychopharmacology, 33(4), 901–912. Monk, C. S., Peltier, S. J., Wiggins, J. L., Weng, S. J., Carrasco, M., Risi, S., & Lord, C. (2009). Abnormalities of intrinsic functional connectivity in autism spectrum disorders. Neuroimage, 47(2), 764–772. doi:http:// dx.doi.org/10.1016/j.neuroimage.2009.04.069 Monto, S., Palva, S., Voipio, J., & Palva, J. M. (2008). Very slow EEG fluctuations predict the dynamics of stimulus detection and oscillation amplitudes in humans. Journal of Neuroscience, 28(33), 8268–8272. doi:10.1523/ jneurosci.1910–08.2008 Palva, J. M., & Palva, S. (2012). Infra-slow fluctuations in electrophysiological recordings, blood-oxygenationlevel-dependent signals, and psychophysical time series. Neuroimage, 62(4), 2201–2211. doi:http://dx.doi. org/10.1016/j.neuroimage.2012.02.060 Pfurtscheller, G. (1976). Ultralangsame schwankungen innerhalb der rhythmischen aktivität im alpha-band und deren mögliche ursachen. Pflügers Archiv, 367(1), 55–66. doi:10.1007/BF00583657 Pineda, J. A., Brang, D., Hecht, E., Edwards, L., Carey, S., Bacon, M., . . . Rork, A. (2008). Positive behavioral and electrophysiological changes following neurofeedback training in children with autism. Research in Autism Spectrum Disorders, 2(3), 557–581. doi:http://dx.doi.org/10.1016/j.rasd.2007.12.003 Pineda, J. A., Juavinett, A., & Datko, M. (2012). Self-regulation of brain oscillations as a treatment for aberrant brain connections in children with autism. Medical Hypotheses, 79(6), 790–798. doi:10.1016/j. mehy.2012.08.031 Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences of the United States of America, 98(2), 676–682. Raichle, M. E., & Snyder, A. Z. (2007). A default mode of brain function: A brief history of an evolving idea. Neuroimage, 37(4), 1083–1090. doi:http://dx.doi.org/10.1016/j.neuroimage.2007.02.041 Scolnick, B. (2005). Effects of electroencephalogram biofeedback with Asperger’s syndrome. International Journal of Rehabilitation Research, 28(2), 159–163. Sichel, A. G., Fehmi, L. G., & Goldstein, D. M. (1995). Positive outcome with neurofeedback treatment in a case of mild autism. Journal of Neurotherapy, 1(1), 60–64. Smith, M. L. (2013). Infra-slow fluctuation training: On the down-low in neuromodulation. Neuroconnections, Fall, 38 & 42. Smith, M. L., Collura, T. F., Ferrara, J., & de Vries, J. (2014). Infra-slow fluctuation training in clinical practice: A technical history. NeuroRegulation, 1(2), 187–207. doi:10.15540/nr.1.2.187

498

Infra-Slow Fluctuation (ISF) Training Vanhatalo, S., Palva, J. M., Holmes, M. D., Miller, J. W., Voipio, J., & Kaila, K. (2004). Infraslow oscillations modulate excitability and interictal epileptic activity in the human cortex during sleep. Proceedings of the National Academy of Sciences, 101(14), 5053–5057. doi:10.1073/pnas.03053751010305375101 [pii]. Vanhatalo, S., Voipio, J., & Kaila, K. (2005). Full-band EEG (FbEEG): An emerging standard in electroencephalography. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology, 116(1), 1–8.

499

27 TRANSCRANIAL DIRECT CURRENT STIMULATION IN REHABILITATION Roger H. Riss and Frederick Ulam

Abstract Functional neuroimaging research has given rise to a tantalizing and provocative new viewpoint on the role of rehabilitation therapy, to wit: that neuroplastic cortical reorganization following neuropsychological insult or illness is the norm (Kim, Ko, Parrish & Kim, 2002; Warren, Crinion, Lambon Ralph & Wise, 2009), and that a necessary and sufficient condition for any effective rehabilitation intervention is that it facilitates and directs this process. Over the past decade neuroplasticity research has revolutionized rehabilitation therapy, leading to the introduction of neuroplasticity based, high-intensity interventions such as Constraint Induced Movement Therapy (Morris, Crago, Deluca, Pidikiti & Taub, 1997), and Intensive Language Action Therapy (Pulvermüller & Berthier, 2008) designed to maximize cortical reorganization through intensive massed practice. The introduction of non-invasive brain stimulation techniques into the therapy session holds promise to take these advances to the next level of learning efficiency, by directly targeting brain structures to facilitate or inhibit their activity during concurrent traditional therapy interventions, further optimizing therapy-induced cortical reorganization. Among neurostimulatory techniques, transcranial direct current stimulation (tDCS) appears to hold particular promise as a practical clinical tool for non-invasive therapeutic stimulation of brain cortex. Advantages of this method include its portability, low cost, negligible risk, and the ease which it can be delivered concurrently with traditional cognitive, motor, or psychotherapy interventions to potentiate their impact (Schlaug, Renga, & Nair, 2008).

Introduction Transcranial direct current stimulation (tDCS) is a simple, non-invasive technique for brain activity modulation that induces prolonged functional changes in the cerebral cortex (Priori, 2003). In this technique, a weak direct current (usually 1–2 mA) is delivered to scalp through two sponge electrodes. The resulting constant electric field penetrates the skull, modulating neuronal function (Mahdavi, Yavari, Gharibzadeh, & Towhidkhah, 2014). Whereas other neurostimulation techniques, such as repetitive transcranial magnetic stimulation (rTMS) or deep brain stimulator implants (DBS), directly override normal brain activity to induce, or inhibit, an action potential (Stade, 2011), tDCS does not directly induce neuronal firing, because the current densities that it produces in underlying cortex—between 0.77 and 2.00 mA/cm2 according to a computer model—are many orders of magnitude below the threshold to directly propagate action potentials in the brain. However, even 500

Transcranial Direct Current Stimulation

small voltage gradients of this magnitude influence background neuronal firing rates, inducing subtle shifts in polarity to facilitate or inhibit normal background cortical excitability by a factor of up to 20%, with effects that persist up to 90 minutes after stimulation has ceased.

Practical Advantages In contrast to alternative neural stimulation techniques, such as deep brain stimulation (DBS), which requires invasive surgical implantation, or repetitive transcranial magnetic stimulation (rTMS), which relies upon costly equipment, highly trained lab personnel, and time consuming targeting procedures (neuronavigation), tDCS holds distinct practical advantages for use in the therapy clinic. tDCS can be readily administered by clinicians via low-cost ($500) devices not dissimilar to equipment long used in physical therapy and pain medicine. tDCS combines portability and ease of use with negligible risk and minimal side effect profile, adding to its appeal. Minimal equipment demands make it ideal for application within the therapy session concurrent with motor or cognitive tasks. Whereas the rTMS coil emits a loud click for each stimulus delivered which is readily discerned by the patient, tDCS stimulation levels are sufficiently subtle that research participants are typically unable to discern the difference between actual and sham stimulation, supporting an easily implemented control condition in clinical research trials (Gandiga, Hummel, & Cohen, 2006).

Historical Perspective Therapeutic use of electricity on excitable tissues is not new (Sparing & Mottaghy, 2008). As early as AD 43, Scribonius Largus, a physician of the Roman emperor Claudius, provided a detailed account of the use of the (electric) torpedo fish to treat headache, while the 11th century Muslim physician Ibn-Sidah proposed that the electric catfish might play a useful role for the treatment of epilepsy (Kellaway, 1946). The introduction of the battery in the 18th century made DC stimulation or faradization, as it was termed at the time, possible. Luigi Galvani (1791, 1797) and Alessandro Volta (1745–1827) pioneered use of transcranial electrical stimulation to explore the relationship between muscle movement and motor cortex (Gross, 2007). Georges Duchenne de Bologne (1806–1875), a student of neurology pioneer Jean-Martin Charcot MD, became the first to systematically use electricity in the diagnosis and treatment of disease, and even employed an early form of cardiac electro-shock to revive a patient from carbon monoxide induced coma (Priori, 2003). The application of electric currents to relieve mental illness was pioneered a by Italian physicist Giovanni Aldini (1762–1834), who reported the successful treatment of hospitalized patients suffering from melancholia using low-intensity galvanic currents in 1804 (Parent, 2004). With the emergence of biological psychiatry in the 20th century, this history was largely ignored in the favor of efforts to develop pharmacological agents, and low-intensity electrotherapy in medicine was largely abandoned, until its rapid re-emergence in the last decade (Utz, Dimova, Oppenlander, & Kerkhoff, 2010).

A Methodology on the Move Over the past decade, neuroplasticity research has revitalized interest in tDCS, for its potential role in promoting cortical reorganization to support learning, recovery from neurological insult, and treatment of psychiatric illness. Whereas only 11 tDCS studies were published during 2003, a PubMed search for the terms “tDCS” or “transcranial direct current stimulation” identified total of 1761 tDCS-related papers published by early 2015, with 1387 peer reviewed papers appearing during the last five years alone. Finally, a search made on clinicaltrials.gov in early 2015 revealed 328 tDCSrelated randomized controlled clinical trials either completed or underway. 501

Roger H. Riss and Frederick Ulam

Basic Principles Equipment and Application DC stimulator: Mechanically, the most important component of a tDCS delivery system is a simple DC current generator, which operates on a standard 9-volt battery, quite similar to devices that have been routinely utilized in peripheral applications by physical therapists for decades. Electrodes: Electrodes are positioned on the surface of the scalp according to the International 10–20 system. The electrodes used for tDCS are thick (0.3 cm), rectangular saline-soaked synthetic sponges, or alternatively, re-usable flat carbon rubber electrode pads, covered by sponge sleeves (typically sized between 25 to 35 cm2), which are soaked in normal saline solution and held in place by a non-conductive band or net. The relatively large electrode surface diffuses and limits the focality of stimulation, keeping current densities low, constituting one of the critical safety parameters of the current generation of tDCS protocols (Sparing & Mottaghy, 2008). Rounded or circular electrodes may be substituted in order to reduce undesirable “edge effects,” which concentrate electrical current at sharp corners of rectangular pads (Minhas, Datta, & Bikson, 2011). Current strength and duration: A weak electrical current of 1 to 2 mA is typically applied for 20–30 min/session, offering an ideal duration for concurrent use during therapy. Early clinical trials have utilized treatment protocols of up to 10 sessions; recently, researchers have identified continued incremental benefit with protocols of 30 sessions or more. Up to half of the applied current is shunted away from underlying cortex via the scalp. Using stimulating currents of 2.0 mA, the magnitude of the current density in relevant regions of the brain is of the order of 0.1 A/m2, corresponding to an electric field of 0.22 V/m (Miranda, Lomarev, & Hallett, 2006). Montage selection: tDCS montages rely upon a circuit created between an “active” electrode (placed over a region of interest related to training goals) and a return signal at a so-called “reference” electrode, which is traditionally placed over either an extra cephalic site (such as contralateral shoulder) or a contralateral cephalic site thought to be unrelated to the function of interest (for example, motor rehabilitation studies in stroke survivors frequently utilize a montage in which the active electrode is placed over the motor strip of the affected hemisphere, while the reference electrode is placed over the contralateral forehead). In order to further approximate neutral effect at the return site, the “reference” electrode may utilize an oversized sponge, which serves to diffuse, and therefore diminish, electrical signal propagated within cortex beneath the reference. Therapeutically tDCS may be used to either facilitate or inhibit activation of specific neural networks, depending upon the direction or “polarity” of current flow. Depending upon treatment goals, the “active” electrode may be either stimulatory (anodal tDCS) or inhibitory (cathodal tDCS). Recently, attention has begun to turn to so-called “dual hemisphere stimulation” montages, in which both electrodes are treated as “active” with the goal of stimulating a target in one hemisphere while simultaneously inhibiting a second region, typically a mirror site within the contralateral hemisphere. These protocols are of particular interest in disorders characterized by hemispheric imbalance in cortical activation, such as frontal asymmetry in depression, or motor hemiparesis following a stroke. For example, a “dual hemisphere stimulation” montage to address mood might feature anode (+) at F3 and cathode (–) at F4. To promote upper extremity motor recovery after stroke, the anode may be placed over the stroke-affected motor cortex to stimulate reactivation within perilesional cortex, while the cathode is placed over motor cortex in the contralesional hemisphere to inhibit any compensatory activation patterns generated by the healthy hemisphere which have outlived their usefulness to recovery. Alternatively, some high-end tDCS systems now utilize a dedicated cap, featuring an array of small “high density” electrodes, according to the International 10–20 system, facilitating consistent reproduction of the montage across treatment sections and significantly reducing patient preparation time.

502

Transcranial Direct Current Stimulation

Mode of Action Therapeutic efficacy of tDCS depends upon short-term, transient excitatory effects, as well as potentiation of long-term neuroplastic effects similar to those underlying learning and memory. Short-term effects: Brain excitability is increased beneath the positively charged electrode, or anode, via depolarization of neural membrane barriers, while propensity for neural firing is decreased beneath the negatively charged electrode, or cathode; inducing a shift toward neural hyperpolarization, in turn raising the threshold for action potentials to be generated (Nitsche & Paulus, 2000; Nitsche et al., 2005; Sparing & Mottaghy, 2008). Thus, depending upon whether the anode or cathode is placed over the targeted region, the clinician may either subtly activate or inhibit a particular cortical region or neural network. Transitory changes in cortical excitability due to polarity-specific shifts of neuronal resting membrane potential continue to be detectable for up to 90 minutes following cessation of stimulation, while associated transitory behavioral effects on motor or cognitive performance persist for at least 30 minutes beyond a typical 20–30-minute treatment session (Nitsche & Paulus, 2000, 2001). Long-term effects: At a molecular level, tDCS appears to modulate a complex cascade of events involving NMDA, glutamatergic, GABAergic, dopaminergic, serotonergic, and cholinergic pathways (Medeiros et al., 2012). At a behavioral level, tDCS-related therapeutic gains may have delayed onset or even continue to grow in magnitude over time following termination of treatment (Fridriksson, Richardson, Baker, & Rorden, 2011; Segrave, Arnold, Hoy, & Fitzgerald, 2013), providing indirect evidence for a process of neuroplastic change which cannot be explained by short-term cortical excitatory effects alone (Ranieri et al., 2012). While these mechanisms are not fully understood, we can conclude that tDCS not only alters spontaneous neuronal firing rate by polarity-specific alteration of membrane potentials, but also has the capacity to induce long-term changes in synaptic function via neuroplastic cortical reorganization processes similar to the long-term potentiation (LTP) and long-term depression (LTD) effects which are understood to play a key role in memory and learning (Monte-Silva et al., 2013). These important mechanisms were anticipated over 60 years ago by Canadian psychologist Donald Hebb who proposed that, “cells which fire together wire together” (Hebb, 1949). While “Hebb’s law” at that time was merely a theory, it is now well established that, on a cellular level, processes supporting learning are best described by repeated engagement of transient, task-specific neural networks. With repetition, the initially transient association between neurons during task performance becomes consolidated, or “hard wired,” a process known as long-term potentiation (LTP; Rioult-Pedotti, Friedman, & Donoghue, 2000). To the extent that application of anodal tDCS concurrent with task practice increases the readiness of relevant neural networks supporting that task to co-activate, LTP-like cortical reorganization processes are facilitated. By contrast, repeated cathodal tDCS may promote long-term depression (LTD) or inhibition of maladaptive activation patterns (as, for example, those that are associated with pain) which are the target of treatment (Brasil-Neto, 2012).

Safety Contraindications and precautions: Presence of cardiac pacemaker or other implanted electrical medical device is a tDCS exclusion criterion. Precautions should also be taken to avoid electrode placement directly over skull fractures, skull flap seams, or metallic skull plates, due to unpredictable signal transmission effects and potential for skull defects to alter the path and intensity of current flow through the brain, (Datta, Bikson, & Fregni, 2010; Madhavan & Shah, 2012). Safety profile: When applied according to established guidelines, tDCS is widely regarded as extremely safe and causing minimal patient discomfort (Bikson, Datta, & Elwassif, 2009; Iyer et al., 2005; Liebetanz et al., 2009; Nitsche et al., 2008; Priori, 2003). Induction of seizures by tDCS has

503

Roger H. Riss and Frederick Ulam

never been reported, and indeed, cathodal tDCS has been found effective for suppression of seizures in susceptible patients (N. Auvichayapat et al., 2013; Yook, Park, Seo, Kim, & Ko, 2011). While now in use clinically in Europe and Canada, tDCS is still considered investigational by FDA in the United States (Arul-Anandam, Loo, & Sachdev, 2009). Highlighting its safety profile, several manufacturers have already cleared their devices with the FDA as Non-Significant Risk (NSR) devices and exempt from the Investigational Device Equipment requirements for use with human subjects. Tolerability: Adverse effects are typically mild and transitory. Transient tingling or itching sensation at the electrode site, which diminishes after the first 30 seconds of stimulation, is experienced by about 70% of participants (Nitsche et al., 2008). Transient redness or irritation of skin beneath the electrode sponge, more common in fair-skinned or skin-sensitive individuals, is typically due to neurally driven vasodilation rather than burn injury, and only rarely have skin lesions been reported. Reported cases of skin burn or lesion following repeated treatment sessions appear to be associated with improper technique and stimulation at higher intensity stimulation settings (Poreisz, Boros, Antal, & Paulus, 2007). Less common symptoms, such as headache, appear to occur as frequently in the sham condition as when the device is actually turned on (Fertonani, Rosini, Cotelli, Rossini, & Miniussi, 2010). Optimizing comfort: While tDCS is generally well tolerated, clinicians can reduce or prevent minor discomfort with skillful technique. To minimize awareness of tingling sensation during stimulation and risk of skin irritation, thoroughly soak electrode sponges with saline solution and remoisten as needed during treatment (many devices are equipped with an alarm to alert the clinician to increased impedances caused by a dry electrode). Research indicates that a diluted, halfstrength saline solution provides more uniform electrode current densities beneath the electrode vs. full strength 0.9% normal saline, potentially improving patient comfort (Minhas et al., 2011). However, substitution of tap water is discouraged. Compared with saline solution, tap water is associated with higher impedance level, leading to application of higher voltage, and increased risk of skin burns or lesions with repeated treatment sessions (Frank et al., 2010). Topical application of an over-thecounter anti-inflammatory gel (ketoprofen 2%) to skin underlying the electrode sponge may also enhance patient comfort (Guarienti et al., 2014). Patients can be desensitized to transient tinglingassociated discomfort by slowly “ramping up” the current during the first 10 to 60 seconds until the desired current is reached (a built-in feature of many devices), or by briefly reducing the current setting until the patient becomes habituated to the sensation.

tDCS as a Standalone vs. Combined Intervention Therapeutically tDCS may be delivered via either “offline” or “online” treatment protocols. “Offline” mode refers to administration of stimulation during a resting brain state. “Offline” treatment may be offered as a standalone intervention (as in treatment of depression) or may be administered immediately prior to task practice with goal of inducing a “priming” effect. By contrast, “online” treatment refers to administration of tDCS concurrently with task and in a task activation brain state. Results of tDCS intervention may differ depending upon whether stimulation modulates a resting brain state or a task-activated neural network. For example, when delivered “offline” prior to a verbal naming task, only inhibitory, cathodal tDCS to Broca’s area yields improvement in subsequent naming efficiency (Monti et al., 2008), whereas when delivered in “online” mode, concurrent with the speech task, only stimulatory, anodal tDCS to Broca’s area improves speech production in both healthy and aphasic patients (Baker, Rorden, & Fridriksson, 2010; Cattaneo, Pisoni, & Papagno, 2011). As a standalone, “offline” intervention, tDCS appears to offer relatively modest short-term benefit, with perhaps 10 to 20% impact on subsequent performance or clinical outcome. Attention has therefore increasingly shifted to the potential synergistic benefit of tDCS when delivered “online” and concurrent with traditional physical, cognitive or psychotherapy interventions, with goals of priming 504

Transcranial Direct Current Stimulation

brain readiness for learning and facilitating neuroplastic change associated with repeated task practice (Bolognini, Pascual-Leone, & Fregni, 2009). Just as repeated practice of a bad golf swing will not improve one’s game, repeated practice of a therapy activity will not necessarily promote optimal recovery, if execution of the task is being supported by less than optimal task-related cortical recruitment patterns. This is where tDCS comes in, offering the therapist the potential to optimize neuroplastic efficiency of therapy exercises. By subtly inhibiting any maladaptive cortical recruitment patterns and facilitating resumption of task control by optimal networks, the therapist is able to combine “bottom-up” therapy drills with “top down” neurostimulatory direction of task-specific cortical reorganization processes.

Clinical Applications tDCS studies on cognition in the healthy human brain have largely focused on short-term improvements in performance induced by a single session of stimulation, often delivered immediately before or during task. These preliminary, proof of concept studies have yielded transient improvement to a range of cognitive functions. In healthy subjects, tDCS has capacity to modulate short-term visual (Fregni et al., 2005) and auditory working memory (Elmer, Burkard, Renz, Meyer, & Jancke, 2009; Ferrucci, Marceglia et al., 2008), memory encoding and retrieval (Boggio, Fregni, et al., 2009; Boggio, Khoury et al., 2009), and declarative memory consolidation during slow-wave sleep (Marshall, Molle, Hallschmid, & Born, 2004). Effects of tDCS have also been demonstrated for attention (Stone & Tesche, 2009), decision-making (Hecht, Walsh, & Lavidor, 2010), impulse control (Fecteau, Knoch et al., 2007; Fecteau, Pascual-Leone et al., 2007), and language (Fertonani et al., 2010; Sparing, Dafotakis, Meister, Thirugnanasambandam, & Fink, 2008). A growing body of research supports a potential for tDCS in treatment of disorders including epilepsy (Yook et al., 2011), Parkinson’s disease (Benninger et al., 2010; Hess, 2013; Leite, Goncalves, & Carvalho, 2014), depression (Berlim, Van den Eynde, & Daskalakis, 2013; Fregni, Boggio, Nitsche et al., 2006), addiction, chronic pain (Antal & Paulus, 2010; Fregni, Freedman, & Pascual-Leone, 2007; Zaghi, Heine, & Fregni, 2009), and neuropathic pain following spinal cord injuries (Kumru et al., 2013; Nardone et al., 2014; Yoon et al., 2013). In the past decade, the potential of tDCS in rehabilitation of cognitive, motor, and sensory function after stroke has also been a major focus of investigation (Feng, Bowden, & Kautz, 2013; Gomez Palacio Schjetnan, Faraji, Metz, Tatsuno, & Luczak, 2013; Stagg & Johansen-Berg, 2013). A recent systematic literature review of tDCS in major psychiatric disorders (Mondino et al., 2014) yielded 40 publications: 22 in major depressive disorder (MDD), nine in schizophrenia, seven in substance use disorder, one in obsessive-compulsive disorder, and one in mania. Preliminary findings indicated modest efficacy of tDCS for MDD as well as a promising emerging literature in substance use disorder.

Depression History: Studies of tDCS stimulation in the treatment of depression dating from the 1960s were flawed by inadequate current intensities (0.02–0.5 mA) and considerable variability in stimulation technique, yielding equivocal outcomes, which resulted in premature abandonment of the method (Nitsche, Boggio, Fregni, & Pascual-Leone, 2009). Since that time, optimization of protocols and refinement of equipment providing safe and reliable stimulation in the 1 to 2 mA range has fostered renewed interest in tDCS in clinical research and improved therapeutic response. Since 2000, eight high-quality randomized sham controlled trials investigating the efficacy of tDCS for treating depression have emerged with encouraging results (Boggio et al., 2008; Brunoni et al., 2014; Fregni, Boggio, Nitsche, Marcolin et al., 2006; Fregni, Boggio, Nitsche, Rigonatti, & Pascual-Leone, 2006; Loo et al., 505

Roger H. Riss and Frederick Ulam

2010; Loo et al., 2012; Palm et al., 2012; Rigonatti et al., 2008), with all but one trial (Loo et al., 2010), indicating statistically significant antidepressant effects. Protocol selection: Protocols for the treatment of major depressive disorder (MDD) have most frequently applied anodal stimulation to left dorsolateral prefrontal cortex (DLPFC, i.e., F3; International 10–20 system) paired with reference cathodal electrode to the right supraorbital forehead area (i.e., Fp2), for an average of 10 treatment sessions (1–2 mA; 20-–30 min) over two weeks (Berlim, Dias Neto, & Turecki, 2009). Alternatively, bifrontal application (anode at F3/cathode at F4) has been utilized with success (D’Urso, Mantovani, Micillo, Priori, & Muscettola, 2013). Adverse events: While tDCS treatment of depression is generally safe and well tolerated, clinicians should be aware that induction of hypomania has been reported as a rare side effect, with both bifrontal montage (i.e. anode to Fp1, cathode to Fp2; Brunoni et al., 2013), and when a fronto-extra cephalic montage (i.e. anode to Fp1, cathode to right shoulder) was utilized (Galvez et al., 2011). Efficacy: Results from a recent meta-analysis of randomized controlled trials, including a total of 200 subjects with MDD, reported modest but encouraging benefit of active vs. sham tDCS in terms of response to treatment (23.3% vs. 12.4%), and depression remission (12.2% vs. 5.4%) after an average of 10.8 tDCS sessions (Berlim et al., 2013). Standalone tDCS vs. combined therapy: A recent meta-analysis reported that when tDCS is offered as a standalone, or “offline” treatment for depression, mean duration of symptom remission is about 11.7 weeks, with significantly higher relapse rate in more severely depressed patients (Brunoni et al., 2014; similar concerns about persistence of gains have been raised for rTMS depression protocols). In contrast, a recent study found that when tDCS was combined synergistically with cognitive behavioral therapies, symptom remission continued to be measureable at one-year follow-up (D’Urso et al., 2013). Additional support for a tDCS “priming effect” in treatment of depression emerged from a recent randomized controlled study contrasting antidepressant effect of (1) standalone anodal tDCS applied to left DLPFC, vs. (2) a standalone cognitive behavioral therapy intervention, Cognitive Control Training (CCT), which features computer-based cognitive exercises designed to recruit and activate prefrontal neural networks supporting euthymic mood, vs. (3) a combined tDCS + CCT intervention. While all three treatment conditions were associated with an immediate reduction in depression severity at the end of five treatment sessions, only those who received the tDCS + concurrent CCT intervention demonstrated sustained antidepressant response at 3 week follow-up. Moreover, only the combined tDCS + concurrent CCT group demonstrated greater improvement at follow-up than had been evident immediately following treatment, implicating a true neuroplastic change in recovery trajectory (Segrave et al., 2013). These findings have been replicated in a followup study by Brunoni and colleagues, who reported that those patients who demonstrated highest levels of task engagement demonstrated the largest benefit from the combined tDCS + cognitive therapy intervention (Brunoni et al., 2014). Interestingly, this synergistic priming effect is also evident when tDCS is paired with pharmacological treatment of depression. A controversial article (Fournier et al., 2010) published in the prestigious Journal of the American Medical Association concluded that antidepressant medications, as a standalone intervention, are of minimal efficacy for all but the most severely depressed patients. “The magnitude of benefit of antidepressant medication compared with placebo may be minimal or nonexistent, on average, in patients with mild or moderate symptoms” (p. 47). Further limiting their utility, full therapeutic response to these medications may take 4 to 8 weeks, begging the question whether a tDCS “booster” effect might increase the efficacy of this class of treatment. An important double blind, randomized, controlled clinical trial (Sertraline vs. Electrical Current Therapy for Treating Depression Clinical Trial—SELECT TDCS) addressed this question. Investigators followed 120 depressed outpatients over six weeks, documenting that the combination of tDCS and the commonly prescribed antidepressant sertraline increased the efficacy of each, with 506

Transcranial Direct Current Stimulation

the combined treatment group achieving greater change in depression scores, and improvement in a broader spectrum of depression symptoms, than those receiving either tDCS or pharmacotherapy alone (Brunoni et al., 2013). Indeed, this outcome engendered sufficient excitement to fuel speculation that tDCS’s potential to boost efficacy of traditional pharmacotherapeutics may be the factor that drives funding toward final FDA approval of this technique.

Addiction and Craving Preliminary findings indicate potential for targeted brain stimulation of the DLPFC to modulate reward circuits associated with craving and dependency (Luigjes, Breteler, Vanneste, & de Ridder, 2013). A recent meta-analysis of 17 controlled studies reported “clear evidence that non-invasive neurostimulation of the DLPFC decreases craving levels in substance dependence,” with a medium effect size favoring active vs. sham stimulation (Jansen et al., 2013, p. 2472). Anodal tDCS stimulation appears to be equally effective when applied to either the left or the right DLPFC, and without regard to substance of abuse. Studies to date have focused on shortterm efficacy in modulating reward circuits associated with craving; as yet studies are lacking to demonstrate long-term impact on abstinence rates and relapse prevention (Herremans & Baeken, 2012; Wing et al., 2013).

Post-traumatic Stress Disorder Rationale: Individuals suffering from post-traumatic stress disorder (PTSD) exhibit dysfunctional activation of the fear extinction network, including the ventromedial prefrontal cortex, amygdala, and hippocampus, raising question whether the neural nodes of fear extinction could be targeted to reduce cognitive and neuropsychiatric symptoms of PTSD (Marin, Camprodon, Dougherty, & Milad, 2014). Findings: Literature review yielded a single tDCS pilot study (Saunders et al., 2014) which explored the feasibility of treating people suffering from PTSD and associated working memory difficulties by employing a combination of computerized working memory training and transcranial direct current stimulation (tDCS). After treatment, participants showed clinically significant improvements on a range of cognitive and emotional performance measures. Behavioral changes were supported by significant neurophysiological changes between pre- and post-treatment electroencephalographic (EEG) recordings including increase in both frequency and amplitude of participants’ initially slow alpha peak frequency as well as a significant shift in the P3a component of participants’ event-related potentials, characteristically abnormal in this clinical population, toward database norms.

Chronic Pain Rationale: One important finding is that neuroplasticity is not always adaptive. Chronic central pain and tinnitus (discussed in a following section) are just two examples of conditions now understood to result in maladaptive cortical reorganization, offering a potential target for tDCS-based therapies. The discovery that cortical neuroplasticity plays an important role in maintenance of chronic pain states raises question whether these maladaptive patterns might be reversible in response to neuromodulatory interventions such as tDCS (Moseley & Flor, 2012). Pathophysiology: We now know that chronic pain remodels both the structure and functional organization of the brain. Gray matter alterations have been observed in chronic pain sufferers across multiple pain syndromes and diagnoses, suggesting a common, overlapping “brain signature” in areas known to be involved in the affective experience of pain, including the cingulate cortex, the orbitofrontal cortex, and the insula and dorsal pons (May, 2008). Moreover, the extent of functional cortical 507

Roger H. Riss and Frederick Ulam

reorganization in patients suffering both neuropathic and musculoskeletal pain increases progressively with magnitude and chronicity of pain, apparently as a result of cumulative excitatory nociceptive input (Flor, 2003). Mechanism of action: Proposed mechanisms for cortical modulation of pain by tDCS include effects on the μ-opioid receptor, increase in glutamine and glutamate levels under the stimulating electrode, and restoration of the defective intracortical inhibition that is the cumulative end product of chronic pain-related excitatory input (Ngernyam, Jensen, Auvichayapat, Punjaruk, & Auvichayapat, 2013). Clinical indications: tDCS-mediated pain control has been demonstrated for experimentally induced pain in healthy subjects, in acute post-operative pain, and in a range of chronic pain syndromes, including headache (Pinchuk, Pinchuk, Sirbiladze, & Shugar, 2013), fibromyalgia (Fregni, Gimenes et al., 2006; Riberto et al., 2011), and neuropathic pain following spinal cord injury (Fregni, Boggio, Lima et al., 2006). Protocol selection: Therapeutic effects have been observed with tDCS stimulation of both sensorimotor and frontal cortex, targeting, respectively, the sensory and affective components of neural networks comprising the “pain matrix” (Garcia-Larrea & Peyron, 2013). Several specific tDCS montages have successfully demonstrated analgesic effects: • • • • •

Anodal tDCS delivered over the primary motor cortex (i.e. T3 or T4, 10–20 system; Fregni, Gimenes et al., 2006) Cathodal tDCS over the somatosensory cortex (Antal et al., 2008) Anodal tDCS over the left dorsolateral prefrontal cortex (DLPFC) (Riberto et al., 2011) Combined anodal tDCS to left DLPFC and cathodal tDCS to contralateral somatosensory cortex (Borckardt et al., 2011). Anodal tDCS over supraorbital cortex (FP1 or Fp2 depending on pain side), with extra cephalic cathode reference (opposite side neck or shoulder; Mendonca et al., 2011)

Dosage: Analgesic effects have been shown to be cumulative and dose-dependent, with a majority of clinical trials requiring 5 to 10 sessions of tDCS to consolidate gains (Knotkova, Nitsche, & Cruciani, 2013). Moreover, as with other neuromodulatory interventions for pain, such as rTMS, patients who have completed a course of treatment may require regular booster sessions to maintain optimal pain control. Approximately 50% of patients suffering from chronic central pain following traumatic spinal cord injury achieved at least 50% reduction in pain following five daily sessions of tDCS (20 min, 2 mA, anodal stimulation motor cortex; Fregni, Boggio, Lima et al., 2006). Fibromyalgia patients completing 10 daily sessions of stimulation to DLPFC or motor cortex reported an average 20 to 30% pain reduction (Zaghi et al., 2009).

Headache Headache treatment via tDCS has provided mixed results in a few small studies and several migrainespecific protocols have been identified (Antal, Kriener, Lang, Boros, & Paulus, 2011; P. Auvichayapat et al., 2012; Dasilva et al., 2012). Recently, a Russian neurology research group (Pinchuk et al., 2013) has contributed an extremely well done study demonstrating clinical effectiveness of tDCS comparable to that of modern pharmacological drugs, with no negative side effects. Patients receiving stimulation at 0.70–1.50 mA for 30–45 min via 6.25 cm2 electrodes demonstrated an average 50% decrease in headache days and headache severity and duration, with clinical outcomes maintained on average from 5 to 9 months. Duration of course of treatment was determined by stabilization of headaches. The researchers contrasted response to a family of three montages reflecting variations of anodal stimulation to forehead and cathode to mastoid process, reporting that optimal montage was 508

Transcranial Direct Current Stimulation Table 27.1 Relationship of headache subtype to tDCS treatment montage. Headache Type

Effective Montage

• Frequent episodic tension-type HA

1; 2; 3

• Migraine without aura

1; 2

• Chronic post-traumatic HA following mild head injury

1

• Chronic tension-type HA

None effective

Montage description Montage 1: Anode over the frontal pole, with medial edge of electrode at midline, non-dominant hemisphere (midway between Fz and Fp2; 10–20 system); cathode at the ipsilateral mastoid process. Montage 2: Anode at midline of the forehead (Fz; 10–20 system); cathode 2 cm above the mastoid process of the non-dominant hemisphere. Montage 3: Anode centered over frontal pole of non-dominant hemisphere (Fp2; 10–20 system); cathode 2 cm above the ipsilateral mastoid process. Source: Table adapted from Pinchuk et al. (2013).

dependent upon headache subtype. Relationship of headache subtype to effective tDCS treatment montage, as identified by the authors, is summarized in Table 27.1 above.

Tinnitus Pathophysiology: Tinnitus, a subjective phantom sound perception in the absence of an external sound source, afflicts 5%–21% of adults at some point in their lifetime. Risk factors include hearing loss due to environmental noise exposure and diffuse axonal injury of the central auditory pathway due to whiplash or concussion (Nolle, Todt, Seidl, & Ernst, 2004). The process of maladaptive neuroplasticity observed as tinnitus becoming chronic has been compared to that observed in chronic pain, and is characterized by a spreading activation pattern from auditory cortex to eventually encompassing a widespread emotional attention network reflecting the affective response to unremitting noxious stimuli. Depending upon symptom severity, tinnitus may increase risk for cognitive inefficiency, sleep disturbances, anxiety, and depression, causing great distress for patients and their families. Treatment response: Unfortunately, a recent systemic review of 17 studies and 2 randomized controlled trials revealed only modest benefit in tinnitus symptom management; while 39.5% of tinnitus sufferers responded to active tDCS, they achieved a mean tinnitus intensity reduction of only 13.5%. Protocol selection: Protocols utilizing left temporal and bifrontal stimulation appeared to yield similar results (Song, Vanneste, Van de Heyning, & De Ridder, 2012). Recently, Vanneste and colleagues compared the efficacy of tDCS to two alternative neurostimulation techniques which rely upon pulsed, AC stimulation using weak electrical currents—transcranial alternating current stimulation (tACS) and transcranial random noise stimulation (tRNS)—the latter technique relying upon stimulation at randomly changing frequencies. In a head-to-head comparison, tRNS induced the largest transient suppressive effect on both tinnitus loudness and the tinnitus related distress as compared to tDCS and tACS, supporting additional research of this innovative modality (Vanneste, Fregni, & De Ridder, 2013).

Neurodegenerative Disorders Given the progressive neurodegenerative nature of this family of diseases, tDCS is more likely to serve as an adjunct in symptom management rather than as a first-line therapy. This being said, tDCS in management of these disorders continues to hold appeal for its low cost, ease of use, non-invasive nature, and minimal side effect profile (Brunoni et al., 2013). 509

Roger H. Riss and Frederick Ulam

Parkinson’s Disease Rationale: Animal models have shown that key cortical plasticity mechanisms such as long-term potentiation (LTP) and long-term depression (LTD) can be impaired in Parkinson’s disease (PD), raising the question whether these altered mechanisms of cortical plasticity can serve as a rational model for electrical stimulation protocols targeting PD symptoms. Neural networks implicated in PD reflect cortical-subcortical, rather than primarily cortical activity. While this network has been successfully addressed invasively via deep brain stimulator (DBS) implant surgery, investigators hypothesize that dysfunctional cortical-subcortical networks implicated in PD might be accessed more readily via cortical nodes of that network which remain accessible to non-invasive scalp electrode placements (Hess, 2013). This effect-at-a-distance was confirmed in a recent proof-of-concept animal study in which 10 min application of tDCS stimulation via a scalp electrode resulted in persisting increase in extracellular dopamine levels within the striatum, a subcortical structure (Tanaka et al., 2013). In addition to modulating basal ganglia-thalamocortical network activity, and promoting cortical compensation for basal ganglia network dysfunction (Fregni, Boggio, Santos et al., 2006), investigators believe that tDCS holds potential to amplify neuroplastic response to physical therapies, potentiating the impact of traditional rehabilitation interventions for PD (Hess, 2013). Findings: Clinical literature supporting use of tDCS in treatment of PD is sparse, though intriguing. In regard to motor symptoms, eight sessions of anodal tDCS applied to the motor and prefrontal cortices over two weeks resulted in short-term improvement in walking speed, while upper extremity bradykinesia remained significantly improved on 3 month post-treatment follow-up (Benninger et al., 2010). tDCS also shows promise in the treatment of cognitive symptoms in PD, for which current therapies are quite limited. Anodal (but not sham) stimulation of left DLPFC in PD patients yielded improved working memory performance (Boggio et al., 2006), as well as improved verbal fluency (Pereira et al., 2013). Executive functioning and mental flexibility improved in PD patients following 10 sessions of anodal (but not sham) stimulation applied to either right or left DLPFC, with gains persisting at one month follow-up (Doruk, Gray, Bravo, Pascual-Leone, & Fregni, 2014). To date, a single study investigating tDCS efficacy in treatment of depression in PD was identified, yielding null outcome (Benninger et al., 2010). The authors reported that a novel protocol providing anodal stimulation (2 mA) with alternating treatments over premotor cortex (centered over Fz-Cz; 10–20 system) or prefrontal cortex (Fp1; 10–20 system), and cathode on mastoids delivered for 20 minutes in eight sessions over 2.5 weeks, failed to yield significant improvement in depression.

Multiple Sclerosis Findings: A handful of studies have explored the efficacy of tDCS in amelioration of MS deficits, including tactile sensory loss, pain, and fatigue. A small randomized, double blind pilot study by Mori and colleagues reported that five daily sessions of anodal (but not sham) tDCS over the somatosensory cortex resulted in significant improvement in tactile discrimination thresholds (Mori et al., 2013), while a 5-day period of anodal tDCS to motor cortex was able to significantly reduce pain-scale scores in MS patients with central chronic pain, with evidence of long-lasting clinical effects (Mori et al., 2010). MS patients reported a 30% reduction in debilitating fatigue symptoms following a 5-day course of anodal tDCS over motor cortex (Ferrucci et al., 2014) and 25% fatigue reduction with anodal stimulation of bilateral somatosensory cortex (Tecchio et al., 2014). By contrast, anodal stimulation over the left frontal cortex failed to yield significant improvement in post-treatment fatigue (Saiote et al., 2014).

510

Transcranial Direct Current Stimulation

Amyotrophic Lateral Sclerosis Absence of treatment effect: In contrast to Parkinson’s disease, tDCS has been found ineffective in impacting motor symptoms of Amyotrophic lateral sclerosis (ALS; “Lou Gehrig’s disease”), a progressive neuromuscular disorder marked by motor neuron degeneration. Whereas either cathodal or anodal tDCS successfully induced a consistent +/- 45% change in corticospinal excitability in healthy control subjects, no change could be induced in ALS patients after tDCS (Munneke et al., 2011; Quartarone et al., 2007).

Alzheimer’s Disease Rationale: Alzheimer’s disease (AD) is a neurodegenerative disease characterized by a progressive decline in cognitive functions such as memory, attention, perceptual-spatial abilities, language, and executive functions. Preliminary evidence suggests that interventions promoting neural plasticity may hold potential to induce at least transitory cognitive gains especially for patients in early stages or with mild AD (Boggio et al., 2011). Findings: Studies published to date have confirmed that anodal tDCS in patients with AD can induce acute and short-duration beneficial effects on cognitive function, but the long-term therapeutic clinical significance in AD is unclear (Freitas, Mondragon-Llorca, & Pascual-Leone, 2011). Three small N studies have yielded preliminary evidence of transitory cognitive improvement following tDCS application in Alzheimer’s patients. Because temporoparietal areas are thought to be hypoactive in AD, Ferrucci and colleagues contrasted the impact of a anodal tDCS, cathodal tDCS, or sham tDCS over bilateral temporoparietal cortex (P3-T5 on left side and P6-T4 over right side, with cathode placement to right shoulder) in three separate sessions (15 min at 1.5 mA, at least one week apart). They found that anodal tDCS transiently improved word recognition memory in patients with mild AD, while cathodal stimulation reversed the effect, impeding word recognition performance (Ferrucci, Mameli et al., 2008). Boggio and colleagues investigated the impact of tDCS on visual recognition memory in a group of 10 patients with mild to moderately severe AD, alternating a single session each (30 min at 2 mA, sessions 48 hours apart) of (1) anodal tDCS to the left DLPFC (F3, 10–20 system); (2) anodal tDCS to the left temporal cortex (T7, 10–20 system); and (3) sham stimulation with cathode on right supraorbital area (FP2, 10–20 system). Stimulation over both prefrontal and temporal areas resulted in a modest transient improvement of visual recognition memory, which was not attributable to nonspecific attention processes (Boggio, Khoury et al., 2009). With adequate treatment, these effects seemed to persist, as demonstrated in a multi-session tDCS study (Boggio et al., 2012), where 11.4% improvement in patients’ visual recognition memory continued to be evident at 1 month follow-up, following five treatment sessions administered over one week. A single study investigating the potential for tDCS to impact neuropsychiatric symptoms of AD yielded null outcome (Suemoto et al., 2014); anodal stimulation of left DLPFC (6 sessions over 2 weeks) failed to have measurable impact on symptoms of apathy, depression, or level of caregiver burden.

Acquired Neurological Disorders Stroke Rationale: Several studies have elucidated the role of maladaptive plasticity in sustaining motor and language deficits following stroke. Neuroimaging studies have identified increased cortical excitability in the intact primary motor cortex of the unaffected hemisphere, while the level cortical

511

Roger H. Riss and Frederick Ulam

excitability within the intact hemisphere is inversely correlated with the level of paresis in the affected extremity. These observations appear to reflect a persistent maladaptive consequence of early compensatory efforts by the unaffected motor cortex to assume activity previously supported by the affected perilesional motor cortex. As recovery progresses, re-assertion of control by perilesional motor cortex continues to be maladaptively suppressed by the contralesional motor cortex in a process known as interhemispheric inhibition. A similar process of maladaptive reciprocal inhibition has been identified related to right hemisphere Broca’s homologue and recovery of aphasic speech. Therefore, blocking or reducing maladaptive compensatory activation patterns (typically via inhibitory or cathodal tDCS placements over contralesional hemisphere) has been an attractive target for tDCS rehabilitation research. A second approach is to potentiate adaptive neuroplastic change arising from traditional therapy interventions by enhancing recruitment of task-related cortical networks (typically via stimulatory or anodal tDCS over perilesional and/or task-related cortex) during skill acquisition (Schlaug et al., 2008). Efficacy: Clinical data indicates that tDCS, when applied concurrently with motor practice, potentiates motor skill acquisition by a factor of 10 to 20%, as compared to physical therapy alone. In human subjects, we now know that anodal stimulation over M1 area of the motor cortex (e.g. 10–20 locations C3 or C4), concurrent with motor skill practice, modulates encoding, consolidation, and retention of motor learning (Stagg et al., 2011), as well as speed and accuracy of task execution (Reis et al., 2009). In comparison to individuals receiving sham stimulation, those who received active tDCS concurrent with physical therapy demonstrated better retention of gains in motor skills at long-term follow-up as well as stronger generalization of skills to untrained tasks (Orban de Xivry et al., 2011). Arm and hand movement: To facilitate restoration of balance of corticospinal excitability between hemispheres, studies using tDCS have targeted motor cortex, referred to as M1 (i.e. C3 or C4, 10–20 system). Following a left hemisphere stroke, for example, tDCS might be applied to rebalance cortical excitability by (1) inhibition of compensatory activation of motor cortex within the healthy right hemisphere (e.g. cathode placement to C4, reference anode to contralateral supraorbital forehead), or by (2) excitatory facilitation of the lesioned motor cortex (anode placement to C3, reference cathode to contralateral supraorbital forehead), or (3) via application of a dual hemisphere stimulation protocol (cathode to C4, anode to C3) in order to facilitate the stroke-affected motor cortex, while simultaneously inhibiting the healthy “helper” hemisphere’s compensatory efforts during execution of a motor task. Spasticity: Excessive muscle tone, or spasticity, is another common barrier to motor recovery following both stroke and traumatic brain injury. A 4-week course of cathodal tDCS applied to M1 of the affected hemisphere (2 mA, 20 min, 20 sessions), applied concurrently with physical therapy, yielded 80% reduction in spasticity, with associated improvement in motor function as compared to 4% reduction with physical therapy alone, with gains maintained at 4 week follow-up (Wu et al., 2013). Dysphagia: Currently, treatment options for acquired swallowing disorders (dysphagia) due to loss of muscle tone following stroke include both behavioral swallowing training exercises and peripherally applied electrical stimulation (Chetney & Waro, 2004). Results of at least three sham controlled trials (Kumar et al., 2011; Shigematsu, Fujishima, & Ohno, 2013; Yang et al., 2012) have demonstrated that non-invasive cortical stimulation may now also be added to this list of therapy options, as an add-on strategy during swallowing training. Anodal tDCS to the stroke-affected motor cortex (1 mA, 20 min, 5 to 10 sessions) with concurrent behavioral therapies generated significantly larger improvement in swallowing function, as compared to behavioral therapy + sham tDCS. Preliminary evidence for neuroplastic change was found in gains at follow-up that were significantly largely than those that had been observed immediately post-treatment. Future investigations may include direct comparison of cortical vs. peripheral stimulation when administered during behavioral swallowing therapies. 512

Transcranial Direct Current Stimulation

Hemi spatial neglect: Survivors of right hemisphere stroke often demonstrate unawareness of items within the left visual field, hindering safety and reducing their functional independence in life activities. Unfortunately, current rehabilitation interventions for neglect, which include visual scanning training, optokinetic stimulation, and limb activation training, have produced varying results, with only limited generalization to untrained tasks (Fasotti & van Kessel, 2013). In an early proofof-concept study, Sparing demonstrated that stroke survivors demonstrated a transient reduction in symptoms of visuospatial neglect following a single session of either inhibitory (cathodal) tDCS applied over the contralesional posterior parietal cortex or facilitatory (anodal) tDCS applied over the lesioned posterior parietal cortex (Sparing et al., 2009). In another example of the synergistic effects, investigators demonstrated that tDCS amplified the efficacy of a computer-based therapy for visual hemi-neglect following stroke (Vision Restoration Therapy, VRT, Nova Vision Inc.) which had achieved mixed reviews as a standalone therapy, with questions about generalization and persistence of gains (Horton, 2005). Researchers sought to boost the efficacy of VRT training by concurrently up-regulating the excitability of surviving visual networks within the occipital cortex via application of anodal transcranial direct current stimulation (tDCS) during VRT practice sessions. They found that patients receiving the combined intervention did, in fact, attain superior visual rehabilitative outcomes compared to computer training drills alone, including better stimulus detection accuracy in the affected visual field and a greater expansion of visual field border at follow-up (Plow, Obretenova, Fregni, Pascual-Leone, & Merabet, 2012). A follow-up study utilized high-resolution electrical field modeling of current flow in perilesional cortex during combined VRT and tDCS, to demonstrate that functional improvement was regionspecific to visual cortical areas stimulated by the anode electrode, consistent with effective tDCS facilitated rehabilitation (Halko et al., 2011).

Aphasia Rationale: Aphasia is an acquired language disorder often accompanying left hemisphere stroke. Functional brain imaging has consistently shown that, during early recovery, aphasic stroke survivors rely upon compensatory right hemisphere cortical activation to support speech output. However, in the long-term, persistent reliance on compensatory activation of the non-language specialized right hemisphere to support speech represents a maladaptive strategy that interferes with, rather than promotes, aphasia recovery. This finding has guided researchers to focus on development of tDCS neurostimulation protocols designed to re-establish a balance between hemispheres and facilitate return to activation patterns associated with optimal language processing in recovering aphasic patients (Schlaug et al., 2008). Temporal recovery trajectory: Imaging research indicates that best outcomes in aphasia recovery are associated with an eventual re-assertion of dominance by perilesional left hemisphere cortex to support language, as recovery progresses and perilesional cortex comes back on line in three phases: 1. 2. 3.

Reduced activation of left hemisphere language areas following stroke, accompanied by aphasia. Compensatory recruitment of right hemisphere to support speech, supporting initial improvement in speech output. Later in recovery, patients who demonstrate best outcomes demonstrate gradual re-assertion of dominance by left hemisphere language cortex, as perilesional language areas come back on line (Crosson et al., 2007; Postman-Caucheteux et al., 2010; Saur et al., 2006).

Efficacy: Despite individual differences in study design and stimulation parameters, some 15 studies have now demonstrated that tDCS administered concurrently with speech therapy facilitates recovery of expressive and receptive speech by a factor of up to 30% as compared to traditional therapy alone 513

Roger H. Riss and Frederick Ulam

(Monti et al., 2013). In several studies, aphasic patients receiving excitatory “anodal” stimulation to facilitate activation of left hemisphere regions comprising the language network, concurrent with speech training, demonstrated faster skill acquisition than those who received sham stimulation + speech therapy (Floel, Rosser, Michka, Knecht, & Breitenstein, 2008; Iyer et al., 2005; Sparing et al., 2008) and aphasic stroke patients (Baker et al., 2010; Fridriksson et al., 2011; Kang, Kim, Sohn, Cohen, & Paik, 2011). Protocol selection: Naming accuracy and fluency can be improved by applying anodal tDCS to the language-dominant left cortex, including both Broca’s (Baker et al., 2010; Fridriksson et al., 2011; Monti et al., 2008) and Wernicke’s (Fiori et al., 2011) areas, while aphasic patients, but not healthy normals, appear to benefit from cathodal inhibition of maladaptive compensatory activation emerging from right hemisphere homologue sites which “mirror” Broca’s and Wernicke’s language areas (Floel et al., 2011; Kang et al., 2011).

Traumatic Brain Injury Rationale: Research on use of tDCS in traumatic brain injury (TBI) rehabilitation is currently quite limited. Support for its use in this population to date is primarily theoretical or inferred from work with other diagnostic groups, where tDCS has already demonstrated its capacity to enhance learning efficiency by potentiating synaptic strengthening during task acquisition (Demirtas-Tatlidede, Vahabzadeh-Hagh, & Pascual-Leone, 2013; Rioult-Pedotti, Friedman, Hess, & Donoghue, 1998). The results from these studies suggest that neurostimulation may augment improvements in both motor and cognitive deficits after brain injury (Shin, Dixon, Okonkwo, & Richardson, 2014). Coma stimulation: More than 30 years after the pioneering neurofeedback-based coma stimulation studies by Margaret Ayers, therapeutic options for patients in minimally conscious state (MCS) or persistent vegetative state (PVS) are limited, giving impetus to two recent studies investigating efficacy of tDCS in emergence from coma. Angelakis and colleagues found that patients in MCS following severe cerebral insult demonstrated improved arousal on the JFK Coma Recovery Scale Revised following 10 sessions of anodal, but not sham, tDCS. One patient who received a second round of tDCS three months after initial participation showed further improvement, emerging into consciousness after stimulation, despite no change in the interval between treatments. Patients who were in a more severe, persistent vegetative state (PVS) demonstrated smaller benefit, suggesting that severity of pathology predicts capacity to benefit from tDCS (Angelakis et al., 2014). These findings have received additional support in a randomized controlled crossover study by Belgian researchers (Thibaut, Bruno, Ledoux, Demertzi, & Laureys, 2012). Attention regulation: Persistent attention deficits are a ubiquitous finding during early recovery from traumatic brain injury (TBI). Attention is not a unitary construct, and specific factors contributing to attention impairment following TBI may include speed of information-processing, span of attention, sustained attention, focused/selective attention, and executive attentional control. Depending upon severity of TBI, these deficits may persist into later recovery stages (Mathias & Wheaton, 2007). In early stages of recovery, pharmacological agents have been used with success to reduce disabling impact of disrupted attention in rehabilitation. Among these agents, the dopamine agonist amantadine, thought to be involved in frontal lobe stimulation, has been found particularly effective (Wheaton, Mathias, & Vink, 2009), safely improving arousal and cognition in early recovery from 5 days to 3 months post-injury at doses of 200–400 mg/day (Sawyer, Mauro, & Ohlinger, 2008). Behavioral emphasis during early recovery is placed on environmental management to reduce susceptibility to overstimulation, while in subacute and chronic recovery phase; specific cognitive behavioral training techniques have been used with success to address attention inefficiency. A limitation is that training effects appear to be task specific and may not generalize to other components 514

Transcranial Direct Current Stimulation

of attention. For example, Attention Process Training has effectively addressed specific components of executive attention, yet was ineffective in concurrently addressing deficits in arousal and vigilance (Sohlberg, McLaughlin, Pavese, Heidrich, & Posner, 2000). Another behavioral intervention, Dual Task Training, demonstrates similar specificity, with significant training-related effect found on divided attention and dual-task measures but limited generalization to untrained, non-target tasks and no effect on measures of executive attention. A search of PubMed in early 2015, at the time of this writing, identified only three studies investigating use of tDCS in traumatic brain injury rehabilitation, including the most definitive of the studies to date, conducted by one of this chapter’s authors (FAU) and his research group (Ulam et al., 2015). This study will be discussed in some detail in order to provide insight into some of the important challenges and considerations associated with the use of tDCS in the TBI population.

Ulam et al. Study Methods A sample of 26 individuals with moderate to severe traumatic brain injuries participated in the study. The subjects were hospitalized, undergoing subacute neurorehabilitation for their injuries. A randomized, double blind design was used in the study. Subjects were randomly assigned to receive active anodal tDCS (n = 13) to the left dorsolateral prefrontal cortex, or sham (n = 13) tDCS. The anodal electrode was placed at the F3 electrode location according to the International 10/20 System of Electrode Placement, with the cathodal electrode placed over the right supraorbital area (Fp2 electrode location). A battery of neuropsychological tests emphasizing attention, working memory, learning, and executive inhibitory control was administered before and after the series of 10 consecutive, daily tDCS treatments. Subjects underwent 10 consecutive daily tDCS treatments, with the active tDCS group receiving 1 mA direct current stimulation for 20 minutes each day. The sham group had tDCS electrodes placed in the same locations and received 30 seconds of stimulation that faded in over 8 seconds and then faded out over 8 seconds. Subjects were unable to determine accurately if they were in the active or sham groups. EEG was recorded at 6 different time points in order to assess (1) the stability of the quantified EEG metrics used, (2) immediate effects of tDCS on EEG oscillations, and (3) cumulative effects of tDCS on EEG oscillations. Relative power Z scores based on comparison of each subject’s FFT derived EEG spectrum with the NeuroGuide Reference Database (Applied Neuroscience, Inc., St. Petersburg, FL) were calculated for the F3 electrode, where the anode was placed, and the Fp2 electrode, where the cathode was placed. All EEGs were recorded in the eyes closed, resting but awake state.

Findings Both immediate and cumulative effects of anodal tDCS on EEG oscillations were found in this study. First of all, no significant changes in EEG relative power were present for either group between the first two pretreatment EEGs conducted one day apart, suggesting adequate short-term stability of the EEG measures. A significant decrease in theta relative power at the F3 electrode location (p = 0.03) was present immediately following the first tDCS session compared to immediately before the session. This reduction in theta was present for the active tDCS group only, with no significant change present for the sham group. When the pretreatment EEG was compared to the EEG obtained immediately following the final tDCS session, a significant decrease in delta relative power was present for the active group (p = 0.012) but not the sham group. Additionally, an increase in alpha relative power was documented for 515

Roger H. Riss and Frederick Ulam

the active group (p = 0.001), with no significant change noted for the sham group. These cumulative changes in EEG oscillations were tested with an additional EEG recording obtained one day after the final tDCS session. Again, compared to the pretreatment EEG obtained one day before the initiation of treatment, the EEG obtained one day after the completion of 10 tDCS sessions was characterized by a significant decrease in delta relative power (p = 0.004) and an increase in alpha relative power (p = 0.002) for the active tDCS group only. A graph showing progressive changes in alpha over the course of the study is presented in Figure 27.1, below. An intriguing aspect of the cumulative EEG changes associated with active anodal tDCS was that the exact same pattern of change (i.e. decreased delta, increased alpha) was present not only at the F3 electrode, where the anode had been placed, but also at the cathode location (Fp2). Delta was significantly decreased immediately following the final tDCS session compared to the EEG recorded prior to the initiation of treatment (p = 0.024), and alpha was significantly increased (p = 0.006). Similarly, for the EEG recorded one day following the completion of tDCS treatments, delta was significant decreased (p = 0.043) and alpha was significantly increased (p = 0.007). Regarding changes in neuropsychological tests from before to after treatment, we anticipated that both the active and sham groups would show significant improvements. This is owing to the fact that the subacute phase of recovery from traumatic brain injury is very dynamic, with dramatic changes in function often taking place. In fact, repeated measures ANOVAs comparing pre- versus post-neuropsychological tests showed extensive improvements for both groups, with no overall significant differences between groups. A challenge in this study involved attempting to distinguish neuropsychological improvements that occurred spontaneously or in response to traditional rehabilitation therapies from those that might have been stimulated by the tDCS. To further explore changes in neuropsychological functioning, correlation analyses were conducted. The intent was to see if meaningful relationships between EEG changes and neuropsychological changes were present for both the active and sham groups. For the active tDCS group, in the alpha frequency, significant positive correlations were present on 4 out of 21 neuropsychological measures, with a fifth comparison closely approaching significance (p = 0.06). For the sham group, 2 out of 21 comparisons were significantly positively correlated. In the delta frequency, 10 out of 21 neuropsychological measures were negatively correlated at statistically significant levels for the active group, while two negative correlations reached significance for

Mean Alpha Relative Power Z Scores at F3: Active vs Sham tDCS 0.6

10th tDCS Treatment 0.5 0.4

t(25) = 2, p = 0.028 1st tDCS Treatment

0.3 0.2 Mean Z Score 0.1 0 –0.1 –0.2 –0.3

Figure 27.1

Q1

Q2

Q3

Q4

Q5

Q6

Active tDCS

–0.03

0.12

0.18

0.36

0.46

0.52

Sham tDCS

0.01

0.11

0.23

0.08

0.07

–0.23

Cumulative changes in alpha relative power from the F3 electrode during 10 sessions of active anodal versus sham tDCS. Active anodal tDCS, n = 13; Sham tDCS, n = 13.

516

Transcranial Direct Current Stimulation

the sham group. A z test comparing the proportion of significant correlations present for the active tDCS group versus the proportion present for the sham group was conducted. The greater proportion of significant correlations present for the active group compared to the sham group was itself significant (p = 0.006). These results strongly suggest that the changes in EEG oscillations resulting from active anodal tDCS were meaningfully associated with a large number of the improved neuropsychological performances achieved by the active tDCS group. It is important to note that the direction of the correlations present in this analysis is consistent with the hypothesis of improved performance on neuropsychological tests due to better regulation of cortical excitability. Specifically, all significant correlations between neuropsychological tests and changes in alpha are positive—the greater the amount of alpha relative power, the better the performance on the tests. In contrast, all significant correlations between neuropsychological test changes and delta relative power are negative—the larger the decrease in delta, the greater the improvement in neuropsychological test performance. Previous work by our group had tracked quantitative EEG and neuropsychological changes occurring as individuals with TBI progress through subacute neurorehabilitation (Ulam et al., 2013). Using simple linear regression, we had shown that decreases in delta and theta power over a twoweek period predicted improved neuropsychological performance, while increases in alpha predicted improvement over that same period of time. These results suggested that increased regulation of cortical excitability is an important facet of recovery from TBI, and that resting EEG is a satisfactory measure of this regulation of cortical excitability. Based on our previous work, we entertained the post-hoc hypothesis that the extent of response to anodal tDCS might depend upon the degree of EEG slowing present in the initial, pretreatment EEG. We thus divided our sample of subjects with TBI into those with and without slowing in their initial EEGs. Slowing was defined as having two contiguous electrodes with delta or theta spectral values equal to or greater than 2 standard deviations above the age-appropriate mean as determined by comparison with the NeuroGuide Reference Database. Based on this criterion, 7 of the active tDCS group had slowing and 6 did not, while 5 in the sham group had slowing, and 8 did not. We then re-examined neuropsychological changes over the course of treatment within the EEG-defined subgroups. Figure 27.2 shows the results of this analysis.

Figure 27.2

Number of significantly improved neuropsychological test performances by EEG-defined subgroups (Active tDCS with slowing, n = 7; Active tDCS without slowing, n = 6; Sham tDCS with slowing, n = 5; Sham tDCS without slowing, n = 8).

517

Roger H. Riss and Frederick Ulam

As can be seen from Figure 27.2, the active tDCS group with initial EEG slowing improved to a statistically significant degree on a larger number of neuropsychological measures than all other groups. Further post-hoc statistical analysis using a Chi2 test showed that the distribution of improvements among EEG-defined groups was highly unlikely by chance (p = 0.02). A Marascuilo procedure analyzing multiple pair wise comparisons showed that the significant difference between EEG-defined groups was primarily driven by the greater number of improvements within the active tDCS group with slowing. The potential significance of the post-hoc analysis discussed above relates to the notion of tDCS as a modulator of cortical excitability. If the effects of anodal tDCS are indeed associated with its ability to increase cortical excitability, then it is reasonable to hypothesize that it will be more effective among individuals with neurological or neuropsychiatric conditions in which decreased cortical excitability is prominent. This appears to the case in our study. Our results further suggest that quantified EEG measures are useful biological markers of the level of cortical excitability, and that these measures might help guide the selection of individuals with traumatic brain injuries that might benefit from this potential treatment. The EEG measures could also help in determining whether anodal or cathodal stimulation of a given cortical region is most appropriate.

Discussion As a whole, the study described above indicates that tDCS shows great promise as a treatment for cognitive impairments among persons with traumatic brain injury. However, another recent study (Lesniak, Polanowska, Seniow, & Czlonkowska, 2013) using roughly similar methods found nonsignificant differences between active and sham tDCS treatments. Several important differences between the studies may account for these discrepant findings. The Lesniak et al. (2013) study enlisted individuals with TBI in the chronic stages of recovery. Time post-injury was measured in months in their study as opposed to weeks in ours. In the Lesniak et al. study, subjects were administered 15 treatments, 10 minutes in duration at 1 mA, as opposed to our study, where 10 treatments of 20 minutes duration at 1 mA were administered. No electrophysiological measures were reported in the Lesniak et al. study, so it is difficult to determine if the 10 minutes of stimulation at 1 mA had a measurable effect on cortical excitability. On the other hand, in our study we were able to demonstrate an immediate effect on EEG theta oscillations after the first tDCS session, indicating that increased cortical excitability was in fact achieved by the stimulation. Finally, subjects in the Lesniak et al. study underwent computerized cognitive training immediately following the tDCS sessions. This may have induced some beneficial cognitive changes in both groups. It would have been helpful to attempt to disassociate changes that could be reasonably attributed to tDCS as opposed to those more likely attributed to the cognitive exercises. However, without electrophysiological measures, it would be difficult to do so. In spite of a lack of significant differences on neuropsychological measures, the Lesniak et al. study found overall larger effect sizes in the active tDCS group, suggesting a positive response to stimulation. Due to the heterogeneity of injury effects across TBI patients as well as the often multi-focal nature of post-injury lesion distribution, models for effective targeting of tDCS in TBI rehabilitation are necessarily more complex and subject to higher inter-patient variability as compared to disorders with more homogenous pathophysiology such as stroke. Thus, it is understandable that tDCS research with TBI survivors has lagged in comparison to diagnostic groups in which injury effects are more easily modeled. Application of tDCS with the TBI population has been limited to date to remediation of arousal and attention deficits. However, in both healthy adults and other clinical diagnostic groups, tDCS has been shown to effectively address a range of behavioral and cognitive targets, 518

Transcranial Direct Current Stimulation

which often elude traditional TBI rehabilitation efforts. For example, persistent executive deficits are an often disabling feature of traumatic brain injury. Studies confirm that tDCS may successfully modulate neural networks supporting executive processes including working memory (Oliveira et al., 2013), insight (Chi & Snyder, 2011), decision-making (Zaghi, Acar, Hultgren, Boggio, & Fregni, 2010), and judgment and impulse control (Pripfl, Neumann, Kohler, & Lamm, 2013), giving hope for tDCS as a next-generation intervention for survivors of TBI.

The Future of tDCS Emerging Technological Advances In comparison to more technologically sophisticated neurostimulation methods such as rTMS, tDCS has been critiqued for its low spatial resolution and non-focal targeting, resulting in diffuse, widespread regional cortical activation patterns under the stimulation electrode. In an effort to overcome this limitation, research-level tDCS systems are now becoming available offering high-definition, multichannel, dense-array electrode montages, for increased precision in stimulation of discrete cortical targets (e.g. HD-TCS, Soterix, Inc., New York). To support integration of tDCS into concurrent behavioral and physical therapies, new wireless portable systems include a full-array wearable tDCS cap supporting multichannel stimulation with wireless EEG, to support dual use of electrodes for stimulation and concurrent mobile EEG monitoring of response to treatment (e.g. StarStim system; NeuroElectrics, Inc., Barcelona, Spain). Neurotargeting software is also coming to market to automate optimal electrode placement based on clinician-identified targets and subject-specific anatomy on a patient-by-patient basis. In another development, there is increasing interest in use of pretreatment structural MRI and fMRI data to map the margins of spared perilesional cortex in order to guide optimal electrode placement (Baker et al., 2010; Fridriksson et al., 2011). New research-level tDCS systems supporting co-registration of MRI signal concurrent with tDCS stimulation have recently come to market to move research in the field to a higher level of sophistication (e.g. NeuroConn MRI-tDCS system; Rogue Resolutions, Cardiff, Wales). In the therapy clinic, where MRI is typically unavailable or cost prohibitive, database-guided quantitative electroencephalography (QEEG) may offer a practical, cost-effective alternative to the clinician seeking real-time information to guide treatment planning and monitor outcomes of tDCS intervention in a fast paced clinical setting (Schestatsky, Morales-Quezada, & Fregni, 2013). While these exciting technological innovations are critical to high-end research applications, they do not diminish the clinical utility of the simple low-cost devices to which clinicians already have access. In fact, evidence is accumulating that the diffuse stimulation and low-precision targeting offered by the oversized electrode pads used in the current generation of low-cost devices has not necessarily been a disadvantage clinically, but has actually yielded unexpected synergistic benefits, as when depressed patients receiving Left DLPFC tDCS have demonstrated concurrent improvement in working memory (Oliveira et al., 2013), or reduction in chronic pain (Loo & Martin, 2012), or when hemiplegic stroke patients receiving therapeutic stimulation of motor cortex to improve hand movement have demonstrated serendipitous improvement in aphasic speech (Hesse et al., 2007). At least nine published studies to date have noted that depressed patients receiving tDCS have reported unplanned improvement in cognitive symptoms of depression concurrent with improvement in mood (Demirtas-Tatlidede et al., 2013; Tortella, Selingardi, Moreno, Veronezi, & Brunoni, 2014). These reports highlight that the relatively non-focal brain stimulation provided by the large-electrode pads typically utilized in simple clinical tDCS devices, while problematic from a researcher’s standpoint, may actually provide a therapeutic advantage in disorders, such as depression, for which pathophysiology occurs at a widespread network rather than focal level. 519

Roger H. Riss and Frederick Ulam

Future Research Questions As research moves beyond phase-one studies exploring whether tDCS works, focus will increasingly shift to optimizing clinical outcomes, with questions such as: • • • • • • •

What is the optimal dosing regimen (e.g. number and frequency of treatment sessions)? Can we predict who is most likely to benefit from tDCS? How does age interact with response to tDCS? How does severity of lesion burden influence protocol selection and treatment outcome? Do specific patient subtypes require specific tDCS protocols? Must staging of tDCS interventions be aligned with stages of recovery? What is the role of clinician competencies in tDCS outcomes?

What Is the Optimal Dosing Regimen? Studies to date have largely focused on proof-of-concept, leaving questions of duration and frequency of treatment largely unexplored. Nonetheless, evidence is beginning to emerge that past studies may underestimate potential tDCS effect size due to less than optimal duration of treatment. A recent meta-analysis reported that, following an average course of 10 sessions, tDCS for treatment of depression yielded a modest remission rate of only 8.5%, with mean reduction in depression symptom severity of less than 30%. However, in the largest and most definitive sham controlled tDCS/depression study published to date, 48% of subjects who received 30 treatment sessions (2 mA, 30 min) over six weeks demonstrated > 50% reduction in depression severity, suggesting a clear dose/response relationship (Alonzo, Brassil, Taylor, Martin, & Loo, 2012).

Who Is Most Likely to Benefit from tDCS? Research is needed to define specific patient characteristics that predict likely therapeutic response to tDCS. We do have early evidence, from selected diagnostic groups, that development of useful prediction algorithms is an attainable goal. For example, Korean investigators reported that aphasic stroke patients whose pretreatment Aphasia Quotient (AQ) score fell below 10 were nine times less likely to benefit from tDCS supported speech therapy as compared to those whose initial AQ score equaled or exceeded that level (Jung, Lim, Kang, Sohn, & Paik, 2011).

How Does Age Interact with Response to tDCS? Early tDCS studies have been largely limited to adult populations, although a handful of studies extending use of tDCS to pediatric populations have been published. Given an increased level of neural plasticity in the younger brain (Pinto, Poretti, Meoded, Tekes, & Huisman, 2012), interest in tDCS intervention in pediatrics is expected to grow, with initial studies addressing safety, feasibility, and dosage adjustment issues for pediatric populations already beginning to emerge (Gillick et al., 2014; Moliadze et al., 2014). Conversely, the brain’s capacity for experience-dependent neuroplasticity is thought to decline in older age, raising the question of an age related upper limit in capacity to benefit from tDCS. However, any practical upper age limit for tDCS efficacy remains theoretical, and has not been subjected to rigorous inquiry in the tDCS literature.

520

Transcranial Direct Current Stimulation

How Does Severity of Lesion Burden Influence Protocol Selection? We are beginning to learn that the level of lesion burden following cerebral insult can have a powerful impact on which tDCS protocol is likely to be effective. For example, in treatment of aphasia, anodal stimulation of the language-dominant left hemisphere, and cathodal inhibition of compensatory right hemisphere activation patterns, have been the most successful intervention protocols for mildly to moderately severe aphasics. By contrast, for the most severely aphasic patients whose massive left hemisphere lesions leave little room for recovery of language within that hemisphere, uptraining of compensatory right hemisphere Broca’s or Wernicke’s homologue regions (via anodal stimulation) appears to be the most effective strategy. For this more severe group, continued reliance on right hemisphere for language production appears to be the best available outcome (Floel et al., 2011; Kang et al., 2011).

Do Specific Patient Subtypes Require Specific tDCS Protocols? Future studies should elucidate patient-specific factors guiding choice of protocol. An example of this very important approach is found in the excellent headache study by Pinchuk and colleagues, reviewed earlier in this paper, which broke new ground in defining best-match treatment protocols for three specific headache subtypes and identifying one headache subtype for which tDCS was ineffective (Pinchuk et al., 2013).

Should Staging of tDCS Intervention Be Linked with Stages of Recovery? Optimal protocol selection may depend as well upon increased knowledge regarding staging of recovery, as it relates to the brain’s own healing trajectory. For example, a recent study suggested that protocols providing anodal stimulation of left hemisphere perilesional cortex within the first few weeks after stroke might be premature (Polanowska, Lesniak, Seniow, & Czlonkowska, 2013). Rather, within the first 30 days of stroke onset, stroke survivors appear to respond most strongly to protocols focusing on cathodal inhibition of compensatory activation patterns in the (non-lesioned) right hemisphere (Jung et al., 2011). On a theoretical level, it has been proposed that tDCS may offer specific benefits at various stages of recovery from neurological insult. Early in recovery, tDCS may emerge as a neuroprotective intervention, providing a method to modulate the ongoing neurochemical cascade of events that contribute to continuing cell death following initial injury. During the acute recovery stage the neuroinhibitory effects of cathodal tDCS may suppress glutamatergic cortical excitotoxity, which when left unchecked is responsible for continued neuronal death. Later, neurostimulatory effects of anodal tDCS may potentially counter persistent GABAergic inhibition which has been shown to play a role in delayed recovery of working memory in the subacute recovery stage (Demirtas-Tatlidede, Vahabzadeh-Hagh, Bernabeu, Tormos, & Pascual-Leone, 2012). In the chronic stage, tDCS coupled with traditional physical and cognitive rehabilitation therapies may enhance learning efficiency by potentiating synaptic strengthening during task acquisition and by inhibiting maladaptive compensatory activation patterns, which have outlived their usefulness (Villamar, Santos Portilla, Fregni, & Zafonte, 2012).

What Is the Role of Clinician Competencies in tDCS Outcomes? Recent research highlights that advances in tDCS-based therapeutics depend, not only on the growing sophistication of tDCS technology, but importantly on the skills of treating clinicians, in particular with regard to their knowledge of the functionally-connected neural networks which modulate the

521

Roger H. Riss and Frederick Ulam

symptom or disorder under treatment. Following an exhaustive comparative review across a range of invasive and non-invasive neurostimulation devices, Fox and colleagues reported that, regardless of neurostimulation method, clinically effective protocols shared a common feature: those protocols that work for a given disorder all target nodes within a common neural network as defined by resting-state functional-connectivity MRI. This pattern of resting-state functional connectivity was replicated when they examined successful disease-specific protocols, across stimulation techniques, for treatment of depression, Parkinson’s disease, obsessive-compulsive disorder, essential tremor, addiction, pain, minimally conscious states, and Alzheimer’s disease, regardless of whether invasive or noninvasive stimulation devices were utilized. By contrast, lack of resting-state functional connectivity predicted sites where stimulation was likely to be ineffective, and the sign of the correlation predicted whether excitatory or inhibitory stimulation was found clinically effective. These results highlight that advances in tDCS-based therapeutics depend, not only on the growing sophistication of instrumentation, but importantly on the level of sophistication with which tDCS clinicians apply analysis of neural network dynamics to optimizing treatment, and identifying new stimulation targets (Fox et al., 2014).

Conclusion While tDCS has demonstrated promise as a standalone intervention (as for increasing arousal in early brain injury recovery) a growing body of evidence suggests that tDCS’s capacity to synergistically boost efficacy of other therapies is perhaps its most promising application, leading to more rapid and enduring clinical gains than either tDCS or traditional therapies can offer on their own (Bolognini et al., 2009). Hebbian theory predicts that the more frequently and consistently a neuronal circuit is activated in a synchronous manner, the more its network connectivity patterns will be strengthened, and the less frequently that these neurons are allowed to co-activate maladaptively with neurons outside that circuit, the less likely that maladaptive compensatory circuits will be inadvertently reinforced (Taub, Uswatte, & Elbert, 2002). The introduction of cortical neurostimulation techniques into the cognitive, physical, or psychotherapy training session holds promise to take these advances to the next level of learning efficiency. When practice of neuroplasticity-based therapy protocols is paired with carefully targeted stimulation of relevant brain networks, long-term potentiation within relevant neural networks and consolidation of therapeutic change is likely to be maximized.

References Alonzo, A., Brassil, J., Taylor, J. L., Martin, D., & Loo, C. K. (2012). Daily transcranial direct current stimulation (tDCS) leads to greater increases in cortical excitability than second daily transcranial direct current stimulation. Brain Stimulation, 5(3), 208–213. doi:10.1016/j.brs.2011.04.006 Angelakis, E., Liouta, E., Andreadis, N., Korfias, S., Ktonas, P., Stranjalis, G., & Sakas, D. E. (2014). Transcranial direct current stimulation effects in disorders of consciousness. Archives of Physical Medicine and Rehabilitation, 95(2), 283–289. doi:10.1016/j.apmr.2013.09.002 S0003–9993(13)00896–4 [pii] Antal, A., Brepohl, N., Poreisz, C., Boros, K., Csifcsak, G., & Paulus, W. (2008). Transcranial direct current stimulation over somatosensory cortex decreases experimentally induced acute pain perception. Clinical Journal of Pain, 24(1), 56–63. doi:10.1097/AJP.0b013e318157233b Antal, A., Kriener, N., Lang, N., Boros, K., & Paulus, W. (2011). Cathodal transcranial direct current stimulation of the visual cortex in the prophylactic treatment of migraine. Cephalalgia, 31(7), 820–828. doi:10.1177/0333102411399349 Antal, A., & Paulus, W. (2010). Transcranial magnetic and direct current stimulation in the therapy of pain. Schmerz, 24(2), 161–166. doi:10.1007/s00482–010–0899–x Arul-Anandam, A. P., Loo, C., & Sachdev, P. (2009). Transcranial direct current stimulation—what is the evidence for its efficacy and safety? F1000 Medicine Reports, 1, 58. doi:10.3410/M1–58 [pii]

522

Transcranial Direct Current Stimulation Auvichayapat, N., Rotenberg, A., Gersner, R., Ngodklang, S., Tiamkao, S., Tassaneeyakul, W., & Auvichayapat, P. (2013). Transcranial direct current stimulation for treatment of refractory childhood focal epilepsy. Brain Stimulation, 6(4), 696–700. doi:10.1016/j.brs.2013.01.009 S1935–861X(13)00031–4 [pii] Auvichayapat, P., Janyacharoen, T., Rotenberg, A., Tiamkao, S., Krisanaprakornkit, T., Sinawat, S., . . . Auvichayapat, N. (2012). Migraine prophylaxis by anodal transcranial direct current stimulation, a randomized, placebo-controlled trial. Journal of the Medical Association of Thailand, 95(8), 1003–1012. Baker, J. M., Rorden, C., & Fridriksson, J. (2010). Using transcranial direct-current stimulation to treat stroke patients with aphasia. Stroke, 41(6), 1229–1236. doi:10.1161/strokeaha.109.576785 Benninger, D. H., Lomarev, M., Lopez, G., Wassermann, E. M., Li, X., Considine, E., & Hallett, M. (2010). Transcranial direct current stimulation for the treatment of Parkinson’s disease. Journal of Neurology, Neurosurgery and Psychiatry, 81(10), 1105–1111. doi:10.1136/jnnp.2009.202556 81/10/1105 [pii] Berlim, M. T., Dias Neto, V., & Turecki, G. (2009). Transcranial direct current stimulation: A promising alternative for the treatment of major depression? Revista Brasileira de Psiquiatria, 31(Suppl 1) S34–S38. Berlim, M. T., Van den Eynde, F., & Daskalakis, Z. J. (2013). Clinical utility of transcranial direct current stimulation (tDCS) for treating major depression: A systematic review and meta-analysis of randomized, double-blind and sham-controlled trials. Journal of Psychiatric Research, 47(1), 1–7. doi:10.1016/j.jpsychires.2012.09.025 S0022–3956(12)00305–6 [pii] Bikson, M., Datta, A., & Elwassif, M. (2009). Establishing safety limits for transcranial direct current stimulation. Clinical Neurophysiology, 120(6), 1033–1034. doi:10.1016/j.clinph.2009.03.018 S1388–2457(09)00295–8 [pii] Boggio, P. S., Ferrucci, R., Mameli, F., Martins, D., Martins, O., Vergari, M., . . . Priori, A. (2012). Prolonged visual memory enhancement after direct current stimulation in Alzheimer’s disease. Brain Stimulation, 5(3), 223–230. doi:10.1016/j.brs.2011.06.006 Boggio, P. S., Ferrucci, R., Rigonatti, S. P., Covre, P., Nitsche, M., Pascual-Leone, A., & Fregni, F. (2006). Effects of transcranial direct current stimulation on working memory in patients with Parkinson’s disease. Journal of the Neurological Sciences, 249(1), 31–38. doi:10.1016/j.jns.2006.05.062 Boggio, P. S., Fregni, F., Valasek, C., Ellwood, S., Chi, R., Gallate, J., . . . Snyder, A. (2009). Temporal lobe cortical electrical stimulation during the encoding and retrieval phase reduces false memories. PLoS One, 4(3), e4959. doi:10.1371/journal.pone.0004959 Boggio, P. S., Khoury, L. P., Martins, D. C., Martins, O. E., de Macedo, E. C., & Fregni, F. (2009). Temporal cortex direct current stimulation enhances performance on a visual recognition memory task in Alzheimer disease. Journal of Neurology, Neurosurgery and Psychiatry, 80(4), 444–447. doi:10.1136/jnnp.2007.141853 Boggio, P. S., Rigonatti, S. P., Ribeiro, R. B., Myczkowski, M. L., Nitsche, M. A., Pascual-Leone, A., & Fregni, F. (2008). A randomized, double-blind clinical trial on the efficacy of cortical direct current stimulation for the treatment of major depression. International Journal of Neuropsychopharmacology, 11(2), 249–254. doi:10.1017/ S1461145707007833 [pii] Boggio, P. S., Valasek, C. A., Campanha, C., Giglio, A. C., Baptista, N. I., Lapenta, O. M., & Fregni, F. (2011). Non-invasive brain stimulation to assess and modulate neuroplasticity in Alzheimer’s disease. Neuropsychological Rehabilitation, 21(5), 703–716. doi:10.1080/09602011.2011.617943 Bolognini, N., Pascual-Leone, A., & Fregni, F. (2009). Using non-invasive brain stimulation to augment motor training-induced plasticity. Journal of Neuroengineering and Rehabilitation, 6, 8. doi:10.1186/1743–0003–6–8 Borckardt, J. J., Romagnuolo, J., Reeves, S. T., Madan, A., Frohman, H., Beam, W., & George, M. S. (2011). Feasibility, safety, and effectiveness of transcranial direct current stimulation for decreasing post-ERCP pain: A randomized, sham-controlled, pilot study. Gastrointestinal Endoscopy, 73(6), 1158–1164. doi:10.1016/j. gie.2011.01.050 Brasil-Neto, J. P. (2012). Learning, memory, and transcranial direct current stimulation. Frontiers in Psychiatry, 3, 80. doi:10.3389/fpsyt.2012.00080 Brunoni, A. R., Boggio, P. S., De Raedt, R., Bensenor, I. M., Lotufo, P. A., Namur, V., . . . Vanderhasselt, M. A. (2014). Cognitive control therapy and transcranial direct current stimulation for depression: A randomized, double-blinded, controlled trial. Journal of Affective Disorders, 162, 43–49. doi:10.1016/j.jad.2014.03.026 S0165–0327(14)00137–2 [pii] Brunoni, A. R., Valiengo, L., Baccaro, A., Zanao, T. A., de Oliveira, J. F., Goulart, A., . . . Fregni, F. (2013). The sertraline vs. electrical current therapy for treating depression clinical study: Results from a factorial, randomized, controlled trial. JAMA Psychiatry, 70(4), 383–391. doi:10.1001/2013.jamapsychiatry.32 Cattaneo, Z., Pisoni, A., & Papagno, C. (2011). Transcranial direct current stimulation over Broca’s region improves phonemic and semantic fluency in healthy individuals. Neuroscience, 183, 64–70. doi:S03064522(11)00366-6 [pii] Chetney, R., & Waro, K. (2004). A new home health approach to swallowing disorders. Home Healthcare Nurse, 22(10), 703–707; quiz 708–709. doi:00004045–200410000–00011 [pii]

523

Roger H. Riss and Frederick Ulam Chi, R. P., & Snyder, A. W. (2011). Facilitate insight by non-invasive brain stimulation. PLoS One, 6(2), e16655. doi:10.1371/journal.pone.0016655 Crosson, B., McGregor, K., Gopinath, K. S., Conway, T. W., Benjamin, M., Chang, Y. L., . . . White, K. D. (2007). Functional MRI of language in aphasia: A review of the literature and the methodological challenges. Neuropsychol Review, 17(2), 157–177. doi:10.1007/s11065–007–9024-z Dasilva, A. F., Mendonca, M. E., Zaghi, S., Lopes, M., Dossantos, M. F., Spierings, E. L., . . . Fregni, F. (2012). tDCS-induced analgesia and electrical fields in pain-related neural networks in chronic migraine. Headache, 52(8), 1283–1295. doi:10.1111/j.1526–4610.2012.02141.x Datta, A., Bikson, M., & Fregni, F. (2010). Transcranial direct current stimulation in patients with skull defects and skull plates: High-resolution computational FEM study of factors altering cortical current flow. Neuroimage, 52(4), 1268–1278. doi:10.1016/j.neuroimage.2010.04.252 Demirtas-Tatlidede, A., Vahabzadeh-Hagh, A. M., Bernabeu, M., Tormos, J. M., & Pascual-Leone, A. (2012). Noninvasive brain stimulation in traumatic brain injury. Journal of Head Trauma Rehabilitation, 27(4), 274–292. doi:10.1097/HTR.0b013e318217df55 Demirtas-Tatlidede, A., Vahabzadeh-Hagh, A. M., & Pascual-Leone, A. (2013). Can noninvasive brain stimulation enhance cognition in neuropsychiatric disorders? Neuropharmacology, 64, 566–578. doi:10.1016/j.neuro pharm.2012.06.020S0028–3908(12)00278–X [pii] Doruk, D., Gray, Z., Bravo, G. L., Pascual-Leone, A., & Fregni, F. (2014). Effects of tDCS on executive function in Parkinson’s disease. Neuroscience Letters, 582, 27–31. doi:10.1016/j.neulet.2014.08.043S0304–3940(14)00714–9 [pii] D’Urso, G., Mantovani, A., Micillo, M., Priori, A., & Muscettola, G. (2013). Transcranial direct current stimulation and cognitive-behavioral therapy: Evidence of a synergistic effect in treatment-resistant depression. Brain Stimulation, 6(3), 465–467. doi:10.1016/j.brs.2012.09.003 Elmer, S., Burkard, M., Renz, B., Meyer, M., & Jancke, L. (2009). Direct current induced short-term modulation of the left dorsolateral prefrontal cortex while learning auditory presented nouns. Behavioral and Brain Functions, 5, 29. doi:10.1186/1744–9081–5–29 1744–9081–5–29 [pii] Fasotti, L., & van Kessel, M. (2013). Novel insights in the rehabilitation of neglect. Frontiers in Human Neuroscience, 7, 780. doi:10.3389/fnhum.2013.00780 Fecteau, S., Knoch, D., Fregni, F., Sultani, N., Boggio, P., & Pascual-Leone, A. (2007). Diminishing risk-taking behavior by modulating activity in the prefrontal cortex: A direct current stimulation study. Journal of Neuroscience, 27(46), 12500–12505. doi:10.1523/jneurosci.3283–07.2007 Fecteau, S., Pascual-Leone, A., Zald, D. H., Liguori, P., Theoret, H., Boggio, P. S., & Fregni, F. (2007). Activation of prefrontal cortex by transcranial direct current stimulation reduces appetite for risk during ambiguous decision making. Journal of Neuroscience, 27(23), 6212–6218. doi:10.1523/jneurosci.0314–07.2007 Feng, W. W., Bowden, M. G., & Kautz, S. (2013). Review of transcranial direct current stimulation in poststroke recovery. Topics in Stroke Rehabilitation, 20(1), 68–77. doi:10.1310/tsr2001–68 Ferrucci, R., Mameli, F., Guidi, I., Mrakic-Sposta, S., Vergari, M., Marceglia, S., . . . Priori, A. (2008). Transcranial direct current stimulation improves recognition memory in Alzheimer disease. Neurology, 71(7), 493–498. doi:10.1212/01.wnl.0000317060.43722.a3 Ferrucci, R., Marceglia, S., Vergari, M., Cogiamanian, F., Mrakic-Sposta, S., Mameli, F., . . . Priori, A. (2008). Cerebellar transcranial direct current stimulation impairs the practice-dependent proficiency increase in working memory. Journal of Cognitive Neuroscience, 20(9), 1687–1697. doi:10.1162/jocn.2008.20112 Ferrucci, R., Vergari, M., Cogiamanian, F., Bocci, T., Ciocca, M., Tomasini, E., . . . Priori, A. (2014). Transcranial direct current stimulation (tDCS) for fatigue in multiple sclerosis. NeuroRehabilitation, 34(1), 121–127. doi:10.3233/NRE-131019 Y1138T5445003161 [pii] Fertonani, A., Rosini, S., Cotelli, M., Rossini, P. M., & Miniussi, C. (2010). Naming facilitation induced by transcranial direct current stimulation. Behavioural Brain Research, 208(2), 311–318. doi:10.1016/j.bbr.2009.10.030 S0166–4328(09)00653–6 [pii] Fiori, V., Coccia, M., Marinelli, C. V., Vecchi, V., Bonifazi, S., Ceravolo, M. G., . . . Marangolo, P. (2011). Transcranial direct current stimulation improves word retrieval in healthy and nonfluent aphasic subjects. Journal of Cognitive Neuroscience, 23(9), 2309–2323. doi:10.1162/jocn.2010.21579 Floel, A., Meinzer, M., Kirstein, R., Nijhof, S., Deppe, M., Knecht, S., & Breitenstein, C. (2011). Short-term anomia training and electrical brain stimulation. Stroke, 42(7), 2065–2067. doi:10.1161/STROKEAHA.110.609032 [pii] Floel, A., Rosser, N., Michka, O., Knecht, S., & Breitenstein, C. (2008). Noninvasive brain stimulation improves language learning. Journal of Cognitive Neuroscience, 20(8), 1415–1422. doi:10.1162/jocn.2008.20098 Flor, H. (2003). Cortical reorganisation and chronic pain: Implications for rehabilitation. Journal of Rehabilitation Medicine, 41(Suppl), 66–72. Fournier, J. C., DeRubeis, R. J., Hollon, S. D., Dimidjian, S., Amsterdam, J. D., Shelton, R. C., & Fawcett, J. (2010). Antidepressant drug effects and depression severity: A patient-level meta-analysis. JAMA, 303(1), 47–53. doi:10.1001/jama.2009.1943303/1/47 [pii]

524

Transcranial Direct Current Stimulation Fox, Michael D., Buckner, Randy L., Liu, Hesheng, Chakravarty, M. Mallar, Lozano, Andres M., & PascualLeone, Alvaro. (2014). Resting-state networks link invasive and noninvasive brain stimulation across diverse psychiatric and neurological diseases. Proceedings of the National Academy of Sciences, 111(41), E4367–E4375. doi:10.1073/pnas.1405003111 Frank, E., Wilfurth, S., Landgrebe, M., Eichhammer, P., Hajak, G., & Langguth, B. (2010). Anodal skin lesions after treatment with transcranial direct current stimulation. Brain Stimulation, 3(1), 58–59. doi:10.1016/j. brs.2009.04.002 Fregni, F., Boggio, P. S., Lima, M. C., Ferreira, M. J., Wagner, T., Rigonatti, S. P., . . . Pascual-Leone, A. (2006). A sham-controlled, phase II trial of transcranial direct current stimulation for the treatment of central pain in traumatic spinal cord injury. Pain, 122(1–2), 197–209. doi:10.1016/j.pain.2006.02.023 Fregni, F., Boggio, P. S., Mansur, C. G., Wagner, T., Ferreira, M. J., Lima, M. C., . . . Pascual-Leone, A. (2005). Transcranial direct current stimulation of the unaffected hemisphere in stroke patients. Neuroreport, 16(14), 1551–1555. Fregni, F., Boggio, P. S., Nitsche, M. A., Marcolin, M. A., Rigonatti, S. P., & Pascual-Leone, A. (2006). Treatment of major depression with transcranial direct current stimulation. Bipolar Disord, 8(2), 203–204. doi:10.1111/ j.1399–5618.2006.00291.x BDI291 [pii] Fregni, F., Boggio, P. S., Nitsche, M. A., Rigonatti, S. P., & Pascual-Leone, A. (2006). Cognitive effects of repeated sessions of transcranial direct current stimulation in patients with depression. Depression and Anxiety, 23(8), 482–484. doi:10.1002/da.20201 Fregni, F., Boggio, P. S., Santos, M. C., Lima, M., Vieira, A. L., Rigonatti, S. P., . . . Pascual-Leone, A. (2006). Noninvasive cortical stimulation with transcranial direct current stimulation in Parkinson’s disease. Movement Disorders, 21(10), 1693–1702. doi:10.1002/mds.21012 Fregni, F., Freedman, S., & Pascual-Leone, A. (2007). Recent advances in the treatment of chronic pain with noninvasive brain stimulation techniques. Lancet Neurology, 6(2), 188–191. doi:10.1016/s1474–4422(07)70032–7 Fregni, F., Gimenes, R., Valle, A. C., Ferreira, M. J., Rocha, R. R., Natalle, L., . . . Boggio, P. S. (2006). A randomized, sham-controlled, proof of principle study of transcranial direct current stimulation for the treatment of pain in fibromyalgia. Arthritis and Rheumatism, 54(12), 3988–3998. doi:10.1002/art.22195 Freitas, C., Mondragon-Llorca, H., & Pascual-Leone, A. (2011). Noninvasive brain stimulation in Alzheimer’s disease: Systematic review and perspectives for the future. Experimental Gerontology, 46(8), 611–627. doi:10. 1016/j.exger.2011.04.001 Fridriksson, J., Richardson, J. D., Baker, J. M., & Rorden, C. (2011). Transcranial direct current stimulation improves naming reaction time in fluent aphasia: A double-blind, sham-controlled study. Stroke, 42(3), 819– 821. doi:10.1161/strokeaha.110.600288 Galvani, L. (1791). De viribus electricitatis in motu musculari commentarius. Bologna: Accademia delle Scienze. Galvani, L. (1797). Memorie sull’elettricità animale a L. Spallanzani. Bologna: Sassi. Galvez, V., Alonzo, A., Martin, D., Mitchell, P. B., Sachdev, P., & Loo, C. K. (2011). Hypomania induction in a patient with bipolar II disorder by transcranial direct current stimulation (tDCS). J ECT, 27(3), 256–258. doi:10.1097/YCT.0b013e3182012b89 Gandiga, P. C., Hummel, F. C., & Cohen, L. G. (2006). Transcranial DC stimulation (tDCS): A tool for doubleblind sham-controlled clinical studies in brain stimulation. Clinical Neurophysiology, 117(4), 845–850. doi:10. 1016/j.clinph.2005.12.003 Garcia-Larrea, L., & Peyron, R. (2013). Pain matrices and neuropathic pain matrices: A review. Pain, 154(Suppl 1), S29–S43. doi:10.1016/j.pain.2013.09.001 S0304–3959(13)00494–6 [pii] Gillick, B. T., Feyma, T., Menk, J., Krach, L. E., Usset, M., Vaith, A., . . . Worthington, R. (2014). Safety and feasibility of transcranial direct current stimulation in pediatric hemiparesis: A randomized controlled pilot study. Physical Therapy, 95(3), 337–349. doi:10.2522/ptj.20130565 ptj.20130565 [pii] Gomez Palacio Schjetnan, A., Faraji, J., Metz, G. A., Tatsuno, M., & Luczak, A. (2013). Transcranial direct current stimulation in stroke rehabilitation: A review of recent advancements. Stroke Research and Treatment, 2013, 170256. doi:10.1155/2013/170256 Gross, C. G. (2007). The discovery of motor cortex and its background. Journal of the History of the Neurosciences, 16(3), 320–331. doi:10.1080/09647040600630160 780517743 [pii] Guarienti, F., Caumo, W., Shiozawa, P., Cordeiro, Q., Boggio, P. S., Bensenor, I. M., . . . Brunoni, A. R. (2014). Reducing transcranial direct current stimulation-induced erythema with skin pretreatment: Considerations for sham-controlled clinical trials. Neuromodulation, 18(4), 261–265. doi:10.1111/ner.12230 Halko, M. A., Datta, A., Plow, E. B., Scaturro, J., Bikson, M., & Merabet, L. B. (2011). Neuroplastic changes following rehabilitative training correlate with regional electrical field induced with tDCS. Neuroimage, 57(3), 885–891. doi:10.1016/j.neuroimage.2011.05.026 S1053–8119(11)00514–3 [pii] Hebb, D. O. (1949). The organization of behavior: A neuropsychological theory. New York: John Wiley. Hecht, D., Walsh, V., & Lavidor, M. (2010). Transcranial direct current stimulation facilitates decision making in a probabilistic guessing task. Journal of Neuroscience, 30(12), 4241–4245. doi:10.1523/jneurosci.2924–09.2010

525

Roger H. Riss and Frederick Ulam Herremans, S. C., & Baeken, C. (2012). The current perspective of neuromodulation techniques in the treatment of alcohol addiction: A systematic review. Psychiatria Danubina, 24(Suppl 1), S14–S20. Hess, C. W. (2013). Modulation of cortical-subcortical networks in Parkinson’s disease by applied field effects. Frontiers in Human Neuroscience, 7, 565. doi:10.3389/fnhum.2013.00565 Hesse, S., Werner, C., Schonhardt, E. M., Bardeleben, A., Jenrich, W., & Kirker, S. G. (2007). Combined transcranial direct current stimulation and robot-assisted arm training in subacute stroke patients: A pilot study. Restorative Neurology and Neuroscience, 25(1), 9–15. Horton, J. C. (2005). Disappointing results from Nova Vision’s visual restoration therapy. British Journal of Ophthalmology, 89(1), 1–2. doi:10.1136/bjo.2004.058214 89/1/1 [pii] Iyer, M. B., Mattu, U., Grafman, J., Lomarev, M., Sato, S., & Wassermann, E. M. (2005). Safety and cognitive effect of frontal DC brain polarization in healthy individuals. Neurology, 64(5), 872–875. doi:10.1212/01. WNL.0000152986.07469.E9 64/5/872 [pii] Jansen, J. M., Daams, J. G., Koeter, M. W., Veltman, D. J., van den Brink, W., & Goudriaan, A. E. (2013). Effects of non-invasive neurostimulation on craving: A meta-analysis. Neuroscience and Biobehavioral Reviews, 37(10 Pt 2), 2472–2480. doi:10.1016/j.neubiorev.2013.07.009 S0149–7634(13)00180–2 [pii] Jung, I. Y., Lim, J. Y., Kang, E. K., Sohn, H. M., & Paik, N. J. (2011). The factors associated with good responses to speech therapy combined with transcranial direct current stimulation in post-stroke aphasic patients. Annals of Rehabilitation Medicine, 35(4), 460–469. doi:10.5535/arm.2011.35.4.460 Kang, E. K., Kim, Y. K., Sohn, H. M., Cohen, L. G., & Paik, N. J. (2011). Improved picture naming in aphasia patients treated with cathodal tDCS to inhibit the right Broca’s homologue area. Restorative Neurology and Neuroscience, 29(3), 141–152. doi:10.3233/RNN-2011–0587 923V06824T358610 [pii] Kellaway, P. (1946). The part played by electric fish in the early history of bioelectricity and electrotherapy. Bulletin of the History of Medicine, 20(2), 112–137. Kim, Y. H., Ko, M. H., Parrish, T. B., & Kim, H. G. (2002). Reorganization of cortical language areas in patients with aphasia: A functional MRI study. Yonsei Med J, 43(4), 441–445. Knotkova, H., Nitsche, M. A., & Cruciani, R. A. (2013). Putative physiological mechanisms underlying tDCS analgesic effects. Frontiers in Human Neuroscience, 7, 628. doi:10.3389/fnhum.2013.00628 Kumar, S., Wagner, C. W., Frayne, C., Zhu, L., Selim, M., Feng, W., & Schlaug, G. (2011). Noninvasive brain stimulation may improve stroke-related dysphagia: A pilot study. Stroke, 42(4), 1035–1040. doi:10.1161/ strokeaha.110.602128 Kumru, H., Soler, D., Vidal, J., Navarro, X., Tormos, J. M., Pascual-Leone, A., & Valls-Sole, J. (2013). The effects of transcranial direct current stimulation with visual illusion in neuropathic pain due to spinal cord injury: An evoked potentials and quantitative thermal testing study. European Journal of Pain, 17(1), 55–66. doi:10.1002/j.1532–2149.2012.00167.x Leite, J., Goncalves, O. F., & Carvalho, S. (2014). Facilitative effects of bi-hemispheric tDCS in cognitive deficits of Parkinson disease patients. Medical Hypotheses, 82(2), 138–140. doi:10.1016/j.mehy.2013.11.021 S0306– 9877(13)00550–1 [pii] Lesniak, M., Polanowska, K., Seniow, J., & Czlonkowska, A. (2013). Effects of repeated anodal tDCS coupled with cognitive training for patients with severe traumatic brain injury: A pilot randomized controlled trial. Journal of Head Trauma Rehabilitation, 29(3), E20–E29. doi:10.1097/HTR.0b013e318292a4c2 Liebetanz, D., Koch, R., Mayenfels, S., Konig, F., Paulus, W., & Nitsche, M. A. (2009). Safety limits of cathodal transcranial direct current stimulation in rats. Clinical Neurophysiology, 120(6), 1161–1167. doi:10.1016/j. clinph.2009.01.022 S1388–2457(09)00293–4 [pii] Loo, C. K., Alonzo, A., Martin, D., Mitchell, P. B., Galvez, V., & Sachdev, P. (2012). Transcranial direct current stimulation for depression: 3-week, randomised, sham-controlled trial. British Journal of Psychiatry, 200(1), 52–59. doi:10.1192/bjp.bp.111.097634 Loo, C. K., & Martin, D. M. (2012). Could transcranial direct current stimulation have unexpected additional benefits in the treatment of depressed patients? Expert Review of Neurotherapeutics, 12(7), 751–753. doi:10.1586/ern.12.67 Loo, C. K., Sachdev, P., Martin, D., Pigot, M., Alonzo, A., Malhi, G. S., . . . Mitchell, P. (2010). A double-blind, sham-controlled trial of transcranial direct current stimulation for the treatment of depression. International Journal of Neuropsychopharmacology, 13(1), 61–69. doi:10.1017/s1461145709990411 Luigjes, J., Breteler, R., Vanneste, S., & de Ridder, D. (2013). Neuromodulation as an intervention for addiction: overview and future prospects. Tijdschrift voor Psychiatrie, 55(11), 841–852. Madhavan, S., & Shah, B. (2012). Enhancing motor skill learning with transcranial direct current stimulation—a concise review with applications to stroke. Frontiers in Psychiatry, 3, 66. doi:10.3389/fpsyt.2012.00066 Mahdavi, S., Yavari, F., Gharibzadeh, S., & Towhidkhah, F. (2014). Modeling studies for designing transcranial direct current stimulation protocol in Alzheimer’s disease. Frontiers in Computational Neuroscience, 8, 72. doi:10.3389/fncom.2014.00072

526

Transcranial Direct Current Stimulation Marin, M. F., Camprodon, J. A., Dougherty, D. D., & Milad, M. R. (2014). Device-based brain stimulation to augment fear extinction: Implications for PTSD treatment and beyond. Depression and Anxiety, 31(4), 269–278. doi:10.1002/da.22252 Marshall, L., Molle, M., Hallschmid, M., & Born, J. (2004). Transcranial direct current stimulation during sleep improves declarative memory. Journal of Neuroscience, 24(44), 9985–9992. doi:10.1523/jneurosci.2725–04.2004 Mathias, J. L., & Wheaton, P. (2007). Changes in attention and information-processing speed following severe traumatic brain injury: A meta-analytic review. Neuropsychology, 21(2), 212–223. doi:10.1037/0894– 4105.21.2.212 2007–03216–007 [pii] May, A. (2008). Chronic pain may change the structure of the brain. Pain, 137(1), 7–15. doi:10.1016/j. pain.2008.02.034 S0304–3959(08)00128–0 [pii] Medeiros, L. F., de Souza, I. C., Vidor, L. P., de Souza, A., Deitos, A., Volz, M. S., . . . Torres, I. L. (2012). Neurobiological effects of transcranial direct current stimulation: A review. Frontiers in Psychiatry, 3, 110. doi:10.3389/ fpsyt.2012.00110 Mendonca, M. E., Santana, M. B., Baptista, A. F., Datta, A., Bikson, M., Fregni, F., & Araujo, C. P. (2011). Transcranial DC stimulation in fibromyalgia: Optimized cortical target supported by high-resolution computational models. Journal of Pain, 12(5), 610–617. doi:10.1016/j.jpain.2010.12.015 S1526–5900(11)00028–9 [pii] Minhas, Preet, Datta, Abhishek, & Bikson, Marom. (2011). Cutaneous perception during tDCS: Role of electrode shape and sponge salinity. Clinical Neurophysiology, 122(4), 637–638. doi:10.1016/j.clinph.2010. 09.023 Miranda, P. C., Lomarev, M., & Hallett, M. (2006). Modeling the current distribution during transcranial direct current stimulation. Clinical Neurophysiology, 117(7), 1623–1629. doi:10.1016/j.clinph.2006.04.009 Moliadze, V., Schmanke, T., Andreas, S., Lyzhko, E., Freitag, C. M., & Siniatchkin, M. (2014). Stimulation intensities of transcranial direct current stimulation have to be adjusted in children and adolescents. Clinical Neurophysiology, 126(7), 1392–1399. doi:10.1016/j.clinph.2014.10.142 S1388–2457(14)00683–X [pii] Mondino, M., Bennabi, D., Poulet, E., Galvao, F., Brunelin, J., & Haffen, E. (2014). Can transcranial direct current stimulation (tDCS) alleviate symptoms and improve cognition in psychiatric disorders? World Journal of Biological Psychiatry, 15(4):261–275. doi:10.3109/15622975.2013.876514 Monte-Silva, K., Kuo, M. F., Hessenthaler, S., Fresnoza, S., Liebetanz, D., Paulus, W., & Nitsche, M. A. (2013). Induction of late LTP-like plasticity in the human motor cortex by repeated non-invasive brain stimulation. Brain Stimulation, 6(3), 424–432. doi:10.1016/j.brs.2012.04.011 Monti, A., Cogiamanian, F., Marceglia, S., Ferrucci, R., Mameli, F., Mrakic-Sposta, S., . . . Priori, A. (2008). Improved naming after transcranial direct current stimulation in aphasia. Journal of Neurology, Neurosurgery and Psychiatry, 79(4), 451–453. doi:10.1136/jnnp.2007.135277 Monti, A., Ferrucci, R., Fumagalli, M., Mameli, F., Cogiamanian, F., Ardolino, G., & Priori, A. (2013). Transcranial direct current stimulation (tDCS) and language. Journal of Neurology, Neurosurgery and Psychiatry, 84(8), 832–842. doi:10.1136/jnnp-2012–302825 Mori, F., Codeca, C., Kusayanagi, H., Monteleone, F., Buttari, F., Fiore, S., . . . Centonze, D. (2010). Effects of anodal transcranial direct current stimulation on chronic neuropathic pain in patients with multiple sclerosis. Journal of Pain, 11(5), 436–442. doi:10.1016/j.jpain.2009.08.011 Mori, F., Nicoletti, C. G., Kusayanagi, H., Foti, C., Restivo, D. A., Marciani, M. G., & Centonze, D. (2013). Transcranial direct current stimulation ameliorates tactile sensory deficit in multiple sclerosis. Brain Stimulation, 6(4), 654–659. doi:10.1016/j.brs.2012.10.003 S1935–861X(12)00192–1 [pii] Morris, D. M., Crago, J. E., Deluca, S. C., Pidikiti, R. D., & Taub, E. (1997). Constraint-induced movement therapy for motor recovery after stroke. NeuroRehabilitation, 9(1), 29–43. doi:10.3233/NRE-1997–9104 Moseley, G. L., & Flor, H. (2012). Targeting cortical representations in the treatment of chronic pain: A review. Neurorehabilitation and Neural Repair, 26(6), 646–652. doi:10.1177/1545968311433209 1545968311433209 [pii] Munneke, M. A., Stegeman, D. F., Hengeveld, Y. A., Rongen, J. J., Schelhaas, H. J., & Zwarts, M. J. (2011). Transcranial direct current stimulation does not modulate motor cortex excitability in patients with amyotrophic lateral sclerosis. Muscle and Nerve, 44(1), 109–114. doi:10.1002/mus.22012 Nardone, R., Holler, Y., Leis, S., Holler, P., Thon, N., Thomschewski, A., . . . Trinka, E. (2014). Invasive and noninvasive brain stimulation for treatment of neuropathic pain in patients with spinal cord injury: A review. Journal of Spinal Cord Medicine, 37(1), 19–31. doi:10.1179/2045772313Y.0000000140jscm-d-13–00008 [pii] Ngernyam, N., Jensen, M. P., Auvichayapat, N., Punjaruk, W., & Auvichayapat, P. (2013). Transcranial direct current stimulation in neuropathic pain. Journal of Pain & Relief, S3:001. doi:10.4172/2167–0846. S3–001 Nitsche, M. A., Boggio, P. S., Fregni, F., & Pascual-Leone, A. (2009). Treatment of depression with transcranial direct current stimulation (tDCS): A review. Experimental Neurology, 219(1), 14–19. doi:10.1016/j. expneurol.2009.03. 038

527

Roger H. Riss and Frederick Ulam Nitsche, M. A., Cohen, L. G., Wassermann, E. M., Priori, A., Lang, N., Antal, A., . . . Pascual-Leone, A. (2008). Transcranial direct current stimulation: State of the art 2008. Brain Stimulation, 1(3), 206–223. doi:10.1016/j. brs.2008.06.004 Nitsche, M. A., & Paulus, W. (2000). Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation. Journal of Physiology, 527(Pt 3), 633–639. Nitsche, M. A., & Paulus, W. (2001). Sustained excitability elevations induced by transcranial DC motor cortex stimulation in humans. Neurology, 57(10), 1899–1901. Nitsche, M. A., Seeber, A., Frommann, K., Klein, C. C., Rochford, C., Nitsche, M. S., . . . Tergau, F. (2005). Modulating parameters of excitability during and after transcranial direct current stimulation of the human motor cortex. Journal of Physiology, 568(Pt 1), 291–303. doi:10.1113/jphysiol.2005.092429 [pii] Nolle, C., Todt, I., Seidl, R. O., & Ernst, A. (2004). Pathophysiological changes of the central auditory pathway after blunt trauma of the head. Journal of Neurotrauma, 21(3), 251–258. doi:10.1089/089771504322972040 Oliveira, J. F., Zanao, T. A., Valiengo, L., Lotufo, P. A., Bensenor, I. M., Fregni, F., & Brunoni, A. R. (2013). Acute working memory improvement after tDCS in antidepressant-free patients with major depressive disorder. Neuroscience Letters, 537, 60–64. doi:10.1016/j.neulet.2013.01.023 Orban de Xivry, J. J., Marko, M. K., Pekny, S. E., Pastor, D., Izawa, J., Celnik, P., & Shadmehr, R. (2011). Stimulation of the human motor cortex alters generalization patterns of motor learning. Journal of Neuroscience, 31(19), 7102–7110. doi:10.1523/jneurosci.0273–11.2011 Palm, U., Schiller, C., Fintescu, Z., Obermeier, M., Keeser, D., Reisinger, E., . . . Padberg, F. (2012). Transcranial direct current stimulation in treatment resistant depression: A randomized double-blind, placebo-controlled study. Brain Stimulation, 5(3), 242–251. doi:10.1016/j.brs.2011.08.005 Parent, A. (2004). Giovanni Aldini: From animal electricity to human brain stimulation. Canadian Journal of Neurological Sciences, 31(4), 576–584. Pereira, J. B., Junque, C., Bartres-Faz, D., Marti, M. J., Sala-Llonch, R., Compta, Y., . . . Tolosa, E. (2013). Modulation of verbal fluency networks by transcranial direct current stimulation (tDCS) in Parkinson’s disease. Brain Stimulation, 6(1), 16–24. doi:10.1016/j.brs.2012.01.006S1935–861X(12)00014–9 [pii] Pinchuk, D., Pinchuk, O., Sirbiladze, K., & Shugar, O. (2013). Clinical effectiveness of primary and secondary headache treatment by transcranial direct current stimulation. Frontiers in Neurology, 4, 25. doi:10.3389/fneur.2013.00025 Pinto, P. S., Poretti, A., Meoded, A., Tekes, A., & Huisman, T. A. (2012). The unique features of traumatic brain injury in children: Review of the characteristics of the pediatric skull and brain, mechanisms of trauma, patterns of injury, complications and their imaging findings—part 1. Journal of Neuroimaging 22(2), e1–e17. doi:10.1111/j.1552–6569.2011.00688.x Plow, E. B., Obretenova, S. N., Fregni, F., Pascual-Leone, A., & Merabet, L. B. (2012). Comparison of visual field training for hemianopia with active versus sham transcranial direct cortical stimulation. Neurorehabilitation and Neural Repair, 26(6), 616–626. doi:10.1177/1545968311431963 [pii] Polanowska, K. E., Lesniak, M., Seniow, J. B., & Czlonkowska, A. (2013). No effects of anodal transcranial direct stimulation on language abilities in early rehabilitation of post-stroke aphasic patients. Neurologia i Neurochirurgia Polska, 47(5), 414–422. Poreisz, C., Boros, K., Antal, A., & Paulus, W. (2007). Safety aspects of transcranial direct current stimulation concerning healthy subjects and patients. Brain Research Bulletin, 72(4–6), 208–214. doi:10.1016/j.brainresbull.2007.01.004 Postman-Caucheteux, W. A., Birn, R. M., Pursley, R. H., Butman, J. A., Solomon, J. M., Picchioni, D., . . . Braun, A. R. (2010). Single-trial fMRI shows contralesional activity linked to overt naming errors in chronic aphasic patients. Journal of Cognitive Neuroscience, 22(6), 1299–1318. doi:10.1162/jocn.2009.21261 Priori, A. (2003). Brain polarization in humans: A reappraisal of an old tool for prolonged non-invasive modulation of brain excitability. Clinical Neurophysiology, 114(4), 589–595. doi:S1388245702004376 [pii] Pripfl, J., Neumann, R., Kohler, U., & Lamm, C. (2013). Effects of transcranial direct current stimulation on risky decision making are mediated by “hot” and “cold” decisions, personality, and hemisphere. European Journal of Neuroscience, 38(12), 3778–3785. doi:10.1111/ejn.12375 Pulvermüller, F., & Berthier, M. L. (2008). Aphasia therapy on a neuroscience basis. Aphasiology, 22, 563–599. Quartarone, A., Lang, N., Rizzo, V., Bagnato, S., Morgante, F., Sant’angelo, A., . . . Girlanda, P. (2007). Motor cortex abnormalities in amyotrophic lateral sclerosis with transcranial direct-current stimulation. Muscle and Nerve, 35(5), 620–624. doi:10.1002/mus.20737 Ranieri, F., Podda, M. V., Riccardi, E., Frisullo, G., Dileone, M., Profice, P., . . . Grassi, C. (2012). Modulation of LTP at rat hippocampal CA3-CA1 synapses by direct current stimulation. Journal of Neurophysiology, 107(7), 1868–1880. doi:10.1152/jn.00319.2011jn.00319.2011 [pii] Reis, J., Schambra, H. M., Cohen, L. G., Buch, E. R., Fritsch, B., Zarahn, E., . . . Krakauer, J. W. (2009). Noninvasive cortical stimulation enhances motor skill acquisition over multiple days through an effect on consolidation. Proceedings of the National Academy of Sciences, 106(5), 1590–1595. doi:10.1073/pnas.0805413106

528

Transcranial Direct Current Stimulation Riberto, M., Marcon Alfieri, F., Monteiro de Benedetto Pacheco, K., Dini Leite, V., Nemoto Kaihami, H., Fregni, F., & Rizzo Battistella, L. (2011). Efficacy of transcranial direct current stimulation coupled with a multidisciplinary rehabilitation program for the treatment of fibromyalgia. Open Rheumatology Journal, 5, 45–50. doi:10.2174/1874312901105010045 Rigonatti, S. P., Boggio, P. S., Myczkowski, M. L., Otta, E., Fiquer, J. T., Ribeiro, R. B., . . . Fregni, F. (2008). Transcranial direct stimulation and fluoxetine for the treatment of depression. European Psychiatry, 23(1), 74–76. doi:10.1016/j.eurpsy.2007.09.006 S0924–9338(07)01392–2 [pii] Rioult-Pedotti, M. S., Friedman, D., & Donoghue, J. P. (2000). Learning-induced LTP in neocortex. Science, 290(5491), 533–536. Rioult-Pedotti, M. S., Friedman, D., Hess, G., & Donoghue, J. P. (1998). Strengthening of horizontal cortical connections following skill learning. Nature Neuroscience, 1(3), 230–234. doi:10.1038/678678 [pii] Saiote, C., Goldschmidt, T., Timaus, C., Steenwijk, M. D., Opitz, A., Antal, A., . . . Nitsche, M. A. (2014). Impact of transcranial direct current stimulation on fatigue in multiple sclerosis. Restorative Neurology and Neuroscience, 32(3), 423–436. doi:10.3233/RNN-130372 757 P658705HMMK70 [pii] Saunders, N., Downham, R., Turman, B., Kropotov, J., Clark, R., Yumash, R., & Szatmary, A. (2014). Working memory training with tDCS improves behavioral and neurophysiological symptoms in pilot group with post-traumatic stress disorder (PTSD) and with poor working memory. Neurocase, 21(3), 271–278. doi:10.1 080/13554794.2014.890727 Saur, D., Lange, R., Baumgaertner, A., Schraknepper, V., Willmes, K., Rijntjes, M., & Weiller, C. (2006). Dynamics of language reorganization after stroke. Brain, 129(Pt 6), 1371–1384. doi:10.1093/brain/awl090 [pii] Sawyer, E., Mauro, L. S., & Ohlinger, M. J. (2008). Amantadine enhancement of arousal and cognition after traumatic brain injury. Annals of Pharmacotherapy, 42(2), 247–252. doi:10.1345/aph.1K284 [pii] Schestatsky, P., Morales-Quezada, L., & Fregni, F. (2013). Simultaneous EEG monitoring during transcranial direct current stimulation. Journal of Visualized Experiments: JoVE, 76, 1–11. doi:10.3791/50426 Schlaug, G., Renga, V., & Nair, D. (2008). Transcranial direct current stimulation in stroke recovery. Archives of Neurology, 65(12), 1571–1576. doi:10.1001/archneur.65.12.1571 Segrave, R. A., Arnold, S., Hoy, K., & Fitzgerald, P. B. (2013). Concurrent cognitive control training augments the antidepressant efficacy of tDCS: A pilot study. Brain Stimulation, 7(2), 325–331. doi:10.1016/j. brs.2013.12.008 S1935–861X(13)00399–9 [pii] Shigematsu, T., Fujishima, I., & Ohno, K. (2013). Transcranial direct current stimulation improves swallowing function in stroke patients. Neurorehabilitation and Neural Repair, 27(4), 363–369. doi:10.1177/1545968312474116 Shin, S. S., Dixon, C. E., Okonkwo, D. O., & Richardson, R. M. (2014). Neurostimulation for traumatic brain injury. Journal of Neurosurgery, 121(5), 1219–1231. doi:10.3171/2014.7.JNS131826 Sohlberg, M. M., McLaughlin, K. A., Pavese, A., Heidrich, A., & Posner, M. I. (2000). Evaluation of attention process training and brain injury education in persons with acquired brain injury. Journal of Clinical and Experimental Neuropsychology, 22(5), 656–676. doi:10.1076/1380–3395(200010)22:5;1–9;FT656 Song, J. J., Vanneste, S., Van de Heyning, P., & De Ridder, D. (2012). Transcranial direct current stimulation in tinnitus patients: a systemic review and meta-analysis. Scientific World Journal, 2012, 427941. doi:10.1100/2012/427941 Sparing, R., Dafotakis, M., Meister, I. G., Thirugnanasambandam, N., & Fink, G. R. (2008). Enhancing language performance with non-invasive brain stimulation—a transcranial direct current stimulation study in healthy humans. Neuropsychologia, 46(1), 261–268. doi:10.1016/j.neuropsychologia.2007.07.009 S0028– 3932(07)00264–3 [pii] Sparing, R., & Mottaghy, F. M. (2008). Noninvasive brain stimulation with transcranial magnetic or direct current stimulation (TMS/tDCS): From insights into human memory to therapy of its dysfunction. Methods, 44(4), 329–337. doi:10.1016/j.ymeth.2007.02.001 Sparing, R., Thimm, M., Hesse, M. D., Kust, J., Karbe, H., & Fink, G. R. (2009). Bidirectional alterations of interhemispheric parietal balance by non-invasive cortical stimulation. Brain, 132(Pt 11), 3011–3020. doi:10.1093/brain/awp154 Stade, N. K. (2011). Medical Devices; Neurological Devices; Classification of Repetitive Transcranial Magnetic Stimulation System: Final rule. Federal Register, 76(143): 44489–44491. Stagg, C. J., Jayaram, G., Pastor, D., Kincses, Z. T., Matthews, P. M., & Johansen-Berg, H. (2011). Polarity and timing-dependent effects of transcranial direct current stimulation in explicit motor learning. Neuropsychologia, 49(5), 800–804. doi:10.1016/j.neuropsychologia.2011.02.009 Stagg, C. J., & Johansen-Berg, H. (2013). Studying the effects of transcranial direct current stimulation in stroke recovery using magnetic resonance imaging. Frontiers in Human Neuroscience, 7, 857. doi:10.3389/fnhum.2013. 00857 Stone, D. B., & Tesche, C. D. (2009). Transcranial direct current stimulation modulates shifts in global/local attention. Neuroreport, 20(12), 1115–1119. doi:10.1097/WNR.0b013e32832e9aa2

529

Roger H. Riss and Frederick Ulam Suemoto, C. K., Apolinario, D., Nakamura-Palacios, E. M., Lopes, L., Leite, R. E., Sales, M. C., . . . Fregni, F. (2014). Effects of a non-focal plasticity protocol on apathy in moderate Alzheimer’s disease: A randomized, double-blind, sham-controlled trial. Brain Stimulation, 7(2), 308–313. doi:10.1016/j.brs.2013.10.003 S1935–861X(13)00298–2 [pii] Tanaka, T., Takano, Y., Tanaka, S., Hironaka, N., Kobayashi, K., Hanakawa, T., . . . Honda, M. (2013). Transcranial direct-current stimulation increases extracellular dopamine levels in the rat striatum. Frontiers in Systems Neuroscience, 7, 6. doi:10.3389/fnsys.2013.00006 Taub, E., Uswatte, G., & Elbert, T. (2002). New treatments in neurorehabilitation founded on basic research. Nature Reviews: Neuroscience, 3(3), 228–236. doi:10.1038/nrn754nrn754 [pii] Tecchio, F., Cancelli, A., Cottone, C., Zito, G., Pasqualetti, P., Ghazaryan, A., . . . Filippi, M. M. (2014). Multiple sclerosis fatigue relief by bilateral somatosensory cortex neuromodulation. Journal of Neurology, 261(8), 1552–1558. doi:10.1007/s00415–014–7377–9 Thibaut, A., Bruno, M., Ledoux, D., Demertzi, A., & Laureys, S. (2012). Using transcranial direct current stimulation to improve consciousness of severly brain injured patients: A sham-controlled double blind study. Paper presented at the Front. Hum. Neurosci. Conference (Abstract: Belgian Brain Council). Tortella, G., Selingardi, P. M., Moreno, M. L., Veronezi, B. P., & Brunoni, A. R. (2014). Does non-invasive brain stimulation improve cognition in major depressive disorder? A systematic review. CNS & Neurological Disorders Drug Targets, 13(10), 1759–1769. doi:CNSNDDT-EPUB-63708 [pii] Ulam, F., Shelton, C., Richards, L., Davis, L., Hunter, B., & Fregni, F. (2013). Serial norm-referenced quantitative EEG evaluations during subacute recovery from traumatic brain injury: A validation study. Journal of Neurotherapy, 17, 275–276. Ulam, F., Shelton, C., Richards, L., Davis, L., Hunter, B., Fregni, F., & Higgins, K. (2015). Cumulative effects of transcranial direct current stimulation on EEG oscillations and attention/working memory during subacute neurorehabilitation of traumatic brain injury. Clinical Neurophysiology, 126, 486–496. doi:10.1016/j. clinph.2014.05.015 S1388–2457(14)00290–9 [pii] Utz, K. S., Dimova, V., Oppenlander, K., & Kerkhoff, G. (2010). Electrified minds: Transcranial direct current stimulation (tDCS) and galvanic vestibular stimulation (GVS) as methods of non-invasive brain stimulation in neuropsychology—a review of current data and future implications. Neuropsychologia, 48(10), 2789–2810. doi:10.1016/j.neuropsychologia.2010.06.002 Vanneste, S., Fregni, F., & De Ridder, D. (2013). Head-to-head comparison of transcranial random noise stimulation, transcranial AC stimulation, and transcranial DC stimulation for Tinnitus. Frontiers in Psychiatry, 4, 158. doi:10.3389/fpsyt.2013.00158 Villamar, M. F., Santos Portilla, A., Fregni, F., & Zafonte, R. (2012). Noninvasive brain stimulation to modulate neuroplasticity in traumatic brain injury. Neuromodulation, 15(4), 326–338. doi:10.1111/j.1525–1403.2012. 00474.x Warren, J. E., Crinion, J. T., Lambon Ralph, M. A., & Wise, R. J. (2009). Anterior temporal lobe connectivity correlates with functional outcome after aphasic stroke. Brain, 132(Pt 12), 3428–3442. doi:awp270 [pii] Wheaton, P., Mathias, J. L., & Vink, R. (2009). Impact of early pharmacological treatment on cognitive and behavioral outcome after traumatic brain injury in adults: A meta-analysis. Journal of Clinical Psychopharmacology, 29(5), 468–477. doi:10.1097/JCP.0b013e3181b66f04 00004714–200910000–00010 [pii] Wing, V. C., Barr, M. S., Wass, C. E., Lipsman, N., Lozano, A. M., Daskalakis, Z. J., & George, T. P. (2013). Brain stimulation methods to treat tobacco addiction. Brain Stimulation, 6(3), 221–230. doi:10.1016/j.brs.2012.06.008 Wu, D., Qian, L., Zorowitz, R. D., Zhang, L., Qu, Y., & Yuan, Y. (2013). Effects on decreasing upper-limb poststroke muscle tone using transcranial direct current stimulation: A randomized sham-controlled study. Archives of Physical Medicine and Rehabilitation, 94(1), 1–8. doi:10.1016/j.apmr.2012.07.022 Yang, E. J., Baek, S. R., Shin, J., Lim, J. Y., Jang, H. J., Kim, Y. K., & Paik, N. J. (2012). Effects of transcranial direct current stimulation (tDCS) on post-stroke dysphagia. Restorative Neurology and Neuroscience, 30(4), 303–311. doi:10.3233/rnn-2012–110213 Yook, S. W., Park, S. H., Seo, J. H., Kim, S. J., & Ko, M. H. (2011). Suppression of seizure by cathodal transcranial direct current stimulation in an epileptic patient—a case report. Ann Rehabil Med, 35(4), 579–582. doi:10.5535/arm.2011.35.4.579 Yoon, E. J., Kim, Y. K., Kim, H. R., Kim, S. E., Lee, Y., & Shin, H. I. (2013). Transcranial direct current stimulation to lessen neuropathic pain after spinal cord injury: A mechanistic PET study. Neurorehabilitation and Neural Repair, 28(3), 250–259. doi:10.1177/1545968313507632 [pii] Zaghi, S., Acar, M., Hultgren, B., Boggio, P. S., & Fregni, F. (2010). Noninvasive brain stimulation with lowintensity electrical currents: Putative mechanisms of action for direct and alternating current stimulation. Neuroscientist, 16(3), 285–307. doi:10.1177/1073858409336227 Zaghi, S., Heine, N., & Fregni, F. (2009). Brain stimulation for the treatment of pain: A review of costs, clinical effects, and mechanisms of treatment for three different central neuromodulatory approaches. Journal of Pain Management, 2(3), 339–352.

530

28 AN INTEGRATIVE APPROACH TO OPTIMIZING NEURAL FUNCTION Exploring the Brain–Gut Connection Nancy E. White and Leonard M. Richards

Abstract Neuroscience, as with most of the life sciences, continues its advance toward a more systemic approach to both research and treatment, one that is slowly displacing the linear thinking that has long prevailed with a paradigm that is impacting how we might evaluate, diagnose and treat brain function. This new thinking urges us to go beyond symptom toward cause, thereby plunging us into a web of intricate and sometimes mind-boggling interactions between the brain and the physical system-ofsystems that we call the body. One important interaction is called the Brain–Gut Axis and a growing body of literature describes the nature, functioning and relevance of this complex bidirectional signaling process mediated by the Autonomic Nervous System. How the messages coursing this communication system impact brain function and ultimately affect mood and behavior involves many participants, such as the massive colony of mainly friendly bacteria that manage how the gut works, and the intricate nerve pathways that deliver the messages back and forth between gut and brain. The quality and type of nutrients that travel through the digestive system and how those nutrients are processed also affect brain function, while chronic stress not only affects brain function directly, but indirectly by its effect on gut function. Tools are available to assess the condition of the brain–gut relationship and treat to normalize its impact on brain function and behavior. By describing an integrative neuroregulation practice, the reader can get an idea of how to broaden the scope of a practice to include the use of these tools.

Introduction Neuroscience today provides us with many more labels and far more detailed descriptions of the brain and its functions than our predecessors had; we know much more about how the brain works, have plumbed more of its secrets and have begun to decipher more of its relationships with other systems of the human body. One important relationship has given rise to the relatively new field of Neurogastroenterology, which explores the complex, symbiotic communications between the gut, which feeds the brain both nutrients and information, and the brain, which affects gut response and, at another level, seeks to modulate gut activity. The intricate system of pathways that carries these communications has come to be known as the brain–gut axis and the road to explaining it continues under construction. Still, science today can tell us a lot about how highly integrated are the gut and the brain, showing us more almost every day of the intricate bidirectional pathways by which such

531

Nancy E. White and Leonard M. Richards

communication occurs. It becomes possible to see from these discoveries that if the gut isn’t happy the brain can’t be happy either and that by extension the reverse is true. Even popular books, such as Dr. David Perlmutter’s Brain Maker (Perlmutter, 2015), can now describe for laypersons, with adequate scientific reference, what neural disorders can arise from chronic gut problems and what one can do about them. Furthermore, the still-developing field of Neuroregulation, a name we can give to the emerging integration of Neurofeedback, Transcranial Stimulation, Deep Brain Stimulation—and perhaps other as yet uncovered approaches—is being swept up in a paradigm shift, along with elements of medicine, psychology, physics and even religion, away from reductionist thinking and isolated treatments toward a systems approach, based on an emerging understanding of how things interconnect and influence each other. Given all this, perhaps it makes sense to seriously consider the impact this ongoing, intimate interaction between gut and brain has, not only on problems of brain function that the field traditionally encounters, but on protocols to which the field has traditionally turned in addressing them, if for no other reason than to reduce the potential for gut-based problems to sabotage neuroregulation’s outcomes. So, in this chapter we start by locating and identifying major brain structures and physical systems responsible for bidirectional pathways along which brain–gut intercommunications move, indicating the impact brain–gut intercommunication can have on neural development, mood, and behavior. This opens up some significant implications for treating problems of brain function and provides grounds for a rationale that advocates a more integrative approach to treatment on the part of practitioners. To help the practitioner actualize a way that such a treatment approach might look, we characterize this rationale in the form of a Neurotherapy practice that has integrated what science and technology currently offer into a more comprehensive neurobehavioral treatment model.

The Brain–Gut Axis The brain and the gut communicate in a bidirectional manner largely by means of the Autonomic Nervous System (ANS) and the Hypothalamic–Pituitary–Adrenal (HPA) Axis. A growing body of literature outlines the nature and functioning of this specialized bidirectional signaling system between the brain and the gut and is mapping both the ways in which multiple neurochemicals act mainly on nerve pathways of the ANS and how secretions of the HPA Axis act, mainly via the bloodstream, to transmit information back and forth (Jones, Dilley, Drossman & Crowell, 2006; Mayer, Knight, Mazmanian, Cryan & Tillisch, 2014).

The Role of the Autonomic Nervous System Like a route map through a maze of public highways and local roads, the brain–gut axis is defined by those pathways that the gut and the brain generally use to get information back and forth to each other. Like a long distance traveler, brain–gut messages use all three parts of the autonomic nervous system: 1.

2.

the sympathetic nervous system (SNS), via a network of nerves emanating first from the thoracic spine then through ganglia that combine and reroute their messages to individual sites (Elankov et al., 2000); the parasympathetic nervous system (PNS), specifically the visceromotor component of the Vagus nerve which runs from the brain stem all the way through the abdomen and the viscera (Leanage, 2014). The full Vagus nerve serves as the main neural pathway between the brain and the organs of the body, including the esophagus, heart, and lungs which lie along its path to the 532

Exploring the Brain-Gut Connection

3.

gut (Bergland, 2014). About 80–90% of the vagal nerve fibers relay information to the brain, including messages from the enteric nervous system, informing the brain of visceral feelings and gut instincts (Hadhazy, 2014); the enteric nervous system (ENS), an intricate neural network that manages the gut. The enteric nervous system consists of sheaths of neurons—approximately 100 million of them—embedded within the walls of our some 28-foot-long alimentary canal (Hadhazy, 2014). Accordingly, it is sometimes termed “the second brain” because of its innate ability to manage much of the digestive process independent of direct Vagus input (Bergland, 2014).

All these systems are connected and, again like an interstate highway system, the messages they transport seem to know exactly which exits to take in order to connect locally with their intended target organs (Bergland, 2014). Various non-site specific neurotransmitters guide the bidirectional traffic along neural highways, influencing not only gastrointestinal, endocrine and immune function, but human behavior and emotional states as well (Mulak & Bonaz, 2004). Research increasingly demonstrates that brain–gut interactions are the mechanisms of both gastrointestinal function and human mood and behavior. Disturbances at every level of the brain’s connection with the gastrointestinal tract can have a modulating effect on gut function, including both the perception of, and response to, visceral events. Alternatively, studies show that information from the gut to the brain by way of the Vagus nerve has a modulating effect on mood and “distinct forms of anxiety and fear” (Klarer et al., 2014, p. 7067).

The Role of Intestinal Bacteria There is another important, even critical, participant in the brain–gut conversation. Recent research has shown that a principal source of the visceral events and related gut instincts relayed to the brain lies not just with the alimentary canal itself, but with a community of microbes, or commensal microbiota, that inhabit mainly the lower gut, creating an environment called the gut microbiome (Mulak & Bonaz, 2004). The human gut contains between 400 and 1,000 different bacterial species that make up an intricate network of cohabiting organisms (Collins, Surette & Bercik, 2012). This colony of trillions lives in symbiosis with its human host and takes an active role in digestion and metabolism, in fact determining in large part the ability of the gut to function well. Colonization of the gastrointestinal tract, mainly the colon, begins at birth, continues during early development and remains throughout life. While each person’s microbial profile is distinct, certain phylotypes seem to be similar among healthy individuals, which has offered a broad-based way of inducing a healthier gut for most of those in need of one (Rhee, Pothoulakis & Mayer, 2009). The state of the microbiome is passed to the brain from nerve endings embedded along the intestine, then through the enteric nerve to the Vagus nerve, providing a continuous information flow (Rhee et al., 2009). The brain affects gut microbiota indirectly by initiating changes in gastrointestinal motility and secretion and directly by means of signaling molecules released into the gut from cells in the intestinal wall (Forsythe, Bienenstock & Kunze, 2014), thus establishing effective two-way communication between the microbiome and the brain. Accumulated research findings, such as those by Borre, Moloney, Clarke, Dinan, and Cryan (2014) and Cryan and Dinan (2012), indicate that changes in the gut microbiome affect not only endocrine and immune conditions, but by means of this two-way communication network they influence brain function and behavior as well. Studies involving germ-free animals, as well as animals exposed both to various pathogens and to probiotics, indicate that the gut microbiota play a role in regulation of anxiety, mood, cognition and pain (Cryan & Dinan, 2012). Additionally, studies support the idea that normal gut microbiota can help maintain normal brain development and behavioral functions (Borre 533

Nancy E. White and Leonard M. Richards

et al., 2014; Heijtz et al., 2011). Alternatively, dysfunction of the microbiome has been implicated as an important factor in the development of stress-related disorders such as depression, anxiety and irritable bowel syndrome as well as neurodevelopmental disorders such as autism (Borre et al., 2014; Heijtz et al., 2011).

The Role of the HPA Axis The Hypothalamic–Pituitary–Adrenal Axis (HPA), important in the integration of adaptive responses to stress, also has a significant impact on gut function. Both physical and psychological stressors can affect the composition and metabolic activity of the gut microbiota (Elenkov et al., 2000). Further, experimental changes to the gut microbiome have been shown to affect emotional behavior and related brain systems. Exposure to psychological stress in particular activates the HPA Axis by means of a chemical cascade described in some detail by Chrousos (1992), Gold and Chrousos (2002) and Tsigos and Chrousos (2002), leading to altered intestinal barrier function and disruption of the gut microbiome which, when relayed back to the brain, can have, as shown earlier, significant effects on mood and behavior. By way of orientation, the Hypothalamus, together with the Thalamus and part of the Pineal, is part of the Diencephalon, located just posterior to the forebrain and immediately in front of the midbrain, just above the brain stem. The Pituitary is positioned directly below the Hypothalamus, connected to it by a short tubule (The Diencephalon, n.d.). The Hypothalamus controls the autonomic nervous system, among other things, and links the nervous system to the endocrine system by way of the Pituitary, which it governs (Boundless, 2014) Under ordinary, non-stressful conditions the Hypothalamus manages a regular, well-documented chemical cascade, via the Pituitary, ending with the adrenals secreting mainly cortisol in a pulsative circadian cycle. Under conditions of excessive stress such as may originate from physiological and psychosocial sources, a variety of brain nuclei spur the HPA Axis to initiate what Hans Selye (1936) termed a “general adaptation syndrome” the result of which is the release of additional stress hormones including cortisol, epinephrine and norepinephrine in an attempt to help the brain–body system regain overall balance (Tsigos & Chrousos, 2002). The disruptive effects of stress hormones on gut sensation, motility and secretion, when fed back to the brain, are compounded by messages from the Hypothalamus activating other brain structures that respond to stress, creating a measurable effect on pain perception, mood and behavior, such as melancholic depression and anxiety-causing memories (Tsigos & Chrousos, 2002). Chronic stress can make these conditions worse because the Hypothalamus tends to be hyperactive in depression, leading to excessive secretion of such chemicals as vasopression, which can increase suicide risk, and oxytocin, which can fuel eating disorders. In chronic stress these and other factors affecting the brain’s responses have been shown ultimately to induce severe physical problems, progression of which can have their own effects on mood and behavior (Jones et al., 2006; Tache, Martinez, Million & Rivier, 1999; Tsigos & Chrousos, 2002).

The Role of Nutrition Most Neurofeedback practitioners are aware of recommended dietary strategies for conditions they usually treat. A number of well-known experts, including Harvard Medical School (Harvard Mental Health Letter, 2009) and Daniel Amen MD (2013), are clear that most forms of ADHD are helped by a diet high in protein, low glycemic carbohydrates and a sufficient amount of healthy fats. In addition, many ADHD children and a preponderance of persons on the autistic spectrum require gluten free—if not fully grain-free—menus (Celiac disease defined, n.d.) or casein-free diets (Casien Allergy Overview, n.d.). These researchers and others see sugar as generally bad. 534

Exploring the Brain-Gut Connection

While most people can easily get the connection between nutritional deficiencies and physical illness, relatively few people are aware of the connection between nutrition and mood disorders such as depression and anxiety (Gold & Chrousos, 2002). Yet emerging scientific evidence indicates that carefully administered dietary supplements have a positive impact on the most common mental disorders (Lakhan & Vieira, 2008). For example, mental health professionals typically think of depression as emotionally-rooted or biochemically-based, while Neurofeedback therapists may look for well-known neural patterns indicating a particular condition. However, both may tend to overlook “easily noticeable eating patterns that can play a key role in the onset as well as severity and duration of depression” (Sathyanarayana Rao, Asha, Ramesh & Jagannatha Rao, 2008, p. 77). Similarly, in a systematic review of 24 studies, Lakhan and Vieira (2010, p. 1) concluded that: “Based on the available evidence, it appears that nutritional and herbal supplementation is an effective method for treating anxiety and anxiety-related conditions without risk of serious side effects,” benefits of which, they add, may include placebo effects. Emerging evidence also tells us that front-line communication to the brain about the effects on the gut of a person’s food and supplement intake comes from the extensive community of bacteria in the intestine, the microbiota, which, as we have shown, evokes brain responses that include modulation of behavior, such as depression and anxiety, making the gut microbiota an essential part of a network of relationships that govern homeostasis (Cryan & O’Mahony, 2011). It follows, then, that a decrease in, or a chronic major imbalance of, desirable gastrointestinal bacteria is likely to lead to a deterioration in gastrointestinal, neuroendocrine or immune conditions that ultimately can lead to disease (Cryan & O’Mahony, 2011) A pro-inflammatory diet, such as one overly concentrated on red meat, carbohydrates and sugar, especially in conjunction with ongoing excessive stress, has been shown to disrupt the normal process by which the gut sloughs off and renews its mucus lining and the normal process by which it renews intestinal epithelial cells. Disruption of this process, known as apoptosis (programmed normal cell turnover), can lead to leaky gut syndrome, gut inflammation, irritable bowel syndrome and intestinal cell necrosis (excessive, non-programmed cell death) with a potentially more lasting negative impact on brain development, mood and behavior (Ramachandran, Madesh & Balasubramanian, 2000). The literature is increasingly clear regarding pro-inflammatory substances in foods generally considered healthy, such as whole grains and dairy products, and most practitioners are aware of the need to have patients be examined for gluten and casein sensitivity, especially in cases of ADHD and Autism Spectrum Disorder (Amen, 2013), so they are simply mentioned here as elements to be included in the mix rather than examined in detail. Similarly, artificial sweeteners and food colorings, about which numerous articles relate a litany of adverse neural events—and which many Neurofeedback practitioners have observed in their own patients—is a complicated area where the body of formal research gives us little help. With respect to Aspartame in particular, there is a multitude of practitioner reports describing adverse events (Martini, n.d.), while a survey of the formal research through 1998 showed that 100% of industry-funded studies deemed the sweetener safe, while 92% of independently funded studies showed it to be a problem (Walton, n.d.). At the same time, a reliable body of neurochemical research has identified a number of specific nutrients that the brain requires in order to maintain normal functioning, such as the Vitamin B group—elements of which are important to neurotransmitter biosynthesis and insufficiencies of which have been linked to neurological problems—as well as trace minerals such as zinc, copper and selenium, which have been shown to have a vital role in maintaining normal brain function (Gibson & Blass, 1999). The brain is sensitive to diet, dependent on a continuous supply of nutrients, some of which cannot be synthesized by other organs for use by the brain, but have to be furnished by what we eat. Our food can alter brain function in the short run by altering neurotransmitters and neuronal firing and can actually alter membrane structure in the longer run (Gibson & Blass, 1999). On the other hand, 535

Nancy E. White and Leonard M. Richards

development of a diet and nutritional program based on sound neurochemical and valid scientific observations can be a rational way to deal with imbalances in brain-related problems. This has been further demonstrated in recent research studies conducted by Bravo et al. (2011) and Smith et al. (2014) which gave increased recognition to the benefits of probiotics as adjunctive therapy for conditions such as depression, anxiety, ADHD and even autism.

In Summary We now know that diet has the potential to alter brain health and cognition, influencing cognition in particular by acting on cellular processes vital to maintaining cognitive functioning, and it should be clear from a growing body of research that gut condition, and especially inflammation, has a significant influence on neural process and associated behaviors so that: • • •

we can no longer view neural function as fully distinct from and independent of most physical disorders; it is increasingly evident that nearly all degenerative diseases, including those of the brain, have similar biochemical etiologies; and whatever the etiology of a disorder, neural function is likely to be affected, answering a cogent question Seaman (1987) raised years earlier.

The brain is now seen as being in direct communication with the immune and endocrine systems, which means that systemic inflammatory reactions and responses can influence brain function. In that respect, research, such as that by Wilson, Finch and Cohen (2002) has been actively exploring ways that inflammatory cytokines—a group of low molecular weight amino acids with specific receptors that mediate cellular intercommunication by various means—cross the blood-brain barrier to induce changes in the brain that affect cognition and contribute to the development of neurodegenerative diseases. The literature indicates that giving due attention to gut–brain interaction can support, and potentially enhance, the outcomes of Neurofeedback and Neuromodulation protocols, even though the specific impact of such attention has yet to be determined. Nonetheless, it still follows that reducing systemic inflammation and improving gut health should be considered an integral part of treating for a more functional brain.

A Brain–Body Model This conclusion brings up several potentially unsettling questions for a practitioner, like what does it take for a practitioner to put such a program together, how affordable is it and how disruptive to an existing practice—and its income stream—may be the changes implied by the inclusion of a gut health regimen in what has, to date, been a rather straightforward system of diagnosis and treatment. In that respect the field has changed relatively little, fundamentally, over the years, even with increasingly sophisticated equipment and software based on rapid advances in technology and neuroscientific research. The changes implied by broadening a practice to include a brain-gut-microbiome model, thus moving it toward a systems approach to treatment, shouldn’t alter significantly the fundamental clinical work of evaluation, testing and diagnosis; rather, it requires incorporation of what else in the human mega-system is shown to have a significant impact on neuroregulation outcomes. Specifically, since the condition of the gut is shown to have a demonstrable effect on brain function and behavior, and that effective means exist to evaluate gut condition and reregulate the microbiome, a practitioner can feel confident about using such means to support and augment treatment outcomes. 536

Exploring the Brain-Gut Connection

The practitioner would be dealing with, first, a shift in the scope of testing and evaluation to find out what a patient needs to move gut condition toward active support of neural treatment; second, a cost-effective and reliable way for the patient to get done the required testing and results; and third, a way the practitioner can receive the testing results to incorporate in his/her evaluation, treatment plan and monitoring program. Two other important questions a practitioner is bound to ask are: if I am to go through all this adjustment, how do I know it will make a real difference for the patient and if so, how much of a difference would it make? The answers are: we don’t know that it will make a significant difference for everyone and we don’t know exactly how much of any improvement can be attributed solely to treating gut function. What we do know is: the literature demonstrates clearly that problems of the gut and its microbiome adversely affect mood, behavior and brain function, that treating the brain with those problems present is like swimming against the tide and that a practice which includes a gut function module as part of its program seems likely to achieve better results than it did before. Bottom line, if the practitioner’s job is to deliver the patient measurably improved neural functioning in a lasting way, then it would be well for him/her to include gut function in the process of evaluation and treatment.

A Brain–Body Model in Practice What follows is the profile of a neuroregulation practice with a well-established brain–body model in place. This practice has broadened its scope of treatment by: • • •

including in its intake package a questionnaire specifically designed to indicate physical problems impinging on neural function; linking with a functional medicine doctor to order necessary testing, interpret results and recommend dietary and supplement regimens to treat indicated problems; and providing a range of advanced Neurofeedback, Neuromodulation and adjunctive modalities to increase treatment options.

Most patients coming into this practice follow a fairly conventional treatment path: • • •

• •

Initial interview Testing and interpretation of results Evaluation, diagnosis and treatment plan • Evaluation outline from the assembled results • Detailed consultation with patient and caregivers • Treatment orders Ongoing evaluation of progress Follow-up evaluation and next steps • Follow-up testing • Consultation • Ongoing dietary and supplement program • Referrals to further treatment as indicated

Initial Interview, Testing and Interpretation The importance of an initial interview, in addition to letting new patients sense that they’ve been heard, is to ask cogent questions around their presenting problems that include diet and lifestyle habits and to garner other information that may be of value in reviewing test results. 537

Nancy E. White and Leonard M. Richards

Prior to the initial interview the prospective patient fills out a detailed individual and family history, including dietary and lifestyle information, current and past medication history and family patterns of behavior and disease. They also fill out an extensive questionnaire, in person or online, that offers insight into neurophysiologic symptoms for which testing may be indicated in addition to a Quantitative EEG—administered to every incoming patient—and a TOVA or IVA as indicated. The questionnaire used by this particular practice includes the self-ranking of specific symptoms in a number of categories on a 0–3 Likert Scale (nonexistent to severe). Inquiries include symptoms such as gas, bloating, bowel movement frequency, antibiotic and prescription drug use, ringing in ears, ankle swelling, frequent drowsiness, and questions around such conditions as sleep, stress, diet and lifestyle. Results are collated and symptom categories are arranged for the respondent in order of apparent importance. A questionnaire such as this can give the practitioner important information regarding factors impinging on brain function and provide a focus for functional medicine testing. This practice has found the questionnaire form of inquiry to be highly informative. For example, in a sample of 48 current and recent patients presenting at this clinic (ages 14 to mid-70s, mixed gender) the five most frequent symptom categories revealed by their questionnaires, along with the percent of respondents who scored in the particular category, were: • • • • •

Heavy metals (lead, mercury, cadmium, etc.) = 81% Adrenal, Hypothalamus, Pituitary (chronic stress, HPA Axis activation) = 67% Liver-gallbladder (toxicity: pollution, pesticides, hair and skin products) = 63% Thyroid (secondary to stress, toxicity, HPA Axis dysregulation) = 60% Gastrointestinal system (inflammation, Dysbiosis, leaky gut) = 58%

The value of this sample is that it’s made up of a diverse group of individuals coming off today’s streets looking for enhancement of brain function. The percentages indicate that most respondents had several of these categories among their top five. The take-home point of this exercise is that today’s assaults on functional health from lifestyle, diet and the environment are affecting how our brains work—mine, yours and theirs—and, by extension, are likely to impact the effectiveness of Neurofeedback and Neuromodulation protocols, regardless of their sophistication. Only one other symptom category shows up in more than 50% of responses: nutritional deficiencies at 56%. These six categories as a group indicate a general symptomology arising from toxicity, chronic stress and gut problems, all of which have been shown previously to have an adverse impact on brain function, mood and behavior.

A Note on Heavy Metals This paper has focused mainly on gut-biome dysregulation and activation of the HPA Axis and ways these dynamics can affect brain function. These conditions usually can be corrected with appropriate supplements and changes in diet and lifestyle. On the other hand, toxic levels of heavy metals, while fairly easily tested for, are much more difficult to correct. Research, well-summarized by Clarkson (1987), shows that both inorganic and organic compounds of such metals as aluminum, lead and mercury tend to target the nervous system and are demonstrated to be neurotoxic. Chang (1990) demonstrated that toxic levels of the organic compounds of lead and mercury have a generally adverse effect on neural function and selectively damaging effects on neurons. If heavy metals ranks among the top symptom categories in this practice’s questionnaire, a heavy metals test, which can be either blood-based or urine-based, is ordered. If toxic levels are found, the functional medicine doctor can order chelation therapy in intravenous, oral or rectal form.

538

Exploring the Brain-Gut Connection

Balancing Neurotransmitters to Enhance Neuroregulation Protocols Neurotransmitter testing is generally ordered for depression, anxiety and chronic stress because much of the symptomology arising from toxicity, mood disorders, chronic stress and gut problems, including those resulting from poor diet and nutrition, is likely to be reflected in neurotransmitter imbalances. The literature indicates, and intuitively it makes sense, that rebalancing neurotransmitter activity tends to improve overall mood and behavior and to quiet the HPA Axis, reflecting an overall improvement in brain function (Wells, n.d.). Neurotransmitter testing may be blood-based or urine-based (Neuroscience Inc., n.d.) and specially formulated corrective amino acid and dietary supplements are generally recommended based on test results. This practice found that urine-based testing is generally better for young children and those averse to needles. The practitioner can monitor patient supplements during the treatment process and initiate retesting if he/she sees a need for it. Another means of treating neurotransmitter imbalance, and one used by this practice, is to introduce Nexalin™ Advanced Therapy at the front end of treatment. Nexalin™ uses a FDA-cleared form of Transcranial Electrical Stimulation (TES) pulse wave at a specific frequency to stimulate the Hypothalamus, which helps balance neurochemistry and can begin to quiet the HPA Axis (Marshall & Binder, 2013; Reato, Rahman, Bikson & Parra, 2013). Both supplementation and transcranial stimulation have achieved satisfactory improvements in neurochemistry at this practice, although transcranial stimulation appears to work more quickly and with good persistence. In several cases the practice has found it beneficial to use the two methods together. Several other tests relating to functional medicine may be ordered during the evaluation and testing process as indicated by patient responses at the initial interview either on the intake form or in the questionnaire: •

• • •

Minnesota Multiphasic Personality Inventory (MMPI-2 RC) and/or Millon Clinical Multiaxial Inventory (MCMI III): where psychological assessment and intervention may be indicated (Sellbom, Graham & Schenk, 2006). Hormone testing: may be indicated as a factor in neurochemical imbalance affecting cognition, depending on gender and age (Barth, Villringer & Sacher, 2015; Drake et al., 2000). Micronutrient testing: assessment of vitamin, mineral and essential nutrients shown to have an effect on brain function (Gómez-Pinilla, 2008). Food sensitivities testing: an assessment of which fruits, vegetables, meats, dairy, grains, nuts and other foods are likely to cause or increase a patient’s gut inflammation, with potential inflammatory effects on the brain (Klein, 2013).

With respect to psychological testing, results are generally considered as information for the practitioner to use in optimizing the patient treatment plan and the practitioner has to be careful about what he/she shares with the patient and how what’s shared is presented. In the wake of micronutrient testing and a survey of food sensitivities, as was discussed earlier in this paper, a patient may be prescribed a gluten-free, casein-free or grain-free diet, along with a vitamin and probiotic regimen, to heal gut issues and support outcomes of neuroregulation treatments. Of all the factors that influence inflammation, we have seen that diet may have the most direct impact. A number of nutrient-dense foods with specific anti-inflammatory qualities, such as green vegetables, sprouted grains, legumes and healthy fats, are shown to support brain health and cognitive function. On the other hand, junk foods high in sugars and trans-fats fuel inflammation and impair cognitive function. Worse, insulin dysfunction—usually related to chronically elevated blood sugar from an unhealthy diet—is a major longer term risk factor in dementia and cognitive decline (Eliaz, 2013).

539

Nancy E. White and Leonard M. Richards

Evaluation, Diagnosis and Treatment Plan These results and recommendations are added to results of Quantitative EEG and TOVA, or IVA, testing to provide a basis for the practitioner to develop a treatment plan and to outline what he/she considers important to explain to the patient and caregivers during an extended consultation. This practice emphasizes the value of an extended consultation that brings together all the facets of the patient’s condition, those overseen by the functional medicine doctor as well as those to be treated within the practice. This is an extended conversation designed to support the diagnosis, demonstrate the value of a comprehensive treatment plan and make certain that all the patient’s questions are addressed. This consultation also provides the practitioner, the patient and his/her caregivers a basis for ongoing check-ins to improve compliance with the treatment program and communicate progress.

Elements of the Treatment Program 1.

2.

3.

4.

5.

The practitioner oversees compliance with the supplementation and dietary recommendations of the functional medicine doctor to get as much mileage as possible from the neuroregulatory program indicated by the Quantitative EEG. Transcranial Electrical Stimulation (TES) may be introduced early in the treatment regimen. The extensive research behind TES and its effectiveness was reviewed by Gilula and Kirsch (2005), who concluded: “The results suggest there is sufficient data to show that CES technology has equal or greater efficacy for the treatment of depression compared to antidepressant medications, with fewer side effects” (p. 7). The effectiveness of TES continued to be shown by Kadosh (2013) and by Krause and Kadosh (2013), indicating promise for TES modalities to help enhance cognition. The normal protocol used in this practice is designed to move neurotransmitter levels more toward normal, thus quieting anxiety and improving mood. It consists of ten 40-minute sessions over two weeks, initially five consecutive days on and two days off followed by five more consecutive days of treatment. Additional sessions may be indicated, administered on a more flexible schedule. EEG Neurofeedback protocols. At the clinician’s discretion, after several Nexalin™ treatments, EEG Neurofeedback can be started. This clinic uses several configurations of EEG Neurofeedback, depending on the objectives of treatment. Most of the practice’s training protocols are Z-score based. Adjunctive treatments: a. Transcranial Magnetic Stimulation (rTMS) using a variety of frequency-based protocols. This modality provides a diffuse cortical stimulation used mainly to support and amplify the effects of Neurofeedback (Fitzgerald & Daskalakis, 2013; George et al., 1995). b. Syntonic Phototherapy. This modality uses a system that sends specific colors of light from a specially developed source into the eyes at a given flash rate to stimulate the brain through both the optical pathways and a secondary nerve pathway that runs from the eye to deep brain structures. The system has been shown to help not only with sight, but with depression, phobias and reading disabilities (Liberman, 1991; Mischio, 2012) Interim Abbreviated Quantitative EEGs, administered as indicated during the treatment program to assess progress and adjust the treatment program as necessary.

Post-Testing and Consultation This practice finds it helpful in most cases to have a post-treatment Quantitative EEG and IVA to compare with the initial QEEG and IVA. This gives both the practitioner and the patient a tangible representation of progress and supports a conversation that offers either closure or a better understanding of what next steps might be indicated. Follow-up testing on brain-gut issues addressed at the 540

Exploring the Brain-Gut Connection

beginning of treatment should be included in this conversation to clarify issues around continuation of dietary and supplementation programs. The practitioner should make clear, both to the patient and to any caregivers, how important it is to maintain the improvements they have taken the time and expense to obtain.

Putting It Together in Practice Making a shift toward the more integrative brain-gut-biome model for enhancing brain function in a lasting way is, as with most things, easier said than done. While it need not be prohibitively expensive to shift the practice in that direction, the practitioner is likely to face an extensive amount of change in assessment and testing procedures, along with possibly significant problems of finding a good functional medicine practitioner to work with. The patient path is altered significantly and the degree of direct control the practitioner has over it may be diluted by the presence of another practitioner with a substantial role to play. Moreover, there is a learning curve involved in understanding what is being tested, what the interpretations mean and what that implies regarding treatment. Last, but hardly least, the patient is asked to take on additional expense if a significant amount of additional testing is indicated. While most aspects of the testing mentioned above may be covered in whole or in part by insurance, there usually will be some out-of-pocket cost for supplements. Ultimately, the shift is dependent on the successful negotiation of five major hurdles: •

• • •

Finding and creating a satisfactory working relationship with a functional medicine doctor. If there is no one in the immediate town or area of the practice, then arrangements could be made with the laboratories, and with a local phlebotomist, to acquire and submit samples directly from the practice. Laboratory reports could be sent both to the practitioner and to the non-local doctor who could consult by visual software, Skype or email. • Considering financial arrangements: who charges for what and how much to charge. Some health insurers and related services publish a periodic survey of charges for many procedures. This can provide a guideline. Learning what results of the expanded testing means, how to discuss it in the patient consultation and how to monitor patient compliance with ongoing mini-consults. What additional treatment equipment or software upgrades might be required to optimally carry out a more advanced treatment plan. How to maintain a satisfactory patient flow into the integrative practice, marketing to potential patients and other professionals.

Worries attached to owning and managing a professional practice are not reduced, but patient outcomes are likely to be better, with concomitant patient satisfaction and potential for increased referrals. If the reader takes away nothing more from this paper than the sense that he/she is actually dealing with a nonlinear, far-reaching and broadly interactive system when working with the brain, the authors’ point will have been made. If the information provided here actually prompts more practitioners to work from a wider, more systemic point of view, then that would be terrific. Either way, the march of applied neuroscience puts the future on the side of integrative treatment.

References Amen, D. G. (2013). Healing ADD revised edition. New York: Penguin Group. Barth, C., Villringer, A., & Sacher, J. (2015). Sex hormones affect neurotransmitters and shape the adult female brain during hormonal transition periods. Frontiers in Neuroscience, 9, 1–20. doi:10.3389/fnins.2015.00037 Bergland, C. (2014). Think twice: How does the vagus nerve convey gut instincts to the brain? The Athlete’s Way, May 23.

541

Nancy E. White and Leonard M. Richards Borre,Y. E., Moloney, R. D., Clarke, G., Dinan, T. G., & Cryan, J. F. (2014). The impact of microbiota on brain and behavior: Mechanisms & therapeutic potential. Microbial Endocrinology: The Microbiota-Gut-Brain Axis in Health and Disease Advances in Experimental Medicine and Biology, 817, 373–403. doi:10.1007/978–1–4939–0897– 4_17 Boundless. (2014, June 27). Functions of the Diencephalon. Boundless Anatomy and Physiology. Retrieved from https://www.boundless.com/ Bravo, J. A., Forsythe, P., Chew, M. V., Escaravage, E., Savignac, H. M., Dinan, T. G., . . . Cryan, J. F. (2011). Ingestion of Lactobacillus strain regulates emotional behavior and central GABA receptor expression in a mouse via the vagus nerve. Proceedings of the National Academy of Sciences, 108(38), 16050–16055. doi:10.1073/ pnas.1102999108 Casien Allergy Overview. (n.d.). Retrieved from http://webmd.com/ Celiac disease defined. (n.d.). Retrieved May 29, 2015, from http://www.csaceliacs.org/ Chang, L. W. (1990, December). The neurotoxicology and pathology of organomercury, organolead and organotin. Journal of Toxicological Sciences, 15(Suppl 4), 125–151. Chrousos, G. P. (1992). The concepts of stress and stress system disorders: Overview of physical and behavioral homeostasis. JAMA: The Journal of the American Medical Association, 267(9), 1244–1252. doi:10.1001/ jama.267.9.1244 Clarkson, T. W. (1987, November). Metal toxicity in the central nervous system. Environmental Health Perspectives, 75, 59–64. Collins, S., Surette, M., & Bercik, P. (2012). The interplay between the intestinal microbiota and the brain. Nature Reviews Microbiology, 10(11), 735–742. Cryan, J., & Dinan, T. (2012). Mind-altering microorganisms: The impact of the gut microbiota on brain and behavior. Nature Reviews Neuroscience, 13(10), 701–712. Cryan, J. F., & O’Mahony, S. M. (2011). The microbiome-gut-brain axis: From bowel to behavior. Neurogastroenterology & Motility, 23(3), 187–192. doi:10.1111/j.1365–2982.2010.01664.x The Diencephalon. (n.d.). Retrieved from http://bio.sunyorange.edu/updated2/THINKING_EVOLUTION/ disorders/web/B_DIENCEPH.htm Drake, E. B., Henderson, V. W., Stanczyk, F. Z., Mccleary, C. A., Brown, W. S., Smith, C. A., . . . Buckwalter, J. G. (2000). Associations between circulating sex steroid hormones and cognition in normal elderly women. Neurology, 54(3), 599–603. doi:10.1212/wnl.54.3.599 Elenkov, I., Wilder, R., Chrousos, G., & Vizi, E. (2000). The sympathetic nerve—an integrative interface between two supersystems. Pharmacological Reviews, 52(4), 595–638. Eliaz, I. (2013, September 4). Five ways to clear brain fog. Rodale Press. Retrieved from www.rodalenews.com/ brain-fog Fitzgerald, P. B., & Daskalakis, Z. J. (2013). The history of TMS and rTMS treatment of depression. Repetitive Transcranial Magnetic Stimulation Treatment for Depressive Disorders (Ch. 2, pp. 7–12). doi:10.1007/978– 3–642–36467–9_2 Forsythe, P., Bienenstock, J., & Kunze, W. A. (2014). Vagal pathways for microbiome-brain-gut axis communication. Microbial Endocrinology: The Microbiota-Gut-Brain Axis in Health and Disease Advances in Experimental Medicine and Biology, 817, 115–133. doi:10.1007/978–1–4939–0897–4_5 George, M. S., Wassermann, E. M., Williams, W. A., Callahan, A., Ketter, T. A., Basser, P., . . . Post, R. M. (1995). Daily repetitive transcranial magnetic stimulation (rTMS) improves mood in depression. NeuroReport, 6(14), 1853–1856. Gibson, G. E., & Blass, J. P. (1999). Nutrition and functional neurochemistry. In G. J. Siegel & B. W. Agranoff (Eds.), Basic neurochemistry: Molecular, cellular and medical aspects (6th ed., Ch. 33). Philadelphia, PA: Lippincott-Raven. Gilula, M., & Kirsch, D. (2005). Cranial electrotherapy review: A safer alternative to psychopharmaceuticals in the treatment of depression. Journal of Neurotherapy, 9(2), 7–17. Gold, P. W., & Chrousos, G. P. (2002). Organization of the stress system and its dysregulation in melancholic and atypical depression: High vs low CRH/NE states. Molecular Psychiatry, 7(3), 254–275. doi:10.1038/ sj.mp.4001032 Gómez-Pinilla, F. (2008). Brain foods: The effects of nutrients on brain function. Nature Reviews Neuroscience, 9(7), 568–578. doi:10.1038/nrn2421 Hadhazy, A. (2014). Think twice: How the gut’s “second brain” influences mood and well-being. Scientific American Online. Retrieved from www.scientificamerican.com. Harvard Mental Health Letter. (2009, July). Diet and attention deficit hyperactivity disorder. Harvard Health Publications. Accessed at: http://www.health.harvard.edu/newsletter_article/Diet-and-attention-deficithyperactivity-disorder

542

Exploring the Brain-Gut Connection Heijtz, R. D., Wang, S., Anuar, F., Qian, Y., Bjorkholm, B., Samuelsson, A., . . . Pettersson, S. (2011). Normal gut microbiota modulates brain development and behavior. Proceedings of the National Academy of Sciences, 108(7), 3047–3052. doi:10.1073/pnas.1010529108 Jones, M. P., Dilley, J. B., Drossman, D., & Crowell, M. D. (2006). Brain-gut connections in functional GI disorders: Anatomic and physiologic relationships. Neurogastroenterology and Motility, 18(2), 91–103. doi:10.1111/j.1365–2982.2005.00730.x Kadosh, R. (2013, March). Using transcranial electrical stimulation to enhance cognitive functions in the typical and atypical brain. Translational Neuroscience, 4(1), 20–33. Klarer, M., Arnold, M., Gunther, L., Winter, C., Langhans, W., & Meyer, U. (2014). Gut vagal afferents differentially modulate innate anxiety and learned fear. Journal of Neuroscience, 34(21), 7067–7076. doi:10.1523/ jneurosci.0252–14.2014 Klein, S. (2013, March 21). Inflammatory foods: 9 of the worst picks for inflammation. Retrieved from www. huffingtonpost.com Krause, B., & Kadosh, R. C. (2013, October). Can transcranial electrical stimulation improve learning difficulties in atypical brain development? A future possibility for cognitive training. Developmental Cognitive Neuroscience, 6, 176–194. Lakhan, S. E., & Vieira, K. F. (2008). Nutritional therapies for mental disorders. Nutrition Journal, 7(1), 2. doi:10.1186/1475–2891–7–2 Lakhan, S. E., & Vieira, K. F. (2010). Nutritional and herbal supplements for anxiety and anxiety-related disorders: Systematic review. Nutrition Journal, 9(1), 42. doi:10.1186/1475–2891–9–42 Leanage, N. (2014, December 22). The vagus nerve (CN X). Retrieved April 14, 2015, from http://teachmeanatomy.info/ Liberman, J. (1991). Light, medicine of the future: How we can use it to heal ourselves now (pp. 37–52). Santa Fe, NM: Bear & Company. Marshall, L., & Binder, S. (2013). Contribution of transcranial oscillatory stimulation to research on neural networks: An emphasis on hippocampo-neocortical rhythms. Frontiers in Human Neuroscience, 26(7), 614. doi:10.3389/fnhum.2013.00614 Martini, B. (Ed.). (n.d.). Read what doctors have been and are saying about Aspartame. Retrieved from http://dorway.com/ doctors.html Mayer, E. A., Knight, R., Mazmanian, S. K., Cryan, J. F., & Tillisch, K. (2014). Gut microbes and the brain: Paradigm shift in neuroscience. Journal of Neuroscience, 34(46), 15490–15496. doi:10.1523/jneurosci. 3299 Mischio, G. (2012, May 14). Syntonic phototherapy yielding eye-opening results. Retrieved from http://www. thevisiontherpaycenter.com/ Mulak, A., & Bonaz, B. (2004). Irritable bowel syndrome. Medical Science Monitor, 10(4), RA55–RA62. Neuroscience, Inc. (n.d.). Why neuroscience testing. Retrieved from http://www.neurorelief.com/ Perlmutter, D. (2015). Brain maker: The power of gut microbes to heal and protect your brain—for life (pp. 71–95; 116–142). New York: Little, Brown. Ramachandran, A., Madesh, M., & Balasubramanian, K. A. (2000). Apoptosis in the intestinal epithelium: Its relevance in normal and pathophysiological conditions. Journal of Gastroenterology and Hepatology, 15(2), 109–120. doi:10.1046/j.1440–1746.2000.02059.x Reato, D., Rahman, E., Bikson, M., & Parra, L. C. (2013, October). Effects of weak transcranial alternating current stimulation on brain activity—a review of known mechanisms from animal studies. Frontiers in Human Neuroscience, 23(7), 687. Rhee, S. H., Pothoulakis, C., & Mayer, E. A. (2009). Principles and clinical implications of the brain–gut–enteric microbiota axis. Nature Reviews Gastroenterology & Hepatology, 6(5), 306–314. doi:10.1038/nrgastro.2009.35 Sathyanarayana Rao, T., Asha, M., Ramesh, B., & Jagannatha Rao, K. (2008). Understanding nutrition, depression and mental illness. Indian Journal of Psychiatry, 50(2), 77–82. Seaman, D. R. (1987). The diet-induced inflammatory state: A cause of chronic pain and other diseases? Journal of Manipulative and Physiological Therapeutics, 25(3), 168–179. Sellbom, M., Graham, J. R., & Schenk, P. W. (2006). Incremental validity of the MMPI-2 restructured clinical (RC) scales in a private practice sample. Journal of Personality Assessment, 86(2), 196–205. Selye, H. (1936). A syndrome produced by diverse nocuous agents. Nature, 138, 32–36. Smith, C. J., Emge, J. R., Berzins, K., Lung, L., Khamishon, R., Shah, P., . . . Gareau, M. G. (2014). Probiotics normalize the gut-brain-microbiota axis in immunodeficient mice. AJP: Gastrointestinal and Liver Physiology, 307(8), G793–G802. doi:10.1152/ajpgi.00238.2014 Tache, Y., Martinez, V., Million, M., & Rivier, J. (1999, March). Corticotropin-releasing factor and the brain-gut motor response to stress. Canadian Journal of Gastroenterology, 13, 18A–25A.

543

Nancy E. White and Leonard M. Richards Tsigos, C., & Chrousos, G. (2002). Hypothalamic-pituitary-adrenal axis, neuroendocrine factors and stress. Journal of Psychosomatic Research, 53, 865–871. Walton, R. G. (n.d.). Survey of Aspartame studies. Retrieved from http://www.dorway.com/peerrev.html Wells, W. (n.d.). The brain game, balancing neurotransmitters and hormones. Retrieved from http://www. heartspring.net/brain_improving_happy_balance.html Wilson, C. J., Finch, C. E., & Cohen, H. J. (2002). Cytokines and cognition: The case for a head-to-toe inflammatory paradigm. Journal of the American Geriatrics Society, 50(12), 2041–2056. doi:10.1046/j.1532– 5415.2002.50619.x

544

INDEX

Abarbanel, A. 389–90 Abel Screen and Penile Plethysmography 98 Abrams, M. 423 ACE study see Adverse Childhood Experiences (ACE) study AD see Alzheimer’s disease (AD) ADD see attention-deficit disorder (ADD) addiction/craving, tDCS stimulation and 507 ADHD see attention-deficit hyperactivity disorder (ADHD) Admon, R. 303 Adverse Childhood Experiences (ACE) study 95, 96–7 Aguayo-Siquier, B. 390 alcohol, concussions and 194 Aldana, J. 470 Aldini, G. 501 Alexander, M. 49 Almurshedi, A. 394 alpha role in meditation 65 alpha-theta: background 467–8; configuration 469; demonstrating 467; induction script 469–71; as teaching tool 466–7 alpha-theta protocol for neurofeedback introduction 464–73; abstract of 464; background of 467–8; cognitive flexibility case example 472; configuration of 469; demonstrating alpha and theta 467; hyperactivity case example 471; induction script and 469–71; introduction to 464–5; mood improvement case example 471–2; neurofeedback session 465–6; neurofeedback station 465; outcome of 472–3; session conclusion 471; as teaching tool 466–7 alpha-theta training 49, 51 Alpha waves 102 Alstott, J. 388, 392 Alzheimer’s disease (AD): concussions and 194–5; LORETA Z-score neurofeedback cases 173–5; tDCS stimulation and 511

Amen, D. 111, 534 American Academy of Pediatrics 93 American Psychiatric Association 97 American Psychological Association Code of Ethics 51 amnesia, concussions and 188 amygdala 95 amyotrophic lateral sclerosis, tDCS stimulation and 511 Anatomy of an Epidemic: Magic Bullets, Psychiatric Drugs, and the Astonishing Rise of Mental Illness in America (Whitaker) 432–3 anger/anger control disorders, QEEG-guided neurofeedback and 149 angular concussion 190 anterior cingulate cortex (ACC) 67 anterior cingulate gyrus (ACG) 96, 186 anxiety 6; case study 16–21; concussions and 195; LORETA Z-score neurofeedback cases 176–8; neurofeedback as treatment (see neurofeedback as anxiety treatment in adolescents/young adults); psychophysiological bases of 456–7 aphasia, tDCS stimulation and 513–14 Apkarian, A. V. 83 apoptosis 535 Arani, F. D. 427 Arbanal, A. 8 arousal modification 391–2 arousal theory 389–91 ASD see autism spectrum disorder (ASD) Association for the Treatment of Sexual Abusers (ATSA) 97 Association of Applied Psychophysiology and Biofeedback (AAPB) 103, 104 Astruc, L. 214, 223 ATSA see Association for the Treatment of Sexual Abusers (ATSA) ATSA Code of Ethics 97

545

Index attention-deficit disorder (ADD): concussions and 195; LORETA Z-score neurofeedback cases 180–1 attention-deficit hyperactivity disorder (ADHD) 6; biofeedback practices and 93; case study 10–15; equipment options for treating 49; LORETA Z-score neurofeedback cases 180–1; neurofeedback as treatment for 6 auditory evoked potential (AEP) 222 autism spectrum disorder (ASD); see also Infra-slow Fluctuation (ISF) training for ASD: brainwave biofeedback and 100; case study 22–33; LORETA Z-score neurofeedback cases 178–80; symptoms of 488 automatic self-transcending (AST) 70–2; described 66; protocols 72; synchrony 72 autonomic nervous system (ANS), brain-gut axis and 532–3 Avram, J. 391 Ayers, M. 434 Badets, A. 479 Baeher, R. 388, 393 Baehr, E. 73, 388, 392, 393 Baehr, F. 73 Baehr, R. 73 Bakker, D. J. 236 balance, concussions and 195 Baldeweg, T. 222 Baltes, F. R. 391 Bannatyne, A. 236 Beck Anxiety Scale 48 Beck Depression Inventory 48 Beck Inventories 388 behavioral ratings 49 Behavior Assessment Scale for Children 48 behavior problems, concussions and 195–6 Belluck, P. 97 Belmont, I. 238 Belmont, L. 236 Benedetti, F. 62 Benton, A. L. 236 Berger, H. 101 Beta waves 102 biofeedback: certifications in 103; defined 98; forensic populations and use of 94, 98; Lantz description of 94 Biofeedback Certification International Alliance (BCIA) 52, 93, 103 Biofeedback Foundation of Europe (BFE) 103 biological variables affecting neurotherapy progress 59–60, 62 bio-psycho-social assessment/tracking method 395–6 bipolar, concussions and 196 Birch, H. 236 Birklein, F. 83 Black, A. H. 479 Blandin, Y. 479 Blood Oxygen Level Dependent signal 489

Boder, E. 236, 237, 247 Bornas, X. 390 Borre, Y. E. 533 Boutin, A. 479 Boynton, T. 468 Bradley, L. 218 brain 185–7; see also concussions; traumatic brain injury (TBI); anatomy 185–6; basics 185; consistency of 185; contact types affecting 190; features 186; mapping 191–3; plasticity 186; regions 185–6; trauma impact on 95–6 BrainAvatar 8, 324 brain-based approach to neurofeedback see Brain Enrichment Center brain-body model 536–41; evaluation and diagnosis 540; heavy metals and 538; initial interview, testing, and interpretation 537–8; neurotransmitter testing and 539; overview of 536–7; post-testing/ consultation 540–1; in practice 537; treatment program elements 540 brain drain 189 braindriving 414–17; described 404; examples of 415–16; Unconditioned Stimuli (UCS) and 414–15 BrainDx 8 Brain Enhancement Centre Private Limited 312 Brain Enrichment Center 3–44; abstract of 3–6; approach used at 7–9; case studies 10–44; clientele at 7; CNC-1020 8–9; CNS-VS instrument 8; consultation 7; goals of 3; information review 9; initial assessment 7; intake interview 7; neurofeedback training 9; populations who do/ don’t benefit from 6; post-neurofeedback training 9; QEEG 7–8; SCL-90-R 9; therapist-client relationship 3–4, 5; treatment methodologies 4 brain fog: concussions and 196; defined 196 brain-gut axis 531–41; abstract of 531; autonomic nervous system role in 532–3; brain-body model 536–41; HPA axis role in 534; intestinal bacteria role in 533–4; introduction to 531–2; nutrition role in 534–6 Brain Maker (Perlmutter) 532 BrainMaster Technologies, Inc. 283, 335 brain plasticity 4–5, 6, 82; see also neuroplasticity; defined 186 “Brain Rate” 391 brain regions 185–6 brainstem 186 Brain That Changes Itself, The (Doidge) 82 brainwave biofeedback 99–100, 101–2; see also electroencephalography (EEG) measures; neurotherapy; defined 98, 99; effective uses for 100 brainwave distribution and emotional evaluation 140–1 Bravo, J. A. 536 Breakspear, M. 388 breathing skills 98–9 Bressler, S. L. 391, 392 Breteler, M. H. M. 220

546

Index British Dyslexia Association Conference 223 Brown, V. 388 Brownback, T. S. 8 Bryant, P. E. 218 Budzynski, H. 8 Budzynski, T. 8, 415 Burns Anxiety Scale 48 Buzsaki, G. 391 Cammoun, L. 388 Carey, B. 97 Carmen, J. 126 Carmody, D. P. 220 Casarotto, S. 238 Centers for Disease Control and Prevention 96 central nervous system (CNS), neuroplasticity and 82–3, 85–6 cerebellar vermis 95 cerebellum 186 cerebral blood flow 190 cerebrovascular disease (CVA), LORETA Z-score neurofeedback cases 163–5 cerebrum 186 Chang, L. W. 538 Charcot, J-M. 501 Chiarenza, G. A. 238 Child Behavior Checklist (CBCL) 8 Children’s Depression Scale 48 children 6 years or younger, pediatric neurofeedback for 109–33; abstract of 109–12; eyes open condition analysis 120–33; raw EEG acquisition 112–15; symptoms checklist as guideposts 115–20 Choi, S. W. 391 chronic fatigue, concussions and 196 chronic pain; see also neuroplasticity: central nervous system and 82–3, 85–6; described 82; LORETA Z-score neurofeedback cases 166–9; neuroplasticity of 82–90; peripheral nervous system and 83, 86–7; tDCS stimulation and 507–8 Chronic Traumatic Encephalopathy (CTE) 189 Chrousos, G. 534 chunking 480 Clarke, G. 533 Clarkson, T. W. 538 Cleveland Clinic Wellness Institute 4 ClinicalQ assessment 407–14; as clinical data base 406; depression and 411–14; described 404–5; example of 418–20; precision level of 407–8; protocol 408–11 clinical vs. normative data bases 405–7; conditional probability models and 406–7 CNC-1020 see Comprehensive Neurodiagnostic Checklist-1020 (CNC-1020) CNS-Vital Signs (CNS-VS) 8; case study 33, 44 CNS-VS see CNS-Vital Signs (CNS-VS) cognitive behavioral therapy 4 cognitive dysfunction, LORETA Z-score neurofeedback cases 170–3

cognitive event related potentials 192 cognitive impairments, concussions and 196 Cohen, H. J. 536 Collura, T. 46, 313, 324 comorbidity, concussions and 196 Comprehensive Neurodiagnostic Checklist-1020 (CNC-1020) 8–9 Comprehensive Tracking Checklist (CTC) 8–9 computed tomography (CT) scan 192 Concentrative Qigong 69 Concussion in Sport Group (CISG) 187 concussions 184–209; see also brain; traumatic brain injury (TBI); abstract of 184; Alzheimer’s disease and 194–5; anxiety and 195; attention deficit and 195; balance and 195; behavior problems and 195–6; bipolar and 196; brain and 185–7; brain fog and 196; brain mapping and 191–3; chronic fatigue and 196; cognitive/emotional impairments of 194–7; comorbidity and 196; contact types and 190; definitions of 187–8; depression and 196; emotional incontinence case example of 206–7; language/speech difficulties and 196; mechanisms of 189–90; memory/learning difficulties and 196; motor vehicle accident case example of 200–5; neuromodulation treatment protocol for 199–200; pain relief for 198; Parkinson’s disease and 197; pathophysiology and 190–1; physics of 189; repercussion factors of 193–4; severity of 188; signs and symptoms of 188–9; sleep and 197, 198; train wreck case example of 207–9; treatment for 197–9 conditional probability models 406–7 confusion, concussions and 188 Congedo, M. 305 Conner’s Rating Scales 48 Constraint Induced Movement Therapy 500 consultation 7 content 5 contra-coup brain injury 190 Cook, I. A. 423 Coordinated Allocation of Resource (CAR) model of effective reading 250–79; abstract of 250; adolescent/adult performance results 265–7; age/memory scores 255; age related changes in QEEG measures 255–6; background of 250–2; children group results, developmental changes 259–65; cognitive performance assessment 253–4; developmental changes, all participants 256–9; electroencephalography and 250–1; intervention efforts and 278; non-clinical adolescent/adult group results 272–3; non-clinical children group results 270–1; normative group results 268–70; participant overview 254–5; pattern summary 273–4; QEEG measures 253; research goals and methodology 252; viewpoints on data 274–7 corpus collosum 95 cortical modules 115–20 Cott, A. 479, 481

547

Index Council for Accreditation of Counseling and Related Educational Programs (CACREP) 93 coup brain injury 190 Craig, A. D. B. 73 Crottaz-Herbette, S. 67 Cryan, J. F. 533 Cry Help methodology 427 CT scan see computed tomography (CT) scan CVA see cerebrovascular disease (CVA) cytokines 536

dyslexia in children 235–47; abstract of 235; introduction to 235–6; neuropsychology of 236; psychophysiology of 238–47; subtypes of 236–8; symptoms of 236 dysregulation, upstream/downstream contributions to 395

Dalenberg, C. 96 Davidson, R. 68, 388, 391, 392, 411 Davidson depression marker 411–13 Davydov, D. M. 426 deBeus, R. 46 de Bologne, G. D. 501 Default Mode Network (DMN) 67, 390, 489 Degardin, A. 426 Delta waves 101 Demos, J. M. 393 depression 6; concussions and 196; LORETA Z-score neurofeedback cases 176–8; tDCS stimulation and 505–7 Diagnostic and Statistical Manual of Mental Disorders (DSM) 97, 98; anxiety and 195; PTSD definition in 300 Diathesis Stress model 389 Diffusion Tensor Imaging (DTI) studies 222 Dinan, T. G. 533 Direct Test of Reading and Writing (TDLS) 237 direct trauma 190 disequilibrium, concussions and 189 disorientation, concussions and 189 Dogris, N. 131 Doidge, N. 82–3 drug use, concussions and 194 DSM see Diagnostic and Statistical Manual of Mental Disorders (DSM) dual hemisphere stimulation montage 502 Dual Relationships issues 51 Dunne, J. 68 Durgin, G. 324 Dwyer, K. V. 394 dysgraphia, QEEG-guided neurofeedback and 149 dyslexia 213–31; abstract of 213; brain-based studies of 219–20; in children (see dyslexia in children); defined 235, 247; described 213; Event-Related Potentials in 221–4; Evoked Potentials in 221–4; introduction to 213–19; neurological research summary into 213–15; phonics-based teaching for 218; as Phospholipid Spectrum Disorder 218; pre/ post QEEG/ERP case study 226–9; QEEG-guided neurofeedback and 150; QEEG studies in 219–21; retrospective case series of 224–6; rhythmic deficits in 218; treatments for 218–19; visual processing problems and 215–18 Dyslexia Action 218

Earnest, C. 73, 388 EEG see electroencephalography (EEG) measures EEG state discrimination 477–85; abstract of 477–8; clinical applications of 480; Kamiya and human phenome project 484–5; operant conditioning/ discrimination and 478–9; physiological selfregulation and 479–81; psychophysics of 481–2; as sensorimotor process 483–4 effective helping, conditions of 46 Egner, T. 302, 304 Ehlers, C. L. 423 Elder, J. D. 110 electroencephalography (EEG) measures 49; see also EEG state discrimination; neurobehavioral disorders, EEG biomarkers/z-scored analysis of; biological variables affecting 62; neurotherapy treatment progress and 61–2; during OM meditation 69; psychological variables affecting 61–2; raw data acquisition for 112–15; sociocultural variables affecting 62 enteric nervous system (ENS) 533 enuresis, QEEG-guided neurofeedback and 150 environment, concussions and 194 epidural hematoma 190 epilepsy: LORETA Z-score neurofeedback cases 181–2; QEEG-guided neurofeedback and 150 Etienne, M. A. 388, 391 Etkin, A. 302 Evans, J. R. 220, 389–90 Evans, W. 8 Event-Related Potentials (ERPs) 221–4 Evoked Potentials (EPs) 221–4 exercise, concussions and 194 existential therapy 4 eyes closed (EC) EEG acquisition 134 eyes open (EO) EEG acquisition 134 family systems theory 4 faradization 501 FastForWord interventions program 278 Fawcett, A. J. 216, 223, 237 fear conditioning 301–2 Fehmi, L. 72 fibromyalgia 84 Finch, C. E. 536 Fingelkurts, A. A. 426 Fitts, P. M. 479 fixed-role therapy 51 fMRI see functional magnetic resonance imaging (fMRI) focal impact, brain and 190 focus, concussions and 189

548

Index focused attention (FA) 66–8; Anterior Cingulate Cortex and 67; areas of brain involved in 66–7; described 66; example of 66; mind wandering and 67–8; protocols 68 Fonteijn, W. A. 8 Food and Drug Administration (FDA) 102 forensic populations, working with 92–104; abstract of 92; ACE Study and 96–7; biofeedback and 98; brainwave biofeedback and 99–100, 101–2; breathing skills and 98–9; introduction to 92–3; neuroplasticity and 94–5; peripheral biofeedback and 99; practical/ ethical considerations for 97–8; QEEG and 100–2; self-regulation and 93–4; trauma and 95–6 Forssberg, H. 86–7 Fouche, M. 214 4 Channel Surface PZOK 338–50; advantages of 349; clinical application 340–9; concept of 338–40; training considerations 349–50 Franceschini, S. 219 Franken, I. H. 426 Freides, D. 304 Friston, K. 256 frontal midline theta rhythm (FM theta) 69 functional magnetic resonance imaging (fMRI) 50, 114, 192 Galaburda, A. M. 216 Galin, D. 219 Galvani, L. 501 Gate, V. 87 Genardi, D. 144 gender, concussions and 193 general adaptation syndrome 534 Gerson, D. 214, 223 Gerstman group 236 gestalt approach to reading 236 Gevirtz, R. 96 Gilliam Asperger’s Disorder Scale 48 Gilula, M. 540 global approach to reading 236 Gold, P. W. 534 Gomi, C. F. 59 Gontkovsky, S. T. 394 Goswami, U. 214, 218, 223 GoToMeeting 52 Graap, K. 304 Gracefire, P. 324 Granet, D. B. 59 Greden, J. F. 421–2 Greenwald, M. K. 426 Gruzelier, J. 304, 468 Guez, J. 303 Gunkelman, J. 46 gut inflammation 535 gut microbiome 533 Hagmann, P. 388 Haley, J. 47

Hammond, C. 47, 112 Hammond, D. C. 434–5, 440 Handbook of Biofeedback (Schwartz & Andrasik) 389 Handwerker, H. O. 83 Hardt, J. V. 304 Hare Psychopathy Checklist 98 Harvard Medical School 534 Hay, L. 468, 470 headaches: concussions and 189; LORETA Z-score neurofeedback cases 166–9; as neuroplasticity example 88–9; tDCS stimulation and 508–9 Heart-Centered Hypnotherapy 4 heart rate, breathing skills and 98–9 heavy metals, brain-body model and 538 Hebb, D. 503 Helgers, N. A. H. 8 Heller, W. 388, 391 hemorrhage 190–1 Hendriks, V. M. 426 heredity, concussions and 193 Herning, R. 426 Heuer, H. 479 hippocampus 95 Hirschfeld, H. 86–7 Honey, C. J. 392–3, 395 Horizontal Axis 391–2, 394 Hornsby Centre 218 Horvat, J. 388 HPA see hypothalamic-pituitary-adrenal (HPA) axis, brain-gut axis and human phenome project 484–5 Hunter, A. M. 423 hypothalamic-pituitary-adrenal (HPA) 534 Ibn-Sidah 501 individualized neuromeditation 75–6 induction script, alpha-theta and 469–71 Infra-slow Fluctuation (ISF) training for ASD 488–97; abstract of 488–90; case studies 490–7; Default Mode Network and 489 initial assessment 7 Insel, T. 97, 432 insight practice 68; see also open monitoring (OM) intake interview 7 Integrated Visual and Auditory Attention Test (IVA) 48 Intensive Language Action Therapy 500 International Dyslexia Conference 220 International Society for Neurofeedback and Research (ISNR) 52, 103, 104 intestinal bacteria role in brain-gut axis 533–4 Irlen, H. 219 irritable bowel syndrome 535 Ismail, A. K. 394 Janda, V. 87 Jasper, H. 414

549

Index Joffe, D. 305 Johnson, M. 468 Kadosh, R. 540 Kaiser, D. 114, 260, 427, 469 Kaiser Permanente Health Appraisal Clinic 96 Kalisch, R. 302 Kamiya, J. 304, 477–8, 483; see also EEG state discrimination; and human phenome project 484–5 Kelly, G. 51 Kilner, J. M. 393 Kinsbourne, M. 236 Kirk, I. J. 390 Kirsch, D. 540 Klingberg, T. 216 Ko, A. L. 489 Korostil, M. 392–3 Krause, B. 540 Ku, J. 426 Kuhlman, K. 223 Kulkosky, P. J. 304, 469 Kupfer, D. J. 423 Lacroix, J. M. 479 Laird, A. R. 391 Lakhan, S. E. 535 language retardation group 236 language/speech difficulties, concussions and 196 language type dyslexia 236 Lantz, D. 94 Largus, S. 501 Lashley, K. 229 leaky gut syndrome 535 Lehmann, D. 66–7 Leonard, C. 86–7 Leppänen, P. H. T. 222 Lesniak, M. 518 Leuchter, A. F. 423 Libet, B. 483 lifestyle, concussions and 193 Light Barrier, The (Stone) 219 limbic system (LS) 186 Lindamood Bell 218 linear brain injury 190 Livingstone, M. 215, 223 London, E. D. 426 Longo, R. E. 94, 98 long-term memory 196 long-term potentiation (LTP) 390 LORETA see Low Resolution Electromagnetic Tomography (LORETA) LORETA protocol: automatic self-transcending 72; focused attention 68; lovingkindness/compassion 74; open monitoring 69 LORETA Z-score neurofeedback 158–83; abstract of 158; for ADD/ADHD cases 180–1; for Alzheimer Disease cases 173–5; for ASD cases 178–80; for

chronic pain/headaches cases 166–9; for cognitive dysfunction cases 170–3; for CVA cases 163–5; for depression/anxiety cases 176–8; described 159–60; for epilepsy cases 181–2; initial evaluation and 159; introduction to 158–9; PTSD treatment using (see post-traumatic stress disorder (PTSD)); for seizure cases 181–2; for TBI cases 160–3 loss of consciousness, concussions and 188 lovingkindness/compassion (LK-C) 73–5; ACC and insula gamma during 75; areas of brain involved in 73; described 66, 73; protocols 74 Low Energy Neurofeedback System (LENS) 49 Low Frequency Neurofeedback System 46 Low Resolution Electromagnetic Tomography (LORETA) 50, 114; as treatment for PTSD 305–7 Lubar, J. 57, 305, 388 Lutz, A. 68 MacKay, J. C. 390 macroscopic view of traumatic injuries 190–1 magnetic resonance imaging (MRI) 114, 192 Maihöfner, C. 83 Marchi, I. D. 238 Massen, C. 479 McIntosh, A. R. 392–3 mechanisms: of concussion injury 189–90; defined 189 medications, withdrawal from see surface 19-channel z-score training (19 ZNF), medication withdrawal and meditation practices see neuromeditation meditation types 66–75; see also individual types; automatic self-transcending (AST) 66, 70–2; focused attention (FA) 66–8; lovingkindness/ compassion (LK-C) 66, 73–5; open monitoring (OM) 66, 68–70 Meehan, T. P. 391, 392 memory/learning difficulties, concussions and 196 Menard, M. T. 216 Menon, V. 67 Miclea, M. 391 microscopic view of traumatic injuries 191 migraine history, concussions and 193 migraines, QEEG-guided neurofeedback and 150 Mild Traumatic Brain Discriminate Index (MTBI) 86 mild traumatic brain injury (mTBI) 187, 188 Miller, G. A. 388, 391 Miller-Scholte, A. 59 mindfulness-based stress reduction (MBSR) 64 mindfulness meditation see open monitoring (OM) mind wandering 67–8 Mismatch Negativity (MMN) 221 Miu, A. C. 391 MMPI 48 molecular view of traumatic injuries 191 Moloney, R. D. 533 Montgomery, D. D. 394 mood swings, concussions and 189

550

Index Morillas-Romero, A. 390 Moritani, T. 86–7 Motor Cortex Potential (MCP) 242 Motor Evoked Potentials (MEP) 223–4 motor skills development, neuroplasticity and 89–90 MRI see magnetic resonance imaging (MRI) Mulder, D. 8 Multidimensional Anxiety Scale for Children 48 multiple sclerosis, tDCS stimulation and 510 Muneaux, M. 223 Myklebust, H. 236 Myofascial Pain (Travell & Simons) 84 Myss, C. 468 Nash, J. K. 59 National Institute of Mental Health (NIMH) 97, 431–2 nausea/vomiting, concussions and 189 necrosis 535 nervous system, branches of 82 network, defined 256 Neundorfer, B. 83 neurobehavioral disorders, EEG biomarkers/ z-scored analysis of 431–50; abstract of 431; enuresis EEG signature case examples 440–9; introduction to 431–5; raw EEG signature case examples 435–40 neurofeedback; see also brainwave biofeedback; neurotherapy: defined 98; equipment options 49–50; intake interviews 47; introduction of (see alpha-theta protocol for neurofeedback introduction); meditation and (see neuromeditation); pediatric (see pediatric neurofeedback); progress monitoring, data useful for 45; psychotherapeutic relationship and 46–7; rationale/process of 47; training 9; as treatment for ADHD 6; treatments for PTSD 304–5 neurofeedback, clinical practice of; see also individual headings: bio-psycho-social approach to (see two channel bi-hemispheric methodology, QEEG based); Brain Enrichment Center 3–44; forensic populations and 92–104; neuromeditation practices 64–78; neuroplasticity 82–90; neurotherapy and 45–53; treatment progress, variables affecting 55–63 neurofeedback as anxiety treatment in adolescents/ young adults 455–63; abstract of 455; ADD client case study 457–63; options for 457; psychophysiological bases 456–7 neurofeedback session 465–6 neurofeedback station 465 neurofeedback technology, evolutions in 326–82; abstract of 326–7; 4 Channel Surface PZOK 338–50; 9 Channel Surface PZOK 350–60; 19 Channel Surface PZOK 360–7; sLORETA Region of Interest amperage feedback 329–34; sLORETA Region of Interest live Z-scored feedback 367–79; surface amplitude neurofeedback 327–9; Surface

Live Z-Score Feedback (PZOK) 335–8; Z-Plus options 379–82 neurogastroenterology 531 Neuroguide database 114 neuromeditation 64–78; see also meditation types; abstract of 64; background 64–6; clinical applications of 76–7; guidelines for 77–8; individualized 75–6; types of 66–75 neuroplasticity: abstract of 82–3; central nervous system and 82–3, 85–6; of chronic pain 82–90; clients of 83; components of 82; defined 94; evaluation of 83–6; forensic populations and 94–5; headaches example of 88–9; history as relevent to presenting problem and 85; motor skills development and 89–90; peripheral nervous system and 83, 86–7; presenting problem and 83–5; psychophysiological stress profiling and 88 neuropsychological tests 48–9 neuropsychology of dyslexia 236 neurotherapy 45–53; see also neurofeedback; abstract of 45; assessment/progress monitoring 48–9; continued learning/support for 52–3; definition of 45; equipment choices for 49–50; ethical challenges 51; family members/friends and, treating 51–2; intake interviews 47; introduction to 45–6; overtraining/medication changes discussion 47–8; success/failure of (see neurotherapy treatment progress, variables affecting); support team development 53; tactics 50–1 neurotherapy for practicing clinicians 404–20; abstract of 404–5; braindriving 404, 414–17; ClinicalQ 407–14, 418–20; clinical vs. normative data bases 405–7; conditional probability models and 406–7; symptom checklists 407 Neurotherapy Institute symptom assessment form 110–11 neurotherapy treatment progress, variables affecting 55–63; abstract of 55; biological 59–60, 62; EEG measures and 61–2; introduction to 55–6; psychological 56–7, 61–2; socio-cultural 57–9, 62; teen-age clients and 56–7 neurotransmitters, balancing 539 NewMind 8 Nexalin™ Advanced Therapy 539, 540 NFB see LORETA Z-score neurofeedback; neurofeedback Nicolson, R. I. 216, 237 9 Channel Surface PZOK 350–60; advantages of 360; Beck Anxiety Inventory and 350; Beck Depression Inventory and 350; clinical application 352–9; concept of 350–2; training considerations 360 19-Channel pediatric neurofeedback see QEEG and 19-Channel pediatric neurofeedback 19 Channel Surface PZOK 360–7; advantages of 367; clinical application 361–6; concept of 360–1; training considerations 367 Nitschke, J. B. 388, 391 node, defined 256

551

Index Norman, C. A. 220 normative vs. clinical data bases 405–7; conditional probability models and 406–7 Norretrander, T. 466 nutrition: brain-gut axis and 534–6; concussions and 193 observing response 478 obsessive-compulsive disorder (OCD) 57 occipital lobes 186 Och Labs 47 Ochs, L. 46, 47–8, 126 offline tDCS treatment 504 Ogden, P. 96 Olding, S. 407 Olgiati, P. 238 online tDCS treatment 504 Open Focus Synchrony Trainer 72 open monitoring (OM) 68–70; described 66, 68; EEG activity during 69; protocols 69; theta band power and 69–70 operant discrimination 478–9 orthography 216 Orton-Gillingham 218 Orton-Gillingham interventions program 278 Othmer, S. 427, 469 Pammer, K. 216 parasympathetic nervous system (PNS) 532–3 parietal lobes 186 Park, H. J. 256 Park, N.-S. 220 Parkinson, L. A. 468 Parkinson’s disease: concussions and 197; tDCS stimulation and 510 Pascual-Leone, A. 388, 392 passive volition 466 Pathologies (Olding) 407–8 pathophysiology, concussions and 190–1 Pavloski, R. 479 pediatric neurofeedback; see also individual headings: for children 6 years or younger 109–33; QEEG and 19-Channel 134–44 Peniston, E. 304, 467–8, 469 Perceptual type dyslexia 236 peripheral biofeedback: defined 98; measures used in 99 peripheral nervous system, chronic pain and 83, 86–7 Perlmutter, D. 532 Personality Inventory for Children 48 person-centered theory 4 PET see positron emission tomography (PET) Pfurtscheller, G. 489 Phillips, R. L. 426 Phospholipid Spectrum Disorder 218 physiological self-regulation 479–81 Pigott, E. H. 49 plasticity 187; see also brain plasticity

Polunina, A. G. 426 Polygraphy 98 positron emission tomography (PET) 114, 192 Posner, M. I. 479 Posterior Cingulate Cortex (PCC) 67 postmodern approach 4 post-neurofeedback training 9 post-traumatic stress disorder (PTSD) 6, 300–9; abstract of 300; amygdala and 95; case study 33–44; from clinical perspective 300–1; DSM5 definition of 300; fear conditioning and 301–2; LORETA treatments for 305–7; from neurocognitive perspective 302–3; neurofeedback treatments for 304–5; neuronal network implicated in 301–3; QEEG-guided neurofeedback and 150; sLORETA neurofeedback in 306–7; symptom clusters of 300–1; tDCS stimulation and 507; treatment case studies 307–9 power training neurofeedback 9 prefrontal cortex (PFC) 186 pre-natal health, concussions and 193 pre/post maps method 396 Prescott, D. S. 98 previous brain injury, concussions and 193 psychological variables affecting neurotherapy progress 56–7, 61–2 psychophysics of EEG state discrimination 481–2 psychophysiological stress profiling (PSP) 82, 88; chronic headaches example 89 psychophysiology of dyslexia 238–47 psychotherapeutic relationship, neurofeedback and 46–7 PTSD see post-traumatic stress disorder (PTSD) PZME (percentage of z-scores relevant to the mean) 327, 379 PZMO (percentage of z-scores in motion) 327, 379 QEEG see quantitative electroencephalography (QEEG) QEEG and 19-Channel pediatric neurofeedback 134–44; abstract of 134; brainwave distribution/ emotional evaluation step 140–1; evaluation process and therapeutic approach 143–4; familiarization/data collection step of 135–6; introduction to 135; methods used overview 135; neurofeedback training step of 136, 141–3; power, coherence and phase Z-score values overview 137–9 QEEG-guided neurofeedback 149–57; abstract of 149; anger/anger control disorders trained 149; bibliography of 154–7; cases successfully treated with 151–4; dysgraphia and 149; dyslexia and 150; enuresis and 150; epilepsy (drug resistant) and 150; introduction to 149; migraines and 150; PTSD and 150; reference list for 150–1 QEEGPro: case studies using 11–15, 17–21, 23–33, 34–43; Standardized Artifact Rejection Algorithm (SARA) 8

552

Index Quadrant Rules 395 quantitative electroencephalography (QEEG) 15; see also two channel bi-hemispheric methodology, QEEG based; at Brain Enrichment Center 5, 7–8; chronic headaches example 88; chronic pain and 82, 85–6; concussions and 192; conducting 111–12; described 100; dyslexia studies using 219–21; example 100; findings with 101–2; forensic populations and 100–2; as indication of connectivity, timing and amplitude issues 133; individualized neuromeditation 75–6; raw data acquisition for 112–14; reports 8; understanding 101 Raichle, M. E. 489 Rainnie, D. G. 301 rapid automatised naming 215 rapid temporal processing deficit theory 214 Rappelsberger, P. 251 Raymond, J. 468 reading-related potentials (RRPs) 238–42 reality therapy 4 relative rest phase of concussion treatment 198 Ressler, K. J. 301 “Results” neurofeedback tracking system 49 Reynolds, C. F. 423 Rich Club Hubs 390–1 Robb, J. 394 Roehrs, T. A. 426 Rogers, C. 46 Rondout Valley Holistic Health Community 464 Rosen, G. D. 216 Rosenfeld, J. P. 73, 388, 393 Rosenfeld, P. 392 Ross, J. 112 rotational concussion 190 Sahaja Meditation 69 SARA see Standardized Artifact Rejection Algorithm (SARA), QEEGPro Sarnthein, J. 468 Schore, A. 389 Schwartz, M. 389 Schweinhart, L. J. 223 SCL-90-R see Symptom Checklist-90-Revised (SCL-90-R) Scott, W. C. 427, 469 Seaman, D. R. 536 seat of emotion 95 secondary gain concept 56 Sedlacek, K. 3–4 seizure disorders: equipment options for treating 49; LORETA Z-score neurofeedback cases 181–2 self-regulation 93–4; breathing skills and 98–9 Self-Regulation Theory (SRT) 93–4 Selye, H. 391, 534 Sha-gass, C.

Shahim, P. 193 short-term memory 196 Shufman, E. 426 Sideroff, S. 427, 469 Simons, D. 87 single photon emission computed tomography (SPECT) 114, 192 SKIL database 114 sleep, concussions and 189, 193, 194, 197, 198 sLORETA current source density 9 sLORETA neurofeedback in PTSD 306–7 sLORETA Region of Interest amperage feedback 329–34; advantages of 334; characteristics of 327; clinical application 331–4; concept of 329–31; training considerations 327 sLORETA Region of Interest live Z-scored feedback 367–79; advantages of 376–9; characteristics of 327; clinical application 369–76; concept of 367–9; training considerations 379 sLORETA training 283–98; abstract of 283–5; client 1 case study 285–91; client 2 case study 291–8; regions of interest 284 sLORETA Z-score 9 Slow Cortical Potential Training 49 Small World scale free network design 391 Small World with a Rich Club Hub system 392 Smith, C. J. 536 Smith, M. 77, 337, 422 smoking, concussions and 194 Social Rating Scales 48 socio-cultural variables affecting neurotherapy progress 57–9, 62 Somkuwar, S. S. 428 Soutar, R. 393 SPECT see single photon emission computed tomography (SPECT) Sperling, A. J. 217 Sporns, O. 388 sport concussion see concussions Stam, C. J. 426 Standardized Artifact Rejection Algorithm (SARA), QEEGPro 8 standardized rating scales 48 standard operant conditioning 478–9 standard protocol: automatic self-transcending 72; focused attention 68; lovingkindness/compassion 74; open monitoring 69 Stara, V. 87 Stein, J. 215–16 Sterman, B. 114, 121, 181, 348 Sterman, M. B. 389–90, 393 Stöckl-Drax, T. 134–44; see also QEEG and 19-Channel pediatric neurofeedback Stone, R. 219 Stoyva, J. 484 stroke, tDCS stimulation and 511–13 subarachnoid hemorrhage 191 sub-dural hematoma 190–1

553

Index surface 19-channel z-score training (19 ZNF), medication withdrawal and 421–8; abstract of 421; depression case study 423–4; introduction to 421–2; method used 422–3; principles of 422; restless leg syndrome case study 424–7; tics case study 427–8 surface amplitude neurofeedback 327–9; advantages of 329; characteristics of 327; clinical application 328–9; concept of 327–8; training considerations 329 surface electromyographic (SEMG) evaluation 82, 87; chronic headaches example 88; chronic pain and 85 Surface Live Z-Score Feedback (PZOK/PZOKUL): characteristics of 327; concept of 335–8 Swingle, P. G. 415 sympathetic nervous system (SNS) 532 Symptom Checklist (SCL-90) 8 Symptom Checklist-90-Revised (SCL-90-R) 9, 48 symptom checklists, neurotherapy and 407 symptom tracking method 396 talk therapy 5 Tallal, P. 214 Taub, E. 3–4 Taylor, J. 219 TBI see traumatic brain injury (TBI) tDCS see transcranial direct current stimulation (tDCS) Teipal, S. J. 392 temporal lobes 95, 186 Test of Variables of Attention (TOVA) 48, 388, 397 Thatcher, B. 46 Thatcher, R. 86, 114, 121, 313 Theta waves 101–2 Thompson, L. 427 Thompson, M. 427 Thomson, D. M. 302 Thornton, K. 220 thoughtless awareness 69 tinnitus, tDCS stimulation and 509 tone 5 Tortella-Feliu, M. 390 total-tau blood levels, concussions and 193 Training & Research Institute, Inc. 95 transcranial alternating current stimulation (tACS) 509 transcranial direct current stimulation (tDCS) 500–22; abstract of 500; acquired neurological disorders and 511–15; for addiction/craving 507; advantages of 501; for Alzheimer’s disease 511; for amyotrophic lateral sclerosis 511; for aphasia 513–14; for chronic pain 507–8; clinical applications of 505–9; for depression 505–7; described 500–1; equipment/application for 502; future of 519; future research questions for 520–2; for headache 508–9; historical perspective of 501;

introduction to 500–1; mode of action 503; for multiple sclerosis 510; neurodegenerative disorders and 509–11; for Parkinson’s disease 510; principles of 502–4; for PTSD 507; safety of 503–4; as standalone vs. combined intervention 504–5; for stroke 511–13; for tinnitus 509; for traumatic brain injury 514–15; Ulam et al. study of 515–19 Transcranial Electrical Stimulation (TES) 539, 540 transcranial random noise stimulation (tRNS) 509 trauma, brain and 95–6 traumatic brain injury (TBI); see also concussions: brainwave biofeedback and 99–100; defined 194; LORETA Z-score neurofeedback cases 160–3; pathophysiology and 190–1; regions of 185–6, 194–7; tDCS stimulation and 514–15 Travell, J. 87 Trevisan, C. 238 Tsigos, C. 534 Tulving, E. 277, 302 two channel bi-hemispheric methodology, QEEG based 387–401; abstract of 387; applying 393–4; arousal modification, affect regulation, and horizontal axis 391–2; arousal theory, cognition, and vertical axis 389–91; bio-psycho-social assessment/tracking method 395–6; case study 397–401; introduction to 387–9; location and 393; pre/post maps method 396; rationale for 389–92; symptom tracking method 396; theoretical conundrums of 392–3; upstream/ downstream dysregulation and 395 Ulam, F. 515–19; see also transcranial alternating current stimulation (tACS) Unconditioned Stimuli (UCS) 414–15 van den Brink, W. 426 van der Kolk, B. 96 Varney, C. 468 Ventura, R. 59 verbal/motor response delays, concussions and 189 Vertical Axis of arousal 394 vertical occipital fascicus 216 Vidyasagar, T. R. 216 Vieira, K. F. 535 Vipassana 68, 69; see also open monitoring (OM) visual disturbances, concussions and 189 Visual Evoked Potentials (VEPs) 223 visual processing problems, dyslexia and 215–18 Volta, A. 501 von Stein, A. 468 Walker, J. 115, 126, 128, 220 Walsh, V. 215–16 Warrington, E. 236 Weiss, S. 251 What Is Neurofeedback-Update (Hammond) 47 Whitaker, R. 432 Wilson, C. J. 536

554

Index “Window of Vulnerability” model 389 Witton, C. 217 Wolfe, F. 84 Wyckoff, L. B. 478–9 Yeatman, J. D. 216 Yerkes-Dodson Law 390 Zazen 69 Zimberoff, D. 4 Z-Plus 379–82; advantages of 382; characteristics of 327; clinical application 379–82; concept of 379; training considerations 382

Z-score LORETA NFB therapy see LORETA Z-score neurofeedback Z-score neurofeedback training 312–24; abstract of 312–14; ADHD case study 319–22; Autism case study 322–3; bipolar disorder case study 317–18; dementia case study 316; estimators used in 312; papers/articles written on 313–14; Parkinson disorder case study 316; pervasive developmental delay case study 314–15; vertigo case study 315 Z-scores 9, 76; see also neurobehavioral disorders, EEG biomarkers/z-scored analysis of; displayed as instant brain maps 137; displayed as numbers 137; displayed as plots 138–9

555