Neuroimaging of Schizophrenia and Other Primary Psychotic Disorders: Achievements and Perspectives [1st ed.] 978-3-319-97306-7, 978-3-319-97307-4

Table of contents :
Front Matter ....Pages i-x
Neuroimaging: Diagnostic Boundaries and Biomarkers (Silvana Galderisi, Giulia Maria Giordano, Lynn E. DeLisi)....Pages 1-56
Neuroimaging and Psychopathological Domains (Armida Mucci, Silvana Galderisi, Antonella Amodio, Thomas Dierks)....Pages 57-155
Neuroimaging of Neurotransmitter Alterations in Schizophrenia and Its Relevance for Negative Symptoms (Andreas Heinz, Stefan Borgwardt, Lynn E. DeLisi)....Pages 157-169
Neuroimaging and Genetics (Lynn E. DeLisi, Stefan Borgwardt, Andreas Heinz)....Pages 171-182
Neuroimaging and the Longitudinal Course of Schizophrenia (Geraldo F. Busatto, Pedro G. P. Rosa, Paolo Fusar-Poli, Lynn E. DeLisi)....Pages 183-218
Neuroimaging and the At-Risk Mental State (Yu-Shiuan Lin, Paolo Fusar-Poli, Stefan Borgwardt)....Pages 219-265
Neuroimaging and Antipsychotics (Antonio Vita, Florian Schlagenhauf, Stefano Barlati, Andreas Heinz)....Pages 267-301
Research Perspectives for Neuroimaging of Schizophrenia Spectrum Disorders (Eleanor Scutt, Stefan Borgwardt, Paolo Fusar-Poli)....Pages 303-325
Toward Clinical Translation of Neuroimaging Research in Schizophrenia and Other Primary Psychotic Disorders (Annarita Vignapiano, Lynn E. DeLisi, Silvana Galderisi)....Pages 327-345

Citation preview

Neuroimaging of Schizophrenia and Other Primary Psychotic Disorders Achievements and Perspectives Silvana Galderisi Lynn E. DeLisi Stefan Borgwardt Editors

123

Neuroimaging of Schizophrenia and Other Primary Psychotic Disorders

Silvana Galderisi  ·  Lynn E. DeLisi Stefan Borgwardt Editors

Neuroimaging of Schizophrenia and Other Primary Psychotic Disorders Achievements and Perspectives

Editors Silvana Galderisi Department of Psychiatry University of Campania “Luigi Vanvitelli” Naples, Italy

Lynn E. DeLisi Harvard Medical School The VA Boston Healthcare System Brockton, MA, USA

Stefan Borgwardt Department of Psychiatry (UPK) University of Basel Basel, Switzerland

This volume is part of the activities of the WPA Section on Neuroimaging in Psychiatry. ISBN 978-3-319-97306-7    ISBN 978-3-319-97307-4 (eBook) https://doi.org/10.1007/978-3-319-97307-4 Library of Congress Control Number: 2018962261 © Springer Nature Switzerland AG 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

The project of a book on the state of the art of neuroimaging in schizophrenia and other primary psychotic disorders matured within the activities of the Section on Neuroimaging in Psychiatry of the World Psychiatric Association. Deciding the focus was not an easy task, in the light of the lively debate on the limitations of current diagnostic categories in psychiatry. Should we adopt a comprehensive approach (e.g., across all psychiatric disorders) or a specific focus (e.g., schizophrenia)? Should each chapter deal with diagnostic categories or psychopathological dimensions? Should we move from brain circuits and review their involvement in different disorders? Most of us agreed on the opportunity to focus on primary psychotic disorders in a transnosographic perspective (e.g., looking at ­psychopathological dimensions, reviewing data on at-risk mental states). We also agreed on the need to review both structural and functional, as well as neurochemical and multimodal, neuroimaging studies. The nine chapters provide an in-depth coverage of current achievements and limitations of neuroimaging research in psychotic disorders. Throughout the book, the authors highlight that abnormalities of brain structure and function do not reflect boundaries of current diagnostic categories but are relevant to important clinical aspects, such as the severity of the clinical picture, the persistence of the symptoms over time, and the overall response to treatment. The first chapter reviews data supporting the claim that we are close to identifying biomarkers for diagnosis, prediction of conversion from at-risk states to psychotic disorders, and prediction of treatment response, as well as of functional outcome or disease progression. The second chapter reviews research aimed at mapping individual symptoms and psychopathological domains on specific neural systems. It summarizes the contribution provided by research findings to the understanding of pathophysiological underpinnings of psychopathological dimensions of psychotic disorders, highlights gaps in current knowledge, and proposes directions for future research efforts. The third chapter covers research findings on neurotransmitter alterations and their relevance to cognitive dysfunctions and negative symptoms. It reviews PET and spectroscopy data on dopaminergic, gabaergic, and glutamatergic abnormalities and discusses models of dysfunctional network interactions in schizophrenia. The fourth chapter reviews data relevant to the link between genetic and neuroimaging research and highlights recent progress in the field of “imaging genetics.” v

vi

Preface

It discusses the potential contribution of the research approach to the identification of intermediate phenotypes and concludes with the hope that future use of these study designs will ultimately provide an important tool for the clinic and the practice of precision medicine in patients with schizophrenia. The fifth chapter reviews contributions from MRI investigations to the identification of heterogeneous patterns of progression of brain changes over the longitudinal course of schizophrenia after the initial onset of symptoms, their relevance to the outcome of the illness, and the relationship with treatment. The sixth chapter provides a comprehensive coverage of neuroimaging research in at-risk mental states. It reviews diagnostic methods, neuroimaging techniques and paradigms, structural, functional, and neurochemical findings, highlights methodological limitations, and discusses the risks of characterizing mental disorders purely by their biological inherency. The seventh chapter deals with the impact of antipsychotic drugs on brain structure and function and contributes to the lively debate on the role played by antipsychotic treatment on the progressive trajectory of brain abnormalities. It also reviews current evidence relevant to changes of positive and negative symptoms (both primary and secondary) under treatment, with respect to brain structural, functional, and neurochemical correlates. The eighth chapter summarizes very recent neuroimaging findings across schizophrenia spectrum disorders in order to provide an insight into the current trends for research in this area and highlights the progress in our understanding of this disorder spectrum. In the light of the reviewed literature and of the limitations of current diagnostic categories, the ninth chapter addresses the potential of neuroimaging research for translation into psychiatric clinical practice and suggests the need for further investigations with multicenter and multimodal imaging design, integrating clinical measures and imaging data, applying new multivariate approaches, such as different combined machine learning algorithms, to consolidate promising findings and finally lead to the translation into clinical practice. The book represents a unique opportunity for researchers, clinicians, and trainees in psychiatry to improve their knowledge on neuroimaging findings from different techniques, in the frame of a transnosographic approach that takes into account psychopathological dimensions common to the examined group of disorders, different features of at-risk states, and treatment implications and also provides different pathophysiological perspectives. The clear and informative update of neuroimaging research on primary psychotic disorders and its potential for translation into psychiatric clinical practice may guide future research in this area aimed at reducing the gap between current clinical practice and precision psychiatry. Naples, Italy Brockton, MA, USA Basel, Switzerland

Silvana Galderisi Lynn E. DeLisi Stefan Borgwardt

Contents

1 Neuroimaging: Diagnostic Boundaries and Biomarkers������������������������   1 Silvana Galderisi, Giulia Maria Giordano, and Lynn E. DeLisi 2 Neuroimaging and Psychopathological Domains������������������������������������  57 Armida Mucci, Silvana Galderisi, Antonella Amodio, and Thomas Dierks 3 Neuroimaging of Neurotransmitter Alterations in Schizophrenia and Its Relevance for Negative Symptoms �������������������� 157 Andreas Heinz, Stefan Borgwardt, and Lynn E. DeLisi 4 Neuroimaging and Genetics���������������������������������������������������������������������� 171 Lynn E. DeLisi, Stefan Borgwardt, and Andreas Heinz 5 Neuroimaging and the Longitudinal Course of Schizophrenia ������������ 183 Geraldo F. Busatto, Pedro G. P. Rosa, Paolo Fusar-Poli, and Lynn E. DeLisi 6 Neuroimaging and the At-Risk Mental State������������������������������������������ 219 Yu-Shiuan Lin, Paolo Fusar-Poli, and Stefan Borgwardt 7 Neuroimaging and Antipsychotics������������������������������������������������������������ 267 Antonio Vita, Florian Schlagenhauf, Stefano Barlati, and Andreas Heinz 8 Research Perspectives for Neuroimaging of Schizophrenia Spectrum Disorders ���������������������������������������������������������������������������������� 303 Eleanor Scutt, Stefan Borgwardt, and Paolo Fusar-Poli 9 Toward Clinical Translation of Neuroimaging Research in Schizophrenia and Other Primary Psychotic Disorders�������������������� 327 Annarita Vignapiano, Lynn E. DeLisi, and Silvana Galderisi

vii

Contributors

Antonella  Amodio  Department of Psychiatry, University of Campania “Luigi Vanvitelli”, Naples, Italy Stefano  Barlati  Department of Mental Health and Addiction Services, ASST Spedali Civili of Brescia, Brescia, Italy Stefan Borgwardt  Department of Psychiatry (UPK), University of Basel, Basel, Switzerland Geraldo F. Busatto  Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, São Paulo, Brazil Lynn E. DeLisi  VA Boston Healthcare System, Harvard Medical School, Brockton, MA, USA Thomas Dierks  Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, Bern, Switzerland Paolo  Fusar-Poli  Early Psychosis Interventions and Clinical Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry Psychology and Neuroscience (IoPPN), King’s College London, London, UK Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy Silvana  Galderisi  Department of Psychiatry, University of Campania “Luigi Vanvitelli”, Naples, Italy Giulia Maria Giordano  Department of Psychiatry, University of Campania “Luigi Vanvitelli”, Naples, Italy Andreas  Heinz  Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Charite University Medicine, Berlin, Germany Yu-Shiuan  Lin  Department of Psychiatry (UPK), University of Basel, Basel, Switzerland

ix

x

Contributors

Armida  Mucci  Department of Psychiatry, University of Campania “Luigi Vanvitelli”, Naples, Italy Pedro G. P. Rosa  Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, São Paulo, Brazil Florian  Schlagenhauf  Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Charite University Medicine, Berlin, Germany Eleanor Scutt  Department of Psychosis Studies, Institute of Psychiatry Psychology and Neuroscience (IoPPN), King’s College London, London, UK Annarita Vignapiano  Department of Psychiatry, University of Campania “Luigi Vanvitelli”, Naples, Italy Antonio  Vita  Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy Department of Mental Health and Addiction Services, ASST Spedali Civili of Brescia, Brescia, Italy

1

Neuroimaging: Diagnostic Boundaries and Biomarkers Silvana Galderisi, Giulia Maria Giordano, and Lynn E. DeLisi

1.1

Introduction

Neuroimaging research has shown that the abnormalities of brain structure and function, as well as the receptor pharmacology, are associated with psychiatric disorders but do not reflect boundaries of current diagnostic categories. Though this has disappointed scientists and clinicians searching for biomarkers supporting current diagnostic categories, neuroimaging findings are being reconsidered in the light of recent proposals for research aimed at identifying at-risk patients with an early diagnosis, predicting the treatment response and reconceptualizing classification systems in psychiatry (e.g., the Research Domain Criteria, https://www.nimh.nih. gov/research-priorities/rdoc/index.shtml). The present chapter will focus on the use of neuroimaging to identify biomarkers for the wide spectrum of psychotic disorders, with a focus on schizophrenia and other primary psychotic disorders. The state of the art of neuroimaging techniques as diagnostic tools will be depicted; main pitfalls and innovative perspectives will be highlighted. In particular, main contributions provided by neuroimaging techniques in the characterization of schizophrenia and other primary psychotic disorders will be examined for the purposes of diagnosis, prediction of outcome, treatment response, and onset of psychosis in subjects with “at-risk mental state.” Brain abnormalities that might contribute to separate subjects with schizophrenia from healthy controls are observed even before the disease onset and are predictive of illness course [1, 2]. The future of neuroimaging in psychiatry will depend on the possibility to move from the evidence of differences between groups to measures at the individual level, meaningful in the clinical

S. Galderisi (*) · G. M. Giordano Department of Psychiatry, University of Campania “Luigi Vanvitelli”, Naples, Italy L. E. DeLisi VA Boston Healthcare System, Harvard Medical School, Brockton, MA, USA © Springer Nature Switzerland AG 2019 S. Galderisi et al. (eds.), Neuroimaging of Schizophrenia and Other Primary Psychotic Disorders, https://doi.org/10.1007/978-3-319-97307-4_1

1

2

S. Galderisi et al.

practice, supporting diagnosis, and enabling prediction of illness course and response to treatment.

1.2

Biomarkers: Definition and Application in Psychiatry

1.2.1 Definition Different medical disciplines have adopted biomarkers in order to measure specific characteristics, provide a diagnosis, and predict treatment response or outcome of a disease. A variety of terms, such as biological marker, biomarker, surrogate marker, surrogate endpoint, and intermediate endpoint, can be found in the literature. In order to standardize the relevant terminology, the biomarkers definitions working group on “biomarkers and surrogate endpoints preferred definitions and conceptual framework” [3], convened by the National Institutes of Health, and proposed preferred definitions for terms such as biomarker, clinical endpoint, and surrogate endpoint. A biological marker (biomarker) is defined as “a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention” [3]. For instance, in the clinical medical practice, blood glucose level represents the cornerstone of diagnosis and management of diabetes. Therefore, a biomarker can be used as a measure of normal or abnormal biological functions or as a measure of treatment response. For these purposes, different biological parameters, such as receptor structure, electrophysiological correlates, and imaging-related measures, may represent a biomarker. Biomarkers have a great importance in preclinical studies (such as in vitro studies conducted on tissue samples or in vivo studies conducted in animal models) and in the early phase of clinical trials. Furthermore, in clinical practice they can be used for (1) disease staging; (2) diagnosis, i.e., identification of patients with a specific disease or an abnormal condition; (3) prognosis, i.e., to categorize patients according to the risk for disease progression; and (4) prediction of response to treatment. A clinical endpoint represents a clinical measure of disease characteristics used in clinical trials to assess the ratio benefits/risks related to a therapeutic intervention. A surrogate endpoint is defined as a biomarker that might substitute for a clinical endpoint in order to predict the efficacy of therapeutic intervention trial. The use of a biomarker as a surrogate endpoint is based on the accuracy (the correlation between clinical endpoint and surrogate endpoint) and reproducibility values. In drug development, approval by the US Food and Drug Administration (FDA) is based specifically on the effects that the drug produces on a specific surrogate endpoint or on a clinical endpoint, other than survival or irreversible morbidity [4]. Validation and qualification processes are crucial aspects to assess the utility of a surrogate endpoint (http://www.fda.gov/downloads/Drugs/GuidanceCompliance RegulatoryInformation/Guidances/UCM230597.pdf). The validation process refers to the performance of a surrogate endpoint in terms of sensitivity, specificity, and

1  Neuroimaging: Diagnostic Boundaries and Biomarkers

3

reliability. The qualification process is important to determine the ability of a biomarker in predicting the clinical outcome in order to verify its relevance to drug development. In general, in the medical practice, the validity of a biomarker is verified through the evaluation of its sensitivity, specificity, positive predictive value, and negative predictive value [5]. The sensitivity and specificity refer to the ability of a biomarker to determinate, respectively, the cases of patients with disease or the healthy cases correctly. The positive predictive value is the probability that subjects with a positive test truly have the disease, while the negative predictive value refers to the probability that subjects with a negative test truly do not have the disease. The Consensus Report of the Working Group on Molecular and Biochemical Markers of Alzheimer’s Disease [5, 6] suggested that a perfect diagnostic biomarker should identify only true positive cases and no false-negative one in order to reflect accurately the prevalence of the disease of interest. Furthermore, it should have sensitivity and specificity no less than 80% and a positive predictive value around 90%. Moreover, it should be reliable, reproducible, noninvasive, and inexpensive. Finally, it should be confirmed as a valid biomarker by at least two independent studies. A well-established biomarker developed for the β-amyloid pathology in the Alzheimer disease is the [F-18] florbetapir-PET, validated on the basis of relationships between [F-18] florbetapir-PET data antemortem and the β-amyloid in postmortem tissues. Clark and colleagues reported that the results rated as positive or negative for β-amyloid were confirmed in 96% of 29 subjects assessed in the autopsy cohort; in the secondary cohort (the non-autopsy cohort) [F-18], florbetapir-PET allowed to rate as amyloid negative 100% of healthy subjects, suggesting the high negative predictive value of this measure [7].

1.2.2 Neuroimaging Biomarkers In psychiatry, uncertainties about the pathophysiology of different mental disorders as well as about the relationship between identified biological abnormalities and pathophysiological mechanisms have contributed to make the search for biomarkers quite unsuccessful, so far. Historically, neuroimaging biomarkers were developed to allow the discrimination between primary mental disorders and secondary mental disorders caused by lesions such as neoplasm, hematoma, hydrocephalus, atrophy, or cerebrovascular diseases. However, these conditions explained the etiopathogenesis of a low percentage of mental disorders. Therefore, the use of neuroimaging data to differentiate primary and secondary mental disorders did not lead to a large use of biomarkers in the clinical practice. Until now, the diagnosis of mental disorders has been based mainly on the description of subject’s behavior (subjective-descriptive classification). The identification of neuroimaging biomarkers is crucial to move into the era of objective brain measures [8] and, maybe, to genes (see Chap. 4). Indeed, neuroimaging techniques, such as positron emission tomography (PET), as well as single-photon emission computed

4

S. Galderisi et al.

tomography (SPECT) and magnetic resonance spectroscopy (MRS), might be used to study variations in cells and molecular targets, while diffusion tensor imaging (DTI) and structural and functional magnetic resonance imaging (sMRI and fMRI, respectively) might investigate anatomical and functional circuitry [9]. Functional MRI is a measure of the blood-oxygenation-level-­dependent (BOLD) change in levels of blood deoxyhemoglobin that is a measure of variation in brain activity. MRS allows the study of brain metabolism, providing a measure of different metabolites levels within different brain regions, in the absence of structural brain changes. Finally, DTI is based on diffusion characteristics of water molecules and offers a measure of white matter integrity and connectivity between brain regions. All these methods can be used at a single time point or in longitudinal studies, which are useful to follow progressive brain changes across time. Currently, in Europe and the USA, neuroimaging is not recommended to diagnose a primary mental disorder, and more research is needed to change this picture and develop biomarkers capable to support the diagnostic work-up, predict illness course and treatment response, and develop new effective treatments.

1.2.2.1 Steps for Neuroimaging Biomarker Discovery The first step in the development of neuroimaging biomarkers consists in defining a clinical relevant question with the aim to improve patients’ quality of life. Biomarkers of the conversion to psychotic disorders in subjects at clinical high risk represent a good example of such a goal [10, 11]. The second step is crucial to ensure that a specific biomarker is linked to brain phenotypes directly associated with physiological mechanisms of interest, and not to the consequences of the disease, treatments, or other confounding factors such as age and personality traits [12]. The third step requires the validation of the biomarker in order to ensure sufficient positive and negative predictive value. Multicenter studies, conducted in independent samples with an adequate size, are needed to achieve this goal. Finally, in the fourth step, researchers have to indicate the clinical utility and costs/benefits of a specific biomarker. To this aim, the biomarker should improve the ability to establish a diagnosis, clinical outcomes, and quality of life (Fig. 1.1). In addition, it has been suggested that longitudinal studies might be crucial to establish a final diagnosis in subjects with unclear presentation of a mental disorder and to examine progressive brain changes [13] that could be related to the pathophysiology of the disease [14], antipsychotic treatments [15], or to substance and alcohol abuse [16].

1.3

 rimary Psychotic Disorders and Neuroimaging P Findings: Which Biomarkers for Psychotic Disorders?

Schizophrenia is a severe disease that affects approximately 0.5–1% of the general population. Since the first descriptions of schizophrenia [17, 18], it has been observed that patients are unaware of their symptoms, disconnected from reality, and exhibit negative symptoms that affect both high-level and basic cognitive functions. Over a hundred years ago, Emil Kraepelin differentiated dementia praecox, to

1  Neuroimaging: Diagnostic Boundaries and Biomarkers

Biomarker development

Phase I Target identification

5

Examples of potential biomarkers: • Striatal dopamine-release capacity • CA1 hippocampal CBV • Multivariate gray-matter markers

• Prediction of conversion to disease in at-risk states • Prediction of treatment response • Differential diagnosis in ambiguous cases

Phase II Internal validation

Ruling out confounding factors associated with outcome (e.g., age, medication, personality traits, etc.)

Phase III External validation

Demonstrating sufficient positive and negative predictive value for the target question in independent, targeted samples

Phase IV Demonstration of clinical utility

Demonstration of adequate NNA/NNT to improve clinical outcomes and quality of life (e.g., in converters from at-risk states, after treatment)

Fig. 1.1  Steps for biomarkers development. On the left, steps for biomarkers development. On the right, examples of potential biomarkers. From Fig. 2 in Abi-Dargham and Horga Abi-Dargham [8]

be called schizophrenia later on, from manic-depressive insanity, later termed bipolar disorder [17]. Conceptualization of endogenous psychoses as two distinct categories and the separation between primary psychotic disorders and primary affective disorders are still highly debated. Moreover, the schizophrenia spectrum disorder includes other categories characterized by psychotic symptoms such as delusions and hallucinations, i.e., schizoaffective disorder, delusional disorder, schizotypal personality disorder, schizophreniform disorder, and brief psychotic disorder. Actually, relation and boundaries between affective and schizophrenia spectrum disorders remain at the center of the debate. A spectrum model points that psychosis severity varies on a continuous scale with schizophrenia and affective disorders at opposite ends and schizoaffective disorder in between. Structural and functional neuroimaging techniques can uncover the neurobiological underpinnings of clinically defined entities, such as schizophrenia, and draw a likely delimitation with respect to bipolar disorder or schizoaffective disorder. In addition, they might

6

S. Galderisi et al.

identify brain alterations that occur since the onset, or even before the onset, of the disease and predict illness course, treatment response, and functional outcome.

1.3.1 Diagnostic Biomarkers 1.3.1.1 Structural Gray and White Matter MRI Findings In subjects with chronic schizophrenia (SCZ) or other schizophrenia spectrum disorders (SSD), the brain structural abnormalities consistently observed include the volume increase of the third and lateral ventricles and the decrease of intracranial and total brain volume [19, 20]. Volume decrease in cortical gray matter (GM), predominantly in prefrontal cortex (PFC) and inferior frontal gyrus, has also been confirmed repeatedly [19]. In antipsychotic-naïve patients (AP-naïve), the volume reductions in the caudate and thalamus seem to be more pronounced than in medicated patients, while less extensive total gray matter loss is detected [19]. A volume decrease in the right and left thalamus is also observed in first-episode schizophrenia (FES) subjects [21], indicating the presence of the abnormality since the early development of the disorder. Several replicated findings indicate the presence of a significant reduction in the right hippocampal volume, corpus callosum area (CC), and total GM and an increase in the right, left, and total lateral ventricle volumes in FES relative to healthy controls (HC) [22–25]. Some structural abnormalities seem to progress over time in schizophrenia. In particular, with the progressive manifestation of the disorder in FES subjects, a pattern of progressive loss of the whole cerebral GM volume, involving frontal, temporal, and parietal lobes, occurs. In chronic conditions, a significant volume loss affects the total cortical GM, primarily in the left superior temporal gyrus (STG), left anterior STG, left Heschl gyrus, left planum temporale, and posterior STG bilaterally [26]. In addition, a volume decrease in frontal, parietal, and temporal white matter [27] and a volume increase in lateral ventricles were found [13, 27, 28]. The possibility to identify structural abnormalities as candidate biomarkers is hindered by the variability of mentioned abnormalities in relationship with illness stage, treatment regimen, and image acquisition. Results from sMRI meta-analyses in psychotic disorders are described in Table 1.1. The diagnostic value of a biomarker also depends on whether it differs between the population of interest and other diagnostic categories. SCZ and subjects with schizoaffective disorder, for instance, show a widespread cortical gray matter volume decrease in numerous and overlapping areas, such as frontal, limbic, and subcortical areas [31–33], while gray matter reduction is observed in anterior cingulate and insular cortex in bipolar disorder (BPD) subjects [34]. In direct comparison with BPD subjects, SCZ showed a reduced right amygdala volume and a larger increase in right and left lateral ventricle volumes, while BDP subjects were characterized by whole brain and prefrontal lobe volume reductions, as well as by an increase in the volume of the globus pallidus [30]. By contrast, these differences appeared less pronounced when comparing first-episode bipolar disorder and schizophrenia subjects. In this case, similar abnormalities including a reduction in

1  Neuroimaging: Diagnostic Boundaries and Biomarkers

7

Table 1.1  Structural gray and white matter MRI findings in psychotic disorders Brain imaging technique, study design Meta-analysis of 21 structural MRI (sMRI) studies

Study Vita et al. [23]

Sample 340 FES; 422 HC

Medication Medicated

Boos et al. [29]

679 SCZ; 1100 HC; 1065 FHR

Unknown

Meta-analysis of 25 sMRI studies

Arnone et al. [24]

894 SCZ/SSD/ FES; 809 HC

Medicated

Meta-analysis of 28 sMRI

Arnone et al. [30]

1823 BPD; 670 SCZ; 29 SSD; 1940 HC

Medicated

Meta-analysis of 72 sMRI studies

Adriano et al. [21]

449 FES/SCZ; 508 HC

Medicated

Kempton et al. [13]

473 SCZ/patients with psychotic disorders (schizoaffective disorder, schizophreniform, psychoses not otherwise specified); 348 HC

Unknown

Meta-analysis of 13 sMRI studies (six studies of first-episode patients only and seven studies of chronic patients) Meta-analysis of 13 longitudinal sMRI studies (median duration of follow-up unknown)

Findings Significant overall effect sizes for lateral and third ventricular volume increase and for volume reduction of whole brain and hippocampus, but not for temporal lobe, amygdala, and total intracranial volumes Smaller hippocampal volume in relatives than in controls. Reduced GM and increased thirdventricle volume in relatives versus HC. Smaller hippocampal volume in SCZ compared to first-degree relatives Corpus callosum area reduced in SCZ in comparison to HC, especially in first-episode patients Whole brain and prefrontal lobe volume reductions, as well as increased volume of the globus pallidus and lateral ventricles volumes in BPD versus HC. Smaller lateral ventricular volume and enlarged amygdala volume in BPD than in SCZ Significant bilateral thalamus volume reduction in both FES and SCZ compared to HC. Left thalamus smaller than right thalamus in both SCZ and HC Increased rates of lateral ventricle dilation over time in patients compared to HC (effect size g = 0.449, 95%CI 0.192–0.707, p = 0.0006). No significant effect of age of onset, duration of illness, or age at baseline scan was found (continued)

8

S. Galderisi et al.

Table 1.1 (continued) Brain imaging technique, study design Meta-analysis of 26 sMRI studies

Study Olabi et al. [27]

Sample 928 SCZ; 867 HC

Medication Unknown

Vita et al. [26]

813 FES/SCZ; 718 HC

Unknown

Meta-analysis of 19 sMRI studies

De Peri et al. [25]

1198 FES; 315 FE BPD; 1382 HC

Medicated

Meta-analysis of 45 sMRI studies

Adriano et al. [22]

1669 FES/SCZ; 2130 HC

Medicated

Meta-analysis of 13 structural MRI studies of FEP and of 22 chronic patient studies

Findings Significantly greater decreases over time in whole brain volume, whole brain GM, frontal GM and WM, parietal WM, and temporal WM volume, as well as larger increases in lateral ventricular volume in SCZ than in HC Significantly higher volume loss over time of total cortical GM, left STG, left anterior STG, left Heschl gyrus, left planum temporale, and posterior STG bilaterally in SCZ. FES showed greater progressive loss of cerebral GM volume involving the frontal, temporal, and parietal lobes, and left Heschl gyrus than HC Significant overall effect sizes (Q = 43.39, p = 0.02) for intracranial, whole brain, total GM, and WM volume reduction, as well as for lateral ventricular volume increase at disease onset in both schizophrenic and bipolar patients Significant bilateral hippocampal volume reduction in patients as compared with HC. Similar hippocampal volume reduction in FES and SCZ. Left smaller than right hippocampus in patients with respect to HC

1  Neuroimaging: Diagnostic Boundaries and Biomarkers

9

Table 1.1 (continued)

Study Fusar-­Poli et al. [28]

Sample 1046 SCZ,; 780 HC

Medication Medicated

Haijma et al. [19]

8327 medicated SCZ/SSD; 771 antipsychoticnaive patients

Medicated; AP naïve

Brain imaging technique, study design Meta-analysis of 30 longitudinal sMRI studies (Median duration of follow-up of 72.4 weeks)

Meta-analysis on cross-­ sectional volumetric (MRI) brain alterations in both medicated and antipsychotic-­ naïve patients

Findings At baseline, whole brain volume reductions and enlarged lateral ventricle (LV) volumes; no volumetric abnormalities in the GM and WM volumes, cerebrospinal fluid, and caudate nucleus in SCZ compared to HC. Progressive GM volume decreases and LV volumes enlargements in patients than in HC Significant decrease of intracranial and total brain volume and increase of third and lateral ventricles in medicated versus AP-naïve patients; volume decreases in cortical GM, prefrontal GM, and inferior frontal gyrus in medicated versus drug-naïve patients. Volume reductions in caudate nucleus and thalamus more pronounced in AP naïve than in medicated patients. White matter volume decreased to a similar extent in both groups; gray matter loss less extensive in AP-naïve patients

SCZ subjects with schizophrenia, SSD schizophrenia spectrum disorder, FES first-episode schizophrenia, FHR familial high risk, FEP first-episode psychosis, BPD subjects with bipolar disorder, HC healthy controls, sMRI structural magnetic resonance imaging, WM white matter, GM gray matter, STG superior temporal gyrus

intracranial, whole brain, and total gray and white matter volumes, as well as an increase in lateral ventricular volume, were observed in both patient groups [25]. Interestingly, similar results were reported in a study based on a dimensional approach to psychosis that included first-degree relatives of bipolar and schizophrenia or schizoaffective subjects. Psychosis probands and relatives with psychosis spectrum personality disorders showed diffuse gray matter reductions in

10

S. Galderisi et al.

overlapping cortical regions, including the frontotemporal, parietal, cingulate, and insular cortices, and the cerebellum. Unaffected relatives did not show any abnormality. Furthermore, psychotic bipolar probands had gray matter volume reductions primarily localized in the frontotemporal, cingulate, and insular cortices, albeit less extensive than probands with schizophrenia or schizoaffective disorder, who showed a widespread cortical and subcortical GM volume reduction [32]. This might indicate a partially divergent GM phenotype across disease categories, with SCZ and schizoaffective disorder subjects demonstrating more generalized decrease in neocortical and subcortical gray matter regions and BPD subjects showing limited reductions in frontotemporal regions. Volume reductions in thalamus and hippocampal subcortical areas were also consistently reported in psychosis spectrum disorders [35–38], with a greater volume loss in SCZ as compared to BPD subjects [35, 39, 40]. Actually, recent evidence suggests that a decreased thalamus volume is present in SCZ but not in BPD subjects [32, 41]. Findings relevant to the amygdala and basal ganglia volumes are more inconsistent. In fact, volume decreases limited to schizophrenia, as well as decreases in both SCZ and BPD but more prominent in SCZ than in BDP, or no decrease in either diagnostic groups, or even increased volumes in SCZ, were reported [31, 42–50]. Few studies provided data on correlations between structural brain abnormalities and clinical characteristics of SCZ and SSD subjects. In line with Ivleva et al. findings supporting the notion of the psychosis spectrum [32], a negative correlation was observed between the severity of delusions and frontal gray matter volumes, as well as between the severity of hallucinations and right uncus gray matter volume along a continuum across diseases [49]. Furthermore, findings obtained by studying firstepisode psychosis (FEP) subjects, such as FES, FEP SSD, and FEP BPD, indicated a direct association of the negative dimension with lateral and third ventricle volume enlargement and of the positive dimension with thalamus and ventral diencephalon nuclei volume increase [20, 51]. It is worth highlighting that the most significant ventricular and basal ganglia enlargement and the greatest frontotemporal cortical volume deficits were reported in subjects with an earlier onset of a schizophrenia spectrum disorder, while the least extensive cortical deficits were found in mood disorder subjects [46].

1.3.1.2 Diffusion Tensor Imaging Findings Increasing evidence suggests regional disconnectivity and white matter (WM) pathology of bundles connecting cortical and subcortical areas in the brain of SCZ subjects [52–54]. Anatomical disconnectivity and myelination abnormalities were confirmed also in postmortem and genetic studies in schizophrenia [55, 56]. DTI analysis, enabling the study of this kind of WM alterations, revealed their presence since the early stages of the disorder [57–59]. Evidence of frontotemporal abnormalities in FEP subjects and fronto-temporo-limbic impairments in subjects with schizophrenia has been provided [57, 59–64]. In FEP, lower fractional anisotropy (FA) values were observed in corpus callosum (CC), uncinate fasciculus (UF), anterior cingulum (AC), superior longitudinal fasciculus (SLF), and fornix (FX). Specifically, in FES subjects decreased FA in medial and middle frontal lobe, precuneus and parietal lobe, anterior and posterior cingulate cortex, and

1  Neuroimaging: Diagnostic Boundaries and Biomarkers

11

predominantly in the temporal lobe confirmed that changes of cortical-subcortical WM integrity occur early at the onset of the disorder. Furthermore, in the same subjects, FA reductions were found in the anterior and posterior internal capsule, external capsule, and bilateral hippocampal gyri [65]. Interestingly, these WM alterations were correlated with specific cognitive deficits in verbal and spatial working memory, as well as with positive more than negative symptoms in FES subjects. The correlation between microstructure abnormalities and psychopathology is yet scant, but recent studies showed that lower FA values in the right SLF, right inferior longitudinal fasciculus (ILF), right arcuate fasciculus (AF), left UF, right cingulum bundle, inferior fronto-occipital fasciculus, and right fornix correlate with negative symptom severity [59, 66–70]. Overall, schizophrenia studies consistently reported decreased FA and WM abnormalities localized preferentially within the fiber bundles connecting prefrontal and temporal lobes, including UF, cingulum bundle, arcuate fasciculus, and genu of the corpus callosum [71, 72]. Results of meta-analyses or reviews of DTI studies in psychotic disorders can be found in Table 1.2. Figure 1.2 shows the trajectories of uncinate fasciculus, arcuate fasciculus, genu, fornix, cingulum, inferior fronto-occipital fasciculus, and inferior longitudinal fasciculus [74]. However, these features cannot be regarded as available biomarkers for schizophrenia diagnosis, especially when we consider the overlap between findings obtained in SCZ and those reported in BPD samples [73, 75–79]. Nevertheless, the disrupted intra- and interhemispheric structural WM connections in SCZ provide a solid evidence for the conceptualization of the disconnectivity syndrome. Indeed, widespread alterations observed in WM bundles connecting heteromodal association cortices (HASC), such as ventral prefrontal cortex and superior temporal gyrus, could explain the disruption in the coordination of those association areas and the cognitive and behavioral deficits observed in schizophrenia [57, 80–82].

1.3.1.3 Functional Neuroimaging Findings Several functional neuroimaging studies reported that HC and subjects with psychotic disorders during task performance engage the same brain networks but with a differential magnitude of activation [83, 84]. Multiple meta-analyses and systematic reviews exploring working memory, executive function, emotion recognition, or positive symptoms have highlighted the differential activation of the prefrontal cortex [84–88], anterior cingulate cortex (ACC) [84, 87, 89], insula [86, 89, 90], thalamus [84, 91], and superior temporal gyrus [86, 87, 89] in SCZ as compared to HC. In particular, SCZ subjects showed reduced activation in the left dorsolateral prefrontal cortex (DLPFC), rostral/dorsal ACC, left thalamus, and inferior and posterior cortical areas, which should support executive task performances [92]. A hyperactivation was observed, instead, in several midline regions, such as ventrolateral prefrontal cortex (VLPFC), posterior temporal and parietal cortices, amygdala, and insula. The largely confirmed hypoactivation of DLPFC and ACC is consistent with an impairment in the cognitive control network that could be associated with the executive dysfunction in SCZ [84]. Most probably, the reduced top-­down regulation provided by the DLPFC leads to a relatively greater activity in

12

S. Galderisi et al.

Table 1.2  DTI findings in psychotic disorders

Study Patel et al. [72]

Sample 202 SCZ; 213 HC

Medication Medicated

Ellison-­ Wright and Bullmore [62]

407; SCZ/ SSD; 383 HC

Medicated

Kuswanto et al. [65]

FES; HC

Medicated; assessment PANSS; Sternberg item recognition task

Brain imaging technique, study design 2 meta-analyses on DTI studies investigating the genu and splenium of the corpus callosum The ALE method hybridized with the rank approach used in genome scan meta-­analysis (GSMA) on DTI studies Review of 22 DTI studies

Findings Lower FA in the splenium in patients (modest effect size) than in HC

Significant FA reductions in the left frontal deep WM and temporal deep WM

Decrease of FA predominantly in the temporal lobe (superior temporal gyrus, inferior temporal gyrus, temporal-occipital region, and posterior temporal regions) but also in medial and middle frontal lobe, precuneus and parietal lobe, anterior and posterior cingulate cortex in patients as compared to HC. FA reductions in the anterior and poster internal capsule, in the fornix, and in bilateral hippocampal gyri in FES. Changes in white matter integrity correlated with specific cognitive deficits (verbal and spatial working memory) as well as psychopathology (positive more than negative symptoms) in patients with FES

SCZ subjects with schizophrenia, SSD schizophrenia spectrum disorder, FES first-episode schizophrenia, FHR familial high risk, FEP first-episode psychosis, BPD subjects with bipolar disorder, HC healthy controls, ALE activation likelihood estimation, WM white matter, FA fractional anisotropy, DTI diffusion tensor imaging

the middle regions as compensatory response or alternative strategies to support task performance [84, 93, 94]. Furthermore, findings of reduced amygdala activation for emotion perception, reduced modulation of visual processing areas, as well as contemporary hyperactivity in the cuneus, superior temporal gyrus, parietal lobule, and precentral gyrus may reflect an aberrant connectivity in networks processing emotional stimuli in SCZ [87, 95]. This suggests that patients have a difficulty in modulating integrative brain areas underlying complex socio-emotional stimuli processing, such as ACC, dorsomedial PFC and occipital pole, and a compensatory recruitment of nonemotional regions [96–98]. The detected functional alterations

1  Neuroimaging: Diagnostic Boundaries and Biomarkers

13

Fig. 1.2 Uncinate fasciculus, arcuate fasciculus, genu, fornix, cingulum, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus. ILF inferior longitudinal fasciculus, IFO inferior fronto-­ occipital fasciculus. From Fig. 1 in Boos et al. [73] genu uncinate cingulum IFO

fornix arcuate ILF splenium

in SCZ frequently involve the same brain areas in which structural abnormalities are observed since the onset of the disorder [89], thus supporting the possibility to use these structural aberrations as biomarkers for early diagnosis. An example is provided by the increased activation in bilateral cortical areas involved in speech generation, i.e., the Broca’s area, the associative auditory cortices of the Wernicke’s convolution, as well as the areas involved in verbal memory, i.e., the left hippocampus/parahippocampal region, demonstrated in SCZ subjects experiencing auditory verbal hallucinations (AVHs) [90]. The involvement of these language-related perceptual and motor areas in AVHs in functional neuroimaging studies is consistent with the correlation between hallucination severity and gray matter volume reductions in the left superior and middle temporal gyri or altered microstructure of white matter bundles (such as superior longitudinal fasciculus) connecting Broca’s and Wernicke’s regions, reported by structural imaging studies [99–101]. When comparing distinct psychosis categories, SCZ subjects showed a pronounced impairment in both neural networks involved in verbal and visuospatial working memory, while schizoaffective disorder subjects were impaired in visuospatial processing only [102]. Similarly, more subtle or no abnormality was seen in BDP subjects with respect to SCZ, when studying activation patterns of networks engaged in working memory tasks [103, 104]. Resting state functional MRI enabled the identification of connectivity patterns in brain areas typically conceptualized as part of the default mode network (DMN). This is a large-scale brain network involved in self-referential thinking, active at rest that recruits the medial PFC, left hippocampus, posterior cingulate cortex, and precuneus [105]. It has been suggested that disorders included in the psychosis spectrum share the connectivity reductions in the DMN; the reduction, however, is more pronounced in SCZ, and selective nodes are differentially affected in different disorders [106]. Specifically, SCZ seems to be characterized by abnormal recruitment of the frontopolar cortex and the basal ganglia [107]. SCZ and schizoaffective disorder subjects show the same degree of altered connectivity, revealing greater coherence in the left frontopolar cortex, right DLPFC, and multiple regions within basal ganglia, compared to HC. BPD subjects, compared to HC, show significantly more coherence in other regions within DMN, i.e., the left parietal cortex, left

14

S. Galderisi et al.

fusiform gyrus, right visual and auditory association cortices, left frontopolar cortex, and the pons [107]. A DMN dysfunction, i.e., reduced deactivation of the medial frontal gyrus, was observed during working memory tasks in acute [108] and in clinical remission [109] phases in subjects with schizoaffective disorder as compared to HC, thus indicating a trait like feature of schizoaffective disorder [110]. Similar results of a failure to deactivate the medial frontal cortex were observed also in bipolar disorder and schizophrenia, supporting the concept of DMN dysfunction as shared feature across the diseases [110–113] (Fig. 1.3). Findings suggesting increased or no aberrant functional connectivity at rest between frontal and mesolimbic areas of the DMN in schizophrenia and affective psychoses were also reported [105, 114, 115]. Interestingly, the aforementioned brain regions found to be dysfunctional in studies cited in this paragraph correspond to those revealing structural abnormalities, such as PFC, DLPFC, temporal and occipital cortex, or hippocampal nuclei. For more details on the findings described in this paragraph, reader is referred to Table 1.3, reporting results of fMRI meta-analyses in psychotic disorders. Healthy controls –32

–26

–20

–14

16

22

28

34

–8

40

–2

4

10

46

52

58

0

8

0

–6

0

7

0

–6

Subjects with schizophrenia –32

–26

–20

–14

16

22

28

34

–8

40

–2

4

10

46

52

58

Fig. 1.3  DMN in subjects with schizophrenia and healthy controls. Brain regions showing a significant effect of the two-back versus baseline contrast in healthy controls and in subjects with schizophrenia. Yellow colors indicate a positive association with the two-back memory task. Blue colors are for areas where the task led to a decrease in the blood oxygenation level-dependent (BOLD) response (areas of deactivation). Numbers refer to Talairach z coordinates of the slice shown. The right side of each image represents the left side of the brain. Color bars indicate z scores from the group-level analysis. From Fig. 1 in Pomarol-Clotet et al. [111]

1  Neuroimaging: Diagnostic Boundaries and Biomarkers

15

1.3.1.4 Other Neuroimaging Findings An extensive body of research provided substantial evidence of abnormal Table 1.3  fMRI findings in psychotic disorders

Study Minzenberg [84]

Sample SCZ; HC

Jardri et al. [90]

SCZ/SSD experiencing AVHs during scanning

Assessment Delayed match to sample or delayed response (including Sternberg item recognition), go/no-go (including AX-CPT), Mental arithmetic, N-back, oddball, sequence recall, Stroop, Wisconsin card sort, and word generation tasks were used to explore the executive functions Patients press online button during auditory verbal hallucinations

Brain imaging technique, study design Meta-­ analysis of 41 fMRI/ PET studies published prior to February 2007

10 meta-­ analysis of fMRI/PET studies

Findings In within-group analyses, activation of a similarly distributed cortical-­ subcortical network prominently include the DLPFC, ventrolateral PFC, ACC, and thalamus in HC and SCZ. In between-group analyses, reduced activation in the left DLPFC, rostral/dorsal ACC, left thalamus (with significant co-occurrence of these areas), and inferior/posterior cortical areas, in patients than in HC. Increased activation in several midline cortical areas in patients than in HC

Significantly increased activation likelihoods in a bilateral neural network, including the Broca’s area, anterior insula, precentral gyrus, frontal operculum, middle and superior temporal gyri, inferior parietal lobule, and hippocampus/ parahippocampal region in patients experiencing AVHs (continued)

16

S. Galderisi et al.

Table 1.3 (continued)

Study Taylor et al. [87]

Sample 450 SCZ; 422 HC

Assessment Tasks aimed to assess (1) emotion perception (2) Emotion experience (3) Emotion valencespecific processing

Radua et al. [89]

sMRI: 965 FEP; 1040 HC; fMRI: 362 FEP; 403 HC; [FEP included both schizophrenia spectrum psychoses (schizophrenia, schizoaffective, schizophreniform) and affective psychoses (bipolar psychosis and psychotic depression)]

Cognitive tasks assessing attention, processing, speed, verbal fluency, working memory, visual memory

Brain imaging technique, study design Meta-­ analysis of 26 fMRI/ PET studies

Multimodal meta-­ analysis of 43 structural (VBM) and functional (fMRI, PET, SPECT) studies

Findings For emotional experience, greater activation in the left occipital pole in HC than in SCZ. For emotional perception, reduced activation in bilateral amygdala, visual processing areas, ACC, DLPFC, medial frontal cortex, and subcortical structures in SCZ than in HC. Greater activation in the cuneus, parietal lobule, precentral gyrus, and superior temporal gyrus in SCZ than in HC Reduced specific reactivity of the amygdala in emotion-neutral contrast and decrease in ACC activity throughout contrasts in SCZ versus HC Conjoint structural and functional differences between FEP and HC in the insula/ superior temporal gyrus and the medial frontal/ anterior cingulate cortex bilaterally. In the same regions, large and robust decreases in gray matter volume were found with either reduced or enhanced activation. Specifically, the anterior parts of the insula and the dorsal part of the mF/ACC showed hypoactivation; the posterior parts of the insula and the ventral part of the mF/ACC showed reduction in deactivation in FEP

SCZ subjects with schizophrenia, SSD schizophrenia spectrum disorder, FES first-episode schizophrenia, FEP first episode psychosis, BPD subjects with bipolar disorder, HC healthy controls, VBM voxel-based morphometry, SPECT single-photon emission computed tomography, PET positron emission tomography, fMRI functional magnetic resonance imaging, ACC anterior cingulate cortex, DLPFC dorsolateral prefrontal cortex, PFC prefrontal cortex, mF medial frontal

1  Neuroimaging: Diagnostic Boundaries and Biomarkers

17

neurometabolite levels in schizophrenia and affective disorders. An increased striatal dopamine synthesis in both caudate and putamen regions has been reported by several PET studies in SCZ and SSD subjects [85]. On the other hand, magnetic resonance spectroscopy provided new insights about glutamatergic neurotransmission. Reductions of N-acetylaspartate (NAA) and glutamate (glu) concentrations have been found in frontal lobe, thalamus, and basal ganglia in SCZ and FES subjects [116, 117], while an increase in glutamine (gln) was observed in frontal regions [118]. Furthermore, both glu and gln levels in the frontal region show a greater progressive decrease with age in patients as compared to healthy controls, suggesting a progressive loss of synaptic activity. These findings are consistent with an increased glutamate turnover in frontal regions among patients that leads to a faster reduction of glu and gln concentrations with age [118]. Moreover, an extensive meta-analysis suggested that the decrease of NAA involves the frontal lobe, hippocampus, thalamus, and basal ganglia in SCZ, while it is limited to the basal ganglia and frontal lobe in BDP [116]. However, no difference was found among SCZ, BPD, or schizoaffective subjects in the degree of NAA reduction in the DLPFC [119]. See Table 1.4.

1.3.2 B  iomarkers of Schizophrenia Subtypes: Deficit and Non-­deficit Schizophrenia The wide heterogeneity of schizophrenia in clinical picture, symptomatic and functional outcomes, as well as neurobiological correlates has led to the identification of different subtypes within the syndrome. To date, although DSM-5 does not include any schizophrenia subtype, deficit schizophrenia (DS), i.e., schizophrenia with primary and enduring negative symptoms [120–122], is largely regarded as the most validated schizophrenia subtype. With respect to non-deficit schizophrenia (NDS), DS is associated to poor response to pharmacological treatment [123], worse functional outcome [122, 124, 125], and greater impairment of neurocognitive functions [124–126].

1.3.2.1 Structural and Functional Neuroimaging Findings Results of MRI studies demonstrated that DS was not associated with lateral ventricular enlargement [127–129]. These findings are surprising since the lateral ventricular enlargement has often been reported in association with negative symptoms and poor outcome. A discrepant finding reported by Arango and colleagues [130] showed an increased ventricular volume in DS subjects, in comparison with HC, without differences between NDS and DS subjects or HC. These discrepant results might be explained by differences in methodological aspects, such as MRI analysis methods and the inclusion in the latter study of the third and fourth ventricles too. Different studies reported greater abnormalities in temporal lobe in DS than in NDS subjects, including an increase of left temporal cerebrospinal fluid volume [131], smaller right temporal lobe volume [127], and reduced gray matter volume in the superior [132, 133] and middle temporal gyrus [133]. In a study conducted in DS, NDS, and BPD subjects and HC, the authors, evaluating network-level properties of cortical thickness, found higher interregional coupling, associated with high regional

18

S. Galderisi et al.

Table 1.4  Other neuroimaging findings in psychotic disorders Brain imaging technique, study design Meta-analysis of 97 1 H-MRS studies

Study Brugger et al. [117]

Sample SCZ; FEP; HC

Medication Medicated

Marsman et al. [118]

647 SCZ/ FEP; HC

Medicated/ antipsychotic naive

Meta-analysis of 28 1H-MRS studies that examined differences in glutamate and glutamine concentrations

Fusar-Poli and Meyer-­ Lindenberg [85]

113 SCZ/ SSD; 131 HC

Medicated

Kraguljac et al. [116]

SCZ; FEP; BPD

Medicated

11 striatal (caudate and putamen) PET studies were included in a quantitative meta-­ analysis of dopamine synthesis capacity Meta-analysis of 146 studies evaluating N-acetylaspartate choline and creatine assessed with -MRS in SCZ and bipolar disorder up to September 2010

Findings Significant reductions in NAA levels in frontal lobe, temporal lobe, and thalamus in both patient groups as compared to HC Decreased glutamate and increased glutamine in medial frontal region in SCZ as compared with HC. In SCZ, glutamate and glutamine concentrations decreased at a faster rate with age as compared with HC Increased striatal (both caudate and putamen) DSC in SCZ as compared with HC

Decreased NAA levels in the basal ganglia and frontal lobe in SCZ versus HC, decreased NAA levels in the basal ganglia in BPD versus HC

SCZ subjects with schizophrenia, SSD schizophrenia spectrum disorder, FES first-episode schizophrenia, FEP first-episode psychosis, BPD subjects with bipolar disorder, HC healthy controls, MRS magnetic resonance spectroscopy, DSC dopamine synthesis capacity, NAA N-acetylaspartate, Cho choline, Cr Creatine, ACC anterior cingulate cortex, DLPFC dorsolateral prefrontal cortex

centrality in the inferior frontal, inferior parietal, and middle and superior temporal cortices, in DS subjects as compared to the other groups [134]. These results are in contrast with previous studies conducted in other (NDS) groups of subjects with schizophrenia, which reported reduced frontotemporal and frontoparietal connectivity (see [135] for a review). Wheeler’s et al. findings [134] suggested that DS might represent a subtype of schizophrenia with a diffuse neurodevelopmental disorder of brain connectivity, since the increased network density could be linked to a reduction of networks differentiation during neurodevelopment. As to DTI data, different studies investigated white matter tracts connecting frontal, parietal, and temporal lobes [66–68] in DS and reported WM abnormalities in frontoparietal and frontotemporal circuits involving the superior longitudinal fasciculus [66], left uncinate fasciculus [67, 68], right inferior longitudinal fasciculus, and right arcuate fasciculus [67]. The disruption of the left uncinate fasciculus, right inferior longitudinal fasciculus,

1  Neuroimaging: Diagnostic Boundaries and Biomarkers

19

and right arcuate fasciculus was reported in DS subjects, as well as in FEP subjects with a “deficit-like” disease [67], suggesting that these abnormalities might represent a neurobiological feature of DS and do not depend on other factors such as pharmacological treatment or duration of illness. These results are in line with studies that showed in DS subjects impairment of emotion expression, social cognition, and socio-emotional functioning, associated with fronto-temporal-parietal circuit [51]. Furthermore, WM abnormalities in DS subjects could be related to the increased density of interstitial cells of WM found in postmortem studies, thus suggesting an abnormal development of WM tracts [136]. However, these findings await replication since these studies were conducted in small samples. Only few functional neuroimaging studies were carried out in DS subjects, and results are not as consistent as expected [51]. The most replicated findings involved abnormalities in the frontoparietal circuit in DS subjects as compared to NDS subjects [51, 135]. In different studies using PET or SPECT, authors reported glucose hypometabolism or hypoperfusion in the frontal and parietal cortices in subjects with DS as compared to those with NDS [51, 135]. In a recent fMRI study, using a monetary incentive delay task to investigate the reward processing, the authors reported a reduced activity of the dorsal caudate during reward anticipation in DS subjects as compared with NDS subjects and healthy controls [137]. On the whole neuroimaging studies, investigating neurobiological correlates of DS refuted the hypothesis of the association between DS and lateral ventricles enlargement and indicated that DS is not just the more severe end of the schizophrenia severity spectrum [51]. However, they failed to produce a coherent picture and identify a robust biomarker.

1.3.3 Prognostic and Predictive Biomarkers 1.3.3.1 Prediction of Treatment Response Several longitudinal MRI studies investigated brain changes associated to early-, medium-, and long-term outcomes of SCZ, either clinical (number of subsequent psychotic episodes, severity of symptoms, hospitalizations, duration of remission, and response to treatment) or functional outcomes (the ability to live independently, maintain employment, or be in a relationship) [138–154] (Table 1.5). Results from these studies have demonstrated that the presence of brain alterations at the baseline is related to worse clinical and functional outcome at follow-up and that these alterations tend to progress in patients with poor outcome. However, both the chronicity of the illness and the effect of medication should be taken into account. In fact, after the illness onset, most patients are treated with antipsychotics; patients with a poor course of illness are more likely to be exposed to a long period or high doses of medications that could have an impact on brain measures [156]. Similar considerations concerning the effects of medication apply to medium and long-term follow-up studies conducted in FEP subjects [157, 158] or in children and adolescents with early onset first-episode schizophrenia (CAFEP) [139]. In a 4-year follow-up study conducted in FEP subjects, Manè and colleagues [157]

20

S. Galderisi et al.

Table 1.5  Relationship between brain structure alterations and medium- and long-term clinical and functional outcomes Assessment SANS; SAPS; CASH; PSYCH BASE Outcome evaluated after 7 years CASH; SCAN Outcome evaluated after 2 years

MRI technique sMRI, manual tracings

Study Wassink et al. [138]

Sample 63 SCZ

Van Haren et al. [155]

109 SCZ (89 FEP; 20 s psychotic episode)

Mitelman et al. [150]

104 SCZ (GF and PF); 41 HC

CASH Outcome evaluated after about 4 years

DTI

Van Haren et al. [153]

96 SCZ; 113 HC

PANSS; CASH Outcome evaluated after 5 years

sMRI

Van Haren et al. [154]

96 SCZ; 113 HC

PANSS; CASH Outcome evaluated after 5 years

sMRI

Jaaskelainen et al. [147]

54 SCZ

sMRI

Friedman et al. [142]

34 SCZ and SCZ-AFF

Honer et al. [140]

39 SCZ; 3 SCZ-AFF

PANSS; SOFAS Outcome evaluated after 16 years BPRS; clozapine Treatment response evaluated after 6 weeks CGI; clozapine Treatment response evaluated after 6 weeks

sMRI

CT

CT

Findings Negative association of cerebellar volume at baseline with negative and positive symptom duration, as well as psychosocial impairment, after a 7-year follow up No association of brain volume parameters with clinical and functional outcome after a 2-year follow-up Reduced overall WM integrity of the prefrontal and temporal areas in SCZ as compared to HC in both PF and GF but more pronounced in PF than in GF Decreases in GM density in the left superior frontal area, left STG, right caudate nucleus, and right thalamus in SCZ as compared to HC. Correlation between GM density reduction in the superior frontal cortex and increased number of hospitalizations, 5 years after the onset More pronounced cortical thinning in SCZ in temporal regions associated with poor outcome, 5 years after the onset Increased density of frontal and limbic areas associated with better clinical and functional outcome at follow-up Prefrontal sulcal enlargement associated with poor response to clozapine

Prefrontal sulcal enlargement associated with poor response to clozapine

1  Neuroimaging: Diagnostic Boundaries and Biomarkers

21

Table 1.5 (continued) MRI technique SPECT, rCBF

Study Molina et al. [148]

Sample 24 SCZ

Assessment SAPS; SANS Neuroleptics; clozapine Treatment response evaluated after 6 months

Molina et al. [149]

39 SCZ

SAPS; SANS Neuroleptics, clozapine Treatment response evaluated after 6 months

SPECT, rCBF

Konicki et al. [141]

36 SCZ (26 GO and 10 PO)

CT

Arango et al. [143]

75 SCZ

Molina et al. [144]

25 SCZ

CGI; clozapine Treatment response evaluated after 6 weeks SANS; BPRS; SARS; clozapine; haloperidol Treatment response evaluated after 10 weeks SANS; SAPS; clozapine Treatment response evaluated after 6 months

Findings During treatment with neuroleptics, subjects who later responded to clozapine showed higher thalamic, left basal ganglia, and right prefrontal perfusion, than nonresponders. As compared to HC, in nonresponders, lower perfusion in prefrontal cortex, while in responders higher perfusion in the left basal ganglia and thalamus. Subcortical perfusion of responders decreased when they received clozapine During neuroleptic treatments nonresponders to clozapine showed lower rCBF in comparison with responders in the thalamus, basal ganglia, and DLPFC After clozapine intake, perfusion reduction in thalamus and basal ganglia in responders only Prefrontal sulcal enlargement associated to poor response to clozapine

sMRI, ROI

Increased GM volume associated with good treatment response in clozapine-treated patients but with poor treatment response in haloperidol-­treated patients

sMRI-­ PET

Positive symptom improvement associated with temporal GM volume; disorganization symptom improvement inversely related to intracranial volume and hippocampal volume. Patients with high baseline DLPFC volume and metabolic activity more likely to experience improvement in negative symptoms (continued)

22

S. Galderisi et al.

Table 1.5 (continued) Study Garver et al. [146]

Sample 13 SCZ (8 responders, 5 nonresponders), 14 HC

Ertugrul et al. [151]

22 SCZ

Lathi et al. [152]

29 SCZ

Molina et al. [145]

30 SCZ; 31 HC

Assessment CASH; SAPS; haloperidol; ziprasidone Treatment response evaluated after 4 weeks PANSS; clozapine Treatment response evaluated after 8 weeks BPRS; haloperidol; olanzapine Treatment response evaluated after 1–6 weeks

Olanzapine; risperidone Treatment response evaluated after 3 weeks

MRI technique DTI

Findings Responders showed lower WM microstructural integrity at baseline, as compared to non-responders This impairment showed a partial reversal after 4 weeks

SPECT, rCBF

Baseline frontal and thalamic rCBF positively predicted changes in the PANSS scores after 8 weeks of clozapine treatment

PECT, rCBF

rCBF changes in cortical-­ subcortical and limbic neuronal networks after antipsychotics intake. After 1 week of treatment, greater rCBF increase in the ventral striatum and greater decrease in hippocampus associated with good response to both haloperidol and olanzapine Smaller volumes of the insular cortex and rectal gyrus and larger volumes of basal ganglia associated with better response after 3 weeks of antipsychotic treatment

sMRI, VBM

FEP first-episode psychosis, SCZ subjects with schizophrenia, SCZ-AFF schizoaffective patients, HC healthy controls, GF good functioning patients, PF poor functioning patients, GAF global assessment of functioning scale, PANSS positive and negative syndrome scale, SOFAS social and occupational functioning assessment scale, SANS scale for the assessment of negative symptoms, SAPS scale for the assessment of positive symptoms, SARS Simpson-Angus rating scale, CASH comprehensive assessment of symptoms and history, SCAN schedule for clinical assessment in neuropsychiatry-section psychosis, PAS premorbid adjustment scale, sMRI structural magnetic resonance, CT computed tomography, DTI diffusion tensor imaging, SPECT single-photon emission computed tomography, PET positron emission tomography, rCBF regional cerebral blood flow, ROI region of interest, GM gray matter, VBM voxel-based morphometry, WM white matter, PFC prefrontal cortex, OFC orbitofrontal cortex, DLPFC dorsolateral prefrontal cortex, FPC fronto-polar cortex, STG superior temporal gyrus, OFG orbitofrontal gyrus

reported gray matter changes in the left lingual gyrus, right insula, and right cerebellum, which were inversely correlated with functional outcome. Furthermore, changes in hippocampal volume over the first 6 years of the illness were associated with illness course, symptoms severity, and functional outcome [158]. Finally, in CAFEP subjects the loss of left frontal gray matter volume was associated with the weeks of hospitalization, while the cerebral spinal fluid increase was associated with the severity of negative symptoms, after a 2-year follow-up [139].

1  Neuroimaging: Diagnostic Boundaries and Biomarkers

23

About 55% of patients respond to antipsychotics in the first 12 months of treatments [159], and generally the effective treatment is identified by trials and errors. Therefore, the development of neuroimaging biomarkers able to predict the treatment response and identify unresponsive patients early on would be extremely useful in the clinical practice [160, 161]. From a clinical perspective, it is interesting and informative to analyze the relationships between brain alterations and early outcomes, including also the response to the initial treatment, at the onset of psychosis in FEP subjects. Results concerning relationships between structural brain alterations and early outcomes in FEP subjects are reported in Table 1.6. Although inconsistent findings have been reported, the use of follow-up evaluations to predict clinical and functional outcomes in FEP subjects appears promising. Smaller hippocampal, parahippocampal, and striato-thalamic volumes are associated with lower rates of symptom remission [168, 169, 173]. Fractional anisotropy of the uncinate, cingulum, and corpus callosum is lower in nonresponders to treatment at baseline, as compared to both healthy controls and treatment responders [172]. After 12  weeks, in both responders and nonresponders, there is an increase in fractional anisotropy, positively correlated with the antipsychotic exposure. Brain structures in treatment-responsive patients are similar to those of healthy controls [172]. Furthermore, treatment nonresponders show prominent hypogyria at bilateral insular cortex, left frontal, and right temporal regions, as compared to responders [171].

1.3.3.2 Prediction of the Transition to Psychosis Before the onset of psychosis, subjects generally experience subthreshold psychotic symptoms and impairment in social functioning [174]. This condition is defined as at-risk mental state (ARMS), for which researchers have developed specific criteria [175, 176], including attenuated psychotic symptoms and/or a brief psychotic episode and/or genetic vulnerability, and impairment in social functioning [177, 178]. Within 2 years following the onset of prodromal symptoms, about 18–36% of ARMS subjects develop a psychotic disorder (ARMS-T) [177]. Most of the ARMS subjects who do not develop a psychosis (ARMS-NT) will present another psychiatric disorder or persistent attenuated symptoms, while about 14% will have symptomatic remission [179–181]. Early pharmacological and psychological treatments are available to prevent or delay the disease onset in people with at-risk mental state [182] but, especially pharmacological treatments, should be addressed only to ARMS subjects that will develop a psychosis [183]. Moreover, the characterization of ARMS subjects entirely based on clinical evaluation enables the prediction of conversion with a sensitivity of 96% and a specificity of 47% [184], thus failing to provide an accurate discrimination between subjects who will develop psychosis from those who will not, with the consequent risk of overtreatment. In the light of these observations, the main goal of the translational research would be to identify neurobiological markers of psychosis conversion in order to provide targeted treatments only to subjects who need it [185]. For a full review of biomarkers relevant to transition to psychosis from at-risk mental state, reader is referred to Chap. 7.

24

S. Galderisi et al.

Table 1.6  Relationships between brain structure alterations and early outcomes in FEP Study Lieberman et al. [162]

Sample 70 FEP

Zipursky et al. [163]

26 FEP; 82 HC

Prasad et al. [164]

27 FEP (25 completed follow-up)

Wobrock et al. [165]

45 FEP (32 included in MRI analyses, divided in GF and PF) 32 male drug naïve FEP (GF and PF), 18 HC

Kasparek et al. [166]

Luck et al. [167]

44 FEP (20 GF and 24 PF), 30 HC

Bodnar et al. [168]

68 FEP

Bodnar et al. [169]

59 FEP

Szeszko et al. [170]

39 FEP (25 responders and 14 non responders), 45 HC

Assessment PANSS; CGI; SADS-CPD Outcome evaluated after 1 year PANSS; ESRS; haloperidol Treatment response evaluated after 1 week Strauss-­ Carpenter scale Outcome evaluated after 1 year PANSS Outcome evaluated after 1 year

MRI technique sMRI

Findings Lateral and third ventricle abnormalities associated with longer time to remission

sMRI

Greater cortical GM volume associated with improvement in positive and negative symptoms

sMRI

DLPFC volume, but not intracranial volume, positively correlated with functional outcome at 1 year

sMRI, ROI

Smaller left anterior limb of the internal capsule in PF as compared to GF patients

GAF Outcome evaluated after 1 year

sMRI, VBM

PANSS Outcome evaluated after 6 months PANSS Outcome evaluated after 6 months PANSS Outcome evaluated after 1 year SADS-CPD Outcome evaluated after 16 weeks

DTI

Bilateral GM reduction in the lateral PFC in FEP as compared with HC. Smaller GM volume in the left OFC and frontopolar cortex in PF than in GF patients, after 1-year follow-up Greater FA decreases along the superior longitudinal fasciculus and the uncinate fasciculus in PF than in GF Parahippocampal gyrus volume associated with symptom remission

sMRI, VBM

sMRI, VBM

Parahippocampal gyrus volume associated with symptom remission

sMRI

Greater cortical thickness and cortical asymmetry in occipital regions in responders, as compared with nonresponders. Among responders, greater cortical thickness in temporal regions associated faster response to treatment

1  Neuroimaging: Diagnostic Boundaries and Biomarkers

25

Table 1.6 (continued) MRI technique sMRI

Study Palaniyappan et al. [171]

Sample 80 FEP; 46 HC

Assessment PANSS; antipsychotics Treatment response evaluated after 12 weeks

Reis Marques et al. [172]

63 FEP; 52 HC

PANSS; antipsychotic Treatment response evaluated after 12 weeks

DTI

Fung et al. [173]

39 drug naïve FEP

GAF Outcome evaluated after 1 year

sMRI, VBM

Findings Significant reduction in gyrification across multiple brain regions in FEP as compared with HC Prominent hypogyria at bilateral insular, left frontal, and right temporal regions in nonresponders when compared with responders At baseline, lower FA in nonresponders than in responders and HC in the uncinate, cingulum, and corpus callosum. After 12 weeks, increase in FA in both responders and nonresponders; the increase positively correlated with the cumulative dose of antipsychotic In females FEP, larger striato-thalamic volume was associated with early remission

FEP first-episode psychosis, SCZ subjects with schizophrenia, HC healthy controls, PANSS positive and negative syndrome scale, ESRS extrapyramidal symptom rating scale, CGI clinical global impression, SARS Simpson-Angus rating scale, GF good functioning patients, PF poor functioning patients, GAF global assessment of functioning scale, SADS-CPD schedule for affective disorders change and psychosis and disorganization, sMRI structural magnetic resonance, DTI diffusion tensor imaging, VBM voxel-based morphometry, ROI region of interest, GM gray matter, WM white matter, FA fractional anisotropy, PFC prefrontal cortex, OFC orbitofrontal cortex, OFG orbitofrontal gyrus, DLPFC dorsolateral prefrontal cortex

1.4

Machine Learning

1.4.1 M  achine Learning and Generation of  Future Instances and Previsions Recently, the focus of neuroimaging research in schizophrenia spectrum disorders has shifted to the possibility to identify diagnostic and prognostic tools that, in individual subjects, could support (1) the identification of the correct diagnosis, (2) the prediction of the disease onset, and (3) the choice of the most effective treatment [186]. The study and construction of computer software that learns from experience constitutes the subject matter of machine learning. Based on the observation of different examples, machine learning enables general hypotheses and previsions about future instances [187, 188]. In a dataset used by machine-learning algorithm,

26

S. Galderisi et al.

each instance is represented using the same set of features [189]. A critical step that influences the classifier performance is the choice of the features, which may be continuous (i.e., education, age), categorical (i.e., gender, race), or binary (i.e., sick, unemployed). An objective of machine-learning process is to identify the set of categories (subpopulations) a new observation belongs to, on the basis of a training set of data containing observations (or instances) whose category membership is known. For instance, machine-learning algorithm may assign a diagnosis to a given patient as described by observed characteristics of the patient (gender, presence or absence of certain symptoms, etc.) (Fig. 1.4). Classification performance, mainly measured by the accuracy, enables the assessment of a model’s predictive power [191, 192].

1.4.1.1 Main Learning Schemes: Unsupervised and Supervised Machine-Learning Methods In machine-learning algorithms, two different schemes can be used. They are characterized by applying or not applying prior information (supervised or unsupervised learning, respectively) to the instances. In the unsupervised learning, no prior information is available, and with this scheme, it is possible to evaluate how the system finds unknown but useful classes of items [193] that share common characteristics. The main method of this typology of learning is the clustering, in which the total amount of data is clustered using measures of similarity between patterns, in order to collect them in different homogenous clusters. Most machine-learning applications involve the supervised learning, in which the instances present a known label. The most applied supervised learning algorithms used in psychiatry are artificial neural networks (ANNs), support vector machines (SVMs), and relevance vector regression (RVR) [194]. ANNs are biologically inspired networks of artificial neurons configured to perform specific tasks. A typical neural network contains a large number of artificial neurons called units, arranged in a series of layers. Units in a net are clustered into three classes, input units, output units, and units in between (hidden units), which transform the input into something that output units can use. ANN depends upon three main aspects: input and activation functions of the unit, network architecture, and weight of each input connection [195, 196]. SVM is a new supervised machine-learning technique, in which the expected generalization error is minimized [197, 198]. Performance of this algorithm, as compared to other algorithms, has proven particularly useful to avoid classification issues by maximizing the margin between classes of data in a high-dimensional space and has recently been widely used in several studies on psychiatric disorders [197, 199, 200]. This algorithm comprises two different phases: (1) the “training phase” in which the dataset is used as input into classifier, in order to discriminate between different established groups and (2) the “testing and evaluation phase” in which the classifier is tested to predict the group to which a new collected case belongs. In order to assess the performance of the SVM classifier, three different measures have been used: accuracy, sensitivity, and specificity [201, 202]. Neural

1  Neuroimaging: Diagnostic Boundaries and Biomarkers Data collected from patient

27

Reference database

Database core

Secure hosting infrastructure

Clinical Demographic Cognitive Genetic

Clinical Demographic Cognitive Genetic Structural MRI Functional MRI Diffusion MRI Structural MRI Functional MRI Diffusion MRI

Plug-in module

Data analysis modules Neuroimaging data

Non-neuroimaging data

Integration of neuroimaging and non-neuroimaging data

Graph analysis

Machine learning

Multi-parametric classification

Fig. 1.4  Multivariate approach. The prediction of the outcome or the diagnosis can be assessed using an integrated approach with neuroimaging, demographic, clinical, cognitive, and genetic data. From Fig. 4 in McGuire et al. [190]

networks and SVMs tend to perform much better when dealing with multidimensions and continuous features. Finally, RVR is a technique that allows the prediction of a quantitative variable without a binary categorical decision (such as patients vs controls) [203].

28

S. Galderisi et al.

1.4.2 Applicability of Machine-Learning Techniques in Psychotic Disorders Recent insights into nosology and neurobiology of psychiatric disorders reveal that traditional categorical diagnoses do not reflect the complexity of psychiatric disorders. Individuals often satisfy criteria for multiple disorders or do not fit any specific category clearly; therefore, the validity of a categorical diagnosis has sometimes been questioned. This has led to the development of the research domain criteria (RDoC) [204, 205], a project aimed at reorienting research on etiology and pathophysiological mechanisms underlying psychopathology from category-based to dimension-based and at incorporating genetics, neuroimaging, and cognitive science methods into future diagnostic schemes [206]. When neuroimaging data have been used as input in the machine-learning classifiers, the algorithms were applied for disease diagnosis [199, 200, 207–209], quantitative predictions about transition from an at-risk mental state to psychosis [10, 199, 200, 210, 211], and predicting global level of functioning and long-term clinical outcome [212]. Therefore, computational psychiatry could be the future of psychiatry with a key role in the development of new treatments and preventive strategies. Main findings of studies that have applied the machine-learning approaches are reviewed in the following paragraphs.

1.4.2.1 Machine-Learning Classifiers and the Prediction of Schizophrenia-Distinctive Patterns and Illness Course A number of studies have investigated the diagnostic and prognostic value of neuroimaging biomarkers in different stages of psychotic disorders. Most of them applied SVMs [199, 213] to various neuroimaging techniques (sMRI, fMRI, DTI) [214, 215] and reported good diagnostic accuracy in the distinction of subjects with schizophrenia from healthy controls (Table 1.7) (Fig. 1.5). The first study applying SVM to sMRI data was conducted by Davatzikos et al. [213]. Their results were able to discriminate between the two groups even when the experimental sample was split by gender, demonstrating good generalizability of the MRI-based diagnostic system in detecting subtle and distributed brain alterations. Although confirmed in the following studies [216–220], on the whole, these findings have to be interpreted with caution, since sample size and stage of illness could influence the performance of the diagnostic algorithm [200]. Relatively high classification accuracy, ranging from 75% to 92%, has been reported in several neuroimaging classification studies using fMRI data [207, 208, 221–223]. Some studies used both structural and functional MRI data to generate diagnostic classifiers with higher sensitivity and specificity than single modality ones. The integration of different MRI modalities into multimodal disease models might provide a superior classification performance [224, 227]. In the study conducted by Cabral et al. [224], the authors demonstrated that the combination of sMRI and rs-­ fMRI reached 75% accuracy compared to 69.7% and 70% accuracy for the single MRI modalities (respectively, sMRI and fMRI). However, sociodemographic and clinical variables such as patients’ age, duration of illness, and symptom domains

Costafreda et al. [223]

63 FE SSD; 63 HC 32 SCZ; 40 HC

Mikolas et al. [222]

sMRI

36 recent onset psychosis; 36 HC 51 FEP; 51 HC

16 ARMS-T; 23 FEP; 22 HC

DTI

37 HC; 27 SCZ

fMRI, Verbal fluency task

rsFC

fMRI, CPT task sMRI

sMRI

92%

SVM

SVM

HC vs FEP 86.7% ARMS-T vs FEP 80.0% 73%

61.8%

86.1%

90.62%

72%

Females HC vs SCZ 91.8% Males HC vs SCZ 90.8% 73.4% 84.37%

Accuracy 81.1%

SVM

LDA

SMLR

SVM

SVM Unsupervised classifier based on C-means MLDA

SVM-RFE

sMRI

sMRI rsFC

Machine-learning approach SVM

MRI technique sMRI

39 FEP; 39 HC

Females: 23 SCZ; 38 HC Males: 46 SCZ; 41 HC 62 FEP; 62 HC 32 SCZ; 20 HC

Sample 69 SCZ; 79 HC

Borgwardt et al. [215]

Yoon et al. [221]

Kasparek et al. [218] Ingalhalikar et al. [219] Sun et al. [220]

Zanetti et al. [217] Shen et al. [207]

Authors Davatzikos et al. [213] Fan et al. [216]

Table 1.7  Machine learning: prediction of schizophrenia-distinctive patterns and illness course

92%

(continued)

HC vs FEP 86.4% ARMS-T vs FEP 66.8% 71.4% HC vs FEP 87% ARMS-T vs FEP 91.3% 74.6% 91%

–

–

–

76.9%

–

–

–

66.7%

67.7% 75.0%

–

–

79.0% 93.75%

Specificity 87.3%

Sensitivity 73.9%

1  Neuroimaging: Diagnostic Boundaries and Biomarkers 29

28 PS-CON; 28 PS-EP; 28 HC

Mourao-Miranda et al. [225, 226]

sMRI rsFC sMRI

sMRI rsFC

MRI technique rsFC

SVM

SVM Linear discriminant Analysis (meta-analytic model bivariate random effects) rdCV

Machine-learning approach Random forest algorithm

EP-PS vs CON-PS = 68% CON-PS vs HC = 61% EP-PS vs HC = 43% EP-PS vs CON-PS = 71% CON-PS vs HC = 71% EP-PS vs HC = 64%

EP-PS vs CON-PS = 70% CON-PS vs HC = 67% EP-PS vs HC = 54%

75%

SCZ vs HC 80.3% rsfMRI SCZ vs HC 76.9% sMRI SCZ vs HC 79.0% 71.2%

SCZ vs HC 80.3% rsfMRI SCZ vs HC 84.46% sMRI SCZ vs HC 76.4% 78.8%

–

Specificity –

Sensitivity –

Accuracy 75%

ARMS at-risk mental state, FEP first-episode psychosis, HC healthy controls, SCZ subjects with schizophrenia, FE-SSD first-episode schizophrenia-spectrum disorder, PS-CON/EP continuous psychosis/episodic psychosis, SVM support vector machines, SVM-RFE support vector machine with recursive feature elimination, SMLR sparse multinomial logistic regression, MLDA maximum-uncertainty linear discrimination analysis, LDA linear discriminant nalysis, rdCV repeated double cross-validation framework, sMRI structural magnetic resonance imaging, fMRI functional magnetic resonance imaging, DTI diffusion tensor imaging, rsFC resting-state functional connectivity, CPT continuous performance task

71 SCZ; 74 HC

38 studies 1602 SCZ; 1637 HC

Sample 18 SCZ; 18 HC

Cabral et al. [224]

Authors Venkataraman et al. [208] Kambeitz et al. [209]

Table 1.7 (continued)

30 S. Galderisi et al.

1  Neuroimaging: Diagnostic Boundaries and Biomarkers

31

Healthy Controls

Gray matter density

Recent-onset Patients

>.40 .35 .30 .25 .20 .15 .10 .05 .01

Fig. 1.5  Machine learning and gray matter density in first-episode psychosis subjects. Maps of average gray matter density (GMD) and P-maps of recent-onset psychosis patients versus healthy control subjects comparison. Patients showed significantly lower GMD, particularly in the lateral surface of the prefrontal and temporal lobes, limbic regions along the cingulate sulci, and areas along the parieto-occipital fissures. The primary sensorimotor cortex and primary visual cortex on the medial surface were relatively spared. From Fig. 1 in Sun et al. [220]

may affect diagnostic accuracy. Indeed, they found that the accuracy of the classifier performance for the rs-fMRI was mediated by age and age at illness onset: older SCZ subjects with a later onset of illness and more pronounced negative symptoms were more reliably assigned to the SCZ group by the rs-fMRI classifier. The authors did not find the same results with the sMRI classifier. In order to investigate the diagnostic accuracy of different multivariate approaches and different imaging modalities in the distinction between SCZ subjects and HC, Kambeitz et al. [209] conducted a meta-analysis of 38 studies. They found that the most frequent multivariate approaches (support vector machine and discriminant analysis) had almost identical sensitivity and specificity. Moreover, they reported a significant difference between the sensitivity of rs-fMRI and structural MRI studies, with rs-fMRI showing a higher sensitivity but a similar specificity with respect to sMRI. According to the authors, better performance of rs-fMRI-based classification algorithms could be related to more homogeneous functional resting-state patterns in schizophrenia that lead to a tighter clustering of patients. The authors also investigated the potential role of clinical variables on the classification performance and observed significant effects of patients’ age and disease stage on sensitivity values, indicating higher sensitivity in older and chronic patients. The specificity was higher in patients with predominantly positive symptoms (evaluated as positive-to-­negative symptom ratio) and treated with higher medication doses. Instead, other variables

32

S. Galderisi et al.

such as gender, illness duration, positive and negative syndrome scale (PANSS) positive scores, and PANSS negative scores did not exhibit significant effects on sensitivity or specificity. Recently, multivariate pattern recognition approaches are applied for the identification of future illness course [226], observing a higher accuracy in classification of “continuous psychosis” than “episodic psychosis.” The findings reported to date suggest that the development of neuroimaging-­ based machine-learning systems could aid in the identification of biomarkers for schizophrenia in order to overcome the limits of clinical evaluation.

1.4.2.2 Neuroimaging Biomarkers for Psychosis Differential Diagnosis A key question in ascertaining the diagnostic value of a biomarker is whether it differs between the population of interest and another diagnostic category. In the light of the heterogeneity of SCZ clinical features and the overlap with other disorders such as bipolar disorder [228], it is likely that objective measures could improve the diagnostic process and increase its reliability and validity. Multivariate pattern analyses might be effective in discriminating schizophrenia from bipolar disorder subjects and improve the prediction of the final diagnosis for patients experiencing a psychotic episode [229]. These analyses could contribute to clarify whether current nosological constructs are subserved by distinct neurobiological features or alternatively whether a significant pathophysiological overlap between these disease entities does exist [209]. Few studies included subjects with bipolar disorder, allowing the clinically relevant discrimination between SCZ and BPD, and provided encouraging results [223, 230–232] (Table 1.8). Based on gray matter density, great accuracy in differentiation between SCZ and BPD subjects and major depression disorder was reported by different studies [230, 232]. The usefulness of gray matter volume data in diagnostic prediction has been confirmed, also excluding the effects of different machine-learning algorithms [229]. Costafreda et al. [223] investigated the diagnostic value of fMRI during a phonological verbal fluency task in a sample of subjects with schizophrenia or bipolar disorder. Since the impairment in executive function and language processing is observed in both disorders, finding a distinct pattern of neural responses in schizophrenia and bipolar disorder could provide an innovative biomarker for differential diagnosis. Authors found more pronounced functional aberrations in prefrontal, striatal, and default mode networks in schizophrenia than bipolar disorder (Fig. 1.6), hypothesizing that fMRI indices of language processing could be a potential diagnostic marker of schizophrenia. BPD and SCZ have shown a partial overlap in clinical, neurocognitive [233, 234], neurophysiological, and genetic features [235], suggesting the existence of a psychosis spectrum disorder [236]. Widespread functional and structural abnormalities in brain regions related to language processing were found in SCZ [237, 238]; however, few studies investigated discriminative patterns between SCZ and BPD subjects [239, 240]. In light of these observations, the identification of a neuroimaging biomarker using machine-learning approach in SCZ or BPD should be regarded as a desirable goal in the debate on nosological boundaries between the two disorders.

1  Neuroimaging: Diagnostic Boundaries and Biomarkers

33

Table 1.8  Machine learning: neuroimaging biomarkers for psychosis differential diagnosis

Authors Costafreda et al. [223]

Sample 32 SCZ; 32 BPD; 40 HC

Bansal et al. 65 SCZ; [230] 26 BPD Schnack et al. [231]

66 SCZ; 66 BPD; 66 HC

Koutsouleris 104 et al. [232] MD; 158 SCZ Salvador 128 et al. [229] SCZ; 128 BPD; 128 HC

MRI Technique fMRI, Verbal fluency task sMRI

sMRI

Machine-­ learning approach SVM

Semi-­supervised learning algorithm SVM

sMRI

Cross-­validation framework

sMRI

 Ridge and lasso logistic  Regression Elastic net regularization  L0 norm regularization  SVC  RDA  GPC  RF

Accuracy BPD vs HC 79% SCZ vs HC 92% 100%

Sensitivity BPD vs HC 56% SCZ vs HC 91% 86%

Specificity BPD vs HC 89% SCZ vs HC 92% 100%

SCZ vs HC 90% SCZ vs BPD 88% BPD vs HC 53% 76%

SCZ vs HC 54% SCZ vs BPD 48% BPD vs HC 49% 79.8%

SCZ vs HC 91% SCZ vs BPD 78% BPD vs HC 70% 72.2%

SCZ vs BPD Ridge 0.66 Lasso 0.609 Elastic 0.616 L0 norm 0.581 SVC 0.652 RDA 0.605 GPC 0.621 RF 0.613

–

–

HC healthy controls, SCZ subjects with schizophrenia, BPD subjects with bipolar disorder, MD subjects with major depression, fMRI functional magnetic resonance imaging, sMRI structural magnetic resonance imaging, SVM support vector machines, SVC support vector classifier, RDA regularized discriminant function analysis, GPC Gaussian process classifier, RF random forests

1.4.2.3 Machine-Learning Algorithms in the Identification of Individuals at High Risk of Psychosis To overcome the limits of univariate analyses, which lead to significant effects at group level only, with no clear implication for clinical translation [190], several studies applied supervised machine-learning techniques to neuroimaging to predict clinical outcome at individual level in ARMS subjects [10, 210, 211, 241] (Table 1.9). SVM has been the most widely used machine-learning approach in this field. It has aimed to provide the distinction between high-risk individuals who did or did not

34

S. Galderisi et al.

a

b

Word Generation > Baseline Word Generation < Baseline

Word Generation with difficult letters > with easy letters

c

Main effect of diagnostic group (Schizophrenia, Bipolar and Healthy Control)

Fig. 1.6  Machine learning and diagnosis in subjects with schizophrenia and bipolar disorder using a verbal fluency task. Patterns of activation during word generation. Significant activations during verbal fluency according to SPM random-effects analysis for the whole subject sample (a and b, slices at x = 0, z = +4, and x = −4) and diagnostic effects (c, slices at z = −8, 16, 40, 48), adjusted by sex and antipsychotic dosage. (MNI space, images are in MNI space and +x on the right). Results are multiple comparisons corrected with cluster-level significance level of p 3 on P3 and a history of P3 > 3 for longer than 1 month)

rs-fMRI (Participants were also scanned while passively listening to auditory stimuli)

Findings Group effect on rCBF signal SCZ showed higher activation in Wernicke’s area during the reading condition compared to HC, and a reversed laterality index (right more than left) for the supplementary motor area was found in the AVH subgroup Correlations of AVHs with rCBF in SCZ+ (direction—region) Positive—right middle frontal gyrus (BA46) [resting metabolism in BA46 positively correlated with the intensity of AVHs] Correlations of AVHs with rCBF (direction—region) Positive—left superior and middle temporal cortices, bilateral superior medial frontal cortex, and left caudate nucleus (relatively) Negative—hippocampus, parahippocampus, temporale lobe bilaterally, and cerebellum Group effect on functional connectivity SCZ showed reduced functional connectivity between left TPJ and the right homotope of Broca Correlation of AVHs with functional connectivity (direction—region) Negative—left TPJ and bilateral anterior cingulate as well as the bilateral amygdala Group effect on functional connectivity AH showed significantly reduced interhemispheric connectivity in both primary and secondary auditory cortices compared to non-AH and HC

(continued)

120

A. Mucci et al.

Table 2.6 (continued) Brain imaging technique rs-fMRI

Study Sommer et al. [272]

Sample 49 SCZ with chronic AVH; 49 HC

Assessment CASH; PANSS

Shinn et al. [273]

27 SSD with lifetime AVHs; 14 SSD with no lifetime AVHs; 28 HC

PSYRATS

rs-fMRI

AlonsoSolis et al. [274]

19 right-handed SCZ with chronic AVHs (HP); 14 right-handed SCZ without AVHs (nHP); 20 right-handed HC

PANSS

rs-fMRI

Findings Group effect on functional connectivity SCZ showed increase between the right parahippocampal gyrus and the rIFG as compared to HC, but reduction was found between the rIFG and the right DLPFC. Reduction was found also in lSTG- left frontal/ parietal opercular area and in lSTG- left hippocampus Correlation of AVHs with functional connectivity (direction-region) Negative—lSTG and hippocampus Group effect on functional connectivity SSD with AVHs showed increase in left Heschl’s gyrus-left frontoparietal regions but decrease in left Heschl’s gyrus-right hippocampal formation and medio-dorsal thalamus compared to SSD without AVHs Correlation of AVHs with functional connectivity in SSD with AVHs (direction-region) Positive—left Heschl’s gyrus-left inferior frontal gyrus (Broca’s area), left lateral STG, right pre- and postcentral gyri, cingulate cortex, and OFC Group effect on functional connectivity HP showed increase between dMPFC and bilateral central opercular cortex, bilateral insular cortex, and bilateral precentral gyrus compared to nHP and HC. HP also showed decrease between vMPFC and bilateral paracingulate and dACC

2  Neuroimaging and Psychopathological Domains

121

Table 2.6 (continued) Study Rolland et al. [275]

Sample 16 SCZ with AVHs (AH); 15 SCZ with visuoauditory hallucinations (VAH); 14 SCZ without hallucinations (NoH)

Assessment PANSS; SAPS

Lefebvre et al. [276]

25 SCZ

PANSS; AHRS; VAS

Li et al. [277]

17 drug-naive FESZ with AVHs; 15 without AVHs; 19 HC

AHRS

Brain imaging technique rs-fMRI

rs-fMRI (Four stages of scanning: periods without hallucination (“OFF”), periods with hallucination (“ON”), transition periods between “OFF” and “ON,” and the extinction of the hallucinatory experience (“END”) rs-fMRI

Findings Group effect on functional connectivity AH showed increase between NAcc and left temporal superior gyrus, cingulate gyri, and VTA as compared to NoH. VAH showed increase between NAcc and bilateral insula, putamen, parahippocampal gyri, and VTA as compared to AH Correlation of AVHs with functional connectivity (direction-region) Positive—strength in NAcc-VTA (but the NAcc connectivity patterns changed with the complexity of these experiences rather than with severity) Correlation of AVH periods with network effective connectivity “ON” periods were linked to memory-based sensory input from the hippocampus to the SAL; “END” periods were associated with a takeover of the CEN in favor of a voluntary process

Group effect on functional connectivity An increased sensitivity of auditory cortex to its thalamic afferents and a decrease in hippocampal sensitivity to auditory inputs in FESZ with AVHs Correlation of AVHs with functional connectivity (direction-region) Positive—Broca’s area and the auditory cortex

(continued)

122

A. Mucci et al.

Table 2.6 (continued) Study Hare et al. [278]

Sample 143 SCZ experiencing AVHs; 155 HC

Assessment PANSS; SAPS

Plaze et al. [234]

15 right-handed SCZ (Daily AVHs for at least 3 months)

PSYRATS; SAPS

Allen et al. [279]

20 SCZ (10 AVH and 10 no AVH); 11 HC

SANS; SAPS

Fu et al. [280]

20 SCZ (10 with current AVHs and delusions and 10 in remission); 13 HC

Brain imaging technique rs-fMRI [Exploring dynamics across 2 low-frequency passbands (slow-4 and slow-5)]

efMRI (The participants listened to 32 sentences in French or to 32 silence periods) fMRI (The participants listened to pre-recorded words: 20 selfundistorted, 20 self-distorted, 20 alien undistorted, 20 alien distorted in each of 4 speech conditions) fMRI (The participants listened to own voice or another male voice)

Findings Group effect on ALFF SCZ showed decreased ALFF in the posterior brain in comparison to HC, except for SCZ that endorsed visual hallucinations. These last one had elevated ALFF in the left hippocampus in comparison to patients that endorsed only auditory hallucinations Correlation of Hallucination (visual and auditory) severity with ALLF (direction-region) Positive—left hippocampus Correlations of AVHs with BOLD signal (direction—region) Negative—left temporal superior region (in the French minus silence condition) Group effect on BOLD signal The AVH group made more external misattributions and showed altered activation in the superior temporal gyrus and anterior cingulate compared with no AVH and HC

Group effect on BOLD signal Patients with active hallucinations and delusions had greater bilateral superior temporal activation with external misattributions than with correct self-attributions, while patients in remission and HC showed the inverse pattern of response. External misattributions were most common when self-generated speech was distorted, particularly in the patient group with active symptoms

2  Neuroimaging and Psychopathological Domains

123

Table 2.6 (continued) Brain imaging technique efMRI (The left-sided or right-sided voices were presented in random order, and subjects were required to determine whether voices were from the left ear or right ear)

Study Zhang et al. [281]

Sample 26 male SCZ (13 with and 13 without AVHs); 13 HC

Assessment SAPS; SANS

Zhang et al. [282]

26 male SCZ (13 with and 13 without AVHs); 13 HC

SAPS

fMR; Voice Recognition task (Familiar and unfamiliar voices)

Simons et al. [283]

15 SCZ; 12 HC

PANSS

fMRI; Inner speech task; Listening speech task (Patients were studied while listening to sentences or imagining sentences)

Findings Group effect on BOLD signal Right precuneus and right superior parietal lobule were significantly activated by left-sided voices than right-sided ones in HC, while no significant difference in activation was found in the two patient subgroups. Right MFG was markedly activated when HC were differentiating right-sided voices if compared to AVH SCZ. Right-sided stimuli significantly activated bilateral MFG and left postcentral gyrus in HC compared to no AVH SCZ. Left Wernicke’s area was significantly activated by both left- and right-sided voices in AVH SCZ when compared to no AVH group Group effect on BOLD signal SCZ with AVHs showed reduction in the right temporal lobe than HC, under the familiar minus unfamiliar contrasts Group effect on BOLD signal SCZ showed greater bilateral activation of the occipital gyri during the active tasks compared to HC, who showed greater activation during the baseline condition. Less pronounced activation in left superior temporal gyrus, bilateral anterior cingulate gyrus, right hippocampus, and the left posterior cingulate gyrus was seen in SCZ compared to HC, during the generation of inner speech. SCZ showed a decrease in activation in right hippocampus and cingulate gyrus during the inner speech vs listening

(continued)

124

A. Mucci et al.

Table 2.6 (continued) Study Diederen et al. [232]

Sample 16 SCZ; 3 SSD; 5 psychosis not otherwise specified; 15 HC

Assessment PANSS; Psychotic Symptom Rating Scales

Escarti et al. [284]

41 SCZ (27 with AVHs and 14 without AVHs); 31 HC

PANSS; BPRS; PSYRATS

Vercammen et al. [285]

22 SCZ

PANSS; AHRS

Brain imaging technique fMRI; (Patients signaled the presence of auditory verbal hallucinations by squeezing a handheld balloon during the scan)

fMRI; (During the scan, participants were presented with an auditory paradigm containing emotional words) fMRI; The metrical stress evaluation task

Findings Group effect on BOLD signal During AVHs, activation was found in bilateral (right more than left) language-related regions (insula and inferior frontal gyrus, middle temporal, superior temporal, and supramarginal gyri) and bilateral motor regions. Preceding AVHs, deactivation was observed in the left parahippocampal gyrus and, in addition, in the superior temporal, right inferior frontal, and left middle frontal gyri as well as in the right insula and left cerebellum Group effect on BOLD signal The group of hallucinatory patients showed an increase of activity in the parahippocampal gyrus and the amygdala during the emotional session, compared to non-hallucinators and HC

Correlations of AVHs severity with BOLD signal (direction—region) Negative—bilateral angular gyrus, anterior cingulate gyrus, left inferior frontal gyrus, left insula, and left temporal cortex Correlations of AVHs reality with BOLD signal (direction—region) Negative—language lateralization

2  Neuroimaging and Psychopathological Domains

125

Table 2.6 (continued) Study Wu et al. [286]

Sample 18 SCZ; 18 HC

Assessment PANSS

Brain imaging technique fMRI; DSI

Findings Group effect on structural connectivity Decreased structural integrity in the left ventral, right ventral, and right dorsal tracts was found in SCZ compared to HC Group effect on functional connectivity Decreased lateralization of the dorsal pathway (arcuate fasciculus) in SCZ compared to HC Correlation of hallucination/ delusion symptoms with structural and functional connectivity (direction—region) Negative—lateralization of the dorsal pathway and integrity of the right dorsal pathway

SCZ subjects with schizophrenia patients, HC healthy controls, FESZ first-episode SCZ, SSD schizophrenia spectrum disorder, AVHs auditory verbal hallucinations, PANSS Positive and Negative Syndrome Scale, SANS Scale for the Assessment of Negative Symptoms, SAPS Scales for the Assessment of Positive Symptom, SAPS-HA subscale of the SAPS containing the three items related to auditory hallucinations, BPRS Brief Psychiatric Rating Scale, GIS Global Impression Scale, SAI-E Schedule for the Assessment of Insight-Expanded Version, DIP diagnostic interview for psychosis, SPQ schizotypal personality questionnaire, SF Sylvian fissure, PT planum temporale, CGI Clinical Global Impression Scale, RHS Revised Hallucination Scale, ESI Eppendorf Schizophrenia Inventory, MWT-B multiple-choice word comprehension test, HAHRS Hoffman Auditory Hallucination Rating Scale, AHRS Auditory Hallucination Rating Scale, PSYRATS Psychotic Symptom Ratings Scale, CASH Comprehensive Assessment of Symptoms and History, VAS visual analogue scale, MUPS Mental Health Research Institute Unusual Perceptions Scale, AHRS Auditory Hallucination Rating Scale, PSYRATS Psychotic Symptom Rating Scale, SLF superior longitudinal fasciculus, ILF inferior longitudinal fasciculus, CC corpus callosum, OFC orbitofrontal cortex, IFOF inferior fronto-occipital fasciculus, IOFF inferior occipito-frontal fasciculus, rIFG right inferior frontal gyrus, DLPFC dorsolateral prefrontal cortex, dMPFC dorsomedial prefrontal cortex, vMPFC ventromedial prefrontal cortex, MFG middle frontal gyrus, dACC dorsal anterior cingulate cortex, lSTG left superior temporal gyrus, CC corpus callosum, NAcc nucleus accumbens, VTA ventral tegmental area, TPJ temporoparietal junction, STG superior temporal gyrus, sMRI structural magnetic resonance imaging, DTI diffusion tensor imaging, DSI diffusion spectrum imaging, fMRI functional magnetic resonance imaging, efMRI event-related functional magnetic resonance imaging, FA fractional anisotropy, RD radial diffusivity, ALFF amplitude of low-frequency fluctuations, TBSS tract-based spatial statistics, STG superior temporal gyrus, GM gray matter, WM white matter, SAL salience network, CEN central executive network

126

A. Mucci et al.

hallucinations and controls, hallucinating patients did not respond to the source of external speech—agency/ownership (alien, own)—in left STG and not to distortion of language in anterior cingulate [279]. These results suggest an impairment in processing and differentiating the source of speech perception. In a similar study, patients with hallucinations demonstrated greater bilateral STG activation during misattribution of external sources in a reading paradigm [280]. In general, those studies support the idea of misattribution of self-generated speech/thoughts, i.e., disturbances in self-monitoring processes, located within language circuits, e.g., especially concerning the superior temporal lobe. Additional studies with language-related stimulus material support the hypothesis of dysfunction within the language system, especially the perceptive parts in (left superior) temporal lobe (reduced activity in voice recognition, competition of neural resources) [281, 282].

2.3.2.4 Resting State MR Studies Recently there has been increasing interest in so-called resting state activity using MRI technologies. Imaginably it is more difficult to standardize a resting state compared to a specific task processing state: there is no behavioral control of processes during the former. A common cerebral activation pattern during this state is the socalled default mode network, which covers the areas “active” during wakefulness and “rest.” The activity here is negatively correlated with the activity in areas related to general or specific task processing. Like other technologies used in AVH research, also regarding resting state network (RSN), few studies used the same methodology especially for data analysis making difficult a comprehensive discussion, as well as to draw clear conclusions from the existing studies (Table  2.6). Most studies used various areas to analyze connectivity in AVH patients. In an early study focusing on the connectivity of the temporoparietal junction (TPJ; suggested to be a key region for AVH generation) during resting state, Vercammen et al. [270] reported reduced connectivity between left TPJ and right Broca homologue in schizophrenia patients suffering from AVH compared to healthy subjects. They interpreted their results as a disintegration between language production and perception areas and in general terms in areas attributed to agency, self-reference, and attentional control. Using primary auditory cortex (PAC) as seeding points, a reduced interhemispheric connectivity between PACS in AVH patients compared to non-AVH patients (both groups suffering from schizophrenia) was reported [271]. Also, changes in connectivity between language-­ related structures in temporal and frontal lobe and medial temporal structures were reported [272]. Abnormal interactions among left Heschl’s gyrus (HG), containing the PAC, and regions involved in speech/language, memory, and monitoring of self-­ generated events were reported by [273]. Alonso-Solis and colleagues [274] used seeds that are more related to the default mode network: posterior cingulate cortex and anterior medial frontal cortex but also medial temporal lobe and temporoparietal junction. Compared to patients without AVH, they reported in patients with AVH increased functional connectivity between regions including insula and dorsomedial prefrontal cortex (dMPFC) as well as reduced connectivity in intermediate

2  Neuroimaging and Psychopathological Domains

127

areas including mainly cingulate regions. This was interpreted as cross-network abnormalities between default mode and salience networks. Considering the sensory networks, Rolland et al. [275] investigated functional connectivity within the mesolimbic pathways also depending on sensory modality of hallucinations. In general, they reported increased connectivity in these networks; additionally, these results were positively correlated with the presence of hallucinations. Using arterial spin labeling (ASL) fMRI (measuring cerebral perfusion), Cui et al. [288] also reported an increased activity between areas in the language network, including processing and inner speech monitoring areas. In a recent interesting study, which was a symptom-catching study during resting state, Lefebvre et al. [276] differentiated between various states during one scanning session. They investigated the interaction between the various networks during these states and reported a permanent control of the salience network when switching between executive and default mode network and an input of information from the hippocampus to the salience network during occurrence of hallucinations. Recently Li et al. [289] reported a positive correlation between the strength of connectivity within the language network and the severity of auditory hallucinations. Lately, low-frequency fluctuations in cerebral activity, reflecting connectivity, demonstrated a different pattern in patients suffering from schizophrenia but also in patients with AVH compared to patients without AVH (e.g., Hare et al. [278]). These results are difficult to interpret and should be put into context of other connectivity studies. Taken together, the studies which have investigated functional connectivity during resting state support the hypothesis of dysfunctional connectivity within language networks in patients with AVH and partly also during the actual occurrence of AVH.

2.3.2.5 Functional Studies Using Radioactive-Tagged Substances (PET/SPECT) Before the development of MRI techniques allowed to measure brain function noninvasively (e.g., fMRI), different nuclear diagnostic techniques—mainly PET and SPECT—were used to access the neuronal activity in AVH.  Mostly radioactive-­ tagged glucose was used to assess glucose metabolism in connection to neuronal activity by PET, with a relative low time resolution, so that these studies can be considered accessing a state or trait depending on investigated populations, but not comparable to symptom-catching studies. Most commonly increased neuronal activity was reported for schizophrenia patients with hallucinations in bilateral, preferable left-sided, temporal [265, 266, 269, 290] reduced activation during inner speech, including temporoparietal Wernicke area [267] as well as frontal regions [268] including Broca language area [264, 269]. But, also increased activity in subcortical and limbic structures was reported [291]. In the first study of this kind by McGuire and coworkers [264], they investigated patients during hallucinations and repeated the measurement after the hallucinations ceased after several weeks. They reported increased activity mainly in Broca’s area during the state of hallucinations. Many of these studies, similar to the fMRI studies, are more or less case studies.

128

A. Mucci et al.

2.3.2.6 Studies of Cerebral Metabolism Using MRI (Magnetic Resonance Spectrography Imaging) Besides structural alterations as well as changes of cerebral blood flow (neuronal activity) of the human brain, MR technologies allow noninvasively to assess neurochemical aspects of brain physiology. Most commonly these relate to the spectrum of hydrogen or phosphorous compounds. Various metabolites containing these compounds have a characteristic peak in the magnetic resonance spectrography (MRS) spectrum, making it possible to quantify metabolites, e.g., choline-­containing compounds (part of cell membranes), creatine (involved in energy metabolism), and neurotransmitters (e.g., glutamate). Until now, the time and spatial resolution of MRS has been quite limited and investigations technically rather complicated. This is probably one reason why the number of studies investigating neurochemical changes in hallucinations is rather limited, all of them until now focused on hydrogen (H-MRS). In a H-MRS study, lower N-acetyl aspartate/choline (NAA/Cho) ratio in the right thalamus in patients with auditory hallucinations compared to patients without auditory hallucinations and control subjects was reported [292]. Significant correlations were found between metabolic ratios and BPRS, PANSS and PSYRATS scores, and age of onset of auditory hallucinations. The results were discussed in relation of thalamus dysfunction in AVH patients, where thalamus is a key location for auditory processing. In a more recent paper by the same group, and probably in overlapping population, the authors reported that metabolic data in the right inferior colliculus (located in the auditory pathway) were correlated with emotional auditory hallucination items [293]. Both studies, similar to most others, used single region of interest approach; if there are metabolic alterations in other regions remains unknown. Homan et al. [294] investigated H-MRS in patients with and without hallucinations as well as in healthy subjects. In Broca’s area, lower levels of N-acetyl-­ aspartate (suggested to be a marker of neuronal cell function) in non-hallucinating patients compared to healthy controls were reported. There were positive associations of NAA levels in the left Heschl’s gyrus with total and negative PANSS scores. In Broca’s area, there was a negative association of myo-inositol levels (glial cell marker) with total PANSS scores. Cautiously this was interpreted as neuronal disturbances in frontal areas and alterations of neuropil in temporal regions. 2.3.2.7 Structural Cerebral Changes in Relation to Auditory Verbal Hallucinations Some studies used a whole brain approach and others a region of interest approach. The various reported parameters are more static compared to functional measurements, and as such, they are mostly regarded as trait markers of the disorder. Thus, these measures are complementary to functional results, and they may be a precondition for dysfunctions. Some studies compared patients with hallucinations to healthy controls, and here we try to limit our report specifically to AVH and thus focus on results comparing patients with AVH and those without AVH, even though the definitions are not always homogeneous throughout published studies.

2  Neuroimaging and Psychopathological Domains

129

2.3.2.8 Gray Matter Anatomical Structures In general, most studies reported reduced cerebral volume in patients with AVH compared both to patients without AVH and healthy controls (Table 2.6). The most affected region was the temporal cortex, more frequently the left one (Table 2.6). Similar results are reported for schizophrenia in general independent of AVH but seem more pronounced in patients suffering from AVH. Furthermore, the amount of reduced volume correlated negatively with severity of hallucinations, e.g., more severe occurrence of hallucinations was related to lower volumes, most commonly in temporal lobe (left sided). More time-consuming and elaborate is the possibility to manually delineate cerebral structures as regions of interest, allowing a more anatomically exact characterization of cerebral structures, like cortical thickness, cortical surface area, and gyrification. Focusing on insular cortex, Crespo-Facorro et al. [235] described a negative correlation between both cortical surface area and gray matter volume for bilateral insula and psychotic symptoms (aggregating hallucinations and delusions) in schizophrenia patients. If other regions also have such a negative correlation in a such approach is unclear. Shapleske et al. reported a more leftward Sylvian fissure in patients with AVH with a slight positive correlation with severity of AVH [236]. Hubl et al. [241] performed a manual segmentation of Heschl’s gyrus in schizophrenia patients with AVH and reported an increased volume of gray and white matter in right HG in patients with AVH compared to patients with no history of AVH. Interestingly, in this study, there was also a tendency to a higher number of so-called duplicated HG in hallucinating patients. Cachia and coworkers [240] took another approach and investigated automated calculated whole-brain cortical folding—gyrification—in schizophrenia patients compared to healthy controls. They reported bilateral superior temporal local gyrification decrease in schizophrenia patients. Since they did not have a control group of schizophrenia patients without a history of hallucinations, the specificity of the results regarding hallucinations is questionable but however interesting. Distinguishing the source of hallucinations—inner or outer space hallucinations—Plaze and coworkers [234] reported, compared to healthy controls, lower white matter volume in right temporoparietal junction in patients with external source and higher white matter volume in the same area in patients with internal source of AVH. The results were interpreted in relation to right hemisphere dominance concerning auditory spatial processing. 2.3.2.9 White Matter Structure and Fibers Recently, white matter alterations have received much attention in the neurobiology of hallucinations. This may not be so surprising considering the fact that schizophrenia and psychosis have been attributed to cerebral structural disconnections. These structural disconnected brain areas may be the origin of reported alterations of functional connectivity, e.g., described in resting state networks.

130

A. Mucci et al.

Considering the number of DTI studies both investigating schizophrenia in general and AVH in part, here we will focus on the AVH results in these studies and not on changes relevant to schizophrenia in general (Table 2.6). Hubl et al. [247] were the first who reported DTI measures for patients with and without a history of AVH. They described increased directionality (FA) in the lateral arcuate fascicle, a fiber tract which connects language regions, in schizophrenia patients with AVH compared to patients without a history of AVH.  Furthermore, increased white matter directionality was reported for the anterior part of interhemispheric commissure fibers, connecting homologous language production regions (Fig. 2.10). Similar results were reported by Shergill and coworkers, where the propensity to experience auditory hallucinations was associated with relatively increased fractional anisotropy in superior longitudinal fasciculi (SLF) and in the anterior cingulum [248] as well as by Bopp et al. [192] for the left uncinate fasciculus (and left corticospinal tract). In this context, it should be mentioned that similar to numerous studies, Shergill et al. [248] additionally reported a general reduction of white matter directionality in schizophrenia in a number of fiber tracts. A mixture of increased and reduced directionality was also reported in a study by Seok and coworkers, where the frontal part of the SLF was positively correlated with the 1

2

3

a

12 3

b

Short Fibers Long Fibers

c

P ARMS| activated HC| deactivated Frontoparietal effective connectivity (r) ↓ Parietal lobule (r, s) ↓ MFG ↓

STG–MFG effective connectivity: FEP > ARMS| positive coupling HC| negative coupling

ARMS| (encoding) MFG (l), mFG (b) and the parahippocampal gyrus (l) ↓ Hippocampal ~/~ correct rejection

Task performance ↓ FP-ec ~ BPRS (negatively)

False alarm for novel words ↑ Discrimination accuracy for target words ↓

Episodic memory Valli et al. [106]

ARMS| Absent: temporal (m) ~ temporal (m) glutamate (+) (normal in HC)

Visual and spatial working memory Benetti et al. [107]

Regional activation; Falkenberg et al. [108] clinical F/U 1.5 – 6 years

Fusar-Poli et al. [109]

Regional activation; F/U 1 year

Visuospatial working memory task (PAL)

HC 15 ARMS 17

Choi et al. [110]

Regional activation

SWM: encoding, maintenance and retrieval

HC 16 ARMS 21 FHR 17 Scz 15

ARMS| IFG (r) ↑ GAF at F/U ~ MFG (+), STG (–), Cuneus, posterior cingulate gyrus (l) ↓ mFG (–), ACG (–), putamen (–) Poor function vs good function| DMN ↓ (Activation ↑ or ↓ deactivation) MFG (r) ↓ vs ↑ STG (r) ↓ vs ↓↓ mFG (l) and ACG (l) and putamen (r) ↑ vs ↓ Accuracy = Latency to answers ↑ Baseline| (demanding level) Cognitive impairment ~ precuneus Follow-up improvement| (l), parietal (l,s), MTG (r) (–) Occipitoparietal ~ clinical symptoms parietal ~/~ task difficulty (–) and global functioning (+) Encoding: Frontoparietal regions STG ↑ Encoding: Maintenance| FHR > HC > ARMS > Scz| frontal Thalamus ↓, STG ↑ Retrieval| N/A

(continued)

236

Y.-S. Lin et al.

Table 6.3 (continued) Motor control

Broome et al. [111]

Regional activation

Random movement generation paradigm

HC 15 ARMS 17 FEP 10

Random movement| Parietal cortex (l, i) ↓

Bernard et al. [112]

Network activation

Postural control; resting state

HC 26 ARMS 32

Cerebello-cortical network ↓

Seiferth et al. [113]

Regional activation

Facial emotion processing

HC 12 ARMS 12

Modinos et al. [114]

Regional activation

Emotional salience

HC 22 ARMS 18 FEP 18

van der Velde et al. [115]

Regional activation

Emotion regulation: International Affective Picture System

HC 16 ARMS 15

Reappraisal| VLPFC ↓

Network activation

Facial emotion recognition (FER) Resting state

HC 33 ARMS 36

ARMS| SN–mPFC ↑ FER ↑| DMN–somatosensory ARMS| FER and SF ↓ cortex ↑ DMN–cerebellum and precuneus ↓

Language processing: naturalistic discourse processing paradigm

HC 24 A-T 15 A-NT 40

HC 24 ARMS 21

Pelletier-Baldelli et al. [116]

High order executive functions

Sabb et al. [117]

Regional activation; clinical F/U 6–24 months

Emotion discrimination| Lingual and fusiform gyrus (r) ↑ Middle occipital gyrus (l) ↑ Neutral face vs emotional| IFG, SFG, cuneus, thalamus, hippocampus ↑ FEP, ARMS vs HC| (Emotional ↓ neutral ↑) Cortico-limbic activity in DmPFC, IFG, AI, amygdala

ARMS| language-associated regions: mPFC (b),LIFG, MTG, AC ↑ A-T| STG, caudate and LIFG ↑

Postural sway ~ cerebello-cortical (–) Postural sway ~ negative symptom (+) Cerebello-cortical connectivity ~ negative symptoms (–)

No group difference

FEP, ARMS| (neutral scenes) Emotional arouse ↑

Behavioural: reappraisal in daily life ↓

Follow-up| Positive formal thought disorder ~ LIFG, frontal gyrus(s), MTG(i) (+) social outcome ~ LIFG, AC (–) Positive symptoms~ anticipation signal (VS and rAI) (+) Negative symptoms ~ outcome-related contrast (VS) (–) Depressive symptoms ~ outcome-related contrast (mOFC) (–)

Wotruba et al. [118]

Regional activation

Reward processing: incentive delay paradigm

Pettersson-Yeo et al. [28]

Regional activation; sMRI

Voice recognition task

HC 21 ARMS 18 FEP 24

FEP < ARMS < HC| STG volume and activation(l) ↓

GMV [PJA2] (cov: functional activation) Activation (cov: GMV)

Rausch et al. [119]

Regional activation

Decision-making: classical beads task

HC 24 ARMS 24

Decision stage: VS (r) ↓

Beads ↓ JTC bias ↑

Regional activation

Motivational salience: salience attribution task (SAT)

HC 19 ARMS 34 FEP-UM 17 FEP-M 12

ARMS| Adaptive salience: Haemodynamic response inferior parietal lobule (r) ↓ No behavioural association

FEP-UM| CG (l, d) Adaptive salience: Hallucinations and delusions correlated ↑ ~ haemodynamic response (insula, AC) ↓

Smieskova et al. [120]

Processing information = Anticipating rewards: PCC, MFG, IFG ↑

Resting state Shim et al. [121]

Network activation

Resting state HC 20 (DMN and TRN) ARMS 19 Seed: bilateral PCC

DMN ↑ PCC-TRN negative correlation -

Dandash et al. [122]

Network activation

Resting state

HC 35 ARMS 74

Decreased caudate (d)DLPFC (r), mPFC (l, rt), thalamus Decreased DPThalamic, lenticular nuclei (l) Increased VP Insula, frontal operculum, STG (b)

Wotruba et al. [123]

Network activation

Resting state Seed: DMN/TRN| MPFC SN| rAI

HC 29 ARMS 19 BS 28

Both at-risk| Antagonistic relationship in mPFC -rDLPFC, rAI-PCC and internetwork connectivity of DMN-TPN was absent

Anticevic et al. [124]

Network activation; clinical F/U 2 years

Resting state

HC 154 A-T 21 A-NT 222

A-T| Thalamus–prefrontal–cerebellar ↓ Thalamic–sensory motor areas ↑

Ventral striatal functional connectivity = Perceptual abnormality, non-bizarre ideation ~ caudate (d)–PFC (–) Unusual thought content and positive symptoms ~ DP–thalamic nuclei (–) Unusual thought content ~ VP–STG, insula (+) Positive symptoms ~ VP-LIFG (+)

mPFC-rDLPFC and TPN-DMN ~ Cognitive functions (–)

6  Neuroimaging and the At-Risk Mental State

237

Table 6.3 (continued) Yoon et al. [125]

Network activation

Wang et al. [81]

ROI (salience network) Seed-based approach Tract-based spatial statistics

Supplement: structural covariance within network regions

Heinze et al. [126]

Structural covariance of whole-brain signal

Resting state Seed: planum temporale/ Heschl’s gyrus

HC 47 ARMS 41 FEP 22

Resting state

HC 37 ARMS 87

Seed regions: visual, auditory, motor, speech, semantic, executive control, salience and default mode network

HC 65 A-T 51 A-NT 82

FEP, ARMS| Planum temporale (l)–DLPFC (b) ↑ Planum temporale (l)–DLPFC (b) ~ Positive symptoms (+) Heschl's gyrus(b)–ACC (d) ↓ ARMS| Functional connectivity: Ventral anterior insula (l)–SN regions ↓ FA and AD: Frontal-striatal-thalamic circuits and cingulum ↓

ARMS| Regional FA ~ symptom severity (–)

ARMS| DMN [PJA3]: Angular gyrus (r) and PCC (r) and OBFC (l) ↓ Motor network: Precentral gyrus (l) ↓ Subcallosal cortex (l) and paracingulate gyrus ↑ Executive control networks: Parietal lobule (r,i) and paracingulate cortex (l) ↑

A-T| Salience network: Fronto-insular cortex (l) and occipital pole (r) and parietal lobule (s) ↑ Executive control network: DLPFC (r) and precentral gyrus (r) ↓ Auditory network: Heschl’s gyrus (b) and ACC (r) ↑ Motor network: Precentral gyrus (l) and SFG (l), IFG (l), MFG (r) ↑

Functional connectivity between insula (b) and FA at the forceps → transition in 2 years

Shading: grey, with connectivity results; white, regional activation Abbreviations of methods: DCM dynamic causal modelling, BMS Bayesian model selection, PAL paired-associate learning test, SWM spatial working memory, DMTS delayed matching-to-sample task, FEP-UM/M FEP without/with medication, clinical F/U scan only at baseline, follow-up by clinical assessment Abbreviations of regions: AC anterior cingulate, r/AI right/anterior insula, MTG middle temporal gyrus, STG superior temporal gyrus, LIFG left inferior frontal, mFG medial frontal gyrus, MFG middle frontal gyrus, IFG inferior frontal gyrus, DmPFC dorsomedial prefrontal cortex, DLPFC dorsolateral prefrontal cortex, CG cingulate gyrus, VS ventral striatum, rAI right anterior insula, mOFC medial orbitofrontal cortex, VLPFC ventrolateral prefrontal cortex, rt rostral, IFC inferior frontal cortex, M middle, m medial Symbols: (+) positively correlated, (−) negatively correlated, (=) no difference, ~ correlated with, ~/~ correlation that exists in HC is absent, → predicts

6.4.2 Verbal Working Memory (N-Back) and Verbal Learning Several verbal working memory tasks, e.g. letter-number span (LNS), simple digit span test, initial learning trials of standard word lists or N-back, have been applied in neurobehavioural studies in ARMS [95]. While LNS and digit span are more commonly utilised in neurobehavioural studies, N-back is more often used in fMRI studies. In our review of imaging studies employing the N-back task, the ARMS group mostly performed about the same as HC [100–104]. However, in comparison with HC, ARMS exhibited decreased activation in the middle frontal gyrus (along with grey matter loss [101, 102, 105]), the prefrontal cortex (associated with grey matter loss in long-term ARMS, [102]) and the parietal cortices [100–103, 105], as well as hyperactivation in the superior temporal cortex (along with its disinhibited coupling with the middle frontal gyrus [104]). In comparison with HC, ARMS also exhibited a reduction in task-related frontoparietal effective connectivity along with poorer performance; the attenuated frontoparietal connectivity was associated with

238

Y.-S. Lin et al.

the greater severity of psychotic symptoms [105]. The enhanced activation in ACC and right parahippocampus gyrus in the ARMS group was associated longitudinally with improved clinical symptoms and global functioning [101]. Some neurobehavioural studies have studied the cognitive function of verbal learning performance in the ARMS group, although the findings have been inconsistent. Nevertheless, one study adopted Deese’s and Roediger’s false memory paradigm to investigate cerebral activation during the task; the authors reported that more mistakes were made in the ARMS group than in HC, irrespective of novel or target words. Moreover, frontal and parahippocampal activation in ARMS was also reduced during the encoding phase, and the distinct hippocampal activation between correct rejection and false alarm that existed in healthy subjects was also absent in the ARMS group [106].

6.4.3 Episodic Memory Valli et al. [107] measured brain activation during clinical assessment (CAARMS and PANSS) when the subject recalled self-related experiences; the authors found reduced medial temporal activation in the ARMS group. Moreover, medial temporal activation and regional glutamate levels were correlated in HC, but not in ARMS.

6.4.4 Visual Working Memory Delayed matching-to-sample task (DMT or DMTS) was used to investigate visual working memory in recent ARMS studies, where subjects were instructed to remember the object during the encoding phase, followed by different durations of visual fixation at one point and then followed by the recognition phase to identify the displayed object from multiple choices. Falkenberg et al. [109] reported increased activation in the left inferior frontal gyrus (IFG) and decreased activation in the right cuneus and left posterior cingulate gyrus. They compared the ARMS subjects with poorer or better functions and showed that the subgroup with poor function exhibited anomalous activation in the middle frontal, medial frontal, anterior cingulate gyri and putamen, whereas activation measured at baseline was always associated with global functions at follow-up. These patterns of cerebral activities were shown to be independent of the phases of the task. The other study with DMT applied dynamic causal modelling to investigate effective connectivity and reported reduced activation in the connectivity between posterior hippocampus and prefrontal lobe in both ARMS and FEP groups, compared to HC [108]. However, neither of these studies found an interaction effect between group and conditions, i.e. no group-by-­ delayed-time interaction in correctness and/or response latencies.

6  Neuroimaging and the At-Risk Mental State

239

6.4.5 Spatial Working Memory Several instruments have been applied in neurobehavioural studies to assess spatial working memory in ARMS subjects compared to HC, e.g. the dot test [129, 130], the position test [136], the visual span task [137] and the spatial location task [134], but no significant discrepancy in performance was found. In the recent fMRI studies, however, brain activities in the ARMS group during tasks were clearly not normal. In the study with the spatial delayed-response task, ARMS subjects performed no differently from HC in the correctness and reaction time [111]. However, in comparison with HC, ARMS displayed reduced activation in frontoparietal regions during the encoding phase of the task, and in the thalamus during the maintenance phase, but increased activation in the superior temporal gyrus during both phases. Brain activation during the retrieval phase of the task was similar in the two groups. The other study assessed visuospatial working memory by paired-associate learning work (PAL) and reported that ARMS group exhibited longer latency than HC to answers (with comparable accuracy) during more demanding tasks [110]. The impaired performance was correlated to decreased activation in the left superior parietal lobule, left precuneus and right middle temporal gyrus. Moreover, occipitoparietal activation in ARMS did not increase much with task difficulty as is the case with HC.  After 12 months of follow-up, improvements in clinical symptoms and global functioning were associated with enhancement of occipitoparietal activation in the ARMS group.

6.4.6 Motor Control In the study of Broome et al. [112], subjects were assessed for their degree of motor control by random movement generation. Each subject was instructed by displaying “Move” or “Rest”, where they either moved the joystick randomly to one of the four directions or stopped and rested. During the random movement, subjects in first-­ episode psychosis had least activation in the left inferior parietal cortex compared to HC, and ARMS was intermediate between two groups. The other study on subjects’ balance control and neural correlates during the resting state revealed that, in ARMS, increased postural sway was associated with more severe negative symptoms and decreased activation in the cerebello-cortical network [113]. Reduced cerebello-cortical connectivity was also associated with the severity of negative symptoms.

6.4.7 Emotional Processing Behavioural studies have reported that clinical high-risk subjects were deficient in social cognition and emotional processing by applying theory of mind (ToM) [134],

240

Y.-S. Lin et al.

facial emotion identification and discrimination [138] and intact emotion processing [139]. In neuroimaging studies with the ARMS group, increased activation was found in the right lingual gyrus, fusiform gyrus and the left middle occipital gyrus during emotional discrimination [114]. During processing of neutral scenes, hyperactivation in the frontal and parietal regions and limbic system, coupled with higher emotional arousal, was found in ARMS compared to HC, although the study did not find a significant difference between groups in behavioural performance [114, 115]. In contrast to the hyperemotional response to neutral scene, both ARMS and FEP groups exhibited hypoactivation in frontal regions, anterior insula and amygdala during the processing of emotional scenes [115]. This apathetic-like pattern in ARMS was also found during the reappraisal of negative emotion—when hypoactivation in the ventrolateral prefrontal cortex was displayed [116]. One study reported that ARMS subjects performed worse in facial emotion recognition (FER) than HC and that better performance within the ARMS group was positively correlated with DMN-somatosensory cortex connectivity and negatively to DMN-cerebellum and precuneus connectivity [117].

6.4.8 Higher-Order Executive Functions Cumulative studies have focused on deficiencies in higher-order executive functions, including planning, reasoning and problem-solving, as part of the symptomatic expression in the development of psychosis [140]. Aside from those perceptive abnormalities, such as hallucination or aberrant salience towards external stimuli, delusions or other thought disorders are derived from bizarre belief as a result of the lack of data collection, misattribution [141], inability to reappraise and adapt their belief [142, 143], the misinterpretation of external events, etc. [144]. Sabb et  al. [118] examined the neural correlates during language processing (topic maintenance and reasoning) between ARMS and HC. Although no difference in performance was found at the behavioural level, the ARMS group exhibited increased activation in the medial prefrontal cortex (mPFC), inferior frontal gyrus (IFG), middle temporal gyrus (MTG) and anterior cingulate cortex (ACC) compared to HC. Moreover, hyperactivation in STG and left IFG in addition to the caudate were associated with manifestation of full-blown psychosis and correlated with the presence of thought disorder and social outcome at follow-up. The relationship between perceptive abnormalities and biased belief towards the world is not only a bottom-up process but also top-down when individuals ignore the information that could challenge their beliefs in order to spare the energy from excessive sensory activity or when cerebral dysconnectivity occurs between motor and sensory regions and leads to the failure of predictability of stimuli and difficulty in distinguishing internal and external information and then ultimately results in hallucination [144, 145]. Earlier study on schizophrenia indicated that patients with schizophrenia possessed reduced adaptive salience but not aberrant salience compared to HC, and the subgroup of schizophrenia patients with delusions had even

6  Neuroimaging and the At-Risk Mental State

241

greater aberrant salience than those without delusions [146]. A model proposed by Kapur [147] demonstrated that the psychotic patient’s aberrant feeling of salience in responding towards external experiences may be modulated by the hyperactive dopaminergic system. Thus, delusion is the consequence of the cognitive effort in response to this sense of salience, and hallucination is the explicit expression of this aberrant salience towards inner belief. The blurred boundary between self and others—as well as between the selfproduced experience and external stimuli—is another characteristic of hallucination [145]. Studies have examined this capability through the task of distinguishing the speech of the individual from those of other people. While misattribution was pronounced in schizophrenia patients compared to HC [148], ARMS did not exhibit significant differences from HC in voice recognition. However, cerebral activation in the superior temporal gyrus along with volumetric loss in the same region was significantly reduced in both ARMS and FEP compared to HC [28], and this was also consistent with the findings in chronic schizophrenia. Adaptive motivational salience is a term referring to the effect that subject’s response is increased to high-probability rewards compared to low-probability rewards, and the biological mechanism underlying this behaviour is considered to be modulated by the dopaminergic system in the ventral striatum [149, 150]. Smieskova et  al. [121] investigated motivational salience in ARMS but did not find consistent behavioural patterns as in schizophrenia. However, during adaptive motivational salience, the haemodynamic response in the right inferior parietal lobule was reduced in the ARMS group compared to HC [121]. Another study also observed the haemodynamic response during reward processing and reported that, despite the lack of a significant difference in processing information, the ARMS group exhibited an elevated haemodynamic response in the posterior cingulate cortex, the middle frontal gyrus and the inferior frontal gyrus relative to HC. Moreover, the haemodynamic response in the ventral striatum and right anterior insula during the anticipation phase was positively correlated with psychotic symptoms, while the responses in the medial orbitofrontal cortex towards the phase of receipt/omission of reward were negatively correlated with depressive symptoms [119]. “Jumping to conclusion” is another characteristic that contributes to the build-up of delusional thoughts; this refers to the tendency that individuals form their belief or make decisions based on insufficient collection of information. This tendency has been found in schizophrenic people and was even more pronounced in the delusion-­ prone subgroup [151–153]. Rausch et al. therefore used the classical beads task to demonstrate ARMS subjects’ pattern of data collection before making a choice, which was followed by questions about their subjective confidence towards the decision. The study showed that the ARMS group speculated on the answer with fewer beads, even though they also reported less confidence in their answers relative to HC. During the phase of decision-making, the ARMS group exhibited reduced activation in the right ventral striatum, which was correlated with more severe negative symptoms and worse global functioning [120].

242

Y.-S. Lin et al.

6.4.9 Resting State The activation of the default mode network, in contrast to task-stimulated activities, is considered to be the cerebral baseline activity when the subject is in a task-free and relaxing state with “freely wandering mind”. During this mental state, one’s attention gradually shifts from external stimuli onto oneself, such as internal, selfreferential processing, autobiographical memory retrieval, conceiving the perspectives of the external world and envisioning the future. The default mode network is inhibited during attention-demanding tasks and is thus also called the “task-negative network” due to its task-related deactivation [154, 155]. The switch between the activation and deactivation of central executive network (CEN) during task performance and DMN during resting is responsible for the salience network (SN) [156, 157], which comprises the right fronto-insular cortex (rAI) and the anterior cingulate cortex (ACC). The posterior cingulate cortex (PCC) plays a key anatomical role in converging the information from the medial temporal cortex (mTL) and the inferior parietal lobe (IPL), communicates with the parahippocampal cortex and reciprocally connects to other connectivity hubs, for instance, the medial prefrontal cortex (mPFC) [158]. The disruption of the default network is also correlated with the manifestation of some neurodevelopmental and neurodegenerative disorders, e.g. autistic spectrum disorder, schizophrenia and Alzheimer’s disease. Cognitive/ perceptive symptoms or thought disorders, such as distorted interpretation of the external world, salient sense of reality and blurred boundary between self and others, are the critical symptomatic expressions in schizophrenic patients. These aberrant functions are thought to be generated from a deviant internal mentation which is led by malfunction of active connectivity in default mode network. Studies in schizophrenia have demonstrated striking aberrance in DMN connectivity with comprehensive indices, such as disrupted homogeneity (e.g. hyperactive PCC [159]); attenuated coupling (e.g. between STG and the temporal pole [160]), between frontocerebellar and thalamocerebellar connectivity [161], with paracingulate cortex associated with difficulties in abstract thinking [162]; and also aberrant counteractivity with other networks (e.g. hyperconnection with central executive network [163], failure of inhibition from the striatum onto DMN [164] and failure of DMN deactivation during a task [165]). These abnormalities are further correlated to clinical symptoms and functions, for instance, hyperactivity or insufficient deactivation of DMN may be associated with attention deficit and cognitive impairments [166] or with positive symptoms [163]. Similar findings are also reported in individuals in at-risk mental state. Decreased coupling within SN [82] and hyperactivated DMN, along with attenuated counteractivity between task-positive network (TPN) and DMN [124], have been observed in ARMS subjects. Moreover, regional counteractive strength was also reduced in ARMS compared to HC, for example, between DMN-PCC and TPN [122] and between DMN-mPFC and the dorsolateral prefrontal cortex [124]. In addition, the antagonistic activation between DMN and salience network (SN) observed in healthy subjects was often absent in ARMS, for instance, between DMN-mPFC and SN [117] and between DMN-PCC and SN-rAI [124]. The insufficient counteractivity between DMN and other networks was associated with the symptomology of

6  Neuroimaging and the At-Risk Mental State

243

ARMS subjects, such as reality distortion and cognitive impairment [124]. The study of Heinze et al. [127] confirmed that these abnormalities in functional connectivity were supported by the structural covariance between regions of the networks and were more extensive in the patients with a later transition to psychosis. More connectivity between regions has been investigated in ARMS studies. Dandash et al. [123] demonstrated decreased connectivity between the dorsal caudate and prefrontal regions in the ARMS group compared to HC, and this was correlated with more severe perceptual abnormalities. In addition, they observed that the significant decrease in the connectivity between dorsal putamen and thalamus in ARMS compared to HC was correlated with more severe positive symptoms, and increased connectivity between the ventral putamen and the superior temporal gyrus (STG) and insula in ARMS compared to HC was correlated with both alien thoughts and positive symptoms. Anticevic et al. [125] examined the thalamocortical connectivity and reported reduced connectivity between cerebellar, thalamus and prefrontal cortex but hyperactive connectivity between thalamus and sensory motor area in ARMS-T compared to ARMS-NT and HC. Yoon et al. [126] investigated the connectivity between subareas of the superior temporal gyrus (STG) and other regions and indicated symptoms-correlated hyperactivity between STG and the dorsal lateral prefrontal cortex (DLPFC), but the hypoconnectivity between STG and ACC in both ARMS and FEP was comparable to that of HC. Moreover, the increase in STG-DLPFC connectivity was correlated with greater positive symptoms.

6.5

Neurotransmission: Dopamine and Glutamate

6.5.1 Dopaminergic Mechanism Dopamine has been known to play a critical role in the biopathology of schizophrenia since the first generation of antipsychotics came out in the 1950s and the effect of elevated metabolism of dopamine after administration was observed [167]. This finding was later indirectly corroborated by the observation of amphetamine-­ induced psychotic-like symptoms and its pharmacological effect on elevating synaptic monoamine levels. Since then, there has been great interest in the dopamine hypothesis, and subsequent pharmacological studies have focused on the efficacy of blockade of D2 receptors in treating psychotic symptoms. Despite the fact that D2 receptor antagonists have been the main medical treatment for schizophrenia since the first generation of antipsychotics, it remains unclear how the alteration of dopaminergic system is induced and then leads to chronic neural impairment. Moreover, the high rate of treatment nonresponders has not been explained. Clozapine is the most active antipsychotic in the treatment of nonresponders, but has rather low affinity and low occupancy of D2 receptors. In addition, a decrease in dopamine metabolite levels was conversely observed in some patients, even though this was correlated with symptom severity [168]. Consequently, the hypothesised alteration in dopamine mechanism has moved from postsynaptic receptors binding to presynaptic capacity. Furthermore, a regionally specific mechanism was proposed in the

244

Y.-S. Lin et al.

review of Davis et al. [169]. The review presented the evidence for elevated levels of dopamine metabolite in subcortical regions and lower dopamine activity in the prefrontal cortex in patients with schizophrenia and, by extension, the evidence from animal studies suggesting that impairment of dopamine neurons in the prefrontal cortex could lead to the elevation or reduction of striatal dopamine levels [169]. Because of the correlation between dopamine metabolite and psychotic symptoms, it was also proposed that striatal dopaminergic hyperfunction might account for negative symptoms and frontal dopaminergic hypofunction for positive symptoms. The advent of in vivo techniques in the recent decades has made it possible to perform studies to examine presynaptic abnormalities in schizophrenia. To examine presynaptic capacity in striatal neurons, 18F-dihydroxyphenylalanine (DOPA) has been commonly used in positron emission tomography (PET) studies as the analog of l-DOPA (l-dihydroxyphenylalanine), which is able to pass through blood-brain barrier and be synthesised into dopamine and stored in the presynaptic vesicle. PET studies have revealed elevated putative presynaptic dopamine synthesis and increased uptake in schizophrenia [170–172]. These abnormal elevations in schizophrenia might result in high synaptic dopamine and increased occupancy of D2 receptors. There is not much positive evidence on altered dopamine synthesis in ARMS individuals as a group comparing to HC, yet Allen et al. [173] found an association between medial temporal dopamine synthesis and brain activation in the corresponding region during the verbal encoding and recognition task. Egerton et  al. [174] also reported that there was no difference in synthesis capacity in the limbic or striatum, but an elevation in associative and sensorimotor cortices. Roiser et al. [175] employed fMRI to investigate the correlation between brain activation and dopamine synthesis capacity during adaptive and aberrant motivational salience and reported no group difference in synthesis capacity. However, they found that there was a negative correlation between striatal dopamine synthesis capacity and hippocampal response to irrelevant stimuli, and this association in HC was inverse. Furthermore, high striatal dopamine uptake was associated with the severity of abnormal thought contents [176]. Striatal dopamine uptake was negatively associated with hippocampal glutamate level, and the alteration in these two functions effectively predicted the future transition [176]. This finding was in line with another study, which found a positive correlation between dopamine uptake and earlier prodromal symptoms in people with schizophrenia [177]. A study reported that this increased striatal dopamine uptake was positively correlated to the activation of the frontal region [178]. Suridjan et al. [179] utilised [11C]-(+)-PHNO— which preferbly bind to D2 and D3 receptors—to measure the binding potential of non-displaceable (BD(ND)) in the dorsal and ventral striatum and in the thalamus but failed to find significant differences between ARMS, HC or even chronic schizophrenia. A longitudinal study confirmed the similar pattern in individuals who later transitioned to psychosis (ARMS-T). ARMS-T showed an increase in striatal dopamine

6  Neuroimaging and the At-Risk Mental State

245

synthesis capacity, which was positively correlated to the severity of their positive symptoms [180]. Elevated dopamine synthesis capacity in ARMS was also found in the brain stem at the baseline examination [97]. One study employed [123I]-IBZM SPECT to investigate putative dopamine concentration by measuring the change in binding potential between baseline and follow-up depletion through a competitive inhibitor of tyrosine hydroxylase—alpha-methyl-para-tyrosine (AMPT). The aim was to calculate the synaptic dopamine concentration and to correlate this with symptoms [181]. The study reported that the ARMS group showed significantly reduced positive symptoms in follow-up depletion by AMPT, despite there being no difference in concentration between HC and ARMS.  Moreover, higher synaptic concentrations predicted better alleviation of positive symptoms following depletion (Table 6.4). Table 6.4  Research findings in dopamine synthesis capacity, uptake and synaptic concentration in ARMS Author

Method

N

Follow-up

HC 29 A-NT 15 A-T 9 Scz-ty 6 HC 24 A-T 7 A-NT 34

3 years

A-T vs HC and A-NT| DA synthesis capacity in the striatum ↑ DA synthesis capacity ~ symptom severity (+)

ROI/regions of findings

2 years

A-T| DA synthesis capacity in the brainstem region ↑

Howes et al. [179]

6-18F-DOPA PET Synthesis capacity

Allen et al. [96]

18

Egerton et al. [173]

18

F-DOPA PET DA synthesis capacity at striatum

HC 32 ARMS 50

ARMS| DA synthesis capacity Total, associative, sensorimotor ↑ but not the limbic, striatum =

Roiser et al. [174]

18

F-DOPA PET DA synthesis capacity Salience Attribution Test (adaptive and aberrant motivational salience) fMRI

HC 18 ARMS 18

Allen et al. [172]

F-DOPA PET Synthesis capacity fMRI: verbal encoding and recognition task 6-18F-DOPA PET DA uptake

HC 14 ARMS 20

ARMS| Behaviour: attribute motivational salience to irrelevant stimulus features (~ delusion-like symptoms) fMRI: Ventral striatal responses to irrelevant stimulus features ~ delusional symptoms PET: Striatal dopamine synthesis capacity ~ fMRI hippocampal responses to irrelevant stimulus features (–) (HC+) Verbal encoding and recognition| MTL activation x DA storage/synthesis capacity altered

Howes et al. [176]

F-DOPA PET Synthesis capacity fMRI

18

HC 12 ARMS 24 Scz 7

Scz > ARMS > HC| Striatal 18F-DOPA uptake in the associative striatum ↑ Scz| striatal 18F-DOPA uptake~ severity of prodromal psychopathologic and neuropsychological impairment (~/~ severity of anxiety or depressive symptoms)

Stone et al. [175]

18

F-DOPA PET DA uptake Proton MRS

HC 12 A-T 4 A-NT 12

Hippocampal glutamate levels ~ striatal DA uptake (–) ARMS| striatal DA uptake ~ severity of abnormal beliefs (+) Hippocampal glutamate x dopamine uptake –> transition (trend)

Fusar-Poli et al. [177]

18

F-DOPA PET DA uptake fMRI: verbal fluency task

HC 14 ARMS 20

ARMS in verbal fluency task| Striatal DA uptake ↑; IFC (l) activation ~ striatal DA uptake (+)

Bloemen et al. [180]

[123I]-IBZM SPECT synaptic DA concentration F/U: DA depletion with AMPT [11C]-(+)-PHNO PET, BP(ND)

HC 15 ARMS 14

ARMS| synaptic DA concentration = Synaptic DA concentration ↑ ® positive symptoms–following depletion Synaptic DA concentration~, positive symptoms (+) and reduction of these symptoms following depletion (–) HC vs ARMS vs Scz| BP(ND) in caudate, putamen, VS, globus pallidus, substantia nigra and thalamus =

BL, F/U: [(123)I]-IBZM SPECT F/U: DA depletion with AMPT PPI

HC 11 ARMS 11

Suridjan et al. [178]

De Koning et al. [181]

HC 12 ARMS 12 Scz 13

ARMS| PPI ↓ ARMS and HC| Striatal synaptic DA ~/~ PPI

Shading: grey, dopamine synthesis capacity or synaptic concentration; white, dopamine reuptake Abbreviations: DA dopamine, AMPT alpha-methyl-para-tyrosine, BP(ND) binding potential non-­ displaceable, VS ventral striatum, PPI prepulse inhibition (PPI) Symbols: (+) positive correlated, (−) negative correlated, (=) no difference, ~ correlation, ~/~ no correlation, → predicts

246

Y.-S. Lin et al.

6.5.2 Glutamatergic Mechanism The interest in the N-methyl-d-aspartate receptor (NMDAR) first arose from the observation of the psychotomimetic effects of PCP and ketamine and their inhibitory action on NMDARs [183]. This psychotomimetic effect incorporates pseudo-­ psychotic symptoms (e.g. hallucination, paranoia), emotional withdrawal and cognitive impairments, and thus so-called PCP/amphetamine-induced psychosis, along with the subsequent signalling abnormalities in striatal dopamine release after administration [184] and cortical glutamate elevation [185]. Animal studies corroborated these cortico-limbic alterations induced by NMDAR-blockade; these resemble the neural abnormalities in subjects with chronic psychosis and could also be effectively reversed by clozapine [186]. Intervention in rodents suggests that the direct application of NMDAR blocker in cortical regions does not lead to similar effects  as the application in the anterior thalamus that induced elevated glutamate release and neurodegeneration in the cortex [187]. This regionsensitive finding implies that the impairment may be derived from the cortices but most probably originated from anomalous thalamic neurofunctions, through the thalamocortical connectivity, and then eventually lead to malfunction and neuronal loss in cortical regions. A recent human randomised double-blind controlled study has confirmed the finding that ketamine enhances functional connectivity between the ventral striatum/nucleus accumbens and the ventromedial prefrontal cortex [188]. The review of Bergeron and Coyle [189] described the details of the abnormal NMDAR system in schizophrenia. In short, the ion channel of NMDAR is only opened after both subunits have been bound—NR1 to glycine/d-serine and NR2 to glutamate. The endogenous NMDAR inhibitor N-acetylaspartylglutamate (NAAG) plays a dual role, as it is both a full antagonist of NMDAR and a weak agonist of mGluR3 (which inhibits glutamate release when activated). In schizophrenia, antagonism from NAAG might be malfunctioning, which would be consistent with the observed reduction of its degrading enzyme, glutamate carboxypeptidase (GCP-II). In schizophrenia, the reductions in GCP-II in the frontal cortex, the hippocampus and the parahippocampus (in particular the CA1 pyramidal neuron) and increased NAAG in the hippocampus have been found in postmortem studies of schizophrenia patients. The abnormal function of GCP-II can also be investigated in vivo  by measuring its catabolite, N-acetyl aspartate (NAA), via MRS, and this shows that NAA is reduced in cortical limbic regions in schizophrenia. Stone [190] described the excitotoxic hypothesis of NMDAR hypofunction and cortical damage in detail. It is postulated that the hypofunction of NMDAR expression on the hippocampal GABA (gamma-aminobutyric acid)ergic interneuron (that modulates the inhibition of glutamatergic transmission) may trigger these GABAergic interneurons to develop anatomical degeneration (e.g. become parvalbumin-positive). Subsequently, these degenerated interneurons exhibit rapid and recurrent firing and then consequently fail to control cortical inhibition of glutamatergic projection in the thalamocortical efferent. The disinhibition may result in burn-out through excitotoxicity to pyramidal neurons in the cortex and ultimately lead to volumetric loss [189].

6  Neuroimaging and the At-Risk Mental State

247

Even with attenuated symptomatic characteristics, studies in people at risk state show similar findings to chronic schizophrenia. In the thalamus, reduced glutamate and NAA were found in the ARMS group [96, 191–193], while glutamate was elevated in ACC [193] and NAA in DLPFC [194]. Moreover, lower thalamic glutamate was correlated with reduced cortical volume of the medial temporal cortex and insula [193]. The cortical and subcortical hyperactivation observed during verbal fluency performance was also correlated with the lower thalamic glutamate level. The hyperactive regions included the hippocampus, the dorsolateral prefrontal cortex and the superior temporal gyrus [96], along with enhanced prefrontal-striatal connectivity [191]. Thalamic glutamate deficit was also correlated with the higher severity of thoughts disorder [192]. It is striking that elevated glutamate levels were also found in the striatum [195], where the hub of dopaminergic system is located. The findings on hippocampal glutamate release are less consistent than in chronic schizophrenia. Despite some negative results [176, 193], one study found decreased hippocampal levels, which were correlated to grey matter loss in the superior temporal lobe [196]. Lower hippocampal glutamate levels were also correlated with increased striatal dopamine uptake (for details, refer to the previous dopamine section [176]), and this finding may implicate the altered signal projection in striatalthalamic-cortical connectivity. In cortical regions, medial temporal and prefrontal cortices are the most frequently investigated regions, with varying metabolites of interests, including glutamate/glutamine, NAA/NAAG, creatine and choline. Although the levels of medial temporal metabolites were not significantly different between ARMS and HC [42], the intrinsic coupling between brain activation (measured by the BOLD signal) and glutamate levels in mTL during episodic memory was absent in the ARMS group [107]. On the other hand, one study reported elevation of NAA/creatine and choline/creatine ratios in the dorsolateral prefrontal cortex compared to both HC and FEP. Higher ratios of NAA/creatine imply that there is hypometabolism in the prodromal state [194], so is in established schizophrenia. Similarly, hypofunction of NMDAR in the ARMS group was indicated by the elevated GABA and glutamate levels but with unchanged levels of the metabolites (NAA, choline or creatine) in the medial frontal cortex and caudate compared to both HC [197] and FEP (dorsal caudate [198]). Somewhat inconsistent findings were reported in the study of Liemburg et  al. [199], where ARMS group displayed elevated NAA, with a trend to higher glutamine levels in the prefrontal cortex compared to HC; however, this group difference was also highly dependent on the age of the subjects in this study (Table 6.5).

6.6

 iscussion: From Methodological Limitations D to Concepts

6.6.1 Issue 1: Methodological Heterogeneity Studies may be heterogenous in the following respects: (1) inconsistent measures (e.g. diagnostic instruments, clinical assessments), (2) control for nonpsychotic subclinical complaints (e.g. depressive symptoms, anxiety level, history of

248

Y.-S. Lin et al.

Table 6.5  Research findings on glutamate levels in ARMS Author

Method

Stone et al. [191] MRS; volumetric proton MRI

N

ROI/regions of findings

HC 27 ARMS 27

Glutamate level: ARMS | thalamus–; ACC ↑; hippocampus= Thalamic glutamate ~ GM in mTC and insula

HC 12

ARMS | Hippocampal glutamate ~ striatal [18F] DOPA uptake (–)

ARMS 16 HC 17 ARMS 24

Striatal [18F] DOPA uptake ~ abnormal beliefs (+)

HC 14 ARMS 22

ARMS vs HC| mTL activation ↓ Episodic encoding: coupling relationship with glutamate absent

HC 40 ARMS 18 FEP 18 HC 24/19/25 ARMS 24 FEP 19 Scz 25 HC 56/33 ARMS 75 (A-NR 29 A-R 22) HC 27 ARMS 33

ARMS, FEP vs HC| Precommissural dorsal caudate ↑ Cerebellum =

Proton MRS; de la Fuente-Sandoval gamma-aminobutyric acid et al. [195] Glx, NAA, choline, creatine caudate(d,b); mPFC

HC 24 ARMS 23

ARMS vs HC| Caudate and mPFC gamma-aminobutyric acid and Glx ↑ Caudate and mPFC NAA, choline, creatine =

Nenadic et al. [194]

HC 42 ARMS 31 FEP 18

ARMS | Hippocampal NAA ↓ Hippocampal glutamate ~ SFC GM(l) (+) FEP | hippocampal NAA ~ PF GM(l) (–) ARMS, FEP | reverse correlation (–) between hippocampal glutamate and caudate clusters (HC +) ARMS | PFC Glx and NAA ~ age (+) Scz | chronicity of schizophrenia ~ Glx and NAA (–)

Glutamate level at ACC; hippocampus (l); thalamus (l) Stone et al. [175] Proton MRS; [18] DOPA PET

Fusar-Poli et al. [103]

Hippocampal glutamate; striatal dopamine fMRI; proton MRS Verbal fluency Regional BOLD; levels of glutamate in the ACC, thalamus (l) and hippocampus (l)

Valli et al. [114]

fMRI; proton MRS Episodic memory task Medial temporal activation and local glutamate levels ((1) H-MRS), glutamate levels in the de la Fuente-Sandoval precommissural dorsal caudate (a dopamine-rich region) and the et al. [196] cerebellar cortex Natsubori et al. (1) H-MRS [198] mPFC

Egerton et al. [190]

Proton MRS; 18 months Glutamate concentration at thalamus (l) and ACC

Allen et al. [189]

Proton MRS (1H-MRS) Verbal fluency task Cortical responses and thalamic glutamate levels

1-H proton MRS; VBM in whole brain; Glu and NAA in hippocampus

Liemburg et al. [197]

(1) H-MRS Glutamate + Glx NAA + NAAG PFC Wood et al. [192] NAA/creatine and choline/creatine ratios at medial temporal (l); DLPFC (l)

Wood et al. [42]

MRS; MRI; 24 months Hippocampal volume, hippocampal T2 relaxation time and mTL metabolite concentrations (including NAA)

Proton MRS; 2 years de la Fuente-Sandoval et al. [193]

HC 36 ARMS 16 FEP 31 Scz 60 HC 21 A-NT 24 A-T 6 FEP 56 HC 29 A-NT 59 A-T 7 HC 26 A-NT 12 A-T 7

ARMS vs HC| Activation in frontal gyrus ↑ (verbal fluency task) Thalamic glutamate levels ↓; hippocampal glutamate level ↓ (trend) ARMS (opposite to HC) | Thalamic glutamate levels ~ cortical activation (DLPFC(r), hippocampus (r), STG) (–)

Scz vs ARMS, FEP, HC| mPFC NAA and glutamate ↓ Glutamine (Glx) levels ↓ A-NR | Thalamic glutamate ↓ (~abnormal thought (–)) ARMS | ACC glutamate ↓ over time ARMS with poorer function| Activation in cortical and subcortical ↑ (verbal fluency task) Thalamic glutamate ↓ Thalamic glutamate levels ~ prefrontal-striatal activation (–)

ARMS vs HC | medial temporal (l) =; DLPFC ↑ FEP vs HC | =

ARMS | hippocampal volume (l) ↓; T2 relaxation time in hippocampal head (l) ~ positive symptoms (+) A-NT | hippocampal volume (r) ↓ A-T | T2 relaxation time in hippocampal head (l) ↑ mTL metabolite concentrations = A-T vs A-NT and HC| Glutamate in associative striatum ↑

Shading: grey, without differentiation of transition; white, comparison between transition and non-transition Abbreviations: A-R who developed remission later in follow-­up, A-NR who did not develop remission (still in ARMS or has transitioned), ACC anterior cingulate cortex, DLPFC dorsolateral prefrontal cortex, STG superior temporal gyrus, mTL medial temporal lobe, SFC superior frontal cortex, PF prefrontal, mPFC medial prefrontal cortex, NAA N-acetyl aspartate, GM grey matter Symbols: (+) positive correlation, (−) negative correlation, (=) no difference, ~ correlation, → predicts

6  Neuroimaging and the At-Risk Mental State

249

substance use, sleep disturbance, etc.) and (3) different cognitive paradigms employed for similar domains. Some of these variances may not be detrimental to the integration of the results; for instance, different paradigms for the same cognitive domain may have different sensitivities to types of response. However, the first two points may cause bias in selecting and diagnosing subjects and lead to the difficulties when we attempt to compare the findings on the non-comparable populations from different “systems”. Nonpsychotic subclinical symptoms (or complaints) may also be potential confounding factors for the disrupted neurobiological mechanism, if a specific disturbance, perhaps not even a major diagnosis, commonly coincides in the population with subclinical psychotic symptoms, or is convariant with the severity of psychotic symptoms, it may hence result in confounded, inconsistent or even counterintuitive outcomes. It is important not only to investigate group differences but also to compare different clinical expressions irrespective of diagnoses, especially as one of the underlying concepts in neuroimaging in ARMS studies is to optimise the diagnosis on the basis of neurocognitive and biological characteristics. It would therefore be paradoxical if we compare the biological variables between groups classified by the existing (possibly flawed) criteria. A recent trend in statistical analysis is to employ the data-driven approach, for instance, to employ principle component analysis or machine learning. With the precondition of large enough sample size, the data-­ driven approach allows us to illustrate how the data may be clustered and to identify different powerful predictors among all the hypothetical factors that can contribute to the predefined or intrinsic classification.

6.6.2 Issue 2: Power and Sample Size The deficiencies regarding the small sample size and methodological heterogeneity among studies have long been criticised. However, this is still an important issue in AMRS—including the studies in the present review. Imaging studies on psychotic biomarkers inherently lead to relatively small effect sizes. A meta-analysis reported that the observed effect size among 283 sMRI studies in established schizophrenia ranged from −0.2 to −0.6, particularly in reduction in grey matter [201]. In order to achieve ideal power, and with an effect size of 0.5, it was suggested that the sample size should be greater than 45 in each group, yet the mean sample size of the studies included in the meta-analysis was only half this value. The effect size may depend on the nature of the measured effect, the rigour of data acquisition or the designed methodology, and current ARMS research may have as great a problem in this respect as studies on established psychosis. If we examine the ARM studies included in this review by the assumed effect size, few studies reach the suggested sample size and are potentially underpowered—except those derived from large multisite projects. This is mainly because the access to the targeted population is limited, so that individuals in ARMS may be underrepresented in clinic due to lack of insight or an efficient pathway for help-seeking.

250

Y.-S. Lin et al.

6.6.3 Issue 3: Multimodal Approach Traditionally, a study design is derived from a hypothetical effect on a specific region of interest as based on existing evidence, and scientists accordingly choose the most adequate measure as primary outcome to test the hypothesis. However, development of mental disorder involves a multitude of minor contribution from individual genetic differences, neural activities, cognition, behaviour patterns and environments. The results for some abnormalities in ARMS or established psychosis are promising, for instance, increased striatal dopamine or reduced hippocampal grey matter, but the causality of these findings has not yet been established. Many hypothetical models have tried to integrate, not only the factors from these domains (e.g. the epigenetic approach) but also within the domain (e.g. neurotoxicity in psychosis), and these models comprise multiple factors and even temporal features, with several primary measures to corroborate their concomitant occurrence and possibly their causality. As a data-driven multimodal approach is necessary, with a sample of adequate size, multicentre projects have been developed in recent years, with large samples, multimodal analysis (e.g. genetics, neurobiology, cognitive psychology, clinical assessment, life history, geodemography, etc.) and methodological integration, followed by the development and clinical application of predictive modals. The integration of resources not only provides way to attain better statistical power by expanding the sample size but also provides massive data of multiple types of measures, incorporated with multivariate classification that may give rise to supplementary diagnostic and/or prognostic tools. Furthermore, this new trend may raise the possibility of breaking through the limitation of the interpretation of monomodal findings in the current state and systematically map out the mechanism of psychosis development by comprising endogenous and exogenous factors. Indeed, the existing between-centre methodological barriers to acquiring and synthesising neuroimaging data also demand that a rigorous protocol for data collection must be assured, along with an established pipeline of data calibration, such as the contrast, signal-­ to-­noise ratio, voxel size and scanning parameters for sensitivity activation, as well as the standardised process of presenting task stimuli, recording responses, controlling movement, etc. [202]. There are currently three major EU-funded projects of high-risk to psychosis in Europe—PRONIA (Personalised Prognostic Tools for Early Psychosis Management), PSYSCAN [203] and EU-GEI, involving in total 17 countries throughout the world—and one NIMH-funded project in North America, NAPLS (North American Prodrome Longitudinal Study). PRONIA and PSYSCAN tend to emphasise neurobiological and cognitive psychological approaches. Their principle objective is to develop a machine learning algorithm with highly accurate predictive power, which could be used as a novel support tool for clinical decisions related to diagnosis, prognosis and adequate client management. EU-GEI exploits the social psychiatric perspective of genetic-environmental interaction. The scope of assessment ranges from genetic biology, cognitive psychology, to environmental

6  Neuroimaging and the At-Risk Mental State

251

determinants, in order to map out the pathway—from genetic factors through the endogenotypes to the development of mental illness, as moderated by socioenvironmental factors [204, 205]. NAPLS was originally a consortium to combine the data from eight independent NIMH-funded prodromal studies located in multiple sites across North America. The consortium later awarded a grant for the multisite longitudinal project focusing on multiple biological approaches (i.e. neuroimaging, electrophysiological, hormonal and genomics) and reported a clear correlation between volumetric reduction in the prefrontal cortex and neuroinflammation. The current extended project now examines the correlation between neuroinflammation and deficient synaptic plasticity [206, 207].

6.6.4 Issue 4: From Externalism to Social Psychiatry Current identification of high-risk to psychosis is mainly in accordance with the clinical interview that contains the evaluation of salient experiences or cognitive functions. Whether the patient’s ideas, perceptions and interpretation of environmental inputs are “delusional” or not relies on the interviewer’s sympathetic understanding and judgement of the “degree deviant to reality” contextualised in the sociocultural circumstances on which the patient depends. Since the genetic and in vivo  techniques have greatly advanced, psychiatrists have shifted their attention away from the mind-body dualism and are dedicated to search for the holy grail of the biological aetiology and treatment of the illness. However, Broome et al. [208] pointed out that it might not be adequate to solve the puzzle only in accordance with the biological dimension. He adopted the doctrine of externalism and emphasised the undeniable relevance of integrated information from psychological, social psychiatric and epidemiological approaches. The debate  between internalism and externalism may throw light on how inseparable the subjective perception and the objective  existence of the external environment are. While internalism considers that the justification of one’s knowledge is purely an intrinsic mechanism between their existing belief, their recognition of their belief, and the new information, Alvin Goldman has argued in What Is Justified Belief?: Granted that principles of justified belief must make reference to causes of belief, what kinds of causes confer justifiedness? We can gain insight into this problem by reviewing some faulty processes of belief-formation, i.e., processes whose belief-outputs would be classed as unjustified. Here are some examples: confused reasoning, wishful thinking, reliance on emotional attachment, mere hunch or guesswork, and hasty generalization. What do these faulty processes have in common? They share the feature of unreliability: they tend to produce error a large proportion of the time. By contrast, which species of belief-forming (or belief-sustaining) processes are intuitively justification-conferring? They include standard perceptual processes, remembering, good reasoning, and introspection. What these processes seem to have in common is reliability: the beliefs they produce are generally true. My positive proposal, then, is this. The justificational status of a belief is a function of the reliability of the process or processes that cause it, where (as a first approximation) reliability consists in the tendency of a process to produce beliefs that are true rather than false. [209]

252

Y.-S. Lin et al.

Accordingly, we may understand that knowledge may be formed and consolidated as the product of a biased cognitive process through the interpretation of external information. Kapur [147] presented an explanatory model incorporating life event, the dopaminergic system and aberrant salient experiences. He showed how dysregulation of dopamine driven by the concomitant occurrence of multiple risk factors (e.g. obstetric complex, genetic proneness, stress, trauma, etc.) yields the abnormal salience towards the external world, with the consequence of a disorder in thought or perception. In the last decade, the renaissance of social psychiatry has been advocated, as—given the multifactorial inherency of mental disorders—the aetiological model cannot be complete if the interaction between environment and psychological processes is absent. AESOP (Aetiology and Ethnicity of Schizophrenia and Other Psychoses) is an MRC-funded multisite longitudinal project that aims to probe the causes of schizophrenia and onset of other psychoses, on the basis of a long-term investigation of the clinical and social outcomes of epidemiological cohorts [210, 211].

6.6.5 Additional Issue 1: Validity of ARMS Recruitment As explained above, we must keep in mind the problem that all the research work to expand our knowledge of ARMS and to develop predictive and diagnostic instruments is based on selection criteria which are less than ideal or perfect. Thus, we should be more conservative when interpreting the neurobiological findings as either pathological or predictive biomarkers for psychosis onset. Broome et  al. [208] have argued that current criteria to identify the high-risk state imply major selective bias for the following reasons: 1. The established criteria are based on the clinical and research evidence derived from the highly selected represented samples. “Selection” refers to inclusion or exclusion during the referral process, the socio-demographic determinants that may decide the accessibility of resources, or the ignorance of people who never seek help for the brief subclinical symptoms yet still bear the diathesis. 2. As the psychotic spectrum is a continuum and the diagnosis of high-risk relies on wide-ranging and polymorphic subclinical experiences, the definition of the cut-­ off point between normal and abnormal, i.e. the border of reality and delusion or hallucination, can be subjectively determined by the interview and the sympathetic skills of clinicians, as well as their understanding of the cultural background. It is also possible that the polymorphism of brief/attenuated symptoms is weighted differently. Do they represent the pathological origins with equal weight, or do specific characteristics determine the “high-risk state”? 3. Just like the biological reference, the environmental context is also ignored in the assessment tools—which are based on the stated symptoms and traits. The recent meta-analysis of Fusar-Poli et al. [212] compared psychotic recurrence in the two subtypes of high-risk state and two diagnoses in the psychotic spectrum with slightly different definitions of a brief psychotic episode with respect to

6  Neuroimaging and the At-Risk Mental State

253

duration and severity: (1) brief limited intermittent psychotic symptoms (BLIPS), one of the subtypes of the high-risk state; (2) brief intermittent psychotic symptoms (BIPS), one of the subtypes of the high-risk state, mostly employed in the North American study assessed by SIPS; (3) acute and transient disorder (ATPS), coded as a subtype in the psychotic spectrum by ICD-10; and (4) brief psychotic disorder (BPD), coded as a psychotic subtype by DSM-V. Surprisingly enough, there was no prognostic difference between these four subtypes, even though ATPS/BPD are considered to be diagnoses of established psychosis, while BLIPS/BIPS are the criteria for at risk mental state. This finding may implicate that the confusion is not only within the heterogenous diagnosed high-risk group—as mentioned in the introduction [18]—but also in the admixture of individuals with frank psychosis and at risk, and eventually leads to inadequate treatment. This can be also one of the explanations why some cerebral changes did not progress in sequence of the diagnoses (i.e. HC, ARMS, then FEP and/or Scz). Thus, combining samples at different stages may not simply cancel out the effect of interest, but detect false effects from the misrepresented population.

6.6.6 A  dditional Issue 2: Can We Define One Type of “Disorder” by a Purely Biological Index? Finally, we would like to repeat the question raised by Broome et al. [208]: What is the core character of mental disorders? Furthermore, when making a diagnosis, can these neurobiological markers in the future be the only determinants to whether the individual is healthy or disordered as a diagnosis in physical medicine? If so, does it mean that we can characterise mental disorders purely by their biological inherency? If not, then how should psychological and biological features be weighted? In contrast to any other physical illness, mental disorders are manifested through both mental and physical expressions. Although the existence of neurobiological changes in the progression of mental disorders is promising, it is unclear whether the phenotypes are formed in concordance with the changes. Meanwhile, the definition of “disorder” still relies on the understanding about where the behaviours or cognition lies within the normal distribution of the general population in different societies and cultures, as these are eventually how we define “mental” as a distinct inherency of human beings.

References 1. Mayer-Gross W.  Die Klinik der Schizophrenie. In: Bunke O, editor. Handbuch der Geisteskrankheiten. Berlin, Germany: Springer; 1932. 2. Huber G, Gross G.  The concept of basic symptoms in schizophrenic and schizoaffective psychoses. Recenti Prog Med. 1989;80:646–52. 3. Häfner H, Maurer K, Loffler W, an der Heiden W, Munk-Jorgensen P, Hambrecht M, Riecher-Rossler A. The ABC schizophrenia study: a preliminary overview of the results. Soc Psychiatry Psychiatr Epidemiol. 1998;33:380–6. 4. Häfner H, Maurer K, Loffler W, Riecher-Rossler A. The influence of age and sex on the onset and early course of schizophrenia. Br J Psychiatry. 1993;162:80–6.

254

Y.-S. Lin et al.

5. Häfner H, Riecher-Rossler A, Hambrecht M, Maurer K, Meissner S, Schmidtke A, Fatkenheuer B, Loffler W, van der Heiden W. IRAOS: an instrument for the assessment of onset and early course of schizophrenia. Schizophr Res. 1992;6:209–23. 6. Häfner H, Riecher-Rossler A, Maurer K, Fatkenheuer B, Loffler W.  First onset and early symptomatology of schizophrenia. A chapter of epidemiological and neurobiological research into age and sex differences. Eur Arch Psychiatry Clin Neurosci. 1992;242:109–18. 7. Riecher A, Maurer K, Loffler W, Fatkenheuer B, an der Heiden W, Häfner H.  SchizophreniaDOUBLEHYPHENa disease of young single males? Preliminary results from an investigation on a representative cohort admitted to hospital for the first time. Eur Arch Psychiatry Neurol Sci. 1989;239:210–2. 8. Fusar-Poli P, Borgwardt S, Bechdolf A, Addington J, Riecher-Rossler A, Schultze-Lutter F, Keshavan M, Wood S, Ruhrmann S, Seidman LJ, Valmaggia L, Cannon T, Velthorst E, de Haan L, Cornblatt B, Bonoldi I, Birchwood M, Mcglashan T, Carpenter W, Mcgorry P, Klosterkotter J, Mcguire P, Yung A. The psychosis high-risk state: a comprehensive state-ofthe-art review. JAMA Psychiatry. 2013;70:107–20. 9. Mcgorry PD, Yung AR, Phillips LJ. The “close-in” or ultra high-risk model: a safe and effective strategy for research and clinical intervention in prepsychotic mental disorder. Schizophr Bull. 2003;29:771–90. 10. Yung AR, Mcgorry PD. The initial prodrome in psychosis: descriptive and qualitative aspects. Aust N Z J Psychiatry. 1996;30:587–99. 11. Yung AR, Mcgorry PD.  The prodromal phase of first-episode psychosis: past and current conceptualizations. Schizophr Bull. 1996;22:353–70. 12. Jackson HJ, Mcgorry PD, Mckenzie D. The reliability of DSM-III prodromal symptoms in first-episode psychotic patients. Acta Psychiatr Scand. 1994;90:375–8. 13. Miller TJ, Mcglashan TH, Rosen JL, Cadenhead K, Cannon T, Ventura J, Mcfarlane W, Perkins DO, Pearlson GD, Woods SW. Prodromal assessment with the structured interview for prodromal syndromes and the scale of prodromal symptoms: predictive validity, interrater reliability, and training to reliability. Schizophr Bull. 2003;29:703–15. 14. Miller TJ, Mcglashan TH, Rosen JL, Somjee L, Markovich PJ, Stein K, Woods SW. Prospective diagnosis of the initial prodrome for schizophrenia based on the Structured Interview for Prodromal Syndromes: preliminary evidence of interrater reliability and predictive validity. Am J Psychiatr. 2002;159:863–5. 15. Riecher-Rossler A, Aston J, Ventura J, Merlo M, Borgwardt S, Gschwandtner U, Stieglitz RD. The Basel Screening Instrument for Psychosis (BSIP): development, structure, reliability and validity. Fortschr Neurol Psychiatr. 2008;76:207–16. 16. Fusar-Poli P, Cappucciati M, Rutigliano G, Schultze-Lutter F, Bonoldi I, Borgwardt S, Riecher-Rossler A, Addington J, Perkins D, Woods SW, Mcglashan TH, Lee J, Klosterkotter J, Yung AR, Mcguire P. At risk or not at risk? A meta-analysis of the prognostic accuracy of psychometric interviews for psychosis prediction. World Psychiatry. 2015;14:322–32. 17. Nelson B, Yuen K, Yung AR. Ultra high risk (UHR) for psychosis criteria: are there different levels of risk for transition to psychosis? Schizophr Res. 2011;125:62–8. 18. Fusar-Poli P, Cappucciati M, Borgwardt S, Woods SW, Addington J, Nelson B, Nieman DH, Stahl DR, Rutigliano G, Riecher-Rossler A, Simon AE, Mizuno M, Lee TY, Kwon JS, Lam MM, Perez J, Keri S, Amminger P, Metzler S, Kawohl W, Rossler W, Lee J, Labad J, Ziermans T, An SK, Liu CC, Woodberry KA, Braham A, Corcoran C, Mcgorry P, Yung AR, Mcguire PK. Heterogeneity of psychosis risk within individuals at clinical high risk: a metaanalytical stratification. JAMA Psychiatry. 2016;73:113–20. 19. Hurlemann R, Jessen F, Wagner M, Frommann I, Ruhrmann S, Brockhaus A, Picker H, Scheef L, Block W, Schild HH, Moller-Hartmann W, Krug B, Falkai P, Klosterkotter J, Maier W.  Interrelated neuropsychological and anatomical evidence of hippocampal pathology in the at-risk mental state. Psychol Med. 2008;38:843–51. 20. Takahashi T, Yung AR, Yucel M, Wood SJ, Phillips LJ, Harding IH, Soulsby B, Mcgorry PD, Suzuki M, Velakoulis D, Pantelis C. Prevalence of large cavum septi pellucidi in ultra highrisk individuals and patients with psychotic disorders. Schizophr Res. 2008;105:236–44.

6  Neuroimaging and the At-Risk Mental State

255

21. Witthaus H, Kaufmann C, Bohner G, Ozgurdal S, Gudlowski Y, Gallinat J, Ruhrmann S, Brune M, Heinz A, Klingebiel R, Juckel G. Gray matter abnormalities in subjects at ultrahigh risk for schizophrenia and first-episode schizophrenic patients compared to healthy controls. Psychiatry Res. 2009;173:163–9. 22. Jung WH, Kim JS, Jang JH, Choi JS, Jung MH, Park JY, Han JY, Choi CH, Kang DH, Chung CK, Kwon JS.  Cortical thickness reduction in individuals at ultra-high-risk for psychosis. Schizophr Bull. 2011;37:839–49. 23. Bohner G, Milakara D, Witthaus H, Gallinat J, Scheel M, Juckel G, Klingebiel R.  MTR abnormalities in subjects at ultra-high risk for schizophrenia and first-episode schizophrenic patients compared to healthy controls. Schizophr Res. 2012;137:85–90. 24. Iwashiro N, Suga M, Takano Y, Inoue H, Natsubori T, Satomura Y, Koike S, Yahata N, Murakami M, Katsura M, Gonoi W, Sasaki H, Takao H, Abe O, Kasai K, Yamasue H.  Localized gray matter volume reductions in the pars triangularis of the inferior frontal gyrus in individuals at clinical high-risk for psychosis and first episode for schizophrenia. Schizophr Res. 2012;137:124–31. 25. Benetti S, Pettersson-Yeo W, Hutton C, Catani M, Williams SC, Allen P, Kambeitz-Ilankovic LM, Mcguire P, Mechelli A.  Elucidating neuroanatomical alterations in the at risk mental state and first episode psychosis: a combined voxel-based morphometry and voxel-based cortical thickness study. Schizophr Res. 2013;150:505–11. 26. Nakamura K, Takahashi T, Nemoto K, Furuichi A, Nishiyama S, Nakamura Y, Ikeda E, Kido M, Noguchi K, Seto H, Suzuki M. Gray matter changes in subjects at high risk for developing psychosis and first-episode schizophrenia: a voxel-based structural MRI study. Front Psychiatry. 2013;4:16. 27. Tepest R, Schwarzbach CJ, Krug B, Klosterkotter J, Ruhrmann S, Vogeley K. Morphometry of structural disconnectivity indicators in subjects at risk and in age-matched patients with schizophrenia. Eur Arch Psychiatry Clin Neurosci. 2013;263:15–24. 28. Pettersson-Yeo W, Benetti S, Frisciata S, Catani M, Williams SC, Allen P, Mcguire P, Mechelli A. Does neuroanatomy account for superior temporal dysfunction in early psychosis? A multimodal MRI investigation. J Psychiatry Neurosci. 2015;40:100–7. 29. Valli I, Crossley NA, Day F, Stone J, Tognin S, Mondelli V, Howes O, Valmaggia L, Pariante C, Mcguire P. HPA-axis function and grey matter volume reductions: imaging the diathesisstress model in individuals at ultra-high risk of psychosis. Transl Psychiatry. 2016;6:e797. 30. Dean DJ, Orr JM, Bernard JA, Gupta T, Pelletier-Baldelli A, Carol EE, Mittal VA. Hippocampal shape abnormalities predict symptom progression in neuroleptic-free youth at ultrahigh risk for psychosis. Schizophr Bull. 2016;42:161–9. 31. Reniers RL, LIN A, Yung AR, Koutsouleris N, Nelson B, Cropley VL, Velakoulis D, Mcgorry PD, Pantelis C, Wood SJ. Neuroanatomical predictors of functional outcome in individuals at ultra-high risk for psychosis. Schizophr Bull. 2017;43:449–58. 32. Yucel M, Wood SJ, Phillips LJ, Stuart GW, Smith DJ, Yung A, Velakoulis D, Mcgorry PD, Pantelis C. Morphology of the anterior cingulate cortex in young men at ultra-high risk of developing a psychotic illness. Br J Psychiatry. 2003;182:518–24. 33. Velakoulis D, Wood SJ, Wong MT, Mcgorry PD, Yung A, Phillips L, Smith D, Brewer W, Proffitt T, Desmond P, Pantelis C. Hippocampal and amygdala volumes according to psychosis stage and diagnosis: a magnetic resonance imaging study of chronic schizophrenia, firstepisode psychosis, and ultra-high-risk individuals. Arch Gen Psychiatry. 2006;63:139–49. 34. Walterfang M, Yung A, Wood AG, Reutens DC, Phillips L, Wood SJ, Chen J, Velakoulis D, Mcgorry PD, Pantelis C. Corpus callosum shape alterations in individuals prior to the onset of psychosis. Schizophr Res. 2008;103:1–10. 35. Koutsouleris N, Schmitt GJ, Gaser C, Bottlender R, Scheuerecker J, Mcguire P, Burgermeister B, Born C, Reiser M, Moller HJ, Meisenzahl EM. Neuroanatomical correlates of different vulnerability states for psychosis and their clinical outcomes. Br J Psychiatry. 2009;195:218–26. 36. Smieskova R, Fusar-Poli P, Aston J, Simon A, Bendfeldt K, Lenz C, Stieglitz RD, Mcguire P, Riecher-Rossler A, Borgwardt SJ. Insular volume abnormalities associated with different transition probabilities to psychosis. Psychol Med. 2012;42:1613–25.

256

Y.-S. Lin et al.

37. Phillips LJ, Velakoulis D, Pantelis C, Wood S, Yuen HP, Yung AR, Desmond P, Brewer W, Mcgorry PD. Non-reduction in hippocampal volume is associated with higher risk of psychosis. Schizophr Res. 2002;58:145–58. 38. Garner B, Pariante CM, Wood SJ, Velakoulis D, Phillips L, Soulsby B, Brewer WJ, Smith DJ, Dazzan P, Berger GE, Yung AR, van den Buuse M, Murray R, Mcgorry PD, Pantelis C. Pituitary volume predicts future transition to psychosis in individuals at ultra-high risk of developing psychosis. Biol Psychiatry. 2005;58:417–23. 39. Borgwardt SJ, Mcguire PK, Aston J, Berger G, Dazzan P, Gschwandtner U, Pfluger M, D’Souza M, Radue EW, Riecher-Rossler A. Structural brain abnormalities in individuals with an at-risk mental state who later develop psychosis. Br J Psychiatry Suppl. 2007;51:s69–75. 40. Fornito A, Yung AR, Wood SJ, Phillips LJ, Nelson B, Cotton S, Velakoulis D, Mcgorry PD, Pantelis C, Yucel M. Anatomic abnormalities of the anterior cingulate cortex before psychosis onset: an MRI study of ultra-high-risk individuals. Biol Psychiatry. 2008;64:758–65. 41. Witthaus H, Mendes U, Brune M, Ozgurdal S, Bohner G, Gudlowski Y, Kalus P, Andreasen N, Heinz A, Klingebiel R, Juckel G. Hippocampal subdivision and amygdalar volumes in patients in an at-risk mental state for schizophrenia. J Psychiatry Neurosci. 2010;35:33–40. 42. Wood SJ, Kennedy D, Phillips LJ, Seal ML, Yucel M, Nelson B, Yung AR, Jackson G, Mcgorry PD, Velakoulis D, Pantelis C. Hippocampal pathology in individuals at ultra-high risk for psychosis: a multi-modal magnetic resonance study. NeuroImage. 2010;52:62–8. 43. Mechelli A, Riecher-Rossler A, Meisenzahl EM, Tognin S, Wood SJ, Borgwardt SJ, Koutsouleris N, Yung AR, Stone JM, Phillips LJ, Mcgorry PD, Valli I, Velakoulis D, Woolley J, Pantelis C, Mcguire P. Neuroanatomical abnormalities that predate the onset of psychosis: a multicenter study. Arch Gen Psychiatry. 2011;68:489–95. 44. Meijer JH, Schmitz N, Nieman DH, Becker HE, van Amelsvoort TA, Dingemans PM, Linszen DH, de Haan L. Semantic fluency deficits and reduced grey matter before transition to psychosis: a voxelwise correlational analysis. Psychiatry Res. 2011;194:1–6. 45. Dazzan P, Soulsby B, Mechelli A, Wood SJ, Velakoulis D, Phillips LJ, Yung AR, Chitnis X, Lin A, Murray RM, Mcgorry PD, Mcguire PK, Pantelis C. Volumetric abnormalities predating the onset of schizophrenia and affective psychoses: an MRI study in subjects at ultrahigh risk of psychosis. Schizophr Bull. 2012;38:1083–91. 46. Tognin S, Riecher-Rossler A, Meisenzahl EM, Wood SJ, Hutton C, Borgwardt SJ, Koutsouleris N, Yung AR, Allen P, Phillips LJ, Mcgorry PD, Valli I, Velakoulis D, Nelson B, Woolley J, Pantelis C, Mcguire P, Mechelli A. Reduced parahippocampal cortical thickness in subjects at ultra-high risk for psychosis. Psychol Med. 2014;44:489–98. 47. Pantelis C, Velakoulis D, Mcgorry PD, Wood SJ, Suckling J, Phillips LJ, Yung AR, Bullmore ET, Brewer W, Soulsby B, Desmond P, Mcguire PK. Neuroanatomical abnormalities before and after onset of psychosis: a cross-sectional and longitudinal MRI comparison. Lancet. 2003;361:281–8. 48. Borgwardt SJ, Mcguire PK, Aston J, Gschwandtner U, Pfluger MO, Stieglitz RD, Radue EW, Riecher-Rossler A. Reductions in frontal, temporal and parietal volume associated with the onset of psychosis. Schizophr Res. 2008;106:108–14. 49. Sun D, Phillips L, Velakoulis D, Yung A, Mcgorry PD, Wood SJ, van Erp TG, Thompson PM, Toga AW, Cannon TD, Pantelis C. Progressive brain structural changes mapped as psychosis develops in ‘at risk’ individuals. Schizophr Res. 2009;108:85–92. 50. Takahashi T, Wood SJ, Yung AR, Phillips LJ, Soulsby B, Mcgorry PD, Tanino R, Zhou SY, Suzuki M, Velakoulis D, Pantelis C. Insular cortex gray matter changes in individuals at ultrahigh-risk of developing psychosis. Schizophr Res. 2009;111:94–102. 51. Walter A, Studerus E, Smieskova R, Kuster P, Aston J, Lang UE, Radue EW, Riecher-Rossler A, Borgwardt S. Hippocampal volume in subjects at high risk of psychosis: a longitudinal MRI study. Schizophr Res. 2012;142:217–22. 52. Ziermans TB, Schothorst PF, Schnack HG, Koolschijn PC, Kahn RS, van Engeland H, Durston S. Progressive structural brain changes during development of psychosis. Schizophr Bull. 2012;38:519–30. 53. Cannon TD. How schizophrenia develops: cognitive and brain mechanisms underlying onset of psychosis. Trends Cogn Sci. 2015;19:744–56.

6  Neuroimaging and the At-Risk Mental State

257

54. Ashburner J, Friston KJ.  Voxel-based morphometryDOUBLEHYPHENthe methods. NeuroImage. 2000;11:805–21. 55. Iwashiro N, Koike S, Satomura Y, Suga M, Nagai T, Natsubori T, Tada M, Gonoi W, Takizawa R, Kunimatsu A, Yamasue H, Kasai K. Association between impaired brain activity and volume at the sub-region of Broca’s area in ultra-high risk and first-episode schizophrenia: a multi-modal neuroimaging study. Schizophr Res. 2016;172:9–15. 56. Cannon TD, Cadenhead K, Cornblatt B, Woods SW, Addington J, Walker E, Seidman LJ, Perkins D, Tsuang M, Mcglashan T, Heinssen R.  Prediction of psychosis in youth at high clinical risk: a multisite longitudinal study in North America. Arch Gen Psychiatry. 2008;65:28–37. 57. Peters BD, Karlsgodt KH.  White matter development in the early stages of psychosis. Schizophr Res. 2015;161:61–9. 58. Bartzokis G, Beckson M, Lu PH, Nuechterlein KH, Edwards N, Mintz J. Age-related changes in frontal and temporal lobe volumes in men: a magnetic resonance imaging study. Arch Gen Psychiatry. 2001;58:461–5. 59. Yakovlev PL, Lecour AR. Regional development of the brain in early life. Oxford: Blackwell; 1967. 60. Asato MR, Terwilliger R, Woo J, Luna B. White matter development in adolescence: a DTI study. Cereb Cortex. 2010;20:2122–31. 61. Perrin JS, Leonard G, Perron M, Pike GB, Pitiot A, Richer L, Veillette S, Pausova Z, Paus T.  Sex differences in the growth of white matter during adolescence. NeuroImage. 2009;45:1055–66. 62. Weinberger DR. Implications of normal brain development for the pathogenesis of schizophrenia. Arch Gen Psychiatry. 1987;44:660–9. 63. Murray RM, Lewis SW. Is schizophrenia a neurodevelopmental disorder? Br Med J. (Clin Res Ed.). 1987;295:681–2. 64. Fatemi SH, Folsom TD.  The neurodevelopmental hypothesis of schizophrenia, revisited. Schizophr Bull. 2009;35:528–48. 65. Basser PJ, Pierpaoli C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996;111:209–19. 66. Beaulieu C.  The basis of anisotropic water diffusion in the nervous system  - a technical review. NMR Biomed. 2002;15:435–55. 67. Ashburner J, Friston KJ.  Why voxel-based morphometry should be used. NeuroImage. 2001;14:1238–43. 68. Bookstein FL. “Voxel-based morphometry” should not be used with imperfectly registered images. NeuroImage. 2001;14:1454–62. 69. Davatzikos C. Why voxel-based morphometric analysis should be used with great caution when characterizing group differences. NeuroImage. 2004;23:17–20. 70. Friston KJ, Ashburner J. Generative and recognition models for neuroanatomy. NeuroImage. 2004;23:21–4. 71. Jones DK, Griffin LD, Alexander DC, Catani M, Horsfield MA, Howard R, Williams SC.  Spatial normalization and averaging of diffusion tensor MRI data sets. NeuroImage. 2002;17:592–617. 72. Jones DK, Symms MR, Cercignani M, Howard RJ. The effect of filter size on VBM analyses of DT-MRI data. NeuroImage. 2005;26:546–54. 73. Park HJ, Westin CF, Kubicki M, Maier SE, Niznikiewicz M, Baer A, Frumin M, Kikinis R, Jolesz FA, Mccarley RW, Shenton ME.  White matter hemisphere asymmetries in healthy subjects and in schizophrenia: a diffusion tensor MRI study. NeuroImage. 2004;23: 213–23. 74. Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, Watkins KE, Ciccarelli O, Cader MZ, Matthews PM, Behrens TE. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. NeuroImage. 2006;31:1487–505. 75. Witthaus H, Brune M, Kaufmann C, Bohner G, Ozgurdal S, Gudlowski Y, Heinz A, Klingebiel R, Juckel G. White matter abnormalities in subjects at ultra high-risk for schizophrenia and first-episode schizophrenic patients. Schizophr Res. 2008;102:141–9.

258

Y.-S. Lin et al.

76. Epstein KA, Cullen KR, Mueller BA, Robinson P, Lee S, Kumra S. White matter abnormalities and cognitive impairment in early-onset schizophrenia-spectrum disorders. J Am Acad Child Adolesc Psychiatry. 2014;53:362–72. 77. Karlsgodt KH, Niendam TA, Bearden CE, Cannon TD. White matter integrity and prediction of social and role functioning in subjects at ultra-high risk for psychosis. Biol Psychiatry. 2009;66:562–9. 78. Katagiri N, Pantelis C, Nemoto T, Zalesky A, Hori M, Shimoji K, Saito J, Ito S, Dwyer DB, Fukunaga I, Morita K, Tsujino N, Yamaguchi T, Shiraga N, Aoki S, Mizuno M. A longitudinal study investigating sub-threshold symptoms and white matter changes in individuals with an ‘at risk mental state’ (ARMS). Schizophr Res. 2015;162:7–13. 79. Rigucci S, Santi G, Corigliano V, Imola A, Rossi-Espagnet C, Mancinelli I, de Pisa E, Manfredi G, Bozzao A, Carducci F, Girardi P, Comparelli A.  White matter microstructure in ultra-high risk and first episode schizophrenia: a prospective study. Psychiatry Res. 2016;247:42–8. 80. Bloemen OJ, de Koning MB, Schmitz N, Nieman DH, Becker HE, de Haan L, Dingemans P, Linszen DH, van Amelsvoort TA. White-matter markers for psychosis in a prospective ultrahigh-risk cohort. Psychol Med. 2010;40:1297–304. 81. Cho KI, Shenton ME, Kubicki M, Jung WH, Lee TY, Yun JY, Kim SN, Kwon JS. Altered thalamo-cortical white matter connectivity: probabilistic tractography study in clinical-high risk for psychosis and first-episode psychosis. Schizophr Bull. 2016;42:723–31. 82. Wang C, Ji F, Hong Z, Poh JS, Krishnan R, Lee J, Rekhi G, Keefe RS, Adcock RA, Wood SJ, Fornito A, Pasternak O, Chee MW, Zhou J. Disrupted salience network functional connectivity and white-matter microstructure in persons at risk for psychosis: findings from the LYRIKS study. Psychol Med. 2016;46:2771–83. 83. Mittal VA, Dean DJ, Bernard JA, ORR JM, Pelletier-Baldelli A, Carol EE, Gupta T, Turner J, Leopold DR, Robustelli BL, Millman ZB. Neurological soft signs predict abnormal cerebellar-thalamic tract development and negative symptoms in adolescents at high risk for psychosis: a longitudinal perspective. Schizophr Bull. 2014;40:1204–15. 84. Bernard JA, Orr JM, Mittal VA. Abnormal hippocampal-thalamic white matter tract development and positive symptom course in individuals at ultra-high risk for psychosis. NPJ Schizophr. 2015;1 85. Pierpaoli C, Basser PJ.  Toward a quantitative assessment of diffusion anisotropy. Magn Reson Med. 1996;36:893–906. 86. Aung WY, Mar S, Benzinger TL. Diffusion tensor MRI as a biomarker in axonal and myelin damage. Imaging Med. 2013;5:427–40. 87. Alexander AL, Hurley SA, Samsonov AA, Adluru N, Hosseinbor AP, Mossahebi P, Tromp do PM, Zakszewski E, Field AS. Characterization of cerebral white matter properties using quantitative magnetic resonance imaging stains. Brain Connect. 2011;1:423–46. 88. von Hohenberg CC, Pasternak O, Kubicki M, Ballinger T, Vu MA, Swisher T, Green K, Giwerc M, Dahlben B, Goldstein JM, Woo TU, Petryshen TL, Mesholam-Gately RI, Woodberry KA, Thermenos HW, Mulert C, Mccarley RW, Seidman LJ, Shenton ME. White matter microstructure in individuals at clinical high risk of psychosis: a whole-brain diffusion tensor imaging study. Schizophr Bull. 2014;40:895–903. 89. Schmidt A, Lenz C, Smieskova R, Harrisberger F, Walter A, Riecher-Rossler A, Simon A, Lang UE, Mcguire P, Fusar-Poli P, Borgwardt SJ. Brain diffusion changes in emerging psychosis and the impact of state-dependent psychopathology. Neurosignals. 2015;23:71–83. 90. Fox RJ, Beall E, Bhattacharyya P, Chen JT, Sakaie K. Advanced MRI in multiple sclerosis: current status and future challenges. Neurol Clin. 2011;29:357–80. 91. Carletti F, Woolley JB, Bhattacharyya S, Perez-Iglesias R, Fusar Poli P, Valmaggia L, Broome MR, Bramon E, Johns L, Giampietro V, Williams SC, Barker GJ, Mcguire PK. Alterations in white matter evident before the onset of psychosis. Schizophr Bull. 2012;38:1170–9. 92. Walterfang M, Mcguire PK, Yung AR, Phillips LJ, Velakoulis D, Wood SJ, Suckling J, Bullmore ET, Brewer W, Soulsby B, Desmond P, Mcgorry PD, Pantelis C.  White matter volume changes in people who develop psychosis. Br J Psychiatry. 2008;193:210–5.

6  Neuroimaging and the At-Risk Mental State

259

93. Peters BD, de Haan L, Dekker N, Blaas J, Becker HE, Dingemans PM, Akkerman EM, Majoie CB, van Amelsvoort T, den Heeten GJ, Linszen DH. White matter fibertracking in first-episode schizophrenia, schizoaffective patients and subjects at ultra-high risk of psychosis. Neuropsychobiology. 2008;58:19–28. 94. Peters BD, Dingemans PM, Dekker N, Blaas J, Akkerman E, van Amelsvoort TA, Majoie CB, den Heeten GJ, Linszen DH, de Haan L.  White matter connectivity and psychosis in ultra-high-risk subjects: a diffusion tensor fiber tracking study. Psychiatry Res. 2010;181:44–50. 95. Pukrop R, Ruhrmann S.  Neurocognitive indicators of high-risk states for psychosis. In: Fusar Poli PB, Borgwardt SJ, Mcguire P, editors. Vulnerability to psychosis. Great Britain: Psychology Press on behalf of The Maudsley; 2012. 96. Fusar-Poli P, Stone JM, Broome MR, Valli I, Mechelli A, Mclean MA, Lythgoe DJ, O’Gorman RL, Barker GJ, Mcguire PK. Thalamic glutamate levels as a predictor of cortical response during executive functioning in subjects at high risk for psychosis. Arch Gen Psychiatry. 2011;68:881–90. 97. Allen P, Luigjes J, Howes OD, Egerton A, Hirao K, Valli I, Kambeitz J, Fusar-Poli P, Broome M, Mcguire P. Transition to psychosis associated with prefrontal and subcortical dysfunction in ultra high-risk individuals. Schizophr Bull. 2012;38:1268–76. 98. Jung WH, Jang JH, Shin NY, Kim SN, Choi CH, An SK, Kwon JS. Regional brain atrophy and functional disconnection in Broca’s area in individuals at ultra-high risk for psychosis and schizophrenia. PLoS One. 2012;7:e51975. 99. Allen P, Stephan KE, Mechelli A, Day F, Ward N, Dalton J, Williams SC, Mcguire P.  Cingulate activity and fronto-temporal connectivity in people with prodromal signs of psychosis. NeuroImage. 2010;49:947–55. 100. Broome MR, Matthiasson P, Fusar-Poli P, Woolley JB, Johns LC, Tabraham P, Bramon E, Valmaggia L, Williams SC, Brammer MJ, Chitnis X, Mcguire PK.  Neural correlates of executive function and working memory in the ‘at-risk mental state’. Br J Psychiatry. 2009;194:25–33. 101. Fusar-Poli P, Broome MR, Woolley JB, Johns LC, Tabraham P, Bramon E, Valmaggia L, Williams SC, Mcguire P. Altered brain function directly related to structural abnormalities in people at ultra high risk of psychosis: longitudinal VBM-fMRI study. J Psychiatr Res. 2011;45:190–8. 102. Smieskova R, Allen P, Simon A, Aston J, Bendfeldt K, Drewe J, Gruber K, Gschwandtner U, Klarhoefer M, Lenz C, Scheffler K, Stieglitz RD, Radue EW, Mcguire P, Riecher-Rossler A, Borgwardt SJ. Different duration of at-risk mental state associated with neurofunctional abnormalities. A multimodal imaging study. Hum Brain Mapp. 2012;33:2281–94. 103. Falkenberg I, Chaddock C, Murray RM, Mcdonald C, Modinos G, Bramon E, Walshe M, Broome M, Mcguire P, Allen P. Failure to deactivate medial prefrontal cortex in people at high risk for psychosis. Eur Psychiatry. 2015;30:633–40. 104. Crossley NA, Mechelli A, Fusar-Poli P, Broome MR, Matthiasson P, Johns LC, Bramon E, Valmaggia L, Williams SC, Mcguire PK. Superior temporal lobe dysfunction and frontotemporal dysconnectivity in subjects at risk of psychosis and in first-episode psychosis. Hum Brain Mapp. 2009;30:4129–37. 105. Schmidt A, Smieskova R, Simon A, Allen P, Fusar-Poli P, Mcguire PK, Bendfeldt K, Aston J, Lang UE, Walter M, Radue EW, Riecher-Rossler A, Borgwardt SJ.  Abnormal effective connectivity and psychopathological symptoms in the psychosis high-risk state. J Psychiatry Neurosci. 2014;39:239–48. 106. Allen P, Seal ML, Valli I, Fusar-Poli P, Perlini C, Day F, Wood SJ, Williams SC, Mcguire PK. Altered prefrontal and hippocampal function during verbal encoding and recognition in people with prodromal symptoms of psychosis. Schizophr Bull. 2011;37:746–56. 107. Valli I, Stone J, Mechelli A, Bhattacharyya S, Raffin M, Allen P, Fusar-Poli P, Lythgoe D, O’Gorman R, Seal M, Mcguire P.  Altered medial temporal activation related to local glutamate levels in subjects with prodromal signs of psychosis. Biol Psychiatry. 2011;69:97–9.

260

Y.-S. Lin et al.

108. Benetti S, Mechelli A, Picchioni M, Broome M, Williams S, Mcguire P. Functional integration between the posterior hippocampus and prefrontal cortex is impaired in both first episode schizophrenia and the at risk mental state. Brain. 2009;132:2426–36. 109. Falkenberg I, Valli I, Raffin M, Broome MR, Fusar-Poli P, Matthiasson P, Picchioni M, Mcguire P. Pattern of activation during delayed matching to sample task predicts functional outcome in people at ultra high risk for psychosis. Schizophr Res. 2016;181:86–93. 110. Fusar-Poli P, Broome MR, Matthiasson P, Woolley JB, Johns LC, Tabraham P, Bramon E, Valmaggia L, Williams SC, Mcguire P. Spatial working memory in individuals at high risk for psychosis: longitudinal fMRI study. Schizophr Res. 2010;123:45–52. 111. Choi JS, Park JY, Jung MH, Jang JH, Kang DH, Jung WH, Han JY, Choi CH, Hong KS, Kwon JS. Phase-specific brain change of spatial working memory processing in genetic and ultra-high risk groups of schizophrenia. Schizophr Bull. 2012;38:1189–99. 112. Broome MR, Matthiasson P, Fusar-Poli P, Woolley JB, Johns LC, Tabraham P, Bramon E, Valmaggia L, Williams SC, Brammer MJ, Chitnis X, Mcguire PK. Neural correlates of movement generation in the ‘at-risk mental state’. Acta Psychiatr Scand. 2010;122:295–301. 113. Bernard JA, Dean DJ, Kent JS, Orr JM, Pelletier-Baldelli A, Lunsford-Avery JR, Gupta T, Mittal VA. Cerebellar networks in individuals at ultra high-risk of psychosis: impact on postural sway and symptom severity. Hum Brain Mapp. 2014;35:4064–78. 114. Seiferth NY, Pauly K, Habel U, Kellermann T, Shah NJ, Ruhrmann S, Klosterkotter J, Schneider F, Kircher T. Increased neural response related to neutral faces in individuals at risk for psychosis. NeuroImage. 2008;40:289–97. 115. Modinos G, Tseng HH, Falkenberg I, Samson C, Mcguire P, Allen P. Neural correlates of aberrant emotional salience predict psychotic symptoms and global functioning in high-risk and first-episode psychosis. Soc Cogn Affect Neurosci. 2015;10:1429–36. 116. van der Velde J, Opmeer EM, Liemburg EJ, Bruggeman R, Nieboer R, Wunderink L, Aleman A.  Lower prefrontal activation during emotion regulation in subjects at ultrahigh risk for psychosis: an fMRI-study. NPJ Schizophr. 2015;1:15026. 117. Pelletier-Baldelli A, Bernard JA, Mittal VA. Intrinsic functional connectivity in salience and default mode networks and aberrant social processes in youth at ultra-high risk for psychosis. PLoS One. 2015;10:e0134936. 118. Sabb FW, van Erp TG, Hardt ME, Dapretto M, Caplan R, Cannon TD, Bearden CE. Language network dysfunction as a predictor of outcome in youth at clinical high risk for psychosis. Schizophr Res. 2010;116:173–83. 119. Wotruba D, Heekeren K, Michels L, Buechler R, Simon JJ, Theodoridou A, Kollias S, Rossler W, Kaiser S.  Symptom dimensions are associated with reward processing in unmedicated persons at risk for psychosis. Front Behav Neurosci. 2014;8:382. 120. Rausch F, Mier D, Eifler S, Fenske S, Schirmbeck F, Englisch S, Schilling C, MeyerLindenberg A, Kirsch P, Zink M.  Reduced activation in the ventral striatum during probabilistic decision-making in patients in an at-risk mental state. J Psychiatry Neurosci. 2015;40:163–73. 121. Smieskova R, Roiser JP, Chaddock CA, Schmidt A, Harrisberger F, Bendfeldt K, Simon A, Walter A, Fusar-Poli P, Mcguire PK, Lang UE, Riecher-Rossler A, Borgwardt S. Modulation of motivational salience processing during the early stages of psychosis. Schizophr Res. 2015;166:17–23. 122. Shim G, Oh JS, Jung WH, Jang JH, Choi CH, Kim E, Park HY, Choi JS, Jung MH, Kwon JS.  Altered resting-state connectivity in subjects at ultra-high risk for psychosis: an fMRI study. Behav Brain Funct. 2010;6:58. 123. Dandash O, Fornito A, Lee J, Keefe RS, Chee MW, Adcock RA, Pantelis C, Wood SJ, Harrison BJ. Altered striatal functional connectivity in subjects with an at-risk mental state for psychosis. Schizophr Bull. 2014;40:904–13. 124. Wotruba D, Michels L, Buechler R, Metzler S, Theodoridou A, Gerstenberg M, Walitza S, Kollias S, Rossler W, Heekeren K.  Aberrant coupling within and across the default mode, task-positive, and salience network in subjects at risk for psychosis. Schizophr Bull. 2014;40:1095–104.

6  Neuroimaging and the At-Risk Mental State

261

125. Anticevic A, Haut K, Murray JD, Repovs G, Yang GJ, Diehl C, Mcewen SC, Bearden CE, Addington J, Goodyear B, Cadenhead KS, Mirzakhanian H, Cornblatt BA, Olvet D, Mathalon DH, Mcglashan TH, Perkins DO, Belger A, Seidman LJ, Tsuang MT, van Erp TG, Walker EF, Hamann S, Woods SW, Qiu M, Cannon TD. Association of thalamic dysconnectivity and conversion to psychosis in youth and young adults at elevated clinical risk. JAMA Psychiatry. 2015;72:882–91. 126. Yoon YB, Yun JY, Jung WH, Cho KI, Kim SN, Lee TY, Park HY, Kwon JS. Altered frontotemporal functional connectivity in individuals at ultra-high-risk of developing psychosis. PLoS One. 2015;10:e0135347. 127. Heinze K, Reniers RL, Nelson B, Yung AR, Lin A, Harrison BJ, Pantelis C, Velakoulis D, Mcgorry PD, Wood SJ. Discrete alterations of brain network structural covariance in individuals at ultra-high risk for psychosis. Biol Psychiatry. 2015;77:989–96. 128. Frommann I, Pukrop R, Brinkmeyer J, Bechdolf A, Ruhrmann S, Berning J, Decker P, Riedel M, Moller HJ, Wolwer W, Gaebel W, Klosterkotter J, Maier W, Wagner M. Neuropsychological profiles in different at-risk states of psychosis: executive control impairment in the earlyDOUBLEHYPHENand additional memory dysfunction in the lateDOUBLEHYPHENprodromal state. Schizophr Bull. 2011;37:861–73. 129. Hawkins KA, Addington J, Keefe RS, Christensen B, Perkins DO, Zipurksy R, Woods SW, Miller TJ, Marquez E, Breier A, Mcglashan TH.  Neuropsychological status of subjects at high risk for a first episode of psychosis. Schizophr Res. 2004;67:115–22. 130. Keefe RS, Perkins DO, Gu H, Zipursky RB, Christensen BK, Lieberman JA.  A longitudinal study of neurocognitive function in individuals at-risk for psychosis. Schizophr Res. 2006;88:26–35. 131. Pukrop R, Schultze-Lutter F, Ruhrmann S, Brockhaus-Dumke A, Tendolkar I, Bechdolf A, Matuschek E, Klosterkotter J.  Neurocognitive functioning in subjects at risk for a first episode of psychosis compared with first- and multiple-episode schizophrenia. J Clin Exp Neuropsychol. 2006;28:1388–407. 132. Simon AE, Cattapan-Ludewig K, Zmilacher S, Arbach D, Gruber K, Dvorsky DN, Roth B, Isler E, Zimmer A, Umbricht D. Cognitive functioning in the schizophrenia prodrome. Schizophr Bull. 2007;33:761–71. 133. Brewer WJ, Francey SM, Wood SJ, Jackson HJ, Pantelis C, Phillips LJ, Yung AR, Anderson VA, Mcgorry PD. Memory impairments identified in people at ultra-high risk for psychosis who later develop first-episode psychosis. Am J Psychiatry. 2005;162:71–8. 134. Chung YS, Kang DH, Shin NY, Yoo SY, Kwon JS. Deficit of theory of mind in individuals at ultra-high-risk for schizophrenia. Schizophr Res. 2008;99:111–8. 135. Ozgurdal S, Littmann E, Hauser M, von Reventlow H, Gudlowski Y, Witthaus H, Heinz A, Juckel G. Neurocognitive performances in participants of at-risk mental state for schizophrenia and in first-episode patients. J Clin Exp Neuropsychol. 2009;31:392–401. 136. Parnas J, Vianin P, Saebye D, Jansson L, Volmer-Larsen A, Bovet P. Visual binding abilities in the initial and advanced stages of schizophrenia. Acta Psychiatr Scand. 2001;103:171–80. 137. Wood SJ, Pantelis C, Proffitt T, Phillips LJ, Stuart GW, Buchanan JA, Mahony K, Brewer W, Smith DJ, Mcgorry PD. Spatial working memory ability is a marker of risk-for-psychosis. Psychol Med. 2003;33:1239–47. 138. Addington J, Penn D, Woods SW, Addington D, Perkins DO.  Facial affect recognition in individuals at clinical high risk for psychosis. Br J Psychiatry. 2008;192:67–8. 139. Pinkham AE, Penn DL, Perkins DO, Graham KA, Siegel M. Emotion perception and social skill over the course of psychosis: a comparison of individuals “at-risk” for psychosis and individuals with early and chronic schizophrenia spectrum illness. Cogn Neuropsychiatry. 2007;12:198–212. 140. Garety PA, Hemsley DR, Wessely S.  Reasoning in deluded schizophrenic and paranoid patients. Biases in performance on a probabilistic inference task. J Nerv Ment Dis. 1991;179:194–201. 141. Bentall RP, Kaney S, Dewey ME. Paranoia and social reasoning: an attribution theory analysis. Br J Clin Psychol. 1991;30(Pt 1):13–23.

262

Y.-S. Lin et al.

142. Warman DM. Reasoning and delusion proneness: confidence in decisions. J Nerv Ment Dis. 2008;196:9–15. 143. Woodward TS, Moritz S, Menon M, Klinge R. Belief inflexibility in schizophrenia. Cogn Neuropsychiatry. 2008;13:267–77. 144. Fletcher PC, Frith CD. Perceiving is believing: a Bayesian approach to explaining the positive symptoms of schizophrenia. Nat Rev Neurosci. 2009;10:48–58. 145. Blakemore SJ, Smith J, Steel R, Johnstone CE, Frith CD. The perception of self-produced sensory stimuli in patients with auditory hallucinations and passivity experiences: evidence for a breakdown in self-monitoring. Psychol Med. 2000;30:1131–9. 146. Roiser JP, Stephan KE, den Ouden HE, Barnes TR, Friston KJ, Joyce EM. Do patients with schizophrenia exhibit aberrant salience? Psychol Med. 2009;39:199–209. 147. Kapur S. Psychosis as a state of aberrant salience: a framework linking biology, phenomenology, and pharmacology in schizophrenia. Am J Psychiatry. 2003;160:13–23. 148. Allen P, Amaro E, Fu CH, Williams SC, Brammer MJ, Johns LC, Mcguire PK. Neural correlates of the misattribution of speech in schizophrenia. Br J Psychiatry. 2007;190:162–9. 149. Milstein DM, Dorris MC.  The influence of expected value on saccadic preparation. J Neurosci. 2007;27:4810–8. 150. Wyvell CL, Berridge KC. Intra-accumbens amphetamine increases the conditioned incentive salience of sucrose reward: enhancement of reward “wanting” without enhanced “liking” or response reinforcement. J Neurosci. 2000;20:8122–30. 151. Moritz S, Woodward TS. Jumping to conclusions in delusional and non-delusional schizophrenic patients. Br J Clin Psychol. 2005;44:193–207. 152. Ross RM, Mckay R, Coltheart M, Langdon R. Jumping to conclusions about the beads task? A meta-analysis of delusional ideation and data-gathering. Schizophr Bull. 2015;41:1183–91. 153. Speechley WJ, Whitman JC, Woodward TS. The contribution of hypersalience to the “jumping to conclusions” bias associated with delusions in schizophrenia. J Psychiatry Neurosci. 2010;35:7–17. 154. Fox MD, Snyder AZ, Vincent JL, Corbetta M, van Essen DC, Raichle ME. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A. 2005;102:9673–8. 155. Spreng RN. The fallacy of a “task-negative” network. Front Psychol. 2012;3:145. 156. Goulden N, Khusnulina A, Davis NJ, Bracewell RM, Bokde AL, Mcnulty JP, Mullins PG. The salience network is responsible for switching between the default mode network and the central executive network: replication from DCM. NeuroImage. 2014;99:180–90. 157. Sridharan D, Levitin DJ, Menon V.  A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc Natl Acad Sci U S A. 2008;105:12569–74. 158. Buckner RL, Andrews-Hanna JR, Schacter DL. The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci. 2008;1124:1–38. 159. Guo W, Yao D, Jiang J, Su Q, Zhang Z, Zhang J, Yu L, Xiao C.  Abnormal default-mode network homogeneity in first-episode, drug-naive schizophrenia at rest. Prog NeuroPsychopharmacol Biol Psychiatry. 2014;49:16–20. 160. Pankow A, Deserno L, Walter M, Fydrich T, Bermpohl F, Schlagenhauf F, Heinz A. Reduced default mode network connectivity in schizophrenia patients. Schizophr Res. 2015;165:90–3. 161. Wang L, Zou F, Shao Y, Ye E, Jin X, Tan S, Hu D, Yang Z. Disruptive changes of cerebellar functional connectivity with the default mode network in schizophrenia. Schizophr Res. 2014;160:67–72. 162. Orliac F, Naveau M, Joliot M, Delcroix N, Razafimandimby A, Brazo P, Dollfus S, Delamillieure P.  Links among resting-state default-mode network, salience network, and symptomatology in schizophrenia. Schizophr Res. 2013;148:74–80. 163. Manoliu A, Riedl V, Zherdin A, Muhlau M, Schwerthoffer D, Scherr M, Peters H, Zimmer C, Forstl H, Bauml J, Wohlschlager AM, Sorg C. Aberrant dependence of default mode/central executive network interactions on anterior insular salience network activity in schizophrenia. Schizophr Bull. 2014;40:428–37.

6  Neuroimaging and the At-Risk Mental State

263

164. Wang X, Li F, Zheng H, Wang W, Zhang W, Liu Z, Sun Y, Chan RC, Chen A. Breakdown of the striatal-default mode network loop in schizophrenia. Schizophr Res. 2015;168:366–72. 165. Landin-Romero R, Mckenna PJ, Salgado-Pineda P, Sarro S, Aguirre C, Sarri C, Compte A, Bosque C, Blanch J, Salvador R, Pomarol-Clotet E.  Failure of deactivation in the default mode network: a trait marker for schizophrenia? Psychol Med. 2015;45:1315–25. 166. Whitfield-Gabrieli S, Ford JM. Default mode network activity and connectivity in psychopathology. Annu Rev Clin Psychol. 2012;8:49–76. 167. Carlsson A, Lindqvist M. Effect of chlorpromazine or haloperidol on formation of 3methoxytyramine and normetanephrine in mouse brain. Acta Pharmacol Toxicol. (Copenh.). 1963;20:140–4. 168. Howes O. Does dopamine start the psychotic “fire”? In: Fusar Poli P, Borgwardt S, Mcguire P, editors. Vulnerability to psychosis. London: Psychology Press; 2012. 169. Davis KL, Kahn RS, Ko G, Davidson M. Dopamine in schizophrenia: a review and reconceptualization. Am J Psychiatry. 1991;148:1474–86. 170. Kumakura Y, Cumming P, Vernaleken I, Buchholz HG, Siessmeier T, Heinz A, Kienast T, Bartenstein P, Grunder G.  Elevated [18F]fluorodopamine turnover in brain of patients with schizophrenia: an [18F]fluorodopa/positron emission tomography study. J Neurosci. 2007;27:8080–7. 171. Mcgowan S, Lawrence AD, Sales T, Quested D, Grasby P. Presynaptic dopaminergic dysfunction in schizophrenia: a positron emission tomographic [18F]fluorodopa study. Arch Gen Psychiatry. 2004;61:134–42. 172. Reith J, Benkelfat C, Sherwin A, Yasuhara Y, Kuwabara H, Andermann F, Bachneff S, Cumming P, Diksic M, Dyve SE, Etienne P, Evans AC, Lal S, Shevell M, Savard G, Wong DF, Chouinard G, Gjedde A. Elevated dopa decarboxylase activity in living brain of patients with psychosis. Proc Natl Acad Sci U S A. 1994;91:11651–4. 173. Allen P, Chaddock CA, Howes OD, Egerton A, Seal ML, Fusar-Poli P, Valli I, Day F, Mcguire PK. Abnormal relationship between medial temporal lobe and subcortical dopamine function in people with an ultra high risk for psychosis. Schizophr Bull. 2012;38:1040–9. 174. Egerton A, Chaddock CA, Winton-Brown TT, Bloomfield MA, Bhattacharyya S, Allen P, Mcguire PK, Howes OD. Presynaptic striatal dopamine dysfunction in people at ultra-high risk for psychosis: findings in a second cohort. Biol Psychiatry. 2013;74:106–12. 175. Roiser JP, Howes OD, Chaddock CA, Joyce EM, Mcguire P. Neural and behavioral correlates of aberrant salience in individuals at risk for psychosis. Schizophr Bull. 2013;39:1328–36. 176. Stone JM, Howes OD, Egerton A, Kambeitz J, Allen P, Lythgoe DJ, O’Gorman RL, Mclean MA, Barker GJ, Mcguire P.  Altered relationship between hippocampal glutamate levels and striatal dopamine function in subjects at ultra high risk of psychosis. Biol Psychiatry. 2010;68:599–602. 177. Howes OD, Montgomery AJ, Asselin MC, Murray RM, Valli I, Tabraham P, BramonBosch E, Valmaggia L, Johns L, Broome M, Mcguire PK, Grasby PM. Elevated striatal dopamine function linked to prodromal signs of schizophrenia. Arch Gen Psychiatry. 2009;66:13–20. 178. Fusar-Poli P, Howes OD, Allen P, Broome M, Valli I, Asselin MC, Montgomery AJ, Grasby PM, Mcguire P.  Abnormal prefrontal activation directly related to pre-synaptic striatal dopamine dysfunction in people at clinical high risk for psychosis. Mol Psychiatry. 2011;16:67–75. 179. Suridjan I, Rusjan P, Addington J, Wilson AA, Houle S, Mizrahi R. Dopamine D2 and D3 binding in people at clinical high risk for schizophrenia, antipsychotic-naive patients and healthy controls while performing a cognitive task. J Psychiatry Neurosci. 2013;38:98–106. 180. Howes OD, Bose SK, Turkheimer F, Valli I, Egerton A, Valmaggia LR, Murray RM, Mcguire P. Dopamine synthesis capacity before onset of psychosis: a prospective [18F]-DOPA PET imaging study. Am J Psychiatry. 2011;168:1311–7. 181. Bloemen OJ, de Koning MB, Gleich T, Meijer J, de Haan L, Linszen DH, Booij J, van Amelsvoort TA. Striatal dopamine D2/3 receptor binding following dopamine depletion in subjects at Ultra High Risk for psychosis. Eur Neuropsychopharmacol. 2013;23:126–32.

264

Y.-S. Lin et al.

182. de Koning MB, Bloemen OJ, van Duin ED, Booij J, Abel KM, de Haan L, Linszen DH, van Amelsvoort TA. Pre-pulse inhibition and striatal dopamine in subjects at an ultra-high risk for psychosis. J Psychopharmacol. 2014;28:553–60. 183. Javitt DC, Zukin SR. Recent advances in the phencyclidine model of schizophrenia. Am J Psychiatry. 1991;148:1301–8. 184. Kegeles LS, Abi-Dargham A, Zea-Ponce Y, Rodenhiser-Hill J, Mann JJ, van Heertum RL, Cooper TB, Carlsson A, Laruelle M.  Modulation of amphetamine-induced striatal dopamine release by ketamine in humans: implications for schizophrenia. Biol Psychiatry. 2000;48:627–40. 185. Rowland LM, Bustillo JR, Mullins PG, Jung RE, Lenroot R, Landgraf E, Barrow R, Yeo R, Lauriello J, Brooks WM. Effects of ketamine on anterior cingulate glutamate metabolism in healthy humans: a 4-T proton MRS study. Am J Psychiatry. 2005;162:394–6. 186. Mohn AR, Gainetdinov RR, Caron MG, Koller BH.  Mice with reduced NMDA receptor expression display behaviors related to schizophrenia. Cell. 1999;98:427–36. 187. Lorrain DS, Baccei CS, Bristow LJ, Anderson JJ, Varney MA.  Effects of ketamine and N-methyl-D-aspartate on glutamate and dopamine release in the rat prefrontal cortex: modulation by a group II selective metabotropic glutamate receptor agonist LY379268. Neuroscience. 2003;117:697–706. 188. Dandash O, Harrison BJ, Adapa R, Gaillard R, Giorlando F, Wood SJ, Fletcher PC, Fornito A.  Selective augmentation of striatal functional connectivity following NMDA receptor antagonism: implications for psychosis. Neuropsychopharmacology. 2015;40:622–31. 189. Bergeron R, Coyle JT.  NAAG, NMDA receptor and psychosis. Curr Med Chem. 2012;19(9):1360–4. 190. Stone J. Glutamate: gateway to psychosis? London: Psychology Press; 2012. 191. Allen P, Chaddock CA, Egerton A, Howes OD, Barker G, Bonoldi I, Fusar-Poli P, Murray R, Mcguire P. Functional outcome in people at high risk for psychosis predicted by thalamic glutamate levels and prefronto-striatal activation. Schizophr Bull. 2015;41:429–39. 192. Egerton A, Stone JM, Chaddock CA, Barker GJ, Bonoldi I, Howard RM, Merritt K, Allen P, Howes OD, Murray RM, Mclean MA, Lythgoe DJ, O’Gorman RL, Mcguire PK. Relationship between brain glutamate levels and clinical outcome in individuals at ultra high risk of psychosis. Neuropsychopharmacology. 2014;39:2891–9. 193. Stone JM, Day F, Tsagaraki H, Valli I, Mclean MA, Lythgoe DJ, O’Gorman RL, Barker GJ, Mcguire PK. Glutamate dysfunction in people with prodromal symptoms of psychosis: relationship to gray matter volume. Biol Psychiatry. 2009;66:533–9. 194. Wood SJ, Berger G, Velakoulis D, Phillips LJ, Mcgorry PD, Yung AR, Desmond P, Pantelis C.  Proton magnetic resonance spectroscopy in first episode psychosis and ultra high-risk individuals. Schizophr Bull. 2003;29:831–43. 195. de la Fuente-Sandoval C, Leon-Ortiz P, Azcarraga M, Favila R, Stephano S, Graff-Guerrero A. Striatal glutamate and the conversion to psychosis: a prospective 1H-MRS imaging study. Int J Neuropsychopharmacol. 2013;16:471–5. 196. Nenadic I, Maitra R, Basu S, Dietzek M, Schonfeld N, Lorenz C, Gussew A, Amminger GP, Mcgorry P, Reichenbach JR, Sauer H, Gaser C, Smesny S.  Associations of hippocampal metabolism and regional brain grey matter in neuroleptic-naive ultra-high-risk subjects and first-episode schizophrenia. Eur Neuropsychopharmacol. 2015;25:1661–8. 197. de la Fuente-Sandoval C, Reyes-Madrigal F, Mao X, Leon-Ortiz P, Rodriguez-Mayoral O, Solis-Vivanco R, Favila R, Graff-Guerrero A, Shungu DC. Cortico-striatal GABAergic and glutamatergic dysregulations in subjects at ultra-high risk for psychosis investigated with proton magnetic resonance spectroscopy. Int J Neuropsychopharmacol. 2015;19:pyv105. 198. de la Fuente-Sandoval C, Leon-Ortiz P, Favila R, Stephano S, Mamo D, Ramirez-Bermudez J, Graff-Guerrero A.  Higher levels of glutamate in the associative-striatum of subjects with prodromal symptoms of schizophrenia and patients with first-episode psychosis. Neuropsychopharmacology. 2011;36:1781–91. 199. Liemburg E, Sibeijn-Kuiper A, Bais L, Pijnenborg G, Knegtering H, van der Velde J, Opmeer E, de Vos A, Dlabac-De Lange J, Wunderink L, Aleman A. Prefrontal NAA and Glx levels in different stages of psychotic disorders: a 3T 1H-MRS study. Sci Rep. 2016;6:21873.

6  Neuroimaging and the At-Risk Mental State

265

200. Natsubori T, Inoue H, Abe O, Takano Y, Iwashiro N, Aoki Y, Koike S, Yahata N, Katsura M, Gonoi W, Sasaki H, Takao H, Kasai K, Yamasue H. Reduced frontal glutamate + glutamine and N-acetylaspartate levels in patients with chronic schizophrenia but not in those at clinical high risk for psychosis or with first-episode schizophrenia. Schizophr Bull. 2014;40:1128–39. 201. Haijma SV, van Haren N, Cahn W, Koolschijn PC, Hulshoff Pol HE, Kahn RS. Brain volumes in schizophrenia: a meta-analysis in over 18 000 subjects. Schizophr Bull. 2013;39:1129–38. 202. Kempton MJ, Mcguire P. How can neuroimaging facilitate the diagnosis and stratification of patients with psychosis? Eur Neuropsychopharmacol. 2015;25:725–32. 203. PSYSCAN.  Translating neuroimaging findings from research into clinical practice. http:// www.psyscan.eu. 204. van Os J, Rutten BP, Myin-Germeys I, Delespaul P, Viechtbauer W, van Zelst C, Bruggeman R, Reininghaus U, Morgan C, Murray RM, Di Forti M, Mcguire P, Valmaggia LR, Kempton MJ, Gayer-Anderson C, Hubbard K, Beards S, Stilo SA, Onyejiaka A, Bourque F, Modinos G, Tognin S, Calem M, O’Donovan MC, Owen MJ, Holmans P, Williams N, Craddock N, Richards A, Humphreys I, Meyer-Lindenberg A, Leweke FM, Tost H, Akdeniz C, Rohleder C, Bumb JM, Schwarz E, Alptekin K, Ucok A, Saka MC, Atbasoglu EC, Guloksuz S, GumusAkay G, Cihan B, Karadag H, Soygur H, Cankurtaran ES, Ulusoy S, Akdede B, Binbay T, Ayer A, Noyan H, Karadayi G, Akturan E, Ulas H, Arango C, Parellada M, Bernardo M, Sanjuan J, Bobes J, Arrojo M, Santos JL, Cuadrado P, Rodriguez Solano JJ, Carracedo A, Garcia Bernardo E, Roldan L, Lopez G, Cabrera B, Cruz S, Diaz Mesa EM, Pouso M, Jimenez E, Sanchez T, Rapado M, Gonzalez E, Martinez C, Sanchez E, Olmeda MS, de Haan L, Velthorst E, van der Gaag M, Selten JP, van Dam D, van der Ven E, van der Meer F, Messchaert E, Kraan T, Burger N, Leboyer M, Szoke A, Schurhoff F, Llorca PM, Jamain S, Tortelli A, Frijda F, Vilain J, Galliot AM, Baudin G, Ferchiou A, et al. Identifying geneenvironment interactions in schizophrenia: contemporary challenges for integrated, largescale investigations. Schizophr Bull. 2014;40:729–36. 205. van Os J, Rutten BP, Poulton R. Gene-environment interactions in schizophrenia: review of epidemiological findings and future directions. Schizophr Bull. 2008;34:1066–82. 206. Addington J, Cadenhead KS, Cannon TD, Cornblatt B, Mcglashan TH, Perkins DO, Seidman LJ, Tsuang M, Walker EF, Woods SW, Heinssen R. North American Prodrome Longitudinal Study: a collaborative multisite approach to prodromal schizophrenia research. Schizophr Bull. 2007;33:665–72. 207. Addington J, Liu L, Buchy L, Cadenhead KS, Cannon TD, Cornblatt BA, Perkins DO, Seidman LJ, Tsuang MT, Walker EF, Woods SW, Bearden CE, Mathalon DH, Mcglashan TH. North American Prodrome Longitudinal Study (NAPLS 2): the prodromal symptoms. J Nerv Ment Dis. 2015;203:328–35. 208. Broome M, Dale J, Marriott C, Bortolotti L. Neuroscience, continua and the prodromal phase of psychosis. In: Fusar Poli PB, Borgwardt SJ, Mcguire P, editors. Vulnerability to psychosis. London: Psychology Press; 2012. 209. Goldman A.  What is justified belief? In: Pappas G, editor. Justification and knowledge. Dordrecht: D. Reidel; 1979. 210. Morgan C, Dazzan P, Morgan K, Jones P, Harrison G, Leff J, Murray R, Fearon P.  First episode psychosis and ethnicity: initial findings from the AESOP study. World Psychiatry. 2006;5:40–6. 211. Revier CJ, Reininghaus U, Dutta R, Fearon P, Murray RM, Doody GA, Croudace T, Dazzan P, Heslin M, Onyejiaka A, Kravariti E, Lappin J, Lomas B, Kirkbride JB, Donoghue K, Morgan C, Jones PB. Ten-year outcomes of first-episode psychoses in the MRC AESOP-10 study. J Nerv Ment Dis. 2015;203:379–86. 212. Fusar-Poli P, Cappucciati M, Bonoldi I, Hui LM, Rutigliano G, Stahl DR, Borgwardt S, Politi P, Mishara AL, Lawrie SM, Carpenter WT Jr, Mcguire PK. Prognosis of brief psychotic episodes: a meta-analysis. JAMA Psychiatry. 2016;73:211–20.

7

Neuroimaging and Antipsychotics Antonio Vita, Florian Schlagenhauf, Stefano Barlati, and Andreas Heinz

7.1

Introduction

Schizophrenia is a common chronic and disabling brain disorder, but the nature of the disease process remains obscure. Since the first MRI study of schizophrenia, the use of this technique allowed for the quantification of gray (GM) and white matter (WM) and for measurement of discrete, cortical, and subcortical brain structures [1]. Early morphological studies [2, 3] of schizophrenia primarily assessed specific brain regions of interest (ROIs). More recently, functional neuroimaging provides a direct way of investigating regional brain activity and the pathophysiology of schizophrenia in vivo. The presence of multiple small structural brain abnormalities in schizophrenia is now well established [4]. Findings about the progressive brain changes over time in schizophrenia are controversial, and the potential confounding effect of antipsychotics on brain structure is still under debate. This chapter reviews the existing longitudinal neuroimaging studies addressing the impact of antipsychotic drug treatment on brain structure, as assessed with MRI, and function, as A. Vita (*) Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy Department of Mental Health and Addiction Services, ASST Spedali Civili of Brescia, Brescia, Italy e-mail: [email protected] F. Schlagenhauf Department of Psychiatry and Psychotherapy, Charite University Medicine, Berlin, Germany S. Barlati Department of Mental Health and Addiction Services, ASST Spedali Civili of Brescia, Brescia, Italy A. Heinz Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Charite University Medicine, Berlin, Germany © Springer Nature Switzerland AG 2019 S. Galderisi et al. (eds.), Neuroimaging of Schizophrenia and Other Primary Psychotic Disorders, https://doi.org/10.1007/978-3-319-97307-4_7

267

268

A. Vita et al.

assessed by functional neuroimaging techniques (i.e., positron emission tomography, PET; single-photon emission computed tomography, SPECT; functional magnetic resonance imaging, fMRI; magnetic resonance spectroscopy, MRS), in first-episode and in chronic schizophrenic patients, both with and without antipsychotic therapy. The goal of this chapter is to highlight the structural and functional abnormalities found in schizophrenia and to discuss whether antipsychotic treatment plays a role in structural and functional brain changes in these patients.

7.2

 rain Structural and Functional Abnormalities B in Schizophrenia

The presence of multiple structural brain abnormalities in schizophrenia has been demonstrated by a large number of computed tomography (CT) and MRI studies in the past 40 years and confirmed by a series of meta-analytic reviews [4–9]. These are particularly evident in some cerebral regions as the ventricular system, cortical GM, and subcortical region [5, 10]. Reductions in whole-brain (3%) and GM volume (2%), primarily in the frontal and temporal lobes, and enlargement of the lateral ventricles (16%) are among the most replicated findings. A small but significant reduction was found also in WM (1%) [5, 9, 11]. A more detailed examination of regional brain structural changes has been achieved by voxel-based morphometry (VBM) studies that confirmed earlier observed patterns of distributed GM reductions in bilateral medial frontal and temporal regions, inferior parietal lobe, limbic and striatal regions, insula, thalamus, and basal ganglia [12–21]. In their VBM meta-analysis, Bora et al. [17] indicated a reduction in GM density in dorsal and rostral anterior cingulate cortex, left lateral prefrontal areas, superior frontal gyrus, and orbitofrontal and fusiform regions. Additionally, studies of WM tracts showed evidence of disorganization and lack of alignment in white fiber bundles in frontal and temporoparietal brain regions and a reduction in WM diffusion anisotropy in schizophrenia subjects [22–26]. More recently, diffusion tensor imaging (DTI) studies in schizophrenia have identified numerous regions with decreased fractional anisotropy (FA), reflecting altered WM connections and supporting the “disconnection model of schizophrenia” [27–33]. Regarding functional neuroimaging, this technique was used to study patterns of increased or decreased activity within the brains of subjects with and without schizophrenia during rest and various assigned behavioral tasks, revealing that the affected parts of the central nervous system (CNS) are not contained within a single brain region, but rather lie within neural networks including several brain regions [34]. Functional brain abnormalities in schizophrenia include alterations in information storage and retrieval by the dorsolateral prefrontal cortex (dlPFC); inhibitory responses to sensory stimuli by the anterior cingulate cortex (ACC); encoding and retrieval of memory by the hippocampus; reception and integration of sensory information by thalamic nuclei, primary sensory cortices, and multimodal cortices; and impaired performance of cognitive tasks associated with the basal ganglia, thalamus, and cerebellum [5, 26, 35–38]. fMRI studies revealed patterns of widespread alterations in task-induced activity, which overlap with patterns of GM findings: one of the

7  Neuroimaging and Antipsychotics

269

most consistent findings is diminished activation of frontal regions during cognitive tasks (i.e., “hypofrontality”) [36, 39–42]. However, this finding surprisingly was not consistently replicated when SPECT semiquantitative assessments were replaced by fMRI [43]. Furthermore, functional studies of social cognition and emotional processing suggest altered responses of the amygdala and hippocampus, potentially with respect to aversive stimuli [44, 45]. The pathogenesis of structural and functional alterations in schizophrenia is still poorly understood and only an ongoing integration of structural data with functional imaging may provide insight into these issues [34].

7.3

 rain Abnormalities in Schizophrenia: Are They Static B or Progressive Over Time?

The nature and meaning of such brain abnormalities, their time of occurrence, and whether they are static or progressive have been investigated by cross-sectional comparisons of patients with first-episode and chronic schizophrenia and by a number of within-subject longitudinal MRI studies [46–48]. Currently, there is some debate between conflicting models of brain pathology related to the disorder. With respect to the neurodevelopmental model of schizophrenia, structural brain abnormalities are thought to stem from (early) developmental processes that are linked to genetic, prenatal, and environmental factors [49]. The post-onset progression (neurodegenerative) model implies that structural brain abnormalities progress over time after illness onset [50, 51]. The question whether brain structure is already altered before illness onset might be valuable for prognosis and treatment and lead to a better understanding of disorder-related pathophysiological mechanisms [52]. There is an ongoing debate whether psychotic disorders are progressive or not, and it is not clear when structural alterations occur and how they develop over time [7, 8, 53] (for more details, see Chap. 5. In this context, longitudinal studies in individuals at ultrahigh risk (UHR) of developing psychosis and patients with first-episode psychosis (FEP) are of importance (for more details, see Chap. 6). Studying patients at these stages provides an opportunity to examine the brain structure without the potential confounding effects of antipsychotic medication, or secondary effects such as social deprivation, which might affect longitudinal brain changes in and of themselves [54, 55]. To dissect the longitudinal course of the illness into different stages, probands with UHR, FEP, and chronic schizophrenia might disclose different dynamical and pathophysiological processes including early effects of medication that occur in the brain at different times. It has been argued that brain changes appear to be especially prominent in the first years of illness compared to changes in later stages of the illness [49, 56–58], although other studies did not confirm these findings [59].

7.3.1 Gray Matter Abnormalities in FEP and in UHR Subjects A number of studies concluded that there is evidence for accelerated loss of GM in schizophrenia compared with healthy individuals [46, 47] and such progressive

270

A. Vita et al.

brain changes may be especially prominent in the first years of illness [47, 57, 58, 60–64]. Several MRI studies examined different cortical and subcortical regions of the brain in patients with first-episode schizophrenia using either ROI method [62, 63, 65–67] or VBM approach [68–70]. The presence of specific brain volume reductions in patients with first-episode schizophrenia has also been confirmed by a number of meta-analyses that showed the occurrence of multiple subtle brain alterations at the onset of schizophrenia [28, 71, 72]. Furthermore, several investigations of GM changes found greater progressive decreases over time in FEP compared with healthy controls [50, 73–77]. Recently, in a 5-year follow-up MRI study, van Haren et  al. [58] showed differences in age-related trajectories of subcortical volume change between a sample of recent-onset and chronically ill schizophrenia patients and healthy individuals. These brain alterations are already detectable before illness onset in prodromal/high-risk individuals [46, 78–80] and progress over time [81], suggesting that there is a pathophysiological trajectory of brain alterations since the very beginning or even before the onset of the disease, which may accelerate during early psychosis [71, 76, 78, 79, 82].

7.3.2 White Matter Abnormalities in FEP and in UHR Subjects Regarding WM changes, some evidences suggest differential changes over time in early schizophrenia patients and controls [61, 83]. As reported in the review by Peters et  al. [84], DTI studies have produced some evidence of widespread WM abnormalities in patients with first-episode schizophrenia, but findings are not unequivocal and some studies showed no differences between patients and healthy controls [85–89], whereas others found no FA abnormalities but identified abnormalities with other diffusion indices [86, 90, 91]. Complementary to GM changes, UHR individuals also have WM alterations similar to, albeit less extensive than, first-episode patients. These findings, integrated with evidence from meta-analyses, suggest that brain alterations become more extensive at the time of a first full psychotic episode, and then possibly even more marked over the years, when the illness becomes established [21, 28, 54].

7.3.3 G  ray and White Matter Abnormalities in Chronic Schizophrenia at Different Stages of Illness Studying patients with chronic schizophrenia allows to investigate which ongoing processes may underlie brain changes. In this regard, numerous studies of GM changes found a greater progressive decreases over time in chronic schizophrenia patients [48, 64, 83, 92, 93], while others report a lack of longitudinal changes [94, 95]. Furthermore, a meta-analysis of longitudinal controlled MRI studies supported the notion that schizophrenia is associated with progressive loss of cerebral tissue and that cortical GM appears particularly sensitive to change [6]. Recently, a comprehensive review of 35 selected human studies reported an ongoing frontal, but not

7  Neuroimaging and Antipsychotics

271

temporal, lobe volume reduction and a progressive GM reduction in patients with schizophrenia, greater in FEP [96], although cognitive deficits remained stable over time. In a more recent selective review, Dietsche et al. [97] summarized structural brain MRI longitudinal alterations at different stages of illness, with the aim to disclose different trajectories that occur in the brain of subjects with schizophrenia. The authors underlined that there is adequate evidence to suggest that schizophrenia (and its treatment) is associated with progressive GM abnormalities, particularly during the initial stages of illness. Overall, despite these relevant findings, a causal relationship between brain changes and illness course should be interpreted with caution, because results might be confounded by longer periods of treatment and higher doses of antipsychotics that can affect changes in brain structure independent of the illness [61, 97]. Effects of antipsychotic medication and factors secondary to the illness—such as substance abuse or lifestyle—on brain structure, remain difficult to control [98]. Finally, due to very few studies that investigated WM alterations in the longitudinal course of the illness, it is not possible to draw firm conclusions on changes in WM microstructure over time [97], although some investigations reported progressive WM change over time in schizophrenia [96]. Furthermore, only a few studies have investigated longitudinal functional changes in schizophrenia, and, taken together, the available functional imaging studies suggest changes following initial treatment but stability in chronic schizophrenia [96]. Although structural and functional imaging have produced a rich data set uncovering a number of consistent brain alterations, several questions merit further study: (i) the neurodevelopmental timeline of observed alterations is not fully understood; and (ii) it is unclear whether structural changes result from intrinsic disease pathology or reflect an adaptation to the disease state or to environmental factors including antipsychotic treatment. Continued research with healthy relatives, UHR, and first-­ episode populations may differentiate the effects of confounding factors (e.g., antipsychotic exposure) from intrinsic disease effects on brain structure.

7.4

Mechanism of Action of Antipsychotic Drugs

Antipsychotics are grouped into “typical” or first-generation antipsychotics (FGAs) and “atypical” or second-generation antipsychotics (SGAs) [99]. Actually FGAs are characterized by undesirable side effects such as extrapyramidal symptoms (EPS), hyperprolactinemia, tardive dyskinesia, and possible neuroleptic malignant syndrome. SGAs can be differentiated from FGAs by their low levels of these side effects, by effectiveness, and in general by supposed increased safety. The latter has been recently questioned for the incidence of symptoms linked to metabolic syndrome [100]. FGAs were commonly classified on their chemical structure (i.e., phenothiazines, thioxanthenes, butyrophenones, and diphenylpiperidines) and on a spectrum of low potency to high potency, where potency referred to the ability of the drug to bind to dopamine receptors. High-potency antipsychotics such as haloperidol, in general, cause less sleepiness and calming effects than low-potency

272

A. Vita et al.

antipsychotics such as chlorpromazine, which have a greater degree of anticholinergic and antihistaminergic activity [99–101]. SGA classification is linked essentially to their pharmacodynamic properties, which reflect their affinities for specific receptors. SGAs with a high selectivity for serotonin 5-HT2A receptors and dopamine D2 receptors (and also α1-adrenoceptors) are called serotonin–dopamine antagonists (SDA) (such as risperidone, paliperidone, ziprasidone). Drugs showing an affinity for 5-HT2A, D2, and receptors of other systems (cholinergic, histaminergic, 5-HT1A, 5-HT1C, and others) are designated as multi-acting receptor-targeted antipsychotics (MARTA) (such as clozapine, olanzapine, quetiapine). Antipsychotics that preferentially block D2 and D3 subtypes of the D2-like receptors are classified as combined D2/D3 receptor antagonists (such as amisulpride). A final class of atypical antipsychotics is the partial dopamine receptor agonists (such as aripiprazole) [102]. The validity of such a classification (FGAs vs SGAs), however, is debated since there are many similarities between drugs within these two groups and since SGAs represent a rather inhomogeneous group [99, 101]. Molecular imaging studies with both SPECT and PET have extended the in vitro studies on dopamine receptors and antipsychotics from the 1970s in several crucial ways [103]. First, they have demonstrated that all antipsychotic drugs cross the blood–brain barrier. Second, they have shown that they block D2/3 striatal receptors in  vivo at clinically effective doses. These data extended the in  vitro findings to patients and provided the foundation for studies in which the relationship between D2 occupancy and clinical response could be established [104, 105]. A dose– response relationship between human D2 receptor affinity and clinical profile is very likely, albeit it is challenged by the findings that the highly effective antipsychotic clozapine and also quetiapine only loosely bind to this receptor [106]. As measured by PET or SPECT, the level of D2 receptor blockade is directly related to the antipsychotic effect and clinical efficacy of FGAs is associated with a blockade of 60–80% of D2 receptors in the brain [107]. Numerous clinical and preclinical experiments link the effects of antipsychotics to the dopaminergic system, in which the acute administration of FGAs leads to an increased firing rate and neurotransmitter turnover in dopaminergic neurons, while these effects are reversed after chronic administration [108]. In this respect, FGAs, such as haloperidol, are different from SGAs, such as clozapine, insofar as haloperidol demonstrates these characteristics both in neurons originating in the substantia nigra (A9 dopaminergic neurons) and in those which project from the ventral tegmentum (A10 neurons), while clozapine only blocks A10 neurons. This has been replicated many times using different electrophysiological, neurochemical, and imaging techniques and is considered the reason why clozapine and other SGAs exert antipsychotic effects without affecting the motor system [103, 109, 110]. For SGAs the situation is more complicated than FGAs, and the historical theories for atypical antipsychotic action, such as the “dopamine-serotonin antagonism theory” and the “fast-off-D2 theory,” are not sufficient to explain the atypical features of these compounds [102, 109, 111–116].

7  Neuroimaging and Antipsychotics

273

All currently licensed antipsychotics with the exception of aripiprazole, a partial D2 agonist, block postsynaptic dopamine D2 receptors; however they also work on different receptor systems in the brain, such as other dopamine (e.g., D1, D3) and serotonin receptor subtypes (e.g., 5-HT2A, 5-HT1A), histamine, norepinephrine, and acetylcholine, and on the glutamatergic system, including both ionotropic and metabotropic receptors [101, 104, 105, 109, 117]. Clozapine, for example, has high affinity to a number of other neurotransmitter receptors, including serotonin, histamine, and noradrenaline. These neurotransmitter systems have been explored regarding their potential contribution to the antipsychotic’s benefit–risk profile [104, 105]. Finally, it is important to remember that dopaminergic transmission is also regulated by other neurotransmitter systems, such as inhibitory GABAergic and excitatory glutamatergic neurons, which require further elucidation to provide clinically applicable drug targets in schizophrenia [118].

7.5

 ntipsychotic Treatment and Structural Brain Changes A in Schizophrenia

The role played by antipsychotic treatment on the pathophysiologic trajectory of brain abnormalities in schizophrenia is currently a matter of lively debate. The actions of antipsychotic drugs can be mediated, in part, by their cellular effects and consequently also linked to morphometric changes [61]. Most early MRI studies of schizophrenia examined chronically ill patients, for whom findings are potentially influenced by both illness duration and prolonged exposure to antipsychotic medication [9, 119]. Some authors argued that antipsychotic medication may cause or moderate the longitudinal changes in GM volume, with unclear differences between the effects of FGAs and SGAs, pointing toward the possibility of morphologic changes in the brain during the course of the disease, as a result of the amount and type of drug treatment received [120–124]. Cumulative evidence has revealed significant associations between cortical and subcortical brain structural change and antipsychotic medication exposure [9, 123, 125–127]. However, qualitative reviews addressing the topic of brain changes in schizophrenia in relation to antipsychotic treatment have yielded inconclusive results [120–122, 125]. Type and dosage of medication as well as specific brain areas may play a decisive role [59, 124]. Conversely, a VBM meta-analytic review (N = 298 patients on antipsychotic drugs; N = 250 controls) on the association between antipsychotic use and structural brain changes in schizophrenia revealed seven clusters of areas with consistent structural brain changes in patients on antipsychotics compared to controls [127]. The seven clusters included four areas of relative volumetric decrease in the left lateral temporal cortex, left inferior frontal gyrus, superior frontal gyrus extending to the left middle frontal gyrus, and right rectal gyrus and three areas of relative volumetric increase in the left dorsal anterior cingulate cortex, left ventral anterior cingulate cortex, and right putamen. Authors concluded that additional longitudinal VBM studies including larger and more homogeneous samples of schizophrenia patients may be needed to further disentangle such alterations from those possibly linked to

274

A. Vita et al.

the intrinsic pathological progressive process in schizophrenia. It would be very informative to perform new analyses on patients with schizophrenia using computational methods to take into account the known or hypothetical effect of drugs and chronicity in order to better understand whether a component of later structural changes in the brain is still demonstrable and whether a higher specificity of brain pathomorphology could also be demonstrated in chronic cases after separating out the effects of treatment and chronicity. Thus, some of the abnormalities occur early, probably predating the clinical onset of schizophrenia, while other changes occur later, in the course of the disease and after pharmacological treatment [4]. Whether this later and possibly progressive component of brain abnormalities is just an epiphenomenon of the disease course, due to medication treatment or other environmental events, or may be already embedded in the pathophysiologic trajectory of these diseases, possibly under some degree of genetic control, is still a matter for research and discussion [4]. To overcome these confounding factors, several schizophrenia research programs began to study patients at illness onset and follow them longitudinally with MRI studies. As mentioned above, studies of first-episode schizophrenia were especially important, providing an opportunity to study brain systems without the potential confounding effects of antipsychotic medication and to study changes over the early course of illness. Moreover, first-episode schizophrenia studies provide a strategy for studying short-term brain changes that occur after initiation of antipsychotic treatment [4, 120]. In a recent review about structural and functional changes before and after treatment in FEP, Gong et  al. [55] found anatomical changes, medial prefrontal cortex hypofunction, and hippocampus and striatum hyperactivity before starting antipsychotic treatment. Also there is indirect evidence of moderate progressive changes provided by another study performed by the same research group in drug-naive first-episode patients [128], in which the authors found an accelerated age-related decline in cortical thickness, relative to healthy controls, that could not be attributed to medication effects. Overall, sMRI and fMRI studies showed that antipsychotic drugs can cause GM loss in the neocortex, an increase in striatal volumes, and changes in local resting-­ state function and functional connectivity across the brain [55]. Table 7.1 summarizes main findings of meta-analytical studies on antipsychotic treatment and brain structural and functional changes in patients with schizophrenia. Below we separately analyzed the amount and the type (FGAs and SGAs) of antipsychotic treatment to evaluate the effect of these variables on brain changes in schizophrenia.

7.5.1 A  mount of Antipsychotic Treatment and Brain Changes in Schizophrenia The amount of antipsychotic medication intake may moderate the progressive cortical GM loss over time [4]. In this regard, a loss of GM, observed in 34 FEP patients during the first year of the illness, was significantly correlated with higher cumulative dosage of antipsychotic medication used during the follow-up period [131]. In addition, a longitudinal MRI investigation of a large cohort of patients from the first

Vita et al. [7]

Number of included studies (total sample) 15 (162 NN-FES; 336 NT-FES; 649 HC)

Longitudinal 19 (813 SCZ patients; 718 HC)

Type of included studies Authors Leung et al. Cross-­ [129] sectional

Main investigated areas Cortical and subcortical GM structures

Cortical GM structures (WB, FL, TL, PL, OL, HG, PT, STG)

Subjects NN-FES NT-FES HC

First-episode SCZ (DOI no>24 months; chronic SCZ; COS HC

Type of antipsychotic treatment FGAs and SGAs (approximately 76% on SGAs) Outcome Detect antipsychotic-­ related GM alterations in FES patients Compare structural brain changes between NN-FES patients and NT-FES patients

Structural MRI FGAs and SGAs Influence of antipsychotic (ROI volumetric medications on analysis) changes in GM volume over time Possible different impacts of SGAs versus FGAs on changes in GM volume over time The ES of the difference between groups was affected by the moderators “percentage of patients treated with atypical antipsychotics”

Neuroimaging technique Structural VBM MRI (ALE technique)

Table 7.1  Antipsychotic treatment and brain changes in schizophrenia: findings from meta-analysis

(continued)

Main findings GM volume deficits were more extensive in NT-FES than NN-FES in bilateral insula, medial frontal and inferior frontal gyrus, left parahippocampal gyrus (amygdala), superior temporal gyrus, and right precentral gyrus Progressive cortical GM changes in SCZ occur with regional and temporal specificity, appear especially active in the first stages of SCZ, affect more the left hemisphere and the superior temporal structures, and are at least partly moderated by the type of antipsychotic treatment Treatment with SGAs may be associated with less progressive loss of GM

7  Neuroimaging and Antipsychotics 275

Authors Haijma et al. [9]

Type of included studies Cross-­ sectional

Table 7.1 (continued)

Number of included studies (total sample) 283 (8327 medicated patients; 8292 HC) 33 (771 antipsychotic-­ naïve SCZ; 939 HC)

Main investigated areas Subjects Cortical and Medicated subcortical patients Antipsychotic-­ structures (IC naïve patients volume, WB, GM, WM, FL, HC TL, CSF, LV, etc.) Neuroimaging technique Structural (volumetric) MRI

Type of antipsychotic treatment Outcome Main findings FGAs and SGAs Effect of antipsychotic Medicated SCZ patients (CPZ-Eq) medication on changes show a reduction in IC, WB volumes (2.0% and in cortical and subcortical structures 2.6%, respectively), and total GM volume at time of scanning In antipsychotic-naive Volumetric MRI patients, volume studies in reductions in Cd and antipsychotic-­naive thalamus are > in SCZ patients medicated SCZ. Decrease in WM volume is similar in both groups. GM loss is < in antipsychotic-naïve patients. WB and GM reductions are associated with higher dose of antipsychotics

276 A. Vita et al.

10 (298 Torres et al. Cross-­ [127] sectional and medicated longitudinal patients; 250 controls)

Medicated patients Drug-free patients HC

Structural VBM FGAs and SGAs Cortical and (SCAs: 234 MRI (ALE subcortical patients; 78.5%) structures (GM technique) and WM)

Association between antipsychotic treatment and specific structural brain changes in schizophrenia patients

(continued)

Patients on antipsychotics show four areas of volumetric decrease (left lateral temporal cortex, left inferior frontal gyrus, superior frontal gyrus extending to the left middle frontal gyrus, and right rectal gyrus) and three areas of volumetric increase (left dorsal anterior cingulate cortex, left ventral anterior cingulate cortex, and right putamen) Patients on SGAs show volumetric decrease in left temporal lobe and volumetric increase in right putamen and in left thalamus

7  Neuroimaging and Antipsychotics 277

Goozée et al. [130]

Authors Fusar-­Poli et al. [8]

Number of included studies (total sample) 30 (1046 SCZ patients; 780 HC)

Longitudinal 3 (56 SCZ patients)

Type of included studies Longitudinal

Table 7.1 (continued)

SCZ patients

Subjects First-episode SCZ (DOI no>5 years); chronic SCZ; COS HC when available

Cortical and subcortical regions

Main investigated areas Cortical and subcortical structures WB, GM, WM, CSF, LV, and Cd global volumes

Type of antipsychotic treatment FGAs, SGAs, and mixed (CPZ-Eq/d)

rCBF before and FGAs and SGAs after treatment. VBM studies were subjected to SDM

Neuroimaging technique Structural (volumetric) MRI

Association between antipsychotic medications and changes in global or regional rCBF

Outcome Influence of duration and doses of antipsychotic medication on brain structure in patients with SCZ were analyzed

Main findings SCZ is characterized by progressive GM volume decreases and LV volume increases Longitudinal GM volume decrease in SCZ patients is associated with higher cumulative exposure to antipsychotic over time Antipsychotics exert regional effects on rCBF, particularly in frontal and BG regions Increase in rCBF after treatment in the left Cd. Decrease in rCBF after treatment in the right medial frontal gyrus, cerebellum (right uvula and left declive), and right thalamus

278 A. Vita et al.

Longitudinal 18 (1155 SCZ patients; 911 HC)

First-episode SCZ; chronic SCZ HC

Cortical GM structures (WB, FL, TL, and PL)

Structural MRI FGAs, SGAs, (ROI volumetric and mixed (CPZ-Eq; analysis) cumulative dose and MDD)

Influence of antipsychotic medications on changes in GM volume over time Possible different impacts of SGAs versus FGAs on changes in GM volume over time

SCZ patients show a higher loss of total cortical (WB) GM volume, related to cumulative antipsychotic intake over time More progressive GM loss with higher MDD antipsychotic intake in patients treated with at least one FGA Less progressive GM loss with higher MDD antipsychotic intake in patients treated only with SGAs

ALE anatomic likelihood estimation, BG basal ganglia, Cd caudate nucleus, CPZ-Eq/d chlorpromazine equivalent per day, CSF cerebrospinal fluid, COS childhood-onset schizophrenia, DOI duration of illness, ES effect size, FGAs first-generation antipsychotics, FL frontal lobe, GM gray matter, HC healthy controls, HG Heschl gyrus, IC intracranial, LV lateral ventricles, MDD mean daily dose, MRI magnetic resonance imaging, NN-FES first-episode schizophrenia neuroleptic-naïve, NT-FES first-episode schizophrenia neuroleptic-treated, OL occipital lobe, PL parietal lobe, PT planum temporale, rCBF resting cerebral blood flow, ROI region of interest, SCZ schizophrenia, SDM signed differential mapping, SGAs second-generation antipsychotics, STG superior temporal gyrus, TL temporal lobe, VBM voxel-based morphometric, WB whole brain, WM white matter

Vita et al. [4]

7  Neuroimaging and Antipsychotics 279

280

A. Vita et al.

episode of schizophrenia followed for up to 14  years (Iowa Longitudinal Study) showed that decreases in whole-brain GM and WM volumes were associated with higher exposure to antipsychotics [123, 132]. In another 9-year follow-up MRI study, the amount of antipsychotic medication exposure over the follow-up period predicted total brain volume loss in schizophrenia [93]. Furthermore, a significant negative correlation was detected between lifetime cumulative exposure to antipsychotics and total (but not regional) GM and WM volumes in schizophrenia patients [133]. Findings from a large meta-analysis of cross-sectional studies on schizophrenia [9] indicated that the reduction in whole-brain GM volume is associated with the dose of antipsychotics taken at the time of scanning. In line with these results, a meta-analysis of longitudinal MRI studies showed a correlation between cumulative antipsychotic intake during the interscan follow-up period and the decrease in whole-brain GM volume in patients with schizophrenia [8]. Similar results were also achieved in a recent meta-analysis, performed by Vita et  al. [4], in which patients with schizophrenia showed a significantly higher loss of total cortical GM volume over time, related to cumulative antipsychotic intake during the interval between scans in the whole study sample. Conversely, no association between cumulative dose of lifetime antipsychotic medication and brain morphological changes was found in other studies [134].

7.5.2 T  ype of Antipsychotic Treatment and Brain Changes in Schizophrenia The type of antipsychotic treatment may moderate cortical changes over time in schizophrenia. In this regard, the relationship between antipsychotic treatment and loss of cortical GM and the potential differential effect of SGAs versus FGAs on such loss is a topic of crucial clinical and heuristic interest. FGAs and SGAs may exert neurotrophic and neuroprotective or neurotoxic effects differentially [135]. Animal and cell biology studies have demonstrated the differential effects of FGAs and SGAs on cellular morphology and brain growth factors, with SGAs representing a mixed bag of pharmacological agents associated with diverse actions compared to haloperidol [136, 137]. MRI studies of brain morphology in patients at different stages of illness and after varying times of exposure and longitudinal studies show possible different effects of FGAs and SGAs. Despite mixed results, a number of studies have reported different impacts of FGAs versus SGAs on changes in brain volume in schizophrenia, especially GM. FGAs may increase basal ganglia volume and reduce cortical GM in different brain regions [8, 20, 138–142], whereas SGAs were associated with a lesser decrease (or in some studies even an increase) in longitudinal cortical GM volume than FGAs over time [4, 7, 20, 64, 119, 120, 122, 123, 131, 143, 144]. With respect to frontal brain volumes, some but not all studies reported cumulative antipsychotic medication dosage [60, 61, 119, 123, 145, 146]. In studies assessing cumulative doses, two studies did not find significant differences between FGAs and SGAs [60, 61], while one study did not distinguish between FGAs and SGAs [146]. A fourth study did not find significant correlations

7  Neuroimaging and Antipsychotics

281

between frontal brain volumes and antipsychotic medication, regardless of whether FGAs or SGAs were assessed [61]. These findings are in contrast with the study of Lieberman et al. [119] which reported volume increases in the frontal cortex during the first 12  weeks of medication with olanzapine, compared to haloperidol treatment. However, the same research group, analyzing the brain MRI scans of a subsample of patients from the previous study, showed a volume reduction after 24 weeks of treatment and no significant difference between haloperidol and olanzapine after 12 months of treatment [145]. Antipsychotic drugs seem to act regionally rather than globally on the brain, with different effects on different brain structures, with some studies suggesting that effect sizes of these volumetric changes differ between FGAs and SGAs [122]. With respect to the caudate, McClure et al. [144] found no longitudinal changes in caudate volume after brief periods of atypical antipsychotic treatment. Also with respect to the striatum, 5-year intake of olanzapine was correlated with less reduction in GM volume and even with a subtle GM increment [147]. Other research groups suggested that progressive regional volume loss in quetiapine treated first-episode schizophrenia patients and found a potential dose dependency [148]. The same group suggested that an induction of striatal volume increases was not regularly induced by FGAs, while both volumetric increases and decreases in the basal ganglia were reported following SGA medication [126]. With respect to cortical thickness, in a 5-year follow-up MRI study, higher FGA intake was associated with more pronounced decreases in cortical thickness, whereas higher SGA intake was related to fewer decreases in cortical thickness [131]. Further increases in prefrontal cortical thickness following short-term (4–8  weeks) atypical treatment with risperidone or quetiapine were observed by Goghari et al. [149]. Ansell et al. [150] observed that decreased frontoparietal cortical thickness was present in schizophrenia patients treated with FGAs but not SGAs, with the SGA group even showing increases in addition to decreases in these brain areas. In chronically ill schizophrenia patients, van Haren and coworkers [64, 131] also observed fewer reductions in GM of the superior frontal gyrus as well as cortical thickness in the left medial frontal cortex, with cumulative clozapine doses being associated with less reduction of frontal cortical brain areas. However, other research led to different results. A study performed in first-­ episode patients over 1 year of treatment concluded that low doses of haloperidol, risperidone, or olanzapine may have similar effects on the overall change in GM volume [135, 151]. In a more recent MRI longitudinal study, Guo et al. [140] investigated progressive regional brain reductions in schizophrenia and observed that both FGAs and SGAs treatment exposure partially explain progressive brain reductions in schizophrenia. In a structural MRI VBM study, Moilanen et al. [134] found no associations between type of antipsychotic medication and brain morphometry in schizophrenia patients after an average of 10 years of illness. In line with these data, in a MRI study, Jørgensen et al. [152] have not found significant differences in subcortical structure volumes in patients with schizophrenia treated with FGAs (N = 40) or with SGAs (N = 42). Of some interest, enlargements of the basal ganglia were observed in both FGAs and SGAs users, but not in clozapine-treated patients, in which basal ganglia volumes were similar to healthy controls.

282

A. Vita et al.

A recent quantitative review of longitudinal cortical GM changes in schizophrenia suggested that treatment with SGAs may be associated with less progressive loss of GM, but a very rough index of intake of different classes of antipsychotics (the percentage of patients using SGAs in each study included in the review) was used [7]. Furthermore, a recent meta-analysis on 18 longitudinal MRI studies compared changes in cortical GM volume over time between patients with schizophrenia (N = 1155) and healthy controls (N = 911). Over time, patients with schizophrenia showed a significantly higher loss of total cortical GM volume. Subgroup meta-­analyses of studies on patients treated with SGAs and FGAs revealed a different and contrasting moderating role of medication intake on cortical GM changes: more progressive GM loss correlated with higher mean daily antipsychotic intake in patients treated with at least one FGA and less progressive GM loss with higher mean daily antipsychotic intake in patients treated only with SGAs (Fig. 7.1). Authors highlighted that these findings add useful information to the controversial debate on the brain structural effects of antipsychotic medication and may have both clinical relevance and theoretical implications [4]. a

Regression of MDD (CPZ-Eq) on Hedges’ g

0.80 0.64 0.48 Hedges’ g

0.32 0.16 0.00 -0.16 -0.32 -0.48 -0.64 -0.80 116.30 168.74 221.18 273.62 326.06 378.50 430.94 483.38 535.82 588.26 640.70

MDD (CPZ-Eq)

Fig. 7.1  Meta-regression analyses. GM volume changes and mean daily dose (MDD) of antipsychotic medication administered to patients during the interscan interval: (a) in the whole sample (Z = −2.19, ρ = 0.028); (b) in the subgroup of patients treated with first-generation antipsychotics or mixed treatment [second-generation antipsychotics (SGAs) + first-generation antipsychotics] (Z = −2.88, ρ = 0.003); and (c) in the studies including patients treated exclusively with SGAs (Z = −2.95, ρ = 0.003). The size of the circles reflects the sample size of the study. (c) The colors indicate the different SGAs used as follows: red clozapine, green olanzapine, pink quetiapine, yellow risperidone, blue ziprasidone, black more atypicals (risperidone or olanzapine or quetiapine), brown more atypicals (risperidone or olanzapine or quetiapine or ziprasidone or aripiprazole or clozapine), violet more atypicals (risperidone or olanzapine or quetiapine or clozapine). CPZ-Eq chlorpromazine equivalents. From Vita et al. [4] with permission of authors

7  Neuroimaging and Antipsychotics

b

283

Regression of MDD (CPZ-Eq) on Hedges’ g

0.00 -0.08 -0.16 Hedges’ g

-0.24 -0.32 -0.40 -0.48 -0.56 -0.64 -0.72 -0.80 116.30 168.74 221.18 273.62 326.06 378.50 430.94 483.38 535.82 588.26 640.70

MDD (CPZ-Eq)

c

Regression of MDD (CPZ-Eq) on Hedges’ g

0.80 0.66 0.52

Hedges’ g

0.38 0.24 0.10 -0.04 -0.18 -0.32 -0.46 -0.60 164.50 187.90

211.30 234.70 258.10

281.50 304.50 328.30

351.70 375.10 398.50

MDD (CPZ-Eq)

Fig. 7.1 (continued)

Finally, regarding WM changes, some studies found that higher SGA doses were significantly associated with larger WM volumes [123].

284

7.6

A. Vita et al.

 ntipsychotic Treatment and Functional Brain Changes A in Schizophrenia

During the last two decades, several functional neuroimaging studies (fMRI, SPECT, PET, and MRS) have provided important insights about the differences in pharmacological action and treatment effect among a diverse range of antipsychotics and the subsequent functional changes in the CNS.

7.6.1 A  ntipsychotic Effect on Blood Flow and Metabolism in Schizophrenia A number of studies have shown that FGAs such as haloperidol reduce blood flow and metabolism in the frontal lobe. These effects were repeatedly replicated in studies of both acute [153, 154] and chronic administration [155–159]. An increase in regional cerebral blood flow (rCBF) and regional glucose metabolic ratios (rGMR) in the basal ganglia in patients with schizophrenia by FGAs, in particular haloperidol, is the most consistent finding among numerous reports on antipsychotics. This has been replicated very well in the acute effect [134] as well as the chronic effect [155, 157–164]. This notion is in line with the increase of volume in this area following haloperidol treatment in sMRI studies [10] and with increased rCBF and rGMR in Parkinson’s disease with a dopamine deficit [165]. Studies on the effects of SGAs on brain perfusion/metabolism have been examined on an anatomically more detailed level compared to studies on typical antipsychotics due to the introduction of voxel-wise analysis methodology. Although risperidone has less effect on the reduction of blood flow in the frontal lobe than haloperidol [159], the drug induces a significant reduction in the PFC relative to baseline [166–169]. Liddle et al. [167] demonstrated that treatment with risperidone for 6 weeks showed a significant positive relation between decrease in the hippocampus and decrease in reality distortion. In the basal ganglia, the degree of increase in blood flow/metabolism by risperidone is likely smaller than that by haloperidol [159, 167]. Olanzapine’s effect on blood flow/metabolism in the frontal lobe is lesser [161, 170, 171] than the reduction due to risperidone in blood flow/metabolism in the PFC [155, 169, 172, 173]. Interestingly, some studies have reported that clozapine induced a significant increase in several parts of the PFC, including the ACC [155], and decreases in the hippocampus [155, 173]. Responders to clozapine exhibited more prominent changes in blood flow/metabolism rather than nonresponders [169, 173]. Only few fMRI studies investigated the effect of antipsychotic treatment on functional activation during cognitive tasks, mostly by comparing unmedicated with medicated patients in cross-sectional designs [174, 175]. Some fMRI studies evidenced medication-related increases in the PFC blood oxygenation level-­ dependent (BOLD) response during executive function tasks after starting SGA treatment (quetiapine) [176, 177] or after switching patients with schizophrenia from typical to an atypical antipsychotic (risperidone or aripiprazole) [178, 179],

7  Neuroimaging and Antipsychotics

285

with other studies reporting no significant difference with respect to risperidone [180]. In longitudinal designs increased ACC, but not dlPFC, activation was observed during a cognitive task in N = 11 patients after 4 weeks of antipsychotic treatment [181], and increased activation in left ventrolateral prefrontal cortex (vlPFC) was observed in N = 12 patients after quetiapine treatment for 12 weeks [176]. Another study observed no modulation of dlPFC activation by treatment with secondary antipsychotics, but found that dlPFC activation at baseline predicted poor treatment response [182]. Recent systematic review papers summarized longitudinal fMRI and resting rCBF studies addressing the issue of the effects of antipsychotic drugs in schizophrenia [130, 174]. Abbott et al. [174] provided a systematic review of eight longitudinal fMRI studies focusing on pharmacological treatment effects in schizophrenia. Results of seven out of eight studies indicated a normalization of the BOLD fMRI signal, basically reflecting a reversal of the anomalous fMRI signal, whereas five out of eight investigations reported concurrent alterations of BOLD fMRI signal associated with antipsychotic treatment from baseline brain functional assessment in different brain areas [174, 181, 183]. In particular, normalization was observed in the right cerebellum, primary motor and sensory cortices, anterior cingulate, intraparietal sulci, the insula, superior temporal gyri, ventral medial PFC, the default mode network, and frontoparietal and temporal networks. On the contrary, changes from baseline assessment were observed in the left cerebellum, the cingulate motor area, the caudate and putamen, supramarginal gyri, dlPFC, dorsal medial thalamus, and the right ventral lateral PFC [174]. A recent systematic meta-analytic review focused on rCBF, in order to propose a neuroimaging marker as a potential predictor of symptomatic improvement following antipsychotic treatments [130]. Results showed that antipsychotic medications induce significant rCBF changes in the striatum, frontal, temporal, and, to a lesser extent, thalamic and cerebellar regions, all areas known to undergo structural changes after antipsychotic treatment. Intriguingly, different antipsychotic drugs may have differential effects on regional rCBF. In fact FGAs may have greater rCBF subcortical effects (especially within the basal ganglia), while SGAs could have greater cortical effects [130]. Regarding neurocognitive behavioral effect, large-scale studies showed only small to negligible neurocognitive improvements due to antipsychotic treatment without evidence for a superiority of SGAs [184, 185], although a recent meta-analysis suggests differential effects of different SGAs on certain subcomponents of neurocognition, e.g., related to differences in anticholinergic effects [186]. Finally, in a casecontrol cross-sectional study by Lesh et al. [187], the effects of atypical antipsychotics on brain structure and function in patients with first-episode schizophrenia were examined, using cortical thickness measurements and administration of the AX version of the Continuous Performance Task (AX-CPT) during event-related fMRI. Significant cortical thinning was identified in the SGAs medicated patient group (N = 23) relative to the control group (N = 37) in prefrontal, temporal, parietal, and occipital cortices. The unmedicated patient group (N = 22) showed no significant cortical thickness differences from the control group, and the medicated patient group showed thinner cortex compared with the unmedicated patient group in the dlPFC and in the temporal cortex. During the AX-CPT, both patient groups showed reduced dlPFC activity

286

A. Vita et al.

compared with the control group, but the medicated patient group demonstrated higher dlPFC activation and better behavioral performance than the unmedicated patient group. The authors remark that these findings confirm the complex relationship between antipsychotic treatment and the structural, functional, and behavioral deficits in schizophrenia and underscore that short-­term treatment with antipsychotics was associated both with prefrontal cortical thinning but at the same time with better cognitive performance and increased prefrontal functional activity. Considerable heterogeneity regarding the assessed cognitive function, task designs, and data analyses as well as diversity of the investigated patients (e.g., with respect to stage of illness, duration of illness, previous treatment, etc.) hinder conclusive results. Small sample sizes are an important limitation of the current imaging literature as they limit the possibility to assess neural imaging markers of interindividual differences in treatment response. Pharmacological challenge studies provide proof of concept but due to individual variations in pharmacokinetics cannot identify individual differences in neurotransmitter systems. This is possible with neurochemical SPECT/PET studies; however, here case numbers are often limited due to radiation exposure and costs.

7.6.2 Antipsychotic Effect on Neurochemistry in Schizophrenia Available antipsychotic medication acts on the dopaminergic system mainly via D2 receptor antagonism which is thought to downregulate the hyperdopaminergic state by depolarization block [188, 189]. Howes et al. [190] reviewed the existing literature to describe the nature of dopaminergic abnormalities in schizophrenia through SPECT/PET dopaminergic occupancy with specific ligands. From SPECT/PET studies, we are aware that a rather precise D2-blockade threshold is crucial, but not sufficient, to achieve an antipsychotic response [191]. However, high levels of D2 receptor blockade were associated with decreased negative affect and motivational deficits [192, 193]. While the D2 receptor blockade by antipsychotics has been investigated extensively, there is very limited data on the effect of antipsychotic medication on the presynaptic dopamine synthesis capacity, although the latter is more closely associated with psychosis [194, 195]. A meta-analysis of molecular brain imaging studies confirmed increased striatal dopamine synthesis and release in schizophrenia patients with large effect size (Cohen d = 0.79) [190]. Dopamine synthesis capacity predicted the development of schizophrenia from an at-risk mental state and showed a progressive increase in subjects who made the transition from the prodromal phase to the first episode of psychosis [196]. In schizophrenia patients, there is only one longitudinal study (N = 9) showing a 25% decrease in striatal dopamine synthesis capacity after 5 weeks of haloperidol treatment [197]. One cross-sectional study found that baseline presynaptic dopaminergic activity was elevated only in the subgroup of patients who responded to antipsychotic treatment but not in nonresponders [198]. This suggests dopamine synthesis capacity as measured by FDOPA PET a potential predictor of response to antipsychotic treatment in schizophrenia (and potentially also of longterm treatment outcome). But also other neurotransmitter systems and their reciprocal

7  Neuroimaging and Antipsychotics

287

interactions are implicated in the pathophysiology of schizophrenia and determine the “whole-brain response” [199–201]. MRS is a functional imaging technique that allows the in vivo study of biochemical and metabolic parameters in the brain. Schizophrenia is associated with brain glutamate dysfunction, and MRS studies mainly focused on N-acetylaspartate (NAA), glutamate, and glutamine concentrations in multiple pivotal brain areas. The most replicated finding in schizophrenia is the reduction of NAA concentration in the prefrontal lobe (GM and WM) and hippocampus [199, 202, 203]. NAA is considered a marker of neuronal integrity and predicts severity of illness in several neurodegenerative disorders, while a low glutamate to glutamine ratio is generally consistent with increased synaptic glutamatergic activity and enhanced neurotoxicity [199]. MRS studies addressing brain functional effects of antipsychotic treatments are relatively scarce, and it is currently unclear whether antipsychotic administration impacts on the extent of glutamatergic abnormality. Fannon et al. [204] found that treatment with SGAs was associated with an increase in NAA hippocampal levels in a sample of drug-naïve patients early in the course of their illness. A longitudinal study on an analogous sample of patients with minimal previous antipsychotic exposure determined NAA concentrations in the frontal and occipital lobes, caudate nucleus, and cerebellum before and after a 2-year treatment with either haloperidol or quetiapine, reporting no significant NAA level changes in any of the anatomical regions studied [205]. Another study on this topic found NAA levels restored in adolescents diagnosed with early onset schizophrenia after a 6-month treatment with SGAs within the PFC and thalamus [206]. Regarding the potential impact of long-term antipsychotic treatments, a recent MRS study showed increased levels of glutamine within the anterior dorsal cingulate cortex of chronic schizophrenia patients with a history of long-term antipsychotic treatment, along with a significant reduction of NAA levels [207]. In a more recent systematic review of proton MRS (1H-MRS) studies, Egerton et al. [201] examined the effects of antipsychotic treatment on brain glutamate levels in schizophrenia, reporting a reduction in brain glutamate metabolites with antipsychotic treatment and an overall mean reduction of 6.5% in Glx (the combined glutamate plus glutamine signal) across brain regions.

7.7

Interindividual Response Variability to Antipsychotic Treatment

Antipsychotic (D2-antagonistic) treatment reduces positive psychotic symptoms in most patients, but there is considerable heterogeneity in treatment response with roughly a third of patients showing insufficient clinical response [208]. Furthermore, there is variability regarding the time to clinical response after antipsychotic treatment onset [209] and variability regarding the re-emergence of symptoms despite sufficient D2-receptor blockade [210]. In a cross-sectional study, striatal dopamine synthesis capacity (FDOPA PET) was higher in patients who had responded to antipsychotic treatment compared to patients with treatment-resistant illness and healthy volunteers [198], whereas treatment-resistant patients were characterized

288

A. Vita et al.

by elevated ACC glutamate (MRS) levels [211]. Although there is need to operationalize treatment resistance [212], a recent review summarizes that treatment-­ resistant compared to treatment-responsive patients have more GM decreases and show glutamatergic but no dopaminergic abnormalities [213]. Multimodal imaging studies beyond dopaminergic (e.g., glutamatergic) measures are warranted in longitudinal settings to provide a pathophysiological understanding of antipsychotic response variability.

7.8

Summary of Main Results and Evidences

Neuroimaging studies support the presence of structural and functional alterations in schizophrenia, from its putative prodromal phase to the chronic disorder. Abnormalities, even if subtle in magnitude and variable in extent across studies, are now reported in many brain regions [214]. The above reported studies consistently indicate that FGAs may increase basal ganglia volume and reduce cortical GM in different brain regions, whereas SGAs may be associated with a lesser decrease in longitudinal cortical GM volume over time, although such differences are a matter of current debate [8, 20, 64, 118–120, 122–124, 131, 140, 142–144]. While some studies suggest differential drug effects with respect to neurotoxic and (particularly regarding SGAs) neuroprotective effects [119, 215–217], two studies in nonhuman primates observed identical brain volume reductions (around 8–20% in frontal and parietal brain areas) with both haloperidol and olanzapine [136, 218]. Altogether, these studies suggest to cautiously monitor drug effects on brain volume and to inform patients accordingly. Despite their heterogeneity, SPECT, PET, and fMRI data obtained in schizophrenic patients on antipsychotic treatment clearly indicate that antipsychotic drugs are able to modify cerebral flow and metabolism patterns after both acute and chronic treatment. There is a different modulation of such parameters by different antipsychotic drugs both at rest and during tasks involving activation of different cortical brain regions [219]. Despite some evidences, MRS studies addressing brain functional effects of antipsychotic treatments are relatively scarce, and it is still currently unclear whether antipsychotic administration can reduce the extent of glutamatergic abnormality [201].

7.9

Conclusions and Future Directions

The present chapter investigated the effect of antipsychotic medication on brain structural and functional changes found in patients with schizophrenia. The influence of antipsychotic medication in relation to longitudinal brain changes suggests that brain volume reductions appear both before and during antipsychotic treatment, with medication contributing to volume loss. Overall, there are several methodological limitations in the existing literature, including lack of reliability data, clinical heterogeneity among studies, and inadequate study designs and statistics [120–122, 125]. However, a correlation between brain volumes and antipsychotic use reported by some studies

7  Neuroimaging and Antipsychotics

289

does not necessarily imply any causality, as high neuroleptic doses may be initiated due to symptom severity, which they reduce when administered. Nevertheless, studies of monkeys suggest brain volume loss to be caused both by SGA and FGA [136]. Beyond medication, illness duration, changes in clinical severity, cognitive deficits, substance abuse, and a combination of all these can contribute to volume loss [4, 59, 122]. Further studies that are specifically designed to investigate drug-induced effects on brain volume and function in interaction with other factors are needed. More consistent answers could be achieved by studies of brain morphology in first-episode, drug-naive schizophrenic patients and UHR subjects, with high statistical power performed longitudinally, homogeneously, with large samples, ideally multicentric. Further investigation is needed into the differential effects of different classes of antipsychotic drugs interacting with drugs of abuse, institutionalization, and poor physical health. Future studies should therefore be aided by employing more homogeneous classifications of psychotic disorders (e.g., using symptom-, cognitive-, genetic-, environmental risk-­based approaches), comparing them with other diagnoses (e.g., using a dimensional approach), and relating imaging findings with other biological data [188]. The use of multimodal imaging across neurochemical, functional, and structural imaging modalities, combined with animal models and postmortem studies of the cellular and molecular mechanisms of brain changes, is required to address the complex issue of what is going on in the brain over time before and during the illness [220]. Such studies may lead to a reduction of unwanted medication effects and a better understanding of the biologic meaning of brain abnormalities in schizophrenia and, through this, of the pathogenesis of the diseases [4, 7].

References 1. Smith RC, Calderon M, Ravichandran GK, Largen J, Vroulis G, Shvartsburd A, Gordon J, Schoolar JC. Nuclear magnetic resonance in schizophrenia: a preliminary study. Psychiatry Res. 1984;12(2):137–47. 2. Hirayasu Y, Tanaka S, Shenton ME, Salisbury DF, DeSantis MA. Prefrontal gray matter volume reduction in first episode schizophrenia. Cereb Cortex. 2001;11:374–81. 3. Wible CG, Anderson J, Shenton ME, Kricun A, Hirayasu Y, Tanaka S, Levitt JJ, O’Donnell BF, Kikinis R, Jolesz FA, McCarley RW. Prefrontal cortex, negative symptoms, and schizophrenia: an MRI study. Psychiatry Res. 2001;108:65–78. 4. Vita A, De Peri L, Deste G, Barlati S, Sacchetti E.  The effect of antipsychotic treatment on cortical gray matter changes in schizophrenia: does the class matter? A meta-analysis and meta-regression of longitudinal magnetic resonance imaging studies. Biol Psychiatry. 2015;78(6):403–12. 5. Wright IC, Rabe-Hesketh S, Woodruff PWR, David AS, Murray RM, Bullmore ET. Meta-­ analysis of regional brain volumes in schizophrenia. Am J Psychiatry. 2000;157:16–25. 6. Olabi B, Ellison-Wright I, McIntosh AM, Wood SJ, Bullmore E, Lawrie SM. Are there progressive brain changes in schizophrenia? A meta-analysis of structural magnetic resonance imaging studies. Biol Psychiatry. 2011;70:88–96. 7. Vita A, De Peri L, Deste G, Sacchetti E. Progressive loss of cortical gray matter in schizophrenia: a meta-analysis and meta-regression of longitudinal MRI studies. Transl Psychiatry. 2012;2:e190.

290

A. Vita et al.

8. Fusar-Poli P, Smieskova R, Kempton MJ, Ho BC, Andreasen NC, Borgwardt S. Progressive brain changes in schizophrenia related to antipsychotic treatment? A meta-analysis of longitudinal MRI studies. Neurosci Biobehav Rev. 2013;37:1680–91. 9. Haijma SV, Van Haren N, Cahn W, Koolschijn PC, Hulshoff Pol HE, Kahn RS. Brain volumes in schizophrenia: a meta-analysis in over 18 000 subjects. Schizophr Bull. 2013;39:1129–38. 10. Shenton ME, Dickey CC, Frumin M, McCarley RW. A review of MRI findings in schizophrenia. Schizophr Res. 2001;49(1–2):1–52. 11. Glahn DC, Laird AR, Ellison-Wright I, Thelen SM, Robinson JL, Lancaster JL, Bullmore E, Fox PT. Meta-analysis of gray matter anomalies in schizophrenia: application of anatomic likelihood estimation and network analysis. Biol Psychiatry. 2008;64:774–81. 12. Shenton ME, Whitford TJ, Kubicki M. Structural neuroimaging in schizophrenia: from methods to insights to treatments. Dialogues Clin Neurosci. 2010;12(3):317–32. 13. Honea R, Crow TJ, Passingham D, Mackay CE.  Regional deficits in brain volume in schizophrenia: a meta-analysis of voxel-based morphometry studies. Am J Psychiatry. 2005;162(12):2233–45. 14. Pearlson GD, Calhoun V. Structural and functional magnetic resonance imaging in psychiatric disorders. Can J Psychiatry. 2007;52(3):158–66. 15. Fornito A, Yucel M, Patti J, Wood SJ, Pantelis C. Mapping grey matter reductions in schizophrenia: an anatomical likelihood estimation analysis of voxel-based morphometry studies. Schizophr Res. 2009;108(1–3):104–13. 16. Levitt JJ, Bobrow L, Lucia D, Srinivasan P. A selective review of volumetric and morphometric imaging in schizophrenia. Curr Top Behav Neurosci. 2010;4:243–81. 17. Bora E, Fornito A, Radua J, Walterfang M, Seal M, Wood SJ, Yücel M, Velakoulis D, Pantelis C. Neuroanatomical abnormalities in schizophrenia: a multimodal voxelwise meta-analysis and meta-regression analysis. Schizophr Res. 2011;127(1–3):46–57. 18. Schmitt A, Hasan A, Gruber O, Falkai P. Schizophrenia as a disorder of disconnectivity. Eur Arch Psychiatry Clin Neurosci. 2011;261(Suppl 2):S150–4. 19. Palaniyappan L, Balain V, Liddle PF. The neuroanatomy of psychotic diathesis: a metaanalytic review. J Psychiatr Res. 2012;46(10):1249–56. 20. Dazzan P, Morgan KD, Orr K, Hutchinson G, Chitnis X, Suckling J, Fearon P, McGuire PK, Mallett RM, Jones PB, Leff J, Murray RM. Different effects of typical and atypical antipsychotics on grey matter in first episode psychosis: the AESOP study. Neuropsychopharmacology. 2005;30(4):765–74. 21. Ellison-Wright I, Glahn DC, Laird AR, Thelen SM, Bullmore E.  The anatomy of first-­ episode and chronic schizophrenia: an anatomical likelihood estimation meta-analysis. Am J Psychiatry. 2008;165(8):1015–23. 22. Lim KO, Helpern JA.  Neuropsychiatric applications of DTI  - a review. NMR Biomed. 2002;15:587–93. 23. Ardekani BA, Nierenberg J, Hoptman MJ, Javitt DC, Lim KO. MRI study of white matter diffusion anisotropy in schizophrenia. Neuroreport. 2003;14:2025–9. 24. Kubicki M, Westin CF, Nestor PG, Wible CG, Frumin M, Maier SE, Kikinis R, Jolesz FA, McCarley RW, Shenton ME.  Cingulate fasciculus integrity disruption in schizophrenia: a magnetic resonance diffusion tensor imaging study. Biol Psychiatry. 2003;54:1171–80. 25. Burns J, Job D, Bastin ME, Whalley H, Macgillivray T, Johnstone EC, Lawrie SM. Structural disconnectivity in schizophrenia: a diffusion tensor magnetic resonance imaging study. Br J Psychiatry. 2003;182:439–43. 26. Davis KL, Stewart DG, Friedman JI, Buchsbaum M, Harvey PD, Hof PR, Buxbaum J, Haroutunian V. White matter changes in schizophrenia: evidence for myelin-related dysfunction. Arch Gen Psychiatry. 2003;60:443–56. 27. White T, Nelson M, Lim KO. Diffusion tensor imaging in psychiatric disorders. Top Magn Reson Imaging. 2008;19(2):97–109. 28. Ellison-Wright I, Bullmore E. Meta-analysis of diffusion tensor imaging studies in schizophrenia. Schizophr Res. 2009;108(1–3):3–10.

7  Neuroimaging and Antipsychotics

291

29. Yao L, Lui S, Liao Y, Du MY, Hu N, Thomas JA, Gong QY. White matter deficits in first episode schizophrenia: an activation likelihood estimation meta-analysis. Prog Neuro-­ Psychopharmacol Biol Psychiatry. 2013;45C:100–6. 30. Liu CC, Chien YL, Hsieh MH, Hwang TJ, Hwu HG, Liu CM. Aripiprazole for drug-naïve or antipsychotic-short-exposure subjects with ultra-high risk state and first-episode psychosis: an open-label study. J Clin Psychopharmacol. 2013;33:18–23. 31. Crossley NA, Marques TR, Taylor H, Chaddock C, Dell’Acqua F, Reinders AA, Mondelli V, DiForti M, Simmons A, David AS, Kapur S, Pariante CM, Murray RM, Dazzan P. Connectomic correlates of response to treatment in first-episode psychosis. Brain. 2017;140(Pt 2):487–96. 32. Henze R, Brunner R, Thiemann U, Parzer P, Klein J, Resch F, Stieltjes B. White matter alterations in the corpus callosum of adolescents with first-admission schizophrenia. Neurosci Lett. 2012;513(2):178–82. 33. Guo W, Liu F, Liu Z, Gao K, Xiao C, Chen H, Zhao J. Right lateralized white matter abnormalities in first-episode, drug-naive paranoid schizophrenia. Neurosci Lett. 2012;531(1):5–9. 34. Gur RE, Gur RC. Functional magnetic resonance imaging in schizophrenia. Dialogues Clin Neurosci. 2010;12(3):333–43. 35. Lewis DA, Lieberman JA. Catching up on schizophrenia: natural history and neurobiology. Neuron. 2000;28:325–34. 36. Glahn DC, Ragland JD, Abramoff A, Barrett J, Laird AR, Bearden CE, Velligan DI. Beyond hypofrontality: a quantitative meta- analysis of functional neuroimaging studies of working memory in schizophrenia. Hum Brain Mapp. 2005;25:60–9. 37. Hubl D, Koenig T, Strik WK, Garcia LM, Dierks T. Competition for neuronal resources: how hallucinations make themselves heard. Br J Psychiatry. 2007;190:57–62. 38. Sim K, Cullen T, Ongur D, Heckers S. Testing models of thalamic dysfunction in schizophrenia using neuroimaging. J Neural Transm. 2006;113:907–28. 39. Berman KF, Meyer-Lindenberg A.  Functional brain imaging studies in schizophrenia. In: Charney D, Nestler E, editors. Neurobiology of mental illness. 2nd ed. Oxford, MA: Oxford University Press; 2004. 40. Minzenberg MJ, Laird AR, Thelen S, Carter CS, Glahn DC.  Meta-analysis of 41 functional neuroimaging studies of executive function in schizophrenia. Arch Gen Psychiatry. 2009;66(8):811–22. 41. Potkin SG, Turner JA, Brown GG, McCarthy G, Greve DN, Glover GH, Manoach DS, Belger A, Diaz M, Wible CG, Ford JM, Mathalon DH, Gollub R, Lauriello J, O’Leary D, van Erp TG, Toga AW, Preda A, Lim KO, FBIRN.  Working memory and DLPFC inefficiency in schizophrenia: the FBIRN study. Schizophr Bull. 2009;35(1):19–31. 42. Ragland JD, Laird AR, Ranganath C, Blumenfeld RS, Gonzales SM, Glahn DC. Prefrontal activation deficits during episodic memory in schizophrenia. Am J Psychiatry. 2009;166(8): 863–74. 43. Callicott JH, Mattay VS, Verchinski BA, Marenco S, Egan MF, Weinberger DR. Complexity of prefrontal cortical dysfunction in schizophrenia: more than up or down. Am J Psychiatry. 2003;160(12):2209–15. 44. Pankow A, Friedel E, Sterzer P, Seiferth N, Walter H, Heinz A, Schlagenhauf F.  Altered amygdala activation in schizophrenia patients during emotion processing. Schizophr Res. 2013;150(1):101–6. 45. Li H, Chan RC, McAlonan GM, Gong QY. Facial emotion processing in schizophrenia: a meta-analysis of functional neuroimaging data. Schizophr Bull. 2010;36(5):1029–39. 46. Pantelis C, Yucel M, Wood SJ, Velakoulis D, Sun D, Berger G, Stuart GW, Yung A, Phillips L, McGorry PD.  Structural brain imaging evidence for multiple pathological processes at different stages of brain development in schizophrenia. Schizophr Bull. 2005;31:672–96. 47. Hulshoff Pol HE, Kahn RS.  What happens after the first episode? A review of progressive brain changes in chronically ill patients with schizophrenia. Schizophr Bull. 2008;34: 354–66. 48. van Haren NE, Cahn W, Hulshoff Pol HE, Kahn RS. Schizophrenia as a progressive brain disease. Eur Psychiatry. 2008;23:245–54.

292

A. Vita et al.

49. Kochunov P, Hong LE. Neurodevelopmental and neurodegenerative models of schizophrenia: white matter at the center stage. Schizophr Bull. 2014;40(4):721–8. 50. Andreasen NC, Nopoulos P, Magnotta V, Pierson R, Ziebell S, Ho BC.  Progressive brain change in schizophrenia: a prospective longitudinal study of first-episode schizophrenia. Biol Psychiatry. 2011;70:672–9. 51. Meyer-Lindenberg A. Neuroimaging and the question of neurodegeneration in schizophrenia. Prog Neurobiol. 2011;95:514–6. 52. McGuire PK, Sato JR, Mechelli A, Jackowski A, Bressan RA, Zugman A. Can neuroimaging be used to predict the onset of psychosis? Lancet Psychiatry. 2015;2(12):1117–22. 53. Zipursky RB, Reilly TJ, Murray RM. The myth of schizophrenia as a progressive brain disease. Schizophr Bull. 2013;39:1363–72. 54. Fusar-Poli P, Bonoldi I, Yung AR, Borgwardt S, Kempton MJ, Valmaggia L, Barale F, Caverzasi E, McGuire P. Predicting psychosis: meta-analysis of transition outcomes in individuals at high clinical risk. Arch Gen Psychiatry. 2012;69:220–9. 55. Gong Q, Lui S, Sweeney JA. A selective review of cerebral abnormalities in patients with first-episode schizophrenia before and after treatment. Am J Psychiatry. 2016;173:232–43. 56. Schnack HG, Van Haren NE, Nieuwenhuis M, Hulshoff Pol HE, Cahn W, Kahn RS. Accelerated brain aging in schizophrenia: a longitudinal pattern recognition study. Am J Psychiatry. 2016;173:607–16. 57. Van Haren NE, Cahn W, Hulshoff Pol HE, Kahn RS. The course of brain abnormalities in schizophrenia: can we slow the progression? J Psychopharmacol. 2012;26:8–14. 58. Van Haren NE, Schnack HG, Koevoets MGJC, Cahn W, Hulshoff Pol HE, Kahn RS.  Trajectories of subcortical volume change in schizophrenia: a 5-year follow-up. Schizophr Res. 2016;173:140–5. 59. Roiz-Santiañez R, Suarez-Pinilla P, Crespo-Facorro B. Brain structural effects of antipsychotic treatment in schizophrenia: a systematic review. Curr Neuropharmacol. 2015;13(4):422–34. 60. Cahn W, Hulshoff Pol HE, Lems EB, van Haren NE, Schnack HG, van der Linden JA, Schothorst PF, van Engeland H, Kahn RS. Brain volume changes in first-episode schizophrenia: a 1-year follow-up study. Arch Gen Psychiatry. 2002;59(11):1002–10. 61. Ho BC, Andreasen NC, Nopoulos P, Arndt S, Magnotta V, Flaum M. Progressive structural brain abnormalities and their relationship to clinical outcome: a longitudinal magnetic resonance imaging study early in schizophrenia. Arch Gen Psychiatry. 2003;60:585–94. 62. Yoshida T, McCarley RW, Nakamura M, Lee K, Koo MS, Bouix S, Salisbury DF, Morra L, Shenton ME, Niznikiewicz MA. A prospective longitudinal volumetric MRI study of superior temporal gyrus gray matter and amygdale-hippocampal complex in chronic schizophrenia. Schizophr Res. 2009;113:84–94. 63. Takahashi T, Suzuki M, Zhou SY, Tanino R, Nakamura K, Kawasaki Y, Seto H, Kurachi M. A follow-up MRI study of the superior temporal subregions in schizotypal disorder and first-­ episode schizophrenia. Schizophr Res. 2010;119:65–74. 64. van Haren NE, Hulshoff Pol HE, Schnack HG, Cahn W, Mandl RC, Collins DL, Evans AC, Kahn RS.  Focal gray matter changes in schizophrenia across the course of the illness: a 5-year follow-up study. Neuropsychopharmacology. 2007;32:2057–66. 65. Chua SE, Cheung C, Cheung V, Tsang JT, Chen EY, Wong JC, Cheung JP, Yip L, Tai KS, Suckling J, McAlonan GM.  Cerebral grey, white matter and csf in never-medicated, first-­ episode schizophrenia. Schizophr Res. 2007;89(1–3):12–21. 66. Ebdrup BH, Glenthøj B, Rasmussen H, Aggernaes B, Langkilde AR, Paulson OB, Lublin H, Skimminge A, Baaré W. Hippocampal and caudate volume reductions in antipsychotic-naive first-episode schizophrenia. J Psychiatry Neurosci. 2010;35(2):95–104. 67. Morgan KD, Dazzan P, Orr KG, Hutchinson G, Chitnis X, Suckling J, Lythgoe D, Pollock SJ, Rossell S, Shapleske J, Fearon P, Morgan C, David A, McGuire PK, Jones PB, Leff J, Murray RM. Grey matter abnormalities in first-episode schizophrenia and affective psychosis. Br J Psychiatry Suppl. 2007;51:s111–6.

7  Neuroimaging and Antipsychotics

293

68. Kaspárek T, Prikryl R, Mikl M, Schwarz D, Cesková E, Krupa P. Prefrontal but not temporal grey matter changes in males with first-episode schizophrenia. Prog Neuro-Psychopharmacol Biol Psychiatry. 2007;31(1):151–7. 69. Meisenzahl EM, Koutsouleris N, Bottlender R, Scheuerecker J, Jäger M, Teipel SJ, Holzinger S, Frodl T, Preuss U, Schmitt G, Burgermeister B, Reiser M, Born C, Möller HJ. Structural brain alterations at different stages of schizophrenia: a voxel-based morphometric study. Schizophr Res. 2008;104(1–3):44–60. 70. Lui S, Deng W, Huang X, Jiang L, Ma X, Chen H, Zhang T, Li X, Li D, Zou L, Tang H, Zhou XJ, Mechelli A, Collier DA, Sweeney JA, Li T, Gong Q. Association of cerebral deficits with clinical symptoms in antipsychotic-naive first-episode schizophrenia: an optimized voxel-based morphometry and resting state functional connectivity study. Am J Psychiatry. 2009;166(2):196–205. 71. Vita A, De Peri L, Silenzi C, Dieci M.  Brain morphology in first-episode schizophrenia: a meta-analysis of quantitative magnetic resonance imaging studies. Schizophr Res. 2006;82(1):75–88. 72. Steen RG, Mull C, McClure R, Hamer RM, Lieberman JA.  Brain volume in first-episode schizophrenia: systematic review and meta-analysis of magnetic resonance imaging studies. Br J Psychiatry. 2006;188:510–8. 73. Fraguas D, Díaz-Caneja CM, Pina-Camacho L, Janssen J, Arango C.  Progressive brain changes in children and adolescents with early-onset psychosis: a meta-analysis of longitudinal MRI studies. Schizophr Res. 2016;173(3):132–9. 74. Gutiérrez-Galve L, Chu EM, Leeson VC, Price G, Barnes TR, Joyce EM, Ron MA. A longitudinal study of cortical changes and their cognitive correlates in patients followed up after first-episode psychosis. Psychol Med. 2015;45(1):205–16. 75. Théberge J, Williamson KE, Aoyama N, Drost DJ, Manchanda R, Malla AK, Northcott S, Menon RS, Neufeld RW, Rajakumar N, Pavlosky W, Densmore M, Schaefer B, Williamson PC. Longitudinal grey-matter and glutamatergic losses in first-episode schizophrenia. Br J Psychiatry. 2007;191:325–34. 76. Koo MS, Levitt JJ, Salisbury DF, Nakamura M, Shenton ME, McCarley RW.  A cross-­ sectional and longitudinal magnetic resonance imaging study of cingulate gyrus gray matter volume abnormalities in first-episode schizophrenia and first-episode affective psychosis. Arch Gen Psychiatry. 2008;65(7):746–60. 77. Nakamura M, Salisbury DF, Hirayasu Y, Bouix S, Pohl KM, Yoshida T, Koo MS, Shenton ME, McCarley RW. Neocortical gray matter volume in first-episode schizophrenia and first-­ episode affective psychosis: a cross-sectional and longitudinal MRI study. Biol Psychiatry. 2007;62(7):773–83. 78. Pantelis C, Velakoulis D, McGorry PD, Wood SJ, Suckling J, Phillips LJ, Yung AR, Bullmore ET, Brewer W, Soulsby B, Desmond P, McGuire PK. Neuroanatomical abnormalities before and after onset of psychosis: a cross-sectional and longitudinal MRI comparison. Lancet. 2003;361(9354):281–8. 79. Borgwardt SJ, McGuire PK, Aston J, Berger G, Dazzan P, Gschwandtner U, Pflüger M, D’Souza M, Radue EW, Riecher-Rössler A. Structural brain abnormalities in individuals with an at-risk mental state who later develop psychosis. Br J Psychiatry Suppl. 2007;51:s69–75. 80. Nenadic I, Dietzek M, Schönfeld N, Lorenz C, Gussew A, Reichenbach JR, Sauer H, Gaser C, Smesny S. Brain structure in people at ultra-high risk of psychosis, patients with first-episode schizophrenia, and healthy controls: a VBM study. Schizophr Res. 2015;161(2–3):169–76. 81. Cannon TD, Chung Y, He G, Sun D, Jacobson A, van Erp TG, McEwen S, Addington J, Bearden CE, Cadenhead K, Cornblatt B, Mathalon DH, McGlashan T, Perkins D, Jeffries C, Seidman LJ, Tsuang M, Walker E, Woods SW, Heinssen R, North American Prodrome Longitudinal Study Consortium. Progressive reduction in cortical thickness as psychosis develops: a multisite longitudinal neuroimaging study of youth at elevated clinical risk. Biol Psychiatry. 2015;77(2):147–57. 82. Palaniyappan L, Balain V, Liddle PF.  The neuroanatomy of psychotic diathesis: a meta-­ analytic review. J Psychiatr Res. 2012;46(10):1249–56.

294

A. Vita et al.

83. Brans RG, van Haren NE, van Baal GC, Staal WG, Schnack HG, Kahn RS, Hulshoff Pol HE.  Longitudinal MRI study in schizophrenia patients and their healthy siblings. Br J Psychiatry. 2008;193(5):422–3. 84. Peters BD, Blaas J, de Haan L. Diffusion tensor imaging in the early phase of schizophrenia: what have we learned? J Psychiatr Res. 2010;44(15):993–1004. 85. Price G, Bagary MS, Cercignani M, Altmann DR, Ron MA.  The corpus callosum in first episode schizophrenia: a diffusion tensor imaging study. J Neurol Neurosurg Psychiatry. 2005;76(4):585–7. 86. Price G, Cercignani M, Parker GJ, Altmann DR, Barnes TR, Barker GJ, Joyce EM, Ron MA. White matter tracts in first-episode psychosis: a DTI tractography study of the uncinate fasciculus. NeuroImage. 2008;39(3):949–55. 87. Friedman JI, Tang C, Carpenter D, Buchsbaum M, Schmeidler J, Flanagan L, Golembo S, Kanellopoulou I, Ng J, Hof PR, Harvey PD, Tsopelas ND, Stewart D, Davis KL. Diffusion tensor imaging findings in first-episode and chronic schizophrenia patients. Am J Psychiatry. 2008;165(8):1024–32. 88. Qiu A, Zhong J, Graham S, Chia MY, Sim K.  Combined analyses of thalamic volume, shape and white matter integrity in first-episode schizophrenia. NeuroImage. 2009;47(4): 1163–71. 89. White T, Magnotta VA, Bockholt HJ, Williams S, Wallace S, Ehrlich S, Mueller BA, Ho BC, Jung RE, Clark VP, Lauriello J, Bustillo JR, Schulz SC, Gollub RL, Andreasen NC, Calhoun VD, Lim KO. Global white matter abnormalities in schizophrenia: a multisite diffusion tensor imaging study. Schizophr Bull. 2011;37(1):222–32. 90. Mendelsohn A, Strous RD, Bleich M, Assaf Y, Hendler T. Regional axonal abnormalities in first episode schizophrenia: preliminary evidence based on high b-value diffusion-weighted imaging. Psychiatry Res. 2006;146(3):223–9. 91. Chan WY, Yang GL, Chia MY, Lau IY, Sitoh YY, Nowinski WL, Sim K. White matter abnormalities in first-episode schizophrenia: a combined structural MRI and DTI study. Schizophr Res. 2010;119(1–3):52–60. 92. Mathalon DH, Sullivan EV, Lim KO, Pfefferbaum A.  Progressive brain volume changes and the clinical course of schizophrenia in men: a longitudinal magnetic resonance imaging study. Arch Gen Psychiatry. 2001;58(2):148–57. 93. Veijola J, Guo JY, Moilanen JS, Jääskeläinen E, Miettunen J, Kyllönen M, Haapea M, Huhtaniska S, Alaräisänen A, Mäki P, Kiviniemi V, Nikkinen J, Starck T, Remes JJ, Tanskanen P, Tervonen O, Wink AM, Kehagia A, Suckling J, Kobayashi H, Barnett JH, Barnes A, Koponen HJ, Jones PB, Isohanni M, Murray GK. Longitudinal changes in total brain volume in schizophrenia: relation to symptom severity, cognition and antipsychotic medication. PLoS One. 2014;9(7):e101689. 94. Schaufelberger MS, Lappin JM, Duran FL, Rosa PG, Uchida RR, Santos LC, Murray RM, PK MG, Scazufca M, Menezes PR, Busatto GF. Lack of progression of brain abnormalities in first-episode psychosis: a longitudinal magnetic resonance imaging study. Psychol Med. 2011;41(8):1677–89. 95. Haukvik UK, Hartberg CB, Nerland S, Jørgensen KN, Lange EH, Simonsen C, Nesvåg R, Dale AM, Andreassen OA, Melle I, Agartz I. No progressive brain changes during a 1-year follow-up of patients with first-episode psychosis. Psychol Med. 2016;46(3):589–98. 96. Heilbronner U, Samara M, Leucht S, Falkai P, Schulze TG.  The longitudinal course of schizophrenia across the lifespan: clinical, cognitive, and neurobiological aspects. Harv Rev Psychiatry. 2016;24(2):118–28. 97. Dietsche B, Kircher T, Falkenberg I. Structural brain changes in schizophrenia at different stages of the illness: a selective review of longitudinal magnetic resonance imaging studies. Aust N Z J Psychiatry. 2017;51(5):500–8. 98. Weinberger DR, Radulescu E. Finding the elusive psychiatric “lesion” with 21st-century neuroanatomy: a note of caution. Am J Psychiatry. 2016;173(1):27–33.

7  Neuroimaging and Antipsychotics

295

99. Janicak PG.  Acute management of schizophrenia. In: Janicak PG, Marder SR, Tandon R, Goldman M, editors. Schizophrenia: recent advances in diagnosis and treatment. New York: Springer; 2014. 100. Mauri MC, Paletta S, Maffini M, Colasanti A, Dragogna F, Di Pace C, Altamura AC. Clinical pharmacology of atypical antipsychotics: an update. EXCLI J. 2014;13:1163–91. 101. Fleischhacker WW.  Antipsychotic drugs. In: Fleischhacker WW, Stolerman IP, editors. Encyclopedia of schizophrenia: focus on management options. London: Springer; 2011. 102. Horacek J, Bubenikova-Valesova V, Kopecek M, Palenicek T, Dockery C, Mohr P, Höschl C. Mechanism of action of atypical antipsychotic drugs and the neurobiology of schizophrenia. CNS Drugs. 2006;20(5):389–409. 103. Miyamoto S, Duncan GE, Marx CE, Lieberman JA. Treatments for schizophrenia: a critical review of pharmacology and mechanisms of action of antipsychotic drugs. Mol Psychiatry. 2005;10(1):79–104. 104. Howes OD, Egerton A, Allan V, McGuire P, Stokes P, Kapur S. Mechanisms underlying psychosis and antipsychotic treatment response in schizophrenia: insights from PET and SPECT imaging. Curr Pharm Des. 2009;15(22):2550–9. 105. Howes O, McCutcheon R, Stone J. Glutamate and dopamine in schizophrenia: an update for the 21st century. J Psychopharmacol. 2015;29(2):97–115. 106. Stone JM, Davis JM, Leucht S, Pilowsky LS. Cortical dopamine D2/D3 receptors are a common site of action for antipsychotic drugs  – an original patient data meta-analysis of the SPECT and PET in vivo receptor imaging literature. Schizophr Bull. 2009;35(4):789–97. 107. Farde L, Nordstrom AL, Wiesel FA, Pauli S, Halldin C, Sedvall G. Positron emission tomographic analysis of central D1 and D2 dopamine receptor occupancy in patients treated with classical neuroleptics and clozapine. Relation to extrapyramidal side effects. Arch Gen Psychiatry. 1992;49:538–44. 108. Grace AA. The depolarization block hypothesis of neuroleptic action: implications for the etiology and treatment of schizophrenia. J Neural Transm. 1992;36:91–131. 109. Miyamoto S, Lieberman JA, Fleischhacker WW, Aoba A, Marder SR. Antipsychotic drugs. In: Tasman A, Kay J, Lieberman JA, editors. Psychiatry. 2nd ed. Chichester: Wiley; 2003. 110. Remington G, Kapur S. D2 and 5-HT2 receptor effects of antipsychotics: bridging basic and clinical findings using PET. J Clin Psychiatry. 1999;60(Suppl 10):15–9. 111. Duncan GE, Zorn S, Lieberman JA. Mechanisms of typical and atypical antipsychotic drug action in relation to dopamine and NMDA receptor hypofunction hypotheses of schizophrenia. Mol Psychiatry. 1999;4:418–28. 112. Kapur S, Seeman P. Does fast dissociation from the dopamine D2 receptor explain the action of atypical antipsychotics? A new hypothesis. Am J Psychiatry. 2001;158:360–9. 113. Lieberman JA.  Understanding the mechanism of action of atypical antipsychotic drugs: a review of compounds in use and development. Br J Psychiatry. 1993;163:7–18. 114. Meltzer HY, Matsubara S, Lee JC.  Classification of typical and atypical antipsychotic drugs on the basis of dopamine D1, D2 and Serotonin2 pKi values. J Pharmacol Exp Ther. 1989;251:238–46. 115. Meltzer HY, Li Z, Kaneda Y, Ichikawa J. Serotonin receptors: their key role in drugs to treat schizophrenia. Prog Neuro-Psychopharmacol Biol Psychiatry. 2003;27:1159–72. 116. Seeman P. Atypical antipsychotics: mechanism of action. Can J Psychiatry. 2002;47:27–38. 117. Stahl SM. Stahl’s essential psychopharmacology. 4th ed. New York: Cambridge University Press; 2013. 118. Abi-Dargham A, Meyer JM.  Schizophrenia: the role of dopamine and glutamate. J Clin Psychiatry. 2014;75(3):274–5. 119. Lieberman JA, Tollefson GD, Charles C, Zipursky R, Sharma T, Kahn RS, Keefe RS, Green AI, Gur RE, McEvoy J, Perkins D, Hamer RM, Gu H, Tohen M, HGDH Study Group. Antipsychotic drug effects on brain morphology in first-episode psychosis. Arch Gen Psychiatry. 2005;62(4):361–70. 120. Vita A, De Peri L. The effects of antipsychotic treatment on cerebral structure and function in schizophrenia. Int Rev Psychiatry. 2007;19(4):429–36.

296

A. Vita et al.

121. Smieskova R, Fusar-Poli P, Allen P, Bendfeldt K, Stieglitz RD, Drewe J, Radue EW, McGuire PK, Riecher-Rössler A, Borgwardt SJ. The effects of antipsychotics on the brain: what have we learnt from structural imaging of schizophrenia? -- A systematic review. Curr Pharm Des. 2009;15(22):2535–49. 122. Navari S, Dazzan P. Do antipsychotic drugs affect brain structure? A systematic and critical review of MRI findings. Psychol Med. 2009;39(11):1763–77. 123. Ho BC, Andreasen NC, Ziebell S, Pierson R, Magnotta V. Long-term antipsychotic treatment and brain volumes: a longitudinal study of first-episode schizophrenia. Arch Gen Psychiatry. 2011;68(2):128–37. 124. Aderhold V, Weinmann S, Hägele C, Heinz A. Frontal brain volume reduction due to antipsychotic drugs? Nervenarzt. 2015;86(3):302–23. 125. Moncrieff J, Leo J. A systematic review of the effects of antipsychotic drugs on brain volume. Psychol Med. 2010;40(9):1409–22. 126. Ebdrup BH, Nørbak H, Borgwardt S, Glenthøj B. Volumetric changes in the basal ganglia after antipsychotic monotherapy: a systematic review. Curr Med Chem. 2013;20(3):438–47. 127. Torres US, Portela-Oliveira E, Borgwardt S, Busatto GF.  Structural brain changes associated with antipsychotic treatment in schizophrenia as revealed by voxel-based morphometric MRI: an activation likelihood estimation meta-analysis. BMC Psychiatry. 2013;13:342. 128. Ren W, Lui S, Deng W, Li F, Li M, Huang X, Wang Y, Li T, Sweeney JA, Gong Q. Anatomical and functional brain abnormalities in drug-naive first-episode schizophrenia. Am J Psychiatry. 2013;170(11):1308–16. 129. Leung M, Cheung C, Yu K, Yip B, Sham P, Li Q, Chua S, McAlonan G. Gray matter in first-­ episode schizophrenia before and after antipsychotic drug treatment. Anatomical likelihood estimation meta-analyses with sample size weighting. Schizophr Bull. 2011;37(1):199–211. 130. Goozée R, Handley R, Kempton MJ, Dazzan P. A systematic review and meta-analysis of the effects of antipsychotic medications on regional cerebral blood flow (rCBF) in schizophrenia: association with response to treatment. Neurosci Biobehav Rev. 2014;43:118–36. 131. van Haren NE, Schnack HG, Cahn W, van den Heuvel MP, Lepage C, Collins L, Evans AC, Hulshoff Pol HE, Kahn RS.  Changes in cortical thickness during the course of illness in schizophrenia. Arch Gen Psychiatry. 2011;68(9):871–80. 132. Andreasen NC, Liu D, Ziebell S, Vora A, Ho BC. Relapse duration, treatment intensity, and brain tissue loss in schizophrenia: a prospective longitudinal MRI study. Am J Psychiatry. 2013;170(6):609–15. 133. Torres US, Duran FL, Schaufelberger MS, Crippa JA, Louzã MR, Sallet PC, Kanegusuku CY, Elkis H, Gattaz WF, Bassitt DP, Zuardi AW, Hallak JE, Leite CC, Castro CC, Santos AC, Murray RM, Busatto GF.  Patterns of regional gray matter loss at different stages of schizophrenia: a multisite, cross-sectional VBM study in first-episode and chronic illness. Neuroimage Clin. 2016;12:1–15. 134. Moilanen J, Huhtaniska S, Haapea M, Jääskeläinen E, Veijola J, Isohanni M, Koponen H, Miettunen J. Brain morphometry of individuals with schizophrenia with and without antipsychotic medication  – The Northern Finland Birth Cohort 1966 Study. Eur Psychiatry. 2015;30(5):598–605. 135. Roiz-Santianez R, Tordesillas-Gutierrez D, de la Foz Ortiz-Garcia V, Ayesa-Arriola R, Gutierrez A, Tabares-Seisdedos R, Vazquez-Barquero JL, Crespo-Facorro B. Effect of antipsychotic drugs on cortical thickness. A randomized controlled one-year follow-up study of haloperidol, risperidone and olanzapine. Schizophr Res. 2012;141(1):22–8. 136. Dorph-Petersen KA, Pierri JN, Perel JM, Sun Z, Sampson AR, Lewis DA. The influence of chronic exposure to antipsychotic medications on brain size before and after tissue fixation: a comparison of haloperidol and olanzapine in macaque monkeys. Neuropsychopharmacology. 2005;30:1649–61. 137. Takahashi T, Wood SJ, Soulsby B, McGorry PD, Tanino R, Suzuki M, Velakoulis D, Pantelis C. Follow-up MRI study of the insular cortex in first-episode psychosis and chronic schizophrenia. Schizophr Res. 2009;108(1–3):49–56.

7  Neuroimaging and Antipsychotics

297

138. Keshavan MS, Bagwell WW, Haas GL, Sweeney JA, Schooler NR, Pettegrew JW. Changes in caudate volume with neuroleptic treatment. Lancet. 1994;344(8934):1434. 139. Chakos MH, Lieberman JA, Bilder RM, Borenstein M, Lerner G, Bogerts B, Wu H, Kinon B, Ashtari M. Increase in caudate nuclei volumes of first-episode schizophrenic patients taking antipsychotic drugs. Am J Psychiatry. 1994;151(10):1430–6. 140. Guo JY, Huhtaniska S, Miettunen J, Jääskeläinen E, Kiviniemi V, Nikkinen J, Moilanen J, Haapea M, Mäki P, Jones PB, Veijola J, Isohanni M, Murray GK. Longitudinal regional brain volume loss in schizophrenia: relationship to antipsychotic medication and change in social function. Schizophr Res. 2015;168(1–2):297–304. 141. Gur RE, Maany V, Mozley PD, Swanson C, Bilker W, Gur RC.  Subcortical MRI volumes in neuroleptic-naive and treated patients with schizophrenia. Am J Psychiatry. 1998;155(12):1711–7. 142. Tomelleri L, Jogia J, Perlini C, Bellani M, Ferro A, Rambaldelli G, Tansella M, Frangou S, Brambilla P, Neuroimaging Network of the ECNP networks initiative. Brain structural changes associated with chronicity and antipsychotic treatment in schizophrenia. Eur Neuropsychopharmacol. 2009;19(12):835–40. 143. Garver DL, Holcomb JA, Christensen JD. Cerebral cortical gray expansion associated with two second-generation antipsychotics. Biol Psychiatry. 2005;58(1):62–6. 144. McClure RK, Carew K, Greeter S, Maushauer E, Steen G, Weinberger DR.  Absence of regional brain volume change in schizophrenia associated with short-term atypical antipsychotic treatment. Schizophr Res. 2008;98(1–3):29–39. 145. Thompson PM, Bartzokis G, Hayashi KM, Klunder AD, Lu PH, Edwards N, Hong MS, Yu M, Geaga JA, Toga AW, Charles C, Perkins DO, McEvoy J, Hamer RM, Tohen M, Tollefson GD, Lieberman JA, HGDH Study Group. Time-lapse mapping of cortical changes in schizophrenia with different treatments. Cereb Cortex. 2009;19(5):1107–23. 146. Ho BC, Andreasen NC, Dawson JD, Wassink TH. Association between brain-derived neurotrophic factor Val66Met gene polymorphism and progressive brain volume changes in schizophrenia. Am J Psychiatry. 2007;164(12):1890–9. 147. Tauscher-Wisniewski S, Tauscher J, Logan J, Christensen BK, Mikulis DJ, Zipursky RB. Caudate volume changes in first episode psychosis parallel the effects of normal aging: a 5-year follow-up study. Schizophr Res. 2002;58(2-3):185–8. 148. Ebdrup BH, Skimminge A, Rasmussen H, Aggernaes B, Oranje B, Lublin H, Baaré W, Glenthøj B. Progressive striatal and hippocampal volume loss in initially antipsychotic-naive, first-episode schizophrenia patients treated with quetiapine: relationship to dose and symptoms. Int J Neuropsychopharmacol. 2011;14(1):69–82. 149. Goghari VM, Smith GN, Honer WG, Kopala LC, Thornton AE, Su W, Macewan GW, Lang DJ. Effects of eight weeks of atypical antipsychotic treatment on middle frontal thickness in drug-naive first-episode psychosis patients. Schizophr Res. 2013;149(1–3):149–55. 150. Ansell BR, Dwyer DB, Wood SJ, Bora E, Brewer WJ, Proffitt TM, Velakoulis D, McGorry PD, Pantelis C. Divergent effects of first-generation and second-generation antipsychotics on cortical thickness in first-episode psychosis. Psychol Med. 2015;45(3):515–27. 151. Crespo-Facorro B, Roiz-Santiáñez R, Pérez-Iglesias R, Pelayo-Terán JM, Rodríguez-­ Sánchez JM, Tordesillas-Gutiérrez D, Ramírez M, Martínez O, Gutiérrez A, de Lucas EM, Vázquez-Barquero JL. Effect of antipsychotic drugs on brain morphometry. A randomized controlled one-year follow-up study of haloperidol, risperidone and olanzapine. Prog Neuro-­ Psychopharmacol Biol Psychiatry. 2008;32(8):1936–43. 152. Jørgensen KN, Nesvåg R, Gunleiksrud S, Raballo A, Jönsson EG, Agartz I. First- and second-­ generation antipsychotic drug treatment and subcortical brain morphology in schizophrenia. Eur Arch Psychiatry Clin Neurosci. 2016;266(5):451–60. 153. Bartlett EJ, Brodie JD, Simkowitz P, Schlösser R, Dewey SL, Lindenmayer JP, Rusinek H, Wolkin A, Cancro R, Schiffer W. Effect of a haloperidol challenge on regional brain metabolism in neuroleptic-responsive and nonresponsive schizophrenic patients. Am J Psychiatry. 1998;155(3):337–43.

298

A. Vita et al.

154. Lahti AC, Weiler MA, Medoff DR, Tamminga CA, Holcomb HH. Functional effects of single dose first- and second-generation antipsychotic administration in subjects with schizophrenia. Psychiatry Res. 2005;139(1):19–30. 155. Lahti AC, Holcomb HH, Weiler MA, Medoff DR, Tamminga CA. Functional effects of antipsychotic drugs: comparing clozapine with haloperidol. Biol Psychiatry. 2003;53(7):601–8. 156. Bartlett EJ, Wolkin A, Brodie JD, Laska EM, Wolf AP, Sanfilipo M. Importance of pharmacologic control in PET studies: effects of thiothixene and haloperidol on cerebral glucose utilization in chronic schizophrenia. Psychiatry Res. 1991;40(2):115–24. 157. Buchsbaum MS, Potkin SG, Siegel BV Jr, Lohr J, Katz M, Gottschalk LA, Gulasekaram B, Marshall JF, Lottenberg S, Teng CY, Abel L, Plon L, Bunney WE. Striatal metabolic rate and clinical response to neuroleptics in schizophrenia. Arch Gen Psychiatry. 1992;49(12):966–74. 158. Miller DD, Rezai K, Alliger R, Andreasen NC.  The effect of antipsychotic medication on relative cerebral blood perfusion in schizophrenia: assessment with technetium-99m hexamethyl-propyleneamine oxime single photon emission computed tomography. Biol Psychiatry. 1997;41(5):550–9. 159. Miller DD, Andreasen NC, O’Leary DS, Watkins GL, Boles Ponto LL, Hichwa RD. Comparison of the effects of risperidone and haloperidol on regional cerebral blood flow in schizophrenia. Biol Psychiatry. 2001;49(8):704–15. 160. Buchsbaum MS, Wu JC, DeLisi LE, Holcomb HH, Hazlett E, Cooper-Langston K, Kessler R.  Positron emission tomography studies of basal ganglia and somatosensory cortex neuroleptic drug effects: differences between normal controls and schizophrenic patients. Biol Psychiatry. 1987;22(4):479–94. 161. Buchsbaum MS, Haznedar MM, Aronowitz J, Brickman AM, Newmark RE, Bloom R, Brand J, Goldstein KE, Heath D, Starson M, Hazlett EA.  FDG-PET in never previously medicated psychotic adolescents treated with olanzapine or haloperidol. Schizophr Res. 2007;94(1–3):293–305. 162. Scottish Schizophrenia Research Group. Regional cerebral blood flow in first-episode schizophrenia patients before and after antipsychotic drug treatment. Acta Psychiatr Scand. 1998;97(6):440–9. 163. Desco M, Gispert JD, Reig S, Sanz J, Pascau J, Sarramea F, Benito C, Santos A, Palomo T, Molina V. Cerebral metabolic patterns in chronic and recent-onset schizophrenia. Psychiatry Res. 2003;122(2):125–35. 164. Corson PW, O’Leary DS, Miller DD, Andreasen NC.  The effects of neuroleptic medications on basal ganglia blood flow in schizophreniform disorders: a comparison between the neuroleptic-naïve and medicated states. Biol Psychiatry. 2002;52(9):855–962. 165. Borghammer P, Hansen SB, Eggers C, Chakravarty M, Vang K, Aanerud J, Hilker R, Heiss WD, Rodell A, Munk OL, Keator D, Gjedde A.  Glucose metabolism in small subcortical structures in Parkinson’s disease. Acta Neurol Scand. 2012;125(5):303–10. 166. Berman I, Merson A, Sison C, Allan E, Schaefer C, Loberboym M, Losonczy MF. Regional cerebral blood flow changes associated with risperidone treatment in elderly schizophrenia patients: a pilot study. Psychopharmacol Bull. 1996;32(1):95–100. 167. Liddle PF, Lane CJ, Ngan ET. Immediate effects of risperidone on cortico-striato-thalamic loops and the hippocampus. Br J Psychiatry. 2000;177:402–7. 168. Ngan ET, Lane CJ, Ruth TJ, Liddle PF.  Immediate and delayed effects of risperidone on cerebral metabolism in neuroleptic naïve schizophrenic patients: correlations with symptom change. J Neurol Neurosurg Psychiatry. 2002;72(1):106–10. 169. Molina V, Tamayo P, Montes C, De Luxán A, Martin C, Rivas N, Sancho C, Domínguez-­ Gil A. Clozapine may partially compensate for task-related brain perfusion abnormalities in risperidone-resistant schizophrenia patients. Prog Neuro-Psychopharmacol Biol Psychiatry. 2008;32(4):948–54. 170. Molina V, Gispert JD, Reig S, Pascau J, Martínez R, Sanz J, Palomo T, Desco M. Olanzapine-­ induced cerebral metabolic changes related to symptom improvement in schizophrenia. Int Clin Psychopharmacol. 2005;20(1):13–8.

7  Neuroimaging and Antipsychotics

299

171. Gonul AS, Kula M, Sofuoglu S, Tutus A, Esel E. Tc-99 HMPAO SPECT study of regional cerebral blood flow in olanzapine-treated schizophrenic patients. Eur Arch Psychiatry Clin Neurosci. 2003;253(1):29–33. 172. Molina V, Gispert JD, Reig S, Sanz J, Pascau J, Santos A, Desco M, Palomo T. Cerebral metabolic changes induced by clozapine in schizophrenia and related to clinical improvement. Psychopharmacology. 2005;178(1):17–26. 173. Potkin SG, Basile VS, Jin Y, Masellis M, Badri F, Keator D, Wu JC, Alva G, Carreon DT, Bunney WE Jr, Fallon JH, Kennedy JL. D1 receptor alleles predict PET metabolic correlates of clinical response to clozapine. Mol Psychiatry. 2003;8(1):109–13. 174. Abbott CC, Jaramillo A, Wilcox CE, Hamilton DA. Antipsychotic drug effects in schizophrenia: a review of longitudinal fMRI investigations and neural interpretations. Curr Med Chem. 2013;20(3):428–37. 175. Kani AS, Shinn AK, Lewandowski KE, Öngür D. Converging effects of diverse treatment modalities on frontal cortex in schizophrenia: a review of longitudinal functional magnetic resonance imaging studies. J Psychiatr Res. 2017;84:256–76. 176. Meisenzahl EM, Scheuerecker J, Zipse M, Ufer S, Wiesmann M, Frodl T, Koutsouleris N, Zetzsche T, Schmitt G, Riedel M, Spellmann I, Dehning S, Linn J, Brückmann H, Möller HJ. Effects of treatment with the atypical neuroleptic quetiapine on working memory function: a functional MRI follow-up investigation. Eur Arch Psychiatry Clin Neurosci. 2006;256(8):522–31. 177. Jones HM, Brammer MJ, O’Toole M, Taylor T, Ohlsen RI, Brown RG, Purvis R, Williams S, Pilowsky LS. Cortical effects of quetiapine in first-episode schizophrenia: a preliminary functional magnetic resonance imaging study. Biol Psychiatry. 2004;56(12):938–42. 178. Honey GD, Bullmore ET, Soni W, Varatheesan M, Williams SC, Sharma T.  Differences in frontal cortical activation by a working memory task after substitution of risperidone for typical antipsychotic drugs in patients with schizophrenia. Proc Natl Acad Sci U S A. 1999;96(23):13432–7. 179. Schlagenhauf F, Dinges M, Beck A, Wüstenberg T, Friedel E, Dembler T, Sarkar R, Wrase J, Gallinat J, Juckel G, Heinz A. Switching schizophrenia patients from typical neuroleptics to aripiprazole: effects on working memory dependent functional activation. Schizophr Res. 2010;118(1–3):189–200. 180. Schlagenhauf F, Wüstenberg T, Schmack K, Dinges M, Wrase J, Koslowski M, Kienast T, Bauer M, Gallinat J, Juckel G, Heinz A. Switching schizophrenia patients from typical neuroleptics to olanzapine: effects on BOLD response during attention and working memory. Eur Neuropsychopharmacol. 2008;18(8):589–99. 181. Snitz BE, MacDonald A 3rd, Cohen JD, Cho RY, Becker T, Carter CS.  Lateral and medial hypofrontality in first-episode schizophrenia: functional activity in a medication-­naive state and effects of short-term atypical antipsychotic treatment. Am J Psychiatry. 2005;162(12):2322–9. 182. van Veelen NM, Vink M, Ramsey NF, van Buuren M, Hoogendam JM, Kahn RS. Prefrontal lobe dysfunction predicts treatment response in medication-naive first-episode schizophrenia. Schizophr Res. 2011;129(2–3):156–62. 183. Lui S, Li T, Deng W, Jiang L, Wu Q, Tang H, Yue Q, Huang X, Chan RC, Collier DA, Meda SA, Pearlson G, Mechelli A, Sweeney JA, Gong Q. Short-term effects of antipsychotic treatment on cerebral function in drug-naive first-episode schizophrenia revealed by “resting state” functional magnetic resonance imaging. Arch Gen Psychiatry. 2010;67(8):783–92. 184. Davidson M, Galderisi S, Weiser M, Werbeloff N, Fleischhacker WW, Keefe RS, Boter H, Keet IP, Prelipceanu D, Rybakowski JK, Libiger J, Hummer M, Dollfus S, López-Ibor JJ, Hranov LG, Gaebel W, Peuskens J, Lindefors N, Riecher-Rössler A, Kahn RS.  Cognitive effects of antipsychotic drugs in first-episode schizophrenia and schizophreniform disorder: a randomized, open-label clinical trial (EUFEST). Am J Psychiatry. 2009;166(6):675–82. 185. Keefe RS, Bilder RM, Davis SM, Harvey PD, Palmer BW, Gold JM, Meltzer HY, Green MF, Capuano G, Stroup TS, McEvoy JP, Swartz MS, Rosenheck RA, Perkins DO, Davis CE, Hsiao JK, Lieberman JA, CATIE Investigators; Neurocognitive Working Group. Neurocognitive effects of antipsychotic medications in patients with chronic schizophrenia in the CATIE Trial. Arch Gen Psychiatry. 2007;64(6):633–47.

300

A. Vita et al.

186. Nielsen RE, Levander S, Kjaersdam Telléus G, Jensen SO, Østergaard Christensen T, Leucht S.  Second-generation antipsychotic effect on cognition in patients with schizophrenia--a meta-analysis of randomized clinical trials. Acta Psychiatr Scand. 2015;131(3):185–96. 187. Lesh TA, Tanase C, Geib BR, Niendam TA, Yoon JH, Minzenberg MJ, Ragland JD, Solomon M, Carter CS. A multimodal analysis of antipsychotic effects on brain structure and function in first-episode schizophrenia. JAMA Psychiatry. 2015;72(3):226–34. 188. Grace AA, Bunney BS, Moore H, Todd CL. Dopamine-cell depolarization block as a model for the therapeutic actions of antipsychotic drugs. Trends Neurosci. 1997;20(1):31–7. 189. Miyamoto S, Miyake N, Jarskog LF, Fleischhacker WW, Lieberman JA.  Pharmacological treatment of schizophrenia: a critical review of the pharmacology and clinical effects of current and future therapeutic agents. Mol Psychiatry. 2012;17(12):1206–27. 190. Howes OD, Kambeitz J, Kim E, Stahl D, Slifstein M, Abi-Dargham A, Kapur S. The nature of dopamine dysfunction in schizophrenia and what this means for treatment. Arch Gen Psychiatry. 2012;69(8):776–86. 191. Heinz A, Knable MB, Weinberger DR. Dopamine D2 receptor imaging and neuroleptic drug response. J Clin Psychiatry. 1996;57(Suppl 11):84–8. 192. Heinz A, Knable MB, Coppola R, Gorey J, Jones D, Lee KS, Weinberger DR. Psychomotor slowing, negative symptoms, and dopamine receptor availability - an IBZM SPECT study in neuroleptically treated and drug-free schizophrenic patients. Schizophr Res. 1998;31:19–26. 193. Lataster J, van Os J, de Haan L, Thewissen V, Bak M, Lataster T, Lardinois M, Delespaul PA, Myin-Germeys I. Emotional experience and estimates of D2 receptor occupancy in psychotic patients treated with haloperidol, risperidone, or olanzapine: an experience sampling study. J Clin Psychiatry. 2011;72(10):1397–404. 194. Seeman P, Lee T. Antipsychotic drugs: direct correlation between clinical potency and presynaptic action on dopamine neurons. Science. 1975;188:1217–9. 195. Stone JM, Davis JM, Leucht S, Pilowsky LS. Cortical dopamine D2/D3 receptors are a common site of action for antipsychotic drugs  – an original patient data meta-analysis of the SPECT and PET in vivo receptor imaging literature. Schizophr Bull. 2008;35:789–97. 196. Howes O, Bose S, Turkheimer F, Valli I, Egerton A, Stahl D, Valmaggia L, Allen P, Murray R, McGuire P. Progressive increase in striatal dopamine synthesis capacity as patients develop psychosis: a PET study. Mol Psychiatry. 2011;16(9):885–6. 197. Gründer G, Vernaleken I, Müller MJ, Davids E, Heydari N, Buchholz HG, Bartenstein P, Munk OL, Stoeter P, Wong DF, Gjedde A, Cumming P.  Subchronic haloperidol downregulates dopamine synthesis capacity in the brain of schizophrenic patients in  vivo. Neuropsychopharmacology. 2003;28(4):787–94. 198. Demjaha A, Murray RM, McGuire PK, Kapur S, Howes OD. Dopamine synthesis capacity in patients with treatment-resistant schizophrenia. Am J Psychiatry. 2012;169:1203–10. 199. Merritt K, Egerton A, Kempton MJ, Taylor MJ, McGuire PK. Nature of glutamate alterations in schizophrenia: a meta-analysis of proton magnetic resonance spectroscopy studies. JAMA Psychiatry. 2016;73(7):665–74. 200. Egerton A, Modinos G, Ferrera D, McGuire P. Neuroimaging studies of GABA in schizophrenia: a systematic review with meta-analysis. Transl Psychiatry. 2017;7(6):e1147. 201. Egerton A, Bhachu A, Merritt K, McQueen G, Szulc A, McGuire P. Effects of antipsychotic administration on brain glutamate in schizophrenia: a systematic review of longitudinal 1H-­ MRS studies. Front Psychiatry. 2017;8:66. 202. Steen RG, Hamer RM, Lieberman JA. Measurement of brain metabolites by 1H magnetic resonance spectroscopy in patients with schizophrenia: a systematic review and meta-­ analysis. Neuropsychopharmacology. 2005;30(11):1949–62. 203. Marsman A, van den Heuvel MP, Klomp DW, Kahn RS, Luijten PR, Hulshoff Pol HE.  Glutamate in schizophrenia: a focused review and meta-analysis of 1H-MRS studies. Schizophr Bull. 2013;39(1):120–9. 204. Fannon D, Simmons A, Tennakoon L, O’Céallaigh S, Sumich A, Doku V, Shew C, Sharma T.  Selective deficit of hippocampal N-acetylaspartate in antipsychotic-naive patients with schizophrenia. Biol Psychiatry. 2003;54(6):587–98.

7  Neuroimaging and Antipsychotics

301

205. Bustillo JR, Rowland LM, Jung R, Brooks WM, Qualls C, Hammond R, Hart B, Lauriello J. Proton magnetic resonance spectroscopy during initial treatment with antipsychotic medication in schizophrenia. Neuropsychopharmacology. 2008;33(10):2456–66. 206. Gan JL, Cheng ZX, Duan HF, Yang JM, Zhu XQ, Gao CY. Atypical antipsychotic drug treatment for 6 months restores N-acetylaspartate in left prefrontal cortex and left thalamus of first-episode patients with early onset schizophrenia: a magnetic resonance spectroscopy study. Psychiatry Res. 2014;223(1):23–7. 207. Bustillo JR, Chen H, Jones T, Lemke N, Abbott C, Qualls C, Canive J, Gasparovic C. Increased glutamine in patients undergoing long-term treatment for schizophrenia: a proton magnetic resonance spectroscopy study at 3 T. JAMA Psychiatry. 2014;71(3):265–72. 208. Lindenmayer JP. Treatment refractory schizophrenia. Psychiatr Q. 2000;71(4):373–84. 209. Emsley R, Rabinowitz J, Medori R. Time course for antipsychotic treatment response in first-­ episode schizophrenia. Am J Psychiatry. 2006;163(4):743–5. 210. Rubio JM, Kane JM.  Psychosis breakthrough on antipsychotic maintenance medication (BAMM): what can we learn? NPJ Schizophr. 2017;3(1):36. 211. Demjaha A, Egerton A, Murray RM, Kapur S, Howes OD, Stone JM, McGuire PK. Antipsychotic treatment resistance in schizophrenia associated with elevated glutamate levels but normal dopamine function. Biol Psychiatry. 2014;75(5):e11–3. 212. Howes OD, McCutcheon R, Agid O, de Bartolomeis A, van Beveren NJ, Birnbaum ML, et al. Treatment-resistant schizophrenia: treatment response and resistance in psychosis (TRRIP) working group consensus guidelines on diagnosis and terminology. Am J Psychiatry. 2017;174(3):216–29. 213. Gillespie AL, Samanaite R, Mill J, Egerton A, MacCabe JH. Is treatment-resistant schizophrenia categorically distinct from treatment-responsive schizophrenia? A systematic review. BMC Psychiatry. 2017;17(1):12. 214. Falkenberg LE, Westerhausen R, Craven AR, Johnsen E, Kroken RA, L Berg EM, Specht K, Hugdahl K.  Impact of glutamate levels on neuronal response and cognitive abilities in schizophrenia. Neuroimage Clin. 2014;4:576–84. 215. Halim ND, Weickert CS, McClintock BW, Weinberger DR, Lipska BK. Effects of chronic haloperidol and clozapine treatment on neurogenesis in the adult rat hippocampus. Neuropsychopharmacology. 2004;29(6):1063–9. 216. Angelucci F, Aloe L, Iannitelli A, Gruber SH, Mathé AA.  Effect of chronic olanzapine treatment on nerve growth factor and brain-derived neurotrophic factor in the rat brain. Eur Neuropsychopharmacol. 2005;15(3):311–7. 217. Millan MJ.  N-Methyl-d-aspartate receptors as a target for improved antipsychotic agents: novel insights and clinical perspectives. Psychopharmacology. 2005;179(1):30–53. 218. Konopaske GT, Dorph-Petersen KA, Pierri JN, Wu Q, Sampson AR, Lewis DA.  Effect of chronic exposure to antipsychotic medication on cell numbers in the parietal cortex of macaque monkeys. Neuropsychopharmacology. 2007;32(6):1216–23. 219. De Rossi P, Chiapponi C, Spalletta G.  Brain functional effects of psychopharmacological treatments in schizophrenia: a network-based functional perspective beyond neurotransmitter systems. Curr Neuropharmacol. 2015;13(4):435–44. 220. Van Haren NE, Cahn W, Hulshoff Pol HE, Kahn RS. Confounders of excessive brain volume loss in schizophrenia. Neurosci Biobehav Rev. 2013;37(10 Pt 1):2418–23.

8

Research Perspectives for Neuroimaging of Schizophrenia Spectrum Disorders Eleanor Scutt, Stefan Borgwardt, and Paolo Fusar-Poli

Abbreviations CHR Clinical high risk DTI Diffusion tensor imaging EEG Electroencephalography FA Fractional anisotropy FEP First-episode psychosis fMRI Functional magnetic resonance imaging MRI Magnetic resonance imaging PET Positron emission tomography SSDs Schizophrenia spectrum disorders SVM Support vector modelling

8.1  Introduction Current evidence from neuroimaging studies investigating schizophrenia spectrum disorders (SSDs) has suggested alterations in grey and white matter [1–3], ventricular volume [4, 5], structural and functional connectivity [6, 7] and neurotransmitter

E. Scutt · P. Fusar-Poli (*) Department of Psychosis Studies, Institute of Psychiatry Psychology and Neuroscience (IoPPN), King’s College London, London, UK e-mail: [email protected] S. Borgwardt Department of Psychiatry (UPK), University of Basel, Basel, Switzerland e-mail: [email protected] © Springer Nature Switzerland AG 2019 S. Galderisi et al. (eds.), Neuroimaging of Schizophrenia and Other Primary Psychotic Disorders, https://doi.org/10.1007/978-3-319-97307-4_8

303

304

E. Scutt et al.

levels [8]. Some of these findings have been consistent, for example, in the case of reduced cortical grey matter [1] and increased lateral ventricle volume [4]; however, others have been less clear with findings of both increased and decreased connectivity across several brain regions [6, 7]. Also of interest are regions that have consistently been associated with structural and neurochemical abnormalities, such as the striatum [8, 9] and the growing area of the role of the immune system in the pathology of SSDs [10]. Evidence from populations who are said to be at clinical high risk of developing psychosis [11] and patients who have experienced a first-episode psychosis (FEP) may shed further light on developmental biomarkers of these illnesses, free from the influence of antipsychotic medication. This review aims to summarise neuroimaging findings from 2017 across schizophrenia spectrum disorders. Only studies from 2017 have been included in order to give an insight into the current trends for research in this area and to summarise the progress made in increasing our understanding of SSDs. In particular the review will focus on progress that has been made using structural, functional, neurochemical and multimodal imaging techniques and discuss this in the context of existing knowledge (Fig. 8.1).

8.2  Methods We completed a search of the PubMed database on 1 October 2017 combining the terms “psychosis” and “neuroimaging” and “2017”. This yielded 173 results of which we included 60 studies. Studies were included in this review if they were original studies, used neuroimaging techniques, investigated people with a schizophrenia spectrum disorder, defined as a diagnosis of schizophrenia, schizophreniform disorder or schizoaffective disorder. To gain an understanding of imaging findings as these illnesses progress, we also included studies which focussed on first-episode psychosis patients (FEPs) or people at clinical high risk (CHR) of psychosis. For the purposes of this review, the CHR population includes studies that have defined high-risk populations as an at-risk and ultrahigh-risk mental state; for ease of understanding, these are referred to henceforth as CHR. Not included in this review are studies that have not specifically investigated a schizophrenia spectrum disorder, FEP or CHR population separately from other psychoses, for example, in studies where SSD diagnosis included psychotic disorder not otherwise specified [12] or substance-induced psychosis [13]. We also checked reference lists of the articles included for relevant studies that were not found in the initial search. Following these checks, 51 studies remained and were included in this review; these included 29 structural imaging studies, 14 functional imaging studies, 1 neurochemical study and 7 multimodal imaging studies. As this review encompasses CHR, FEP and SSDs, the paper review covered a wide age range and had a variety of medication use.

8  Research Perspectives for Neuroimaging of Schizophrenia Spectrum Disorders

a

Cortical thickness L R

MRI

(i)

(ii)

T1

b

(i)

DTI

R

Volume

305

L

T2

(iii)

(ii)

c

(i)

fMRI

(iii)

(ii)

Fig. 8.1  Structural and functional imaging techniques. (a) Magnetic resonance imaging (MRI) uses a strong magnetic field which causes hydrogen atoms to spin, and a radio-frequency pulse is applied which moves the spins onto a transverse plane (a, i).The scanner measures the relaxation time for atoms to realign with the longitudinal plane in T1 imaging or the transverse plane in T2 imaging. Relaxation times vary according to a proton’s environment, allowing for the distinction of different types of tissue. (a, ii) depicts MRI images in people with schizoaffective disorder, showing abnormalities in cortical thickness and grey matter volume compared with healthy controls [23]. (b) Diffusion tensor imaging (DTI) measures the diffusion of water molecules in white matter tracts. When the movement of water molecules is restricted, they move faster and are said to be more anisotropic (b, ii), rather than isotropic (b, i) where molecules can move freely. Fractional anisotropy is a measure of this where greater anisotropy is thought to reflect increased organisation of white matter fibres and myelination. (b, iii) illustrates areas of reduced fractional anisotropy in patients with schizophrenia compared with healthy controls [36]. (c) In functional magnetic resonance imaging (fMRI), activation of specific brain areas is detected through the measurement of the magnetic properties of the blood. Active areas receive an increased ratio of oxygenated (red circles) to deoxygenated blood (white circles) (c, ii), compared with a reduced ratio when resting (c, ii). A BOLD (blood-oxygen-level-dependent) response is measured through the detection of the different magnetic properties of oxygenated and deoxygenated blood. (c, iii) shows increased functional connectivity between the putamen and the right anterior insula in patients with schizophrenia during a psychotic episode [42]

8.3  Results 8.3.1 Structural Studies 8.3.1.1 Ventricle Volume and Grey Matter Three recent studies have looked at ventricular volume across different stages of schizophrenia, with all indicating potential abnormalities across each stage [14–16]. Although CHR patients did not appear to have significantly enlarged ventricles

306

E. Scutt et al.

compared with controls [16], there was evidence to suggest enlargement specifically in the left temporal horn of the lateral ventricle in CHR [17] which corresponded with a higher ratio to total brain volume. Furthermore, one of these studies suggested that enlargements in ventricle size over time in CHR youths was associated with reductions in cortical grey matter [15]. Berger’s [16] study suggested that although ventricular enlargement was not significant in FEPs or CHR, there was a linear trend for volume increase over the stages of illness. Enlargement of the lateral ventricles was associated with carrying two single-nucleotide polymorphisms which have been associated with psychosis transition in people with schizophrenia [18]. The size of the nucleus accumbens was investigated in FEPs in three studies, with one finding volume increases in this area compared with controls independent of medication use [19], another finding increases that were associated with exposure to antipsychotic medication [14] and the third suggesting that reductions in the volume of the nucleus accumbens were associated with poorer cognitive performance in a medication-naive sample [20]. Other studies also linked antipsychotic medication exposure with increases in volumes of the lateral ventricle and areas of the hippocampus [21] in FEPs. A possible explanation for abnormalities in limbic system structures was provided by Knöchel who reported that increased changes in apolipoprotein levels were associated with cognitive impairments and with reduced volume of the right hippocampus [22]. Reductions in cortical grey matter were reported in four studies [23–26] which included FEP, schizophrenia and schizoaffective patients. Notably, no studies reported increases in cortical grey matter and grey matter reductions in people with schizoaffective disorder were associated with cortical thinning rather than a reduction in surface area [23]. Reductions in cortical grey matter were found to be associated with auditory hallucinations in schizophrenia [26] and with reduced clinical insight in FEPs over the course of a year [24]. However a separate pilot study suggested that clinical insight was not related to structural covariance between the ventrolateral prefrontal cortex and the medial frontal cortex in FEPs [27]. Further investigation into cognition in FEPs found that the presence of FEP altered the relationship between grey matter volume and cognitive ability [28]. No studies investigated cortical thickness in a CHR population. Fronto-temporal cortical thickness was identified as a factor which could be used to accurately classify patients according to a diagnosis [29]; however, a further study found that structural measurements could not accurately predict a diagnosis or prognosis of a SSD illness [30]. Structural changes in white and grey matter were found to change across the lifespan of people with SSDs, suggesting that age may be a confounding factor in research [31]. Grey matter density was also found to be predictive biomarker in responses to repetitive transcranial magnetic stimulation in people with schizophrenia [32].

8.3.1.2 White Matter Abnormalities Of the studies using DTI, all six studies that investigated fractional anisotropy (FA) found reductions in both FEPs and people with SSDs in several brain regions, including the corpus callosum, and areas of the frontal and limbic regions [33–38].

8  Research Perspectives for Neuroimaging of Schizophrenia Spectrum Disorders

307

Three of these studies suggested that these abnormalities may have a genetic component [33, 34, 38], with Ren’s [38] study identifying genes which may be linked to the abnormalities in FA found in FEPs. As an extension to these findings, Rae [37] used a neurite orientation dispersion and density imaging technique which suggested that the reductions in FA and increases in mean diffusivity found were due to abnormalities in fibre number, density and myelination and not spatial organisation. One of these studies suggested that FA increases in FEPs following antipsychotic treatment [35]; this is in line with a further study which found that white matter streamlines increased following medication in FEPs [39]. Further evidence of structural network disorganisation comes from Schmidt’s [40] study which found that there was disorganisation of the highly interconnected “rich club” hubs in CHR patients, suggesting that abnormalities in connectivity may be evident prior to psychosis onset. Additionally Li [41] found that increased structural connectivity was predictive of response to electroconvulsive therapy.

8.3.2 Functional Studies 8.3.2.1 Functional Connectivity Of the five studies that investigated functional connectivity in SSDs, four found decreases in connectivity in several areas including the prefrontal cortex [43], the orbitofrontal cortex [44], the striatum and insula [42] and the visual cortex [45]; the fifth study found both increases and decreases in connectivity [46]. Gong’s study suggested that decreases in connectivity may be related to the presence of a psychiatric illness and not psychosis; specifically, the study actually found an increase in connectivity between different brain networks that was present only in FEP and not in other psychiatric illnesses [46]. Schmack [47] investigated functional connectivity during an experimentally induced belief task and found higher connectivity between the orbitofrontal cortex and the visual cortex in patients with schizophrenia. Connectivity abnormalities were also found in adolescents with schizophrenia during a mismatch negativity task [48]. Mason [49] investigated connectivity further and identified connective pathways which could be used to predict long-term positive symptoms (prefrontal cortex with postcentral gyrus) and affective symptoms (amygdala and inferior parietal lobe) following a course of cognitive behavioural therapy for psychosis. Two studies investigated functional connectivity as a potential biomarker for diagnosis of schizophrenia [50, 51]. One found that using naturalistic observations did not yield good prediction in FEPs than task-based fMRI techniques [51]. The other found that the use of functional near-infrared spectroscopy had a minimum of 80% accuracy in classing people into their different stages of schizophrenia using measurements of the intensity and timing of activity in the prefrontal cortex [50]. 8.3.2.2 Brain Activation Other studies have used functional magnetic resonance imaging (fMRI) techniques to investigate differences in brain activation during a task. Two studies found

308

E. Scutt et al.

Table 8.1  A summary of recently investigated potential biomarkers for schizophrenia spectrum disorders for each neuroimaging method Structural

Biomarker Reductions in cortical thickness

Functional

Abnormalities in functional connectivity

Neurochemical

Microglial activation

Mixed methodology

Altered striatal activity linked with increased synthesis of dopamine

Findings In addition to reduced cortical thickness, particularly in the frontal lobe [56], evidence suggests that an abnormal ratio between ventricular volumes and total brain grey matter is evident in the early stages of psychosis [17] Abnormalities in the default mode network and the rich club organisation have been identified in people with SSDs and FEPs [57] An increase in the immune response, resulting in abnormal activation of microglial cells, has been suggested in SSDs [10] Increased striatal dopaminergic activity has been associated with positive symptoms of SSDs [8], and recent evidence suggests that increased functional activity in the striatum may also be associated with these symptoms in CHR patients [58]

reductions in deactivation of the posterior cingulate cortex and precuneus and occipital cortex during an emotional processing task [52] and in the posterior cingulate cortex during a working memory task [53]. These regions are associated with the default mode network, a brain network of several brain regions that have a large amount of interactions with each other, and abnormalities in activation were apparent in patients and relatives of people with SSDs [52] and in CHR [53]. Altered activity in the somatosensory cortex was also found in patients with SSDs during a force processing task [54]. A further study also investigated brain complexity, suggesting that people with schizophrenia showed abnormalities in complexity in several brain regions, some of which were also seen in other psychoses [55] (Table 8.1).

8.3.3 Neurochemical Only one study has used solely neurochemical imaging methods, this used PET to measure binding to microglia [59], and this found no difference in microglial activity in the brains of people with schizophrenia, those at CHR or FEPs.

8.3.4 Multimodal Two studies have combined PET and MRI scanning to look at the association between activation of microglia and structural changes [60, 61]. One found that activation of microglia in people with schizophrenia was linked to reductions in cortical grey matter [60] with a non-significant trend for this in people at CHR; in contrast, the other found that there were no increases in microglial activation in

8  Research Perspectives for Neuroimaging of Schizophrenia Spectrum Disorders

309

people at CHR [61]. An additional study using PET and MRI found a higher availability of dopamine transporters in the striatum, midbrain and thalamus in people with chronic schizophrenia [62]. This is consistent with a study which found increased blood flow in the striatum but decreased blood flow in the prefrontal cortex across all stages of schizophrenia and that striatal activity correlated with positive symptoms [58]. Two further studies investigated neural connectivity with one finding disruptions in connectivity in first-episode adolescents which was associated with white matter alterations [63]; the other study also reported reduced and aberrant signalling during a mismatch negativity task using EEG [64]. Poorer cognitive performance was also linked to genetic risk in people with schizophrenia and their relatives; however this was not significantly linked to ventricular volume [65].

8.4  Discussion The majority of the studies in this review concerned patients with schizophrenia or FEP with few looking specifically at schizoaffective disorder, schizophreniform disorder or CHR populations. Overall findings suggest decreases in white and grey matter particularly in the frontal and temporal cortices and abnormalities in both structural and functional connectivity. Studies suggest early, albeit less marked abnormalities in CHR populations, however, given the small number of studies that investigated CHR, these findings require replication. Reductions in cortical grey matter and in total brain volume have been frequently reported in SSDs [1, 2, 66]. There is also suggestion that these deficits are apparent in the early stages of psychosis in people at CHR who go on to develop schizophrenia [67, 68] and FEPs [69, 70]. Recent studies also appear to suggest reductions in cortical grey matter in SSDs [23, 25], with some suggestion that this may be a biomarker for psychosis in FEPs [29]. The studies reviewed also suggest associations between cortical thickness and symptoms such as clinical insight [24], auditory hallucinations [26] and cognition [20, 28]. These alterations in grey matter have been linked to the increases in ventricular volume that are commonly reported in SSDs [4, 5, 15]. Consistent with this idea, increasing in ventricle volume [16] and decreasing grey matter volume [71] have been reported over the course of SSDs. However, there is some question as to whether the use of antipsychotic medication [72] could influence structural changes in people with SSDs. In support of this, two meta-analyses have suggested that changes in grey matter and ventricle volume in SSDs may be linked with the use of antipsychotic medication [73, 74]. Of the structural studies included in this review, two out of five studies that looked at ventricular volume and two of the six investigating cortical grey matter did not control for medication use. This may mean that any differences found are attributable to medication use. However, given that findings of ventricular volume increases and cortical grey matter decreases were consistent across studies that did control for medication use and abnormalities were found in antipsychotic

310

E. Scutt et al.

medication-naive CHR patients, differences appear to be apparently independent of medication use. Furthermore, two of the studies reviewed suggest that these structural abnormalities may have a genetic component and therefore may not be dependent on lifestyle factors [18, 25]. DTI and fMRI techniques have been widely used to investigate structural and functional connectivity, respectively. As suggested by Pettersson-Yeo [6], reductions in both structural and functional connectivities are apparent across the stages of schizophrenia; although these differences were more apparent in those with chronic schizophrenia, there was strong evidence for differences in FEPs and some evidence of deficits in CHR. The structural connectivity studies discussed have suggested widespread reductions in FA in SSDs [33, 35, 36]; this is consistent with the previous evidence which suggests structural disconnectivity [6, 75] that is apparent in white matter alterations in all stages of SSDs, including CHR and FEP [3]. Functional connectivity abnormalities have also been widely reported in SSDs [6]; consistent with this, the studies described in this review found abnormalities in brain connectivity in SSDs, with most reporting reduced connectivity across a range of regions including the prefrontal cortex [43]and the visual cortex [45, 47]. These studies were all conducted on people with chronic SSDs, and more work is needed to investigate functional connectivity in the earlier stages of these illnesses. Solé-Padullés’ [63] study used both functional and structural imaging techniques in FEPs, finding that reduced functional connectivity in the right/middle inferior cortex was correlated with less FA in this area. Only one study found evidence of increased internetwork functional connectivity [46], and this was accompanied by decreased intranetwork connectivity. This is consistent with Lynall’s suggestion that people with schizophrenia have less hub-dominated connections and instead a wider network of less integrated connectivity [76]. In support of this theory, disorganisation of the “rich club” network has been found in schizophrenia [77]. However, several studies have reported increased functional connectivity in corticostriatal brain areas and in the default mode network [7, 78–81]. These increases have also been found to correlate with symptom severity [82]. As suggested by Fornito [7], there may be several explanations for these discrepancies, including methodological limitations of global correction techniques, abnormalities in the crossing of fibre pathways or a compensatory response for reduced integrity. However, Rae’s [37] study suggests that reduced FA in FEPs was a product of reduced neuronal density, myelination and axonal counts in FEPs rather than disrupted fibre organisation. Alternatively, abnormalities in connectivity may be state dependent and subject to change following a course of treatment [9]. In particular, two of the reviewed studies have suggested that treatments for schizophrenia, CBT or ECT, may result in increased connectivity [41, 49]. This may signify that better connectivity at the baseline predicts response to treatment [39, 41] or that treatments could alter connectivity [49].

8  Research Perspectives for Neuroimaging of Schizophrenia Spectrum Disorders

311

Three of these studies [42, 53, 58] found abnormalities in connectivity with the striatum across all patient populations. The striatum has been implicated in schizophrenia as an area of hyper-dopaminergic activity [8], something which was corroborated by Artiges’ study which also found increased availability of dopamine transporters in the midbrain and limbic regions [62]. All three studies that investigated the size of the nucleus accumbens, a major area of the striatum, found abnormalities in FEPs, with two reporting increases [14, 19] and the other decreased volume [20]. Furthermore Peters suggested that aberrant connectivity of the putamen and the right anterior insula was associated with patient’s hallucinations [42]. This evidence complements existing research which implicates the striatum as a key structure in our understanding of SSDs and points to the need for further research using multimodal imaging techniques to investigate the relationships between these neural structures and functioning. Another avenue of investigation in SSD research is the idea that inflammation of the immune system may play a part in the development of the illness and in symptoms. Three studies in this review investigated microglial activation as a marker of inflammation, two of which found no significant changes in activation compared with healthy controls [59, 61] and the other found a link between microglial activation and cortical grey matter loss in schizophrenia [60]. As found by a recent systematic review, this is an area which needs further investigation [10]. A major goal for research into SSDs is to be able to identify individuals who are likely to develop a SSD and to use targeted interventions that are effective in preventing transition [83]. The CHR population is very heterogeneous, with around 27% of patients converting to psychosis [84]; therefore identification of those most likely to develop a SSD could allow for more targeted treatments. One possible method for the identification of biomarkers which could more accurately predict transition is stratification of the CHR population [83]. This may be done in a variety of ways from gene identification, the presence of mismatch negativity, or grey matter abnormalities or through immune or biochemical markers [83]. Multimodal techniques, which combine several biological or symptomatic measures, could also be used to improve predictions. Three studies have used the machine learning technique, support vector modelling (SVM), to identify individuals amongst a CHR group that will transition to psychosis with an 80–85% accuracy rate using MRI data [85–87]. However, as discussed by McGuire [88], these studies used small samples, and the findings need replication across different test sites. Additionally, further work is needed to investigate whether measures from different neuroimaging modalities can be used for prediction and could potentially be combined to increase accuracy [89]. A further aim of research in this area is to use biomarkers to target treatments to the individual. In order to achieve this, a better understanding of the ways in which treatments affect brain structure and connectivity is required. Antipsychotic

312

E. Scutt et al.

medication, commonly used to treat SSDs, works primarily on neurotransmitter systems [90, 91]; however, the precise mechanisms by which each type works and the effect that they have on brain structure and connectivity are not well understood [92, 93]. There is a lot of variation in the medication that patients respond to, and there has been suggestion that treatment response could depend on levels of presynaptic dopamine and glutamate [94, 95]. Further research is needed to investigate whether response to certain medications can be predicted using levels of neurotransmitters as a biomarker. Non-pharmacological therapies such as CBT have been associated with alterations in neural responses to threat through reductions in the activation of frontolimbic connections and alterations in cortico-limbic connectivity [96]. However, the studies reviewed in this meta-analysis included small, overlapping populations, and further evidence is needed on this and on the mechanisms involved in cognitive reappraisal of emotions and the effect of CBT on specific symptoms. Additionally, changes in neural connectivity following CBT can vary between people, and this may be associated with the subsequent outcomes of treatment [49]. Other psychological treatments such as cognitive remediation therapy or family-­based therapies may also have some benefit in altering the course of SSDs [97–99]; however, imaging studies are needed to investigate these in further detail. Other potential treatments such as moderation of the immune response through the use of minocycline, which can have anti-inflammatory and neuroprotective effects [100], have suggested some benefits in both animals and humans [100– 102]. However, as discussed, there is some question as to whether microglial abnormalities are apparent in SSDs [59–61]. Current trials of minocycline as an adjunctive therapy are ongoing, and more evidence is needed as to their efficacy.

8.5  Conclusion Current research reinforces the idea that SSDs are associated with structural, functional and neurochemical impairments in a wide range of brain areas. The research reviewed is consistent with previous evidence that suggests reductions in cortical grey and white matter and increases in ventricular volume in both FEPs and SSDs. The majority of evidence collected suggests both structural and functional impairments in connectivity, particularly in the prefrontal cortex of people with SSDs, and some abnormalities in connections in the striatum, previous findings on functional connectivity have been mixed, and more research is needed. Not enough evidence has been gathered this year on CHR populations to come to any conclusions; however, studies do appear to show some neural abnormalities in this early stage. Future research should identify biomarkers which could be used to predict transition to SSDs and to identify the treatments that are most likely to be effective for any given individual.

8  Research Perspectives for Neuroimaging of Schizophrenia Spectrum Disorders

313

 ppendix: Neuroimaging Studies Published in 2017 A Investigated Schizophrenia Spectrum Disorders, First-Episode Psychosis or Clinical High-Risk Populations

Authors Berger [16]

Neuroimaging method MRI

Participants 89 SCZ, 162 FEP, 135 CHR, 87 HC

Diagnostic manual DSM-III

Bousman [18] MRI + DTI

290 SCZ, 175 HC

DSM-IV

Buchy [24]

MRI

128 FEP

DSM-IV

Castro-de-­ Araujo [28]

MRI

100 FEP, 94 HC

DSM-IV

Chung [15]

MRI

267 CHR, 132 HC

DSM-IV

Cropley [31]

MRI + DTI

326 SCZ and schizoaffective, 197 HC

Crossley [39]

DTI

76 FEP 74 HC

Outcome A linear trend for increasing ventricular volume across progression of illness, reaching significance only for SCZ patients SCZ patients with risk single-nucleotide polymorphisms had larger lateral ventricle volumes (left and right) compared to schizophrenics without risk genes Clinical insight was not associated with cortical thickness at baseline, but worsening of clinical insight over time was linked with thinning in the dorsal postcentral and precentral gyri The presence of FEP altered the relationship between grey matter volume and cognition Ventricular enlargement over time was linked to grey matter reduction in the prefrontal cortex, superior temporal gyrus and parietal cortices in people at CHR Significant loss of grey matter progressively across illness, reduced FA after age of 35 At baseline FEPs who subsequently responded to antipsychotic medication showed better efficiency in structural connectomes, and these group differences were not apparent after 12 weeks of treatment (continued)

314

Authors Cuesta [14]

E. Scutt et al. Neuroimaging method MRI

Dempster [20] MRI

Participants 50 FEP, 24 HC, 21 unaffected relatives

Diagnostic manual DSM-IV

16 FEP

DSM-IV

Forns-Nadal [19]

MRI

31 FEP + 27 HC

DSM-IV

Klauser [36]

DTI

326 SSD, 197 HC

DSM-IV

Konishi [17]

MRI

19 CHR, 20 HC

DSM-IV

Koutsouleris [32]

MRI

92 SCZ

ICD-10

Knöchel [22]

MRI

29 SCZ, 25 BPD, 93 HC

DSM-IV

Outcome Patients had enlarged left lateral and right lateral ventricles compared with family and larger third ventricle than controls Reductions in grey matter in the nucleus accumbens, right globus pallidus, left inferior parietal lobe, Brodmann’s areas 40 and 7 and left superior parietal lobule were associated with poorer cognitive performance over time FEPs had increased nucleus accumbens volumes, and this was not correlated with negative symptoms of psychosis Patients showed reduced FA and increased MD in all lobes, and differences were pronounced in the thalamus, cingulum, corpus callosum and areas involved in the rich club organisation Enlarged temporal horn area of the lateral ventricle but reductions in amygdala and whole-brain volume leading to an abnormal ratio of temporal horn to total brain volume in CHR patients Individual responses to transcranial magnetic stimulation were predicted with 85% accuracy using measures of grey matter density Increased changes in apolipoprotein levels were associated with cognitive impairments and reduced volume in the right hippocampus of SCZ patients

8  Research Perspectives for Neuroimaging of Schizophrenia Spectrum Disorders

Authors Kuang [27]

Neuroimaging method MRI

Participants 15 FEP, 15 HC (historical)

Diagnostic manual

Landin-­ Romero [23]

MRI

45 schizoaffective, 45 HC

DSM-IV

Li [41]

MRI

34 SCZ, 34 HC

Mallas [34]

DTI

63 SCZ, 124 HC

DSM-IV

Mørch-­ Johnsen [26]

MRI

194 SSD

DSM-IV

Nieuwenhuis [30]

MRI

389 FEP

DSM-IV

Picchioni [53]

MRI

70 SCZ, 16 MZ discordant twins, 6 DZ discordant twins, 76 HC

DSM-IV

315

Outcome Cortical thickness in ventrolateral PFC did not covary with other brain areas in FEPs People with schizoaffective disorder showed grey and white matter reductions in the frontal cortices, left insula, bilateral temporal lobes and posterior cingulate cortex and precuneus and fusiform cortex Better connectivity in the default mode network, the temporal lobe, the language network, the corticostriatal network, the frontoparietal network and the cerebellum was predictive of increased response to electroconvulsive therapy in patients People with CACNA1C gene and SCZ had reduced FA compared to those without the gene, and FA was reduced in SCZ in general compared with controls Patients with auditory hallucinations had thinner cortex in left Heschl’s gyrus; no differences in planum temporale or superior temporal gyrus compared with those without hallucinations Gender but not diagnosis or prognosis of psychotic disorders could be accurately predicted Whole-brain, grey matter and white matter volumes were reduced in SCZ, and there was a correlation between these volume reductions and schizophrenia liability in discordant co-twins (continued)

316

E. Scutt et al.

Authors Rae [37]

Neuroimaging method DWI + MRI

Participants 35 FEP, 19 HC

Diagnostic manual

Ren [38]

DTI

100 FEP, 140 HC

DSM-IV

Rhindress [21]

MRI

29 FEP, 29 HC

DSM-IV

Schmidt [40]

DTI

24 CHR, 24 HC

ICD-10

Serpa [35]

MRI + DTI

25 FEP, 1 HC

DSM-IV

Squarcina [29]

MRI

127 FEP, 127 HC

ICD-10

Outcome FEP patients had reduced FA in multiple commissural, corticospinal and association tracts; this was associated with abnormalities in fibre number, density and myelination Reduced FA in left anterior cingulate cortex, right anterior cingulate cortex, left inferior parietal cortex, left posterior cingulate cortex and right posterior cingulate cortex which were associated with eight gene variations and one cell cycle pathway variation Following antipsychotic treatment, patients showed reductions in dentate gyrus/CA4 volume and increases in subiculum, and there were no significant changes in hippocampal volume in healthy controls Rich club organisation was impaired in people at risk of psychosis, and greater impairments were correlated with increased severity of negative symptoms Reduced FA in white matter tracts in the fronto-limbic and the associative, projective and commissural fasciculi in FEP; FA increased upon symptom remission following antipsychotic medication Fronto-temporal cortical thickness can be used as a potential marker to classify patients with FEP

8  Research Perspectives for Neuroimaging of Schizophrenia Spectrum Disorders

Authors Zhou [33]

Neuroimaging method DTI

Participants 48 FEP, 37 GHR, 67 HC

Diagnostic manual DSM-IV

Anderson [45]

fMRI

18 SCZ, 2 HC

DSM-IV

Braeutigam [48]

MEG

15 SCZ, 16 BPD, 14 HC (all adolescents)

DSM-IV

Falkenberg [53]

fMRI

34 CHR, 20 HC

Gong [46]

fMRI

50 FEP, 122 HC

DSM-IV

Hager [55]

fMRI

107 SCZ, 156 HC, 125 BPD, 98 schizoaffective, 230 healthy relatives

DSM-IV

317

Outcome Decreased FA in corpus callosum, anterior cingulum and uncinate fasciculus for both SCZ and GHR groups, decreased FA in fornix and superior longitudinal fasciculus in SCZ Patients showed a reduction in the population receptive field of neurons and a reduction in the inhibitory surround in V1, V2 and V4 Adolescents with schizophrenia (FEP) displayed reduced amplitude of MEG waves following mismatch negativity task, and connections appeared to be dominated by the right hemisphere Altered frontal and cuneus/posterior cingulate activation in UHR; amongst those with poor outcome, there was altered activation of frontal temporal and striatal regions FEPs showed decreased intranetwork connectivity and increased internetwork connectivity in drug-naive FEP compared with HC, and aberrant internetwork connectivity was particularly associated with psychotic symptoms and not MDD or PTSD People with SCZ showed decreased neural complexity towards a regular signal in hypothalamus, and SCZ and schizoaffective patients showed increased complexity in PFC (continued)

318

Authors Koike [50]

E. Scutt et al. Neuroimaging method fNIRS

Participants 47 CHR, 30 FEP, 34 SCZ, 33 HC

Diagnostic manual DSM-IV

Martinelli [54]

fMRI

21 SCZ, 26 HCs

DSM-IV

Mason [49]

fMRI

22 SCZ intervention, 16 SCZ controls

Rikandi [51]

fMRI

46 FEP 32 HC

DSM-IV

Peters [42]

fMRI

21 SCZ, 42 HC

DSM-IV

Schmack [47]

fMRI

21 SCZ, 28 HC

ICD-10

Spilka [52]

fMRI

28 SCZ, 27 GHR, 27 HC

DSM-IV

Outcome The sum of signal changes during the task and the timing of the blood response relative to the task had an 80–90% accuracy in classifying people as non-patient, UHR, FEP or SCZ Patients showed greater brain activation when force was self-generated as opposed to externally produced; this was the opposite of HCs Following CBT for psychosis, long-term psychotic symptoms were predicted by alterations in connectivity in the prefrontal cortex; alterations in connectivity between the amygdala and parietal lobe were predictive of long-term affective symptoms Activity in the precuneus during a fantasy film could be used to classify patients as FEP or healthy controls with 79.5% accuracy Decreased in functional connectivity between the putamen and right interior insula, dorsomedial and dorsolateral PFC and ventral striatum and left anterior insula in people with SCZ during a psychotic episode compared with healthy controls Belief-related connectivity between the orbitofrontal cortex and visual cortex was higher in patients compared with HC Patients with SCZ showed impairments in both age and emotion discrimination during a task and showed reduced activation of the medial prefrontal cortex

8  Research Perspectives for Neuroimaging of Schizophrenia Spectrum Disorders

Authors Xu [44]

Neuroimaging method fMRI

Participants 98 SCZ, 102 HC

Diagnostic manual DSM-IV

Wu [43]

fMRI

45 SCZ, 45 HC

DSM-IV

Di Biase [59]

PET

10 CHR, 18 FEP, 15 SCZ, 27 HC

DSM-IV

Artiges [62]

PET + MRI

21 SCZ, 30 HC

DSM-IV

Hafizi [61]

PET + MRI

24 CHR, 23 HC

DSM-IV

Kim [64]

EEG + MRI

29 SCZ, 40 CHR, 47 HC

DSM-IV

Kindler [58]

MRI + fMRI

32 SCZ, 31 HC, 29 CHR, 12 FEP, 31 HC

ICD-10

Ranlund [65]

MRI + EEG

703 SCZ, 68 schizophreniform, 60 schizoaffective, 2794 HC

DSM-IV

319

Outcome Patients showed decreased functional connectivity between the orbitofrontal cortex subregions Patients with impaired working memory capacity and decreased brain activation/deactivation showed decreased functional activation of the dorsolateral prefrontal cortex with the angular cortex compared with controls No evidence of altered microglial activation in UHR, FE or SCZ Higher dopamine transporter availability in the midbrain, in striatal and limbic regions and in amygdala/hippocampus positively correlated with positive symptoms No significant activation of microglia in dorsolateral prefrontal cortex or hippocampus in CHR compared with HC Significant deficits in mismatch negativity and current source density strength found in temporal and frontal cortices in people with SCZ and CHR Increased regional blood flow in the striatum and decreased regional blood flow in the prefrontal cortex in CHR, FEP and SCZ compared with controls Polygenic risk scores predicted poorer performance on a cognitive block task for people with SCZ, relatives and controls; SCZ patients had higher polygenic risk scores (continued)

320

Authors Selvaraj [60]

Solé-­ Padullés [63]

E. Scutt et al. Neuroimaging method PET + MRI

fMRI + DTI

Participants 14 SCZ, 14 CHR, 22 HC

44 CHR, 34 FEP, 35 HC (all adolescents)

Diagnostic manual DSM-IV

DSM-IV

Outcome Microglia activation was associated with cortical grey matter loss in SCZ, and there was a trend for this in UHR Reduced intrinsic functional connectivity in the right middle/inferior gyrus in patients compared with controls; values for CHR were intermediate between FEP and controls

References 1. Ellison-Wright I, Glahn DC, Laird AR, Thelen SM, Bullmore E.  The anatomy of first-­ episode and chronic schizophrenia: an anatomical likelihood estimation meta-analysis. Am J Psychiatry. 2008;165(8):1015–23. 2. Birur B, Kraguljac NV, Shelton RC, Lahti AC. Brain structure, function, and neurochemistry in schizophrenia and bipolar disorder-a systematic review of the magnetic resonance neuroimaging literature. NPJ Schizophr. 2017;3:15. 3. Canu E, Agosta F, Filippi M. A selective review of structural connectivity abnormalities of schizophrenic patients at different stages of the disease. Schizophr Res. 2015;161(1):19–28. 4. van Erp TG, Hibar DP, Rasmussen JM, Glahn DC, Pearlson GD, Andreassen OA, et  al. Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol Psychiatry. 2016;21(4):585. 5. Horga G, Bernacer J, Dusi N, Entis J, Chu K, Hazlett EA, et al. Correlations between ventricular enlargement and gray and white matter volumes of cortex, thalamus, striatum, and internal capsule in schizophrenia. Eur Arch Psychiatry Clin Neurosci. 2011;261(7):467–76. 6. Pettersson-Yeo W, Allen P, Benetti S, McGuire P, Mechelli A. Dysconnectivity in schizophrenia: where are we now? Neurosci Biobehav Rev. 2011;35(5):1110–24. 7. Fornito A, Bullmore ET. Reconciling abnormalities of brain network structure and function in schizophrenia. Curr Opin Neurobiol. 2015;30:44–50. 8. Howes OD, Kapur S. The dopamine hypothesis of schizophrenia: version III—the final common pathway. Schizophr Bull. 2009;35(3):549–62. 9. Sarpal DK, Robinson DG, Lencz T, Argyelan M, Ikuta T, Karlsgodt K, et al. Antipsychotic treatment and functional connectivity of the striatum in first-episode schizophrenia. JAMA Psychiatry. 2015;72(1):5–13. 10. Laskaris LE, Di Biase MA, Everall I, Chana G, Christopoulos A, Skafidas E, et  al. Microglial activation and progressive brain changes in schizophrenia. Br J Pharmacol. 2016;173(4):666–80. 11. Yung AR, Phillips LJ, Yuen HP, Francey SM, McFarlane CA, Hallgren M, et al. Psychosis prediction: 12-month follow up of a high-risk (“prodromal”) group. Schizophr Res. 2003;60(1):21–32. 12. Sarpal DK, Robinson DG, Fales C, Lencz T, Argyelan M, Karlsgodt KH, et al. Relationship between duration of untreated psychosis and intrinsic corticostriatal connectivity in patients with early phase schizophrenia. Neuropsychopharmacology. 2017;42(11):2214–21. 13. Altamura AC, Delvecchio G, Paletta S, Di Pace C, Reggiori A, Fiorentini A, et al. Gray matter volumes may predict the clinical response to paliperidone palmitate long-acting in acute psychosis: a pilot longitudinal neuroimaging study. Psychiatry Res. 2017;261:80–4.

8  Research Perspectives for Neuroimaging of Schizophrenia Spectrum Disorders

321

14. Cuesta MJ, Lecumberri P, Cabada T, Moreno-Izco L, Ribeiro M, López-Ilundain JM, et al. Basal ganglia and ventricle volume in first-episode psychosis. A family and clinical study. Psychiatry Res. 2017;269:90–6. 15. Chung Y, Haut KM, He G, van Erp TG, McEwen S, Addington J, et al. Ventricular enlargement and progressive reduction of cortical gray matter are linked in prodromal youth who develop psychosis. Schizophr Res. 2017;189:169. 16. Berger GE, Bartholomeusz CF, Wood SJ, Ang A, Phillips LJ, Proffitt T, et  al. Ventricular volumes across stages of schizophrenia and other psychoses. Aust N Z J Psychiatry. 2017;51(10):1041–51. 17. Konishi J, Del Re EC, Bouix S, Blokland GAM, Mesholam-Gately R, Woodberry K, et al. Abnormal relationships between local and global brain measures in subjects at clinical high risk for psychosis: a pilot study. Brain Imaging Behav. 2017. https://doi.org/10.1007/ s11682-017-9758-z. 18. Bousman CA, Cropley V, Klauser P, Hess JL, Pereira A, Idrizi R, et al. Neuregulin-1 (NRG1) polymorphisms linked with psychosis transition are associated with enlarged lateral ventricles and white matter disruption in schizophrenia. Psychol Med. 2018;48:801–9. 19. Forns-Nadal M, Bergé D, Sem F, Mané A, Igual L, Guinart D, et al. Increased nucleus accumbens volume in first-episode psychosis. Psychiatry Res. 2017;263:57–60. 20. Dempster K, Norman R, Théberge J, Densmore M, Schaefer B, Williamson P.  Cognitive performance is associated with gray matter decline in first-episode psychosis. Psychiatry Res. 2017;264:46–51. 21. Rhindress K, Robinson DG, Gallego JA, Wellington R, Malhotra AK, Szeszko PR. Hippocampal subregion volume changes associated with antipsychotic treatment in first-­ episode psychosis. Psychol Med. 2017;47(10):1706–18. 22. Knöchel C, Kniep J, Cooper JD, Stäblein M, Wenzler S, Sarlon J, et al. Altered apolipoprotein C expression in association with cognition impairments and hippocampus volume in schizophrenia and bipolar disorder. Eur Arch Psychiatry Clin Neurosci. 2017;267(3):199–212. 23. Landin-Romero R, Canales-Rodríguez EJ, Kumfor F, Moreno-Alcázar A, Madre M, Maristany T, et  al. Surface-based brain morphometry and diffusion tensor imaging in schizoaffective disorder. Aust N Z J Psychiatry. 2017;51(1):42–54. 24. Buchy L, Makowski C, Malla A, Joober R, Lepage M.  Longitudinal trajectory of clinical insight and covariation with cortical thickness in first-episode psychosis. J Psychiatr Res. 2017;86:46–54. 25. Picchioni MM, Rijsdijk F, Toulopoulou T, Chaddock C, Cole JH, Ettinger U, et al. Familial and environmental influences on brain volumes in twins with schizophrenia. J Psychiatry Neurosci. 2017;42(2):122–30. 26. Mørch-Johnsen L, Nesvåg R, Jørgensen KN, Lange EH, Hartberg CB, Haukvik UK, et al. Auditory cortex characteristics in schizophrenia: associations with auditory hallucinations. Schizophr Bull. 2017;43(1):75–83. 27. Kuang C, Buchy L, Barbato M, Makowski C, MacMaster FP, Bray S, et al. A pilot study of cognitive insight and structural covariance in first-episode psychosis. Schizophr Res. 2017;179:91–6. 28. Castro-de-Araujo LFS, Kanaan RAA. First episode psychosis moderates the effect of gray matter volume on cognition. Psychiatry Res. 2017;266:108–13. 29. Squarcina L, Castellani U, Bellani M, Perlini C, Lasalvia A, Dusi N, et al. Classification of first-episode psychosis in a large cohort of patients using support vector machine and multiple kernel learning techniques. NeuroImage. 2017;145(Pt B):238–45. 30. Nieuwenhuis M, Schnack HG, van Haren NE, Lappin J, Morgan C, Reinders AA, et  al. Multi-center MRI prediction models: predicting sex and illness course in first episode psychosis patients. NeuroImage. 2017;145(Pt B):246–53. 31. Cropley VL, Klauser P, Lenroot RK, Bruggemann J, Sundram S, Bousman C, et  al. Accelerated gray and white matter deterioration with age in schizophrenia. Am J Psychiatry. 2017;174(3):286–95. 32. Koutsouleris N, Wobrock T, Guse B, Langguth B, Landgrebe M, Eichhammer P, et  al. Predicting response to repetitive transcranial magnetic stimulation in patients with schizo-

322

E. Scutt et al.

phrenia using structural magnetic resonance imaging: a multisite machine learning analysis. Schizophr Bull. 2017. https://doi.org/10.1093/schbul/sbx114. 33. Zhou Y, Liu J, Driesen N, Womer F, Chen K, Wang Y, et al. White matter integrity in genetic high-risk individuals and first-episode schizophrenia patients: similarities and disassociations. Biomed Res Int. 2017;2017:3107845. 34. Mallas E, Carletti F, Chaddock CA, Shergill S, Woolley J, Picchioni MM, et al. The impact of CACNA1C gene, and its epistasis with ZNF804A, on white matter microstructure in health, schizophrenia and bipolar disorder(1). Genes Brain Behav. 2017;16(4):479–88. 35. Serpa MH, Doshi J, Erus G, Chaim-Avancini TM, Cavallet M, van de Bilt MT, et al. State-­ dependent microstructural white matter changes in drug-naïve patients with first-episode psychosis. Psychol Med. 2017;47(15):2613–27. 36. Klauser P, Baker ST, Cropley VL, Bousman C, Fornito A, Cocchi L, et  al. White matter disruptions in schizophrenia are spatially widespread and topologically converge on brain network hubs. Schizophr Bull. 2017;43(2):425–35. 37. Rae CL, Davies G, Garfinkel SN, Gabel MC, Dowell NG, Cercignani M, et al. Deficits in neurite density underlie white matter structure abnormalities in first-episode psychosis. Biol Psychiatry. 2017;82(10):716–25. 38. Ren HY, Wang Q, Lei W, Zhang CC, Li YF, Li XJ, et al. The common variants implicated in microstructural abnormality of first episode and drug-naïve patients with schizophrenia. Sci Rep. 2017;7(1):11750. 39. Crossley NA, Marques TR, Taylor H, Chaddock C, Dell’Acqua F, Reinders AA, et  al. Connectomic correlates of response to treatment in first-episode psychosis. Brain. 2017;140(2):487–96. 40. Schmidt A, Crossley NA, Harrisberger F, Smieskova R, Lenz C, Riecher-Rössler A, et al. Structural network disorganization in subjects at clinical high risk for psychosis. Schizophr Bull. 2017;43(3):583–91. 41. Li P, Jing RX, Zhao RJ, Ding ZB, Shi L, Sun HQ, et al. Electroconvulsive therapy-induced brain functional connectivity predicts therapeutic efficacy in patients with schizophrenia: a multivariate pattern recognition study. NPJ Schizophr. 2017;3(1):21. 42. Peters H, Riedl V, Manoliu A, Scherr M, Schwerthöffer D, Zimmer C, et al. Changes in extra-­ striatal functional connectivity in patients with schizophrenia in a psychotic episode. Br J Psychiatry. 2017;210(1):75–82. 43. Wu S, Wang H, Chen C, Zou J, Huang H, Li P, et al. Task performance modulates functional connectivity involving the dorsolateral prefrontal cortex in patients with schizophrenia. Front Psychol. 2017;8:56. 44. Xu Y, Qin W, Zhuo C, Xu L, Zhu J, Liu X, et al. Selective functional disconnection of the orbitofrontal subregions in schizophrenia. Psychol Med. 2017;47(9):1637–46. 45. Anderson EJ, Tibber MS, Schwarzkopf DS, Shergill SS, Fernandez-Egea E, Rees G, et al. Visual population receptive fields in people with schizophrenia have reduced inhibitory surrounds. J Neurosci. 2017;37(6):1546–56. 46. Gong Q, Hu X, Pettersson-Yeo W, Xu X, Lui S, Crossley N, et al. Network-level dysconnectivity in drug-naïve first-episode psychosis: dissociating transdiagnostic and diagnosis-­ specific alterations. Neuropsychopharmacology. 2017;42(4):933–40. 47. Schmack K, Rothkirch M, Priller J, Sterzer P. Enhanced predictive signalling in schizophrenia. Hum Brain Mapp. 2017;38(4):1767–79. 48. Braeutigam S, Dima D, Frangou S, James A. Dissociable auditory mismatch response and connectivity patterns in adolescents with schizophrenia and adolescents with bipolar disorder with psychosis: a magnetoencephalography study. Schizophr Res. 2018;193:313. 49. Mason L, Peters E, Williams SC, Kumari V. Brain connectivity changes occurring following cognitive behavioural therapy for psychosis predict long-term recovery. Transl Psychiatry. 2017;7(8):e1209. 50. Koike S, Satomura Y, Kawasaki S, Nishimura Y, Kinoshita A, Sakurada H, et al. Application of functional near infrared spectroscopy as supplementary examination for diagnosis of clinical stages of psychosis spectrum. Psychiatry Clin Neurosci. 2017;71:794.

8  Research Perspectives for Neuroimaging of Schizophrenia Spectrum Disorders

323

51. Rikandi E, Pamilo S, Mäntylä T, Suvisaari J, Kieseppä T, Hari R, et al. Precuneus functioning differentiates first-episode psychosis patients during the fantasy movie Alice in Wonderland. Psychol Med. 2017;47(3):495–506. 52. Spilka MJ, Goghari VM. Similar patterns of brain activation abnormalities during emotional and non-emotional judgments of faces in a schizophrenia family study. Neuropsychologia. 2017;96:164–74. 53. Falkenberg I, Valli I, Raffin M, Broome MR, Fusar-Poli P, Matthiasson P, et al. Pattern of activation during delayed matching to sample task predicts functional outcome in people at ultra high risk for psychosis. Schizophr Res. 2017;181:86–93. 54. Martinelli C, Rigoli F, Shergill SS. Aberrant force processing in schizophrenia. Schizophr Bull. 2017;43(2):417–24. 55. Hager B, Yang AC, Brady R, Meda S, Clementz B, Pearlson GD, et al. Neural complexity as a potential translational biomarker for psychosis. J Affect Disord. 2017;216:89–99. 56. Fornito A, Yücel M, Patti J, Wood SJ, Pantelis C. Mapping grey matter reductions in schizophrenia: an anatomical likelihood estimation analysis of voxel-based morphometry studies. Schizophr Res. 2009;108(1-3):104–13. 57. Hu ML, Zong XF, Mann JJ, Zheng JJ, Liao YH, Li ZC, et al. A review of the functional and anatomical default mode network in schizophrenia. Neurosci Bull. 2017;33(1):73–84. 58. Kindler J, Schultze-Lutter F, Hauf M, Dierks T, Federspiel A, Walther S, et al. Increased striatal and reduced prefrontal cerebral blood flow in clinical high risk for psychosis. Schizophr Bull. 2018;44:182. 59. Di Biase MA, Zalesky A, O’keefe G, Laskaris L, Baune BT, Weickert CS, et al. PET imaging of putative microglial activation in individuals at ultra-high risk for psychosis, recently diagnosed and chronically ill with schizophrenia. Transl Psychiatry. 2017;7(8):e1225. 60. Selvaraj S, Bloomfield PS, Cao B, Veronese M, Turkheimer F, Howes OD. Brain TSPO imaging and gray matter volume in schizophrenia patients and in people at ultra high risk of psychosis: an [(11)C]PBR28 study. Schizophr Res. 2018;195:206. 61. Hafizi S, Da Silva T, Gerritsen C, Kiang M, Bagby RM, Prce I, et al. Imaging microglial activation in individuals at clinical high risk for psychosis: an in vivo PET study with [(18)F] FEPPA. Neuropsychopharmacology. 2017;42:2474. 62. Artiges E, Leroy C, Dubol M, Prat M, Pepin A, Mabondo A, et al. Striatal and extrastriatal dopamine transporter availability in schizophrenia and its clinical correlates: a voxel-based and high-resolution PET study. Schizophr Bull. 2017;43(5):1134–42. 63. Solé-Padullés C, Castro-Fornieles J, de la Serna E, Sánchez-Gistau V, Romero S, Puig O, et al. Intrinsic functional connectivity of fronto-temporal networks in adolescents with early psychosis. Eur Child Adolesc Psychiatry. 2017;26(6):669–79. 64. Kim M, Cho KI, Yoon YB, Lee TY, Kwon JS. Aberrant temporal behavior of mismatch negativity generators in schizophrenia patients and subjects at clinical high risk for psychosis. Clin Neurophysiol. 2017;128(2):331–9. 65. Ranlund S, Calafato S, Thygesen JH, Lin K, Cahn W, Crespo-Facorro B, et al. A polygenic risk score analysis of psychosis endophenotypes across brain functional, structural, and cognitive domains. Am J Med Genet B Neuropsychiatr Genet. 2018;177:21. 66. Amann BL, Canales-Rodríguez EJ, Madre M, Radua J, Monte G, Alonso-Lana S, et al. Brain structural changes in schizoaffective disorder compared to schizophrenia and bipolar disorder. Acta Psychiatr Scand. 2016;133(1):23–33. 67. Dazzan P, Soulsby B, Mechelli A, Wood SJ, Velakoulis D, Phillips LJ, et  al. Volumetric abnormalities predating the onset of schizophrenia and affective psychoses: an MRI study in subjects at ultrahigh risk of psychosis. Schizophr Bull. 2012;38(5):1083–91. 68. Cannon TD, Chung Y, He G, Sun D, Jacobson A, van Erp TG, et al. Progressive reduction in cortical thickness as psychosis develops: a multisite longitudinal neuroimaging study of youth at elevated clinical risk. Biol Psychiatry. 2015;77(2):147–57. 69. Steen RG, Mull C, McClure R, Hamer RM, Lieberman JA.  Brain volume in first-episode schizophrenia: systematic review and meta-analysis of magnetic resonance imaging studies. Br J Psychiatry. 2006;188:510–8.

324

E. Scutt et al.

70. Chan RC, Di X, McAlonan GM, Gong QY. Brain anatomical abnormalities in high-risk individuals, first-episode, and chronic schizophrenia: an activation likelihood estimation meta-­ analysis of illness progression. Schizophr Bull. 2011;37(1):177–88. 71. Olabi B, Ellison-Wright I, McIntosh AM, Wood SJ, Bullmore E, Lawrie SM. Are there progressive brain changes in schizophrenia? A meta-analysis of structural magnetic resonance imaging studies. Biol Psychiatry. 2011;70(1):88–96. 72. Zipursky RB, Reilly TJ, Murray RM. The myth of schizophrenia as a progressive brain disease. Schizophr Bull. 2013;39(6):1363–72. 73. Fusar-Poli P, Smieskova R, Kempton MJ, Ho BC, Andreasen NC, Borgwardt S. Progressive brain changes in schizophrenia related to antipsychotic treatment? A meta-analysis of longitudinal MRI studies. Neurosci Biobehav Rev. 2013;37(8):1680–91. 74. Vita A, De Peri L, Deste G, Barlati S, Sacchetti E.  The effect of antipsychotic treatment on cortical gray matter changes in schizophrenia: does the class matter? A meta-analysis and meta-regression of longitudinal magnetic resonance imaging studies. Biol Psychiatry. 2015;78(6):403–12. 75. Lener MS, Wong E, Tang CY, Byne W, Goldstein KE, Blair NJ, et  al. White matter abnormalities in schizophrenia and schizotypal personality disorder. Schizophr Bull. 2015;41(1):300–10. 76. Lynall ME, Bassett DS, Kerwin R, McKenna PJ, Kitzbichler M, Muller U, et al. Functional connectivity and brain networks in schizophrenia. J Neurosci. 2010;30(28):9477–87. 77. van den Heuvel MP, Sporns O. Rich-club organization of the human connectome. J Neurosci. 2011;31(44):15775–86. 78. Whitfield-Gabrieli S, Thermenos HW, Milanovic S, Tsuang MT, Faraone SV, McCarley RW, et  al. Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proc Natl Acad Sci U S A. 2009;106(4):1279–84. 79. Fornito A, Harrison BJ, Goodby E, Dean A, Ooi C, Nathan PJ, et  al. Functional dysconnectivity of corticostriatal circuitry as a risk phenotype for psychosis. JAMA Psychiatry. 2013;70(11):1143–51. 80. Dandash O, Fornito A, Lee J, Keefe RS, Chee MW, Adcock RA, et al. Altered striatal functional connectivity in subjects with an at-risk mental state for psychosis. Schizophr Bull. 2014;40(4):904–13. 81. Anticevic A, Cole MW, Repovs G, Murray JD, Brumbaugh MS, Winkler AM, et  al. Characterizing thalamo-cortical disturbances in schizophrenia and bipolar illness. Cereb Cortex. 2014;24(12):3116–30. 82. Hoffman RE, Fernandez T, Pittman B, Hampson M. Elevated functional connectivity along a corticostriatal loop and the mechanism of auditory/verbal hallucinations in patients with schizophrenia. Biol Psychiatry. 2011;69(5):407–14. 83. Millan MJ, Andrieux A, Bartzokis G, Cadenhead K, Dazzan P, Fusar-Poli P, et  al. Altering the course of schizophrenia: progress and perspectives. Nat Rev Drug Discov. 2016;15(7):485–515. 84. Fusar-Poli P, Bonoldi I, Yung AR, Borgwardt S, Kempton MJ, Valmaggia L, et al. Predicting psychosis: meta-analysis of transition outcomes in individuals at high clinical risk. Arch Gen Psychiatry. 2012;69(3):220–9. 85. Koutsouleris N, Meisenzahl EM, Davatzikos C, Bottlender R, Frodl T, Scheuerecker J, et al. Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition. Arch Gen Psychiatry. 2009;66(7):700–12. 86. Koutsouleris N, Borgwardt S, Meisenzahl EM, Bottlender R, Möller HJ, Riecher-Rössler A. Disease prediction in the at-risk mental state for psychosis using neuroanatomical biomarkers: results from the FePsy study. Schizophr Bull. 2012;38(6):1234–46. 87. Koutsouleris N, Riecher-Rössler A, Meisenzahl EM, Smieskova R, Studerus E, Kambeitz-­ Ilankovic L, et al. Detecting the psychosis prodrome across high-risk populations using neuroanatomical biomarkers. Schizophr Bull. 2015;41(2):471–82.

8  Research Perspectives for Neuroimaging of Schizophrenia Spectrum Disorders

325

88. McGuire P, Sato JR, Mechelli A, Jackowski A, Bressan RA, Zugman A. Can neuroimaging be used to predict the onset of psychosis? Lancet Psychiatry. 2015;2(12):1117–22. 89. Gifford G, Crossley N, Fusar-Poli P, Schnack HG, Kahn RS, Koutsouleris N, et al. Using neuroimaging to help predict the onset of psychosis. NeuroImage. 2017;145(Pt B):209–17. 90. Kapur S, Seeman P.  Antipsychotic agents differ in how fast they come off the dopamine D2 receptors. Implications for atypical antipsychotic action. J Psychiatry Neurosci. 2000;25(2):161–6. 91. Tamminga CA. Treatment mechanisms: traditional and new antipsychotic drugs. Dialogues Clin Neurosci. 2000;2(3):281–6. 92. Navari S, Dazzan P. Do antipsychotic drugs affect brain structure? A systematic and critical review of MRI findings. Psychol Med. 2009;39(11):1763–77. 93. Abbott CC, Jaramillo A, Wilcox CE, Hamilton DA. Antipsychotic drug effects in schizophrenia: a review of longitudinal FMRI investigations and neural interpretations. Curr Med Chem. 2013;20(3):428–37. 94. Demjaha A, Murray RM, McGuire PK, Kapur S, Howes OD. Dopamine synthesis capacity in patients with treatment-resistant schizophrenia. Am J Psychiatry. 2012;169(11):1203–10. 95. Demjaha A, Egerton A, Murray RM, Kapur S, Howes OD, Stone JM, et al. Antipsychotic treatment resistance in schizophrenia associated with elevated glutamate levels but normal dopamine function. Biol Psychiatry. 2014;75(5):e11–3. 96. Kumari V, Tercer T. Cognitive behaviour therapy for psychosis: insights from neuroimaging. J Neuroimaging Psychiatry Neurol. 2017;2:11–9. 97. Wykes T, Huddy V, Cellard C, McGurk SR, Czobor P. A meta-analysis of cognitive remediation for schizophrenia: methodology and effect sizes. Am J Psychiatry. 2011;168(5):472–85. 98. Stafford MR, Jackson H, Mayo-Wilson E, Morrison AP, Kendall T. Early interventions to prevent psychosis: systematic review and meta-analysis. BMJ. 2013;346:f185. 99. van der Gaag M, Smit F, Bechdolf A, French P, Linszen DH, Yung AR, et al. Preventing a first episode of psychosis: meta-analysis of randomized controlled prevention trials of 12 month and longer-term follow-ups. Schizophr Res. 2013;149(1-3):56–62. 100. Sommer IE, van Westrhenen R, Begemann MJ, de Witte LD, Leucht S, Kahn RS. Efficacy of anti-inflammatory agents to improve symptoms in patients with schizophrenia: an update. Schizophr Bull. 2014;40(1):181–91. 101. Zhu F, Zhang L, Ding YQ, Zhao J, Zheng Y. Neonatal intrahippocampal injection of lipopolysaccharide induces deficits in social behavior and prepulse inhibition and microglial activation in rats: implication for a new schizophrenia animal model. Brain Behav Immun. 2014;38:166–74. 102. Müller N. The role of anti-inflammatory treatment in psychiatric disorders. Psychiatr Danub. 2013;25(3):292–8.

9

Toward Clinical Translation of Neuroimaging Research in Schizophrenia and Other Primary Psychotic Disorders Annarita Vignapiano, Lynn E. DeLisi, and Silvana Galderisi

9.1

 linical Relevance of Diagnostic C and Predictive Biomarkers

A biomarker is defined as “a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention” [1]. It represents a key component of modern medicine. Biomarkers might index the biological processes associated with a disease (diagnostic biomarkers), with the risk of developing a disease, or with the treatment response or outcome of a disease (predictive biomarkers). In psychiatry, the search for biomarkers turned out to be more complex than in other medical disciplines. For instance, it is still debated whether mental disorders should be conceptualized as discrete entities (categorical approach) or as phenomena along a continuum of severity (dimensional approach). Main current diagnostic systems, i.e., the Diagnostic and Statistical Manual of Mental Disorder (DSM) or the International Classification of Diseases (ICD), are based on the categorical approach to diagnosis, which may hinder the discovery of pathophysiological mechanisms and biomarkers of psychopathology. In fact, there is little empirical evidence supporting this approach. It is difficult to identify reliable and clear-cut boundaries between normal and pathological conditions and among different disorders; no specific pathophysiology or biomarker has been identified for any category so far; the same diagnosis can apply to two individuals who have no symptom in common, and more than one mental disorder is diagnosed in the same individual [2]. The US National Institute of Mental Health proposed a new approach for research on mental disorders, the Research Domain Criteria (RDoC) [3, 4], a project

A. Vignapiano · S. Galderisi (*) Department of Psychiatry, University of Campania “Luigi Vanvitelli”, Naples, Italy L. E. DeLisi VA Boston Healthcare System, Harvard Medical School, Brockton, MA, USA © Springer Nature Switzerland AG 2019 S. Galderisi et al. (eds.), Neuroimaging of Schizophrenia and Other Primary Psychotic Disorders, https://doi.org/10.1007/978-3-319-97307-4_9

327

328

A. Vignapiano et al.

aimed at re-orienting research on etiology and pathophysiology of psychopathological phenomena from category-based to dimension-based and at incorporating genetics, neuroimaging, and cognitive science methods into future diagnostic schemes [5]. Neuroimaging, providing in  vivo access to the brain, can be used to measure variations in molecular and cellular disease targets or in specific neuronal networks, which are associated with specific behaviors, found to be aberrant in psychotic disorders. Recently, the development of brain imaging techniques has substantially increased knowledge about the pathophysiology of psychotic disorders, showing their association with distributed, complex, multimodal patterns of brain aberrations. In the light of these observations, neuroimaging findings might represent perfect candidates for the development of both diagnostic and predictive biomarkers that could be applied in the current clinical practice and could help in redefining diagnostic criteria according to the RDoC system [6]. The translation of these neuroimaging-­based findings into clinical practice has become a priority in order to improve clinical care and lead to a future in which the clinical evaluation for early episodes of psychotic disorders will incorporate also brain imaging data [7–9]. As highlighted in the first chapter, meta-analyses of structural magnetic resonance imaging (sMRI) studies in chronic and medicated subjects with schizophrenia reported a large number of structural alterations such as smaller hippocampus, amygdala, thalamus, nucleus accumbens and intracranial volumes, as well as larger globus pallidus and lateral ventricle volumes [10–12]. These aberrations showed small to moderate effect sizes, with strong replicability and consistency across studies [13]. Lateral ventral volume is also increased in first-episode schizophrenia [11, 14] and in antipsychotic naive subjects in relation to psychosis onset [15]. Volumetric reduction in thalamus and hippocampal subcortical areas was also consistently reported in psychosis spectrum disorders [16–19], with more pronounced differences in subjects with schizophrenia with respect to subjects with bipolar disorder [16, 20, 21]. Longitudinal studies found that some structural aberrations, identified at the onset of psychosis, worsened in the course of disease. In fact, subjects with schizophrenia showed a decrease of the whole cerebral gray matter volume, involving frontal, temporal, and parietal lobes [10, 14, 22], with an annual percent volume increase of 36% for bilateral lateral ventricles [22]. Functional alterations, assessed in functional magnetic resonance imaging (fMRI) studies as neuro-functional correlates of schizophrenia, were found in the prefrontal cortex [23–27], anterior cingulate cortex [24, 26, 28], insula [25, 28, 29], thalamus [24, 30], and superior temporal gyrus [25, 26, 28]. The described functional aberrations in subjects with schizophrenia frequently involved the same brain areas in which structural abnormalities are observed since the onset of the disorder [28], thus supporting the possibility that structural alterations could be used as biomarkers for early diagnosis. Diffusion tensor imaging findings showed lower fractional anisotropy in fronto-temporo-limbic regions in subjects with schizophrenia and in frontotemporal regions in first-episode subjects [31–37]. Among the other imaging modalities, the meta-analysis of several positron emission tomography (PET) studies by Fusar-Poli and Meyer-Lindenberg [23] reported an increase in

9  Toward Clinical Translation of Neuroimaging Research in Schizophrenia and Other… 329

striatal dopamine synthesis capacity, while Kraguljac et al. [38] showed in proton magnetic resonance spectroscopy (1H-MRS) studies of N-acetylaspartate (NAA) a reduction of NAA in the frontal lobe and basal ganglia for first-episode and chronic subjects. Overall, due to inconsistencies among studies, none of these functional features has been regarded as a biomarker, and no clinical translation, so far, can be envisaged for any of them. In schizophrenia, negative symptoms have been recognized as core features of disease [39], and particularly, primary and persistent negative symptoms, termed as deficit symptoms [40], showed important prognostic implications since no known effective treatments exist [41]; they are associated with worse functional outcome [42–44] and impairment of neurocognitive functioning [42, 44, 45]. Several studies observed the validity of subgrouping people with schizophrenia into those with deficit symptoms (deficit schizophrenia) on the basis of neuroimaging features such as an increase of left temporal cerebrospinal fluid volume [46], smaller right temporal lobe volume [47], reduced gray matter volume in the superior and middle temporal gyrus [48, 49] and white matter abnormalities in frontoparietal and frontotemporal circuits [50], left uncinate fasciculus [51, 52], and right inferior longitudinal and right arcuate fasciculus [52]. Functional neuroimaging studies of subjects with negative symptoms showed, in the most replicated findings, aberrations in the fronto-­ parietal circuit in subjects with deficit symptoms compared to those with a non-deficit illness [53, 54]. Identification of robust neuroimaging biomarkers, able to define subgroups of people with deficit symptoms, is an important goal in order to implement appropriate new therapeutic approaches [55]. However, currently, these markers are not clear. The successful translation of the described potential biomarkers to real-world clinical practice requires an extensive knowledge of etiopathological models of psychotic disorders in order to identify which neuroimaging aberrations could add value over the existing clinical assessment [7]. Combined use of clinical and imaging biomarker data for distinguishing subjects with well-established schizophrenia from healthy controls (see Chaps. 1 and 2), for identifying subjects “at risk mental state” (ARMS) who will develop psychosis (see Chap. 7), and also for differentiating patients who will respond to treatment from those that will not has provided promising results. The availability of biomarkers supporting the prediction of conversion to overt psychosis, as well as early diagnosis and prediction of treatment response, could contribute to improve both clinical and functional outcome.

9.1.1 C  linical Relevance of Neuroimaging Biomarkers for Transition to Psychosis Predicting transition to psychosis, on the basis of clinical assessment, has failed to provide an accurate discrimination between subjects who will develop psychosis and those who will not [56], with the consequent risk of over- or undertreatment. The challenge of translational research would be to identify neurobiological

330

A. Vignapiano et al.

markers of psychosis conversion in order to provide targeted treatments only to subjects who need it [57]. Furthermore, to date, since the response to a pharmacological treatment in a given patient is determined by trial and error, the development of neuroimaging biomarkers able to predict the treatment response and identify unresponsive patients early on would be extremely useful in the clinical practice [58, 59]. Therefore, the identification of people at risk who will develop a psychosis, based on neuroimaging phenotypes, would be a great clinical opportunity [60]. As we learned from Chap. 7, cross-sectional neuroimaging studies comparing ARMS subjects that developed psychosis and those that did not develop psychosis demonstrated differences in the two groups in terms of gray matter volume reduction in anterior cingulate cortex [61–63] and in temporal, frontal, parietal, and insular cortices [61, 63–65], gyrification [66], decreased integrity of white matter [67], and increased glutamate levels in the striatum [68]. Longitudinal studies, conducted in ARMS subjects before and after the onset of psychosis, showed in ARMS who develop a psychotic disorder progressive brain changes in gray matter volume of medial temporal lobe and prefrontal cortex, in white matter volume and integrity, as well as in striatal dopamine function [65, 69–72]. The translation of these findings into clinical applications is still under scrutiny. Machine learning represents a promising method; its potential translation into clinical applications might lead to early identification of subjects who will develop the illness, and early treatment with medications and other psychosocial interventions, and perhaps prevention of further deterioration of brain structure and function, as well as clinical picture and social functioning.

9.1.2 F  rom Neurochemical Biomarker Approaches to Personalized Medicine: The Prediction of Treatment Response Treatment-resistant schizophrenia affects almost 30% of subjects with a diagnosis of schizophrenia [73–75]. The definition of treatment resistance is not clear and may be due to a variety of causes, potentially leading to inconsistent clinical management and treatment delays [76]. Relevant neuroimaging studies reported, in treatment non-responders compared to treatment responders, showed reduced fractional anisotropy of the uncinate, cingulum, and corpus callosum [77] at baseline and prominent hypogyria at the bilateral insular cortices and left frontal and right temporal regions [78] (for more details see Chap. 1). It has been suggested that treatment-resistant schizophrenia subjects do not respond to dopaminergic antipsychotic medication because their illness is not characterized by a dopaminergic abnormality [79]. Recent evidence, particularly from in vivo imaging studies and preclinical findings on the role of dopamine and glutamate, have refined understanding of the nature of these neurotransmitter dysfunctions in schizophrenia. Advances in PET, single-photon emission computed tomography (SPECT), and 1H-MRS techniques and in their application enabled further testing and refinement of the major aspects of the neurotransmitter hypotheses of schizophrenia [80].

9  Toward Clinical Translation of Neuroimaging Research in Schizophrenia and Other… 331

Increased synaptic dopamine is predictive of treatment response to the conventional antipsychotic medications [81]. In a 3,4-dihydroxy-6-[18F]-fluoro-l-phenylalanine (18F-DOPA) PET study, Demjaha et al. [82] observed that changes in presynaptic dopamine transmission usually seen in subjects who had responded to antipsychotic treatment were absent in treatment-resistant subjects. Studies in the prodromal phase of schizophrenia found elevated dopamine synthesis capacity [83, 84] in individuals who later developed a psychotic disorder [72]. These findings taken together suggested that there may be a “non-dopaminergic” subtype of schizophrenia [79]. With respect to the “hyperdopaminergic” subtype, characterized by prominent striatal dopamine synthesis and release capacity, the “non-dopaminergic” subtype exhibited normal dopaminergic function, and symptoms of the disorder were not related to dopaminergic transmission. This classification based on a neurobiological mechanism shows several advantages: it could lead to the identification of PET scanning tests that guide treatment choice at illness onset and could provide a basis for research in order to develop new treatment options [79]. Some studies suggested that glutamatergic alterations may underlie the “non-dopaminergic subtype” of schizophrenia. More specifically, treatment responders seem to have more marked dopaminergic aberrations, whereas treatment non-responders seem to have more marked glutamatergic abnormalities [85]. In order to disentangle this issue, 1H-MRS has been used to quantify glutamate and glutamine levels in individuals at high risk of psychosis, as well as patients with first-episode psychosis and chronic schizophrenia [86, 87]. Egerton et al. [88] found elevated glutamate levels in the anterior cingulate cortex in patients who had persistent psychotic symptoms despite antipsychotic treatment. Treatment resistance in schizophrenia could be related to a combination of normal striatal dopamine synthesis and elevated anterior cingulate cortex glutamate levels. In contrast, elevation in striatal dopamine synthesis, reduction of NAA, and normal glutamate levels might be associated with remission after antipsychotic treatment [89]. Association between treatment-resistant schizophrenia and the increased level of anterior cingulate glutamate seems to be a stable neurobiological trait of treatment-­ resistant schizophrenia as reported by Mouchlianitis et al. [90]. As data acquisition was cross-sectional, we cannot determine whether the glutamate increase in anterior cingulate was associated with treatment resistance or was a surrogate marker of increased psychopathology at the time of the scan [85]. Recently, Egerton et  al. [91], in their review of longitudinal 1H-MRS studies, concluded that more research is needed to clarify whether lower glutamate levels before treatment may predict what kind of medication patients may respond to. Ultimately, it would be valuable to be able to use 1H-MRS to detect whether patients would respond to medications that regulate the glutamate system primarily, rather than medications that target the dopaminergic system. Since 1H-MRS is safe, and a relatively cost-effective examination, it could be a potential clinical tool for detection of treatment resistance in early stages of the disease or before the administration of antipsychotic medications [90]. Dopaminergic and glutamatergic indices have the potential to be biomarkers that could be used to assess drug effects. The inclusion of biomarkers in the design of pharmaceutical clinical trials could be helpful in the identification of the causes

332

A. Vignapiano et al.

of drug resistance [74, 75, 92]. The stratification of patient groups using PET or 1H-MRS could contribute to the ability to accurately test the efficacy of innovative non-pharmacologic treatment strategies as well, such as transcranial direct current stimulation (tDCS) or transcranial magnetic stimulation (TMS) in patient subgroups, who might benefit from an increase in neural plasticity and connectivity or from an enhancement in cortical activation by cognitive remediation interventions [93]. The most promising opportunities for the future will be the extensive application of multivariate pattern recognition methods to deepen our understanding of psychotic disorders [94, 95] and the inclusion of clinical, behavioral, and metabolic factors to multimodal imaging approaches [96] in order to improve the individual prediction of treatment response.

9.2

 alidation of Potential Neuroimaging Biomarkers: V Prospective Multicenter Studies

Another important step to define the clinical relevance of a potential neuroimaging biomarker is the validation in independent samples. Biomarkers must be determined objectively, avoiding the lack of consistency in the methodology used to obtain and analyze imaging data. Validation studies are essential and should include subjects who are representative of the population of interest compared with healthy subjects. Multicenter studies are particularly useful for this purpose, since they might test differences in prevalence, recruitment, and clinical assessment at different sites, and they have the potential to improve the statistical power of results. In addition, they are most valuable in obtaining very large numbers that are needed for clearly defining biomarkers for clinical use. In order to promote a collaborative approach for addressing the issue of insufficient power and reproducibility in neuroimaging studies, in 2009 the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium (http:// enigma.ini.usc.edu/) was founded with the goal of identifying genetic influences on brain structure [97]. The aim of the first project of the Consortium was the identification of common variants in the genome associated with hippocampal volume and other subcortical brain volumes [98, 99]. Later on, clinical working groups have been created to study genetic influences on specific brain aberrations in schizophrenia and other major psychiatric disorders [100]. An even more recent extension is the establishment of an ENIGMA early-onset psychosis (EOP) working group. This working group has been formed with the primary goal of identifying the genetic factors and brain neuroanatomical and functional features those robustly discriminate adolescents with EOP from healthy controls. The main aims of their meta-­ analyses will be to understand how genetic or other risk factors could influence the abnormalities of brain structures and the different developmental trajectories, in order to discriminate among EOP syndromes, the subjects with the early-onset schizophrenia from those with an early-onset bipolar spectrum, and to establish a valuable transdiagnostic model for studying the pathophysiology of different psychoses, particularly in individuals at symptom onset.

9  Toward Clinical Translation of Neuroimaging Research in Schizophrenia and Other… 333

In recent years, a number of prospective multicenter studies have been initiated, such as Translating Neuroimaging Findings From Research Into Clinical Practice (PSYSCAN) (http://intranet.psyscan.eu), Personalised Prognostic Tools for Early Psychosis Management (PRONIA) (http://pronia.eu), and the Optimization of Treatment and Management of Schizophrenia in Europe (OPTiMiSE) (www.optimisetrial.eu) [101], funded by the European Community’s Seventh Framework Programme, and one NIMH-funded project in North America, the North American Prodrome Longitudinal Study (NAPLS) (https://campuspress.yale.edu/napls) [69, 102, 103]. These projects allow testing simultaneously the role of neuroimaging biomarkers and clinical aspects in the prediction of the onset or the treatment response in large samples, using multimodal application and methodological integration [104]. The NAPLS project [69] is a resting state functional magnetic resonance imaging (fMRI) study conducted in 243 high-risk subjects focusing on thalamo-cortical connectivity. The study observed, in ARMS subjects who developed psychosis compared to those that did not convert to psychosis, hypo-connectivity between the thalamus on the one hand and the prefrontal and cerebellar areas on the other hand, as well as hyper-­ connectivity between the thalamus and sensory-motor regions. This abnormal connectivity was correlated with prodromal symptom severity. OPTiMiSE is a clinical trial developed to optimize treatment in first-episode subjects with schizophrenia and to examine the ability of neuroimaging data to predict treatment response. Since the prediction of therapeutic response in psychosis using neuroimaging data may be enhanced if demographic, psychopathological, cognitive, and genetic data and other metabolomic, autoimmune, and inflammatory measures are incorporated into the assessment, OPTiMiSE study will also examine the impact of integrating these non-­ imaging measures on the prediction of response at early onset of psychosis [101]. These studies are aimed to support clinicians in their decision-making process, to make more effective clinical decisions in the early stage of the disorder. This might result in fewer ineffective trials and higher remission rates. In subjects with psychotic symptoms, in early intervention or chronic adult services, it would be very useful to distinguish those in need for treatment because spontaneous resolution is unlikely or would take too long from those who benefit from ongoing treatment to avoid relapse and optimize daily functioning [105].

9.3

 ow Can We Move from Neuroimaging H Findings to Clinical Applications?

In order to develop clinical tools, we have to move from differences at the group level to the individual level and identify measures supporting clinical decisions (e.g., Is the subject ill or healthy? Is the subject at high risk of developing the disorder? Will the patient have a good response to treatment?). Indeed, in clinical practice, decisions need to be made based on data from an individual person. Promising results in this field, with low error rates for diagnostic and predictive purposes, come from machine learning algorithms [106] as well as deep learning approaches [107]. Machine learning algorithms are able to learn from experience,

334

A. Vignapiano et al.

thus correctly attributing brain images to different samples, such as patients or healthy controls. These methods consider brain imaging acquisitions as patterns and are based on a multivariate approach, which allows the integration of different variables, such as clinical, neurocognitive, and genetic data, thus improving the predictive accuracy in diagnosis and prognosis. Machine learning studies are able to detect more complex brain patterns of abnormalities that account for the increased sensitivity of this technique with respect to conventional group analysis [108, 109]. Various research groups are now incorporating machine learning algorithms in the service of individual prediction [100]. Applying multivariate pattern recognition methods to MRI data, machine learning could provide an innovative instrument to classify patients, to predict the development of a psychotic disorder in ARMS subjects, and to predict the clinical and functional outcome (see Chap. 1). Studies that examined the diagnostic power of machine learning in distinguishing between healthy controls and subjects with psychotic disorders showed encouraging results in term of accuracy [105, 110–113]. Furthermore, using machine learning, the reduced gray matter volume within the cerebellum, thalamus, prefrontal cortex, precuneus, occipital, temporal, and supplementary motor areas allowed the distinction between ARMS subjects and healthy controls, while lateral and medial temporal aberrations were found to separate converters to schizophrenia from nonconverters [108, 114, 115]. In addition, recent research has focused on the prediction in ARMS subjects of functional and social outcomes, independently of the eventual subsequent transition, which has found to be associated with the baseline subcortical volumes [116, 117]. In the light of these observations, computational methods could have a future in psychiatry, as they might provide prognostic tools for predicting functional outcome at the individual level that could be used in clinical practice in order to identify the specific subgroup of ARMS subjects that will benefit from preventive interventions [118]. Deep learning is a family of machine learning methods that have recently showed notable progresses in the tasks of classification and representation learning for its ability of automatic feature learning from dataset which contributes to improve the accuracy [107] in the validation of diagnostic and prognostic neuroimaging biomarkers. For a future clinical application, after the identification of predictors of diagnosis, prognosis, and therapeutic response, the next step would be to integrate the relevant measures into tools that could be used in clinical setting. The development of userfriendly and easy to handle clinical tools for clinicians with no research and statistical background will lead a fundamental challenge in the clinical management [8]. Just as a clinician takes a detailed clinical report of clinical features to evaluate an ARMS subject or to diagnose a subject with schizophrenia, so might the integration of psychopathological features and other neurocognitive, genetic, metabolomic, autoimmune, and inflammatory measures, along with MRI scans, aid the classification process overcoming the subjectivity in traditional clinical assessments.

9  Toward Clinical Translation of Neuroimaging Research in Schizophrenia and Other… 335

9.4

Conclusions

In summary, neuroimaging studies have reported complex, distributed, multimodal patterns of brain abnormalities in subjects with psychotic disorders. Currently, the main goal is to translate these neuroimaging findings into clinical practice. It is clear that neuroimaging biomarkers could add value to the existing clinical assessment. However, when neuroimaging biomarkers and machine learning algorithms, applied to clinical tools, will seriously impact upon clinical practice in terms of future diagnosis, outcome, and treatment response is difficult to predict. Neuroimaging biomarker translation from the research area to the clinical setting should take into account the availability of well-functioning imaging centers, the cost-effectiveness, and the automated analysis methods that have to produce quantitative, standardized, and not susceptible to inter-rater variability results. In this chapter, we also highlight the potential role of machine learning algorithms in translating group neuroimaging findings to the individual assessment, thus helping to establish the diagnosis and outcome of ARMS and schizophrenia subjects. More research is needed before it is recommended that all clinicians use specific neuroimaging biomarkers from PET, MRS, or fMRI in people at risk for developing schizophrenia or those who have just developed the illness and need personalized treatment. However, currently it could be recommended that at least these subjects receive a baseline structural MRI scan to determine whether any structural abnormalities can be identified. Neuroimaging techniques might support differential diagnoses with neurological disorders such as brain tumors, epileptic seizures, or traumatic lesions that often show the same clinical features of psychotic disorders, and therefore their assessment is so important to deserve a careful evaluation [2]. When researchers will be able to identify a robust neuroimaging biomarker for diagnosis, prognosis or treatment response for ARMS subjects and subjects with psychoses, the procedures of approvals by the US Food and Drug Administration (FDA) and European Medicines Agency (EMA) will require an extensive consensus reached with large-scale multicenter studies using standardized protocols, before utilizing the biomarker in clinical trials and in clinical setting. Until now, however, no neuroimaging biomarker has obtained approval and the validation in terms of scanning protocol and data processing by regulatory bodies [57]. In the light of the findings and considerations reported in the previous paragraphs, the clinical tools that will be able to evaluate the neuroimaging data with other clinical and genetic biomarkers represent the most promising innovation for predicting outcome and treatment response of ARMS subjects and subjects with psychoses in an objective and quantitative way, and thus they will have real potential to revolutionize psychiatric practice.

336

A. Vignapiano et al.

References 1. Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001;69(3):89–95. https://doi. org/10.1067/mcp.2001.113989. 2. Galderisi S.  Current trends in diagnosis and treatment of mental disorders. Eur Psychiatry. 2018;50:4–6. https://doi.org/10.1016/j.eurpsy.2017.11.008. 3. Cuthbert BN, Kozak MJ. Constructing constructs for psychopathology: the NIMH research domain criteria. J Abnorm Psychol. 2013;122(3):928–37. https://doi.org/10.1037/ a0034028. 4. Morris SE, Cuthbert BN. Research Domain Criteria: cognitive systems, neural circuits, and dimensions of behavior. Dialogues Clin Neurosci. 2012;14(1):29–37. 5. Cuthbert BN.  The RDoC framework: facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology. World Psychiatry. 2014;13(1):28–35. https://doi.org/10.1002/wps.20087. 6. Abi-Dargham A, Horga G. The search for imaging biomarkers in psychiatric disorders. Nat Med. 2016;22(11):1248–55. https://doi.org/10.1038/nm.4190. 7. Fu CH, Costafreda SG.  Neuroimaging-based biomarkers in psychiatry: clinical opportunities of a paradigm shift. Can J Psychiatry. 2013;58(9):499–508. https://doi. org/10.1177/070674371305800904. 8. McGuire P, Sato JR, Mechelli A, Jackowski A, Bressan RA, Zugman A. Can neuroimaging be used to predict the onset of psychosis? Lancet Psychiatry. 2015;2(12):1117–22. https://doi. org/10.1016/S2215-0366(15)00308-9. 9. Borgwardt S, Schmidt A.  Implementing magnetic resonance imaging into clinical routine screening in patients with psychosis? Br J Psychiatry. 2017;211(4):192–3. https://doi. org/10.1192/bjp.bp.117.199919. 10. Kempton MJ, Stahl D, Williams SC, DeLisi LE. Progressive lateral ventricular enlargement in schizophrenia: a meta-analysis of longitudinal MRI studies. Schizophr Res. 2010;120(1– 3):54–62. https://doi.org/10.1016/j.schres.2010.03.036. 11. Haijma SV, Van Haren N, Cahn W, Koolschijn PC, Hulshoff Pol HE, Kahn RS. Brain volumes in schizophrenia: a meta-analysis in over 18,000 subjects. Schizophr Bull. 2013;39(5):1129– 38. https://doi.org/10.1093/schbul/sbs118. 12. van Erp TG, Hibar DP, Rasmussen JM, Glahn DC, Pearlson GD, Andreassen OA, Agartz I, Westlye LT, Haukvik UK, Dale AM, Melle I, Hartberg CB, Gruber O, Kraemer B, Zilles D, Donohoe G, Kelly S, McDonald C, Morris DW, Cannon DM, Corvin A, Machielsen MW, Koenders L, de Haan L, Veltman DJ, Satterthwaite TD, Wolf DH, Gur RC, Gur RE, Potkin SG, Mathalon DH, Mueller BA, Preda A, Macciardi F, Ehrlich S, Walton E, Hass J, Calhoun VD, Bockholt HJ, Sponheim SR, Shoemaker JM, van Haren NE, Pol HE, Ophoff RA, Kahn RS, Roiz-Santianez R, Crespo-Facorro B, Wang L, Alpert KI, Jonsson EG, Dimitrova R, Bois C, Whalley HC, McIntosh AM, Lawrie SM, Hashimoto R, Thompson PM, Turner JA. Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol Psychiatry. 2016;21(4):585. https://doi.org/10.1038/ mp.2015.118. 13. Hager BM, Keshavan MS.  Neuroimaging biomarkers for psychosis. Curr Behav Neurosci Rep. 2015;2015:1–10. 14. Fusar-Poli P, Smieskova R, Kempton MJ, Ho BC, Andreasen NC, Borgwardt S. Progressive brain changes in schizophrenia related to antipsychotic treatment? A meta-analysis of longitudinal MRI studies. Neurosci Biobehav Rev. 2013;37(8):1680–91. https://doi.org/10.1016/j. neubiorev.2013.06.001. 15. Fusar-Poli P, Radua J, McGuire P, Borgwardt S.  Neuroanatomical maps of psychosis onset: voxel-wise meta-analysis of antipsychotic-naive VBM studies. Schizophr Bull. 2012;38(6):1297–307. https://doi.org/10.1093/schbul/sbr134.

9  Toward Clinical Translation of Neuroimaging Research in Schizophrenia and Other… 337 16. Mathew I, Gardin TM, Tandon N, Eack S, Francis AN, Seidman LJ, Clementz B, Pearlson GD, Sweeney JA, Tamminga CA, Keshavan MS. Medial temporal lobe structures and ­hippocampal subfields in psychotic disorders: findings from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) study. JAMA Psychiatry. 2014;71(7):769–77. https://doi. org/10.1001/jamapsychiatry.2014.453. 17. Brown GG, Lee JS, Strigo IA, Caligiuri MP, Meloy MJ, Lohr J. Voxel-based morphometry of patients with schizophrenia or bipolar I disorder: a matched control study. Psychiatry Res. 2011;194(2):149–56. https://doi.org/10.1016/j.pscychresns.2011.05.005. 18. Hartberg CB, Sundet K, Rimol LM, Haukvik UK, Lange EH, Nesvag R, Melle I, Andreassen OA, Agartz I.  Subcortical brain volumes relate to neurocognition in schizophrenia and bipolar disorder and healthy controls. Prog Neuro-Psychopharmacol Biol Psychiatry. 2011;35(4):1122–30. https://doi.org/10.1016/j.pnpbp.2011.03.014. 19. Watson DR, Anderson JM, Bai F, Barrett SL, McGinnity TM, Mulholland CC, Rushe TM, Cooper SJ.  A voxel based morphometry study investigating brain structural changes in first episode psychosis. Behav Brain Res. 2012;227(1):91–9. https://doi.org/10.1016/j. bbr.2011.10.034. 20. Haukvik UK, Westlye LT, Morch-Johnsen L, Jorgensen KN, Lange EH, Dale AM, Melle I, Andreassen OA, Agartz I. In vivo hippocampal subfield volumes in schizophrenia and bipolar disorder. Biol Psychiatry. 2015;77(6):581–8. https://doi.org/10.1016/j.biopsych.2014.06.020. 21. Knochel C, Stablein M, Storchak H, Reinke B, Jurcoane A, Prvulovic D, Linden DE, van de Ven V, Ghinea D, Wenzler S, Alves G, Matura S, Kroger A, Oertel-Knochel V. Multimodal assessments of the hippocampal formation in schizophrenia and bipolar disorder: evidences from neurobehavioral measures and functional and structural MRI.  Neuroimage Clin. 2014;6:134–44. https://doi.org/10.1016/j.nicl.2014.08.015. 22. Olabi B, Ellison-Wright I, McIntosh AM, Wood SJ, Bullmore E, Lawrie SM. Are there progressive brain changes in schizophrenia? A meta-analysis of structural magnetic resonance imaging studies. Biol Psychiatry. 2011;70(1):88–96. https://doi.org/10.1016/j.biopsych.2011.01.032. 23. Fusar-Poli P, Meyer-Lindenberg A.  Striatal presynaptic dopamine in schizophrenia, part II: meta-analysis of [(18)F/(11)C]-DOPA PET studies. Schizophr Bull. 2013;39(1):33–42. https://doi.org/10.1093/schbul/sbr180. 24. Minzenberg MJ, Laird AR, Thelen S, Carter CS, Glahn DC.  Meta-analysis of 41 functional neuroimaging studies of executive function in schizophrenia. Arch Gen Psychiatry. 2009;66(8):811–22. https://doi.org/10.1001/archgenpsychiatry.2009.91. 25. Smieskova R, Marmy J, Schmidt A, Bendfeldt K, Riecher-Rssler A, Walter M, Lang UE, Borgwardt S. Do subjects at clinical high risk for psychosis differ from those with a genetic high risk? A systematic review of structural and functional brain abnormalities. Curr Med Chem. 2013;20(3):467–81. 26. Taylor SF, Kang J, Brege IS, Tso IF, Hosanagar A, Johnson TD. Meta-analysis of functional neuroimaging studies of emotion perception and experience in schizophrenia. Biol Psychiatry. 2012;71(2):136–45. https://doi.org/10.1016/j.biopsych.2011.09.007. 27. Fusar-Poli P.  Voxel-wise meta-analysis of fMRI studies in patients at clinical high risk for psychosis. J Psychiatry Neurosci. 2012;37(2):106–12. https://doi.org/10.1503/jpn.110021. 28. Radua J, Borgwardt S, Crescini A, Mataix-Cols D, Meyer-Lindenberg A, McGuire PK, Fusar-Poli P.  Multimodal meta-analysis of structural and functional brain changes in first episode psychosis and the effects of antipsychotic medication. Neurosci Biobehav Rev. 2012;36(10):2325–33. https://doi.org/10.1016/j.neubiorev.2012.07.012. 29. Jardri R, Pouchet A, Pins D, Thomas P. Cortical activations during auditory verbal hallucinations in schizophrenia: a coordinate-based meta-analysis. Am J Psychiatry. 2011;168(1):73– 81. https://doi.org/10.1176/appi.ajp.2010.09101522. 30. Cooper D, Barker V, Radua J, Fusar-Poli P, Lawrie SM.  Multimodal voxel-based meta-­ analysis of structural and functional magnetic resonance imaging studies in those at elevated genetic risk of developing schizophrenia. Psychiatry Res. 2014;221(1):69–77. https://doi. org/10.1016/j.pscychresns.2013.07.008.

338

A. Vignapiano et al.

31. Szeszko PR, Robinson DG, Ashtari M, Vogel J, Betensky J, Sevy S, Ardekani BA, Lencz T, Malhotra AK, McCormack J, Miller R, Lim KO, Gunduz-Bruce H, Kane JM, Bilder RM. Clinical and neuropsychological correlates of white matter abnormalities in recent onset schizophrenia. Neuropsychopharmacology. 2008;33(5):976–84. https://doi.org/10.1038/ sj.npp.1301480. 32. Price G, Cercignani M, Parker GJ, Altmann DR, Barnes TR, Barker GJ, Joyce EM, Ron MA.  White matter tracts in first-episode psychosis: a DTI tractography study of the uncinate fasciculus. NeuroImage. 2008;39(3):949–55. https://doi.org/10.1016/j. neuroimage.2007.09.012. 33. Hovington CL, Bodnar M, Chakravarty MM, Joober R, Malla AK, Lepage M. Investigation of white matter abnormalities in first episode psychosis patients with persistent negative symptoms. Psychiatry Res. 2015;233(3):402–8. https://doi.org/10.1016/j. pscychresns.2015.06.017. 34. Ellison-Wright I, Bullmore E. Meta-analysis of diffusion tensor imaging studies in schizophrenia. Schizophr Res. 2009;108(1–3):3–10. https://doi.org/10.1016/j.schres.2008.11.021. 35. Zhou Y, Liu J, Driesen N, Womer F, Chen K, Wang Y, Jiang X, Zhou Q, Bai C, Wang D, Tang Y, Wang F. White matter integrity in genetic high-risk individuals and first-episode schizophrenia patients: similarities and disassociations. Biomed Res Int. 2017;2017:3107845. https://doi. org/10.1155/2017/3107845. 36. Zhou Y, Shu N, Liu Y, Song M, Hao Y, Liu H, Yu C, Liu Z, Jiang T. Altered resting-state functional connectivity and anatomical connectivity of hippocampus in schizophrenia. Schizophr Res. 2008;100(1–3):120–32. https://doi.org/10.1016/j.schres.2007.11.039. 37. Price G, Cercignani M, Parker GJ, Altmann DR, Barnes TR, Barker GJ, Joyce EM, Ron MA.  Abnormal brain connectivity in first-episode psychosis: a diffusion MRI tractography study of the corpus callosum. NeuroImage. 2007;35(2):458–66. https://doi.org/10.1016/j. neuroimage.2006.12.019. 38. Kraguljac NV, Reid M, White D, Jones R, den Hollander J, Lowman D, Lahti AC.  Neurometabolites in schizophrenia and bipolar disorder  - a systematic review and meta-analysis. Psychiatry Res. 2012;203(2–3):111–25. https://doi.org/10.1016/j. pscychresns.2012.02.003. 39. Galderisi S, Farden A, Kaiser S.  Dissecting negative symptoms of schizophrenia: history, assessment, pathophysiological mechanisms and treatment. Schizophr Res. 2017;186:1–2. https://doi.org/10.1016/j.schres.2016.04.046. 40. Carpenter WT Jr, Heinrichs DW, Wagman AM. Deficit and nondeficit forms of schizophrenia: the concept. Am J Psychiatry. 1988;145(5):578–83. https://doi.org/10.1176/ajp.145.5.578. 41. Buchanan RW, Breier A, Kirkpatrick B, Ball P, Carpenter WT Jr. Positive and negative symptom response to clozapine in schizophrenic patients with and without the deficit syndrome. Am J Psychiatry. 1998;155(6):751–60. https://doi.org/10.1176/ajp.155.6.751. 42. Galderisi S, Maj M, Mucci A, Cassano GB, Invernizzi G, Rossi A, Vita A, Dell’Osso L, Daneluzzo E, Pini S.  Historical, psychopathological, neurological, and neuropsychological aspects of deficit schizophrenia: a multicenter study. Am J Psychiatry. 2002;159(6):983–90. https://doi.org/10.1176/appi.ajp.159.6.983. 43. Kirkpatrick B, Buchanan RW, Ross DE, Carpenter WT Jr. A separate disease within the syndrome of schizophrenia. Arch Gen Psychiatry. 2001;58(2):165–71. 44. Galderisi S, Bucci P, Mucci A, Kirkpatrick B, Pini S, Rossi A, Vita A, Maj M. Categorical and dimensional approaches to negative symptoms of schizophrenia: focus on long-term stability and functional outcome. Schizophr Res. 2013;147(1):157–62. https://doi.org/10.1016/j. schres.2013.03.020. 45. Cohen AS, Saperstein AM, Gold JM, Kirkpatrick B, Carpenter WT Jr, Buchanan RW. Neuropsychology of the deficit syndrome: new data and meta-analysis of findings to date. Schizophr Bull. 2007;33(5):1201–12. https://doi.org/10.1093/schbul/sbl066. 46. Turetsky B, Cowell PE, Gur RC, Grossman RI, Shtasel DL, Gur RE. Frontal and temporal lobe brain volumes in schizophrenia. Relationship to symptoms and clinical subtype. Arch Gen Psychiatry. 1995;52(12):1061–70.

9  Toward Clinical Translation of Neuroimaging Research in Schizophrenia and Other… 339 47. Galderisi S, Quarantelli M, Volpe U, Mucci A, Cassano GB, Invernizzi G, Rossi A, Vita A, Pini S, Cassano P, Daneluzzo E, De Peri L, Stratta P, Brunetti A, Maj M. Patterns of structural MRI abnormalities in deficit and nondeficit schizophrenia. Schizophr Bull. 2008;34(2):393–401. https://doi.org/10.1093/schbul/sbm097. 48. Cascella NG, Fieldstone SC, Rao VA, Pearlson GD, Sawa A, Schretlen DJ.  Gray-matter abnormalities in deficit schizophrenia. Schizophr Res. 2010;120(1–3):63–70. https://doi. org/10.1016/j.schres.2010.03.039. 49. Fischer BA, Keller WR, Arango C, Pearlson GD, McMahon RP, Meyer WA, Francis A, Kirkpatrick B, Carpenter WT, Buchanan RW. Cortical structural abnormalities in deficit versus nondeficit schizophrenia. Schizophr Res. 2012;136(1–3):51–4. https://doi.org/10.1016/j. schres.2012.01.030. 50. Rowland LM, Spieker EA, Francis A, Barker PB, Carpenter WT, Buchanan RW. White matter alterations in deficit schizophrenia. Neuropsychopharmacology. 2009;34(6):1514–22. https:// doi.org/10.1038/npp.2008.207. 51. Kitis O, Ozalay O, Zengin EB, Haznedaroglu D, Eker MC, Yalvac D, Oguz K, Coburn K, Gonul AS. Reduced left uncinate fasciculus fractional anisotropy in deficit schizophrenia but not in non-deficit schizophrenia. Psychiatry Clin Neurosci. 2012;66(1):34–43. https://doi. org/10.1111/j.1440-1819.2011.02293.x. 52. Voineskos AN, Foussias G, Lerch J, Felsky D, Remington G, Rajji TK, Lobaugh N, Pollock BG, Mulsant BH.  Neuroimaging evidence for the deficit subtype of schizophrenia. JAMA Psychiatry. 2013;70(5):472–80. https://doi.org/10.1001/jamapsychiatry.2013.786. 53. Galderisi S, Merlotti E, Mucci A. Neurobiological background of negative symptoms. Eur Arch Psychiatry Clin Neurosci. 2015;265(7):543–58. https://doi.org/10.1007/s00406-015-0590-4. 54. Mucci A, Merlotti E, Ucok A, Aleman A, Galderisi S. Primary and persistent negative symptoms: concepts, assessments and neurobiological bases. Schizophr Res. 2017;186:19–28. https://doi.org/10.1016/j.schres.2016.05.014. 55. Galderisi S, Mucci A, Buchanan RW, Arango C. Negative symptoms of schizophrenia: new developments and unanswered research questions. Lancet Psychiatry. 2018; https://doi. org/10.1016/S2215-0366(18)30050-6. 56. Fusar-Poli P, Cappucciati M, Rutigliano G, Schultze-Lutter F, Bonoldi I, Borgwardt S, RiecherRossler A, Addington J, Perkins D, Woods SW, McGlashan TH, Lee J, Klosterkotter J, Yung AR, McGuire P. At risk or not at risk? A meta-analysis of the prognostic accuracy of psychometric interviews for psychosis prediction. World Psychiatry. 2015;14(3):322–32. https://doi. org/10.1002/wps.20250. 57. Kempton MJ, McGuire P.  How can neuroimaging facilitate the diagnosis and stratification of patients with psychosis? Eur Neuropsychopharmacol. 2015;25(5):725–32. https://doi. org/10.1016/j.euroneuro.2014.07.006. 58. Emsley R, Rabinowitz J, Medori R, Early Psychosis Global Working Group. Remission in early psychosis: rates, predictors, and clinical and functional outcome correlates. Schizophr Res. 2007;89(1–3):129–39. https://doi.org/10.1016/j.schres.2006.09.013. 59. Lambert M, Naber D, Schacht A, Wagner T, Hundemer HP, Karow A, Huber CG, Suarez D, Haro JM, Novick D, Dittmann RW, Schimmelmann BG. Rates and predictors of remission and recovery during 3 years in 392 never-treated patients with schizophrenia. Acta Psychiatr Scand. 2008;118(3):220–9. https://doi.org/10.1111/j.1600-0447.2008.01213.x. 60. Borgwardt S, Schmidt A. Is neuroimaging clinically useful in subjects at high risk for psychosis? World Psychiatry. 2016;15(2):178–9. https://doi.org/10.1002/wps.20333. 61. Pantelis C, Velakoulis D, McGorry PD, Wood SJ, Suckling J, Phillips LJ, Yung AR, Bullmore ET, Brewer W, Soulsby B, Desmond P, McGuire PK.  Neuroanatomical abnormalities before and after onset of psychosis: a cross-sectional and longitudinal MRI comparison. Lancet (London, England). 2003;361(9354):281–8. https://doi.org/10.1016/ s0140-6736(03)12323-9. 62. Walterfang M, Yung A, Wood AG, Reutens DC, Phillips L, Wood SJ, Chen J, Velakoulis D, McGorry PD, Pantelis C. Corpus callosum shape alterations in individuals prior to the onset of psychosis. Schizophr Res. 2008;103(1–3):1–10. https://doi.org/10.1016/j.schres.2008.04.042.

340

A. Vignapiano et al.

63. Meijer JH, Schmitz N, Nieman DH, Becker HE, van Amelsvoort TA, Dingemans PM, Linszen DH, de Haan L. Semantic fluency deficits and reduced grey matter before transition to psychosis: a voxelwise correlational analysis. Psychiatry Res. 2011;194(1):1–6. https:// doi.org/10.1016/j.pscychresns.2011.01.004. 64. Borgwardt SJ, McGuire PK, Aston J, Berger G, Dazzan P, Gschwandtner U, Pfluger M, D’Souza M, Radue EW, Riecher-Rossler A. Structural brain abnormalities in individuals with an at-risk mental state who later develop psychosis. Br J Psychiatry Suppl. 2007;51:s69–75. https://doi.org/10.1192/bjp.191.51.s69. 65. Borgwardt SJ, McGuire PK, Aston J, Gschwandtner U, Pfluger MO, Stieglitz RD, Radue EW, Riecher-Rossler A. Reductions in frontal, temporal and parietal volume associated with the onset of psychosis. Schizophr Res. 2008;106(2–3):108–14. https://doi.org/10.1016/j. schres.2008.08.007. 66. Das T, Borgwardt S, Hauke DJ, Harrisberger F, Lang UE, Riecher-Rossler A, Palaniyappan L, Schmidt A. Disorganized gyrification network properties during the transition to psychosis. JAMA Psychiatry. 2018; https://doi.org/10.1001/jamapsychiatry.2018.0391. 67. Bloemen OJ, de Koning MB, Schmitz N, Nieman DH, Becker HE, de Haan L, Dingemans P, Linszen DH, van Amelsvoort TA.  White-matter markers for psychosis in a prospective ultra-high-risk cohort. Psychol Med. 2010;40(8):1297–304. https://doi.org/10.1017/ s0033291709991711. 68. de la Fuente-Sandoval C, Leon-Ortiz P, Azcarraga M, Favila R, Stephano S, Graff-­ Guerrero A.  Striatal glutamate and the conversion to psychosis: a prospective 1H-MRS imaging study. Int J Neuropsychopharmacol. 2013;16(2):471–5. https://doi.org/10.1017/ s1461145712000314. 69. Cannon TD, Chung Y, He G, Sun D, Jacobson A, van Erp TG, McEwen S, Addington J, Bearden CE, Cadenhead K, Cornblatt B, Mathalon DH, McGlashan T, Perkins D, Jeffries C, Seidman LJ, Tsuang M, Walker E, Woods SW, Heinssen R, North American Prodrome Longitudinal Study Consortium. Progressive reduction in cortical thickness as psychosis develops: a multisite longitudinal neuroimaging study of youth at elevated clinical risk. Biol Psychiatry. 2015;77(2):147–57. https://doi.org/10.1016/j.biopsych.2014.05.023. 70. Ziermans TB, Schothorst PF, Schnack HG, Koolschijn PC, Kahn RS, van Engeland H, Durston S.  Progressive structural brain changes during development of psychosis. Schizophr Bull. 2012;38(3):519–30. https://doi.org/10.1093/schbul/sbq113. 71. Peters BD, Dingemans PM, Dekker N, Blaas J, Akkerman E, van Amelsvoort TA, Majoie CB, den Heeten GJ, Linszen DH, de Haan L. White matter connectivity and psychosis in ultra-­ high-­risk subjects: a diffusion tensor fiber tracking study. Psychiatry Res. 2010;181(1):44–50. https://doi.org/10.1016/j.pscychresns.2009.10.008. 72. Howes OD, Bose SK, Turkheimer F, Valli I, Egerton A, Valmaggia LR, Murray RM, McGuire P.  Dopamine synthesis capacity before onset of psychosis: a prospective [18F]-DOPA PET imaging study. Am J Psychiatry. 2011;168(12):1311–7. https://doi.org/10.1176/appi. ajp.2011.11010160. 73. Lindenmayer JP. Treatment refractory schizophrenia. Psychiatr Q. 2000;71(4):373–84. 74. Li J, Meltzer HY. A genetic locus in 7p12.2 associated with treatment resistant schizophrenia. Schizophr Res. 2014;159(2–3):333–9. https://doi.org/10.1016/j.schres.2014.08.018. 75. Teo C, Zai C, Borlido C, Tomasetti C, Strauss J, Shinkai T, Le Foll B, Wong A, Kennedy JL, De Luca V.  Analysis of treatment-resistant schizophrenia and 384 markers from candidate genes. Pharmacogenet Genomics. 2012;22(11):807–11. https://doi.org/10.1097/ FPC.0b013e3283586c04. 76. Howes OD, Vergunst F, Gee S, McGuire P, Kapur S, Taylor D. Adherence to treatment guidelines in clinical practice: study of antipsychotic treatment prior to clozapine initiation. Br J Psychiatry. 2012;201(6):481–5. https://doi.org/10.1192/bjp.bp.111.105833. 77. Reis Marques T, Taylor H, Chaddock C, Dell’Acqua F, Handley R, Reinders AATS, Mondelli V, Bonaccorso S, DiForti M, Simmons A, Murray RM, Pariante CM, Kapur S, Dazzan P.  White matter integrity as a predictor of response to treatment in first episode psychosis. Brain. 2014;137:172–82. (0006-8950 (Print)).

9  Toward Clinical Translation of Neuroimaging Research in Schizophrenia and Other… 341 78. Palaniyappan L, Marques TR, Taylor H, Handley R, Mondelli V, Bonaccorso S, Giordano A, McQueen G, DiForti M, Simmons A, David AS, Pariante CM, Murray RM, Dazzan P.  Cortical folding defects as markers of poor treatment response in firstepisode psychosis. JAMA Psychiatry. 2013;70(10):1031–40. https://doi.org/10.1001/ jamapsychiatry.2013.203. 79. Howes OD, Kapur S. A neurobiological hypothesis for the classification of schizophrenia: type A (hyperdopaminergic) and type B (normodopaminergic). Br J Psychiatry. 2014;205(1):1–3. https://doi.org/10.1192/bjp.bp.113.138578. 80. McGuire P, Howes OD, Stone J, Fusar-Poli P. Functional neuroimaging in schizophrenia: diagnosis and drug discovery. Trends Pharmacol Sci. 2008;29(2):91–8. https://doi.org/10.1016/j. tips.2007.11.005. 81. Abi-Dargham A, Rodenhiser J, Printz D, Zea-Ponce Y, Gil R, Kegeles LS, Weiss R, Cooper TB, Mann JJ, Van Heertum RL, Gorman JM, Laruelle M. Increased baseline occupancy of D2 receptors by dopamine in schizophrenia. Proc Natl Acad Sci U S A. 2000;97(14):8104–9. 82. Demjaha A, Murray RM, McGuire PK, Kapur S, Howes OD. Dopamine synthesis capacity in patients with treatment-resistant schizophrenia. Am J Psychiatry. 2012;169(11):1203–10. https://doi.org/10.1176/appi.ajp.2012.12010144. 83. Howes OD, Montgomery AJ, Asselin MC, Murray RM, Valli I, Tabraham P, Bramon-Bosch E, Valmaggia L, Johns L, Broome M, McGuire PK, Grasby PM. Elevated striatal dopamine function linked to prodromal signs of schizophrenia. Arch Gen Psychiatry. 2009;66(1):13–20. https://doi.org/10.1001/archgenpsychiatry.2008.514. 84. Egerton A, Chaddock CA, Winton-Brown TT, Bloomfield MA, Bhattacharyya S, Allen P, McGuire PK, Howes OD. Presynaptic striatal dopamine dysfunction in people at ultra-high risk for psychosis: findings in a second cohort. Biol Psychiatry. 2013;74(2):106–12. https:// doi.org/10.1016/j.biopsych.2012.11.017. 85. Howes O, McCutcheon R, Stone J.  Glutamate and dopamine in schizophrenia: an update for the 21st century. J Psychopharmacol. 2015;29(2):97–115. https://doi. org/10.1177/0269881114563634. 86. Poels EM, Kegeles LS, Kantrowitz JT, Javitt DC, Lieberman JA, Abi-Dargham A, Girgis RR.  Glutamatergic abnormalities in schizophrenia: a review of proton MRS findings. Schizophr Res. 2014;152(2–3):325–32. https://doi.org/10.1016/j.schres.2013.12.013. 87. Poels EM, Kegeles LS, Kantrowitz JT, Slifstein M, Javitt DC, Lieberman JA, Abi-Dargham A, Girgis RR. Imaging glutamate in schizophrenia: review of findings and implications for drug discovery. Mol Psychiatry. 2014;19(1):20–9. https://doi.org/10.1038/mp.2013.136. 88. Egerton A, Brugger S, Raffin M, Barker GJ, Lythgoe DJ, McGuire PK, Stone JM. Anterior cingulate glutamate levels related to clinical status following treatment in first-episode schizophrenia. Neuropsychopharmacology. 2012;37(11):2515–21. https://doi.org/10.1038/ npp.2012.113. 89. Demjaha A, Egerton A, Murray RM, Kapur S, Howes OD, Stone JM, McGuire PK.  Antipsychotic treatment resistance in schizophrenia associated with elevated glutamate levels but normal dopamine function. Biol Psychiatry. 2014;75(5):e11–3. https://doi. org/10.1016/j.biopsych.2013.06.011. 90. Mouchlianitis E, Bloomfield MA, Law V, Beck K, Selvaraj S, Rasquinha N, Waldman A, Turkheimer FE, Egerton A, Stone J, Howes OD. Treatment-resistant schizophrenia patients show elevated anterior cingulate cortex glutamate compared to treatment-responsive. Schizophr Bull. 2016;42(3):744–52. https://doi.org/10.1093/schbul/sbv151. 91. Egerton A, Bhachu A, Merritt K, McQueen G, Szulc A, McGuire P. Effects of antipsychotic administration on brain glutamate in schizophrenia: a systematic review of longitudinal (1) H-MRS studies. Front Psychiatry. 2017;8:66. https://doi.org/10.3389/fpsyt.2017.00066. 92. Mamdani F, Martin MV, Lencz T, Rollins B, Robinson DG, Moon EA, Malhotra AK, Vawter MP. Coding and noncoding gene expression biomarkers in mood disorders and schizophrenia. Dis Markers. 2013;35(1):11–21. https://doi.org/10.1155/2013/748095. 93. Insel TR, Cuthbert BN. Medicine. Brain disorders? Precisely. Science. 2015;348(6234):499– 500. https://doi.org/10.1126/science.aab2358.

342

A. Vignapiano et al.

94. O’Halloran R, Kopell BH, Sprooten E, Goodman WK, Frangou S. Multimodal neuroimaging-­ informed clinical applications in neuropsychiatric disorders. Front Psychiatry. 2016;7:63. https://doi.org/10.3389/fpsyt.2016.00063. 95. Calhoun VD, Sui J.  Multimodal fusion of brain imaging data: a key to finding the missing link(s) in complex mental illness. Biol Psychiatry Cogn Neurosci Neuroimaging. 2016;1(3):230–44. https://doi.org/10.1016/j.bpsc.2015.12.005. 96. Moser DA, Doucet GE, Lee WH, Rasgon A, Krinsky H, Leibu E, Ing A, Schumann G, Rasgon N, Frangou S.  Multivariate associations among behavioral, clinical, and multimodal imaging phenotypes in patients with psychosis. JAMA Psychiatry. 2018;75(4):386–95. https://doi. org/10.1001/jamapsychiatry.2017.4741. 97. Thompson PM, Stein JL, Medland SE, Hibar DP, Vasquez AA, Renteria ME, Toro R, Jahanshad N, Schumann G, Franke B, Wright MJ, Martin NG, Agartz I, Alda M, Alhusaini S, Almasy L, Almeida J, Alpert K, Andreasen NC, Andreassen OA, Apostolova LG, Appel K, Armstrong NJ, Aribisala B, Bastin ME, Bauer M, Bearden CE, Bergmann O, Binder EB, Blangero J, Bockholt HJ, Boen E, Bois C, Boomsma DI, Booth T, Bowman IJ, Bralten J, Brouwer RM, Brunner HG, Brohawn DG, Buckner RL, Buitelaar J, Bulayeva K, Bustillo JR, Calhoun VD, Cannon DM, Cantor RM, Carless MA, Caseras X, Cavalleri GL, Chakravarty MM, Chang KD, Ching CR, Christoforou A, Cichon S, Clark VP, Conrod P, Coppola G, Crespo-Facorro B, Curran JE, Czisch M, Deary IJ, de Geus EJ, den Braber A, Delvecchio G, Depondt C, de Haan L, de Zubicaray GI, Dima D, Dimitrova R, Djurovic S, Dong H, Donohoe G, Duggirala R, Dyer TD, Ehrlich S, Ekman CJ, Elvsashagen T, Emsell L, Erk S, Espeseth T, Fagerness J, Fears S, Fedko I, Fernandez G, Fisher SE, Foroud T, Fox PT, Francks C, Frangou S, Frey EM, Frodl T, Frouin V, Garavan H, Giddaluru S, Glahn DC, Godlewska B, Goldstein RZ, Gollub RL, Grabe HJ, Grimm O, Gruber O, Guadalupe T, Gur RE, Gur RC, Goring HH, Hagenaars S, Hajek T, Hall GB, Hall J, Hardy J, Hartman CA, Hass J, Hatton SN, Haukvik UK, Hegenscheid K, Heinz A, Hickie IB, Ho BC, Hoehn D, Hoekstra PJ, Hollinshead M, Holmes AJ, Homuth G, Hoogman M, Hong LE, Hosten N, Hottenga JJ, Hulshoff Pol HE, Hwang KS, Jack CR Jr, Jenkinson M, Johnston C, Jonsson EG, Kahn RS, Kasperaviciute D, Kelly S, Kim S, Kochunov P, Koenders L, Kramer B, Kwok JB, Lagopoulos J, Laje G, Landen M, Landman BA, Lauriello J, Lawrie SM, Lee PH, Le Hellard S, Lemaitre H, Leonardo CD, Li CS, Liberg B, Liewald DC, Liu X, Lopez LM, Loth E, Lourdusamy A, Luciano M, Macciardi F, Machielsen MW, Macqueen GM, Malt UF, Mandl R, Manoach DS, Martinot JL, Matarin M, Mather KA, Mattheisen M, Mattingsdal M, Meyer-­Lindenberg A, McDonald C, McIntosh AM, McMahon FJ, McMahon KL, Meisenzahl E, Melle I, Milaneschi Y, Mohnke S, Montgomery GW, Morris DW, Moses EK, Mueller BA, Munoz Maniega S, Muhleisen TW, Muller-Myhsok B, Mwangi B, Nauck M, Nho K, Nichols TE, Nilsson LG, Nugent AC, Nyberg L, Olvera RL, Oosterlaan J, Ophoff RA, Pandolfo M, Papalampropoulou-Tsiridou M, Papmeyer M, Paus T, Pausova Z, Pearlson GD, Penninx BW, Peterson CP, Pfennig A, Phillips M, Pike GB, Poline JB, Potkin SG, Putz B, Ramasamy A, Rasmussen J, Rietschel M, Rijpkema M, Risacher SL, Roffman JL, RoizSantianez R, Romanczuk-Seiferth N, Rose EJ, Royle NA, Rujescu D, Ryten M, Sachdev PS, Salami A, Satterthwaite TD, Savitz J, Saykin AJ, Scanlon C, Schmaal L, Schnack HG, Schork AJ, Schulz SC, Schur R, Seidman L, Shen L, Shoemaker JM, Simmons A, Sisodiya SM, Smith C, Smoller JW, Soares JC, Sponheim SR, Sprooten E, Starr JM, Steen VM, Strakowski S, Strike L, Sussmann J, Samann PG, Teumer A, Toga AW, Tordesillas-Gutierrez D, Trabzuni D, Trost S, Turner J, Van den Heuvel M, van der Wee NJ, van Eijk K, van Erp TG, van Haren NE, van’t Ent D, van Tol MJ, Valdes Hernandez MC, Veltman DJ, Versace A, Volzke H, Walker R, Walter H, Wang L, Wardlaw JM, Weale ME, Weiner MW, Wen W, Westlye LT, Whalley HC, Whelan CD, White T, Winkler AM, Wittfeld K, Woldehawariat G, Wolf C, Zilles D, Zwiers MP, Thalamuthu A, Schofield PR, Freimer NB, Lawrence NS, Drevets W, Alzheimer’s Disease Neuroimaging Initiative, EPIGEN Consortium, IMAGEN Consortium, Saguenay Youth Study (SYS) Group. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. 2014;8(2):153–82. https://doi.org/10.1007/ s11682-013-9269-5.

9  Toward Clinical Translation of Neuroimaging Research in Schizophrenia and Other… 343 98. Hibar DP, Adams HHH, Jahanshad N, Chauhan G, Stein JL, Hofer E, Renteria ME, Bis JC, Arias-Vasquez A, Ikram MK, Desrivieres S, Vernooij MW, Abramovic L, Alhusaini S, Amin N, Andersson M, Arfanakis K, Aribisala BS, Armstrong NJ, Athanasiu L, Axelsson T, Beecham AH, Beiser A, Bernard M, Blanton SH, Bohlken MM, Boks MP, Bralten J, Brickman AM, Carmichael O, Chakravarty MM, Chen Q, Ching CRK, Chouraki V, Cuellar-Partida G, Crivello F, Den Braber A, Doan NT, Ehrlich S, Giddaluru S, Goldman AL, Gottesman RF, Grimm O, Griswold ME, Guadalupe T, Gutman BA, Hass J, Haukvik UK, Hoehn D, Holmes AJ, Hoogman M, Janowitz D, Jia T, Jorgensen KN, Karbalai N, Kasperaviciute D, Kim S, Klein M, Kraemer B, Lee PH, Liewald DCM, Lopez LM, Luciano M, Macare C, Marquand AF, Matarin M, Mather KA, Mattheisen M, McKay DR, Milaneschi Y, Munoz Maniega S, Nho K, Nugent AC, Nyquist P, Loohuis LMO, Oosterlaan J, Papmeyer M, Pirpamer L, Putz B, Ramasamy A, Richards JS, Risacher SL, Roiz-Santianez R, Rommelse N, Ropele S, Rose EJ, Royle NA, Rundek T, Samann PG, Saremi A, Satizabal CL, Schmaal L, Schork AJ, Shen L, Shin J, Shumskaya E, Smith AV, Sprooten E, Strike LT, Teumer A, Tordesillas-Gutierrez D, Toro R, Trabzuni D, Trompet S, Vaidya D, Van der Grond J, Van der Lee SJ, Van der Meer D, Van Donkelaar MMJ, Van Eijk KR, Van Erp TGM, Van Rooij D, Walton E, Westlye LT, Whelan CD, Windham BG, Winkler AM, Wittfeld K, Woldehawariat G, Wolf C, Wolfers T, Yanek LR, Yang J, Zijdenbos A, Zwiers MP, Agartz I, Almasy L, Ames D, Amouyel P, Andreassen OA, Arepalli S, Assareh AA, Barral S, Bastin ME, Becker DM, Becker JT, Bennett DA, Blangero J, van Bokhoven H, Boomsma DI, Brodaty H, Brouwer RM, Brunner HG, Buckner RL, Buitelaar JK, Bulayeva KB, Cahn W, Calhoun VD, Cannon DM, Cavalleri GL, Cheng CY, Cichon S, Cookson MR, Corvin A, Crespo-Facorro B, Curran JE, Czisch M, Dale AM, Davies GE, De Craen AJM, De Geus EJC, De Jager PL, De Zubicaray GI, Deary IJ, Debette S, DeCarli C, Delanty N, Depondt C, DeStefano A, Dillman A, Djurovic S, Donohoe G, Drevets WC, Duggirala R, Dyer TD, Enzinger C, Erk S, Espeseth T, Fedko IO, Fernandez G, Ferrucci L, Fisher SE, Fleischman DA, Ford I, Fornage M, Foroud TM, Fox PT, Francks C, Fukunaga M, Gibbs JR, Glahn DC, Gollub RL, Goring HHH, Green RC, Gruber O, Gudnason V, Guelfi S, Haberg AK, Hansell NK, Hardy J, Hartman CA, Hashimoto R, Hegenscheid K, Heinz A, Le Hellard S, Hernandez DG, Heslenfeld DJ, Ho BC, Hoekstra PJ, Hoffmann W, Hofman A, Holsboer F, Homuth G, Hosten N, Hottenga JJ, Huentelman M, Hulshoff Pol HE, Ikeda M, Jack CR Jr, Jenkinson M, Johnson R, Jonsson EG, Jukema JW, Kahn RS, Kanai R, Kloszewska I, Knopman DS, Kochunov P, Kwok JB, Lawrie SM, Lemaitre H, Liu X, Longo DL, Lopez OL, Lovestone S, Martinez O, Martinot JL, Mattay VS, McDonald C, McIntosh AM, McMahon FJ, McMahon KL, Mecocci P, Melle I, Meyer-Lindenberg A, Mohnke S, Montgomery GW, Morris DW, Mosley TH, Muhleisen TW, Muller-Myhsok B, Nalls MA, Nauck M, Nichols TE, Niessen WJ, Nothen MM, Nyberg L, Ohi K, Olvera RL, Ophoff RA, Pandolfo M, Paus T, Pausova Z, Penninx B, Pike GB, Potkin SG, Psaty BM, Reppermund S, Rietschel M, Roffman JL, Romanczuk-Seiferth N, Rotter JI, Ryten M, Sacco RL, Sachdev PS, Saykin AJ, Schmidt R, Schmidt H, Schofield PR, Sigursson S, Simmons A, Singleton A, Sisodiya SM, Smith C, Smoller JW, Soininen H, Steen VM, Stott DJ, Sussmann JE, Thalamuthu A, Toga AW, Traynor BJ, Troncoso J, Tsolaki M, Tzourio C, Uitterlinden AG, Hernandez MCV, Van der Brug M, van der Lugt A, van der Wee NJA, Van Haren NEM, van’t Ent D, Van Tol MJ, Vardarajan BN, Vellas B, Veltman DJ, Volzke H, Walter H, Wardlaw JM, Wassink TH, Weale ME, Weinberger DR, Weiner MW, Wen W, Westman E, White T, Wong TY, Wright CB, Zielke RH, Zonderman AB, Martin NG, Van Duijn CM, Wright MJ, Longstreth WT, Schumann G, Grabe HJ, Franke B, Launer LJ, Medland SE, Seshadri S, Thompson PM, Ikram MA. Novel genetic loci associated with hippocampal volume. Nat Commun. 2017;8:13624. https://doi.org/10.1038/ncomms13624. 99. Stein JL, Medland SE, Vasquez AA, Hibar DP, Senstad RE, Winkler AM, Toro R, Appel K, Bartecek R, Bergmann O, Bernard M, Brown AA, Cannon DM, Chakravarty MM, Christoforou A, Domin M, Grimm O, Hollinshead M, Holmes AJ, Homuth G, Hottenga JJ, Langan C, Lopez LM, Hansell NK, Hwang KS, Kim S, Laje G, Lee PH, Liu X, Loth E, Lourdusamy A, Mattingsdal M, Mohnke S, Maniega SM, Nho K, Nugent AC, O’Brien C,

344

A. Vignapiano et al.

Papmeyer M, Putz B, Ramasamy A, Rasmussen J, Rijpkema M, Risacher SL, Roddey JC, Rose EJ, Ryten M, Shen L, Sprooten E, Strengman E, Teumer A, Trabzuni D, Turner J, van Eijk K, van Erp TG, van Tol MJ, Wittfeld K, Wolf C, Woudstra S, Aleman A, Alhusaini S, Almasy L, Binder EB, Brohawn DG, Cantor RM, Carless MA, Corvin A, Czisch M, Curran JE, Davies G, de Almeida MA, Delanty N, Depondt C, Duggirala R, Dyer TD, Erk S, Fagerness J, Fox PT, Freimer NB, Gill M, Goring HH, Hagler DJ, Hoehn D, Holsboer F, Hoogman M, Hosten N, Jahanshad N, Johnson MP, Kasperaviciute D, Kent JW Jr, Kochunov P, Lancaster JL, Lawrie SM, Liewald DC, Mandl R, Matarin M, Mattheisen M, Meisenzahl E, Melle I, Moses EK, Muhleisen TW, Nauck M, Nothen MM, Olvera RL, Pandolfo M, Pike GB, Puls R, Reinvang I, Renteria ME, Rietschel M, Roffman JL, Royle NA, Rujescu D, Savitz J, Schnack HG, Schnell K, Seiferth N, Smith C, Steen VM, Valdes Hernandez MC, Van den Heuvel M, van der Wee NJ, Van Haren NE, Veltman JA, Volzke H, Walker R, Westlye LT, Whelan CD, Agartz I, Boomsma DI, Cavalleri GL, Dale AM, Djurovic S, Drevets WC, Hagoort P, Hall J, Heinz A, Jack CR Jr, Foroud TM, Le Hellard S, Macciardi F, Montgomery GW, Poline JB, Porteous DJ, Sisodiya SM, Starr JM, Sussmann J, Toga AW, Veltman DJ, Walter H, Weiner MW, Alzheimer’s Disease Neuroimaging I, Consortium E, Consortium I, Saguenay Youth Study G, Bis JC, Ikram MA, Smith AV, Gudnason V, Tzourio C, Vernooij MW, Launer LJ, DeCarli C, Seshadri S, Cohorts for Heart Aging Research in Genomic Epidemiology Consortium, Andreassen OA, Apostolova LG, Bastin ME, Blangero J, Brunner HG, Buckner RL, Cichon S, Coppola G, de Zubicaray GI, Deary IJ, Donohoe G, de Geus EJ, Espeseth T, Fernandez G, Glahn DC, Grabe HJ, Hardy J, Hulshoff Pol HE, Jenkinson M, Kahn RS, McDonald C, AM MI, FJ MM, KL MM, Meyer-Lindenberg A, Morris DW, Muller-Myhsok B, Nichols TE, Ophoff RA, Paus T, Pausova Z, Penninx BW, Potkin SG, Samann PG, Saykin AJ, Schumann G, Smoller JW, Wardlaw JM, Weale ME, Martin NG, Franke B, Wright MJ, Thompson PM, Enhancing Neuro Imaging Genetics Through Meta-Analysis Consortium. Identification of common variants associated with human hippocampal and intracranial volumes. Nat Genet. 2012;44(5):552–61. https://doi.org/10.1038/ng.2250. 100. Bearden CE, Thompson PM. Emerging Global Initiatives in Neurogenetics: The Enhancing Neuroimaging Genetics through Meta-analysis (ENIGMA) Consortium. Neuron. 2017;94(2):232–6. https://doi.org/10.1016/j.neuron.2017.03.033. 101. Dazzan P, Arango C, Fleischacker W, Galderisi S, Glenthoj B, Leucht S, Meyer-Lindenberg A, Kahn R, Rujescu D, Sommer I, Winter I, McGuire P. Magnetic resonance imaging and the prediction of outcome in first-episode schizophrenia: a review of current evidence and directions for future research. Schizophr Bull. 2015;41(3):574–83. https://doi.org/10.1093/schbul/ sbv024. 102. Cannon TD, Cadenhead K, Cornblatt B, Woods SW, Addington J, Walker E, Seidman LJ, Perkins D, Tsuang M, McGlashan T, Heinssen R.  Prediction of psychosis in youth at high clinical risk: a multisite longitudinal study in North America. Arch Gen Psychiatry. 2008;65(1):28–37. https://doi.org/10.1001/archgenpsychiatry.2007.3. 103. Anticevic A, Haut K, Murray JD, Repovs G, Yang GJ, Diehl C, McEwen SC, Bearden CE, Addington J, Goodyear B, Cadenhead KS, Mirzakhanian H, Cornblatt BA, Olvet D, Mathalon DH, McGlashan TH, Perkins DO, Belger A, Seidman LJ, Tsuang MT, van Erp TG, Walker EF, Hamann S, Woods SW, Qiu M, Cannon TD. Association of thalamic dysconnectivity and conversion to psychosis in youth and young adults at elevated clinical risk. JAMA Psychiatry. 2015;72(9):882–91. https://doi.org/10.1001/jamapsychiatry.2015.0566. 104. Chung Y, Cannon TD.  Brain imaging during the transition from psychosis prodrome to schizophrenia. J Nerv Ment Dis. 2015;203(5):336–41. https://doi.org/10.1097/ NMD.0000000000000286. 105. Zarogianni E, Moorhead TW, Lawrie SM. Towards the identification of imaging biomarkers in schizophrenia, using multivariate pattern classification at a single-subject level. Neuroimage Clin. 2013;3:279–89. https://doi.org/10.1016/j.nicl.2013.09.003.

9  Toward Clinical Translation of Neuroimaging Research in Schizophrenia and Other… 345 106. Linden DE.  The challenges and promise of neuroimaging in psychiatry. Neuron. 2012;73(1):8–22. https://doi.org/10.1016/j.neuron.2011.12.014. 107. Vieira S, Pinaya WH, Mechelli A. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: methods and applications. Neurosci Biobehav Rev. 2017;74(Pt A):58–75. https://doi.org/10.1016/j.neubiorev.2017.01.002. 108. Koutsouleris N, Meisenzahl EM, Davatzikos C, Bottlender R, Frodl T, Scheuerecker J, Schmitt G, Zetzsche T, Decker P, Reiser M, Moller HJ, Gaser C. Use of n­ euroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition. Arch Gen Psychiatry. 2009;66(7):700–12. https://doi.org/10.1001/ archgenpsychiatry.2009.62. 109. Fu CH, Mourao-Miranda J, Costafreda SG, Khanna A, Marquand AF, Williams SC, Brammer MJ. Pattern classification of sad facial processing: toward the development of neurobiological markers in depression. Biol Psychiatry. 2008;63(7):656–62. https://doi.org/10.1016/j. biopsych.2007.08.020. 110. Orru G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A.  Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci Biobehav Rev. 2012;36(4):1140–52. https://doi.org/10.1016/j. neubiorev.2012.01.004. 111. Shen H, Wang L, Liu Y, Hu D. Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI.  NeuroImage. 2010;49(4):3110–21. https://doi.org/10.1016/j.neuroimage.2009.11.011. 112. Venkataraman A, Whitford TJ, Westin CF, Golland P, Kubicki M. Whole brain resting state functional connectivity abnormalities in schizophrenia. Schizophr Res. 2012;139(1–3):7–12. https://doi.org/10.1016/j.schres.2012.04.021. 113. Kambeitz J, Kambeitz-Ilankovic L, Leucht S, Wood S, Davatzikos C, Malchow B, Falkai P, Koutsouleris N. Detecting neuroimaging biomarkers for schizophrenia: a meta-analysis of multivariate pattern recognition studies. Neuropsychopharmacology. 2015;40(7):1742–51. https://doi.org/10.1038/npp.2015.22. 114. Koutsouleris N, Borgwardt S, Meisenzahl EM, Bottlender R, Moller HJ, Riecher-Rossler A. Disease prediction in the at-risk mental state for psychosis using neuroanatomical biomarkers: results from the FePsy study. Schizophr Bull. 2012;38(6):1234–46. https://doi. org/10.1093/schbul/sbr145. 115. Borgwardt S, Koutsouleris N, Aston J, Studerus E, Smieskova R, Riecher-Rossler A, Meisenzahl EM.  Distinguishing prodromal from first-episode psychosis using neuroanatomical single-subject pattern recognition. Schizophr Bull. 2013;39(5):1105–14. https://doi. org/10.1093/schbul/sbs095. 116. Kambeitz-Ilankovic L, Meisenzahl EM, Cabral C, von Saldern S, Kambeitz J, Falkai P, Moller HJ, Reiser M, Koutsouleris N.  Prediction of outcome in the psychosis prodrome using neuroanatomical pattern classification. Schizophr Res. 2016;173(3):159–65. https:// doi.org/10.1016/j.schres.2015.03.005. 117. de Wit S, Ziermans TB, Nieuwenhuis M, Schothorst PF, van Engeland H, Kahn RS, Durston S, Schnack HG.  Individual prediction of long-term outcome in adolescents at ultra-high risk for psychosis: applying machine learning techniques to brain imaging data. Hum Brain Mapp. 2017;38(2):704–14. https://doi.org/10.1002/hbm.23410. 118. Fusar-Poli P, Yung AR, McGorry P, van Os J.  Lessons learned from the psychosis high-­ risk state: towards a general staging model of prodromal intervention. Psychol Med. 2014;44(1):17–24. https://doi.org/10.1017/S0033291713000184.