Neurocognitive Development: Normative Development, 173 0444641505, 9780444641502

Table of contents :
Front Cover
Neurocognitive Development: Normative Development
Copyright
Available titles
Foreword
Preface
Contributors
Contents
Section I: Introduction to neurodevelopmental disabilities
Chapter 1: Description and classification of neurodevelopmental disabilities
Classification Systems for NDDs
Benefits and Challenges Related to the Use of Classification Systems
The Diagnosis of NDDs: A Challenging Process
Conclusion
References
Chapter 2: Neurodevelopmental and cognitive disabilities: Historical overview
Introduction
Developmental Sciences 1900-50
Deinstitutionalization, Diagnosis, Classification, and Epidemiology
Cognitive Science, Genetics, and Neuroimaging
Conclusion
References
Chapter 3: Ethical views and considerations
Introduction
Ethical Considerations of Researchers Conducting Pediatric Study in Healthy Children
An Example of an fMRI Study on Healthy School Children
MRI familiarization at school
fMRI imaging protocol
Results and Perspectives
Ethical Considerations
Conclusion
References
Section II: Biological basis of typical neurodevelopment
Chapter 4: Neurogenesis, neuronal migration, and axon guidance
Neurulation and Formation of the Primary Brain Vesicles and Ventricular Zone
Neurogenesis and Neuronal Proliferation
Dynamics of Neurogenesis
Regulation of Neurogenesis
Intrinsic Factors
Transcriptional regulators
Epigenetic modifications and post-translational regulation
Extrinsic Factors
Signaling pathways
Environmental cues
Neurotransmitters
Adult Neurogenesis
Programmed Cell Death in Neuronal Development
Neuronal Migration
Radial Migration: Multipolar Migration, Somal Translocation and Locomotion
Tangential Migration
Terminal Translocation
Regulation of Neuronal Migration
Intrinsic Factors
Transcriptional, post-transcriptional, and epigenetic regulation of neural migration
Small GTP binding proteins, protein kinases
Extrinsic Factors
Extracellular matrix proteins
Cell-cell-adhesion molecules
Others: Signaling pathways and axonal guidance molecules
Folding and Gyrification of Human Cortex
Axon Guidance and Pathfinding
Conclusion
References
Chapter 5: Development of neuronal circuits: From synaptogenesis to synapse plasticity
Synapse Formation: A Complex Problem
How Does an Axonal Terminal Choose Its Synapse Location and Identity?
Process of Synapse Formation
Experience Dependent Modulation of Synapse Formation
Synaptic Plasticity in Mature Synapses
Synaptogenesis and Plasticity in Neurodevelopmental Disorders
References
Further reading
Section III: Plasticity, vulnerability and evolutionary constraints of the developing brain
Chapter 6: A conceptual framework for plasticity in the developing brain
Introduction
Definitions of Brain Plasticity
A Conceptual Framework of Early Brain Plasticity
Mechanisms and constraints of early brain plasticity
Mechanism of neuronal plasticity
Constraints in the developing brain
Temporal associations (critical and sensitive periods)
Functions of early brain plasticity
Development/maturation through learning
Compensation/adaptation following injury or sensory deprivation
Clinical Contexts
Genetic disorders
Prematurity
Epilepsy
Perinatal stroke
Intervention-related plasticity
Enhanced vulnerability
Concluding Remarks
References
Chapter 7: Resilience
Introduction
Definition of Resilience
Developmental Psychopathology
Outline of the Chapter
Neurobiology and Resilient Functioning
Stress Response Systems
Genetics
Psychosocial Factors
Conclusion and Future Directions
References
Chapter 8: Critical periods of brain development
Introduction
What Are Critical Periods?
Key Critical Periods of Sensory Systems
Ocular dominance
Amblyopia and stereopsis
Auditory processing and frequency tuning
Spatial hearing and binaural processing
How Are Critical Periods Regulated?
Local regulation of cortical plasticity
Neuromodulation
Timing of Critical Periods: Can CP Plasticity Be Extended, Limited, or Reactivated?
Extending critical periods
Premature closing of critical periods
Reactivation of CP plasticity in the adult brain
Concluding Remarks
References
Chapter 9: The vulnerability of the immature brain
Introduction
Evidence for a Vulnerability of the Developing Brain
Cognitive outcome pattern after an early brain lesion
The fundamental basis of the Hebbian learning model that relies on synaptogenesis
Vulnerability and synaptic homeostasis
Vulnerability of Immature Brain Among Species
Vulnerability and immaturity in mammals: Not always evidence
Vulnerability of neural progenitor cells in neocortex
Vulnerability and Inherited Constraints of Cerebral Networks in the Developing Human Brain
Inherited structural constraints in the developing brain
Functional connectivity in the developing brain: The new face of vulnerability
Conclusion
References
Chapter 10: Development of handedness, anatomical and functional brain lateralization
Introduction
Developmental Course of Anatomical Asymmetries
Developmental Course of Functional Asymmetries of Language Networks
Developmental Course of Functional Asymmetries of Visuospatial Networks
Development of Manual Asymmetries and Relationships With Language and Visuospatial Lateralization Through Development
Conclusion
References
Section IV: Neuroscientific basis of typical functional neurodevelopment
Chapter 11: Intellectual abilities
Definition of Intelligence
Typical Development of Intellectual Abilities
Evaluation of Intellectual Abilities
Woodcok-Johnson IV
Beyond the Conventional Testing Paradigm
Neurometrics
References
Chapter 12: Visual development
Introduction
Outline of Visual Processing
Neurobiologic Model of Infant Visual Development
Subcortical and cortical visual systems
Dorsal and ventral streams
Development of Visual Acuity, Contrast Sensitivity, Refraction, and Focusing
Visual acuity and contrast sensitivity
Development of focusing ability (accommodation) and refraction
Hyperacuity
Developing Cortical Selectivity for Orientation and Motion
More Complex Cortical Motion Processing
Global motion processing
Processing of global static form and global motion compared
Development of Binocularity and 3D Vision
Development of binocularity
Development of 3-D shape and depth perception
Segmentation and Figure-Ground
Visual Face Processing
Development of Color Vision
Development of Action Systems
Action modules in the dorsal stream
Action systems early in life: Head and eye movements linked to visual attention
Optokinetic nystagmus and smooth pursuit eye movements
Visually guided reaching and grasping
``End-state´´ planning of actions
Action modules for locomotion
Development of Attention and Executive Function
Development of Visuospatial Localization and Spatial Memory
Abnormalities and Plasticity of Visual Development
Binocularity and strabismus
Amblyopia and plasticity
Cerebral visual impairment
Dorsal stream vulnerability
Conclusions
Acknowledgments
References
Chapter 13: The development of auditory functions
Embryology of the Peripheral Auditory System
Outer ear
Middle ear
Inner ear
Development of the Central Auditory System
Measuring the Peripheral Auditory System in Infants
Development of Auditory Perception
Auditory detection
Auditory discrimination
Auditory identification
Auditory temporal processing
Auditory temporal integration
Auditory temporal resolution
Auditory temporal sequencing
Binaural auditory processing
Binaural interaction
Sound localization
Binaural integration
Auditory streaming
Conclusion
References
Chapter 14: Motor functions
Definition of Motor Function, Motor Skills, and Praxis
Motor function and motor skills
Praxis
Typical Development of Motor and Praxis Abilities
Early motor development
Integration of motor- and praxis development
Evaluation of Motor Function and Praxis
Evaluation of motor function
Evaluation of praxis
Conclusion
References
Chapter 15: Typical language development
Introduction
Theoretical Approaches to Language Acquisition
Newborns Speech Perception Abilities
Broad-based, universal abilities
Abilities shaped by prenatal experience
Early Phoneme Perception and the Attunement to the Native Language
Segmentation and Word Learning
Segmenting continuous speech
Attaching meaning to word forms
The Beginnings of Grammar
Language Development in Multilinguals
Conclusions
Acknowledgment
References
Chapter 16: Literacy acquisition: Reading development
Introduction
Emergent Literacy
Early Literacy
Conventional Literacy
Developmental Neural Changes Underlying Reading Acquisition
Conclusion
References
Chapter 17: Memory: Normative development of memory systems
Introduction
Development of Short-Term and Working Memory
Working memory in infants
Development of working memory from childhood to adulthood
Development of Nondeclarative Memory
Simple classic conditioning
Procedural memory
Perceptual priming
Conceptual priming
Development of Declarative Memory
Early declarative memory abilities
Development of semantic memory
Development of episodic memory
Conclusions and Future Directions
Working memory
Nondeclarative memory
Declarative memory
Interaction between memory systems
References
Chapter 18: Developing attention in typical children related to disabilities
Introduction
Methods of Examining Brain Changes
Anatomy
Structural images
Task-related functional imaging
Resting-state imaging
Brain Networks of Attention
Alerting
Orienting
Executive attention
Individual Differences
Network scores
Molecular mechanisms
Genes and development
Theory of mind (ToM)
Deficits of Attention
Autistic spectrum disorder (ASD)
Attention deficit hyperactivity disorder (ADHD)
Acknowledgments
References
Further reading
Chapter 19: Executive functions
Definition of Executive Functions
Development of Executive Functions in Children
Infancy (0-2 years)
Preschool period (2-5 years of age)
Middle childhood (6-11 years of age)
Speed of processing and developmental improvements in EFs
Evaluation of Executive Functioning in Children
References
Chapter 20: Learning abilities
Definition of Learning
Types of Learning and Their Development in Children
Repetition-based learning: Habituation as a nonassociative-based learning
Behavioral perspective
Developmental perspective
Neurophysiologic perspective
Associative learning
Classical and operant conditioning
Developmental perspective
More complex forms of associative learning
Associative learning in categorization
Generalization of associative learning
Associative learning in language acquisition
The impact of context for associative learning
Associative learning as an essential building block of learning and memory
How to evaluate learning
System Biology of Learning
Molecular biology of learning
Structural mechanisms of learning
Conclusion
References
Chapter 21: Social cognition
Introduction
Development of Social Cognition in Children
Social cognition emerges as a result of a developmental cascade
Sociocognitive and global development are mutually dependent
Sociocognitive development is driven by biologic processes
Sociocognitive development is subject to environmental influence
Sociocognitive development is protracted and continues across the lifespan
Social Cognition Assessment in Children
Social cognition cannot always be inferred from everyday social behavior
Diverse cognitive deficits can lead to the same manifestation of social dysfunction
Distinct conditions present unique sociocognitive profiles
Subcomponents of a sociocognitive ability may not be uniformly affected within a single condition
Sociocognitive assessment must be developmentally appropriate
Sociocognitive assessment should rely on measures that have good psychometric properties
Conclusion
References
Chapter 22: The role of cerebellum in the child neuropsychological functioning
Introduction
General Anatomy
Cerebellum and Cognition
The Universal Cerebellar Tranform (UCT) Hypothesis and Internal Models
Cerebellar Somatotopy and Functional Organization
Cerebellar Development
Cerebellum and Specific Cognitive Functions
Verbal working memory and articulatory subvocal rehearsal
Language
Time
Other functions
Cerebellum and Eye Movements
Cerebellar Cognitive Associations During Typical Development and in Subjects Born Preterm
Cerebellar Pathology in Children and Cognitive Development
Cerebellar malformations and dysplasias (Table 22.1)
Cerebellar agenesis
Ataxias
Pediatric ataxias (Table 22.1)
Overview of the neuropsychologic profile in ataxias in adults
Cerebellar tumors (Table 22.1)
Cerebellar pilocytic astrocytomas or other benign cerebellar tumors treated by surgery only
Medulloblastoma and other cerebellar tumors (treated by surgery, generally associated with radiotherapy and chemotherapy)
Cerebellar cysts
Other acquired pediatric cerebellar lesions: Stroke, traumatic brain injury (Table 22.1)
Cerebellar stroke
Traumatic brain injury
Cerebellar mutism syndrome/posterior fossa syndrome (Table 22.1)
Cerebellum and Neurodevelopmental Disorders
Cerebellum and autism
Cerebellum and developmental dyslexia
The Cerebellar Cognitive Affective Syndrome (CCAS)
Summary of the Topic and New Debatable Concepts
Motor and cognitive cerebellum
Functional organization of the cerebellum and anatomo-functional associations
The cerebellar cognitive affective syndrome (CCAS)
UCT and common components in cerebellar motor and neuropsychologic deficits
Learning, and cerebellar damage in children and in adults
Environment, remediation, rehabilitation
Conclusion
Ackowledgment
References
Section V: Etiologies of neurodevelopmental disorders
Chapter 23: Genetic mechanisms of neurodevelopmental disorders
Introduction-Historic Perspective and Overview
Role of Chromosomal Microarray
Impact and Insight From Copy Number Variation
Variably Penetrant CNVs
Counseling Considerations for CNVs
Next-Generation Sequencing Technology
The De Novo Paradigm for Neurodevelopmental Disease
Insights Into Disease Mechanism Gained From De Novo Variation
Recessive Disease
Current Limitations of NGS in Neurodevelopmental Disorders
Role of Multifactorial or Complex Inheritance in Neurodevelopmental Disorders
Insights From Genome-Wide Association Studies
Role of Normal Population Variation in Neurodevelopmental Disease
Impact of Common Variation on Rare Monogenic Neurodevelopmental Disorders
Genetic Architecture for Neurodevelopmental Disease Is a Combination of Rare and Common Variation
Gene-Environment Interactions
New Technologies and the Practical Application to Clinic Today
Conclusion
References
Chapter 24: The effects of sex on prevalence and mechanisms underlying neurodevelopmental disorders
Introduction
Sex Differences Within Disorders
Autism spectrum disorder
Intellectual disability
Communication disorders
Language disorder or specific language impairment
Speech sound disorder
Childhood-onset fluency disorder (stuttering)
Specific learning disability
Attention deficit/hyperactivity disorder
Epilepsy
Gilles de la Tourette syndrome
Comorbidity
Factors Underlying Sex Differences
Dimorphism in typical development
Cognition
Problematic behaviors
Hormonal factors
Potential interactions between risk factors and sex
Genetic factors
Environmental insult and injury
Future Directions
References
Chapter 25: Impact of prematurity on neurodevelopment
Introduction
Normal Structural and Functional Brain Maturation
A brief overview of the timeline of the maturation processes
Electroencephalographic monitoring of functional brain development
Properties of developing networks
Endogenous generators
Impact of Prematurity on Structural and Functional Brain Development and Maturation
Hemorrhagic damage
Periventricular leukomalacia
Trophic and maturation disturbances
Impact of Prematurity on the Motor System
Structural maturation of the motor system
Functional maturation of the motor system
Impact of the prematurity on motor functions
Cerebral palsy
Noncerebral palsy motor impairment
Impact of Prematurity on the Neurosensory Systems
The somatosensory system
Structural maturation
Functional maturation
Impact of prematurity
The chemosensory systems
Structural maturation of the olfactory and taste systems
Functional maturation of the olfactory and taste systems
Impact of prematurity
The vestibular and auditory systems
Structural maturation of the auditory system
Functional maturation of the auditory system
Impact of prematurity
The visual system
Structural maturation of the visual system
Functional maturation of the visual system
Impact of prematurity on the visual system
Impact of Prematurity on the Cognitive System
Maturation of the cognitive system
Impact of prematurity on cognitive functions
An example of how the perturbations of building of linguistic networks leads to consequent deficiencies in prematurity
Impact of Prematurity on the Neurovegetative System
Conclusion
References
Chapter 26: Pregnant women, prescription, and fetal risk
Introduction
Why Would the Fetus be at Risk?
Function and histology
Transfer mechanisms
Passive diffusion
Active transport
Other mechanisms
Pregnancy Timeline and Fetal Risk
Teratogenicity
Fetal risk
Neonatal risk
Dangerous Drugs During Pregnancy
Drugs associated with teratogenicity
Drugs associated with a fetal risk
Drugs of Specific Interest for Neurodevelopment
Diethylstilbestrol
Valproic acid
Antidepressants
What Should Be Done to Limit Risk?
Prescribing to pregnant women
Studying fetal risk for novel drugs
Ex vivo model
In vitro model
In vivo model
Informing practitioners
References
Chapter 27: Effects of prenatal alcohol and cannabis exposure on neurodevelopmental and cognitive disabilities
Introduction
Alcohol and Pregnancy
Epidemiology
Prenatal alcohol exposure and brain development
Neurobehavioral deficits associated with prenatal alcohol exposure
FASD and mental health
Breastfeeding and alcohol
Conclusion
Cannabis and Pregnancy
Effects of cannabis exposure during pregnancy
Cannabis and breastfeeding
Mechanisms of cannabis effects of neuro and cognitive development
Conclusion
Implications
References
Chapter 28: Maternal infections
Introduction
Physiopathology of Congenital Infection
Review by Infectious Agent
Cytomegalovirus
Epidemiology
Diagnosis
Antenatal diagnosis
Postnatal diagnosis
Specific treatment
Antenatal treatment
Postnatal treatment
Neurodevelopmental outcome
Toxoplasmosis
Epidemiology
Diagnosis
Antenatal diagnosis
Postnatal diagnosis
Specific treatment
Antenatal
Postnatal
Neurodevelopmental outcome
Rubella
Epidemiology
Diagnosis
Antenatal diagnosis
Postnatal diagnosis
Specific treatment
Antenatal
Postnatal
Neurodevelopmental outcome
Syphilis
Epidemiology
Diagnosis
Antenatal findings
Postnatal findings
Neurologic manifestations
Pathology and neuroimaging findings
Specific treatment
Antenatal
Postnatal
Neurodevelopmental outcome
Herpes
Epidemiology
Diagnosis
Antenatal findings
Postnatal findings
Neuroimaging findings
Specific treatment
Pregnant women
Newborn
Neurodevelopmental outcome
Zika
Epidemiology
Diagnosis
Antenatal diagnosis
Postnatal diagnosis
Specific treatment
Neurodevelopmental outcome
HIV
Epidemiology
Diagnosis
Antenatal findings
Infant findings
Imaging findings
Specific treatment
Antenatal
Postnatal
Neurodevelopmental outcome
Varicella
Epidemiology
Diagnosis
Antenatal findings
Postnatal findings
Specific treatment
Neurodevelopmental outcome
Lymphocytic choriomeningitis virus (LCMV)
Epidemiology
Diagnosis
Antenatal findings
Postnatal findings
Specific treatment
Neurodevelopmental outcome
References
Chapter 29: Environmental toxic agents: The impact of heavy metals and organochlorides on brain development
Introduction11Abbreviations used in the chapter are listed at the end of the chapter before References section.
Neuropsychologic Impairments in Association With Environmental Contaminant Exposure
Lead-related neurodevelopmental impairments
Methylmercury-related neuropsychologic impairments
PCB-related neuropsychologic impairments
The Action Mechanisms of Environmental Contaminants
Neuropathologic damage
Alterations in neurotransmission
Endocrine-disrupting mechanism
Future Perspective: MRI as a Novel and Powerful Tool
References
Further reading
Chapter 30: The effects of socio-affective environment
Introduction: Nature/Nurture Dilemma and Bonfenbrenner's Ecologic Perspective on Early Development
Sensitive periods
The need for transcultural validity in developmental research
Clinical Evidence of the Effects of Socio-Affective Environment on Mental Development
Disentangling SES and Poverty
Exposure to Stressful Events or Violence/Abuse
Comparative effects of abuse and neglect in regard to other risk factors of attachment disorganization
Affective Deprivation: New Data From the Bucharest Study; the Reactive Attachment Disorder Category (RAD) and the Descri
Prematurity and Developmental Risk: Detecting Social Withdrawal Behavior in Infancy. Lessons From Longitudinal Cohort Studies
The Prader-Willi Syndrome as a Paradigmatic Situation to Understand Developmental Psychopathology
Autism Spectrum Disorders: New Insights Stemming From Both Developmental and Interventional Research
Intermodal matching, affect attunement, audiovisual synchrony in infants with ASD
A Growing Number of Gene-Environment Effects on Development Are Now Identified: Lessons From Attachmen
Therapeutic approaches
Conclusion
References
Further reading
Chapter 31: Pediatric traumatic brain injury and abusive head trauma
Introduction
Incidence and Mortality
Mild Pediatric TBI
Moderate and Severe Pediatric TBI
Neuroimaging
Outcome after Pediatric TBI
Overall level of disability
Posttraumatic epilepsy
Sensorimotor impairments
Olfactory disorders
Visual, oculomotor and vestibulo-ocular impairments
Endocrine deficits
Sleep-wake disturbances
Fatigue
Cognitive deficits
General overview
Overall intellectual ability and its components
Language and communication
Visual-spatial skills
Attention
Memory
Executive functions
Social cognition
Behavioral and psychological/psychiatric disorders
Academic achievement
Participation
Health-related quality of life
Long-term outcome (adulthood)
Factors Associated with Outcomes Following Childhood TBI
Specificities of the ``shaken baby syndrome´´ (SBS) and abusive head trauma (AHT)
Definition, incidence, and diagnosis
Long-term outcomes following AHT
Interventions Following Pediatric TBI
General principles
Rehabilitation and treatment of specific deficits (see Chapters 21 and 26 of Volume 174)
Swallowing and communication disorders
Cognitive deficits
Behavioral disorders
Support, education, and interventions with families/caregivers
Management of mild TBI
Conclusion
References
Chapter 32: Ischemic sequelae and other vascular diseases
Introduction
Profile of Perinatal and Childhood Strokes
Intellectual abilities
Language
Visual-spatial ability
Executive function
Behavioral and social functioning
Cognitive Profile of Specific Cerebrovascular Conditions in Children
Congenital heart disease
Moyamoya
Sickle cell disease
Conclusion
References
Back Cover

Citation preview

NEUROCOGNITIVE DEVELOPMENT: NORMATIVE DEVELOPMENT

HANDBOOK OF CLINICAL NEUROLOGY Series Editors

MICHAEL J. AMINOFF, FRANÇOIS BOLLER, AND DICK F. SWAAB VOLUME 173

NEUROCOGNITIVE DEVELOPMENT: NORMATIVE DEVELOPMENT Series Editors

MICHAEL J. AMINOFF, FRANÇOIS BOLLER, AND DICK F. SWAAB

Volume Editors

ANNE GALLAGHER, CHRISTINE BULTEAU, DAVID COHEN, AND JACQUES L. MICHAUD VOLUME 173 3rd Series

ELSEVIER Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States Copyright © 2020 Elsevier B.V. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. With respect to any drug or pharmaceutical products identified, readers are advised to check the most current information provided (i) on procedures featured or (ii) by the manufacturer of each product to be administered, to verify the recommended dose or formula, the method and duration of administration, and contraindications. It is the responsibility of practitioners, relying on their own experience and knowledge of their patients, to make diagnoses, to determine dosages and the best treatment for each individual patient, and to take all appropriate safety precautions. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-444-64150-2 For information on all Elsevier publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Nikki Levy Editorial Project Manager: Kristi Anderson Production Project Manager: Punithavathy Govindaradjane Cover Designer: Alan Studholme Typeset by SPi Global, India

Handbook of Clinical Neurology 3rd Series Available titles Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol.

79, The human hypothalamus: basic and clinical aspects, Part I, D.F. Swaab, ed. ISBN 9780444513571 80, The human hypothalamus: basic and clinical aspects, Part II, D.F. Swaab, ed. ISBN 9780444514905 81, Pain, F. Cervero and T.S. Jensen, eds. ISBN 9780444519016 82, Motor neurone disorders and related diseases, A.A. Eisen and P.J. Shaw, eds. ISBN 9780444518941 83, Parkinson’s disease and related disorders, Part I, W.C. Koller and E. Melamed, eds. ISBN 9780444519009 84, Parkinson’s disease and related disorders, Part II, W.C. Koller and E. Melamed, eds. ISBN 9780444528933 85, HIV/AIDS and the nervous system, P. Portegies and J. Berger, eds. ISBN 9780444520104 86, Myopathies, F.L. Mastaglia and D. Hilton Jones, eds. ISBN 9780444518996 87, Malformations of the nervous system, H.B. Sarnat and P. Curatolo, eds. ISBN 9780444518965 88, Neuropsychology and behavioural neurology, G. Goldenberg and B.C. Miller, eds. ISBN 9780444518972 89, Dementias, C. Duyckaerts and I. Litvan, eds. ISBN 9780444518989 90, Disorders of consciousness, G.B. Young and E.F.M. Wijdicks, eds. ISBN 9780444518958 91, Neuromuscular junction disorders, A.G. Engel, ed. ISBN 9780444520081 92, Stroke – Part I: Basic and epidemiological aspects, M. Fisher, ed. ISBN 9780444520036 93, Stroke – Part II: Clinical manifestations and pathogenesis, M. Fisher, ed. ISBN 9780444520043 94, Stroke – Part III: Investigations and management, M. Fisher, ed. ISBN 9780444520050 95, History of neurology, S. Finger, F. Boller and K.L. Tyler, eds. ISBN 9780444520081 96, Bacterial infections of the central nervous system, K.L. Roos and A.R. Tunkel, eds. ISBN 9780444520159 97, Headache, G. Nappi and M.A. Moskowitz, eds. ISBN 9780444521392 98, Sleep disorders Part I, P. Montagna and S. Chokroverty, eds. ISBN 9780444520067 99, Sleep disorders Part II, P. Montagna and S. Chokroverty, eds. ISBN 9780444520074 100, Hyperkinetic movement disorders, W.J. Weiner and E. Tolosa, eds. ISBN 9780444520142 101, Muscular dystrophies, A. Amato and R.C. Griggs, eds. ISBN 9780080450315 102, Neuro-ophthalmology, C. Kennard and R.J. Leigh, eds. ISBN 9780444529039 103, Ataxic disorders, S.H. Subramony and A. Durr, eds. ISBN 9780444518927 104, Neuro-oncology Part I, W. Grisold and R. Sofietti, eds. ISBN 9780444521385 105, Neuro-oncology Part II, W. Grisold and R. Sofietti, eds. ISBN 9780444535023 106, Neurobiology of psychiatric disorders, T. Schlaepfer and C.B. Nemeroff, eds. ISBN 9780444520029 107, Epilepsy Part I, H. Stefan and W.H. Theodore, eds. ISBN 9780444528988 108, Epilepsy Part II, H. Stefan and W.H. Theodore, eds. ISBN 9780444528995 109, Spinal cord injury, J. Verhaagen and J.W. McDonald III, eds. ISBN 9780444521378 110, Neurological rehabilitation, M. Barnes and D.C. Good, eds. ISBN 9780444529015 111, Pediatric neurology Part I, O. Dulac, M. Lassonde and H.B. Sarnat, eds. ISBN 9780444528919 112, Pediatric neurology Part II, O. Dulac, M. Lassonde and H.B. Sarnat, eds. ISBN 9780444529107 113, Pediatric neurology Part III, O. Dulac, M. Lassonde and H.B. Sarnat, eds. ISBN 9780444595652 114, Neuroparasitology and tropical neurology, H.H. Garcia, H.B. Tanowitz and O.H. Del Brutto, eds. ISBN 9780444534903 115, Peripheral nerve disorders, G. Said and C. Krarup, eds. ISBN 9780444529022 116, Brain stimulation, A.M. Lozano and M. Hallett, eds. ISBN 9780444534972 117, Autonomic nervous system, R.M. Buijs and D.F. Swaab, eds. ISBN 9780444534910 118, Ethical and legal issues in neurology, J.L. Bernat and H.R. Beresford, eds. ISBN 9780444535016 119, Neurologic aspects of systemic disease Part I, J. Biller and J.M. Ferro, eds. ISBN 9780702040863 120, Neurologic aspects of systemic disease Part II, J. Biller and J.M. Ferro, eds. ISBN 9780702040870 121, Neurologic aspects of systemic disease Part III, J. Biller and J.M. Ferro, eds. ISBN 9780702040887 122, Multiple sclerosis and related disorders, D.S. Goodin, ed. ISBN 9780444520012 123, Neurovirology, A.C. Tselis and J. Booss, eds. ISBN 9780444534880 124, Clinical neuroendocrinology, E. Fliers, M. Korbonits and J.A. Romijn, eds. ISBN 9780444596024 125, Alcohol and the nervous system, E.V. Sullivan and A. Pfefferbaum, eds. ISBN 9780444626196 126, Diabetes and the nervous system, D.W. Zochodne and R.A. Malik, eds. ISBN 9780444534804 127, Traumatic brain injury Part I, J.H. Grafman and A.M. Salazar, eds. ISBN 9780444528926 128, Traumatic brain injury Part II, J.H. Grafman and A.M. Salazar, eds. ISBN 9780444635211

vi

AVAILABLE TITLES (Continued)

Vol. 129, The human auditory system: Fundamental organization and clinical disorders, G.G. Celesia and G. Hickok, eds. ISBN 9780444626301 Vol. 130, Neurology of sexual and bladder disorders, D.B. Vodušek and F. Boller, eds. ISBN 9780444632470 Vol. 131, Occupational neurology, M. Lotti and M.L. Bleecker, eds. ISBN 9780444626271 Vol. 132, Neurocutaneous syndromes, M.P. Islam and E.S. Roach, eds. ISBN 9780444627025 Vol. 133, Autoimmune neurology, S.J. Pittock and A. Vincent, eds. ISBN 9780444634320 Vol. 134, Gliomas, M.S. Berger and M. Weller, eds. ISBN 9780128029978 Vol. 135, Neuroimaging Part I, J.C. Masdeu and R.G. González, eds. ISBN 9780444534859 Vol. 136, Neuroimaging Part II, J.C. Masdeu and R.G. González, eds. ISBN 9780444534866 Vol. 137, Neuro-otology, J.M. Furman and T. Lempert, eds. ISBN 9780444634375 Vol. 138, Neuroepidemiology, C. Rosano, M.A. Ikram and M. Ganguli, eds. ISBN 9780128029732 Vol. 139, Functional neurologic disorders, M. Hallett, J. Stone and A. Carson, eds. ISBN 9780128017722 Vol. 140, Critical care neurology Part I, E.F.M. Wijdicks and A.H. Kramer, eds. ISBN 9780444636003 Vol. 141, Critical care neurology Part II, E.F.M. Wijdicks and A.H. Kramer, eds. ISBN 9780444635990 Vol. 142, Wilson disease, A. Członkowska and M.L. Schilsky, eds. ISBN 9780444636003 Vol. 143, Arteriovenous and cavernous malformations, R.F. Spetzler, K. Moon and R.O. Almefty, eds. ISBN 9780444636409 Vol. 144, Huntington disease, A.S. Feigin and K.E. Anderson, eds. ISBN 9780128018934 Vol. 145, Neuropathology, G.G. Kovacs and I. Alafuzoff, eds. ISBN 9780128023952 Vol. 146, Cerebrospinal fluid in neurologic disorders, F. Deisenhammer, C.E. Teunissen and H. Tumani, eds. ISBN 9780128042793 Vol. 147, Neurogenetics Part I, D.H. Geschwind, H.L. Paulson and C. Klein, eds. ISBN 9780444632333 Vol. 148, Neurogenetics Part II, D.H. Geschwind, H.L. Paulson and C. Klein, eds. ISBN 9780444640765 Vol. 149, Metastatic diseases of the nervous system, D. Schiff and M.J. van den Bent, eds. ISBN 9780128111611 Vol. 150, Brain banking in neurologic and psychiatric diseases, I. Huitinga and M.J. Webster, eds. ISBN 9780444636393 Vol. 151, The parietal lobe, G. Vallar and H.B. Coslett, eds. ISBN 9780444636225 Vol. 152, The neurology of HIV infection, B.J. Brew, ed. ISBN 9780444638496 Vol. 153, Human prion diseases, M. Pocchiari and J.C. Manson, eds. ISBN 9780444639455 Vol. 154, The cerebellum: From embryology to diagnostic investigations, M. Manto and T.A.G.M. Huisman, eds. ISBN 9780444639561 Vol. 155, The cerebellum: Disorders and treatment, M. Manto and T.A.G.M. Huisman, eds. ISBN 9780444641892 Vol. 156, Thermoregulation: From basic neuroscience to clinical neurology Part I, A.A. Romanovsky, ed. ISBN 9780444639127 Vol. 157, Thermoregulation: From basic neuroscience to clinical neurology Part II, A.A. Romanovsky, ed. ISBN 9780444640741 Vol. 158, Sports neurology, B. Hainline and R.A. Stern, eds. ISBN 9780444639547 Vol. 159, Balance, gait, and falls, B.L. Day and S.R. Lord, eds. ISBN 9780444639165 Vol. 160, Clinical neurophysiology: Basis and technical aspects, K.H. Levin and P. Chauvel, eds. ISBN 9780444640321 Vol. 161, Clinical neurophysiology: Diseases and disorders, K.H. Levin and P. Chauvel, eds. ISBN 9780444641427 Vol. 162, Neonatal neurology, L.S. De Vries and H.C. Glass, eds. ISBN 9780444640291 Vol. 163, The frontal lobes, M. D’Esposito and J.H. Grafman, eds. ISBN 9780128042816 Vol. 164, Smell and taste, Richard L. Doty, ed. ISBN 9780444638557 Vol. 165, Psychopharmacology of neurologic disease, V.I. Reus and D. Lindqvist, eds. ISBN 9780444640123 Vol. 166, Cingulate cortex, B.A. Vogt, ed. ISBN 9780444641960 Vol. 167, Geriatric neurology, S.T. DeKosky and S. Asthana, eds. ISBN 9780128047668 Vol. 168, Brain-computer interfaces, N.F. Ramsey and J. del R. Millán, eds. ISBN 9780444639349 Vol. 169, Meningiomas, Part I, M.W. McDermott, ed. ISBN 9780128042809 Vol. 170, Meningiomas, Part II, M.W. McDermott, ed. ISBN 9780128221983 Vol. 171, Neurology and pregnancy: Pathophysiology and patient care, E.A.P. Steegers, M.J. Cipolla and E.C. Miller, eds. ISBN 9780444642394 Vol. 172, Neurology and pregnancy: Neuro-obstetric disorders, E.A.P. Steegers, M.J. Cipolla and E.C. Miller, eds. ISBN 9780444642400 All volumes in the 3rd Series of the Handbook of Clinical Neurology are published electronically, on Science Direct: http:// www.sciencedirect.com/science/handbooks/00729752.

Foreword

In daily life, we constantly use language, overt or covert, various types of memories, spatial orientation, and other cognitive functions, usually without even thinking about them. The same is true for children’s cognitive development. All of these cognitive functions are taken for granted and only when something goes wrong do we become aware of their complexity. In an earlier volume of the Handbook of Clinical Neurology edited by Olivier Dulac (Paris), Maryse Lassonde (Montreal), and Harvey Sarnat (Calgary), a section of Volume 111 on pediatric neurology was dedicated to developmental abnormalities. Even though that section was prepared less than a decade ago, new advances in our approach to children with neurodevelopmental delays or disabilities have occurred, based on developments in genetics, neuroimaging, cognitive sciences, and artificial intelligence. All these amply justify a new title, “Neurocognitive Development,” which is now covered in two volumes. The first volume, “Normative Development,” includes five sections. The introductory section includes classification as well as historical and ethical considerations. The second and third sections are dedicated to development, plasticity, and vulnerability of the developing brain. This is followed by a section on the neuroscientific basis of typical functional neurodevelopment from intellectual abilities to social cognition, and includes a chapter discussing the role of the cerebellum in neuropsychologic functioning. The final section of the volume deals with the etiologies of neurodevelopmental disorders including genetic mechanisms, the effect of sex, and the impact of prematurity on development, as well as a chapter dedicated to the effect of pregnancy and fetal risks. The second volume, “Disorders and Disabilities,” first deals with specific neurodevelopmental disorders including disorders of coordination, language, attention, reading, memory, and nonverbal dysfunction. The next section, on complex neurodevelopmental disorders, deals with intellectual disability, autism spectrum disorder, epilepsy, and multidimensional impairment. The following section covers assessment, including neurologic, psychiatric, and neuropsychologic assessment, and investigative neurophysiologic techniques, as well as structural and functional neuroimaging. The final section of the volume deals with rehabilitation and long-term outcome including education and quality-of-life issues. This is rightly recognized as a key outcome of chronic health conditions, and its assessment is recommended for both clinical care and clinical trials. For this massive undertaking, we have had the good fortune of having four outstanding and experienced volume editors: Anne Gallagher, PhD, Department of Psychology, Universit
e de Montr
eal, Quebec, Canada; Christine Bulteau, MD, PhD, Institute of Psychology, Sorbonne, and Pediatric Neurosurgery Department, Rothschild Foundation Hospital, Paris, France; David Cohen, MD, PhD, Universit
e Pierre et Marie Curie and Piti
e-Salp^etrière, Paris, France; and Jacques L. Michaud, MD, CHU Sainte-Justine, and Faculty of Medicine, Universit
e de Montr
eal, Quebec, Canada. As series editors, we reviewed all the chapters in the volume and made suggestions for improvement, but we are delighted that the volume editors and chapter authors produced such scholarly and comprehensive accounts of different aspects of neurocognitive development. They deserve even more credit as the chapters were written while the world was gripped by the COVID-19 pandemic. Despite this, they were able to include nearly all the material that needed to be covered. As a result, we hope that the volume will appeal to clinicians and neuroscientists alike. Our goal was to provide clinicians with a state-of-the-art reference that summarizes the clinical features and management of the many neurologic manifestations of neurocognitive development. We also hoped to provide basic researchers with the foundations for new approaches to the study of the complex issues involved. In addition to the print version, the volumes are also available electronically on Elsevier’s Science Direct website. Indeed, all of the volumes in the present series of the Handbook are available electronically on this website. This should make them even more accessible to readers and facilitate searches for specific information.

viii

FOREWORD

As always, it is a pleasure to thank Elsevier, our publisher, and in particular Michael Parkinson in Scotland, Nikki Levy and Kristi Anderson in San Diego, and Punithavathy Govindaradjane at Elsevier Global Book Production in Chennai, for their assistance in the development and production of these two volumes of the Handbook of Clinical Neurology. Michael J. Aminoff Franc¸ois Boller Dick F. Swaab

Preface

Volumes 173 and 174 of the Handbook of Clinical Neurology focus on neurocognitive development. They provide an up-to-date review of the theoretical concepts of typical neurodevelopment as well as the conceptual framework, etiologies, diagnosis, assessment, and treatment of neurodevelopmental dysfunctions found in various cognitive, behavioral, and neurodevelopmental disorders. In the past 20 years, major discoveries have contributed to our understanding of the underlying mechanisms and genetic basis of cognitive deficits, and the relationship between specific neurodevelopmental disorders, their comorbidities, and environmental factors. New technologies, notably in genetics, epigenetics, machine learning, and neuroimaging, have led to various scientific breakthroughs, aided by significant advances in developmental neuropsychology and new research trends including highly interdisciplinary work. These developments are covered in detail in two volumes. The first, Volume 173, focuses on typical neurodevelopment and the second, Volume 174, on altered neurodevelopment. The first volume includes five sections that give the reader an understanding of the basics of neurodevelopment. Section 1 provides definitions as well as historical and ethical views related to neurodevelopmental disabilities. Section 2, on the biological basis of typical neurodevelopment, describes the main basic developmental steps of the central nervous system. Section 3 covers the central concepts related to the specificity of the developing brain that are crucial for an understanding of neurodevelopment and the impacts of an insult at an early age. Section 4 on the neuroscientific basis of typical functional neurodevelopment describes the normal development of motor, sensory, and cognitive functions. The volume ends with Section 5, which addresses the main etiologies of neurodevelopmental disorders including genetic abnormalities, environmental causes, and brain insults. We also detail how sex modulates neurodevelopmental disorders and the effects of brain insults on brain development, which in turn provides new insights into the conceptual basis of typical neurodevelopment and offers new avenues for future brain therapies. The second volume starts with two sections on neurodevelopmental disabilities in the pediatric population, covering specific and complex neurodevelopmental disorders (Sections 1 and 2, respectively). Section 3 covers the methods and tools used for the assessment of neurodevelopment disorders. The final section (Section 4) contains discussions on patient care, rehabilitation, and long-term outcomes. Although major advances in scientific knowledge, diagnosis, and advocacy have been made in the past two decades, additional interdisciplinary research is needed to improve early detection and, importantly, provide individualized efficient interventions. We would like to thank the series editors, Michael J. Aminoff, Franc¸ois Boller, and Dick F. Swaab, who offered us the opportunity to publish this volume in the most prestigious series in neurology. We also sincerely thank Michael Parkinson, development editor, who guided us through this whole experience with endless patience. Together, as volume editors, we provide an interdisciplinary pediatric expertise including child neuropsychology, neurology, psychiatry, and genetics and covering clinical and research domains related to typical and altered neurodevelopment. We are grateful to the 138 authors who contributed comprehensive and clearly structured manuscripts and valuable illustrations. It is our hope that this volume offers a comprehensive coverage of basic and innovative knowledge in the field of neurocognitive development that will be of value to all our readers. Anne Gallagher Christine Bulteau David Cohen Jacques L. Michaud

This page intentionally left blank

Contributors

A. Accogli Unit of Medical Genetics, Istituto Giannina Gaslini Pediatric Hospital; Departments of Neuroscience, Rehabilitation, Ophthalmology, Genetics and MaternalChild Science, Università degli Studi di Genova, Genova, Italy N. Addour-Boudrahem Research Institute, McGill University Health Centre, Montreal, QC, Canada J. Atkinson Faculty of Brain Sciences, University College London, London, United Kingdom P.Y.B. Au Department of Medical Genetics, Alberta Children's Hospital Research Institute, Calgary, AB, Canada K. Bastien Department of Psychology, Universit
e du Qu
ebec à Montr
eal; Research Centre, Centre Hospitalier Universitaire Sainte-Justine, Universit
e de Montr
eal, Montr
eal, QC, Canada M.H. Beauchamp Research Centre, Centre Hospitalier Universitaire Sainte-Justine, Department of Psychology, Universit
e de Montr
eal, Montr
eal, QC, Canada C. Beaudoin Research Centre, Centre Hospitalier Universitaire Sainte-Justine, Department of Psychology, Universit
e de Montr
eal, Montr
eal, QC, Canada

A. Bouyeure Translational and Applicative Neuroimaging Research
Unit, NeuroSpin, Commissariat à l'Energie Atomique et
aux Energies Alternatives, Universit
e Paris-Saclay, Gif-sur-Yvette, France O. Braddick Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom C. Bulteau Department of Pediatric Neurosurgery, Rothschild Foundation Hospital, Paris; University of Paris, MC2 Lab, Institute of Psychology, Boulogne-Billancourt, France H. C^amara-Costa GRC 24, Handicap Moteur et Cognitif et R
eadaptation, Sorbonne Universit
e; Centre d’Etudes en Sant
e des Populations, INSERM U1018, Paris, France B. Chattopadhyaya Research Centre, Centre Hospitalier Universitaire Sainte-Justine, Universit
e de Montr
eal, Montr
eal, QC, Canada M. Chevignard Rehabilitation Department for Children with Acquired Neurological Injury and Outreach Team for Children and Adolescents with Acquired Brain Injury, Saint Maurice Hospitals, Saint Maurice; Laboratoire d'Imagerie Biom
edicale, Sorbonne Universit
e; GRC 24, Handicap Moteur et Cognitif et R
eadaptation, Sorbonne Universit
e, Paris, France

I. Boucoiran Mother and Child Infection Center, Centre Hospitalier Universitaire Sainte-Justine; Departments of Obstetrics and Gynecology and Social and Preventive Medicine, University of Montreal, Montreal, QC, Canada

D. Cicchetti Institute of Child Development, University of Minnesota, Minneapolis, MN; Mt. Hope Family Center, University of Rochester, Rochester, NY, United States

E. Bourel-Ponchel Research Group on Multimodal Analysis of Brain Function, Jules Verne Picardie University; Department of Pediatric Functional Exploration of the Nervous System, University Hospital, Picardie, Amiens, France

J.M. Cisneros-Franco Department of Neurology and Neurosurgery, Montreal Neurological Institute; Centre for Research on Brain, Language and Music, McGill University, Montreal, QC, Canada

xii

CONTRIBUTORS

H. Cochet Laboratoire Cognition, Langues, Langage, et Ergonomie, Toulouse University, CNRS, UT2J, Toulouse, France D. Cohen Service de Psychiatrie de l'Enfant et de l'Adolescent, APHP.Sorbonne Universit
e, Groupe Hospitalier Piti
eSalp^etrière; Institut des Systèmes Intelligents et Robotiques, Sorbonne Universit
e, Paris, France R. Colom Department of Biological and Health Psychology, Universidad Autónoma de Madrid, Madrid, Spain J.L. Cook Department of Obstetrics and Gynecology, University of Ottawa; Chief Scientific Officer, Society of Obstetricians and Gynaecologists of Canada, Ottawa, ON, Canada F. Crivello Institut des Maladies Neurodegeneratives, University of Bordeaux, Bordeaux, France G. Dellatolas GRC 24, Handicap Moteur et Cognitif et R
eadaptation, Sorbonne Universit
e, Paris, France E. de Villers-Sidani Department of Neurology and Neurosurgery, Montreal Neurological Institute; Centre for Research on Brain, Language and Music, McGill University, Montreal, QC, Canada A. Diamond Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada G. Di Cristo Research Centre, Centre Hospitalier Universitaire Sainte-Justine; Department of Neurosciences, Universit
e de Montr
eal, Montr
eal, QC, Canada N. Dlamini Department of Pediatrics, Division of Neurology, Hospital for Sick Children, Toronto, ON, Canada I. Dupong Department of Perinatal, Child and Adolescent Mental Health, Policlinique Ney Jenny Aubry, Hospital Bichat Claude Bernard, Paris, France D.A. Dyment Department of Genetics, Children's Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada

A. Eaton Department of Medical Genetics, The Stollery Children's Hospital, Edmonton, AB, Canada E. El
efant Centre de R
ef
erence sur les Agents T
eratogènes, H^opital Armand-Trousseau, Paris, France B. Evans School of Languages, Linguistics and Film and School of History, Queen Mary University of London, London, United Kingdom A. Gagnon-Chauvin Department of Psychology, Universit
e du Qu
ebec à Montr
eal; Research Centre, Centre Hospitalier Universitaire Sainte-Justine, Universit
e de Montr
eal, Montr
eal, QC, Canada A. Gallagher Neurodevelopment Optical Imaging Laboratory (LIONlab), Centre Hospitalier Universitaire SainteJustine, Department of Psychology, Universit
e de Montr
eal, Montr
eal, QC, Canada I. Gaudet Neurodevelopment Optical Imaging Laboratory (LIONlab), Centre Hospitalier Universitaire SainteJustine, Department of Psychology, Universit
e de Montr
eal, Montr
eal, QC, Canada J. Gervain Integrative Neuroscience and Cognition Center, CNRS & Universit
e de Paris, Paris, France H. Ghassemzadeh Department of Psychiatry, Tehran University of Medical Sciences, Tehran, Iran A. Guedeney Department of Perinatal, Child and Adolescent Mental Health, Policlinique Ney Jenny Aubry, Hospital Bichat Claude Bernard, Paris, France A. Hallemans Research Group MOVANT (Movement Antwerp), Department of Rehabilitation Sciences and Physiotherapy, Faculty of Medicine and Health Science, University of Antwerp, Antwerp, Belgium C. Hanin Service de Psychiatrie de l'Enfant et de l'Adolescent, APHP.Sorbonne Universit
e, Groupe Hospitalier Piti
e-Salp^etrière, Paris, France

CONTRIBUTORS O. Houd
e Laboratory for the Psychology of Child Development and Education, University of Paris, Paris, France F.Y. Ismail Department of Pediatrics, United Arab Emirates University, Al-Ain, United Arab Emirates; Department of Neurology (adjunct), Johns Hopkins School of Medicine, Baltimore, MD, United States S. Jacquemont Department of Pediatrics, University of Montreal, Montreal, QC, Canada M.V. Johnston Department of Neurology and Developmental Medicine, Kennedy Krieger Institute, Baltimore, MD, United States B. Jutras School of Speech-Language Pathology and Audiology, Universit
e de Montr
eal, Research Centre, Centre Hospitalier Universitaire Sainte-Justine, Montr
eal, QC, Canada F. Kakkar Mother and Child Infection Center, Centre Hospitalier Universitaire Sainte-Justine; Department of Pediatrics, University of Montreal, Montreal, QC, Canada M.L. Kaseka Department of Pediatrics, Division of Neurology, Hospital for Sick Children, Toronto, ON, Canada I.S. Knoth Research Centre, Centre Hospitalier Universitaire Sainte-Justine, Department of Psychology, Universit
e de Montr
eal, Montr
eal, QC, Canada A. Koravand Audiology and Speech-Language Pathology Program, University of Ottawa, Ottawa, ON, Canada M.P. Lafontaine Research Centre, Centre Hospitalier Universitaire Sainte-Justine, Department of Psychology, Universit
e de Montr
eal, Montr
eal, QC, Canada J. Lagac
e Audiology and Speech-Language Pathology Program, University of Ottawa, Ottawa, ON, Canada P. Lefebvre School of Speech Language Pathology, Laurentian University, Sudbury, ON, Canada

xiii

S. Lipp
e Research Centre, Centre Hospitalier Universitaire Sainte-Justine, Department of Psychology, Universit
e de Montr
eal, Montr
eal, QC, Canada M.R. Ljubisavljevic Department of Physiology, United Arab Emirates University, Al-Ain, United Arab Emirates J.L. Michaud Research Centre, Centre Hospitalier Universitaire Sainte-Justine, Université de Montr
eal, Montr
eal, QC, Canada M. Nader Department of Linguistics, Universit
e du Qu
ebec à Montr
eal, Montr
eal, QC, Canada M. Noulhiane Translational and Applicative Neuroimaging Research
Unit, NeuroSpin, Commissariat à l'Energie Atomique et
aux Energies Alternatives, Universit
e Paris-Saclay, Gif-sur-Yvette, France S. Nowak Department of Pediatrics, University of Montreal, Montreal, QC, Canada M.I. Posner Department of Psychology, University of Oregon, Eugene, OR, United States C. Renaud Mother and Child Infection Center, Centre Hospitalier Universitaire Sainte-Justine; Department of Microbiology and Immunology, University of Montreal, Montreal, QC, Canada M.K. Rothbart Department of Psychology, University of Oregon, Eugene, OR, United States L. Routier Research Group on Multimodal Analysis of Brain Function, Jules Verne Picardie University; Department of Pediatric Functional Exploration of the Nervous System, University Hospital, Picardie, Amiens, France D. Safi Department of Speech Language Pathology, Universit
e du Qu
ebec à Trois-Rivières, Trois-Rivières, QC, Canada D. Saint-Amour Department of Psychology, Universit
e du Qu
ebec à Montr
eal; Research Centre, Centre Hospitalier Universitaire Sainte-Justine, Universit
e de Montr
eal, Montr
eal, QC, Canada

xiv

CONTRIBUTORS

M. Srour Research Institute, McGill University Health Centre; Department of Pediatrics, Division of Pediatric Neurology, McGill University, Montreal, QC, Canada M.E. Thomas Department of Neurology and Neurosurgery, Montreal Neurological Institute; Centre for Research on Brain, Language and Music, McGill University, Montreal, QC, Canada N. Tzourio-Mazoyer Institut des Maladies Neurodegeneratives, University of Bordeaux, Bordeaux, France P. Van de Walle Research Group MOVANT (Movement Antwerp), Department of Rehabilitation Sciences and Physiotherapy, Faculty of Medicine and Health Science, University of Antwerp, Antwerp, Belgium F. VanMeter Institute of Child Development, University of Minnesota, Minneapolis, MN, United States

E. Verbeque Department of Rehabilitation Sciences and Physiotherapy, Hasselt University, Hasselt, Belgium P. Voss Department of Neurology and Neurosurgery, Montreal Neurological Institute; Centre for Research on Brain, Language and Music, McGill University, Montreal, QC, Canada F. Wallois Research Group on Multimodal Analysis of Brain Function, Jules Verne Picardie University; Department of Pediatric Functional Exploration of the Nervous System, University Hospital, Picardie, Amiens, France R. Westmacott Department of Psychology, Division of Neurology, Hospital for Sick Children, Toronto, ON, Canada L. Zago Institut des Maladies Neurodegeneratives, University of Bordeaux, Bordeaux, France

Contents Foreword vii Preface ix Contributors xi SECTION I

Introduction to neurodevelopmental disabilities

1. Description and classification of neurodevelopmental disabilities I. Gaudet and A. Gallagher (Montreal, Canada)

3

2. Neurodevelopmental and cognitive disabilities: Historical overview B. Evans (London, United Kingdom)

7

3. Ethical views and considerations O. Houd
e (Paris, France) SECTION II

15

Biological basis of typical neurodevelopment

4. Neurogenesis, neuronal migration, and axon guidance A. Accogli, N. Addour-Boudrahem, and M. Srour (Genova, Italy and Montreal, Canada)

25

5. Development of neuronal circuits: From synaptogenesis to synapse plasticity G. Di Cristo and B. Chattopadhyaya (Montreal, Canada)

43

SECTION III

Plasticity, vulnerability and evolutionary constraints of the developing brain

6. A conceptual framework for plasticity in the developing brain F.Y. Ismail, M.R. Ljubisavljevic, and M.V. Johnston (Al-Ain, United Arab Emirates and Baltimore, United States)

57

7. Resilience F. VanMeter and D. Cicchetti (Minneapolis and Rochester, United States)

67

8. Critical periods of brain development J.M. Cisneros-Franco, P. Voss, M.E. Thomas, and E. de Villers-Sidani (Montreal, Canada)

75

9. The vulnerability of the immature brain C. Bulteau (Paris and Boulogne-Billancourt, France)

89

10. Development of handedness, anatomical and functional brain lateralization N. Tzourio-Mazoyer, L. Zago, H. Cochet, and F. Crivello (Bordeaux and Toulouse, France) SECTION IV

99

Neuroscientific basis of typical functional neurodevelopment

11. Intellectual abilities R. Colom (Madrid, Spain)

109

xvi

CONTENTS

12. Visual development J. Atkinson and O. Braddick (London and Oxford, United Kingdom)

121

13. The development of auditory functions B. Jutras, J. Lagac
e, and A. Koravand (Montreal and Ottawa, Canada)

143

14. Motor functions A. Hallemans, E. Verbeque, and P. Van de Walle (Antwerp and Hasselt, Belgium)

157

15. Typical language development J. Gervain (Paris, France)

171

16. Literacy acquisition: Reading development D. Safi, P. Lefebvre, and M. Nader (Trois-Rivières, Sudbury, and Montreal, Canada)

185

17. Memory: Normative development of memory systems A. Bouyeure and M. Noulhiane (Gif-sur-Yvette, France)

201

18. Developing attention in typical children related to disabilities M.I. Posner, M.K. Rothbart, and H. Ghassemzadeh (Eugene, United States and Tehran, Iran)

215

19. Executive functions A. Diamond (Vancouver, Canada)

225

20. Learning abilities M.P. Lafontaine, I.S. Knoth, and S. Lipp
e (Montreal, Canada)

241

21. Social cognition C. Beaudoin and M.H. Beauchamp (Montreal, Canada)

255

22. The role of cerebellum in the child neuropsychological functioning G. Dellatolas and H. C^ amara-Costa (Paris, France)

265

SECTION V

Etiologies of neurodevelopmental disorders

23. Genetic mechanisms of neurodevelopmental disorders P.Y.B. Au, A. Eaton, and D.A. Dyment (Calgary, Edmonton, and Ottawa, Canada)

307

24. The effects of sex on prevalence and mechanisms underlying neurodevelopmental disorders S. Nowak and S. Jacquemont (Montreal, Canada)

327

25. Impact of prematurity on neurodevelopment F. Wallois, L. Routier, and E. Bourel-Ponchel (Amiens, France)

341

26. Pregnant women, prescription, and fetal risk E. El
efant, C. Hanin, and D. Cohen (Paris, France)

377

27. Effects of prenatal alcohol and cannabis exposure on neurodevelopmental and cognitive disabilities J.L. Cook (Ottawa, Canada) 28. Maternal infections I. Boucoiran, F. Kakkar, and C. Renaud (Montreal, Canada)

391

401

CONTENTS 29. Environmental toxic agents: The impact of heavy metals and organochlorides on brain development A. Gagnon-Chauvin, K. Bastien, and D. Saint-Amour (Montreal, Canada)

xvii 423

30. The effects of socio-affective environment A. Guedeney and I. Dupong (Paris, France)

443

31. Pediatric traumatic brain injury and abusive head trauma M. Chevignard, H. C^ amara-Costa, and G. Dellatolas (Saint Maurice and Paris, France)

451

32. Ischemic sequelae and other vascular diseases M.L. Kaseka, N. Dlamini, and R. Westmacott (Toronto, Canada)

485

Index

493

This page intentionally left blank

Section I Introduction to neurodevelopmental disabilities

This page intentionally left blank

Handbook of Clinical Neurology, Vol. 173 (3rd series) Neurocognitive Development: Normative Development A. Gallagher, C. Bulteau, D. Cohen and J.L. Michaud, Editors https://doi.org/10.1016/B978-0-444-64150-2.00001-0 Copyright © 2020 Elsevier B.V. All rights reserved

Chapter 1

Description and classification of neurodevelopmental disabilities ISABELLE GAUDET AND ANNE GALLAGHER* Neurodevelopment Optical Imaging Laboratory (LIONlab), Centre Hospitalier Universitaire Sainte-Justine, Department of Psychology, Universite de Montreal, Montreal, QC, Canada

Abstract Classification is a tool for communication so that when clinicians, policy-makers, or researchers refer to some features they talk about the same thing. The classification of neurodevelopmental problems in children and adolescents is crucial to better understand their prevalence and the intervention or treatment that should be provided. However, such classification might be challenging, especially when development aspects have to be taken into account. This chapter aims to provide a better understanding of the classification of neurodevelopmental disabilities. Thus, we provide an overview of the different classification systems that are the most commonly used, such as the well-known Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases (ICD). Moreover, we address opportunities and challenges inherent to the classification of neurodevelopmental disorders and the implications for clinical practice and research areas.

Despite major advances in medicine, there is still a lack of valid biomarkers to accurately discriminate and diagnose most neurodevelopmental disorders (NDDs). Currently, NDD’s diagnosis relies mainly on behavioral and cognitive manifestations. Different diagnosis scoring systems and classification algorithms have been developed over time and are used worldwide. NDDs classification and diagnosis may differ depending on the algorithm used. Despite their differences, almost all algorithms share the concept that NDDs arise in the developmental period and impair the child’s development and functioning. NDDs can hence be defined as a heterogeneous set of chronic conditions, characterized by a delay or an impairment in cognition, communication, behavior, and/or motor development, having functional impacts in school or in social, family, or daily life (Mullin et al., 2013; Jeste, 2015).

CLASSIFICATION SYSTEMS FOR NDDs Two of the most widely used classification systems are the Diagnostic and Statistical Manual of Mental Disorders (DSM), developed by the American Psychiatric Association (APA, 2013) and the International Classification of Diseases (ICD), developed by the World Health Organization (WHO, 2018). Both systems have a section on NDDs and provide criteria to identify the different subtypes and to improve the accuracy of the diagnosis. Although the DSM is mainly used in North America and the ICD in Europe, these tools share internationally recognized references and consensus on diagnosis definitions, which provide guidance in informing and shaping clinical decisions at various levels. These levels include etiologic evaluation, rehabilitation referrals, service needs provision, programming access, counseling, and prognostics.

*Correspondence to: Anne Gallagher, M.Ps., Ph.D., Associate Professor, Psychology, Universite de Montreal, 2212 Mont-Royal Est #301, Montreal, QC, Canada H2H 1K4. Tel: +1-514-224-5824, Fax: +1-514-345-2372, E-mail: [email protected]

4

I. GAUDET AND A. GALLAGHER

The first version of the ICD was published at the end of the nineteenth century and has been regularly updated since then. It is now published in 41 languages and is used for a wide range of purposes, including morbidity and mortality statistics, health services reimbursement, and healthcare planning (Carr, 2015). Disorders of mental health were included in the ICD since 1949 only (IDC-6; WHO, 1962). Its eleventh version was released in June 2018 and includes a section on NDDs (WHO, 2018). In the latest version, NDDs are defined as impairments in acquisition or exaction of functioning, either in the intellectual, social, or motor coordination areas (WHO, 2018). The DSM is a North American classification system widely used internationally for both clinical diagnosis and research activities. Since its first publication in 1952, the DSM has been periodically revised until its fifth and most recent version was published in 2013. The writing of this fifth edition was different from the previous editions. Health professionals and scientists have defined the content first, and the final version involved the input of hundreds of individuals, including numerous stakeholders, patients, families, lawyers, consumer organizations, and advocacy groups (APA, 2013). According to the definition provided by the APA, NDDs usually arise in early childhood (preschool age) and involve significant difficulties in the social, academic, or personal functioning of the child (APA, 2013). Differences between NDDs can be more or less specific since most of the time they are not clear-cut and easy to segregate conditions. Clinical presentation is diversified and may include, among others, deficits, delays in development, repetitive behaviors, or hyperactivity. Given this heterogeneity, the use of specifiers is needed to better characterize the clinical presentation, in terms of etiology (e.g., is it associated with a known medical condition, genetic susceptibility, or environmental factors), severity (from mild to severe), and symptomatology. It happens that more than one NDD affects the same individual. The coexpression of two or more NNDs of different etiologies and severities in the same child is often associated with more functional limitation and requires specific interventions, emphasizing the importance of an accurate diagnosis (APA, 2013). Although the ICD and DSM classification systems are generally harmonized, the presence of two major classifications of mental disorders might sometimes be a hurdle from a clinical standpoint or a scientific perspective. Indeed, it has been previously reported that using the two systems on the same patient population may occasionally lead to different diagnosis (APA, 2013). As a consequence, scientific results may be harder to replicate and, more importantly, this could also possibly affect the treatment plan. To avoid discrepancies and

harmful consequences, the APA and the WHO have agreed to harmonize the approaches and to collaborate closely in the next editions of the DSM and ICD. The next editions should thus reflect a consensual and harmonized understanding of NDD diagnosis and impact the clinical approaches. It should be noted that the ICD and DSM are not the only existing NDD classification systems. There is still some controversy in the clinical and scientific community because of the limited evidence on the validity of the current diagnostic categories (Carr, 2015). As a consequence, parallel classification systems have recently emerged, such as the Research Domain Criteria framework (RDoC), developed by the National Institute of Mental Health (NIMH) in 2009. Instead of being based on a categorical model, the RDoC aims to uncover and specify underlying mechanisms influencing cognitive, affective, and behavioral functioning, through a better understanding of the neurobiologic abnormalities affecting the development throughout the lifespan (Patel et al., 2018), including within a neurodevelopmental perspective (Casey et al., 2014). Currently, the RDoC identifies six major domains of functioning that are presumed to underlie core symptoms of psychopathology: cognition (e.g., attention, perception, language), positive (e.g., reward seeking) and negative (e.g., fear, anxiety) valence systems, social process systems (e.g., attachment, social communication), arousal and regulatory systems (circadian rhythms), and the sensorimotor system (National Institute of Mental Health, 2018). Autism research is a good example of how RDoC can be used while searching for biomarkers or specific neural circuitry abnormalities underlying NDDs (Campos et al., 2018; Preckel and Kanske, 2018). In a recent review, Hennesey and colleagues explain the involvement of the amygdala in RDoC domains (negative valence systems, positive valence systems, cognitive systems, social processes, and arousal and regulatory systems) and the association between amygdala dysfunction and altered development with autism symptomatology (e.g., social deficit, rigidity of thinking, impaired attention; Hennessey et al., 2018). The use of the RDoC, therefore, exposes a relationship between an altered brain function or structure and the phenotype related to a NDD.

BENEFITS AND CHALLENGES RELATED TO THE USE OF CLASSIFICATION SYSTEMS Although there are some differences and discordances between the classification systems, these are essential as they provide a language through which clinicians and researchers communicate with each other. Moreover,

NEURODEVELOPMENTAL DISABILITIES they allow the application of research to clinical problems and provide a guidance to clinical practice, explanations to patients, and clinical reimbursement (Carr, 2015). Classification systems also facilitate the development of epidemiologic information about the incidence, prevalence, and course of such disorders, which can be used to plan services (Garralda, 2017). However, since diagnoses need to adapt and change in the light of new knowledges and expertise, the fact that diagnostic criteria change over time may lead to concerns about comparability between past and future studies (Van Herwegen et al., 2015). A recent example relates the important discussions raised since 2013, with the publication of the new DSM-V. This version clubs together the five pervasive development disorders (autism, Asperger’s syndrome, pervasive developmental disorder-not otherwise specified, Rett’s syndrome, child disintegrative disorder) in the diagnosis of the autism spectrum disorder, whereas they were treated individually in the DSM-IV (Nemeroff et al., 2013; Kulage et al., 2014; Maenner et al., 2014).

THE DIAGNOSIS OF NDDs: A CHALLENGING PROCESS Even though the accurate recognition of a child’s NDD is essential at many levels, it may also be challenging in clinical practice for several reasons. NDDs’ diagnostic criteria are based on a constellation of behavioral or cognitive manifestations that contribute to the clinical heterogeneity and represent an important clinical challenge. For one NDD subtype, the symptomatology regularly varies between affected individuals (Van Herwegen et al., 2015), while several clinical expressions overlap between different subtypes (McCary et al., 2012). In addition, NDDs are not always mutually exclusive and having one may increase the likelihood of having another. Hence, especially since these disorders often overlap, a differential diagnosis is necessary to provide appropriate services. For example, a 3-year-old child with language impairment may experience social integration difficulty at day care or school, which increases the disability to communicate and to develop adaptive behavior. As a consequence, if the NDD (language disorder) is not accurately diagnosed at the previous stage, the symptomatology of the patient at the age of five could express different subtypes of NDD, such as autism spectrum disorder or behavioral disorder (Toppelberg and Shapiro, 2000). Many factors contribute to the significant heterogeneity within each subtype of NDD (intraclass heterogeneity). Age, neurologic development, maturation, modes and timing of presentation, etiology, and environmental modifiers are among the factors contributing to this heterogeneity and constitute a challenge for the

5

clinician and the care team. Variability in the support and intervention required, the challenges faced by the patient and his family, and the long-term outcomes are also involved in the NDD classification. Moreover, given that the first 18 years cover the period during which the most profound changes occur in physical, cognitive, and social development, the predominant signs and symptoms of atypical development vary depending on the age of the child, which must be taken into account. The developmental trajectory and the expectations associated with maturation imply that the manifestation of most NDD subtypes evolves over time and the clinical expression of NDDs differs in the developmental process and from one individual to another (Racine et al., 2013; Ball and Karmiloff-Smith, 2015).

CONCLUSION There is an international consensus for the definition of NDDs throughout the two main scales in the United States and Europe even if there is a variety in the available tools. Despite a certain amount of heterogeneity, early identification of NDDs is essential to assure specialized healthcare services at an early stage to improve the neurodevelopmental and functional outcomes. Since lifelong morbidity is often attached to NDD diagnosis, attendant additional medical, rehabilitation, and educational, occupational, and supportive care are usually needed and may be a burden for the individual, the family, and society (Shevell, 2010). A better consensus is expected in the future and will be valuable to clinical and research fields. It will certainly remain an arduous task, in particular because of the implicit diversity of the clinical presentation of these disorders. This challenge is also significant given the presence of different schools of thought, each offering strong theoretical models that are difficult to integrate. Finally, young adults with NDDs require careful consideration, and the issue of transition from the pediatric to the adult-oriented healthcare system, which remains a challenge in clinical practice, should be taken into account (Patel and Greydanus, 2011). Clinical awareness and judgment (Shevell, 2010) and close monitoring of the clinical trajectory over time through a dynamic approach is therefore essential to deal with the clinical heterogeneity of NDDs and to assure an optimal management of the NDD subtypes (Thomas et al., 2009).

REFERENCES American Psychiatric Association (APA) (2013). Diagnostic and statistical manual of mental disorders (DSM-5®), American Psychiatric Pub. Ball G, Karmiloff-Smith A (2015). Why development matters in neurodevelopmental disorders. In: Neurodevelopmental

6

I. GAUDET AND A. GALLAGHER

disorders: research challenges and solutions, Psychology Press 19–33. Campos R, Nieto C, Nu´n˜ez M (2018). Research domain criteria from neuroconstructivism: a developmental view on mental disorders, Wiley Interdisciplinary Reviews: Cognitive Science, e1491. Carr A (2015). Classification, epidemiology and treatment effectiveness. In: The handbook of child and adolescent clinical psychology, Routledge 66–96. https://doi.org/ 10.4324/9781315744230. Casey BJ, Oliveri ME, Insel T (2014). A neurodevelopmental perspective on the research domain criteria (RDoC) framework. Biol Psychiatry 76: 350–353. https://doi.org/ 10.1016/j.biopsych.2014.01.006. Garralda E (2017). New perspectives on the classification of child psychiatric disorders. In: D Skuse, H Bruce, L Dowdney (Eds.), Child psychology and psychiatry: frameworks for clinical training and practice, third edn. John Wiley & Sons, Ltd. pp. 331–337. Hennessey T, Andari E, Rainnie DG (2018). RDoC-based categorization of amygdala functions and its implications in autism. Neurosci Biobehav Rev 90: 115–129. Jeste SS (2015). Neurodevelopmental behavioral and cognitive disorders. Continuum (Minneap Minn) 21 (3): 690–714. Kulage KM, Smaldone AM, Cohn EG (2014). How will DSM-5 affect autism diagnosis? A systematic literature review and meta-analysis. J Autism Dev Disord 44: 1918–1932. https://doi.org/10.1007/s10803-014-2065-2. Maenner MJ, Rice CE, Arneson CL et al. (2014). Potential impact of DSM-5 criteria on autism spectrum disorder prevalence estimates. JAMA Psychiat 71: 292–300. https://doi.org/10.1001/jamapsychiatry.2013.3893. McCary LM, Grefer M, Mounts M et al. (2012). The importance of differential diagnosis in neurodevelopmental disorders: implications for IDEIA. Sch Psychol 66: 1–10. Mullin AP, Gokhale A, Moreno-De-Luca A et al. (2013). Neurodevelopmental disorders: mechanisms and boundary definitions from genomes, interactomes and proteomes. Transl Psychiatry 3 (12): e329. https://doi.org/10.1038/ tp.2013.108. National Institute of Mental Health (2018). Research Domain Criteria (RDoC). Retrieved January 24, 2019, from: https:// www.nimh.nih.gov/research-priorities/rdoc/constructs/ index.shtml.

Nemeroff CB, Weinberger D, Rutter M et al. (2013). DSM-5: a collection of psychiatrist views on the changes, controversies, and future directions. BMC Med 11: 202. https://doi. org/10.1186/1741-7015-11-202. Patel DR, Greydanus DE (2011). Transition from child-oriented to adult-oriented health care. In: DR Patel, DE Greydanus, HA Omar, J Merrick (Eds.), Neurodevelopmental disabilities: clinical care for children and young adults. Springer Netherlands, Dordrecht, pp. 439–447. https://doi.org/10.1007/978-94-007-0627-9_28. Patel V, Saxena S, Lund C et al. (2018). The Lancet Commission on global mental health and sustainable development. Lancet 392 (10157): 1553–1598. Preckel K, Kanske P (2018). Amygdala and oxytocin functioning as keys to understanding and treating autism: commentary on an RDoC based approach. Neurosci Biobehav Rev 94: 45–48. Racine E, Bell E, Shevell M (2013). Ethics in neurodevelopmental disability. In: Handbook of clinical neurology, (first edn, vol. 118). Elsevier B.V. https://doi.org/10.1016/ B978-0-444-53501-6.00021-4. Shevell MI (2010). Present conceptualization of early childhood neurodevelopmental disabilities. J Child Neurol 25: 120–126. https://doi.org/10.1177/0883073809336122. Thomas MSC, Annaz D, Ansari D et al. (2009). Using developmental trajectories to understand developmental disorders. J Speech Lang Hear Res 52: 336. https://doi. org/10.1044/1092-4388(2009/07-0144). Toppelberg CO, Shapiro T (2000). Language disorders: a 10-year research update review. J Am Acad Child Adolesc Psychiatry 39: 143–152. Van Herwegen J, Riby D, Farran EK (2015). Neurodevelopmental disorders: definitions and issues. In: Neurodevelopmental disorders: research challenges and solutions, 3–4. Retrieved from: http://search. ebscohost.com/login.aspx?direct¼true&AuthType¼ip,url, cookie,uid&db¼psyh&AN¼2015-03268-001&site¼ehostlive&scope¼site. World Health Organization (WHO) (1962). WHO and mental health, 1949–1961, World Health Organization, Geneva. World Health Organization (WHO) (2018). International statistical classification of diseases and related health problems (11th revision). Retrieved from: https://icd.who.int/ browse11/l-m/en.

Handbook of Clinical Neurology, Vol. 173 (3rd series) Neurocognitive Development: Normative Development A. Gallagher, C. Bulteau, D. Cohen and J.L. Michaud, Editors https://doi.org/10.1016/B978-0-444-64150-2.00002-2 Copyright © 2020 Elsevier B.V. All rights reserved

Chapter 2

Neurodevelopmental and cognitive disabilities: Historical overview BONNIE EVANS* School of Languages, Linguistics and Film and School of History, Queen Mary University of London, London, United Kingdom

Abstract This chapter explores the history of the developmental sciences and their impact on the study of neurodevelopmental and cognitive disabilities from the start of the 20th century to the present day. It covers the origins of intelligence testing and developmental schedules and the importance of early laboratory testing on identifying the causes of developmental differences. It also explores the importance of major legal and institutional changes after the Second World War in reframing approaches to developmental conditions. Postwar attempts to limit institutional care and to improve educational services had a significant influence on the growth of the cognitive science movement and genetic studies in the 1960s. The chapter argues that this history is critical to understanding contemporary approaches to neurodevelopmental science. Historical investigation demonstrates the complex ways that technological, social, and political changes can directly impact medical and scientific practice. It can therefore play an important role in informing those practices in the present.

INTRODUCTION Throughout the 20th century, variances across the sciences of child psychiatry, developmental psychology, neurology, clinical medicine, and psychoanalysis often created tensions and disagreements over how neurodevelopmental symptoms should be studied and treated. This chapter explores this history and its impact on the study of neurodevelopmental and cognitive disabilities from the start of the 20th century to the present. It argues that this history is critical to understanding how typical and atypical patterns of development are perceived today. In the 1960s, there was an important drive to universalize all clinical concepts in child psychology and neuroscience to support and develop scientific research in neurodevelopmental conditions. This was also supported by political changes that enabled new approaches to children with intellectual disabilities. These changes

provided a backbone for later developments in genetics, neuroimaging, and the cognitive sciences and artificial intelligence. Understanding how classificatory systems have framed these developments is essential to understanding how neurodevelopmental and cognitive disabilities are perceived today.

DEVELOPMENTAL SCIENCES 1900–50 The early 20th century saw the first systematic efforts to track typical childhood psychologic development and, consequently, to classify and identify any atypicalities. One of the most significant creations in this respect was the intelligence quotient (IQ) test developed by Alfred Binet and Th
eodore Simon at Laboratory of Physiological Psychology at Sorbonne Universit
e, France in 1905 (Binet, 1905). Early intelligence tests were designed to measure qualities such as vocabulary, reasoning, spatial

*Correspondence to: Bonnie Evans, Ph.D., Film Department, Arts 1 Queen Mary University of London, Mile End Road, London E1 4NS, United Kingdom. Tel: +44-776-978-0882, E-mail: [email protected]

8

B. EVANS

perception, memory, and judgment. They were hugely successful and began to be used far beyond their original purpose in sorting and selecting the supposed intellectual capacities of individuals and groups (Wooldridge, 1994; Gould, 1996). For example, the British psychologist Cyril Burt used them to measure the mental “capacities” of large school populations; IQ tests also influenced school selection procedures (Burt, 1909). Within the field of developmental psychology, IQ tests supported other new methods to classify and categorize the typical development of social engagement and other capacities. In Switzerland, Jean Piaget developed early concepts of ego development in conjunction with intellectual development to forge systematic models of infants’ progressive attempts to engage with the world around them through play and reality testing. In Language and Thought of the Child (1923), he considered how the development of language enabled children to move toward conceptual thought, logical reasoning, and intellectual capacity (Piaget, 1923). In the same period, Arnold Gesell in the United States started to create simple visual schedules that presented typical actions and behavior of children at particular ages. These schedules were based on systematic studies of children who were placed in a specially designed photographic dome at 4-week intervals with various objects to test typical responses. From this, Gesell developed complex atlases of “normal” infant and child behavior. As many historians have pointed out, Gesell’s norms were based on a select group of children from a unique social and cultural group. Yet they established the idea that typical patterns of psychologic development and associated behavior could be identified (Fig. 2.1) (Gesell, 1929).

Children who did not follow typical patterns were thus identified as outliers, even though the detailed reasons for these differences were still little understood. While developmental psychologists across Europe and the United States sought to create standard models of typical development, the success and influence of psychoanalytic theory encouraged new perspectives on such measures. Sigmund Freud’s work on instinctive drives and unconscious motivations in Austria made a significant mark on theories of child development internationally via psychoanalytic training networks. In the 1920s and 1930s, theorists from across Europe such as Susan Isaacs, Melanie Klein, and Anna Freud attempted to bridge the gap between developmental psychology and psychoanalysis, theorizing how infantile drives concerning human relationships could influence intellectual development from early infancy. Psychoanalysts’ focus on case studies as opposed to large-scale statistical studies created a different approach to “typical” and “atypical” development. In fact, in the work of Melanie Klein and others, concepts used to denote severe psychopathology in adulthood, in particular “psychosis”, were applied liberally to children to denote universal psychologic states in early infancy. The psychoanalytic approach encouraged new views of development based on models of infantile fantasy that were not standard within the wider developmental model. Psychoanalysis had a major influence on developmental psychology from the 1920s, in particular by generating a model of the importance of human relations to children’s psychologic development—something that had often been overlooked in the early theorization of intellectual development (Evans, 2017).

Table II. Comparison of Socially Impaired with Sociable Severely Retarded Children: Behavioral Variablesa Sociable Socially impaired severely retarded Number of children Percentages showing following abnormalities History of typical autism Speech None Echolalia Idiosyncratic speech and/or reversal of pronouns (ever) Symbolic activities None Repetitive Overall interest pattern Repetitive only Repetitive and constructive Elaborate repetitive routines

74

58

(100) 23

(100) 0b

55 35

33b 17

8

0

55 42

10b 14

72 28 23

7b 31 0b

a At b

time of interview, unless otherwise specified. p < .001 (chi-square test).

Fig. 2.1. Table from Wing, L., Gould, J., 1979. Severe impairments of social interaction and associated abnormalities in children: Epidemiology and classification. J Autism Dev Disord 9(1), 11–29.

NEURODEVELOPMENTAL AND COGNITIVE DISABILITIES By the late 1920s, many new professional groups of child psychiatrists, psychologists, and psychoanalysts were collaborating in child guidance clinics in many international settings, creating new settings for psychologic treatment. These collaborations were greatly assisted by the Rockerfeller foundation that funded this collaborative work internationally (Jones, 1999). Medically trained professionals such as William Healy in the United States and Emmanuel Miller in the United Kingdom developed integrative approaches to children’s mental development that drew from statistical methods in psychology, case-based methods in psychoanalysis, as well as physical diagnostic methods. For example, during the global epidemic of encephalitis lethargica, many diagnoses of “post-encephalitis” in the 1920s and 1930s brought child psychiatrists in collaboration with psychologists and educationalists to address complex cases. Teams dealt with children with multiple causes for their developmental atypicalities; this encouraged them to develop integrated approaches to treatment (Evans et al., 2008). However, within the specific discipline of child psychiatry, as with adult psychiatry, the main focus was always on using diagnosis and classification to identify distinct conditions. In the first half of the 20th century, this classificatory method was significantly influenced by the eugenics movement, which was premised on the idea that heredity conditions could be eliminated or reduced by political involvement. Eugenic ideologies developed on markedly different political lines across the world. Nevertheless, there were some similarities with regard to classifying children who presented with low levels of measured intelligence. Classifications such as “mental retardation”, “idiocy,” and “mental deficiency”—dominant from the early 20th century to the 1950s—were often influenced by stereotyping, racism, and prejudice and were divisive and unhelpful in enabling any kind of productive scientific research of clinical cases. In the most abhorrent examples of “race hygiene” in Germany, mentally disabled children were murdered under state-sanctioned laws (Burleigh, 2002; Sheffer, 2018). Sterilizations at adolescence also occurred in multiple global settings (Bashford and Levine, 2010). The political dimension to much work in the 1930s, together with the growing demands of global war, obviously had a marked impact on any kind of international research culture. Nevertheless, in places where research was somewhat protected from political concerns, some important neurologic and laboratory research on developmental conditions was conducted. New studies of hormones and metabolism provided an important site for much research and development in this area in the 1930s. For example, an important aspect of training

9

internationally in this period taught Archibold Garrod’s concept of “inborn errors of metabolism” as a means to understanding how metabolic errors could affect development. Ivar Asbjørn Følling’s publication in 1934 detailing his experiments with Ferric Chloride to determine “imbecilitas phenylpirouvica”—later named “phenylketonuria”—became well known internationally due to its pioneering approach. Følling had worked with the biochemist and physicist Lawrence J. Henderson at Harvard on a Rockefeller scholarship in the late 1920s and had also studied with clinical chemist Donald Van Slyke at the Rockefeller Institute (Paul and Brosco, 2013). Such international collaborations were enabling a number of discoveries in this period, particularly in the field of endocrinology (Le Marquand and Tozer, 1943; Evans and Jones, 2012). In the 1940s, animal experimentation also developed rapidly. For example, Stephen Zamenhof at Columbia University experimented with giving growth hormone to rats, finding an increase in the size of the cerebral hemispheres. Theodore Fainstat at McGill demonstrated that the administration of cortisone had a damaging effect on the offspring of rabbits and mice (Zamenhof, 1941). Animal embryo research was also beginning to launch the field of developmental genetics. In particular, German born US geneticist Salome Glueckson-Waelsh’s studies of genetic mutations in mouse embryos and their impact on spinal development provided an important trigger for new work combing embryologic and genetic expertise in the investigation of developmental conditions (Gluecksohn-Schoenheimer, 1949). By the 1950s, there was already quite a detailed understanding of how gestational nutritional deficiencies, inborn hormonal and chemical imbalances, and maternal viral and bacterial infections such as Rubella and Syphilis, could affect early development. Postnatal causes of developmental atypicalities, such as the effects of encephalitis and meningitis, were also well documented. Furthermore, research was developing rapidly in the 1950s regarding the treatment of both pre- and postnatal chemical, nutritional, and endocrine disturbances, such as the administration of thyroid in hypothyroidism and the nutritional treatment of phenylketonuria. In 1959, Marthe Gautier, J
er^ome Lejeune, and Raymond Turpin published their laboratory research that demonstrated the existence of an additional chromosome in children with the condition then often termed “Down’s syndrome” (Lejeune et al., 1959). Proof of the existence of trisomy 21 provided direct evidence of genetic causes and inspired a new generation of geneticists to investigate developmental conditions. Meanwhile, psychoanalytic researchers such as Margaret Mahler and John Bowlby became increasingly

10

B. EVANS

interested in documenting the impact of early separation on children’s psychologic development, with Bowlby’s “attachment theory” generating much interest in how maternal care influenced a child’s capacity for later socialization. This research replaced some of the earlier, more abstract philosophical reflections on primary narcissism, autism, and ego development that had thrived in the 1930s in the work of Piaget and Isaacs, for example. It increased the scope of psychoanalytic theory but also led to some misunderstandings in the distinction between internal states and those mediated by the environment, leading many to criticize Bowlby for blaming mothers for all psychologic states that developed in their children. Nevertheless, these theories were incredibly popular, particularly during the disruption to families caused by the World War II and influenced a network of psychologic specialists dealing with the general population of children (Evans, 2017). Despite the fact that laboratory research and psychoanalytic theory were thriving in the 1950s, the results of this labor did not always easily translate into better support and treatment for children affected by different kinds of developmental conditions. The specter of the eugenics movement meant that many children were still placed in institutional settings where there was a large amount of despondency with regard to their education and care. Even after the World War II, apathy remained until the late 1950s. However, this attitude did begin to shift by the late 1950s, and this enabled new scientific models to develop alongside it.

DEINSTITUTIONALIZATION, DIAGNOSIS, CLASSIFICATION, AND EPIDEMIOLOGY Up until the late1950s, care and support for children with severe neurodevelopmental conditions in much of Europe, the United States, and elsewhere occurred in institutional settings. This echoed the treatment of psychologic conditions in adults, which largely occurred within the asylum setting. Several attempts were made to build social and therapeutic networks to support mental health conditions outside the asylum from the 1920s, but these were rarely aimed at children who required high levels of support. Things began to change quite rapidly from the 1950s. In 1953, the World Health Organization published a report by the Third Expert Committee on Mental Health that argued that the classical closed asylum system was outdated and that new models of psychologic treatment should focus on outpatient facilities that respected the rights and freedoms of all individuals (World Health Organization, 1953). By the late 1950s, institutions for individuals with “mental

deficiency” and “mental retardation” began to be closed down in many countries in Western Europe, the United States, and elsewhere. These closures followed an interesting pattern, with Scandinavia, North America, and Britain seeing high rates of deinstitutionalization from the 1950s, with other countries such as France, Israel, and Japan seeing relatively low rates. At the same time, countries such as Turkey and South Africa never developed advanced systems of institutionalization of “mentally retarded” children in the first place, and so deinstitutionalization rates were inherently low (Eyal, 2010). In many places where closures occurred, policymakers and clinicians were faced with new issues of monitoring and management. From both a political and medical perspective, institutional care had meant that developmental conditions had often been sidelined and overlooked within broader medical and training networks up until the 1950s. However, in the 1960s, this began to change as children who would previously have been ignored began to be referred to different clinical centers. The process of deinstitutionalization also provided the context for swathes of new statistical and epidemiologic studies of total child populations and their psychologic states. Prior to this, the distinction between children with low IQs, and those with a larger plethora of psychologic conditions, had only been measurable or identifiable via IQ testing. However, deinstitutionalization encouraged new methods for testing and measuring children’s abilities and psychologic states that redefined how psychologic differences could be identified. It thus opened up a number of opportunities for new perspectives and approaches to form on developmental conditions. For example, in the mid-1960s, the term “minimal brain dysfunction” became common for describing different conditions in children that were not accompanied by low IQ levels. Trials for drugs, in particular Ritalin, increased in the 1970s as psychiatrists began to experiment with new treatments (Smith, 2012). By the 1970s, epidemiologic researchers became particularly interested in articulating differences in psychologic conditions across total populations of children. One of the critical pieces of research that enabled that transition was Lorna Wing and Judith Gould’s 1979 study of “Severe impairments of social interaction and associated abnormalities in children: epidemiology and classification” in which the behavior of children with “social impairment” was compared and contrasted with those with low IQ (Fig. 2.2) (Wing and Gould, 1979). This developed new ways to class psychologic differences in children based on their “social interactions” as well as their intelligence, and formed the basis for a burgeoning network of epidemiologic and

NEURODEVELOPMENTAL AND COGNITIVE DISABILITIES

11

Fig. 2.2. Photographs of object exploration age five and six months. Image from Gesell, A., Infancy and human growth, 1929, Macmillan; New York.

neuroscientific research on autism from the 1980s. International research networks ensured that psychologic terms defined in epidemiologic studies—such as “autism” and “hyperactivity or ADHD”—came to hold a similar amount of weight to IQ in the measurement of children’s psychologic differences. The fact that these diagnoses were defined statistically also encouraged international standardization through publications such as DSM-3 and ICD 9 and 10. It was also in this period that the prefix “neuro” began to be increasingly applied to such childhood conditions, as part of a wider and more inclusive term covering a large range of conditions affecting

psychologic development in varying degrees (Rose and Abi-Rached, 2013). Prior to this, notions of developmental disorder in children were not always so focused on the brain and the functions of the brain but were often discussed as bodily or social issues. This new focus on brain development was supported by new diagnostic tools, e.g., the Autism Diagnostic Inventory (1987) that was later developed into the ADOS (Evans, 2017). Scales such as this, as well as the ADHD-RS, are now critical to the establishment and maintenance of psychologic classifications in childhood that have later come to be reinforced by neuroscientific research in brain imaging and genetics.

12

B. EVANS

COGNITIVE SCIENCE, GENETICS, AND NEUROIMAGING Although the cognitive movement in psychology can be traced back to the late 1950s, the 1980s saw the first detailed attempts to track cognitive learning in relation to machine learning, along with the rapid growth of the cognitive movement. Up until the 1950s, the most significant alterative psychoanalysis was J.B. Watson’s behaviorism, based purely on observable behavior and behavioral change. In the 1950s, the work of controversial Soviet psychologist Lev Vygotsky had explored the role of sensory perceptions in the formation of language and mental cognition in childhood (Daniels et al., 2007). Noam Chomsky’s famous critique of behaviorism in 1959 further encouraged scholars internationally to develop new models for understanding the basis for human learning. By the 1960s, many child psychologists, such as Beate Hermilin and Neil O’Connor, built on Vygotsky’s work to explore the way that sensory capacities informed the development of cognitive structures in ways unrelated to IQ and also responded to Chomsky’s call to consider the way that sensory “input” was adopted, stored, recovered, and employed using analogies to machine and computer learning. In the 1970s, Michael Rutter, Lawrence Bartak, and Anthony Cox engaged with Chomsky and Piaget to reconsider developmental disorders of language, arguing that studies of language would provide the critical field through which to explore neurodevelopmental conditions (Bartak et al., 1977). In the 1980s, cognitive modeling advanced rapidly through the growth of “connectionist” models that more closely echoed neural networks than the sensory or rules-based models from the 1960s. In child psychology, social learning was redefined as an inability to understand the sensory inputs that enabled children to learn. For example, in their well-known article on the “theory of mind”, Uta Frith, Simon Baron-Cohen, and Alan Leslie argued that some children were unable to process and understand the mental states of themselves or others (Baron-Cohen et al., 1985). This new vision of cognitive psychology enabled a greater popular understanding and appreciation of developmental milestones. This coincided with the opening of numerous new units for child development or “baby labs” based on these principles of cognitive psychology, which have formed the basis for much theoretical work in child psychology from the 1980s. Work by psycholinguists such as Isabelle Rapin on subtypes of language disorder has attempted to carve up developmental conditions based on language capacities (Rapin and Allen, 1983). Usha Goswami and Peter Bryant have also addressed the way that phonologic skills support literary skills (Goswami and Bryant, 1990). The development of

computer modeling in artificial intelligence has further augmented and supported this cognitive framework for thinking about the processes of learning in child development. Two other vital steps in the establishment of the “neurodevelopmental” understanding of child development were the development of new imaging techniques, in particular, computerized tomography scanning in the 1970s and magnetic resonance imaging (MRI) in the 1980s. These have enabled a flood of studies of the working of children’s brains during the processes of learning, as well as the imaging of children’s brains that appear to function in an atypical way (Raichie, 2009). With regard to typical development, MRI scanning, in particular, enabled a far more expansive mapping of changes in brain structure that occur during development. Previous research drawn from postmortem studies had demonstrated that the expansion of myelin around the axon of neurons continues to adolescence and that synaptic density increases rapidly in postnatal development and is then reduced in maturation. However, MRI scanning, and other technologies used from the 1990s in particular, have enabled a deeper understanding of these processes. One of the most significant findings in this field was the typical pattern of stable growth of white matter and myelination across childhood, together with an inverted U-shape development of gray matter, which follows progressive and regressive change, with the latest peaks occurring in the prefrontal cortex, parietal cortex, and the temporal cortex around puberty. With regard to atypical development, there have been two general trends in the progression of imaging sciences (Crone and Ridderinkhof, 2011). The first has focused on brain scans of children who have experienced some form of abuse in childhood, and the second has focused on brain scans of children who have been diagnosed with different conditions such as language impairment, ADHD, and autism. Historical work that explains changes in the psychologic modeling of all of these conditions has demonstrated that external factors have often played a key role in medical and clinical definitions (Smith, 2012; Evans, 2013). For example, in the case of autism, the closure of deficiency institutions, the growth of epidemiologic methods, and international collaboration itself, led to the emergence of new definitions of “autism,” which challenged earlier understandings. In fact, the meaning of the word “autism” experienced a complete reversal in the 1960s and 1970s from describing a state of excessive fantasy, hallucination, and imagination to one determining an impairment of imagination and a lack of hallucination and fantasy life (Evans, 2013). Since then, neurodiversity advocates and autistic adults have challenged these definitions and some, such as Steven Kapp, have been

NEURODEVELOPMENTAL AND COGNITIVE DISABILITIES involved in influencing DSM V. This has meant that neuroimaging and other neurosciences have sometimes had to play catch-up to the definition of psychologic states, rather than the other way around. Nevertheless, neuroimaging has provided an important new site from which to view mental and psychologic difference and has become a critical tool within the wider neurosciences of child development. Another obviously significant scientific expansion in relation to neurodevelopmental and cognitive disabilities has been in genetic sciences. Although genetic transmission had been tracked through family and biochemical studies in conditions as “Down’s syndrome” and “Martin-Bell syndrome” (later identified as Fragile X) and was already well known by the 1970s, the development of large-scale epidemiologic and longitudinal studies encouraged new family and twin studies in conditions such as autism (Folstein and Rutter, 1977). From the 1970s, there was a lot of enthusiasm in the expanding neurosciences of the possibilities of using animal research on hormonal reaction to stress and to develop wider programs linking genetic, chemical, neurologic, and biochemical research to create more detailed models of development in childhood. The sequencing of the human genome in the early 2000s obviously enabled new research programs that supported this aspiration. Instead of discussions on purely genetic transmission, this enabled new molecular sciences of base pairs that regulate gene expression or code for proteins. As Nikolas Rose and others have argued, this created a new model of the neurosciences in which genetic research was mapped onto the neuromolecular brain (Rose and Abi-Rached, 2013; Vidal and Ortega, 2017). In developmental sciences, this created new possibilities to map the impact of severe deprivation or abuse on the developing brain. These studies drew from information from longitudinal studies of children, animal neuroscientific studies, and biochemical analysis, such as in the work of Avshalom Caspi and Terrie Moffitt in the early 2000s (Caspi et al., 2002). The challenge in such studies was always to create valid links between the multiple sciences to make legitimate claims around cause and effect in the neurosciences. Taking historical changes in definitions into account is also an important factor in such work in many instances.

CONCLUSION Historical factors have had an important influence on the way that cognitive disabilities and neurodevelopmental conditions have been classified, studied, and treated. The inherent complexity of psychologic states mean that they are difficult to categorize and contain, and this is especially true when they are observed in children,

13

whose symptoms develop and change—sometimes very rapidly. Developing an historically informed understanding of the way that scientific approaches to developmental disorders have changed enables a greater understanding and awareness of contemporary approaches. It is important to note that psychologic concepts often go through periods of change and that these changes are usually associated with social and political change. This does not invalidate any diagnosis or concepts; it just illuminates their position within a wider network of sciences—both socially and historically. Such an awareness can be useful for both clinical work and also for the planning of research programs, which engage with multiple scientific methods in observing and understanding neurodevelopmental and cognitive disabilities.

REFERENCES Baron-Cohen S, Leslie A, Frith U (1985). Does the autistic child have a “theory of mind”? Cognition 21: 37–46. Bartak EGL, Rutter M, Cox A (1977). A comparative study of infantile autism and specific developmental receptive language disorders III. Discriminant function analysis. J Autism Dev Disord 7: 383–396. Bashford A, Levine P (2010). The Oxford Handbook of the history of eugenics, Oxford University Press, New York; Oxford. Binet A (1905). New methods for the diagnosis of the intellectual level of subnormals. Annee Psychol 12: 191. Burleigh M (2002). Death and deliverance: ‘Euthanasia’ in germany c. 1900–1945, Pan, London. Burt C (1909). Experimental tests of general intelligence. Br J Psychol 1904–1920 3: 94–177. Caspi A et al. (2002). Role of genotype in the cycle of violence in maltreated children. Science 297: 851–854. Crone EA, Ridderinkhof KR (2011). The developing brain: from theory to neuroimaging and back. Dev Cogn Neurosci 1: 101–109. Daniels H, Cole M, Wertsch JV (2007). The Cambridge companion to vygotsky, Cambridge University Press, Cambridge. Evans B (2013). How autism became autism: the radical transformation of a central concept of child development in britain. Hist Human Sci 26: 3–31. Evans B (2017). The metamorphosis of autism: a history of child development in britain, Manchester University Press, Manchester. Evans B, Jones E (2012). Organ extracts and the development of psychiatry: hormonal treatments at the maudsley hospital 1923–1938. J Hist Behav Sci 48: 251–276. Evans B, Rahman S, Jones E (2008). Managing the ‘unmanageable’: interwar child psychiatry at the maudsley hospital, london. Hist Psychiatry 19: 454–475. Eyal G (2010). The autism matrix: the social origins of the autism epidemic, Polity, Cambridge, pp. 61–63. Folstein S, Rutter M (1977). Infantile autism: a genetic study of 21 twin pairs. J Child Psychol Psychiatry 18: 297–321.

14

B. EVANS

Gesell A (1929). Infancy and human growth, Macmillan, New York. Gluecksohn-Schoenheimer S (1949). Causal analysis of mouse development by the study of mutational effects. Growth 13: 163–176. Goswami UC, Bryant P (1990). Phonological skills and learning to read, Lawrence Erlbaum Associates. Gould SJ (1996). The mismeasure of man, Rev. and expanded edn Penguin, London. 1997. Jones KW (1999). Taming the troublesome child, Harvard University Press, London. Le Marquand HS, Tozer FHW (1943). Endocrine disorders in childhood and adolescence, ed, Hodder and Stoughton, London. Lejeune J, Gauthier M, Turpin R (1959). human chromosomes in tissue cultures. C R Hebd Seances Acad Sci 248: 602–603. Paul DB, Brosco JP (2013). The pku paradox: a short history of a genetic disease, John Hopkins University Press, Baltimore, pp. 10–12. Piaget J (1923). Le langage et la pensee chez l’enfant, etc, Neuchatel, Paris. Raichie ME (2009). A brief history of human brain mapping. Trends Neurosci 32: 118–126.

Rapin I, Allen D (1983). Developmental language disorders: nosologic considerations. In: U Kirk (Ed.), Neuropsychology of language, reading, and spelling. Academic Press, New York; London. Rose NS, Abi-Rached JM (2013). Neuro: the new brain sciences and the management of the mind, Princeton University Press, Princeton, NJ. Sheffer E (2018). Asperger’s children, W. W. Norton & Co., New York. Smith M (2012). Hyperactive: the controversial history of ADHD, Reaktion, London. Vidal F, Ortega F (2017). Being brains: making the cerebral subject, Fordham University Press. Wing L, Gould J (1979). Severe impairments of social interaction and associated abnormalities in children: epidemiology and classification. J Autism Dev Disord 9: 11–29. Wooldridge A (1994). Measuring the mind: education and psychology in England, c.1860-c.1990, Cambridge University Press, Cambridge. World Health Organization (1953). Third expert review committee on mental health, World Health Organization, Geneva. Zamenhof S (1941). Stimulation of the proliferation of neurons by the growth hormone. Nature 148: 143.

Handbook of Clinical Neurology, Vol. 173 (3rd series) Neurocognitive Development: Normative Development A. Gallagher, C. Bulteau, D. Cohen and J.L. Michaud, Editors https://doi.org/10.1016/B978-0-444-64150-2.00003-4 Copyright © 2020 Elsevier B.V. All rights reserved

Chapter 3

Ethical views and considerations  OLIVIER HOUDE* Laboratory for the Psychology of Child Development and Education, University of Paris, Paris, France

Abstract Jean Piaget’s theory is a central reference point in the study of normal development in children. He proposed in the 20th century that distinct stages occur in the development of intellectual abilities from the preoperational period (intuitive stage: 4–7 years old) to the second stage of conceptual intelligence. One of the most famous Piagetian tasks is number conservation. Failures and successes in this task reveal two fundamental stages in children’s thinking and judgment, shifting at approximately 7 years of age from visuospatial intuition to logico-mathematical operation (i.e., number conservation). New emerging techniques in the 21st century, such as functional magnetic resonance imaging, can support this preeminent theory with an understanding of the cerebral basis of the various stages. Since these new technologies are considered to be invasive in children, such techniques are subject to ethical views and concerns due to pediatric participants. The chapter discusses a brain imaging study on Piaget’s conservation-ofnumber task, showing what can be accomplished through careful ethical considerations in the context of healthy children with normal cognitive development.

INTRODUCTION How to better understand the human brain than to study it in children—that is, through its development and construction? Based on abundant scientific data garnered in children of all ages, Piaget (1984) proposed a seminal model of cognitive development according to which children’s cognitive abilities developed through four different stages, from the sensorimotor stage (from birth to 2 years of age) to the formal operational stage (starting at 12 years of age). Between 2 and 7 years of age (the so-called preoperational stage), Piaget assumed that children were mainly illogical in comparison to adults. Importantly, during the concrete operational stage, between 7 and 12 years of age, children start to reason logically in several logico-mathematical domains such as number and categorization. For example, children under approximately 7 years of age fail to solve the famous Piaget’s conservationof-number task (Piaget, 1952). This developmental

evidence is quite robust and manifests in today’s preschoolers over half a century after Piaget’s discovery. The conservation-of-number task (Fig. 3.1) consists of the presentation of two rows containing the same number of objects placed in one-to-one correspondence. Children are asked whether the two rows contain the same number of objects. Once the children acknowledge this equality, one of the rows is transformed in length but not in number (i.e., the objects in the row are spread apart). Children are again asked whether the two rows contain the same number of objects. Until approximately 7 years of age, children are considered to be “nonconservers” because they erroneously state that there are more objects in the longer row. However, after 7 or 8 years of age, children are designated as “conservers” because they correctly state that the numbers of objects in the two rows are equal. According to Piaget’s theory (Piaget, 1952, 1984), total success in this task proves the solidity of the number concept and

*Correspondence to: Olivier Houde, University of Paris, Laboratory for the Psychology of Child Development and Education (CNRS, Centre National de la Recherche Scientifique, UMR 8240), Sorbonne, 46 rue Saint-Jacques, 75005 Paris, France. Tel: +33-140462995, Fax: +33-140462993, E-mail: [email protected]

 O. HOUDE

16

Fig. 3.1. The conservation-of-number task taken from Jean Piaget (1896–1980).

delineates an important step in the acquisition of concrete logico-mathematical skills in children. This conservation-of-number task (like the liquid volume or matter quantity tasks designed by Piaget) has been a seminal test in developmental psychology and pedagogy since the mid-20th century, because it is very simple, replicable, and challenges the cognitive and developing brain. However, Piaget underestimated the rich, precocious, logical knowledge already present in the brains of infants and young children (Mehler and Bever, 1967; Antell and Keating, 1983; Wynn, 1992, 1998, 2000; Lipton and Spelke, 2003; McCrink and Wynn, 2004; Berger et al., 2006; Gopnik, 2012; Dehaene-Lambertz and Spelke, 2015). Therefore, today we think that, more than logic or number per se, it is rather the progressive ability of the prefrontal cortex to inhibit irrelevant or misleading strategies (such as the length-equals-number heuristic in the previous task) and to activate the most logical one (such as the counting algorithm) that sustains the conceptual development of children and the shift from one Piagetian stage to the next (Houde and Borst, 2015). This constitutes the central assumption of our new post-Piagetian approach to brain and cognitive development (Houde, 2000, 2019). At any point in time and at any age, cognitive heuristics (Kahneman, 2011) and algorithms (the logic of Piaget) may compete in the brain. So, the discrete and incremental Piagetian stages theory is replaced by an approach to cognitive development that is analogous to overlapping waves (the cognitive strategies) within a nonlinear dynamic system. Using in vivo brain imaging in healthy children thus becomes essential to understanding the neural networks underlying this dynamic cognitive system.

ETHICAL CONSIDERATIONS OF RESEARCHERS CONDUCTING PEDIATRIC STUDY IN HEALTHY CHILDREN Of course, brain imaging research involving the participation of healthy children must include an assessment of the benefit-risk balance. The benefit corresponds here to the scientific issue: the potential ability to provide a more exact model of cognitive development in children and, hence, to improve pedagogy at school

and neuropsychologic assessment of cognitive abilities or disabilities. For three decades, functional magnetic resonance imaging (fMRI) was conducted first in adults, then in sick children such as in presurgical language fMRI, for example, and finally in healthy children, even in very young infants (Dehaene-Lambertz et al., 2002), for scientific issues. Thus advances in brain imaging techniques have provided significant opportunities for the study of brain development in humans, and fMRI is the most promising and broadly used imaging technology that is also safe for use in pediatric populations (Thomason, 2009). Of course, such research with the participation of healthy children requires their moral consent and that (legal) of their parents. For this purpose, we use a children’s book that explains the human brain, its exploration, and the general aim of the study. Moreover, it is very important to implement a very good and even meticulous preparation procedure before “the lab day.” Each child must be trained and familiarized with the fMRI conditions (stillness inside a tunnel, machine noise, etc.) at school and in a play setting. These precautions are ethically important for the children’s and their parents’ agreement. They are also technically important for the quality of the psychologic and fMRI experiment itself and the data collected. In all of these points, ethics and science must work together.

AN EXAMPLE OF AN fMRI STUDY ON HEALTHY SCHOOL CHILDREN A total of 60 children recruited from preschools and schools in Caen (Calvados, France) participated in this study: 38 5- and 6-year-olds and 22 9- and 10-year-olds. The children had no difficulties with reading and mathematical acquisition, no psychologic or psychomotor therapies (only one child in each age group was under light speech therapy), and no history of neurologic disease. The T1-weighted MRI indicated that none of the children had any cerebral abnormalities. The local ethics committee approved the study. Written consents were obtained from the parents and the children themselves after detailed discussion and explanation (individual consent for children was adapted as a “smiley face” associated with a specific color).

MRI familiarization at school To help the children feel confident with the experimenters and experimental material, they participated individually in a half-hour-long familiarization session at school the day before the MRI in the laboratory. The session consisted of a “statue game” in which they needed to stay as still as a statue in a toy tunnel imitating the MRI scanner and its technological environment,

ETHICAL VIEWS AND CONSIDERATIONS

17

Fig. 3.2. Photographs of the experimental environment. (A) Training procedure at the school. (B) Inside the toy tunnel, a child wearing the cardboard head coil and looking at the screen while playing the “statue game.” (C) Preparation of a 5-year-old in the MRI scanner with a child-friendly environment. Reprinted with permission from Houde, O., Pineau, A., Leroux, G., et al., 2011. Functional MRI study of Piaget’s conservation-of-number task in preschool and school-age children: a neo-Piagetian approach. J Exp Child Psychol 110, 332–346.

including the recorded noises of all MRI sequences, cardboard head coil, medical tape on the forehead, and response pad (with practice trials) (Fig. 3.2A and B). The day of the MRI in the lab, the same familiarization process was repeated just prior to the experiment.

fMRI imaging protocol Images were acquired using a 3T MRI scanner (Achieva, Philips Medical System, Netherlands) (Fig. 3.1C). In a first anatomic session, three-dimensional (3D) T1-weighted spoiled gradient images (field of view [FOV] ¼ 256 mm, slice thickness ¼ 1.33 mm, 128 slices, matrix

size ¼ 192  192 voxels, and duration ¼ 5 min 7 s) were acquired while the children passively watched a cartoon on an MRI-compatible screen. The sedative effect of audiovisual systems on children in MRI scanners has been demonstrated; the systems reduce motion, provide a positive experience, and decrease wait times (Lemaire et al., 2009). After a break outside the scanner, the fMRI session, consisting of two different runs, was conducted with T2-weighted, gradient echo planar images acquired with a repetition time of 2 s, an echo time of 35 ms, and a flip angle of 80 degrees for 31 axial slices, 3.5-mm thick, with a 224-mm FOV and a 64  64 grid (210 volumes in 7 min for each run).

18

 O. HOUDE

Additional anatomic T2-weighted images were acquired with 60 slices, 2.3-mm thick, and a 112  112 grid (2 min 10 s duration) to facilitate realignment between T1 and echo planar images. Throughout this second MRI session, the children performed two different runs: Piaget’s conservation of number task (called the Number task) first and then the control task (called the Color task). Although this may be a limitation of the current study, we decided to adhere to the classical Piagetian design (in which no control task was used). This meant always presenting the control task at the end of the functional session. Such a precaution is important because it avoids any control task priming and/or interference effects on the children’s judgments during the conservation-of-number task. During the Number task, the children were presented with conservation trials. Each trial consisted of two rows, each containing the same number of objects (5, 6, or 7). According to the conservation principle, the number of objects was always the same in both rows. Hence, even though the number of objects (5, 6, or 7) varied across trials, all conservation trials, by definition, involved the same correct response (i.e., numerical equivalence), leading to a possible habituation effect as the fMRI experiment progressed. To minimize this potential bias inherent in Piaget’s conservation design, the experimenter told the children before the functional session that they needed to pay close attention, because there could be traps in the task. For each trial, the children were asked to judge the numerical equivalence of two rows of objects when the rows had the same length. After a jittered interstimulus interval of 750  250 ms, the objects in one of the rows were spread apart by apparent movement on the computer screen. After the objects in one of the two rows had been moved, the children were again instructed to judge the numerical equivalence of the two rows of objects when their length, but not their number, differed. Children responded by pressing the “same” button or the “not the same” button of the response box. The question “Is the number of objects the same in both rows?” was verbally delivered for each trial (2.7 s duration each), and each trial remained present on the screen until the children responded. Children were then presented with the Color task, in which they were asked to make a qualitative decision about the two rows of objects. Children were presented with the same experimental stimuli as during the Number task, except that the two rows consisted of either the same colors or different colors. Same and different trials were presented randomly. Children were asked, “Are the objects in both rows the same color?” and responded by pressing the “same” button or the “not the same” button on the response box. Children used their preferred hand to give

their answers with the response pad. During both tasks, the children were instructed to look passively at a line on the screen between each experimental trial, defining a control rest condition. The intertrial interval was jittered and lasted 9 s, with standard deviation (SD) ¼ 1.

RESULTS AND PERSPECTIVES In this first fMRI study, we found that the cognitive change that allows children to access conservation (i.e., the shift from Stage 2 to Stage 3 in Piaget’s theory) was related to the neural contribution of a bilateral parietofrontal network involved in numerical and executive functions (Houde et al., 2011). These imaging results highlighted how the behavioral and cognitive stages that Piaget formulated during the 20th century manifest in the brain with age. In a second fMRI study (Poirel et al., 2012), we demonstrated that the prefrontal activation (i.e., the blood-oxygen-level-dependent signal), specifically in the right inferior frontal gyrus, observed when schoolchildren succeeded at the Piaget number conservation task was correlated to their behavioral performance on a Stroop-like measure of inhibitory function development (Fuster, 2003; Wright et al., 2003; Aron et al., 2004, 2014). These new results in schoolchildren fit well with previous brain imaging data showing a key role of prefrontal inhibitory control training when adolescents or adults (belonging to Stage 4 in Piaget’s theory) spontaneously fail to block their perceptual intuitions (or bias, heuristics) to activate logico-mathematical algorithms (i.e., deductive rules) in reasoning tasks (Houde, 2000, 2007, 2019; Houde et al., 2000, 2001; Houde and Tzourio-Mazoyer, 2003). If we have “two minds in one brain” as stated by Evans (2003) or, in other words, two ways of thinking and reasoning, i.e., “fast and slow” (Kahneman, 2011), currently called “System 1” (intuitive system) and “System 2” (analytic system), then the crucial challenge is to learn to inhibit the misleading heuristics from System 1 when the more analytic and effortful System 2 (logico-mathematical algorithms) is the way to solve the problem (Houde, 2000, 2019; Borst et al., 2013). Within this post-Piagetian theoretical approach, we can now understand why, despite rich, precocious knowledge of physical and mathematical principles observed in infants and young children, older children, adolescents, and adults so often have poor reasoning. The cost of blocking our intuitions is high and depends on the late maturation of the prefrontal cortex. Moreover, this executive ability remains delicate throughout our lifetime, and adults may sometimes need “prefrontal pedagogy” to learn to inhibit intuitive heuristics (or biases) in reasoning tasks (Houde, 2007).

ETHICAL VIEWS AND CONSIDERATIONS

ETHICAL CONSIDERATIONS Such experimental studies involve both ethical and technical concerns. Ethics of neuroimaging in healthy minors of different ages or vulnerable patients can be particularly problematic. Children’s assent requires that the minor’s will should be considered while the rational capacity necessary to make informed decisions is not present. The variability and change associated with cognitive development heightens the risks/benefits balance of any procedure and may further limit interpretation of data. As a consequence, fewer pediatric than adult neuroimaging research studies have been published. In 2015, Weiss-Croft and Baldeweg (2015) reviewed all the language fMRI studies reported between 1992 and 2014 in healthy children and identified only 39 fMRI studies of language development, encompassing 1114 children. One study was performed in infants aged 1–3 years and the majority were done after the age of 8 years old. The rationale behind informed consent in pediatrics needs to incorporate new strategies to improve communication with the participant, including videotapes, simulations, cartoons, exposure to the research materials, trained parents, and more. The purpose is addressed through a combination of parental permission and minor’s assent. Functional MRI studies in healthy children are nontherapeutic trials without any benefit for the participant in the exposure to the MRI. The research may only be conducted if the burden and any other foreseeable risks are as small as possible. Both the degree of burden and the risk threshold must be defined specifically in the protocol and monitored constantly by the investigator. The same methodology, with variants depending on the cognitive tasks, and the same ethical rules were applied in the next studies using brain imaging with healthy children (Poirel et al., 2012; Borst et al., 2014, 2016; Cachia et al., 2014, 2016, 2018; Houde and Borst, 2014, 2015; Tissier et al., 2018; Mevel et al., 2019). This methodology was to define an official partnership between the parents, the research team, and the teaching staff in order to explain the purpose of the research dedicated to increasing the fundamental knowledge of learning mechanisms and brain plasticity. Each partner became progressively more confident. Specific booklets or posters were prepared for each child according to their age. The possibility of taking measurements of the body was described to the child, the tool for taking these measurements was described, and the child’s knowledge of the brain was discussed. To familiarize the child with the MRI, a sham tunnel was introduced at school and the child could go inside

19

it and play with a knight’s helmet. When the child was comfortable and had learned to be relaxed in the tunnel, the metaphor of a space shuttle was used to teach how to use the computer’s buttons. The children were trained in the tunnel, and also on the school playground or at home, to become immobile, like a statue, in order to avoid movement in the MRI. The majority of the children were pleased to participate in the study, and the families said they would not have expected their child to be able to participate and learn the context so quickly. The experimentation was then concretely explained. The child was required to remain immobile for 15 min for brain MRI morphology, while he/she watched a favorite cartoon. The next 15 min was dedicated to the fMRI and the cognitive task. Both the children and their parents were notified that they could withdraw their assent at any time. The use of this procedure allowed the family to determine the levels of risk and potential benefits, which are the basis for ethical approvability, required to obtain the informed consent of the legal representative. The information process and the training provided to the children helped them to make a voluntary informed decision. This methodology is based on good communication and trust between all the partners. The literature on clinical ethics in pediatrics, however, remains scarce. As the legal and ethical requirements in various cultures differ from each other, we expect that these ethical considerations could help to develop training programs on informed consent and the specificity needed to obtain the minor’s agreement.

CONCLUSION Since Plato, we have known that the three values that are the foundations of human life in general, and of life sciences in particular, are the Good, the True, and the Beautiful (Changeux, 2012; Houde, 2019). Here, the True is the scientific method and its results. The Beautiful is the surprise, the consistency, and the parsimony within each study (namely its design). But first of all, the Good, in each case, is the perfect ethical framework involving informed consent of both children and their parents. The Good is also the extraordinary thoroughness with which researchers have worked to adapt everything to the children! Science without conscience is but the ruin of the soul, as Rabelais said. Using fMRI or any other new technologies in cognitive developmental neurosciences without conscience is but the ruin of neurocognitive psychology. However, the reverse represents a treasure to come.

20

 O. HOUDE

REFERENCES Antell S, Keating D (1983). Perception of numerical invariance in neonates. Child Dev 54: 695–701. Aron A, Robbins T, Poldrack R (2004). Inhibition and the right inferior frontal cortex. Trends Cogn Sci 8: 170–177. Aron A, Robbins T, Poldrack R (2014). Inhibition and the right inferior frontal cortex: one decade on. Trends Cogn Sci 18: 177–185. Berger A, Tzur G, Posner M (2006). Infant brains detect arithmetic errors. PNAS 103: 12649–12653. Borst G, Moutier S, Houde O (2013). Negative priming in logicomathematical reasoning. In: W De Neys, M Osman (Eds.), New approaches in reasoning research. Psychology Press, New York, pp. 34–50. Borst G, Cachia A, Vidal J et al. (2014). Folding of the anterior cingulate cortex partially explains inhibitory control during childhood: a longitudinal study. Dev Cogn Neurosci 9: 126–135. Borst G, Cachia A, Tissier C et al. (2016). Early cerebral constraints on reading skills of school-age children: an MRI study. Mind Brain Educ 10: 47–54. Cachia A, Borst G, Vidal J et al. (2014). The shape of the Anterior Cingulate Cortex (ACC) contributes to cognitive control efficiency in preschoolers. J Cogn Neurosci 26: 96–106. Cachia A, Borst G, Tissier C et al. (2016). Longitudinal stability of the folding pattern of the anterior cingulate cortex during development. Dev Cogn Neurosci 19: 122–127. Cachia A, Roell M, Mangin J-F et al. (2018). How interindividual differences in brain anatomy shape reading accuracy. Brain Struct Funct 223: 701–712. Changeux J-P (2012). The good, the true, and the beautiful: a neuronal approach, Yale University Press, Yale. Dehaene-Lambertz G, Spelke E (2015). The infancy of the human brain. Neuron 88: 93–109. Dehaene-Lambertz G, Dehaene S, Hertz-Pannier L (2002). Functional neuroimaging of speech perception in infants. Science 298: 2013–2015. Evans J (2003). In two minds: dual-process accounts of reasoning. Trends Cogn Sci 7: 454–459. Fuster J (2003). Cortex and mind, Oxford University Press, New York. Gopnik A (2012). Scientific thinking in young children. Theoretical advances, empirical research and policy implications. Science 337: 1623–1627. Houde O (2000). Inhibition and cognitive development. Cogn Dev 15: 63–73. Houde O (2007). First insights on neuropedagogy of reasoning. Think Reason 13: 81–89. Houde O (2019). 3-System theory of the cognitive brain: a postpiagetian approach, Routledge, New York and London. Houde O, Borst G (2014). Measuring inhibitory control in children and adults: brain imaging and mental chronometry. In: Y Moriguchi, P Zelazo, N Chevalier (Eds.),

Frontiers in developmental psychology. Research topic: development of executive function during childhood. https://doi.org/10.3389/fpsyg.2014.00616. Houde O, Borst G (2015). Evidence for an inhibitory-control theory of the reasoning brain. In: V Goel, G Navarrete, J Prado, I Noveck (Eds.), Frontiers in human neuroscience. Research topic: the reasoning brain: the interplay between cognitive neuroscience and theories of reasoning. https:// doi.org/10.3389/fnnhum.2015.00148. Houde O, Tzourio-Mazoyer N (2003). Neural foundations of logical and mathematical cognition. Nat Rev Neurosci 4: 507–514. Houde O et al. (2000). Shifting from the perceptual brain to the logical brain: the neural impact of cognitive inhibition training. J Cogn Neurosci 12: 721–728. Houde O et al. (2001). Access to deductive logic depends on a right ventromedial prefrontal area devoted to emotion and feeling: evidence from a training paradigm. Neuroimage 14: 1486–1492. Houde O, Pineau A, Leroux G et al. (2011). Functional MRI study of Piaget’s conservation-of-number task in preschool and school-age children: a neo-Piagetian approach. J Exp Child Psychol 110: 332–346. Kahneman D (2011). Thinking fast and slow, Allen Lane, London. Lemaire C, Moran G, Swan H (2009). Impact of audio/visual systems on pediatric sedation in magnetic resonance imaging. J Magn Reson Imaging 30: 649–655. Lipton JS, Spelke E (2003). Origins of number sense: largenumber discrimination in human infants. Psychol Sci 14: 396–401. McCrink K, Wynn K (2004). Large-number addition and subtraction by 9-month-old infants. Psychol Sci 15: 776–781. Mehler J, Bever T (1967). Cognitive capacity of very young children. Science 158: 141–142. Mevel K, Borst G, Poirel N et al. (2019). Developmental frontal brain activation differences in overcoming heuristic bias. Cortex 117: 111–121. Piaget J (1952). The child’s conception of number, Routledge and Kegan Paul, New York. Piaget J (1984). Piaget’s theory. In: PH Mussen (Ed.), Handbook of child psychology. vol. 1. Wiley, New York, pp. 103–128. Poirel N, Borst G, Simon G et al. (2012). Number conservation is related to children’s prefrontal inhibitory control: an fMRI study of a Piagetian task. PLoS One 7: e40802. https://doi.org/10.1371/journal.pone.0040802. Thomason ME (2009). Children in non-clinical functional magnetic resonance imaging (fMRI) studies give the scan experience a “thumbs up”. Am J Bioeth 9: 25–27. Tissier C, Linzarini A, Allaire-Duquette G et al. (2018). Sulcal polymorphisms of the IFC and ACC contribute to inhibitory control variability in children and adults. eNeuro 5: ENEURO.0197-17.2018. https://doi.org/10.1523/ ENEURO.0197-17.2018.

ETHICAL VIEWS AND CONSIDERATIONS Weiss-Croft LJ, Baldeweg T (2015). Maturation of language networks in children: a systematic review of 22 years of functional MRI. Neuroimage 123: 269–281. Wright I, Watermann M, Prescott H, Murdcoh-Eaton D (2003). A new Stroop-like measure of inhibitory function development. J Child Psychol Psychiatry 44: 561–575.

21

Wynn K (1992). Addition and subtraction by human infants. Nature 358: 749–750. Wynn K (1998). Psychological foundations of numbers. Trends Cogn Sci 2: 296–303. Wynn K (2000). Findings of addition and subtraction in infants are robust and consistent. Child Dev 71: 1535–1536.

This page intentionally left blank

Section II Biological basis of typical neurodevelopment

This page intentionally left blank

Handbook of Clinical Neurology, Vol. 173 (3rd series) Neurocognitive Development: Normative Development A. Gallagher, C. Bulteau, D. Cohen and J.L. Michaud, Editors https://doi.org/10.1016/B978-0-444-64150-2.00004-6 Copyright © 2020 Elsevier B.V. All rights reserved

Chapter 4

Neurogenesis, neuronal migration, and axon guidance ANDREA ACCOGLI1,2, NASSIMA ADDOUR-BOUDRAHEM3, AND MYRIAM SROUR3,4* 1

Unit of Medical Genetics, Istituto Giannina Gaslini Pediatric Hospital, Genova, Italy

2

Departments of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal-Child Science, Università degli Studi di Genova, Genova, Italy 3

Research Institute, McGill University Health Centre, Montreal, QC, Canada

4

Department of Pediatrics, Division of Pediatric Neurology, McGill University, Montreal, QC, Canada

Abstract Development of the central nervous system (CNS) is a complex, dynamic process that involves a precisely orchestrated sequence of genetic, environmental, biochemical, and physical factors from early embryonic stages to postnatal life. Duringthe past decade, great strides have been made to unravel mechanisms underlying human CNS development through the employment of modern genetic techniques and experimental approaches. In this chapter, we review the current knowledge regarding the main developmental processes and signaling mechanisms of (i) neurogenesis, (ii) neuronal migration, and (iii) axon guidance. We discuss mechanisms related to neural stem cells proliferation, migration, terminal translocation of neuronal progenitors, and axon guidance and pathfinding. For each section, we also provide a comprehensive overview of the underlying regulatory processes, including transcriptional, posttranscriptional, and epigenetic factors, and a myriad of signaling pathways that are pivotal to determine the fate of neuronal progenitors and newly formed migrating neurons. We further highlight how impairment of this complex regulating system, such as mutations in its core components, may cause cortical malformation, epilepsy, intellectual disability, and autism in humans. A thorough understanding of normal human CNS development is thus crucial to decipher mechanisms responsible for neurodevelopmental disorders and in turn guide the development of effective and targeted therapeutic strategies.

The human central nervous system (CNS) contains approximately 86.1 billion neurons, and an even greater number of glial cells (Azevedo et al., 2009; HerculanoHouzel, 2009). In turn, each cortical neuron develops an average of 7000 synaptic connections with other neurons, resulting in a total of 0.15 quadrillion synapses only in the neocortex and more than 150,000 km of myelinated nerve fibers (Pakkenberg et al., 2003). Accordingly,

it is not surprising that the complete development of human CNS takes over 2 decades, beginning in the third week of gestation and extending at least through late adolescence, and requires a precise coordination of a myriad molecular and cellular processes, regulated by both genetic and environmental factors. The main stages of brain development are neurulation, cellular proliferation and differentiation, neuronal

*Correspondence to: Myriam Srour, MDCM, FRCP (C), Ph.D., Montreal Children’s Hospital, MUHC-RI, 1001 Decarie Blvd, EM0.3218, Montreal, QC, Canada H4A 3J1. Tel: +1-514-412-4446, Fax: +1-514-412-4373, E-mail: [email protected]

26

A. ACCOGLI ET AL.

migration, axonal guidance and pathfinding, organization, and myelination. The neural tube is formed at approximately 3–4 weeks gestation, followed by neurogenesis, neural cell proliferation, neuronal migration, and formation of the general brain regions, which are largely complete by birth. Synaptogenesis and myelination start in later gestation, but predominantly occur postnatally, and cerebral connectivity constantly changes throughout the lifespan. Disruptions during any stage of fetal neural development, whether from genetic defects or acquired causes (such as hemorrhage, infection, or ischemia), are associated with cerebral abnormalities and variable neurologic dysfunction such as motor dysfunction, cognitive deficits, and epilepsy.

NEURULATION AND FORMATION OF THE PRIMARY BRAIN VESICLES AND VENTRICULAR ZONE At the end of the second week after conception, the embryo has a two-layered configuration comprised of the epiblast and hypoblast. The embryo will be transformed into a three-layered structure composed of the endoderm, mesoderm, and ectoderm through a process called gastrulation (Stiles and Jernigan, 2010). The first step of gastrulation begins at embryonic day (E) 13 and is characterized by the appearance of a slit-like opening along the caudal midline the epiblast layer called the primitive streak (Fig. 4.1). Epiblast cells detach from

Fig. 4.1. Schematic representation of gastrulation and neural tube formation. The dorsal view of the embryo is shown on the left panel at different developmental stages. At the end of the second week of gestation (E15) the embryo consists of two layers, the epiblast (orange) and hypoblast (wheat). The primitive streak, a slit-like opening along the caudal midline of the epiblast layer, is visible by E18. Epiblast cells detach from the upper layer of the embryo and migrate toward and then through the primitive streak and further migrate toward the rostral end of the embryo. This give rise to the appearance of rostral and caudal regions at E20 that clearly define the anterior neuropore (at the rostral end) and the posterior neuropore (at the caudal end) by E22. A cross-section of the embryo is shown on the right panel. By the end of the third week, the embryo is transformed into a three-layered structure in a process collectively known as gastrulation; the hypoblast and epiblast layers are replaced by the newly formed endodermal and ectodermal layers, respectively, and the novel mesoderm layer lying between the two. Neural progenitor cells are located along the rostral–caudal midline of the upper layer, forming the neural plate. A flexible rod-shaped structure consisting of mesoderm cells, called the notochord, lies below the neural plate, playing a crucial role in developmental pattering. The first sign of neural tube development is the appearance of two ridges along the two sides of the neural plate, containing neural progenitor cells (at E19). The two ridges rise and fold inward giving rise to the neural groove that fuses forming the neural tube. Simultaneously, neural crest cells, the precursor of melanocytes, craniofacial cartilage and bone, smooth muscle, peripheral and enteric neurons, and glia, originate from these ridges and migrate throughout the embryo in a rostrocaudal wave.

NEUROGENESIS, NEURONAL MIGRATION, AND AXON GUIDANCE the upper layer of the embryo and migrate toward and, then through, the primitive streak in a process called ingression. By the end of the third week, the hypoblast and epiblast layers are replaced by the newly formed endodermal and ectodermal layer, respectively, and the novel mesoderm layer lying between the two. The CNS is derived from the neuroectoderm. In the mesoderm, the notochord defines the longitudinal axis of the embryo and induces the overlying ectodermal cells to form the neural plate (Greene and Copp, 2009) composed of the neural progenitor cells (NPCs) (Fig. 4.1). Neurulation, which refers to the formation of the neural tube, begins around E19 when two ridges appear along the two sides of the neural plate. A population of multipotent cells, called the neural crest cells, originate from these ridges and migrate throughout the embryo in a rostrocaudal wave giving rise to melanocytes, craniofacial cartilage and bone, smooth muscle, peripheral and enteric neurons, and glia (Huang and SaintJeannet, 2004). Fusion of the neural tube begins at its center and proceeds simultaneously in the rostral and caudal directions, with the anterior neuropore (at the rostral end) and the posterior neuropore (at the caudal end) closing on E25 and E27, respectively (Fig. 4.1). The NPCs in the most rostral region of the neural tube will give rise to the brain, while more caudally positioned cells will give rise to the hindbrain and spinal column. Just before neural tube closure, the anterior end of the tube begins to expand forming the three primary brain vesicles (Ishikawa et al., 2012). The most anterior brain vesicle is called the “prosencephalon” and is the embryonic precursor of the forebrain. The middle vesicle is the “mesencephalon,” the precursor of midbrain structures, and the most posterior is the “rhombencephalon,” which will become the hindbrain. These three segments further subdivide to form five secondary brain vesicles by the

27

end of the embryonic period. The prosencephalon divides into the “telencephalon” (precursor to the cerebral hemispheres) and the “diencephalon” (precursor to the thalamus and hypothalamus), and the rhombencephalon divides into the “metencephalon” (precursor to the pons and cerebellum) and “myelencephalon” (precursor to the medulla) (Fig. 4.2). Neural tube closure results in the trapping of amniotic fluid within the central canal, eventually forming the primitive ventricular system of the brain. The neural epithelium located around the ventricular system of the neural tube is called the ventricular zone (VZ) (Haubensak et al., 2004). Three membranous layers cover the CNS: the dura mater (derived from the surrounding mesenchyme), the arachnoid, and pia mater (both derived from the neural crest cells).

NEUROGENESIS AND NEURONAL PROLIFERATION Neurogenesis is the process through which neural stem cells (NSCs), or more generally NPCs, generate new neurons. This process occurs not only during the embryonic and perinatal stages but also throughout life.

DYNAMICS OF NEUROGENESIS The VZ is also known as the proliferative zone as it contains the first pool of NPCs that acquire the identity of radial glial cells (RGCs) at the onset of neurogenesis (Paridaen and Huttner, 2014). RGCs are highly polarized cells with their apical membrane exposed to the ventricle and their basal side in contact with the pial basal membrane (Miyata et al., 2014; Chou et al., 2018). RGCs undergo successive rounds of symmetrical cell division from gestational week 4 to 5, resulting in exponential proliferation in the number of progenitor cells, and increased thickness of the VZ surface area

Fig. 4.2. Schematic illustration of primary and secondary brain vesicles formation. With the closure of the neural tube, the anterior end of the tube begins to expand forming the three primary brain vesicles at E28. The most anterior brain vesicle is called the “prosencephalon” and is the embryonic precursor of the forebrain. The middle vesicle, the “mesencephalon,” is the precursor of midbrain structures, and the most posterior is the “rhombencephalon,” which will become the hindbrain. These three segments further subdivide to form five secondary brain vesicles by the end of E49. The prosencephalon divides into the “telencephalon” (precursor to the cerebral hemispheres) and the “diencephalon” (precursor to the thalamus and hypothalamus and the optic cup that gives rise to the retina), and the rhombencephalon divides into the “metencephalon” (precursor to the pons and cerebellum) and “myelencephalon” (precursor to the medulla).

28

A. ACCOGLI ET AL.

Fig. 4.3. Schematic illustration of interkinetic nuclear migration and symmetric versus asymmetric cell division. (A) Early neurogenesis is characterized by interkinetic nuclear migration, an oscillation process during which radial glial cells move back and forth between the basal and apical surface of the ventricular zone during the cell cycle, symmetrically dividing into two daughter cells at the apical surface. (B) Spindle orientation determines whether a division is symmetric, giving rise to two identical NSC daughter cells, or asymmetric, producing a NSC and a BP cell. BP, basal progenitor; NSC, neural stem cell.

(Huttner and Kosodo, 2005). This early neurogenesis is characterized by interkinetic nuclear migration (INM), an oscillation process during which RGCs move back and forth between the basal and apical surface of the VZ during the cell cycle using actomyosin and microtubule motor proteins (Taverna and Huttner, 2010; Miyata et al., 2014) (Fig. 4.3A). Following symmetrical division into two new progenitor cells at the apical surface, the two daughter cells return to their basal position. At the beginning of the 5th week after conception, cell division begins to shift from a symmetrical to an asymmetrical mode (Wodarz and Huttner, 2003), resulting in the production of one neural progenitor and either a postmitotic neuron or an intermediate progenitor (IP) cell (Pontious et al., 2008). Accumulation of basal progenitor cells creates a further compartment above the VZ called the subventricular zone (SVZ) (Ortega et al., 2018). The new progenitor cells remain in the VZ/SVZ and continue to divide, exponentially expanding the number of progenitor cells, whereas the postmitotic neurons migrate to form the developing neocortex (Fig. 4.4). Together these two zones give rise to all excitatory projection neurons (also known as pyramidal neurons) within the telencephalon and subsequently glial cells. Orientation of the cleavage plane determines whether a division is symmetric or asymmetric. In symmetric divisions of NSCs, the cleavage plane is oriented perpendicular to ventricular surface, while during asymmetric cell divisions, the cleavage plane is oblique (Fig. 4.3B) (Paridaen and Huttner, 2014). Consequently, during symmetric stem cell divisions, the basal process is equally split between the daughter cells, while during asymmetric cell division it is inherited by the daughter

cell that retains self-renewing proprieties (Matsuzaki and Shitamukai, 2015). Tight regulation of the spatial organization of the mitotic spindle through the precise assembly and interaction of the centrosome with planar cell polarity components (Lancaster and Knoblich, 2012) are critical for the determination of the plane of cytokinesis, and any abnormalities in this process result in abnormal cell division (Peyre and Morin, 2012). The three glial cell types that play major roles during neurogenesis are the RGCs, oligodendrocytes, and astrocytes. In addition to generating neurons, RGCs also generate progenitors of oligodendrocytes and astrocytes. Oligodendrocytes provide structural support and produce the myelin sheath insulating neuronal axons (analogous to Schwann cells in the peripheral nervous system). The first oligodendrocyte progenitor cells form in the VZ/SVZ around week 10, and their formation peaks around week 15 (Simons and Nave, 2015). Astrocytes develop during the second half of gestation and eventually become the most abundant cell type in the human brain, with a 1.4:1 astrocyte to neuron ratio (Nedergaard et al., 2003). Although, for many years, astrocytes were thought to have only a supportive role, increasing evidence in the last 2 decades has underscored their primary role in a wide range of complex and essential functions in the human CNS, including synaptic transmission and information processing by neuronal circuits, maintenance of the blood–brain barrier, and homeostasis of CNS metabolism (Sofroniew and Vinters, 2010). Microglia, known as the brain’s resident macrophages, are the guests of the CNS. They are derived from erythromyeloid progenitors that reach the CNS early in

NEUROGENESIS, NEURONAL MIGRATION, AND AXON GUIDANCE

29

Fig. 4.4. Schematic representation of the neuronal differentiation and migration process. During early neurogenesis, progenitor cells switch from symmetric to asymmetric cell division, giving rise to either a postmitotic neuron or an intermediate progenitor. At the onset of radial migration, early-born cells located immediately above the VZ, forms the PP that subsequently, around the 7th week, will be split into the MZ and the SP by successive waves of migrating neurons. Radial migration typically requires radial glial cells that provide a scaffold along with new born neurons anchor and migrate in an inside-out fashion to form a six-layered cortex; the earliest born neurons will become the innermost layer 6 while the last born neurons will give rise to the outer layer 2. The MZ remains the most external layer of neurons (layer 1). The IZ consists of tangentially migrating interneurons that together with the axons of other radially migrated neurons will form the white matter. IZ, intermediate zone; MZ, marginal zone; PP, preplate; SP, subplate; SVZ, subventricular zone; VZ, ventricular zone.

development at the onset of neurogenesis colonizing (proliferation/migration) the entire brain and spinal cord (Blumcke et al., 2011).

REGULATION OF NEUROGENESIS Regulation of early neurogenesis relies on the dynamic interaction of several molecular mechanisms that have been traditionally classified as intrinsic and extrinsic factors, depending on whether the origin of the molecular signaling is intracellular or mediated by components of the extracellular microenvironment, respectively (Navarro Quiroz et al., 2018). Alterations in mechanisms controlling cell proliferation result in two main types of cerebral disorders: (1) the primary microcephalies, characterized by a brain size of at least three standard deviations (SD) below normal, are associated with mutations in genes important in neural cell proliferation and spindle orientation regulation (such as ASPM, WDR62, and CDK5RAP2 (Higgins et al., 2010;

Lizarraga et al., 2010; Chen et al., 2014)) and (2) the macrocephalies, characterized by a large brain of over three SD, are associated with mutations in genes resulting in over proliferation of NPCs (such as genes of PI3KAKT-mTORpathway (Dobyns and Mirzaa, 2019), PTEN (Kato et al., 2018), GLI3 (Biesecker, 2008), and PTCH1 (Sim et al., 2018)).

INTRINSIC FACTORS Transcriptional regulators A large number of transcription factors have been implicated in the proliferation and differentiation of neural progenitors, ultimately influencing cell fate via the precise modulation of gene expression. Pax6 has a broad expression in the CNS and has a pivotal role in RGC proliferation, spindle orientation, and progression of cortical neurogenesis (Sansom et al., 2009). Pax6 exerts its function through the induction of multiple target

30

A. ACCOGLI ET AL.

genes, including other transcription factors (e.g., HMGA2 and bHLHs) (Nieto et al., 2001; Asami et al., 2011), signaling molecules (e.g., FABP7) (Arai et al., 2005), and cell cycle regulators (e.g., CDK4) (Manuel et al., 2015). Transcription factors, such as Tlx (Shi et al., 2004), the Sox protein family (Sarkar and Hochedlinger, 2013), Dlx2 (Brill et al., 2008), and Emx2 (Galli et al., 2002), play essential roles in the switch between symmetric and asymmetric cell division. Neurogenin-2 (Ngn2), AP2gamma, and Insm1 are involved in the generation of basal progenitors via the induction of Tbr2 from RGCs (Nieto et al., 2001; Duggan et al., 2008; Pinto et al., 2009). Other factors, such as Foxg1 and Ascl1, act subsequently to promote the division of basal progenitor cells in the cortex and ventral telencephalon (Siegenthaler et al., 2008; Castro et al., 2011). The crucial role of several transcription factors in early brain development has been elucidated by studies in mice mutant for Pax6 (Tuoc et al., 2009), Foxg1 (Mall et al., 2017), Tlx (Roy et al., 2004; Hsu et al., 2015), LIM/Homeobox 2(Lhx2) (Hsu et al., 2015), and Arx (Friocourt et al., 2008), showing defects in progenitor divisions and brain growth. Of note, mutations in the FOXG1 (MIM #613454) (Vegas et al., 2018) and ARX (MIM #300215) (Shoubridge et al., 2010) genes in humans are associated with neurodevelopmental disorders with structural cortical anomalies (Siegenthaler et al., 2008; Castro et al., 2011).

Epigenetic modifications and post-translational regulation Epigenetic modifications, such as DNA methylation and histone modifications, are crucial regulators of spatial and temporal gene expression during neurogenesis (Yao and Jin, 2014; Stricker and Gotz, 2018). For example, the methyl-CpG binding domain 1 protein (MBD1) promotes methylation of FGF-2 promoters, resulting in the downregulation of FGF-2 expression, in turn allowing hippocampal progenitor cells to undergo neuronal differentiation (Li et al., 2008). MeCP2, another methyl-CpG binding domain protein, which is highly expressed in the CNS and linked to Rett syndrome (MIM #312750), is crucial for maintaining neuronal identity by promoting methylation of the glial fibrillary acidic protein (GFAP) promoter in neuronal precursors (Forbes-Lorman et al., 2014). Further, the ablation of Ring1b or Ezh2 (enhancer of zeste homolog 2), members of the Polycomb repressive complex that silences genes through chromatin modifications (Corley and Kroll, 2015), results in an increased rate of neurogenesis, lengthening the neurogenic period and delaying the onset of astrogliogenesis (Hirabayashi et al., 2009). In addition, several post-transcriptional regulation mechanisms including alternative splicing, miRNAs,

and long noncoding RNA have been implicated in the control of neurogenesis (Raj et al., 2011; Bian et al., 2013; Fatica and Bozzoni, 2014; Stappert et al., 2015; Chen et al., 2016; D’Haene et al., 2016; Su et al., 2018). Members of fragile X mental retardation protein family (FMRP, FXR1 and FXR2), for example, regulate translation of mRNA involved in neurogenesis (Khalfallah et al., 2017).

EXTRINSIC FACTORS Signaling pathways Several signaling pathways that are activated through extracellular ligands have a crucial role during neurogenesis, notably the Notch, Wnt, Shh (Sonic hedgehog), and Fgf (fibroblast growth factor) pathways (Paridaen and Huttner, 2014). The Notch pathway activates Hes genes, which in turn repress neurogenic genes such as Neurogenin, and thus inhibit differentiative divisions (Bray and Bernard, 2010). In addition, Wnt/b catenin signaling promotes symmetric RGC division, delaying IP formation during early neurogenesis (Wrobel et al., 2007). Subsequently, Wnt activity promotes IP formation and neuronal differentiation through overexpression of Wnt3a (Munji et al., 2011). Shh promotes symmetric proliferation division through transcription of Hes1 (Dave et al., 2011), whereas decreasing Shh activity mediates the switch from symmetric to asymmetric neurogenic division by changing RGC cell cycle kinetics (Saade et al., 2013). Likewise, FGF is able to both promote symmetric division of RGCs through the downstream activation of Notch signaling (Dave et al., 2011) and inhibit neurogenesis in favor of cortical progenitors proliferation by regulating the duration of the cell cycle (Lukaszewicz et al., 2002). Other signaling pathways such as retinoic acid and bone morphogenetic proteins have been involved in multiple cellular processes during neurodevelopment, including proliferation and differentiation of NPCs (Lukaszewicz et al., 2002; Rash et al., 2011; Janesick et al., 2015), polarity, and dendritogenesis of cortical migrating neurons (Janesick et al., 2015; Jovanovic et al., 2018; Mosher and Schaffer, 2018).

Environmental cues Several environmental cues in proximity to the developing neural tube wall exert an important effect on NPCs (Kazanis et al., 2008). Blood vessels are crucial in regulating stem cell niches during neocortical development (Karakatsani et al., 2019). Embryonic cerebrospinal fluid (CSF) plays an active role during brain and spine development via the oriented transport of several chemical cues (Carnicero et al., 2013; Alonso et al., 2017). The meninges control proliferation and migration of NPCs

NEUROGENESIS, NEURONAL MIGRATION, AND AXON GUIDANCE through the release of diffusible factors (Siegenthaler and Pleasure, 2011). In addition, nonneuronal cells, such as microglia have been shown to regulate the maintenance of the RGC population (Antony et al., 2011; Reemst et al., 2016).

Neurotransmitters An expanding literature supports an important role of neurotransmitters in neurogenesis. Glutamate is involved in many processes including promotion and inhibition of neuronal production (Jansson and Akerman, 2014), whereas the inhibitory neurotransmitter GABA mostly reduces NPC proliferation and neuronal migration (Wang and Kriegstein, 2009). Moreover, dopamine has been shown to affect proliferation rate, with decreased extracellular dopamine levels reducing progenitor cells proliferation in the SVZ (Baker et al., 2004), and increased levels promoting neuroblast proliferation in the adult SVZ (Van Kampen et al., 2004).

ADULT NEUROGENESIS The mature mammalian brain has long been thought to be a structurally rigid, static organ. This long-held view has been completely overturned in recent decades. We know now that new neurons and glia cells are continuously generated from NSCs in specialized niches of the brain throughout life, namely in the SVZ of the lateral ventricles and in the subgranular zone (SGZ) of the hippocampal dentate gyrus (Ming and Song, 2011). Neuronal progenitors are preserved as quiescent, nondividing cells (qNSCs) in both regions until they are activated in response to different physiologic stimuli and start to produce transient amplifying progenitors, which in turn undergo rapid cell divisions into immature neuroblasts (NBs). NBs in the adult SVZ migrate radially and rostrally until they reach the olfactory bulb where, eventually, they differentiate into mature interneurons, whereas NBs in the adult SGZ migrate radially into the granule cell layer to differentiate into dentate granule neurons (Nakafuku and Del Aguila, 2019). Here, they are integrated into hippocampal circuitry, forming synapsis with other neurons (Toda and Gage, 2018). A recent study suggests that adult neurogenesis also occurs in a third cerebral region, the human striatum (Ernst et al., 2014). Similar to neurogenesis during fetal development, adult neurogenesis is tightly regulated by many extrinsic and intrinsic factors (Aimone et al., 2014; Esteves et al., 2019). Once generated and integrated into neuronal circuits of the adult human brain, the newborn neurons play important functions in neural plasticity and underscore crucial roles in regenerative and restorative responses after brain injury and in neurodegenerative diseases (Kempermann et al., 2018; Lucassen et al., 2019).

31

However, many unanswered questions, such as how active and widespread adult neurogenesis is, remain to be elucidated.

PROGRAMMED CELL DEATH IN NEURONAL DEVELOPMENT Developmental cell death occurs in both mitotic neuronal precursor and postmitotic differentiated neuronal populations, involving several programmed cell death mechanisms such as apoptosis and autophagy (Dekkers et al., 2013; Fricker et al., 2018). Removal of excessive neurons that are not completely integrated into the local circuits is crucial to ensure the proper maturation and homeostatic functions of neuronal networks in the developing brain. This mechanism is not cell autonomous, rather it relies on multiple interactions with neighboring neuronal and glial cells. For example, the programmed cell death of specific neurons influences initial microglia cell entry into the CNS (Reemst et al., 2016). Like neurogenesis, programmed cell death is highly regulated by several intrinsic pathways that together with external signals from the brain microenvironment frame the cell survival/death decision for a determinate neuron (Yamaguchi and Miura, 2015). While some transcription factor cascades confer susceptibility to cell death, others promote neuronal survival by activating transcription of prosurvival genes and/or inhibiting proapoptotic genes (Liu and Greene, 2001; Ninkovic et al., 2010). Furthermore, recent evidence suggests that, in addition to common neuronal prosurvival signaling mechanisms, there are a variety of “neuron type-specific” prosurvival constituents that might help different types of neurons adapt for survival in certain brain regions (Pfisterer and Khodosevich, 2017).

NEURONAL MIGRATION Newly born projection neurons reach their target location in the developing cortex either using radial migration (where neurons follow a trajectory that is perpendicular to the ventricular surface moving alongside radial glial fibers) or tangential migration (where neurons move in trajectories that are parallel to the ventricular surface and orthogonal to the radial glia fibers) (Marin and Rubenstein, 2003; Ayala et al., 2007) (Fig. 4.5). Glutamatergic (excitatory) cortical projection neurons are generated in the VZ and reach their final position via radial migration (Rakic, 2007), whereas cortical interneurons, which are GABAergic (inhibitory), reach the cerebral cortex through a tangential migration (Corbin et al., 2001; Marin and Rubenstein, 2001). Migratory processes are highly dynamic, and neurons can switch between radial and tangential migration (Hatanaka et al., 2016).

32

A. ACCOGLI ET AL. migrating cells, cells of the former (PP), and long-range axons (Bystron et al., 2008) that will become the white matter (Fig. 4.4).

RADIAL MIGRATION: MULTIPOLAR MIGRATION, SOMAL TRANSLOCATION AND LOCOMOTION

Fig. 4.5. Schematic representation of radial and tangential migration. There are two modes of neuronal migration; in radial migration, newly born projection neurons follow a trajectory that is perpendicular to the ventricular surface moving alongside radial glial fibers (red), whereas in tangential migration neurons move in trajectories that are parallel to the ventricular surface and orthogonal to the radial glia fibers (blue). Radial migration is typically adopted by glutamatergic (excitatory) cortical neurons generated in the VZ, while tangential migration is used by cortical interneurons, which are GABAergic (inhibitory), to reach their final destination in the developing cortex. LGE, lateral ganglionic eminence; MGE, medial ganglionic eminence; VZ, ventricular zone.

At the onset of radial migration, early-born cells settle immediately above the VZ, forming the preplate (PP) (Bystron et al., 2008), a transient structure of various cell types that will either die or migrate tangentially to become inhibitory interneurons in the cortex. Subsequently, around the 7th week, the migrating neurons give rise to the cortical plate (CP) that splits the PP into the superficial marginal zone (MZ) and the subplate (SP) (Marin and Rubenstein, 2003; Ayala et al., 2007) (Fig. 4.4). Successive waves of migrating neurons occupy cortical layers above the subplate in an insideout fashion to form a six-layered cortex; the earliest born neurons are destined to become the innermost layer 6 while the last born neurons will become the outer layer 2. The MZ remains the most external layer of neurons (layer 1). The compartment of cells between the SV–SVZ and the cortical plate is known as the intermediate zone (IZ); it consists of radially and tangentially

Radial migration occurs in three different modes: (1) multipolar migration, (2) somal translocation, and (3) locomotion (Nadarajah and Parnavelas, 2002; Marin and Rubenstein, 2003; Tabata and Nakajima, 2003; Kriegstein and Noctor, 2004; Ohtaka-Maruyama and Okado, 2015). Newly generated neurons exhibit multipolar morphology in the lower part of the SVZ/IZ and move toward the SP via random directional movements (multipolar migration) (Tabata and Nakajima, 2003). Then, they convert into bipolar cells, enter the CP, and migrate toward the pial surface adopting different strategies; the earliest neurons migrate relatively short distances via somal translocation, whereas the neurons that migrate along longer paths with the subsequent thickening of the cortex use locomotion mode (Nadarajah and Parnavelas, 2002; Nadarajah, 2003; Tabata and Nakajima, 2003). Neuronal locomotion requires the highly polarized RGCs to provide an instructive scaffold for neuronal migration (Marin and Rubenstein, 2003; Nulty et al., 2015). At the cellular level, locomotion is divided into two phases: first, the centrosome advances toward the leading process and subsequently the nucleus moves in the direction of the centrosome, translocating into the leading process (Tsai and Gleeson, 2005). This biphasic process is called nucleokinesis, and relies on highly orchestrated cytoskeletal rearrangements that allow a coordinated coupling between centrosome translocation and subsequent forward nuclear movement in a typical saltatory pattern (Schaar and McConnell, 2005; Sakakibara et al., 2013). Mutations in genes encoding cytoskeleton components disrupt neuronal migration, resulting in various neural migration disorders (Lasser et al., 2018). For example, the Pafah1b1 (formerly Lis1) protein is part of the microtubule organizing center of the centrosome and interacts with dynein via Ndel1, regulating nucleokinesis by promoting centrosome-nucleus coupling (Moon et al., 2014). PAFAH1B1 haploinsufficiency causes lissencephaly (Reiner and Sapir, 2013), a congenital brain malformation characterized by an abnormally smooth and thickened cerebral cortex that lacks normal cerebral convolutions and organized layers (Wynshaw-Boris et al., 2010). X-linked mutations in Doublecortin (DCX), a microtubule-associated protein involved in nucleokinesis (Kappeler et al., 2006), cause lissencephaly in males and subcortical band

NEUROGENESIS, NEURONAL MIGRATION, AND AXON GUIDANCE heterotopia in females (MIM #300067) (Hehr et al., 1993). Of note, some types of lissencephalies are associated with microcephaly (microlissencephalies), as observed in the context of PAFAH1B1 (MIM #607432), TUBA1A (MIM #611603) and WDR62 (MIM #604317) mutations (Bilguvar et al., 2010; Reiner and Sapir, 2013; FalletBianco et al., 2014), pointing to converging mechanisms in centrosome assembly, neuronal proliferation, nucleokinesis, and migration of postmitotic neurons.

REGULATION OF NEURONAL MIGRATION The precise and coordinated migration of neural cells relies on the normal function and regulation of a myriad intrinsic and extrinsic factors, whose disruption can result in various disorders such as brain malformations, epilepsy, and neuropsychiatric diseases (Stouffer et al., 2016; Fukuda and Yanagi, 2017; Roberts, 2018).

TANGENTIAL MIGRATION

INTRINSIC FACTORS

This mode of migration is a strategy by which different neurons generated from spatially and molecularly distinct sites can converge, forming appropriate neural circuits within the cortical plate. Although it was previously believed that only GABAergic cortical interneurons migrate tangentially from the subplial ganglionic eminences in the CP, it is now clear that some populations of glutamatergic neurons also, including Cajal–Retzius cells, subplate neurons, and cortical plate transient neurons, are able to migrate tangentially, playing a critical role in assembling neural circuits in the developing cerebral cortex (Barber and Pierani, 2016).

Transcriptional, post-transcriptional, and epigenetic regulation of neural migration

TERMINAL TRANSLOCATION Neurons migrating by locomotion switch to somal translocation during the final stages of their migration, right after they detach from the RGC fiber and anchor to the MZ for terminal translocation (Nadarajah et al., 2001). It is necessary for migrating neurons to receive stop signals to avoid overmigration. Several molecules such as L1-CAM (Tonosaki et al., 2014), N-cadherin, and Reelin are important regulators of this process. In particular, Rab7-dependent lysosomal degradation of N-cadherin at the cell soma is a critical requirement for the terminal translocation (Kawauchi et al., 2010). The reelin-Dab1-Rap1-cadherin signaling pathway is essential for completion of inside-out lamination at the final stage of radial migration and for terminal translocation (Franco et al., 2011). Impairment of this complex regulating system may cause neuronal overmigration into the meninges, leading to cobblestone-like malformations with a defect in the basement membrane integrity. For instance, bi-allelic mutations in ADGRG1 (MIM #606854) and COL3A1 (MIM #618343), that encode the G protein-coupled receptor 56 (GPR56) and its ligand (type III collagen), respectively, have been associated with cobblestonelike brain malformation, uncovering the importance of Collagen III (ColIII)-GPR56 signaling for pial basement membrane integrity.

33

Many transcription factors that control neurogenesis also have critical roles in neuronal migration (Chedotal and Rijli, 2009; Kwan et al., 2012). Ngn2 is important in transitioning neurons from multipolar to bipolar cells through inhibition of RhoA activity, which regulates actin cytoskeleton (Heng et al., 2008). Moreover, Ngn2 induces expression of the transcriptional repressor RP58 (Xiang et al., 2012) a regulator of cortical development, neuronal differentiation, migration, and neurite outgrowth (Ohtaka-Maruyama et al., 2013). The downstream effect of some transcriptional factors are spatially restricted, as observed for Sox5, responsible for the correct migration of the deep layer neurons (Kwan et al., 2008; Lai et al., 2008) and POU3F2 and POU3F3 that regulate migration of projection neurons specifically destined to form layers 2–5 of the developing neocortex (Kwan et al., 2012). Several transcription factors known to play a crucial role in neural migration, including Arx (Friocourt et al., 2006), Foxg1 (Shen et al., 2019), and the AFF (AF4/FMR2) family (Moore et al., 2014), have been linked to neurodevelopmental disorders in humans. Other key transcription factors for radial migration include Dlx1/2, Scratch2, and the transcriptional repressor REST (Cobos et al., 2007; Mandel et al., 2011; Paul et al., 2014). Although the current understanding of post-transcriptional regulation of neuronal migration is limited, cumulative evidence supports the role of miRNAs in regulating a multitude of transcripts (Rago et al., 2014; Volvert et al., 2014; Rajman and Schratt, 2017), ultimately controlling the spatial and temporal expression of many key neural migration genes. Recently, alternative splicing (Gao and Godbout, 2013; Dhananjaya et al., 2018; Su et al., 2018) and epigenetic mechanisms, such as S-nitrosylation of histone deacetylase 2 (Nott et al., 2013) and histone methyltransferase Ezh2 (Di Meglio et al., 2013; Zhao et al., 2015) have emerged as further important regulators of neural migration.

34

A. ACCOGLI ET AL.

Small GTP binding proteins, protein kinases Small GTP binding proteins, also known as the Ras superfamily, function as molecular switches in many cellular processes including cell proliferation, membrane trafficking, cytoskeletal organization, and cell migration (Lundquist, 2006; Reiner and Lundquist, 2018). Several small GTP binding proteins play a crucial role in radial migration (Govek et al., 2011; Shah and Puschel, 2014). Consequently, a broad range of neurodevelopmental disorders are linked to the small GTP binding protein family, including intellectual disability (e.g., OPHN1, MIM #300486), periventricular nodular heterotopia (e.g., ARFGEF2, MIM #608097), and epilepsy (e.g., ARHGEF9, MIM #300607) (Sheen et al., 2004). Protein kinases are other key cellular regulators that mediate protein phosphorylation of downstream pathways, involved in many cellular functions including neural migration (Ohshima, 2014). Among these, CDK5 acts as a master kinase for neural development as it phosphorylates Dixdc1, modulating microtubule dynamics (Singh et al., 2010). Of note, CDK5 mutations in humans have been associated with an autosomal recessive type of lissencephaly (MIM #616342) (Magen et al., 2015). Classical microtubule-associated proteins (MAPs) are other kinase proteins that play integral roles in maintaining dynamic microtubules to ensure proper neuronal migration through phosphorylation of downstream effectors such as tau, MAP2/4/6, and Dcx (Sapir et al., 2008; Ramkumar et al., 2018). The pseudokinase Ste20-related kinase adaptor a (STRADa) binds to and activates LKB1, a further molecule involved in neuronal migration (Asada et al., 2007). Of note, mutations in STRADa gene have been associated with an autosomal recessive neurodevelopmental disorder, characterized by polyhydramnios, megalencephaly, and epilepsy (PMSE, MIM #611087) (Puffenberger et al., 2007).

EXTRINSIC FACTORS Extracellular matrix proteins During migration, neurons receive important signals from the surrounding environment. Among these, Reelin, a large secreted extracellular matrix glycoprotein, plays a major role in radial neuronal migration. Once secreted by Cajal–Retzius cells, a transient class of neurons in the marginal zone, Reelin activates signaling cascades after binding to the VLDLR/ApoER2 receptor expressed at the membrane of the RGC basal process and thus regulates cytoskeleton organization, nuclear movement, and cell adhesion of migrating neurons (Chai and Frotscher, 2016; Hirota and Nakajima, 2017; Santana and Marzolo, 2017). In Reelin-deficient

mice, neurons do not reach their prospective final position, resulting in a reversed laminar structure of the cortical plate (Landrieu and Goffinet, 1981). Notably, mutations in REELIN (RELN, MIM #257320) and its receptor VLDLR (MIM #224050) are associated with lissencephaly and cerebellar hypoplasia (Valence et al., 2016).

Cell–cell-adhesion molecules The discovery of the locomotion mode of neuronal migration has led to the identification of several celladhesion molecules that support scaffold cell-dependent migration (Kawauchi, 2012). N-cadherin, one of the most studied cell-adhesion molecules of the CNS, is known to mediate radial neural migration in the developing cerebral cortex (Horn et al., 2018) as shown by impaired attachment of migrating neurons to the radial glial fibers in cdh2 / mice (Shikanai et al., 2011). N-cadherin is also required for the somal translocation mode of neuronal migration of early-born neurons (Franco et al., 2011) and it has been implicated in the tangential migration of cortical interneurons (Luccardini et al., 2013; Luccardini et al., 2015). Moreover, N-cadherin regulates the transition from multipolar to bipolar neurons under the control of Reelin and Rap1 (Jossin and Cooper, 2011). Other cell-adhesion molecules (Solecki, 2012), such as Connexin 43 (Cx43) (Matsuuchi and Naus, 2013), Cx26 (Andressen et al., 2005; Elias et al., 2007; Valiente et al., 2011), and beta-1 integrin (Andressen et al., 2005) control neuronal migration.

Others: Signaling pathways and axonal guidance molecules Additional extracellular cues activating retinoic acid (Crandall et al., 2011), Wnt (Bocchi et al., 2017), and GSK-3 signaling (Morgan-Smith et al., 2014) are crucial to ensure proper neural migration. Furthermore, a large number of secreted extracellular molecules involved in axon guidance (i.e., Slits, netrins, semaphorins; see following text) have also been implicated in neural migration (Van Battum et al., 2015; Kim et al., 2019).

FOLDING AND GYRIFICATION OF HUMAN CORTEX Once the majority of cerebral cortical neurons have completed migration from the VZ to the outer layers by the gestational weeks 16–20, the folding process takes place and extends until the end of pregnancy and the early neonatal period (Sun and Hevner, 2014). The expansion of the mammalian cerebral cortex during embryonic development is a complex and tightly regulated process

NEUROGENESIS, NEURONAL MIGRATION, AND AXON GUIDANCE consisting of the development of an intricate pattern of convex folds (gyri) and valleys (sulci) that allows to attain a large surface area relative to the brain volume. Pattern of folds and fissures are very well conserved among individuals of the same species (Borrell and Reillo, 2012) and highly concordant in twins (Hasan et al., 2011), suggesting strong underlying genetic regulation. The mechanisms driving folding of the cerebral cortex remain incompletely understood. Leading hypotheses implicate the following mechanisms: (i) differential tangential growth with expansion of the cortex at a rate greater than adjacent subcortical layers induces folding by a mechanical instability; (ii) spatiotemporal patterns in migrating neurons cause more neurons to proliferate, reach the cortex, and expand in some regions (gyri) more than others (sulci); and (iii) axonal tension, generated as a result of growth difference between upper and lower cortical layers, induce surface buckling (Van Essen, 1997). Current evidence suggests that gyrification is due to combined mechanisms, in which genetically determined spatial–temporal patterns determine the locations of primary folds, and subsequent tangential growth of the CP induces mechanical instability to propagate primary and higher-order folds (Garcia et al., 2018).

AXON GUIDANCE AND PATHFINDING Aside from cell division and migration, the formation of connections and networks are central to normal brain development and function (Nishikimi et al., 2013; Chilton and Guthrie, 2017; Chedotal, 2019). Important axonal tracts include the corpus callosum, which interconnects the right and left cerebral hemispheres, the motor corticospinal tracts, the sensory posterior columns and spinothalamic tract, the oculomotor system, and the multiple other axonal projections connecting cortical and subcortical brain regions. Axons project through considerable distance to their targets in a precise and highly coordinated process, regulated by growth cone motility, guidance cues, and underlying signaling pathways (Ye et al., 2019). The growth cone is a highly motile structure, which is located at the tip of an axon, sensing environmental changes and in turn enabling axons to migrate accurately to their target. Specialized receptor proteins at the growth cone cell surface detect long- and short-range repellent and attractant axon guidance cues that trigger intracellular signaling cascades, which induce axon steering through changes in the growth cone cytoskeleton (Ye et al., 2019) (Fig. 4.6). Hence, actin and microtubules in the cytoskeleton of the growth cone function as an essential scaffold along which organelles, vesicles,

35

and proteins are trafficked ensuring correct axonal pathfinding (Lowery and Van Vactor, 2009; Gomez and Letourneau, 2014; Kahn and Baas, 2016). The Ephrins/Eph receptors, Netrins/DCC, and Unc5, Semaphorins/Neuropilins and Plexins, Slits/Robos and RGM/Neogenin are the canonical axon guidance ligand/receptor protein families (Van Battum et al., 2015; Ye et al., 2019). Other important axon guidance molecules include neural cell-adhesion molecules (Cadherins, L1, L2/HNK-1), neurotrophic factors, morphogens, small GTPases proteins, and lipids (Ye et al., 2019). This relatively small number of guidance cues cooperate to shape the massive complexity of neuronal networks, requiring mechanisms that are far more complex than simple repulsive–attractive forces; indeed, while certain signaling cascades function in parallel, other cascades’ crosstalk and fine-tuning relies on complex synergistic, hierarchical, and permissive guidance cues relationships (Morales and Kania, 2017). The netrins are large soluble proteins produced by the VZ and floor plate, which can function as both attractants (via interaction with DCC receptor (Welniarz et al., 2017)) and repellents (through interaction with UNC family (Colamarino and Tessier-Lavigne, 1995; Hong et al., 1999; Boyer and Gupton, 2018)). Netrin/DCC plays a critical role in midline axonal guidance and commissural formation. Of note, DCC and Netrin-1 mutations in humans have been associated with congenital mirror movements (MIM #157600), which are involuntary movements on one side of the body that mirror voluntary movements on the opposite side (Cincotta and Ziemann, 2008), reflecting aberrant neuronal wiring and axon guidance (MIM #618264) (Meneret et al., 2017). In addition, DCC variants can cause corpus callosum agenesis (MIM #217990) (Marsh et al., 2018). Madd-2 is another molecule that functions along with DCC in netrin-mediated axon attraction and branching (Hao et al., 2010). Of note, MID1, the homologous of MADD2, is associated with Opitz syndrome (MIM #300000) (Alexander et al., 2010), a neurodevelopmental disorder displaying corpus callosum agenesis and other midline defects. Furthermore, TUBB3, the most dynamic b-tubulin isoform in neurons, was revealed to directly interact with DCC and promote netrin-1-mediated microtubule dynamics in guiding commissural axons (Qu et al., 2013) while its interaction with UNC5C is involved in netrin-1 mediated axonal repulsion (Shao et al., 2019). Mutations in TUBB3 cause congenital fibrosis of extraocular muscles (CFEOM3A, MIM #600638), a disorder of oculomotor nerve innervation due to impairment in axon growth and guidance of ocular motor nerves (MacKinnon et al., 2014). The Slit proteins (Slit1, Slit2, and Slit3) are secreted repellents that bind to receptors from the roundabout (Robo1–Robo4) family (Ypsilanti and Chedotal, 2014).

36

A. ACCOGLI ET AL.

Fig. 4.6. Schematic illustration of the highly dynamic mechanism of axon guidance. (A) Actin and microtubules of growth cones cytoskeleton function as an essential scaffold along which organelles, vesicles and proteins are trafficked (B). Specialized receptor proteins at the growth cone cell surface detect long- and short-range repellent ( ) and attractant (+) axon guidance cues that trigger intracellular signaling cascades, inducing axon steering through changes in the growth cone cytoskeleton (C).

Slit proteins are known to play a major role in the control of the midline crossing of axons, acting as a chemorepulsive midline cue (Blockus and Chedotal, 2016). While Slit1 and Slit3 prevent midline recrossing of postcrossing axons through interaction with their receptors Robo1 and Robo2 (Kim et al., 2014), Robo3 does not bind Slit but rather directly interacts with the cytoplasmic tail of DCC to attenuate netrin-1-DCC signaling (Zelina et al., 2014). ROBO1 mutations in humans have been associated with a neurodevelopmental disorder presenting with absence of transverse pontine fibers and thinning of the anterior commissure and corpus callosum (Calloni et al., 2017). ROBO3 mutations are associated with an axon guidance disorder, namely horizontal gaze palsy with progressive scoliosis (MIM #607313), typically characterized by the absence of decussating axons in the pons and medulla (Jen et al., 2004).

CONCLUSION In this chapter, we have tried to highlight some of the main developmental processes and signaling mechanisms underlying neurogenesis, neuronal migration, and axon guidance. In the last decades, research has shed light on many aspects of these complex and dynamic processes that involve genetic, environmental, biochemical, and physical factors. Enhanced understanding of normal human neurodevelopment is critical in deciphering mechanisms responsible for neurodevelopmental

disorders such as epilepsy, autism spectrum disorders, and schizophrenia, and in guiding the development of effective and targeted therapeutic strategies.

REFERENCES Aimone JB, Li Y, Lee SW et al. (2014). Regulation and function of adult neurogenesis: from genes to cognition. Physiol Rev 94: 991–1026. Alexander M, Selman G, Seetharaman A et al. (2010). MADD2, a homolog of the Opitz syndrome protein MID1, regulates guidance to the midline through UNC-40 in Caenorhabditis elegans. Dev Cell 18: 961–972. Alonso MI, Lamus F, Carnicero E et al. (2017). Embryonic cerebrospinal fluid increases neurogenic activity in the brain ventricular-subventricular zone of adult mice. Front Neuroanat 11: 124. Andressen C, Adrian S, Fassler R et al. (2005). The contribution of beta1 integrins to neuronal migration and differentiation depends on extracellular matrix molecules. Eur J Cell Biol 84: 973–982. Antony JM, Paquin A, Nutt SL et al. (2011). Endogenous microglia regulate development of embryonic cortical precursor cells. J Neurosci Res 89: 286–298. Arai Y, Funatsu N, Numayama-Tsuruta K et al. (2005). Role of Fabp7, a downstream gene of Pax6, in the maintenance of neuroepithelial cells during early embryonic development of the rat cortex. J Neurosci 25: 9752–9761. Asada N, Sanada K, Fukada Y (2007). LKB1 regulates neuronal migration and neuronal differentiation in the developing neocortex through centrosomal positioning. J Neurosci 27: 11769–11775.

NEUROGENESIS, NEURONAL MIGRATION, AND AXON GUIDANCE Asami M, Pilz GA, Ninkovic J et al. (2011). The role of Pax6 in regulating the orientation and mode of cell division of progenitors in the mouse cerebral cortex. Development 138: 5067–5078. Ayala R, Shu T, Tsai LH (2007). Trekking across the brain: the journey of neuronal migration. Cell 128: 29–43. Azevedo FA, Carvalho LR, Grinberg LT et al. (2009). Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J Comp Neurol 513: 532–541. Baker SA, Baker KA, Hagg T (2004). Dopaminergic nigrostriatal projections regulate neural precursor proliferation in the adult mouse subventricular zone. Eur J Neurosci 20: 575–579. Barber M, Pierani A (2016). Tangential migration of glutamatergic neurons and cortical patterning during development: lessons from Cajal-Retzius cells. Dev Neurobiol 76: 847–881. Bian S, Xu TL, Sun T (2013). Tuning the cell fate of neurons and glia by microRNAs. Curr Opin Neurobiol 23: 928–934. Biesecker LG (2008). The Greig cephalopolysyndactyly syndrome. Orphanet J Rare Dis 3: 10. Bilguvar K, Ozturk AK, Louvi A et al. (2010). Whole-exome sequencing identifies recessive WDR62 mutations in severe brain malformations. Nature 467: 207–210. Blockus H, Chedotal A (2016). Slit-Robo signaling. Development 143: 3037–3044. Blumcke I, Thom M, Aronica E et al. (2011). The clinicopathologic spectrum of focal cortical dysplasias: a consensus classification proposed by an ad hoc Task Force of the ILAE Diagnostic Methods Commission. Epilepsia 52: 158–174. Bocchi R, Egervari K, Carol-Perdiguer L et al. (2017). Perturbed Wnt signaling leads to neuronal migration delay, altered interhemispheric connections and impaired social behavior. Nat Commun 8: 1158. Borrell V, Reillo I (2012). Emerging roles of neural stem cells in cerebral cortex development and evolution. Dev Neurobiol 72: 955–971. Boyer NP, Gupton SL (2018). Revisiting netrin-1: one who guides (axons). Front Cell Neurosci 12: 221. Bray S, Bernard F (2010). Notch targets and their regulation. Curr Top Dev Biol 92: 253–275. Brill MS, Snapyan M, Wohlfrom H et al. (2008). A dlx2- and pax6-dependent transcriptional code for periglomerular neuron specification in the adult olfactory bulb. J Neurosci 28: 6439–6452. Bystron I, Blakemore C, Rakic P (2008). Development of the human cerebral cortex: Boulder Committee revisited. Nat Rev Neurosci 9: 110–122. Calloni SF, Cohen JS, Meoded A et al. (2017). Compound heterozygous variants in ROBO1 cause a neurodevelopmental disorder with absence of transverse pontine fibers and thinning of the anterior commissure and corpus callosum. Pediatr Neurol 70: 70–74. Carnicero E, Alonso MI, Carretero R et al. (2013). Embryonic cerebrospinal fluid activates neurogenesis of neural precursors within the subventricular zone of the adult mouse brain. Cells Tissues Organs 198: 398–404.

37

Castro DS, Martynoga B, Parras C et al. (2011). A novel function of the proneural factor Ascl1 in progenitor proliferation identified by genome-wide characterization of its targets. Genes Dev 25: 930–945. Chai X, Frotscher M (2016). How does Reelin signaling regulate the neuronal cytoskeleton during migration? Neurogenesis 3: e1242455. Chedotal A (2019). Roles of axon guidance molecules in neuronal wiring in the developing spinal cord. Nat Rev Neurosci 20: 380–396. Chedotal A, Rijli FM (2009). Transcriptional regulation of tangential neuronal migration in the developing forebrain. Curr Opin Neurobiol 19: 139–145. Chen JF, Zhang Y, Wilde J et al. (2014). Microcephaly disease gene Wdr62 regulates mitotic progression of embryonic neural stem cells and brain size. Nat Commun 5: 3885. Chen L, Feng P, Zhu X et al. (2016). Long non-coding RNA Malat1 promotes neurite outgrowth through activation of ERK/MAPK signalling pathway in N2a cells. J Cell Mol Med 20: 2102–2110. Chilton JK, Guthrie S (2017). Axons get ahead: insights into axon guidance and congenital cranial dysinnervation disorders. Dev Neurobiol 77: 861–875. Chou FS, Li R, Wang PS (2018). Molecular components and polarity of radial glial cells during cerebral cortex development. Cell Mol Life Sci 75: 1027–1041. Cincotta M, Ziemann U (2008). Neurophysiology of unimanual motor control and mirror movements. Clin Neurophysiol 119: 744–762. Cobos I, Borello U, Rubenstein JL (2007). Dlx transcription factors promote migration through repression of axon and dendrite growth. Neuron 54: 873–888. Colamarino SA, Tessier-Lavigne M (1995). The axonal chemoattractant netrin-1 is also a chemorepellent for trochlear motor axons. Cell 81: 621–629. Corbin JG, Nery S, Fishell G (2001). Telencephalic cells take a tangent: non-radial migration in the mammalian forebrain. Nat Neurosci 4: 1177–1182. Corley M, Kroll KL (2015). The roles and regulation of polycomb complexes in neural development. Cell Tissue Res 359: 65–85. Crandall JE, Goodman T, McCarthy DM et al. (2011). Retinoic acid influences neuronal migration from the ganglionic eminence to the cerebral cortex. J Neurochem 119: 723–735. Dave RK, Ellis T, Toumpas MC et al. (2011). Sonic hedgehog and notch signaling can cooperate to regulate neurogenic divisions of neocortical progenitors. PLoS One 6: e14680. Dekkers MP, Nikoletopoulou V, Barde YA (2013). Cell biology in neuroscience: death of developing neurons: new insights and implications for connectivity. J Cell Biol 203: 385–393. D’Haene E, Jacobs EZ, Volders PJ et al. (2016). Identification of long non-coding RNAs involved in neuronal development and intellectual disability. Sci Rep 6: 28396. Dhananjaya D, Hung KY, Tarn WY (2018). RBM4 modulates radial migration via alternative splicing of dab1 during cortex development. Mol Cell Biol 38: e00007-18.

38

A. ACCOGLI ET AL.

Di Meglio T, Kratochwil CF, Vilain N et al. (2013). Ezh2 orchestrates topographic migration and connectivity of mouse precerebellar neurons. Science 339: 204–207. Dobyns WB, Mirzaa GM (2019). Megalencephaly syndromes associated with mutations of core components of the PI3K-AKT-MTOR pathway: PIK3CA, PIK3R2, AKT3, and MTOR. Am J Med Genet C Semin Med Genet 181: 582–590. Duggan A, Madathany T, de Castro SC et al. (2008). Transient expression of the conserved zinc finger gene INSM1 in progenitors and nascent neurons throughout embryonic and adult neurogenesis. J Comp Neurol 507: 1497–1520. Elias LA, Wang DD, Kriegstein AR (2007). Gap junction adhesion is necessary for radial migration in the neocortex. Nature 448: 901–907. Ernst A, Alkass K, Bernard S et al. (2014). Neurogenesis in the striatum of the adult human brain. Cell 156: 1072–1083. Esteves M, Serra-Almeida C, Saraiva C et al. (2019). New insights into the regulatory roles of microRNAs in adult neurogenesis. Curr Opin Pharmacol 50: 38–45. Fallet-Bianco C, Laquerriere A, Poirier K et al. (2014). Mutations in tubulin genes are frequent causes of various foetal malformations of cortical development including microlissencephaly. Acta Neuropathol Commun 2: 69. Fatica A, Bozzoni I (2014). Long non-coding RNAs: new players in cell differentiation and development. Nat Rev Genet 15: 7–21. Forbes-Lorman RM, Kurian JR, Auger AP (2014). MeCP2 regulates GFAP expression within the developing brain. Brain Res 1543: 151–158. Franco SJ, Martinez-Garay I, Gil-Sanz C et al. (2011). Reelin regulates cadherin function via Dab1/Rap1 to control neuronal migration and lamination in the neocortex. Neuron 69: 482–497. Fricker M, Tolkovsky AM, Borutaite V et al. (2018). Neuronal cell death. Physiol Rev 98: 813–880. Friocourt G, Poirier K, Rakic S et al. (2006). The role of ARX in cortical development. Eur J Neurosci 23: 869–876. Friocourt G, Kanatani S, Tabata H et al. (2008). Cellautonomous roles of ARX in cell proliferation and neuronal migration during corticogenesis. J Neurosci 28: 5794–5805. Fukuda T, Yanagi S (2017). Psychiatric behaviors associated with cytoskeletal defects in radial neuronal migration. Cell Mol Life Sci 74: 3533–3552. Galli R, Fiocco R, De Filippis L et al. (2002). Emx2 regulates the proliferation of stem cells of the adult mammalian central nervous system. Development 129: 1633–1644. Gao Z, Godbout R (2013). Reelin-disabled-1 signaling in neuronal migration: splicing takes the stage. Cell Mol Life Sci 70: 2319–2329. Garcia KE, Kroenke CD, Bayly PV (2018). Mechanics of cortical folding: stress, growth and stability. Philos Trans R Soc Lond B Biol Sci 373: 20170321. Gomez TM, Letourneau PC (2014). Actin dynamics in growth cone motility and navigation. J Neurochem 129: 221–234. Govek EE, Hatten ME, Van Aelst L (2011). The role of Rho GTPase proteins in CNS neuronal migration. Dev Neurobiol 71: 528–553.

Greene ND, Copp AJ (2009). Development of the vertebrate central nervous system: formation of the neural tube. Prenat Diagn 29: 303–311. Hao JC, Adler CE, Mebane L et al. (2010). The tripartite motif protein MADD-2 functions with the receptor UNC-40 (DCC) in Netrin-mediated axon attraction and branching. Dev Cell 18: 950–960. Hasan A, McIntosh AM, Droese UA et al. (2011). Prefrontal cortex gyrification index in twins: an MRI study. Eur Arch Psychiatry Clin Neurosci 261: 459–465. Hatanaka Y, Zhu Y, Torigoe M et al. (2016). From migration to settlement: the pathways, migration modes and dynamics of neurons in the developing brain. Proc Jpn Acad Ser B Phys Biol Sci 92: 1–19. Haubensak W, Attardo A, Denk W et al. (2004). Neurons arise in the basal neuroepithelium of the early mammalian telencephalon: a major site of neurogenesis. Proc Natl Acad Sci U S A 101: 3196–3201. Hehr U, Uyanik G, Aigner L et al. (1993). DCX-related disorders. In: MP Adam, HH Ardinger, RA Pagon, SE Wallace, LJH Bean, K Stephens, A Amemiya (Eds.), GeneReviews(R). University of Washington, Seattle University of Washington, Seattle, WA. GeneReviews is a registered trademark of the University of Washington, Seattle. All rights reserved. Heng JI, Nguyen L, Castro DS et al. (2008). Neurogenin 2 controls cortical neuron migration through regulation of Rnd2. Nature 455: 114–118. Herculano-Houzel S (2009). The human brain in numbers: a linearly scaled-up primate brain. Front Hum Neurosci 3: 31. Higgins J, Midgley C, Bergh AM et al. (2010). Human ASPM participates in spindle organisation, spindle orientation and cytokinesis. BMC Cell Biol 11: 85. Hirabayashi Y, Suzki N, Tsuboi M et al. (2009). Polycomb limits the neurogenic competence of neural precursor cells to promote astrogenic fate transition. Neuron 63: 600–613. Hirota Y, Nakajima K (2017). Control of neuronal migration and aggregation by Reelin signaling in the developing cerebral cortex. Front Cell Dev Biol 5: 40. Hong K, Hinck L, Nishiyama M et al. (1999). A ligand-gated association between cytoplasmic domains of UNC5 and DCC family receptors converts netrin-induced growth cone attraction to repulsion. Cell 97: 927–941. Horn Z, Behesti H, Hatten ME (2018). N-cadherin provides a cis and trans ligand for astrotactin that functions in glialguided neuronal migration. Proc Natl Acad Sci U S A 115: 10556–10563. Hsu LC, Nam S, Cui Y et al. (2015). Lhx2 regulates the timing of beta-catenin-dependent cortical neurogenesis. Proc Natl Acad Sci U S A 112: 12199–12204. Huang X, Saint-Jeannet JP (2004). Induction of the neural crest and the opportunities of life on the edge. Dev Biol 275: 1–11. Huttner WB, Kosodo Y (2005). Symmetric versus asymmetric cell division during neurogenesis in the developing vertebrate central nervous system. Curr Opin Cell Biol 17: 648–657.

NEUROGENESIS, NEURONAL MIGRATION, AND AXON GUIDANCE Ishikawa Y, Yamamoto N, Yoshimoto M et al. (2012). The primary brain vesicles revisited: are the three primary vesicles (forebrain/midbrain/hindbrain) universal in vertebrates? Brain Behav Evol 79: 75–83. Janesick A, Wu SC, Blumberg B (2015). Retinoic acid signaling and neuronal differentiation. Cell Mol Life Sci 72: 1559–1576. Jansson LC, Akerman KE (2014). The role of glutamate and its receptors in the proliferation, migration, differentiation and survival of neural progenitor cells. J Neural Transm 121: 819–836. Jen JC, Chan WM, Bosley TM et al. (2004). Mutations in a human ROBO gene disrupt hindbrain axon pathway crossing and morphogenesis. Science 304: 1509–1513. Jossin Y, Cooper JA (2011). Reelin, Rap1 and N-cadherin orient the migration of multipolar neurons in the developing neocortex. Nat Neurosci 14: 697–703. Jovanovic VM, Salti A, Tilleman H et al. (2018). BMP/SMAD pathway promotes neurogenesis of midbrain dopaminergic neurons in vivo and in human induced pluripotent and neural stem cells. J Neurosci 38: 1662–1676. Kahn OI, Baas PW (2016). Microtubules and growth cones: motors drive the turn. Trends Neurosci 39: 433–440. Kappeler C, Saillour Y, Baudoin JP et al. (2006). Branching and nucleokinesis defects in migrating interneurons derived from doublecortin knockout mice. Hum Mol Genet 15: 1387–1400. Karakatsani A, Shah B, Ruiz de Almodovar C (2019). Blood vessels as regulators of neural stem cell properties. Front Mol Neurosci 12: 85. Kato K, Mizuno S, Inaba M et al. (2018). Distinctive facies, macrocephaly, and developmental delay are signs of a PTEN mutation in childhood. Brain Dev 40: 678–684. Kawauchi T (2012). Cell adhesion and its endocytic regulation in cell migration during neural development and cancer metastasis. Int J Mol Sci 13: 4564–4590. Kawauchi T, Sekine K, Shikanai M et al. (2010). Rab GTPases-dependent endocytic pathways regulate neuronal migration and maturation through N-cadherin trafficking. Neuron 67: 588–602. Kazanis I, Lathia J, Moss L et al. (2008). The neural stem cell microenvironment. In: StemBook, Harvard Stem Cell Institute, Cambridge, MA. Copyright (c) 2008. Kempermann G, Gage FH, Aigner L et al. (2018). Human adult neurogenesis: evidence and remaining questions. Cell Stem Cell 23: 25–30. Khalfallah O, Jarjat M, Davidovic L et al. (2017). Depletion of the fragile X mental retardation protein in embryonic stem cells alters the kinetics of neurogenesis. Stem Cells 35: 374–385. Kim M, Farmer WT, Bjorke B et al. (2014). Pioneer midbrain longitudinal axons navigate using a balance of Netrin attraction and Slit repulsion. Neural Dev 9: 17. Kim M, Bjorke B, Mastick GS (2019). Motor neuron migration and positioning mechanisms: new roles for guidance cues. Semin Cell Dev Biol 85: 78–83.

39

Kriegstein AR, Noctor SC (2004). Patterns of neuronal migration in the embryonic cortex. Trends Neurosci 27: 392–399. Kwan KY, Lam MM, Krsnik Z et al. (2008). SOX5 postmitotically regulates migration, postmigratory differentiation, and projections of subplate and deep-layer neocortical neurons. Proc Natl Acad Sci U S A 105: 16021–16026. Kwan KY, Sestan N, Anton ES (2012). Transcriptional co-regulation of neuronal migration and laminar identity in the neocortex. Development 139: 1535–1546. Lai T, Jabaudon D, Molyneaux BJ et al. (2008). SOX5 controls the sequential generation of distinct corticofugal neuron subtypes. Neuron 57: 232–247. Lancaster MA, Knoblich JA (2012). Spindle orientation in mammalian cerebral cortical development. Curr Opin Neurobiol 22: 737–746. Landrieu P, Goffinet A (1981). Inverted pyramidal neurons and their axons in the neocortex of reeler mutant mice. Cell Tissue Res 218: 293–301. Lasser M, Tiber J, Lowery LA (2018). The role of the microtubule cytoskeleton in neurodevelopmental disorders. Front Cell Neurosci 12: 165. Li X, Barkho BZ, Luo Y et al. (2008). Epigenetic regulation of the stem cell mitogen Fgf-2 by Mbd1 in adult neural stem/ progenitor cells. J Biol Chem 283: 27644–27652. Liu DX, Greene LA (2001). Regulation of neuronal survival and death by E2F-dependent gene repression and derepression. Neuron 32: 425–438. Lizarraga SB, Margossian SP, Harris MH et al. (2010). Cdk5rap2 regulates centrosome function and chromosome segregation in neuronal progenitors. Development 137: 1907–1917. Lowery LA, Van Vactor D (2009). The trip of the tip: understanding the growth cone machinery. Nat Rev Mol Cell Biol 10: 332–343. Lucassen PJ, Fitzsimons CP, Salta E et al. (2019). Adult neurogenesis, human after all (again): classic, optimized, and future approaches. Behav Brain Res 381: 112458. Luccardini C, Hennekinne L, Viou L et al. (2013). N-cadherin sustains motility and polarity of future cortical interneurons during tangential migration. J Neurosci 33: 18149–18160. Luccardini C, Leclech C, Viou L et al. (2015). Cortical interneurons migrating on a pure substrate of N-cadherin exhibit fast synchronous centrosomal and nuclear movements and reduced ciliogenesis. Front Cell Neurosci 9: 286. Lukaszewicz A, Savatier P, Cortay V et al. (2002). Contrasting effects of basic fibroblast growth factor and neurotrophin 3 on cell cycle kinetics of mouse cortical stem cells. J Neurosci 22: 6610–6622. Lundquist EA (2006). Small GTPases. WormBook 1–18. MacKinnon S, Oystreck DT, Andrews C et al. (2014). Diagnostic distinctions and genetic analysis of patients diagnosed with moebius syndrome. Ophthalmology 121: 1461–1468. Magen D, Ofir A, Berger L et al. (2015). Autosomal recessive lissencephaly with cerebellar hypoplasia is associated with a loss-of-function mutation in CDK5. Hum Genet 134: 305–314.

40

A. ACCOGLI ET AL.

Mall EM, Herrmann D, Niemann H (2017). Murine pluripotent stem cells with a homozygous knockout of Foxg1 show reduced differentiation towards cortical progenitors in vitro. Stem Cell Res 25: 50–60. Mandel G, Fiondella CG, Covey MV et al. (2011). Repressor element 1 silencing transcription factor (REST) controls radial migration and temporal neuronal specification during neocortical development. Proc Natl Acad Sci U S A 108: 16789–16794. Manuel MN, Mi D, Mason JO et al. (2015). Regulation of cerebral cortical neurogenesis by the Pax6 transcription factor. Front Cell Neurosci 9: 70. Marin O, Rubenstein JL (2001). A long, remarkable journey: tangential migration in the telencephalon. Nat Rev Neurosci 2: 780–790. Marin O, Rubenstein JL (2003). Cell migration in the forebrain. Annu Rev Neurosci 26: 441–483. Marsh APL, Edwards TJ, Galea C et al. (2018). DCC mutation update: ongenital mirror movements, isolated agenesis of the corpus callosum, and developmental split brain syndrome. Hum Mutat 39: 23–39. Matsuuchi L, Naus CC (2013). Gap junction proteins on the move: connexins, the cytoskeleton and migration. Biochim Biophys Acta 1828: 94–108. Matsuzaki F, Shitamukai A (2015). Cell division modes and cleavage planes of neural progenitors during mammalian cortical development. Cold Spring Harb Perspect Biol 7: a015719. Meneret A, Franz EA, Trouillard O et al. (2017). Mutations in the netrin-1 gene cause congenital mirror movements. J Clin Invest 127: 3923–3936. Ming GL, Song H (2011). Adult neurogenesis in the mammalian brain: significant answers and significant questions. Neuron 70: 687–702. Miyata T, Okamoto M, Shinoda T et al. (2014). Interkinetic nuclear migration generates and opposes ventricular-zone crowding: insight into tissue mechanics. Front Cell Neurosci 8: 473. Moon HM, Youn YH, Pemble H et al. (2014). LIS1 controls mitosis and mitotic spindle organization via the LIS1NDEL1-dynein complex. Hum Mol Genet 23: 449–466. Moore JM, Oliver PL, Finelli MJ et al. (2014). Laf4/Aff3, a gene involved in intellectual disability, is required for cellular migration in the mouse cerebral cortex. PLoS One 9: e105933. Morales D, Kania A (2017). Cooperation and crosstalk in axon guidance cue integration: additivity, synergy, and fine-tuning in combinatorial signaling. Dev Neurobiol 77: 891–904. Morgan-Smith M, Wu Y, Zhu X et al. (2014). GSK-3 signaling in developing cortical neurons is essential for radial migration and dendritic orientation. eLife 3: e02663. Mosher KI, Schaffer DV (2018). Proliferation versus differentiation: redefining retinoic acid’s role. Stem Cell Rep 10: 1673–1675. Munji RN, Choe Y, Li G et al. (2011). Wnt signaling regulates neuronal differentiation of cortical intermediate progenitors. J Neurosci 31: 1676–1687.

Nadarajah B (2003). Radial glia and somal translocation of radial neurons in the developing cerebral cortex. Glia 43: 33–36. Nadarajah B, Parnavelas JG (2002). Modes of neuronal migration in the developing cerebral cortex. Nat Rev Neurosci 3: 423–432. Nadarajah B, Brunstrom JE, Grutzendler J et al. (2001). Two modes of radial migration in early development of the cerebral cortex. Nat Neurosci 4: 143–150. Nakafuku M, Del Aguila A (2019). Developmental dynamics of neurogenesis and gliogenesis in the postnatal mammalian brain in health and disease: historical and future perspectives. Wiley Interdiscip Rev Dev Biol 9: e369. Navarro Quiroz E, Navarro Quiroz R, Ahmad M et al. (2018). Cell signaling in neuronal stem cells. Cell 7: 75. Nedergaard M, Ransom B, Goldman SA (2003). New roles for astrocytes: redefining the functional architecture of the brain. Trends Neurosci 26: 523–530. Nieto M, Schuurmans C, Britz O et al. (2001). Neural bHLH genes control the neuronal versus glial fate decision in cortical progenitors. Neuron 29: 401–413. Ninkovic J, Pinto L, Petricca S et al. (2010). The transcription factor Pax6 regulates survival of dopaminergic olfactory bulb neurons via crystallin alphaA. Neuron 68: 682–694. Nishikimi M, Oishi K, Nakajima K (2013). Axon guidance mechanisms for establishment of callosal connections. Neural Plast 2013: 149060. Nott A, Nitarska J, Veenvliet JV et al. (2013). S-nitrosylation of HDAC2 regulates the expression of the chromatinremodeling factor Brm during radial neuron migration. Proc Natl Acad Sci U S A 110: 3113–3118. Nulty J, Alsaffar M, Barry D (2015). Radial glial cells organize the central nervous system via microtubule dependant processes. Brain Res 1625: 171–179. Ohshima T (2014). Neuronal migration and protein kinases. Front Neurosci 8: 458. Ohtaka-Maruyama C, Okado H (2015). Molecular pathways underlying projection neuron production and migration during cerebral cortical development. Front Neurosci 9: 447. Ohtaka-Maruyama C, Hirai S, Miwa A et al. (2013). RP58 regulates the multipolar-bipolar transition of newborn neurons in the developing cerebral cortex. Cell Rep 3: 458–471. Ortega JA, Memi F, Radonjic N et al. (2018). The subventricular zone: a key player in human neocortical development. Neuroscientist 24: 156–170. Pakkenberg B, Pelvig D, Marner L et al. (2003). Aging and the human neocortex. Exp Gerontol 38: 95–99. Paridaen JT, Huttner WB (2014). Neurogenesis during development of the vertebrate central nervous system. EMBO Rep 15: 351–364. Paul V, Tonchev AB, Henningfeld KA et al. (2014). Scratch2 modulates neurogenesis and cell migration through antagonism of bHLH proteins in the developing neocortex. Cereb Cortex 24: 754–772. Peyre E, Morin X (2012). An oblique view on the role of spindle orientation in vertebrate neurogenesis. Dev Growth Differ 54: 287–305.

NEUROGENESIS, NEURONAL MIGRATION, AND AXON GUIDANCE Pfisterer U, Khodosevich K (2017). Neuronal survival in the brain: neuron type-specific mechanisms. Cell Death Dis 8: e2643. Pinto L, Drechsel D, Schmid MT et al. (2009). AP2gamma regulates basal progenitor fate in a region- and layer-specific manner in the developing cortex. Nat Neurosci 12: 1229–1237. Pontious A, Kowalczyk T, Englund C et al. (2008). Role of intermediate progenitor cells in cerebral cortex development. Dev Neurosci 30: 24–32. Puffenberger EG, Strauss KA, Ramsey KE et al. (2007). Polyhydramnios, megalencephaly and symptomatic epilepsy caused by a homozygous 7-kilobase deletion in LYK5. Brain 130: 1929–1941. Qu C, Dwyer T, Shao Q et al. (2013). Direct binding of TUBB3 with DCC couples netrin-1 signaling to intracellular microtubule dynamics in axon outgrowth and guidance. J Cell Sci 126: 3070–3081. Rago L, Beattie R, Taylor V et al. (2014). miR379-410 cluster miRNAs regulate neurogenesis and neuronal migration by fine-tuning N-cadherin. EMBO J 33: 906–920. Raj B, O’Hanlon D, Vessey JP et al. (2011). Cross-regulation between an alternative splicing activator and a transcription repressor controls neurogenesis. Mol Cell 43: 843–850. Rajman M, Schratt G (2017). MicroRNAs in neural development: from master regulators to fine-tuners. Development 144: 2310–2322. Rakic P (2007). The radial edifice of cortical architecture: from neuronal silhouettes to genetic engineering. Brain Res Rev 55: 204–219. Ramkumar A, Jong BY, Ori-McKenney KM (2018). ReMAPping the microtubule landscape: how phosphorylation dictates the activities of microtubule-associated proteins. Dev Dyn 247: 138–155. Rash BG, Lim HD, Breunig JJ et al. (2011). FGF signaling expands embryonic cortical surface area by regulating Notch-dependent neurogenesis. J Neurosci 31: 15604–15617. Reemst K, Noctor SC, Lucassen PJ et al. (2016). The indispensable roles of microglia and astrocytes during brain development. Front Hum Neurosci 10: 566. Reiner DJ, Lundquist EA (2018). Small GTPases. WormBook 2018: 1–65. Reiner O, Sapir T (2013). LIS1 functions in normal development and disease. Curr Opin Neurobiol 23: 951–956. Roberts B (2018). Neuronal migration disorders. Radiol Technol 89: 279–295. Roy K, Kuznicki K, Wu Q et al. (2004). The Tlx gene regulates the timing of neurogenesis in the cortex. J Neurosci 24: 8333–8345. Saade M, Gutierrez-Vallejo I, Le Dreau G et al. (2013). Sonic hedgehog signaling switches the mode of division in the developing nervous system. Cell Rep 4: 492–503. Sakakibara A, Ando R, Sapir T et al. (2013). Microtubule dynamics in neuronal morphogenesis. Open Biol 3: 130061. Sansom SN, Griffiths DS, Faedo A et al. (2009). The level of the transcription factor Pax6 is essential for controlling the

41

balance between neural stem cell self-renewal and neurogenesis. PLoS Genet 5: e1000511. Santana J, Marzolo MP (2017). The functions of Reelin in membrane trafficking and cytoskeletal dynamics: implications for neuronal migration, polarization and differentiation. Biochem J 474: 3137–3165. Sapir T, Sapoznik S, Levy T et al. (2008). Accurate balance of the polarity kinase MARK2/Par-1 is required for proper cortical neuronal migration. J Neurosci 28: 5710–5720. Sarkar A, Hochedlinger K (2013). The sox family of transcription factors: versatile regulators of stem and progenitor cell fate. Cell Stem Cell 12: 15–30. Schaar BT, McConnell SK (2005). Cytoskeletal coordination during neuronal migration. Proc Natl Acad Sci U S A 102: 13652–13657. Shah B, Puschel AW (2014). In vivo functions of small GTPases in neocortical development. Biol Chem 395: 465–476. Shao Q, Yang T, Huang H et al. (2019). Disease-associated mutations in human TUBB3 disturb netrin repulsive signaling. PLoS One 14: e0218811. Sheen VL, Ganesh VS, Topcu M et al. (2004). Mutations in ARFGEF2 implicate vesicle trafficking in neural progenitor proliferation and migration in the human cerebral cortex. Nat Genet 36: 69–76. Shen W, Ba R, Su Y et al. (2019). Foxg1 regulates the postnatal development of cortical interneurons. Cereb Cortex 29: 1547–1560. Shi Y, Chichung Lie D, Taupin P et al. (2004). Expression and function of orphan nuclear receptor TLX in adult neural stem cells. Nature 427: 78–83. Shikanai M, Nakajima K, Kawauchi T (2011). N-cadherin regulates radial glial fiber-dependent migration of cortical locomoting neurons. Commun Integr Biol 4: 326–330. Shoubridge C, Fullston T, Gecz J (2010). ARX spectrum disorders: making inroads into the molecular pathology. Hum Mutat 31: 889–900. Siegenthaler JA, Pleasure SJ (2011). We have got you ‘covered’: how the meninges control brain development. Curr Opin Genet Dev 21: 249–255. Siegenthaler JA, Tremper-Wells BA, Miller MW (2008). Foxg1 haploinsufficiency reduces the population of cortical intermediate progenitor cells: effect of increased p21 expression. Cereb Cortex 18: 1865–1875. Sim YC, Kim GH, Choi SW et al. (2018). Novel PTCH1 gene mutation in nevoid basal cell carcinoma syndrome. J Craniofac Surg 29: e252–e255. Simons M, Nave KA (2015). Oligodendrocytes: myelination and axonal support. Cold Spring Harb Perspect Biol 8: a020479. Singh KK, Ge X, Mao Y et al. (2010). Dixdc1 is a critical regulator of DISC1 and embryonic cortical development. Neuron 67: 33–48. Sofroniew MV, Vinters HV (2010). Astrocytes: biology and pathology. Acta Neuropathol 119: 7–35. Solecki DJ (2012). Sticky situations: recent advances in control of cell adhesion during neuronal migration. Curr Opin Neurobiol 22: 791–798.

42

A. ACCOGLI ET AL.

Stappert L, Roese-Koerner B, Brustle O (2015). The role of microRNAs in human neural stem cells, neuronal differentiation and subtype specification. Cell Tissue Res 359: 47–64. Stiles J, Jernigan TL (2010). The basics of brain development. Neuropsychol Rev 20: 327–348. Stouffer MA, Golden JA, Francis F (2016). Neuronal migration disorders: focus on the cytoskeleton and epilepsy. Neurobiol Dis 92: 18–45. Stricker SH, Gotz M (2018). DNA-methylation: master or slave of neural fate decisions? Front Neurosci 12: 5. Su CH, Dhananjaya D, Tarn WY (2018). Alternative splicing in neurogenesis and brain development. Front Mol Biosci 5: 12. Sun T, Hevner RF (2014). Growth and folding of the mammalian cerebral cortex: from molecules to malformations. Nat Rev Neurosci 15: 217–232. Tabata H, Nakajima K (2003). Multipolar migration: the third mode of radial neuronal migration in the developing cerebral cortex. J Neurosci 23: 9996–10001. Taverna E, Huttner WB (2010). Neural progenitor nuclei IN motion. Neuron 67: 906–914. Toda T, Gage FH (2018). Review: adult neurogenesis contributes to hippocampal plasticity. Cell Tissue Res 373: 693–709. Tonosaki M, Itoh K, Umekage M et al. (2014). L1cam is crucial for cell locomotion and terminal translocation of the Soma in radial migration during murine corticogenesis. PLoS One 9: e86186. Tsai LH, Gleeson JG (2005). Nucleokinesis in neuronal migration. Neuron 46: 383–388. Tuoc TC, Radyushkin K, Tonchev AB et al. (2009). Selective cortical layering abnormalities and behavioral deficits in cortex-specific Pax6 knock-out mice. J Neurosci 29: 8335–8349. Valence S, Garel C, Barth M et al. (2016). RELN and VLDLR mutations underlie two distinguishable clinico-radiological phenotypes. Clin Genet 90: 545–549. Valiente M, Ciceri G, Rico B et al. (2011). Focal adhesion kinase modulates radial glia-dependent neuronal migration through connexin-26. J Neurosci 31: 11678–11691. Van Battum EY, Brignani S, Pasterkamp RJ (2015). Axon guidance proteins in neurological disorders. Lancet Neurol 14: 532–546. Van Essen DC (1997). A tension-based theory of morphogenesis and compact wiring in the central nervous system. Nature 385: 313–318.

Van Kampen JM, Hagg T, Robertson HA (2004). Induction of neurogenesis in the adult rat subventricular zone and neostriatum following dopamine D3 receptor stimulation. Eur J Neurosci 19: 2377–2387. Vegas N, Cavallin M, Maillard C et al. (2018). Delineating FOXG1 syndrome: from congenital microcephaly to hyperkinetic encephalopathy. Neurol Genet 4: e281. Volvert ML, Prevot PP, Close P et al. (2014). MicroRNA targeting of CoREST controls polarization of migrating cortical neurons. Cell Rep 7: 1168–1183. Wang DD, Kriegstein AR (2009). Defining the role of GABA in cortical development. J Physiol 587: 1873–1879. Welniarz Q, Morel MP, Pourchet O et al. (2017). Non cell-autonomous role of DCC in the guidance of the corticospinal tract at the midline. Sci Rep 7: 410. Wodarz A, Huttner WB (2003). Asymmetric cell division during neurogenesis in Drosophila and vertebrates. Mech Dev 120: 1297–1309. Wrobel CN, Mutch CA, Swaminathan S et al. (2007). Persistent expression of stabilized beta-catenin delays maturation of radial glial cells into intermediate progenitors. Dev Biol 309: 285–297. Wynshaw-Boris A, Pramparo T, Youn YH et al. (2010). Lissencephaly: mechanistic insights from animal models and potential therapeutic strategies. Semin Cell Dev Biol 21: 823–830. Xiang C, Baubet V, Pal S et al. (2012). RP58/ZNF238 directly modulates proneurogenic gene levels and is required for neuronal differentiation and brain expansion. Cell Death Differ 19: 692–702. Yamaguchi Y, Miura M (2015). Programmed cell death in neurodevelopment. Dev Cell 32: 478–490. Yao B, Jin P (2014). Unlocking epigenetic codes in neurogenesis. Genes Dev 28: 1253–1271. Ye X, Qiu Y, Gao Y et al. (2019). A subtle network mediating axon guidance: intrinsic dynamic structure of growth cone, attractive and repulsive molecular cues, and the intermediate role of signaling pathways. Neural Plast 2019: 1719829. Ypsilanti AR, Chedotal A (2014). Roundabout receptors. Adv Neurobiol 8: 133–164. Zelina P, Blockus H, Zagar Y et al. (2014). Signaling switch of the axon guidance receptor Robo3 during vertebrate evolution. Neuron 84: 1258–1272. Zhao L, Li J, Ma Y et al. (2015). Ezh2 is involved in radial neuronal migration through regulating Reelin expression in cerebral cortex. Sci Rep 5: 15484.

Handbook of Clinical Neurology, Vol. 173 (3rd series) Neurocognitive Development: Normative Development A. Gallagher, C. Bulteau, D. Cohen and J.L. Michaud, Editors https://doi.org/10.1016/B978-0-444-64150-2.00005-8 Copyright © 2020 Elsevier B.V. All rights reserved

Chapter 5

Development of neuronal circuits: From synaptogenesis to synapse plasticity GRAZIELLA DI CRISTO1,2* AND BIDISHA CHATTOPADHYAYA1 1

Research Centre, Centre Hospitalier Universitaire Sainte-Justine, Universit
e de Montr
eal, Montr
eal, QC, Canada 2

Department of Neurosciences, Universit
e de Montr
eal, Montr
eal, QC, Canada

Abstract Optimal brain function critically hinges on the remarkably precise interconnections made among millions of neurons. These specialized interconnected neuronal junctions, termed synapses, are used for neuronal communication, whence the presynaptic neurons releases a specific neurotransmitter, which then binds to the appropriate protein receptor on the membrane of the postsynaptic neuron, activating and eliciting a response in this connected neuron. In this chapter, we discuss how synapses form and are modified as the brain matures. Genetic programs control most of the wiring in the brain, from allowing axons to choose where to target their synapses, to determining synapse identity. However, the final map of neuronal connectivity in the brain crucially relies on incoming sensory information during early childhood to strengthen and refine the preexisting synapses thus allowing both nature and nurture to shape the final structure and function of the nervous system (Fig. 5.1). Finally, we discuss how advances in the knowledge of basic mechanisms governing synapse formation and plasticity can shed light on the pathophysiology of neurodevelopmental disorders.

SYNAPSE FORMATION: A COMPLEX PROBLEM Synapse formation is a multistep process, during which a neuronal growth cone comes in contact with an appropriate target postsynaptic neuron, makes the decision to stop growing, and forms a presynaptic site. The postsynaptic target neuron, in parallel, forms a synapse-specific specialization accumulating the appropriate receptors and associated signaling molecules that will serve as the postsynaptic site (Fig. 5.2). In fact, both the pre- and the postsynaptic neuron generate many of the components needed to form a synapse well before they come into contact, and the formation of the first functional synapse can be quite fast. On the other hand, the final adult phenotype may be reached only weeks or months after the initial contact is established.

One of main barriers to studying synapses is that they are extremely small, ranging from half to a few microns in size, making them almost impossible to visualize with a light microscope, which makes the history of their discovery quite extraordinary. At the end of the 19th century, Camillo Golgi discovered a revolutionary method for staining nerve cells to visualize the basic components of the nervous system, which is still referred to as “Golgi staining.” Golgi staining allowed scientists to visualize nerve cells in various regions of the brain and the spinal cord, clearly distinguishing the soma, axons, and dendrites, for the first time. Golgi claimed to have observed an extremely dense and intricate network, composed of a web of intertwined branches of axons coming from different cell layers, in the gray matter. He proposed that his observations supported the reticular theory, formulated first by Josef von Gerlach in 1871, which postulated

*Correspondence to: Graziella Di Cristo, Ph.D., Centre de Recherche, CHU Sainte-Justine/Universit
e de Montr
eal, 3175, Chemin de la C^ote-Ste-Catherine, Montr
eal, QC, H3T 1C5, Canada. E-mail: [email protected]

44

G. DI CRISTO AND B. CHATTOPADHYAYA

Fig. 5.1. Diagram of neural circuit development. Genetic programs control most of the wiring in the brain, from neuron differentiation and migration to the selection of where axons choose to target their synapses. On the other hand, synapse formation and fine-tuning is likely an activity-dependent process relying on experience from birth until end of adolescence. Synapse restructuration or plasticity occurs throughout life, as we learn and form new memories.

Fig. 5.2. Synapse structure. Micrograph of a typical synapse between cultured hippocampal neurons. The presynaptic site shows numerous vesicles (yellow arrows), which are filled with the neurotransmitter (glutamate in this case), while the postsynaptic site is characterized by the presence of a postsynaptic density (red arrows) formed by neurotransmitter receptors and associated proteins. Scale bar: 200 nm. Adapted from Kaeser PS, Deng L, Wang Y et al. (2011). RIM proteins tether Ca2+ channels to presynaptic active zones via a direct PDZ-domain interaction. Cell 144 (2): 282–295. doi: 10.1016/j.cell.2010.12.029.

that the nervous system was composed of a diffuse, continuous, protoplasmic network of cells fused together. At the end of the 1880s, Cajal used Golgi’s labeling

microscope technique to amass a series of observations supporting the neuron theory, which postulates that the relationship between nerve cells was not one of continuity, but rather of contiguity, where brain cells are individual, separate cells (Cajal, 1906). Golgi, however, did not accept this theory, and a controversy arose between the two scientists that was not put to rest even after the rivals were both awarded the Nobel Prize in 1906 (LópezMuñoz et al., 2006). Charles Sherrington, winner of the Nobel Prize in Physiology or Medicine in 1932, postulated that the separation between nerve cells allows for a new form of intercellular communication, which we now know is chemical in nature, and started to refer to these special communication points between nerve cells as synapses (Foster and Sherrington, 1897). However, the field had to wait until the 1950s, for the advent of Electron Microscopy, to finally have visual proof of the existence of synapses between individual neurons (Fig. 5.2). The average mammalian neuron receives thousands of synapses along their dendrites and soma. The majority of these synapses release glutamate, an excitatory neurotransmitter, or gamma amino butyric acid (GABA), an inhibitory neurotransmitter. A minority of synapses release neuromodulatory neurotransmitters, such as dopamine, serotonin, or acetylcholine. For neurotransmission to be effective, the postsynaptic neuron needs to cluster specific receptors opposing the presynaptic terminal, depending on its identity. For example, glutamatergic receptors (such as NMDA, AMPA, kainate, or mGLURs) must be localized proximal to terminals that release glutamate, while GABAergic receptors (GABAA, GABAB) must be localized proximal to neuronal terminals releasing GABA. The presence of multiple receptors, which can bind to the same neurotransmitter, adds further complexity at the synapse. For example, GABA binding to GABAA receptors causes the direct opening of an ion channel, while its binding to the metabotropic GABAB receptors leads to the activation of intracellular cascades of second messenger signaling molecules. This simplified description of synaptic organization highlights many of the challenges faced by the nervous system during development. First, each growth cone must identify an appropriate membrane area on which to form a presynaptic site. This is not a trivial problem, since most neurons are highly polarized cells, with large and complex dendrites. Most glutamatergic presynapses are located on postsynaptic specializations localized along the dendrites and called dendritic spines (Fig. 5.3). GABAergic neurons, on the other hand, are very diverse in their synapse localization, some form synapses preferentially on the distal dendrites, while others synapse onto the soma and the proximal dendrites of their target cells (Fig. 5.3). There are also specialized GABAergic cells in the cortex called chandelier cells,

DEVELOPMENT OF NEURONAL CIRCUITS

Fig. 5.3. Different neurons form synapses on specific subcellular domains of a target cell. Schematic representation of subcellular synapse specificity of different classes of GABAergic interneurons in the cortex. GABAergic interneurons expressing the neuropeptide somatostatin (yellow) preferentially target distal dendrites of glutamatergic pyramidal cells (red), while interneurons expressing the calcium binding protein parvalbumin (green) target the soma and proximal dendrite of postsynaptic cells. An extremely fascinating case is represented by chandelier interneurons (light blue) that form synapses exclusively along the axon initial segment of projection neurons.

which form synapses exclusively on the axon initial segment of the postsynaptic cell, an amazing feat in itself, to converge on such a precise target in a neuropil crowded with so many cellular components and extracellular substrates. Following the choice of synapse location, suitable receptors have to be localized on the opposite site of a presynaptic terminal to stabilize the nascent synapse and establish its identity. In addition, there must be mechanisms in place to control the total number of synapses of each type (Glutamatergic, GABAergic, etc.) that can form on any given neuron. This number depends on the postsynaptic neuron, e.g., neurons in the medial nucleus of the trapezoid body (MNTB) receive only a single excitatory synapse, while a cortical pyramidal neuron receives over 10,000 synapses, and a Purkinje cell in the cerebellum can receive more than 200,000 synapses. In the next section, we describe the basic principles and mechanisms that govern synapse formation.

HOW DOES AN AXONAL TERMINAL CHOOSE ITS SYNAPSE LOCATION AND IDENTITY? The location of a synapse on a target cell and its distinctive structural and functional properties have a critical

45

impact on efficacy and the flow of information in a neural circuit. Once axons arrive in their target zone, such as in the correct cortical layer, they may form synapses preferentially with a specific cell type that resides in that zone, such as an excitatory glutamatergic neuron or an inhibitory GABAergic cell displaying cellular specificity. Neurons can also form synapses onto specific subcellular domains of a target cell, e.g., on the soma or on different dendritic compartments, such as the dendritic shaft or the dendritic spines with subcellular specificity. Synapses located near the cell soma or even at the axon initial segment, where spikes are initiated, have a greater influence on the decision of the postsynaptic cell to fire an action potential, while synapses located on distal dendrites produce small potentials that must be summated together to effectively change the membrane potential of the postsynaptic cells. Therefore, the formation of synapses at the appropriate, specific site is paramount for neural circuit organization and processing. Furthermore, in the final step of assembling functionally connected circuits, synapses formed by different types of presynaptic neurons are differentiated into structurally distinct synapse types, e.g., as explained earlier, glutamatergic receptors will be localized apposing glutamate releasing neurons (synaptic diversity). The molecular mechanisms that regulate wiring specificity and synaptic diversity in the vertebrate brain are slowly being deciphered. Classic work from Langley (1895) and Sperry (1963) on the regeneration of nerve fibers in the mature nervous system indicate that neurons can rewire in the circuit with remarkable specificity. Their work suggests the presence of “individual identification tags” on cells and fibers that allow neurons to distinguish one another and selectively connect to target cells by “specific chemical affinities”. To better understand this hypothesis, we could envision a system similar to that of a postal service on where to deliver a parcel, based on the presence of precise postal codes. The brain is characterized by a staggering richness and diversity of connections and brain cell types, which begs the question: if every different synapse type is identified by a different protein tag, or postal code, how many different proteins would the brain need to develop properly? The answer is way more than the number of proteins coded by our genome. This observation leads to the hypothesis that no single cue regulates subcellular location and specific synapse identity, but multiple distinct surface molecular tags act in various specific combinations thus creating a combinatorial code. Cell adhesion molecules are attractive candidates for such combinatorial recognition mechanisms. To regulate wiring specificity and synaptic diversity, cell adhesion proteins must meet specific criteria: they should (a) be expressed in distinct populations of neurons, (b) be

46

G. DI CRISTO AND B. CHATTOPADHYAYA

capable of interacting with their binding partners across two different cells (the post- and presynaptic sites), and (c) provide enough molecular diversity to confer cell type- and synapse type-specific identities. Studies in the last 20 years have identified several super families of cell surface proteins that meet these requirements, including the family of cadherins, protocadherins, leucine-rich repeat (LRR) proteins, immunoglobulin (Ig) proteins, and neurexins (de Wit and Ghosh, 2016). The molecular diversity of the adhesion molecule protein families implicated in the generation of synapse diversity arises either through the large size of the underlying gene family or through the alternative splicing of a more limited number of genes, as in the case of neurexins (S€ udhof, 2017). Recent studies have also started to reveal how synapse location is determined. For example, in the cerebellum, basket interneurons make exquisitely precise “pinceau synapses” on the axon initial segment of Purkinje neurons. Basket axons always contact Purkinje soma before innervating the axon initial segment. This process is mediated by the presence of a subcellular gradient of Neurofascin186 (NF186), an L1 family immunoglobulin cell adhesion molecule (L1CAM), along the Purkinje soma–axon initial segment axis. In the absence of the gradient, basket axons lose directional growth along Purkinje neurons and instead follow NF186 to ectopic locations (Ango et al., 2004; Fig. 5.4). While recognition of correct targets can instruct specificity, the capacity to avoid inappropriate targets,

AIS

mediated by repulsive cues is equally important. An example of a protein family coding for repulsive cues are the semaphorins. Sema6A is expressed in a subset of amacrine cells and retinal ganglion cells, and acts noncell autonomously to direct laminar specificity of neurons expressing the appropriate receptor, PlexA4. Mice missing PlexA4 and Sema6A show mistargeting of amacrine cells and retinal ganglion cells in the inner plexiform layer of the retina (Matsuoka et al., 2011). Specificity is often thought of in the context of contact-mediated interactions, but secreted factors can also provide information about specificity of synaptic connectivity. Sonic Hedgehog, a well-studied secreted cue important for nervous system patterning, is expressed by postsynaptic, deep-layer cortical projection neurons while its receptor Brother of CDO (Boc) is expressed by presynaptic callosal projection neurons. Loss of Sonic Hedgehog or Boc specifically disrupts synapse formation between these two neuronal populations (Harwell et al., 2012). The roles of Sonic Hedgehog and Boc illustrate a larger point relevant to other chemotrophic factors, including Netrins and Brain Derived Neurotrophic Factor (BDNF), one of several molecular factors, which play multiple roles at different stages of nervous system development, including the determination of specificity during synaptogenesis. How can secreted factors provide positional information? We can envision different mechanisms, e.g., localized release of a secreted factor by specific tissues and binding to their specific receptor can limit

AIS NF186

Myelin AnkyrinG

Fig. 5.4. Mechanisms directing GABAergic innervation at the axon initial segment of cerebellar Purkinje neurons. Left panel. Subcellular organization of GABAergic cell inputs (green) along Purkinje neurons (orange) in cerebellum. Stellate cells target specifically Purkinje neuron dendrites, while basket cells form specialized synapse at the axon initial segment (AIS). Right panel. A model describing the role of neurofascin (NF186) and ankyrin G in basket cell synapse formation. The neurofascin gradient (in red) along the AIS–soma of the Purkinje cell directs basket axons (green) to the AIS. Adapted from Ango F, Di Cristo G, Higashiyama H et al. (2004). Ankyrin-based subcellular gradient of neurofascin, an immunoglobulin family protein, directs GABAergic innervation at purkinje axon initial segment. Cell 119: 257–72.

DEVELOPMENT OF NEURONAL CIRCUITS its action to a target area (Timofeev et al., 2012). The extracellular matrix provides another substrate capable of binding to and retaining secreted specificity cues. In addition, the presence of specific receptors determines a neuron’s response to its environment and the positioning of its synapses. Therefore, intracellular mechanisms that regulate the expression, localization, and trafficking of these receptors, or of the partner adhesion molecules, also play a major role in controlling synaptic targeting. Synapse formation can also be regulated via a third party, such as the astrocytes. Retinal ganglion cells, isolated and cultured with standard medium, show low synaptic activity. However, when cultured together with astrocytes, they form numerous active synapses (Ullian et al., 2001). Similar observations with different neuron types, suggest that astrocytes play a general role in regulating synapse formation in the brain. Astrocytes can promote synapse formation by releasing factors, such as thrombospondins (Christopherson et al., 2005), or by direct contact via cell adhesion molecules, such as protocadherins (Garrett and Weiner, 2009). Many astrocytedependent factors regulating synapse formation are still unknown. Despite the many advances of the last decades, mapping the precise rules governing how molecular diversity contributes to the encoding of wiring specificity and synaptic diversity remains a considerable challenge. An apparent paradox emerging from the discussed studies is that, although different neurodevelopmental processes depend on similar molecules, interference with these molecular pathways alters very specific neurodevelopmental outcomes, depending on the cellular and developmental stage context. For example, in rodents, the BDNF pathway can regulate axon outgrowth, axon arborization, presynaptic assembly or synaptic plasticity in different neurons or during different developmental time windows. The challenge is to design experiments to understand the specific role played by a specific molecule at a determined time point. Modern genetic manipulation techniques to visualize and manipulate gene expression with exquisite temporal and spatial resolution have greatly contributed to our understanding of how synaptic specificity is determined. Nevertheless, many open questions remain, such as how does a postsynaptic neuron determine, once synapse formation is initiated, whether to recruit excitatory or inhibitory neurotransmitter receptors to the postsynaptic specialization.

PROCESS OF SYNAPSE FORMATION When a growth cone comes in contact with its target, it begins to change, transforming from a spindly structure to a bulbous presynaptic terminal that adheres tightly to

47

its postsynaptic partner. Growth cones are surprisingly equipped with the ability to release neurotransmitters, initially demonstrated in a cellular culture of spinal neurons and myocytes from Xenopus laevis by using an electrode carrying a piece of muscle cell at its tip (Hume et al., 1983; Young and Poo, 1983). The membrane of the muscle cell is rich in acetylcholine (ACh) receptors; when the latter comes in contact with ACh, the channels open and an electrical current is recorded by the electrode. When the electrode was positioned very close to a spinal neuron’s growth cone, a small but significant current was recorded, thus demonstrating that growth cones are already able to release ACh. Once growth cones enter in the right target region, they start to slow down and finally stop when they contact the right target. As discussed above, adhesion molecules, or even secreted factors, concentrated locally, can provide this stop signal and promote synapse formation. One of the first intracellular events occurring after a contact with the right target is an increase in intracellular calcium (Ca2+) ion concentration (Dai and Peng, 1993; Zoran et al., 1993). The rapid influx of Ca2+ likely has an effect on actin dynamics, which mediates changes in growth cone shape and motility. Soon after the first contact is made, the presynaptic terminal shows enhanced capability of neurotransmitter release, while the number of receptors on the postsynaptic sites is dramatically and rapidly increased. The speed at which a synapse becomes functional can be explained by the existence of preassembled packets both at the pre- and postsynaptic sites (Ahmari et al., 2000; Bresler et al., 2004). At the presynaptic sites, these packages contain vesicular proteins, calcium channels and other molecular components needed for the assembly of release sites (or active zones), while in the postsynaptic cells they contain neurotransmitter receptors and associated molecules. Since the pre- and postsynaptic sites are structurally different, a different set of instructions must be delivered to the growth cone and the postsynaptic membranes. A part of this asymmetrical signaling system is mediated by the neurexins, localized at the presynaptic sites, and their ligands, neuroligins, localized at the postsynaptic site. The synapse promoting effects of neurexins and neuroligins was initially demonstrated using artificial synapse formation assays, in which an adhesion protein is expressed in a nonneuronal cell, e.g., fibroblasts, and induces pre- or postsynaptic specializations in cocultured neurons (Scheiffele et al., 2000). Further studies using constitutive and conditional knockout mice suggest that neurexin–neuroligin signaling is not required for the generation of synaptic contacts per se, but for establishing or maintaining proper synaptic transmission; in fact, the elimination of all neurexins or neuroligins

48

G. DI CRISTO AND B. CHATTOPADHYAYA

impairs synaptic transmission (Missler et al., 2003; Varoqueaux et al., 2006; Chen et al., 2017; S€ udhof, 2017). It is important to underline that the neuroligin/ neurexin system is just one of the likely numerous signaling systems regulating synapse formation. The aggregation of neurotransmitter receptors at the postsynaptic membrane opposed to the presynaptic site is a critical step during the synaptogenesis process. Most of what we know about this process comes from studies at the neuromuscular junction (NMJ), due to several reasons. First, a mature muscle cell receives synapses from only one motoneuron, while a cortical glutamatergic cell receives, on average, 10,000 synapses. Second, NMJs are larger than central synapses, making them easier to visualize. Third, they are more easily accessible for monitoring and manipulation than synapses in the central nervous system. AcetylCholine receptor (AChR) clusters form before the motoneuron growth cone arrives (Fischbach and Cohen, 1973); however, once it arrives, the clustering of AChRs under the site of nerve–muscle contact strikingly increases, suggesting that motoneurons induce receptor clustering (Anderson and Cohen, 1977). Several studies show that the motoneuron axon releases a secreted proteoglycan, called agrin that becomes incorporated in the basal lamina, a specialized extracellular matrix structure that enwraps muscle cells (Sanes et al., 1978; Burden et al., 1979; Godfrey et al., 1984; Nitkin et al., 1987). Agrin induces the phosphorylation of the

receptor tyrosine kinase muscle cell-specific kinase MuSK, which is localized on the muscle cell membrane (Valenzuela et al., 1995), likely by binding with the protein Lrp4 (Kim et al., 2008). Together with other cytoplasmic proteins, including one called rapsyn, MuSK appears to act like a shepherd to gather AChRs at the developing neuromuscular junction. Another mechanism that contributes to AchR increase at the postsynaptic membrane of the muscle cell following contact with the presynaptic motoneuron is the activity-dependent regulation of protein synthesis. Muscle cells have numerous nuclei and motoneuron activity regulates the synthesis of the mRNA coding for AChRs in the nuclei closest to the presynaptic terminal (Merlie and Sanes, 1985). In the central nervous system, the steps involved in synapse formation are similar, but the molecular systems involved are different (Yuzaki, 2018). Similar to the NMJ, activity regulates the expression and clustering of postsynaptic receptors. We illustrate this concept in detail further on in the chapter when discussing synaptic plasticity. Overall, the process of synapse formation is relatively fast; however, synapse maturation and the fine-tuning of synaptic function occurs at a slower pace. For example, the formation of complex synaptic innervation by cortical GABAergic neurons is a prolonged process that plateaus only by the end of adolescence in rodents and primates (Fig. 5.5; Chattopadhyaya et al., 2004; Fish et al., 2013). A common theme underlying development

Fig. 5.5. The maturation of cortical GABAergic synaptic innervation is a prolonged process. Top panels show single GABAergic interneurons in organotypic cultures prepared from the cortex of postnatal day 5 (P5) mice and maintained in the incubator for the desired time periods. GABAergic cells are labeled with green fluorescent protein (GFP). Bottom panels show details of synaptic innervations formed by GABAergic cell axon around the soma of glutamatergic neurons (red). At equivalent postnatal day 11 (EP11 ¼ P5 + 6 days in vitro), GABAergic cell axons are still poorly branched and form few distinct synaptic boutons (arrowheads) contacting the postsynaptic glutamatergic cells. At EP28, axon branches are more numerous and carry strings of synaptic boutons (arrowheads). Adapted from Chattopadhyaya B, Di Cristo G, Higashiyama H et al. (2004). Experience and activity-dependent maturation of perisomatic GABAergic innervation in primary visual cortex during a postnatal critical period. J Neurosci 24: 9598–9611.

DEVELOPMENT OF NEURONAL CIRCUITS of different synapse types is that the duration of excitatory or inhibitory synaptic potentials become progressively shorter with age, a process caused by changes in the relative expression levels of different neurotransmitter isoforms. For example, the glutamate receptor NMDA, which plays a critical role in synaptic plasticity, is composed of NR1 and NR2 subunits. NR2 has two isoforms, NR2B, which is relatively more highly expressed in young neurons, and NR2A, whose expression gradually increases as the brain matures. NMDA receptors containing the NR2B subunit remain open for longer, thereby allowing a longer current flow (Sheng et al., 1994). It is hypothesized that the longer-lasting synaptic currents may contribute to the formation of neural circuits, while probably limiting their behavioral and cognitive abilities.

EXPERIENCE DEPENDENT MODULATION OF SYNAPSE FORMATION As synapse formation proceeds, a second, prolonged phase follows, lasting from before birth until the end of adolescence, during which connections are refined, specific synapses are stabilized while others are eliminated. For example, visual cortical neurons in the neonate brain receive 150% more synapses than the equivalent neurons in an adult. What are the mechanisms underlying synapse elimination during the development of the nervous system? Once again, the NMJ has been an extremely useful experimental model for studying this process. Each muscle fiber receives inputs from several motoneurons during early development (polyneuronal innervation), but is whittled down to only one motoneuron in the adult (Fig. 5.6). This elimination process is regulated by electrical activity of the muscle. Silencing the activity of the muscle fiber leads to continued polyneuronal innervation (Thompson et al., 1979), while stimulation of the muscle accelerates the elimination process

49

(O’Brien et al., 1978). In addition, it appears that competition between different inputs for control of the muscle fiber electrical activity is an essential step. In fact, if a portion of the AChRs are partially blocked by the neurotoxin a-bungarotoxin, they will internalize and the overlying axon terminal will withdraw; however, if all the AChRs are blocked, the polyneuronal innervation remains since the muscle is silent and there are no competing inputs (Balice-Gordon and Lichtman, 1994). It is important to understand how and to what extent the development of the nervous system is influenced by the environment. For example, are synapse stabilization and elimination influenced by sensory or motor experience? The seminal work of David Hubel and Torsten Wiesel, 1981 Nobel Prize winners, explored the effects of monocular and binocular visual deprivation on the development of visual coding properties in the central nervous system. Briefly, in the mammalian visual cortex, neurons in layer 4 receive projections that are driven by either one eye or the other. Neurons that respond to only one eye are called monocular. Most neurons outside layer 4 respond, in varying degrees, to both eyes and are referred to as binocular. Monocular deprivation, in which one eyelid is sealed closed, during a particular, critical postnatal period, has dramatic effects: most cortical neurons will no longer respond to visual stimulation from the eye that was deprived during development, although they will continue to respond to the eye that was left open (Wiesel and Hubel, 1963a,b, 1965). Surprisingly, binocular deprivation has little effect on the binocular properties of cortical neurons, suggesting that a process of competition between the inputs from the two eyes may occur. The existence of competition in visual cortex is demonstrated by the occurrence of strabismus (misalignment of the eyes), a condition that in humans may lead to amblyopia, where there is suppression of vision in one eye and the loss of stereoscopic vision. When strabismus is experimentally induced in kittens during a critical postnatal period, it causes most cortical neurons to become monocular (Hubel and Wiesel, 1965), demonstrating

Fig. 5.6. Competition between axons causes elimination of synaptic connections during development. In vivo imaging of the same multiply innervated NMJ in a neonate mouse providing evidence of synapse elimination. Here, the axons of two different motoneurons are labeled by two different fluorescent molecules (blue and green). ACh receptors are labeled in red. At postnatal day (P)11, both axons innervate the same territory; however, in the following 4 days, the blue axon gradually relinquishes its territory and retracts, leaving only one, winner axon at NMJ. Adapted from Walsh MK, Lichtman JW (2003). In vivo time-lapse imaging of synaptic takeover associated with naturally occurring synapse elimination. Neuron 37 (1): 67–73.

50

G. DI CRISTO AND B. CHATTOPADHYAYA

that the disconnection of inputs from one eye is due to competition and not merely lack of use. Altogether, these results demonstrate that visual experience during development regulates the maintenance and elimination of synapses. Parallel studies showed that the same manipulations described above had little effect when started in the adult (Hubel and Wiesel, 1970), therefore supporting the existence of critical or sensitive periods, during which environmental factors can permanently alter brain development. We now know that critical periods exist for the development of many sensory, motor, and cognitive functions. A common example is language learning: learning a language is very easy before 10 years old, but it becomes much harder for adults. Numerous studies in the last four decades have started to elucidate the molecular mechanisms underlying activity-dependent synapse refinement, yet we still do not completely understand why the brain of a child is so much more plastic than an adult brain. Multiple factors likely contribute to the winding down of critical periods, including the maturation of inhibitory circuits (Hensch, 2005; Harauzov et al., 2010) and changes in extracellular components (Pizzorusso et al., 2002) and/or intrinsic transcriptional programs (Putignano et al., 2007). It is puzzling to imagine why so many synapses are eliminated after so much metabolic energy has been spent to build them. A common hypothesis is that experiencedependent synapse refinement optimizes neural circuit processing, and ultimately, behavioral performance in the environment where the organism is born and must live. Allowing sensory experience to shape the finetuning of brain circuits likely improves the specificity and efficiency of connections beyond what could be obtained by exclusively genetically coded cues.

SYNAPTIC PLASTICITY IN MATURE SYNAPSES We have so far discussed how experience can shape the developing brain; however, the role of experience is not limited to the young brain, as we continue to learn and create new memories throughout life. The general consensus is that synaptic plasticity, whereby existing connections among neurons are strengthened or weakened and new synapses are formed or existing ones removed, underlies the ability to store and retrieve information. While a major difference is that the developing nervous system can be altered permanently by specific manipulations, like sensory deprivation, that have limited effect in the adult, adult learning and developmental plasticity share many molecular mechanisms. Synapse plasticity seems to generally follow Hebb’s hypothesis, stating that neurons that fire together, wire together. In other words, if a presynaptic input is active at the same time as its postsynaptic partner, the synapses formed

by the presynaptic axon are strengthened. On the other hand, if the activities of the pre- and postsynaptic cells are out of sync, the synapses between them are weakened. These observations raise the question about the identity of the molecular mechanism(s) able to detect if and when pre- and postsynaptic cells are in or out of sync. The voltage sensitive glutamate NMDA receptor plays the role of coincidence detector at many glutamatergic synapses. NMDA receptors are voltage gated because the ion Mg2+ is lodged in the channel at the resting membrane potential and is displaced when the membrane is depolarized. Therefore, current will flow via the NMDA receptor only when two events occur concurrently: glutamate is released by the presynaptic site and the postsynaptic membrane is depolarized. In addition, NMDA receptors are permeable to Ca2+, therefore, when pre and postsynaptic cells are active at the same time, Ca2+ enters via the NMDA receptors and triggers intracellular signaling pathways that ultimately change the strength of the synapse. Glutamatergic synapses can undergo long-term strengthening, referred to as long-term potentiation (LTP), a process that requires synthesis of new proteins and is often accompanied by a change in the number of postsynaptic receptors, in particular, AMPA glutamate receptors (Bredt and Nicoll, 2003). Conversely, neurons that fire out of sync will lose their connections. Experimental evidence suggests that reduced levels of NMDA receptor activation, and less Ca2+ influx, signals the occurrence of low coincidence activity and triggers long-term depression (LTD) at the synapse. This process, also dependent on new protein synthesis, is accompanied by a loss of AMPA receptors at the synapse, the opposite of what has been observed for LTP. Both LTP and LTD contribute to the selective stabilization of inputs during development. This has been shown by recording tectal neurons that respond to visual stimuli, while simultaneously stimulating two different retinal ganglion cells that converge on the same tectal neuron, in tadpoles (Zhang et al., 1998). When the retinal ganglion cells are stimulated synchronously for 20 s, both their synaptic contacts on the tectal neuron undergo LTP. However, when the two retinal ganglion cells are stimulated asynchronously, the result depends on the correlation between the input stimulus and the tectal cell firing. If the input activity is only weakly correlated with the action potential generation in the tectal cell, the synapses undergo LTD. Of note, in many areas of the brain, LTD is more prominent during early development. For example, LTD can be elicited in layer 4 of the visual cortex in juvenile animals, while it is virtually absent in adults (Dudek and Friedlander, 1996). The ease of LTD induction in the young brain may represent the cellular substrate for the massive synapse elimination occurring during development.

DEVELOPMENT OF NEURONAL CIRCUITS Mammalian neurons are mostly large and highly polarized cells carrying numerous synapses. To be effective, synaptic plasticity must be restricted to the synapses implicated in specific activity patterns. On the other hand, both LTP and LTD require new protein synthesis, the first step being mRNA transcription occurring in the nucleus. So how are the synapse specific changes obtained? Many studies have shown that a subset of mRNA can be transported to the dendrites, often near synapses (Steward and Schuman, 2001). These mRNAs can be locally translated following specific synaptic stimulation, and express relevant proteins, like AMPA receptor subunits, which mediate changes in synaptic transmission (Ju et al., 2004). The signaling networks controlling localized translation are currently heavily investigated since they are one of the core mechanisms controlling synaptic plasticity both during development and in the adult brain (Buffington et al., 2014; Holt et al., 2019).

SYNAPTOGENESIS AND PLASTICITY IN NEURODEVELOPMENTAL DISORDERS Recent advances in microarray and DNA sequencing technologies have enabled sophisticated studies on the genetic architecture of neurodevelopmental and psychiatric disorders at an increasingly larger scale. These studies suggest that mutations in many different genes predispose the brain to neuropsychiatric disorders and that for most mutations, the clinical presentations differ greatly among affected individuals. However, a common theme is that these mutations appear to affect pathways implicated in the basic process governing brain development, including synapse formation and plasticity (Parikshak et al., 2015). Many of the genes encoding adhesion molecules implicated in determining cellular, subcellular, and synapse specificity have been linked to neurodevelopmental and psychiatric disorders (S€ udhof, 2008, 2017). For example, copy number variants showing deletions of NRXN1 (coding for neurexin 1) have been significantly associated with schizophrenia, Tourette syndrome, intellectual disability, epilepsy, and autism spectrum disorders, while mutations in NLGN3 and NLGN4 (coding for neuroligin 3 and 4) has been linked to autism (S€ udhof, 2017). Protocadherins, a family of adhesion molecules regulating neural circuit formation, have been implicated in neuropsychiatric disorders including autism spectrum disorders, intellectual disability, and attention deficit hyperactivity disorder (ADHD) resulting from a duplication of the 16p13.11 locus (Fujitani et al., 2017). Altered local protein synthesis close to the synapse, a process important for synaptic plasticity, is thought to contribute to the physiopathology of Fragile

51

X Syndrome and possibly Tuberous Sclerosis (Winden et al., 2018), while mutations in the synaptic gene SYNGAP1 are associated with abnormal developmental trajectories of synapse maturation in rodent models (Hamdan et al., 2009; Clement et al., 2012; Berryer et al., 2013). Recent imaging studies show that at 10 years of age, children with higher IQ have a slightly thinner cortex than children with a lower IQ. In addition, the thinning of the cortex appears to be faster in children with higher IQ (Schnack et al., 2015). These findings suggest that IQ may be related to the magnitude and timing of changes in brain structure during development. Since cortical thinning is most likely related to the process of synapse elimination discussed earlier, it is possible that defects in experience dependent synapse elimination might contribute to learning deficits. Finally, the groundbreaking studies of Hubel and Wiesel on critical periods of cortical plasticity have clear clinical relevance, such as in congenital cataracts or ocular misalignment, which should be corrected in early childhood, as soon as surgically feasible, to avoid permanent visual disabilities. These studies represent just the tip of the iceberg! Multiple molecular players and cellular processes critical for brain development have been implicated in neurodevelopmental disorders as of today and more will definitely emerge in the future with technological leaps in molecular diagnostics, and large data computational analysis. The challenge then will be to understand how genetic variants may alter the developmental trajectories of neural circuit formation and refinement. Conversely, detailed ongoing research on the myriad mechanisms underlying synapse formation and plasticity will help shed light on the pathophysiology of neurodevelopmental disorders.

REFERENCES Ahmari SE, Buchanan J, Smith SJ (2000). Assembly of presynaptic active zones from cytoplasmic transport packets. Nat Neurosci 3: 445–451. Anderson MJ, Cohen MW (1977). Nerve-induced and spontaneous redistribution of acetylcholine receptors on cultured muscle cells. J Physiol 268: 757–773. Ango F, Di Cristo G, Higashiyama H et al. (2004). Ankyrinbased subcellular gradient of neurofascin, an immunoglobulin family protein, directs GABAergic innervation at purkinje axon initial segment. Cell 119: 257–272. Balice-Gordon RJ, Lichtman JW (1994). Long-term synapse loss induced by focal blockade of postsynaptic receptors. Nature 372: 519–524. Berryer MH, Hamdan FF, Klitten LL et al. (2013). Mutation in SYNGAP1 cause intellectual disability, autism, and a specific form of epilepsy by inducing haploinsufficiency. Hum Mutat 34: 385–394.

52

G. DI CRISTO AND B. CHATTOPADHYAYA

Bredt DS, Nicoll RA (2003). AMPA receptor trafficking at excitatory synapses. Neuron 40 (2): 361–379. Bresler T, Shapira M, Boeckers T et al. (2004). Postsynaptic density assembly is fundamentally different from presynaptic active zone assembly. J Neurosci 24: 1507–1520. Buffington SA, Huang W, Costa-Mattioli M (2014). Translational control in synaptic plasticity and cognitive dysfunction. Annu Rev Neurosci 37: 17–38. Burden SJ, Sargent PB, McMahan UJ (1979). Acetylcholine receptors in regenerating muscle accumulate at original synaptic sites in the absence of the nerve. J Cell Biol 82: 412–425. Cajal SR (1906). Gene`se des fibres nerveuses de l’embryon et observations contraires a` la th
eorie catenaire. Trab Lab Invest Biol Univ Madrid 4: 219–284. Chattopadhyaya B, Di Cristo G, Higashiyama H et al. (2004). Experience and activity-dependent maturation of perisomatic GABAergic innervation in primary visual cortex during a postnatal critical period. J Neurosci 24: 9598–9611. Chen LY, Jiang M, Zhang B et al. (2017). Conditional deletion of all Neurexins defines diversity of essential synaptic organizer functions for neurexins. Neuron 94: 611–625. Christopherson KS, Ullian EM, Stokes CC et al. (2005). Thrombospondins are astrocyte-secreted proteins that promote CNS synaptogenesis. Cell 120: 421–433. Clement JP, Aceti M, Creson TK et al. (2012). Pathogenic SYNGAP1 mutations impair cognitive development by disrupting maturation of dendritic spine synapses. Cell 151: 709–723. Dai Z, Peng HB (1993). Elevation in presynaptic Ca2 + level accompanying initial nerve-muscle contact in tissue culture. Neuron 10: 827–837. de Wit J, Ghosh A (2016). Specification of synaptic connectivity by cell surface interactions. Nat Rev Neurosci 17: 22–35. Dudek SM, Friedlander MJ (1996). Developmental downregulation of LTD in cortical layer IV and its independence of modulation by inhibition. Neuron 16: 1097–1106. Fischbach GD, Cohen SA (1973). The distribution of acetylcholine sensitivity over uninnervated and innervated muscle fibers grown in cell culture. Dev Biol 31: 147–162. Fish KN, Hoftman GD, Sheikh W et al. (2013). Parvalbumincontaining chandelier and basket cell boutons have distinctive modes of maturation in monkey prefrontal cortex. J Neurosci 33: 8352–8358. Foster M, Sherrington CS (1897). A text book of physiology, part III: the central nervous system, seventh edn. Macmillan, London. Fujitani M, Zhang S, Fujiki R et al. (2017). A chromosome 16p13.11 microduplication causes hyperactivity through dysregulation of miR-484/protocadherin-19 signaling. Mol Psychiatry 22: 364–374. Garrett AM, Weiner JA (2009). Control of CNS synapse development by {gamma}-protocadherin-mediated astrocyteneuron contact. J Neurosci 29: 11723–11731.

Godfrey EW, Nitkin RM, Wallace BG et al. (1984). Components of Torpedo electric organ and muscle that cause aggregation of acetylcholine receptors on cultured muscle cells. J Cell Biol 99: 615–627. Hamdan FF, Gauthier J, Spiegelman D et al. (2009). Mutations in SYNGAP1 in autosomal nonsyndromic mental retardation. N Engl J Med 360: 599–605. Harauzov A, Spolidoro M, Di Cristo G et al. (2010). Reducing intracortical inhibition in the adult visual cortex promotes ocular dominance plasticity. J Neurosci 30: 361–371. Harwell CC, Parker PR, Gee SM et al. (2012). Sonic hedgehog expression in corticofugal projection neurons directs cortical microcircuit formation. Neuron 73: 1116–1126. Hensch TK (2005). Critical period plasticity in local cortical circuits. Nat Rev Neurosci 6: 877–888. Holt CE, Martin KC, Schuman EM (2019). Local translation in neurons: visualization and function. Nat Struct Mol Biol 26: 557–566. Hubel DH, Wiesel TN (1965). Binocular interaction in striate cortex of kittens reared with artificial squint. J Neurophysiol 28: 1041–1059. Hubel DH, Wiesel TN (1970). The period of susceptibility to the physiological effects of unilateral eye closure in kittens. J Physiol 206: 419–436. Hume RI, Role LW, Fischbach GD (1983). Acetylcholine release from growth cones detected with patches of acetylcholine receptor-rich membranes. Nature 305: 632–634. Ju W, Morishita W, Tsui J et al. (2004). Activity-dependent regulation of dendritic synthesis and trafficking of AMPA receptors. Nat Neurosci 7: 244–253. Kim N, Stiegler AL, Cameron TO et al. (2008). Lrp4 is a receptor for Agrin and forms a complex with MuSK. Cell 135: 334–342. Langley JN (1895). Note on regeneration of prae-ganglionic fibres of the sympathetic. J Physiol 18: 280–284. Lo´pez-Mun˜oz F, Boya J, Alamo C (2006). Neuron theory, the cornerstone of neuroscience, on the centenary of the Nobel Prize award to Santiago Ramo´n y Cajal. Brain Res Bull 70: 391–405. Matsuoka RL, Nguyen-Ba Charvet KT, Parray A et al. (2011). Transmembrane semaphorin signalling controls laminar stratification in the mammalian retina. Nature 470: 259–263. Merlie JP, Sanes JR (1985). Concentration of acetylcholine receptor mRNA in synaptic regions of adult muscle fibres. Nature 317: 66–68. Missler M, Zhang W, Rohlmann A et al. (2003). Alphaneurexins couple Ca2 + channels to synaptic vesicle exocytosis. Nature 423: 939–948. Nitkin RM, Smith MA, Magill C et al. (1987). Identification of agrin, a synaptic organizing protein from Torpedo electric organ. J Cell Biol 105: 2471–2478. O’Brien RA, Ostberg AJ, Vrbova´ G (1978). Observations on the elimination of polyneuronal innervation in developing mammalian skeletal muscle. J Physiol 282: 571–582. Parikshak NN, Gandal MJ, Geschwind DH (2015). Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders. Nat Rev Genet 16: 441–458.

DEVELOPMENT OF NEURONAL CIRCUITS Pizzorusso T, Medini P, Berardi N et al. (2002). Reactivation of ocular dominance plasticity in the adult visual cortex. Science 298: 1248–1251. Putignano E, Lonetti G, Cancedda L et al. (2007). Developmental downregulation of histone posttranslational modifications regulates visual cortical plasticity. Neuron 53: 747–759. Sanes JR, Marshall LM, McMahan UJ (1978). Reinnervation of muscle fiber basal lamina after removal of myofibers. Differentiation of regenerating axons at original synaptic sites. J Cell Biol 78: 176–198. Scheiffele P, Fan J, Choih J et al. (2000). Neuroligin expressed in nonneuronal cells triggers presynaptic development in contacting axons. Cell 101: 657–669. Schnack HG, van Haren NE, Brouwer RM et al. (2015). Changes in thickness and surface area of the human cortex and their relationship with intelligence. Cereb Cortex 25: 1608–1617. Sheng M, Cummings J, Roldan LA et al. (1994). Changing subunit composition of heteromeric NMDA receptors during development of rat cortex. Nature 368: 144–147. Sperry RW (1963). Chemoaffinity in the orderly growth of nerve fiber patterns and connections. Proc Natl Acad Sci USA 50: 703–710. Steward O, Schuman EM (2001). Protein synthesis at synaptic sites on dendrites. Annu Rev Neurosci 24: 299–325. S€ udhof TC (2008). Neuroligins and neurexins link synaptic function to cognitive disease. Nature 455: 903–911. S€ udhof TC (2017). Synaptic neurexin complexes: a molecular code for the logic of neural circuits. Cell 171: 745–769. Thompson W, Kuffler DP, Jansen JK (1979). The effect of prolonged, reversible block of nerve impulses on the elimination of polyneuronal innervation of new-born rat skeletal muscle fibers. Neuroscience 4: 271–281. Timofeev K, Joly W, Hadjieconomou D et al. (2012). Localized netrins act as positional cues to control layerspecific targeting of photoreceptor axons in Drosophila. Neuron 75: 80–93.

53

Ullian EM, Sapperstein SK, Christopherson KS et al. (2001). Control of synapse number by glia. Science 291: 657–661. Valenzuela DM, Stitt TN, DiStefano PS et al. (1995). Receptor tyrosine kinase specific for the skeletal muscle lineage: expression in embryonic muscle, at the neuromuscular junction, and after injury. Neuron 15: 573–584. Varoqueaux F, Aramuni G, Rawson RL et al. (2006). Neuroligins determine synapse maturation and function. Neuron 51: 741–754. Wiesel TN, Hubel DH (1963a). Single-cell responses in striate cortex of kittens deprived of vision in one eye. J Neurophysiol 26: 1003–1017. Wiesel TN, Hubel DH (1963b). Effects of visual deprivation on morphology and physiology of cells in the cats lateral geniculate body. J Neurophysiol 26: 978–993. Wiesel TN, Hubel DH (1965). Comparison of the effects of unilateral and bilateral eye closure on cortical unit responses in kittens. J Neurophysiol 28: 1029–1040. Winden KD, Ebrahimi-Fakhari D, Sahin M (2018). Abnormal mTOR activation in autism. Annu Rev Neurosci 41: 1–23. Young SH, Poo MM (1983). Spontaneous release of transmitter from growth cones of embryonic neurones. Nature 305: 634–637. Yuzaki M (2018). Two classes of secreted synaptic organizers in the central nervous system. Annu Rev Physiol 80: 243–262. Zhang LI, Tao HW, Holt CE et al. (1998). A critical window for cooperation and competition among developing retinotectal synapses. Nature 395: 37–44. Zoran MJ, Funte LR, Kater SB et al. (1993). Neuron-muscle contact changes presynaptic resting calcium set-point. Dev Biol 158: 163–171.

FURTHER READING Sherrington CS (1906). The integrative action of the nervous system, CT Yale University Press, New Haven.

This page intentionally left blank

Section III Plasticity, vulnerability and evolutionary constraints of the developing brain

This page intentionally left blank

Handbook of Clinical Neurology, Vol. 173 (3rd series) Neurocognitive Development: Normative Development A. Gallagher, C. Bulteau, D. Cohen and J.L. Michaud, Editors https://doi.org/10.1016/B978-0-444-64150-2.00007-1 Copyright © 2020 Elsevier B.V. All rights reserved

Chapter 6

A conceptual framework for plasticity in the developing brain FATIMA Y. ISMAIL1,2*, MILOS R. LJUBISAVLJEVIC3, AND MICHAEL V. JOHNSTON4 1 2

Department of Neurology (adjunct), Johns Hopkins School of Medicine, Baltimore, MD, United States 3

4

Department of Pediatrics, United Arab Emirates University, Al-Ain, United Arab Emirates

Department of Physiology, United Arab Emirates University, Al-Ain, United Arab Emirates

Department of Neurology and Developmental Medicine, Kennedy Krieger Institute, Baltimore, MD, United States

Abstract In this chapter, we highlight the various definitions of early brain plasticity commonly used in the scientific literature. We then present a conceptual framework of early brain plasticity that focuses on plasticity at the level of the synapse (synaptic plasticity) and the level of the network (connectivity). The proposed framework is organized around three main domains through which current theories and principles of early brain plasticity can be integrated: (1) the mechanisms of plasticity and constraints at the synaptic level and network connectivity, (2) the importance of temporal considerations related to the development of the immature brain, and (3) the functions early brain plasticity serve. We then apply this framework to discuss some clinical disorders caused by and/or associated with impaired plasticity mechanisms. We propose that a careful examination of the relationship between mechanisms, constraints, and functions of early brain plasticity in health and disease may provide an integrative understanding of the current theories and principles generated by experimental and observational studies.

INTRODUCTION The term brain plasticity is a broad concept that refers to the process of change of the nervous system as a result of intrinsic factors, environmental inputs, learning experiences, or lesions. Over the past few decades, research and studies on neuroplasticity have demonstrated numerous physiologic processes underlying and governing brain plasticity. They span changes in gene expression, epigenetic modifications, molecular signaling, cellular processes, synaptic proliferation and pruning, neural networks assembly, reorganization and remodeling, and emergence/loss of behavior(s) (Dennis et al., 2013; Ismail et al., 2017a; Kolb et al., 2017).

Brain plasticity originated in adult scientific literature as a term that denotes neuronal changes associated with the formation of new habits, memory formation, and consolidation and learning new skills such as playing a musical instrument (Barrett et al., 2013) or learning a new language (Li et al., 2014). The term evolved over many decades to indicate changes at the level of the synapse (Berlucchi and Buchtel, 2009). Subsequently, the term was broadened to refer to structural and/or functional changes on any hierarchical level of the nervous system organization (from subcellular, cellular, to networks and behaviors). In the pediatric literature, brain plasticity is often used to reflect biologic, psychologic, and cognitive changes

*Correspondence to: Fatima Y. Ismail, MBBS, Assistant Professor of Pediatric Neurology and Developmental Medicine, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain 17666, United Arab Emirates. Tel: +971-3-713-7409, Cell: +971507271888, E-mail: [email protected]

58

F.Y. ISMAIL ET AL.

associated with brain development and maturation, learning, acquisition of new skills, and recovery from injury. Brain plasticity became an all-encompassing and nonspecific semantic descriptor of central nervous system (CNS) changes as a result of internal or external experiences. In this review, we highlight various definitions of early brain plasticity commonly used in the scientific literature generated by experimental and empirical evidence. Given its specificity to the developing brain, we discuss the relationship between innate brain development and experience-dependent plasticity and how they may overlap and lead to enriching theoretical models of early brain plasticity. In this context, we discuss a conceptual framework that focuses on three main domains via which early brain plasticity can be understood: (1) the structural and functional mechanisms that mediate and regulate plasticity, (2) the importance of temporal associations of plasticity concerning brain development, (3) and the functions plasticity serves in the developing brain. We then apply this conceptual framework to discuss selected clinical disorders caused by or associated with impaired plasticity.

DEFINITIONS OF BRAIN PLASTICITY The scientific literature is full of terms that refer to changes in the young brain, including neural plasticity, developmental plasticity, maturational plasticity, network plasticity/remodeling, behavioral plasticity, adaptive and maladaptive plasticity, and restorative and reparative plasticity, to name a few. These terms are drawn from the overlapping fields of neuroscience, developmental biology, developmental psychology, evolutionary biology, and social sciences. Table 6.1 summarizes commonly used plasticity-related terms and their descriptions, as referenced in the literature.

The terms listed in Table 6.1 are a mixture of molecular, biological, anatomical, physiological, behavioral descriptions that are, at times, used interchangeably. We confine our discussion of early brain plasticity to its definition in neuroscience (neuronal/neural plasticity), that is “change in neuronal structure and/or functional responses as a result of experience/input.” We focus on structural and functional changes associated with plasticity at the synaptic and the network levels. Structural brain plasticity refers to changes in the brain’s constitutive elements, including cellular (neurons and glia), synapses (axon buttons, dendritic spines, and cell bodies), cortical map areas, and networks connectivity patterns. Functional brain plasticity refers to altered functional properties of the nervous system due to the reorganization of its structural elements. The two most rigorously investigated hierarchal levels of CNS organization and plasticity are the synapse (synaptic plasticity) and the network (connectivity), which support and mediate plasticity under normal and abnormal conditions and are often studied in relation to behavioral changes making them relevant for clinical translations. Synaptic plasticity refers to the changes in synaptic strength and transmission that result from experience or change in input (Citri and Malenka, 2008). It is a dynamic and responsive property of the developing, developed, and aging brain. Synaptic plasticity is heightened during time-defined periods in early development, and continues, albeit less intensely, throughout the lifetime. Synaptic plasticity is induced, tuned, and sustained through different structural and functional mechanisms. Network connectivity refers to the anatomical, functional and casual patterns of connection between different nervous system areas. A neuronal network is composed of structurally and functionally linked neuronal populations. Patterns of connections shape the dynamics of signaling, communication, and information

Table 6.1 Terminologies and definitions of plasticity-related terms commonly used in the scientific literature Term

Definition/description

Neuronal or neural plasticity

Change in neuronal structure and/or functional responses as a result of experience/input It includes: Synaptic plasticity: activity-dependent modification of synaptic strength and transmission Network plasticity: reorganization and remodeling of connections in a network The extent to which an individual’s behavior changes in different situations The process of change in the organism’s response to change in the environment. Clinically, adaptive plasticity used to refer to plasticity that yields positive functional outcomes Plasticity that yields negative functional outcomes A change that restores or reinstates a lost function secondary to a pathologic process

Behavioral plasticity Adaptive plasticity Maladaptive plasticity Restorative or reparative plasticity

A CONCEPTUAL FRAMEWORK FOR PLASTICITY IN THE DEVELOPING BRAIN processing within a network, which can be changed or maintained as a result of modification and stabilization of synaptic connections (Avena-koenigsberger et al., 2018).

A CONCEPTUAL FRAMEWORK OF EARLY BRAIN PLASTICITY Experimental and observational studies of early brain plasticity have led to the development of many theories related to the diversity of the plasticity principles, rules, and their purposes. Early brain plasticity is mediated and controlled by genetic (Hannan, 2018), epigenetic (Lister et al., 2013), and environmental factors (Berardi et al., 2015). Plasticity is heightened during critical and sensitive periods of brain development (Hensch, 2004; Knudsen, 2004; Meredith, 2015). Plasticity functions are reflected in maturation/development (Galván, 2010), compensation, and recovery following injury (Umeda and Funakoshi, 2014; Williams et al., 2018) or sensory deprivation (Neville and Bavelier, 2002). Brain plasticity can be studied in the context of a typical brain maturation (Dehaene et al., 2015), a disease process (Kirton, 2013), an intervention (Matusz et al., 2018), or an enhanced vulnerability to exaggerated injury (Johnston et al., 2002). We present a conceptual integrative framework that serves as a guide to investigate and correlate current theories and principles governing early brain plasticity. The framework focuses on three main domains through which synaptic plasticity and network connectivity in the developing brain can be examined (Fig. 6.1).

59

Mechanisms and constraints of early brain plasticity MECHANISM OF NEURONAL PLASTICITY Synaptic plasticity is governed by different structural and functional mechanisms that operate over varying timescales (milliseconds to days) at the synaptic and system (connectivity) levels to accommodate for a specific function(s) of the network (reviewed in Suvrathan, 2019 and see Chapter 5 of Volume 173). It has long been postulated that synaptic plasticity is uniformly induced by the repetitive and temporally close firing of adjacent neurons. The cofiring of neurons strengthens the synaptic connection when the presynaptic neuron is excitatory (Hebbian rule), and weakens it when the presynaptic neuron is inhibitory (anti-Hebbian rule). These mechanisms form the basis for long-term potentiation (LTP) and long-term depression (LTD) models of synaptic plasticity that are implicated in associative learning and memory. Recent studies suggest than the strength of synaptic plasticity is dictated by the frequency of presynaptic firing and the order of preand postsynaptic firing that occurs within a precise time window. This form of plasticity mechanism called spike-timing dependent plasticity (STDP) is thought to support the refinement of sensory topographical maps, mediate associative learning, and network remodeling (Feldman, 2012). However, not all synapses conform to the Hebbian rule of plasticity. The strength and direction of synaptic plasticity may be influenced by the nature of presynaptic cell (excitatory vs inhibitory), the status of dendritic depolarization, the neuromodulatory actions of neurotransmitters, and the changes in sensory input (Sj€ostr€om et al., 2001).

Fig. 6.1. A conceptual framework for understanding early brain plasticity. When investigating neurologic and developmental disorders associated with or caused by impaired plasticity mechanisms, we encourage a careful examination of mechanisms and constraints underlying plasticity, specifying the level at which plasticity is investigated and profiling the function plasticity serves and the neurocognitive behavior under question.

60

F.Y. ISMAIL ET AL.

High-resolution imaging studies of neuronal structure demonstrated that activity-dependent formation and pruning of synapses result in modification of numbers of spines and their morphology, redirection of axon terminals, changes in neurotransmitter release, postsynaptic receptor trafficking, and rewiring of synaptic connections (Holtmaat and Svoboda, 2009). On a system level, the changes induced by experience-dependent activity that alter the pattern and the strengths of synaptic connections (according to LTP and LTD processes) lead to a modification in neural network excitability. Changes in neural network activity drive the reconfiguration of the synapses reciprocally to establish, modify, and stabilize new patterns of structural connectivity, which in turn support the dynamic state of neural communication and information processing (Butz et al., 2009). This process of synaptic plasticity is innately dynamic. Therefore, it is regulated by synaptic and extrasynaptic mechanisms that ensure the right amount of change in the system at the right time, in response to a specific input, and for an appropriate duration.

CONSTRAINTS IN THE DEVELOPING BRAIN Here, we present few examples of how early brain plasticity is constrained. Synaptic plasticity is regulated to provide a form of stability to synaptic properties that perpetuate neural network function. Homeostatic plasticity refers to a group of cellular and molecular mechanisms that limit the unopposed persistence of activity-dependent synaptic plasticity (Abraham, 2008). These mechanisms have been partly explained in the last few years and include: (a) regulation of current synaptic activity by history of prior activity at the synapse (homosynaptic metaplasticity) or nearby synapses (heterosynaptic metaplasticity) (Hulme et al., 2014); (b) maintaining an appropriate level of activity within a dynamic range by adjusting postsynaptic strength in the face of abnormally increased or decreased inputs (synaptic scaling) (Chowdhury and Hell, 2018); (c) and modulation of intrinsic neuronal excitability and firing rate in an activity-dependent manner (Shim et al., 2018) (see more details in Chapter 9 of Volume 173). These constraints of plasticity can also be observed at the network level. The neuroplastic potential for a given region/network seems to be predetermined by an ontogenetic potential (Kr€ageloh-Mann et al., 2017). Ontogenetic potential theory suggests that “axonal or cortical structures cannot be recruited beyond early developmental possibilities.” For example, the capacity for reorganization of cortical maps following early brain injury is different among cortical areas. While motor maps show clear evidence for

rewiring following unilateral brain injury in the perinatal period, there is no supportive evidence of an equivalent reorganization of sensory maps beyond their boundaries within the somatosensory cortex (Kr€ageloh-Mann et al., 2017). An example of a “less rigid” ontogenetic constraint is the reorganization of language networks following injury. Young children with early-life left hemispherectomy for the treatment of intractable epilepsy often experience considerable functional recovery of language. This is due to the bilateral representation of language networks in early development that undergoes lateralization later in life. In summary, the developing brain has enhanced, yet restricted, remodeling potential.

Temporal associations (critical and sensitive periods) A growing body of evidence points to the existence of developmental windows during which the presence or absence of experience may lead to long-lasting changes in synaptic plasticity, connectivity, and behavior. The onset and maturation of critical periods seem to be regulated by excitatory–inhibitory balance and the maturation of specific GABA inhibitory circuits (Takesian and Hensch, 2013). The difference between critical and sensitive periods can be elucidated by examining early patterns of network connectivity (Knudsen, 2004). On the one hand, during a critical period, there is a single pattern of connectivity that is genetically encoded for and awaits reinforcement by a preferred experience. In the absence of that experience (e.g., visual or sensory deprivation), the circuit fails to develop. On the other hand, during a sensitive period, there are multiple potential patterns of connectivity. The absence of a preferred stimulus and its specific features (magnitude, duration, and complexity) will steer the circuit into one of these potential patterns. Critical and sensitive periods differ in their spatial and temporal properties and are circuit- or modality-specific. For example, the critical period for ocular dominance and establishing binocular vision occurs early in life, compared to the critical periods for auditory and language development. Moreover, the sensitive period for the development of different subdomains of cognitive functions occurs over different periods, as seen in the development of subdomains of visual perception (e.g., spatial resolution, positional resolution, contrast, and contour sensitivity) (Kiorpes, 2015). The presence of sensitive periods is essential to consider in clinical practice. In children with prelingual deafness, cochlear implants before the age of 12 months improved speech perception, auditory performance,

A CONCEPTUAL FRAMEWORK FOR PLASTICITY IN THE DEVELOPING BRAIN and receptive langue, compared to implantation between 12–24 months of age (Bruijnzeel and Stegeman, 2016). Recognizing intervention-related sensitive periods is essential for designing and implementing time-sensitive interventions that have a high probability of restoring function.

Functions of early brain plasticity The developing brain is characterized by abundant postnatal synaptogenesis. Changes in synaptic plasticity and patterns of network connectivity serve major maturational processes through learning and compensation following injury or sensory deprivation. These processes are highly regulated within and across multiple neuronal domains (cells to networks) to allow precision in achieving the desired aims and functions.

DEVELOPMENT/MATURATION THROUGH LEARNING There is an existing debate on whether experiencedependent plasticity and innate brain development are synonymous, complementary, or independent processes. The interaction between experience-dependent plasticity and preprogrammed processes of growth and maturation is a complex topic. Is every change in the developing nervous system obligatory plastic? When does development/maturation stop and when does experiencedependent plasticity (learning) begin? Brain development, in the biologic sense, is the process of physical growth, which is supported by genetic blueprint, environmental input and is mediated by sequential cellular and biochemical events (Budday et al., 2015). In developmental psychology, brain development also refers to the processes of physical growth, structural and functional maturation, and adaptation. Maturation can be physical, emotional, cognitive, and/ or intellectual and serves to bring the immature organism closer to a fully mature functioning phenotype. Adaptation is defined as the ability to change in response to the environment. Development and maturation occur across time and can be dynamic, interactive, and iterative. Brain development is expressed through the maturation of the underlying brain processes, including biologic processes (genetic, epigenetic, synaptic) and behaviors/ functions (learning, memory, and experience). Experimental and neuroimaging studies support the notion that early stages of physical brain growth and patterning of neural networks are innate and genetically predetermined. Sequential expressions of neurotrophic genes, growth factors, and axon guidance molecules such as netrins, ephrins, and cell adhesion molecules mediate the processes of neuro/gliogenesis, cell migration, and differentiation, synaptic formation and pruning, the emergence of spontaneous electrical

61

activity and neural metabolism, culminating in the rough assembly of primitive neural networks (Kolodkin and Tessier-lavigne, 2011). The induction and progression of these processes during prenatal brain development do not require external input (e.g., visual input to establish early networks in the visual cortex). However, these processes can be derailed by abnormal internal (e.g., genetic disorders) or external challenges (e.g., intrauterine infections, teratogens). In the early stages of postnatal brain development, early patterning of neural networks connectivity is spontaneous and, to a large extent, independent of experience or activity (Leighton and Lohmann, 2016). This concept was primarily illustrated in the postnatal development of sensory networks. In their experimentations to investigate the development and the plasticity of the visual cortex in kittens, Wiesel and Hubel (1963) showed that, during early stages of postnatal development, organization of the topographical maps of the primary visual cortex (V1) occurred regardless of visual input. In contrast to early stages, the later stages of postnatal brain development are experience and activitydependent in which visual input, becomes necessary to modify synaptic plasticity further. This visual inputdependent activity produces long-lasting changes in neuronal responses and circuits specialization and function, leading to a configuration of structurally and functionally more complex networks that serve distinct functions (reviewed in Espinosa and Stryker, 2012). The maturational profile of synaptic plasticity (synaptogenesis and pruning) differs across brain areas and may correlate with gain or loss of function in behavior. For example, synaptogenesis in the auditory cortex peaks at 3 months of age and corresponds to head turning to sounds. Synaptogenesis in the frontal cortex is maximal at 18 months of age, but an activity-dependent synaptic pruning is active until young adulthood and is associated with learning and acquisition of new skills (Huttenlocher and Dabholkar, 1997). Experience-dependent plasticity operates across the life span. During early and late childhood, there is an exponential gain of language, motor, and cognitive skills, which are based on experience-dependent plasticity. For example, early exposure to a second language or musical instrument improves the level of proficiency in adulthood (White et al., 2013). The age of exposure to a second language, has been shown to affect cortical representation differently in native and nonnative speakers. In a recent meta-analysis, the age of acquisition of a second language influences language deficit following a stroke in adulthood. Adult patients who learned a second language before the age of 7 years (early bilinguals) had comparable impairment in both languages, while those who learned a second language after the

62

F.Y. ISMAIL ET AL.

age of 7 years (late bilinguals) had less impairment in their native language, compared to the second language (Kuzmina et al., 2019). During adolescence and early adulthood, the brain continues to undergo major reorganization in functional connectivity that supports the more robust integration of specialized networks between frontal–subcortical and frontal–parietal regions. The adult brain continues to show forms of activity-dependent synaptic plasticity that supports the ability to learn (L€ ovden et al., 2013) and develop higher cognitive skills (e.g., reading) (Dehaene et al., 2015). The mechanisms underlying preprogrammed hardwiring of neural networks as well as those underlying experience-dependent connectivity patterns seem to overlap. For example, there is evidence for an ongoing activity-dependent neurogenesis, synaptic pruning, and structural and functional changes in neural connectivity as a result of learning and aging but also adrenal and gonadal hormones, medications, or disease (Fuchs and Fl€ ugge, 2014). While the original theories of cortical map areas formation suggested that they are configured by predetermined genetic programming “protomap hypothesis” (Rakic, 2007), there is an evidence for interplay between genetic programming, epigenetic modification, and experience-dependent changes in the formation and refinements of cortical maps (protocortex theory) (O’Leary, 1989). The distinction, therefore, between experienceindependent (development) and experience-dependent (plasticity) mechanisms in the developing brain is rather challenging. It has been proposed that development (preprogrammed maturation) and learning (experiencedependent change in neuronal responses) are two processes that exist in a continuum (Galván, 2010). While innate preprogrammed mechanisms of early brain development lay the primitive structural and functional foundations of brain growth, experiencedependent plasticity optimizes the maturational profile. Collectively, a change in the developing brain can be a result of a predetermined path, experience-driven modification, or both.

COMPENSATION/ADAPTATION FOLLOWING INJURY OR SENSORY DEPRIVATION

The notion that the young brain is inherently superior in recovery after brain injury compared to the adult brain is well-rooted in neuroplasticity literature. However, while important, this notion is restrictive as it reduces the complexity of neuroplastic responses to one variable, i.e., age. In her pivotal work, Kennard (1936) laid the important conceptual framework of early principles of brain

neuroplasticity (rseviewed in Dennis, 2011). She made several key observations derived from experiments on infant monkeys seeking to understand factors associated with motor outcomes after early brain lesions and their underlying mechanisms. She demonstrated that age, but more precisely, the developmental stage during which the brain lesion occurs affected functional outcomes, while other factors were equally important, including the topography of the lesion, the total lesion load, and the behavioral outcome(s) under question (Kennard, 1936, 1944). In children with congenital sensory deprivation (e.g., hearing or vision loss), their immature brain adapts and reorganizes its cortical maps within the original cortical area dedicated for a specific sensory input (intramodal plasticity) or between different cortical areas of a different modality (cross-modal plasticity) (Neville and Bavelier, 2002). The pattern of reorganization depends on the timing of sensory deprivation (congenital or acquired), the modality of sensory input (visual, auditory, language, or motor), the severity of sensory deprivation (e.g., mild vs profound hearing loss), and age (neonatal, childhood, or adulthood) (Bavelier and Neville, 2002; Lee and Whitt, 2016). An intriguing phenomenon of the sensory-deprived developing brain is its ability to repurpose the function of the deprived area to support other tasks, referred to as cross-modal plasticity. Evidence for large scale structural and functional changes in auditory pathways and related cortical circuits have been shown in animal models and humans with congenital deafness (Gordon et al., 2011). In cases of congenital deafness, higher order association sensory cortices are particularly subjected to cross-modal reorganization and repurposing (Glick and Sharma, 2017). For example, in children with prelingual hearing loss, enhanced performance in visual motion detection tasks and larger amplitudes of cortical visual evoked potentials have been associated with the recruitment of the temporal cortex for the processing of visual motion stimuli (Campbell and Sharma, 2016). In congenital blindness, large-scale structural reorganization and loss of separate modularity between visual and language areas have been reported (Hasson et al., 2016). Moreover, right-lateralized occipital responses to language and reduced left lateralization of language in the frontotemporal region were observed in individuals with congenital blindness in response to nonlinguistic experiences such as solving a math equation or performing a nonword-based memory task (Lane et al., 2017). Cross-modal plasticity is also subjected to critical periods. For example, cross-modal plasticity for language in occipital regions is associated with occipital responsiveness to language tasks if blindness occurs

A CONCEPTUAL FRAMEWORK FOR PLASTICITY IN THE DEVELOPING BRAIN before the age of 9 years (Bedny et al., 2013). Similarly, the primary visual cortex was shown to be activated during a tactile discrimination task in those who lost sight before the age of 16 years; this was not seen in cases where vision loss occurred after 16 years of age (Sadato et al., 2002). Collectively, the data support the notion that there is a subset of circuits in favor of modality-independent processing of information content, termed supramodality process, and is thought to have implications for sensory rehabilitation strategies through sensory-substitution therapy (reviewed in Cecchetti et al., 2016).

63

parietal regions is seen in children with very premature birth suggesting “stunted” plasticity mechanisms (M€urner-Lavanchy et al., 2014). Children born prematurely with very low birth weight and no clinically apparent neonatal brain injury continue to suffer from abnormal neurodevelopmental profile (e.g., learning disabilities) well into adolescence and show abnormal patterns of functional connectivity (Rowlands et al., 2016).

Epilepsy

Aberrant plasticity mechanisms at the level of the synapse and the network have been associated with abnormal brain development and neurocognitive dysfunction. Common etiologies include faulty genes/ epigenetic machinery, adverse environmental experiences, toxins, or acquired brain injury. In this section, we apply the proposed framework to discuss a selected group of clinical disorders caused by or associated with impaired plasticity mechanisms. We also discuss the mechanisms and functions of early brain plasticity in the setting of neurobehavioral interventions and the context of enhanced vulnerability to injury.

Epileptogenesis, which refers to the process of transforming a circuit from a seizure-naïve status into a circuit that generates recurrent seizures, is a form of excessive uncontrolled plasticity. Seizures and intractable epilepsy lead to changes in neuronal activity, which can induce abnormal axonal sprouting, expression of immediate early genes such as Arc, c-fos, and egr-1, alteration of trophic factors, changes in neurotransmission and glial reactivity that lead to synaptic and network remodeling (Jarero-basulto et al., 2018). This cascade of events may interfere with normal plasticity mechanisms of maturation and development. For example, there is evidence for language network reorganization and atypical speech development in children with epilepsy-onset before 5 years of age (Saltzman et al., 2002).

Genetic disorders

Perinatal stroke

Impaired synaptic plasticity mechanisms can be understood by examining a range of neurogenetic disorders. Mutations in MeCP2 gene in Rett syndrome lead to abnormal homeostatic synaptic scaling that impairs synaptic LTP (Ronnett et al., 2003). In fragile X syndrome, mutations in FMR1 gene are associated with delayed maturation and abnormal stabilization of dendrites leading to an abnormal increase in spine density and impaired LTP (Katz et al., 2016). Targeting specific signaling molecules and pathways have been shown to restore impaired mechanisms of synaptic plasticity in animal models of fragile X and Rett syndrome, with varying impact on behavioral and neurocognitive outcomes (Wang et al., 2015). These approaches show promise but are in their early stages of clinical translation.

Perinatal stroke is a unique condition through which plasticity is often studied. The identified mechanisms of ischemia and their impact on synaptic function, the focality of lesions, and the relatively intact plasticity mechanisms in nonlesioned areas offer advantages in studying plasticity in the developing brain (Kirton, 2013). The motor outcomes in children with perinatal stroke largely depend on the stage of developmental maturation of the corticospinal system at the time of injury. At birth, there is a bilateral representation of corticospinal tracts (CSTs). During the first 2 years of life, a developmentally preprogrammed competitive elimination of ipsilateral CST occurs at the synaptic level in the spinal cord and brainstem to establish motor dominance. A unilateral stroke, during the period of bilateral representation of CSTs, causes a different pattern of structural and functional motor tracts reorganization, compared to a stroke that occurs after complete elimination of ipsilateral projections (reviewed in Williams et al., 2018). The functional outcomes of compensatory plasticity following injury are nevertheless far from ideal. In a recent study, preserved ipsilateral CST projections after unilateral stroke were associated with strong mirror movements and poor motor outcomes (Riddell et al., 2019) .

CLINICAL CONTEXTS

Prematurity Impaired synaptic plasticity mechanisms secondary to premature birth include abnormal dendritic sprouting and synaptogenesis, morphometric changes in gray matter volume in cortical and subcortical structures, and altered resting-state connectivity patterns detected by advanced neuroimaging (Dean et al., 2014). Delay in maturation-related cortical thinning in frontal and

64

F.Y. ISMAIL ET AL.

Intervention-related plasticity

CONCLUDING REMARKS

Plasticity in the developing brain can be induced by neuromodulatory interventions. In children with hemiplegic cerebral palsy, a week of constraint-induced movement therapy (CIMT) induced changes in somatosensory cortical event-related potentials in response to touch in the lesioned hemisphere similar to that of the healthy hemisphere before CIMT intervention (Matusz et al., 2018). In a recent clinical trial (PLASTIC CHAMPS) of intensive therapy in children with hemiplegic cerebral palsy, the combination of CIMT with a repetitive transcranial magnetic stimulation (a noninvasive plasticity-inducing protocol), over the contra-lesioned hemisphere had additive effects on the lesioned hemisphere. The authors pointed out an increase in the size of cortical motor evoked potentials and a decrease in the abnormal intracortical inhibition, which was associated with the improvement of motor functions (Kuo et al., 2018).

The complexity of understanding early brain plasticity is related to its abundant and variable mechanisms that operate at different levels of organization of the nervous system (from neurons to networks) and the multiple functions it serves in early development (maturation, learning, and compensation and recovery following injury). These considerations are an on-going field of research in early brain plasticity and should, therefore, be more precise to reflect these sources of variability. There are many exciting discoveries and new methodologies across the disciplines of neuroscience that allow us to address important questions about the ontogenetic nature of early brain plasticity, how it is regulated in typical development, and affected in disease conditions. A careful examination of the mechanisms that induce or constrain plasticity, according to the specific level at which plasticity is investigated, and profiling the functions that plasticity serves in typical and atypical brain development may allow us to link these seemingly distinct but yet connected aspects of early brain plasticity. Such an approach may enhance an in-depth integration of current theories and principles generated by experimental and observational studies.

Enhanced vulnerability The heightened plasticity of the developing brain renders it particularly susceptible to injury. The high metabolic demands of a continuously changing system and the immaturity of neuroplastic mechanisms in the setting of metabolic failure puts the developing brain at a higher risk for exaggerated injury. For example, while glutamate is a major excitatory neurotransmitter and synaptic plasticity factor, glutamate-mediated excitotoxicity is a key mechanism for excessive excitation and neuronal damage in the setting of energy failure, secondary to neonatal hypoxic–ischemic injury. This is especially observed in glutamate-mediated pathways such as perirolandic area and posterior putamen (Rocha-Ferreira and Hristova, 2016). Subsequent damage is brought by a secondary energy failure that causes mitochondrial dysfunction, inflammation and glial activation, and loss of neurotrophic factors. Delayed CNS injury then ensues, over days to weeks, leading to neurodegeneration, hypomyelination, and abnormal neuronal and vascular remodeling, which may impair normal developmental trajectory (reviewed in Ismail et al., 2017b). The enhanced vulnerability of the developing brain to injury and its long-term negative impact on normal developmental mechanisms underscore the concept that a static brain injury during an early developmental period may lead to impaired plasticity mechanisms mediating maturation and learning, which translate clinically to neurocognitive defects that emerge over time.

REFERENCES Abraham WC (2008). Metaplasticity: tuning synapses and networks for plasticity. Nat Rev Neurosci 9: 387. Avena-koenigsberger A, Misic B, Sporns O (2018). Communication dynamics in complex brain networks. Nat Rev Neurosci 19: 17–33. Barrett KC et al. (2013). Art and science: how musical training shapes the brain. Front Psychol 4: 1–13. Bavelier D, Neville HJ (2002). Cross-modal plasticity: where and how? Nat Rev Neurosci 3: 443–452. Bedny M et al. (2013). A sensitive period for language in the visual cortex: distinct patterns of plasticity in congenitally versus late blind adults. Brain Lang 122: 162–170. Berardi N, Sale A, Maffei L (2015). Brain structural and functional development: genetics and experience. Dev Med Child Neurol 57: 4–9. Berlucchi HA, Buchtel G (2009). Neuronal plasticity: historical roots and evolution of meaning. Exp Brain Res 192: 307–319. Bruijnzeel H, Stegeman I (2016). A systematic review to define the speech and language benefit of early (5 years

Konczak et al. (2005)

(22) MB (7) A (12) Other (3)

M ¼ 8.2 years

Koustenis et al. (2016)

(42) MB (23) A (16) E (3)

M ¼ 2.5 years (A), 4.3 years (MB)

M ¼ 63.7 months

Semi-structured interview: parents considered social functioning as the most important factor, while providers thought that parents cared most about their children’s cognitive functioning In MB, the utilization of sensory and somatosensory information to refine spatiotemporal estimates was compromised Progressive IQ decline in the MB group at 5- and 10-years follow up CT disrupted emotional regulation through cognition control Ataxic dysarthric speech in irradiated MB survivors. Disfluent and slow speech, regardless of tumor type and irradiation history Lower IQ for patients diagnosed before the age of 3 years Adverse perioperative medical events associated with neurocognitive deterioration HFRT associated with better reports of executive function (BRIEF) and poorer growth, compared to STRT. Hair abnormalities: 80% Lower scores in MB compared to A for IQ, attention, verbal memory, visual–motor integration Mean FSIQ ¼ 63. Cognitive deficits associated with age at the time of radiotherapy and initial clinical stage Supratentorial radiation dose associated with impaired intellectual outcome Decline of FSIQ over time, except in case of posterior fossa RT alone (n ¼ 7) Mean FSIQ ¼ 86, range 46–139; VIQ > PIQ and processing speed index; intellectual disability 10/58; intellectual deficit associated with CMS and low parental education Stable employment maintenance was rare (n ¼ 1) and no young adult was married Pronounced risk of hearing impairment, stroke, lower educational attainment, and social independence Postural abnormalities related to lesions of the deep cerebellar nuclei; visual working memory deficits if radiotherapy and/or chemotherapy Executive functioning difficulties (forward thinking, inhibition, mental flexibility), more important in MB Continued

Table 22.1 Continued Author (year)

(n) Population

Follow-up

Main findings

Kulkarni et al. (2013)

(62) PF; (19) MB

M ¼ 5.2 years

Lafay-Cousin et al. (2009) Lafay-Cousin et al. (2013) Laughton et al. (2008)

(29) MB (12 months

Spiegler et al. (2004)

(34) PF tumors; (30) MB

Up to 120 months

Szentes et al. (2018)

(34) MB

M ¼ 2.7 years

Ullrich et al. (2015) Vaquero et al. (2008) Walter et al. (1999)

(52) MB (20) CT; (7) MB (29) young MB

M ¼ 7.5 years M ¼ 6.5 years 5 years

Wegenschimmel et al. (2017) Wolfe et al. (2012) Yock et al. (2016)

(37) MB

4 months to 3 years

(14) PF tumor; (10) MB (49) MB proton RT

2 years Md ¼ 7 years

Other acquired cerebellar lesions Kossorotoff et al. (2010) (5) posterior fossa stroke

>6 months

Limperopoulos et al. (2009)

(20) neonatal cerebellar stroke in-term infants

Md ¼ 32 months

Wingeier et al. (2011)

(8) Pediatric hemorrhagic cerebellar lesions

M ¼ 5 years 6 months

Cerebellar mutism syndrome/posterior fossa syndrome Brinkman et al. (2012a) (220) embryonal BT; (174) MB)

M ¼ 3.6 years

Catsman-Berrevoets et al. (1999) Catsman-Berrevoets and Aarsen (2010)

(42) CT; (12) with CMS

Postoperative period

(41) PFS after resection of CT

Until recurrence of speech

Chua et al. (2017)

(19) MB

>6 months

Lower verbal skills in right cerebellar lesions (n ¼ 4), lower nonverbal/spatial skills in left cerebellar lesions Declines in IQ, visual–motor integration, visual memory, verbal fluency, and executive functioning Mean FSIQ ¼ 86, processing speed most affected; low IQ associated with radiation dose; compulsive disorder and anxiety more prevalent than in controls Incidence of seizures: 7.7% Impaired executive functioning IQ decline. Required hormone replacement therapy ¼ 100% FSIQ considerably impacted by processing speed and visuomotor coordination Impaired cardio respiratory fitness FSIQ decline driven by decrements in processing speed and verbal comprehension Five children with posterior fossa stroke present the CCAS: very early mood disturbances disappeared spontaneously within few days. Initial mutism and anomia were followed by comprehension, planning, visuospatial and attention deficits. Recovery was slow and sometimes incomplete Neurologic abnormalities 39%; cognitive deficits 33%; expressive language deficits 44%. Deficits associated with large cerebellar lesions Motor deficits all; oculomotor deficits most; dysexecutive syndrome 1; verbal fluency and reaction times borderline; verbal performance and reading not pathologic. Strong correlation (0.78) of IQ with age at injury Parent report: largely positive social adjustment; PFS and high-risk treatment status associated with social problems CMS associated with MB, vermal location, and large size of the tumor During recovery, all children were dysarthric. Association of duration of mutism with severity of neurologic symptoms Bilateral abnormalities of the dentate nuclei associated with “permanent” PFS Continued

Table 22.1 Continued Author (year)

(n) Population

Follow-up

Main findings

De Smet et al. (2012)

(24) CT; (12) with CMS

1–12 years

De Smet et al. (2007)

Review of 283 cases of CMS

–

Di Rocco et al. (2011)

(34) PFT; (7) with CMS

Pre- and postsurgery

Gelabert-González and Fernández-Villa (2001) Grønbæk et al. (2020)

Review of 134 cases of CMS; (85) MB

–

Speech analysis revealed more severe deficits in patients with CMS Almost all cases (98.8%) displayed motor speech deficits after the mute period All 7 children with CMS presented preoperative language impairment Lesion of the vermis in 94% of the cases

Review

–

Jabarkheel et al. (2020)

(370) MB molecularly characterized

–

Kieffer et al. (2019)

(58) MB; (13) with CMS

M ¼ 14.9 years

Koh et al. (1997)

(6) cases of CMS

Postsurgical period

Law et al. (2012)

(51) PF tumors; (17) with CMS

M ¼ 3.5 years

Liu et al. (2018)

A 38, MB 32, E 12, other 7

Postoperative period

McEvoy et al. (2016)

(47) CT; (9) with PFS

Preoperative and 1 year postsurgery

Mei and Morgan (2011)

(27) PFT

Postsurgical period

Miller et al. (2010)

(22) CT; (11) PFS

3–4 weeks

Similarities of the supplementary motor area syndrome in adults and the CMS in children CMS 23.8%associated with young age, tumor volume, midline location, and molecular subgroup: higher risk in groups 3 and 4 Strong association between history of CMS and intellectual disability Suggestion of an association of CMS with trauma to dentate nucleus and/or the superior cerebellar peduncle CMS associated with left-handedness, MB histology, and damage within the cerebello-thalamo-cerebral pathway in the right cerebellum CMS 29% associated with tumor location, bilateral middle cerebellar peduncle involvement, dentate nucleus invasion, and age at imaging >12.4y DTI; PFS associated with white matter changes in the superior cerebellar peduncle; no association with handedness Incidence of mutism 33%, dysarthria 30%, dysphasia 33% PFS associated with bilateral damage to the proximal efferent pathway

Oh et al. (2017)

(19) CT; (4) with CMS

1–8.8 years

Ozgur et al. (2006)

Case report and review of 164 cases of CMS

–

Ozimek et al. (2004)

(14) CT; (4) with CMS

Postoperative period

Pols et al. (2017)

71 MB

1, 3, and 5 years post-dg

Robertson et al. (2006)

(450) MB

1 year

Schreiber et al. (2014)

(165) MB)

Up to 5 years

Schreiber et al. (2017)

72 MB, 32 with PFS

1–5 years postsurgery

Toescu et al. (2018)

56 MB

Postoperative period

Wells et al. (2010)

(28) MB; (11) with CMS

1 year

Loss of volume of the efferent cerebello-thalamocerebral pathway associated with history of CMS and degree of ataxia and fine motor dysfunction Association of CMS with vermian or dentate nuclear injury. Melodic speech may be the key to accelerate recovery from CMS Association of CMS with lesions of the deep cerebellar nuclei CMS 35% associated with tumor size, involvement of the brain stem, and higher mean body temperature in the first 4 postoperative days CMS in 24% of the children associated with brain stem invasion Decline in intellectual and academic skills associated with hearing loss, PFS, and young age at diagnosis Lower scores in the PFS group for intellectual ability, processing speed, attention, working memory, and spatial relations. Decline over time for attention and working memory CMS 21.4% associated with changes in the superior cerebellar peduncle and dentate nuclei CMS associated with brain stem invasion, superior and middle cerebellar peduncle edema, and poorer functional outcome

ALL, acute lymphocytic leukemia; BRIEF, behavior rating inventory of executive function; BT, brain tumors; CMS, cerebellar mutism syndrome; CNS, central nervous system; CSR, craniospinal radiation; CT, cerebellar tumors; Dg, diagnosis; FSIQ, full scale intelligence quotient; HFRT, hyperfractionated radiation therapy; HRQoL, health-related quality of life; HUI, health utilities index; IQ, intelligence quotient; M, mean; MB, medulloblastoma; Md, median; PF, posterior fossa; PFS, posterior fossa syndrome; PNET, primitive neuroectodermal tumor; QoL, quality of life; STRT, standard radiation therapy fractionated; TB, tumor bed; RT, radiotherapy.

286

G. DELLATOLAS AND H. CÂMARA-COSTA

OVERVIEW OF THE NEUROPSYCHOLOGIC PROFILE IN ATAXIAS IN ADULTS

Some studies reported preserved cognitive processes in patients with cerebellar degeneration with no difference from controls in tasks such as visuospatial cognition or memory of the temporal order of words (Dimitrov et al., 1996). Other investigations found deficient motor-independent associative learning in patients with isolated cerebellar degenerative disease (Drepper et al., 1999; Timmann, 2002), deficient perception of the absolute timing of single subsecond intervals in patients with spinocerebellar ataxia type 6 (Grube et al., 2010a,b), increased somatosensory temporal discrimination threshold in patients with degenerative cerebellar ataxia (Manganelli et al., 2013), and lower phonemic and semantic fluency and lower performance at a word stem completion task compared to controls in patients with cerebellar degeneration (Stoodley and Schmahmann, 2009). In nine patients with spinocerebellar ataxia type 2, Olivito et al. (2018) reported correlations between gray matter loss in posterior cerebellar lobules (VI, Crus I, Crus II, VIIB, IX) and performance at visuospatial, verbal memory, and executive function tasks, as well as relationships between atrophy of the motor cerebellar lobules and performance at tasks engaging motor and planning components. Finally, Kansal et al. (2017) tried to establish the functional significance of cerebellar lobules investigating correlations between cerebellar lobule volumes and performance at motor and cognitive tasks in 72 patients with ataxias and 36 controls. Motor functions were chiefly associated with the anterior lobe and posterior lobule HVI; verbal fluency, working memory, cognitive flexibility, immediate and delayed recall, verbal learning, and visuomotor coordination were variably associated with HVI, Crus I, Crus II, HVIIB and/or HIX. Immediate and delayed recall showed also associations with the anterior lobe. The authors suggest greater involvement of the cerebellum when immediate recall tasks involve more complex verbal stimuli (e.g., longer words vs digits).

Cerebellar tumors (Table 22.1) CEREBELLAR PILOCYTIC ASTROCYTOMAS OR OTHER BENIGN CEREBELLAR TUMORS TREATED BY SURGERY ONLY

In children treated for a pilocytic astrocytoma by surgery only, reported mean full scale-, verbal-, and performance-IQs are between 91 and 99 but sometimes significantly lower than the norm (i.e., 100) (Steinlin, 2003; Beebe et al., 2005; Aarsen et al., 2009; Moberget et al., 2015). Self- and proxy-reported neurologic deficits

are frequent (30%–60%) (Pompili et al., 2002; Zuzak et al., 2008; Daszkiewicz et al., 2009), and severe disability is present in about 10% of the subjects (Aarsen et al., 2006; Benesch et al., 2006). Overall quality of life has been found to be similar to that of healthy controls in one study (Zuzak et al., 2008) but lower than that of controls in all domains in another (Pompili et al., 2002). Avast majority of children (89/104) had a normal academic curriculum (Daszkiewicz et al., 2009), and 19% presented significant school problems (Zuzak et al., 2008). According to Aarsen et al. (2006), social and behavioral problems were more frequent in infratentorial than in supratentorial astrocytomas. Beebe et al. (2005) reported internalizing and adaptive behavior problems. Daszkiewicz et al. (2009), using mailed questionnaires to patients and parents, found significant behavioral disorders, mainly irritability, in almost half of the subjects (47/104). In nine subjects with astrocytomas affecting the vermis, Richter et al. (2005) reported only minor behavioral and affective changes in 5/9 children and imperfect fit with the description of the behavioral and affective disturbances of the cerebellar cognitive affective syndrome (CCAS). Although some studies found no significant difference in cognitive performances between the cerebellar astrocytoma group and the norm (Pletschko et al., 2018) or a control group (Benavides-Varela et al., 2019), neuropsychologic difficulties in children treated for cerebellar astrocytomas have been repeatedly reported in the domains of processing speed (Steinlin, 2003; Aarsen et al., 2004, 2009; Moberget et al., 2015), attention (Steinlin, 2003; Aarsen et al., 2004), and verbal working memory (Steinlin, 2003; Kirschen et al., 2008; Moberget et al., 2015). Executive function deficits (Moberget et al., 2015), spatial memory deficits (Aarsen et al., 2004), and visuospatial organization deficits (Starowicz-Filip et al., 2017) have also been reported. In the domain of language, there are reports of lower performance compared to the norm at the Boston naming test (Aarsen et al., 2009) and in spelling (Beebe et al., 2005). One study reported difficulties in the acquisition of silent reading and silent counting by young schoolchildren treated for cerebellar astrocytoma, suggesting a role of the cerebellum in automatization of reading and counting (Ait Khelifa-Gallois et al., 2015). A role of the side of the cerebellar lesion, with more visual–spatial difficulties in left cerebellar lesions and more language difficulties in the right cerebellar lesions, has been found in some studies (Frank et al., 2008; Starowicz-Filip et al., 2017), but the side of the cerebellar lesion had no effect in a larger study of children treated for cerebellar astrocytomas (Beebe et al., 2005).

THE ROLE OF CEREBELLUM IN THE CHILD Overall, in children treated for benign cerebellar astrocytomas, we found no clear evidence, neither of a cognitive decline, as reported in children treated for malignant cerebellar tumor nor of an association between age at surgery and cognitive impairment. The relationships between motor difficulties and cognitive difficulties seem poorly explored.

MEDULLOBLASTOMA AND OTHER CEREBELLAR TUMORS (TREATED BY SURGERY, GENERALLY ASSOCIATED WITH RADIOTHERAPY AND CHEMOTHERAPY) MB represents the majority of the investigations about the cognitive consequences of pediatric cerebellar lesions. MB is the most common malignant tumor of the central nervous system in children. It is an embryonic tumor of the cerebellum or fourth ventricle. Mean age at diagnosis is 5–7 years. Four molecular subgroups have been described with different overall outcomes (see Chevignard et al., 2017 for a review). Treatment includes surgery, radiotherapy, and chemotherapy and varies according to age as well as clinical and biopathologic risk factors. Overall survival improved dramatically over the past years. In recent studies, 5-year overall survival is higher than 80% in standard risk MB. In survivors, the tumor itself and its treatments may have devastating long-term side effects. Medical complications include long-term neurologic impairments (Frange et al., 2009; Piscione et al., 2014), sensory and ophthalmic impairments (Cassidy et al., 2000; LafayCousin et al., 2013; Peeler et al., 2017), endocrine deficits and growth problems (Heikens et al., 1998; Walter et al., 1999; Laughton et al., 2008; Frange et al., 2009; Edelstein et al., 2011), secondary tumors (Edelstein et al., 2011), and increased risk of chronic pathologies (Edelstein et al., 2011; Gibson et al., 2018). Cognitive and neuropsychologic impairments are thought to be related to the effects of cranial radiation (Anderson et al., 2000; Mulhern et al., 2004) and to the cerebellar damage (Cantelmi et al., 2008). The respective roles of these two factors remain debatable. Decline of the intelligence quotient (IQ) after treatment of MB has been repeatedly reported (Hoppe-Hirsch et al., 1995; Copeland et al., 1999; Grill et al., 1999; Palmer et al., 2001; Mulhern et al., 2005), with mean IQ lower than the norm but within a very large range (Fay-McClymont et al., 2017; Kieffer et al., 2019). Impairments in sustained attention, processing speed, and working memory have been most often identified (Reeves et al., 2005; Mabbott et al., 2009; Palmer et al., 2010, 2013; Droit-Volet, 2013; Yock et al., 2016; Wegenschimmel et al., 2017; Szentes et al., 2018; Heitzer et al., 2019). In addition, deficits have been documented in a variety of cognitive functions, including executive functions (Levisohn et al., 2000; Spiegler et al.,

287

2004; Vaquero et al., 2008; Koustenis et al., 2016), speech and language (Levisohn et al., 2000; Riva and Giorgi, 2000; Huber et al., 2007; Morgan et al., 2011), learning and memory (Maddrey et al., 2005; Ribi et al., 2005), and visuospatial functions and visual–motor integration (Levisohn et al., 2000; Riva and Giorgi, 2000; Scott et al., 2001; Spiegler et al., 2004; Khajuria et al., 2015). Identified factors associated with unfavorable cognitive outcome are numerous: young age at diagnosis (Johnson et al., 1994; Rønning et al., 2005; Edelstein et al., 2011; Ris et al., 2013; Khalil et al., 2019), presence and dose of supratentorial radiation (Mulhern et al., 1998; Copeland et al., 1999; Grill et al., 1999; KiefferRenaux et al., 2000; Palmer et al., 2003; Lafay-Cousin et al., 2009; Brinkman et al., 2018), white matter integrity (Mulhern et al., 1999, 2001; Mabbott et al., 2006; Brinkman et al., 2012b; Perreault et al., 2014; Law et al., 2017; Moxon-Emre et al., 2016), history of postoperative CMS (Palmer et al., 2010; Kieffer et al., 2019), hydrocephalus (Hardy et al., 2008; MoxonEmre et al., 2014), adverse perioperative medical events and postsurgical complications (Kao et al., 1994; Mabbott et al., 2008; Roncadin et al., 2008), hearing loss (Paulino et al., 2010; King et al., 2017; Orgel et al., 2016), molecular subgroup (Schwalbe et al., 2017; Oyefiade et al., 2019), female gender (Ris et al., 2001), low level of parent education (Kieffer et al., 2019). In addition, the degree of ataxia and deficit in fine motor skills have been found to be associated with the degree of cognitive impairment (Grill et al., 2004; Callu et al., 2009; Puget et al., 2009; Davis et al., 2010; Rueckriegel et al., 2010). Survivors of childhood MB present increased risk of academic underachievement and use of special education services, unemployment, and social isolation (Kiltie et al., 1997; Frange et al., 2009; King et al., 2017; Brinkman et al., 2018). Association between self- or parent-reported quality of life and degree of neurocognitive impairment may be absent (Maddrey et al., 2005; Benesch et al., 2009) or weak (Bull et al., 2015). Findings about long-term emotional and behavioral difficulties are conflicting (Hopyan et al., 2010; Kulkarni et al., 2013; Bull et al., 2014; Szentes et al., 2018). Mabbott et al. (2005) found that neither psychologic distress nor behavior problems were significant.

CEREBELLAR CYSTS Most cerebellar arachnoid cysts are considered asymptomatic and do not require surgical treatment. Cuny et al. (2017) report the case of two brothers who at age 2 years presented motor impairment, dysarthria, and cognitive regression. After surgery, they showed progressive improvement in their motor and cognitive skills.

288

G. DELLATOLAS AND H. CÂMARA-COSTA

Other acquired pediatric cerebellar lesions: Stroke, traumatic brain injury (Table 22.1) CEREBELLAR STROKE In 8 children with hemorrhagic cerebellar lesions that occurred between 9 and 13 years of age and examined years after the accident, Wingeier et al. (2011) report mild and mostly subclinical motor problems in all children, disturbed oculomotor functions, normal mean IQ that correlated positively with age at injury, mild and heterogeneous cognitive difficulties, and internalizing problems in some children with lesion of the vermis. In 5 children with posterior fossa stroke aged 3–14 years, Kossorotoff et al. (2010) observed early poststroke mood disturbances and mutism, which disappeared spontaneously within few days, followed by deficits in comprehension, planning, visuospatial function, and attention. Neuropsychologic testing performed more than 6 months after the accident showed incomplete recovery in some cases. In 20 in-term children with neonatal cerebellar stroke examined at a median age of 32 months, Limperopoulos et al. (2009) reported neurologic abnormalities, cognitive deficits, and expressive language deficits in less than 50% of the cases.

TRAUMATIC BRAIN INJURY In one MRI investigation of 23 children aged 7–13 years with severe traumatic brain injury, examined more than 1 year after the accident, Braga et al. (2007) reported an association between the presence of lesions of the cerebellum and low performance IQ, low visual recognition, and presence of dyscalculia. Total lesion volume and extent of cerebral atrophy were not related to the neuropsychologic evaluation.

Cerebellar mutism syndrome/posterior fossa syndrome (Table 22.1) Some children become mute in the immediate postoperative period after posterior fossa surgery, with restoration of speech within a few weeks (Gelabert-González and Fernández-Villa, 2001). Emotional lability, neuropsychiatric symptoms, and oropharyngeal dyspraxia may also occur, and the term posterior fossa syndrome is sometimes used to describe this phenomenon (Catsman-Berrevoets and Aarsen, 2010). Reported incidence of CMS in children after resection of cerebellar tumors is between 20% and 35% (Mei and Morgan, 2011; Pols et al., 2017; Toescu et al., 2018; Jabarkheel et al., 2020). The occurrence of CMS has been consistently associated with midline location of the tumor and lesions of the

cerebello-thalamo-cerebral pathway, that is lesions of the dentate nuclei and the superior cerebellar peduncle (Koh et al., 1997; Gelabert-González and Fernández-Villa, 2001; Ozimek et al., 2004; Ozgur et al., 2006; Wells et al., 2010; Law et al., 2012; McEvoy et al., 2016; Chua et al., 2017; Oh et al., 2017; Liu et al., 2018; Toescu et al., 2018). Other reported risk factors for the occurrence of CMS are large size of the tumor (Catsman-Berrevoets et al., 1999), preoperative language impairment (Di Rocco et al., 2011), molecular subgroup 3 and 4 of MB (Jabarkheel et al., 2020), and involvement of the brain stem and higher mean body temperature in the first 4 postoperative days (Wells et al., 2010; Pols et al., 2017). Left-handedness was a risk factor in one study (Law et al., 2012) but not in another (McEvoy et al., 2016). CMS has been consistently found to be a risk factor of poorer neurologic, cognitive, and social outcome (Catsman-Berrevoets and Aarsen, 2010; Brinkman et al., 2012a; De Smet et al., 2012; Schreiber et al., 2014, 2017; Oh et al., 2017; Kieffer et al., 2019).

CEREBELLUM AND NEURODEVELOPMENTAL DISORDERS Evidence for the involvement of the cerebellum in neurodevelopmental disorders, emotion, and neuropsychiatric disorders has been reviewed elsewhere (Schmahmann, 2000; Schmahmann et al., 2007; Hoppenbrouwers et al., 2008; Stoodley, 2016; Adamaszek et al., 2017). Hariri (2019) proposed that the risk of general psychopathology in an individual depends on a single factor related to the structural integrity of the cerebellum and its output, the cerebellothalamo-cortical tract. For Sathyanesan et al. (2019), the developmental trajectory of the human cerebellar connectome may be a unifying framework to study diverse complex brain disorders such as ASDs, attention-deficit hyperactivity disorder (ADHD) and Down syndrome. Cerebellar abnormalities have been reported in ADHD (see Dougherty et al., 2016 for a review), together with evidence that stimulant medication may normalize the cerebellar structures (Bledsoe et al., 2009; Schweren et al., 2013). In the following sections, we focus on investigations of the relationships between cerebellum and autism, and between cerebellum and developmental dyslexia.

Cerebellum and autism The cerebellum has been considered as one of the key brain regions affected in ASDs (Becker and Stoodley, 2013). The computational power of the cerebellum may be essential for many processes that are perturbed in autism including language and communication, social interactions, stereotyped behavior, motor activity and

THE ROLE OF CEREBELLUM IN THE CHILD coordination, and higher cognitive functions (Hampson and Blatt, 2015). There seems to be consensus on the presence of abnormal cerebellar anatomy and of cerebellar motor and cognitive deficits in subjects with autism (Fatemi et al., 2012). Many cerebellar anatomical abnormalities have been reported in autism and ASD including: an abnormally low number of Purkinje cells in the cerebellar cortex (Baron-Cohen, 2004; Courchesne et al., 2004), dysfunctional Purkinje cells associated with autism-like behavior in mice (Peter et al., 2016), reduced white matter volume (McAlonan, 2004), increased cerebellar volume (but see Traut et al., 2018 for a metaanalysis), reduced volume of the right anterior cerebellar lobe (Laidi et al., 2017), and diminished or abnormal cerebrocerebellar connectivity (Verly et al., 2014; Khan et al., 2015; Crippa et al., 2016; Igelstr€ om et al., 2017; Stoodley et al., 2017; Olivito et al., 2018; Oldehinkel et al., 2019; Ramos et al., 2019). Functional cerebellar deficits were also reported in individuals with ASD, including abnormalities in eyeblink conditioning (Oristaglio et al., 2013). Autism has been described as a disorder of prediction (Sinha et al., 2014), which may depend on cerebrocerebellar loops (von Hofsten and Rosander, 2012), but acquisition and adaptation of predictive internal models in self-generated movements appeared normal in children with autism (Gidley Larson et al., 2008). Anatomical abnormalities in autism have been identified not only in the cerebellum, but also in many other brain areas, including the brain stem, frontal lobes, parietal lobes, temporal lobes, hippocampus, and the amygdale (Baron-Cohen, 2004; McAlonan, 2004). It has been suggested that disruptions or dysfunctions in specific cerebrocerebellar loops in ASD might impede early development and specialization of specific cortical regions (D’Mello and Stoodley, 2015). Presence or not of an “autistic-like” behavior in children with cerebellar lesions, especially lesions of the vermis, remains questionable. For instance, cerebrocerebellar disconnection after surgery of a cerebellar tumor has been associated with cerebellar mutism (see the CMS section), but it seems difficult to obtain a consensus about the similarity or not of the postsurgical cerebellar mutism with the ASDs.

Cerebellum and developmental dyslexia The cerebellum is positioned to influence both phonologic and word-based decoding procedures for recognizing unfamiliar printed words, according to a recent review of functional connectivity between the cerebellum and the cerebral reading network (Alvarez and Fiez, 2018). Nicolson and Fawcett proposed the cerebellar deficit hypothesis of developmental dyslexia. In a first

289

study using a dual task paradigm in 23 13-year-old children with dyslexia, Nicolson and Fawcett (1990) concluded that the skill of motor balance is poorly automatized in this population and needs conscious compensation. They suggested a failure to fully automatize skills, explaining the reading deficit. In subsequent studies, the same authors attributed this potential automaticity deficit in children with dyslexia to the cerebellum, showing impairments at a battery of cerebellar tasks involving balance, postural stability, muscle tone, and coordination (Fawcett et al., 1996; Fawcett and Nicolson, 1999), as well as in time estimation tasks (Nicolson et al., 1995). More recently, these authors reported difficulties in procedural learning in individuals with dyslexia, even when the tasks were outside the literacy domain (Nicolson et al., 2010). They suggested impairment of procedural learning circuits involving the cerebellar motor circuit in dysgraphia and of the cerebellar language circuit in pure dyslexia (Nicolson and Fawcett, 2011). They proposed a “delayed neural commitment framework” in developmental dyslexia, in which dyslexic children take longer to build the neural networks that underpin the acquisition of reading (Nicolson and Fawcett, 2019). Other studies confirmed the presence of balancing difficulties associated with reading and spelling scores in some (but not all) dyslexic children (Stoodley et al., 2005) and the presence of implicit motor learning deficits in some dyslexic adults (Stoodley et al., 2006). In their reviews on cerebellum and dyslexia, Stoodley and Stein (2011, 2013) conclude that cerebellar dysfunction is probably not the primary cause of dyslexia. Some investigations using MRI are in favor of cerebellar abnormalities in developmental dyslexia, showing lower gray matter volumes in the right cerebellum in dyslexics (Pernet et al., 2009), absence of cerebellar asymmetry (right gray matter > left gray matter) in dyslexics compared to controls (Rae et al., 2002), or diffuse and widespread activation (cerebellar and cerebral) in dyslexics during a noun–verb association paradigm, suggesting a disorder of the processing or transfer of information within the cerebellar cortex (Baillieux et al., 2009). The cerebellar deficit hypothesis in developmental dyslexia has been extensively discussed (e.g., Zeffiro and Eden, 2001; Beaton, 2002). In children with cerebellar lesions, mainly tumors, the most frequently reported core deficits are low processing speed, attention deficits, and working memory deficits. Reading deficits are rarely mentioned as a major neuropsychologic problem. However, there are few detailed investigations of reading skills in children with cerebellar lesions. One study showed difficulties in the transition from reading aloud

290

G. DELLATOLAS AND H. CÂMARA-COSTA

to reading silently in young schoolchildren treated for a cerebellar pilocytic astrocytoma, in line with the hypothesis of a deficit of automatization of reading after a cerebellar lesion (Ait Khelifa-Gallois et al., 2015). A large study of children treated for low-grade cerebellar astrocytoma did not show significant reading difficulties (Beebe et al., 2005).

THE CEREBELLAR COGNITIVE AFFECTIVE SYNDROME (CCAS) In 20 adults with diseases confined to the cerebellum (stroke 14, cerebellitis 3, cerebellar cortical atrophy 3, other 1), examined shortly after the accident in case of acute onset (between 1 week and 3 months), Schmahmann and Sherman (1998) described a constellation of neuropsychologic deficits and behavioral changes, characterized by impairment of executive functions (planning, set-shifting, verbal fluency, abstract reasoning, and working memory), deficient spatial cognition (visual–spatial organization, spatial memory), language deficits (agrammatism, dysprosodia), and personality change (blunting of affect, disinhibited and inappropriate behavior). Cognitive and behavioral changes were associated with lesions of the posterior lobe of the cerebellum and the vermis, whereas lesions of the anterior lobe produced only minor neuropsychologic changes. This “clinical entity” has been called CCAS. Soon after this publication, Levisohn et al. (2000) affirmed that the CCAS is evident in children, as well as in adults. The authors reported impairments in executive function (planning, sequencing), visual–spatial function, expressive language, verbal memory, and modulation of affect in 19 children who received neither cranial irradiation nor methotrexate therapy after resection of benign or malignant cerebellar tumors (astrocytoma 7, MB 11, ependymoma 1). At neuropsychologic testing, deficits were defined as scores more than 1.5 SD lower than the mean, according to available norms. Language was found “abnormal” in seven children, visual–spatial function in seven, visual–spatial memory in five, affect in six, and executive function in five. Seven children presented no deficits at all, and eight children presented one or two cognitive–affective abnormalities, among the five mentioned earlier. Children were evaluated between 1 and 21 months after resection. Dysregulation of affect was associated with lesions of the vermis. Hoche et al. (2018) proposed a 10-item CCAS scale to help to identify CCAS in cerebellar patients. The CCAS scale includes tasks of semantic fluency (animals or living creatures in 1 min), phonemic fluency (words that start with letter F), verbal category shifting (name a vegetable and then a profession or job, and then another

vegetable and another profession and so on), verbal registration and recall of five words, digit span forward and backward, cube draw and cube copy, similarities (e.g., lake/river), a go-no-go task, and a 6-item questionnaire about affect (e.g., “lacks empathy, is apathetic, or has blunted affect”). Recently, a meta-analysis of ten studies in adult patients with isolated cerebellar lesions, examined within 1 year of diagnosis, concludes that cerebellar patients perform significantly worse on phonemic and semantic fluency, Stroop test, block design, and visual memory (Ahmadian et al., 2019). According to a “task-force paper” in the journal dedicated to the cerebellum, the CCAS offers excellent grounds to investigate the functional topography of the cerebellum and the mechanisms by which the cerebellum modulates cognition and affect (Argyropoulos et al., 2019). Schmahmann et al. (2019) revised the theories of the UCT and “dysmetria of thought.” The posterior lobe of the cerebellum modulates thought and emotion in the same way that the anterior lobe modulates motor control. Patients with midline cerebellar lesions show prominent affective impairments. The observation of metalinguistic deficits (e.g., understanding of ambiguous sentences, making inferences) in patients with cerebellar dysfunction (ataxia, stroke), contrasting with relatively preserved grammar and semantic abilities, is proposed as an example of “dysmetria of thought” in the domain of language. Cerebellar injury disrupts modulation but not generation of movement (dysmetria but not weakness) and, in the same manner, modulation but not generation of language (metalinguistic deficits but not aphasia) (Guell et al., 2015). Many subsequent studies in adults and children with cerebellar pathology presented evidence in favor of the CCAS (Riva and Giorgi, 2000; Steinlin, 2003; Gottwald, 2004; Tavano et al., 2007; Baillieux et al., 2010; Kossorotoff et al., 2010; Stoodley et al., 2016), whereas other studies presented negative evidence and criticized the CCAS (Mabbott et al., 2005; Richter et al., 2005; Glickstein, 2006; Frank et al., 2007a,b; Konczak and Timmann, 2007; Timmann and Daum, 2007; Glickstein and Doron, 2008; Alexander et al., 2012; Omar et al., 2014).

SUMMARY OF THE TOPIC AND NEW DEBATABLE CONCEPTS Motor and cognitive cerebellum There is clear evidence of a segregation of the motor and the cognitive functions in the cerebellum, but clinical evidence of a complete segregation of the motor and cognitive deficits after damage of the cerebellum is much

THE ROLE OF CEREBELLUM IN THE CHILD less clear. The idea of exclusively cognitive difficulties, without any motor problem, after damage to the posterior lobe of the cerebellum supposes that many generations of neurologists not only overlooked the cognitive functions of the cerebellum but also missed the absence of any motor problem in many cases of cerebellar damage. Many recent clinical studies in patients after damage to the posterior lobe of the cerebellum focused more on the presence of cognitive problems and much less on eventual motor problems and possible motor-cognitive interactions. In children treated for cerebellar tumors, the lesion generally involves both the motor and the cognitive cerebellum, and strong associations between the cognitive and the motor difficulties have been repeatedly reported. The role of impaired eye movements on visuospatial skills, or other cognitive tasks involving eye movements, remains underexplored. There seems to be some discrepancy between detailed investigations of the oculomotor vermis in monkeys and absence of such investigations in children treated for cerebellar tumors, located precisely in this region. Presence and understanding of motor-cognitive interactions might be essential for the definition of remediation and rehabilitation programs.

Functional organization of the cerebellum and anatomo-functional associations The functional organization of the cerebellum is a rapidly evolving domain. Recent studies showed that lobular boundaries do not coincide with functional subdivisions, as well as multiple motor and nonmotor representations in different regions of the cerebellum. This could explain the difficulty to find specific and consistent anatomofunctional associations in patients with cerebellar damage. An effect of the side of the cerebellar lesion, namely, more language difficulties after right cerebellar lesions and more visuospatial difficulties after left cerebellar lesions, found in some studies has not been consistently confirmed. Furthermore, even at the anatomical level, there are some controversies about the presence and importance of non-crossed cerebellocerebral connections (Wang et al., 2013; Palesi et al., 2015, 2016; Meola et al., 2016; Karavasilis et al., 2019; Schutter, 2019).

The cerebellar cognitive affective syndrome (CCAS) The CCAS certainly triggered investigations on the functions of the cerebellum and offered grounds to investigate its functional topography. The CCAS relies strongly on anatomy. Damage to cerebellar regions connected with specific cerebral association areas should lead to specific cognitive deficits. The description of the CCAS in a

291

small sample of children treated for cerebellar tumors (Levisohn et al., 2000) should be compared to findings of numerous other studies of large samples investigating cognitive difficulties in the same population. The abnormalities of language, visual–spatial organization, visual– spatial memory, and executive function, observed in a small sample, have to be compared with the findings of an extensive literature on the cognitive consequences of pediatric cerebellar tumors, insisting on mean IQs lower than the norm, especially in children treated for MB, low processing speed, and attention and working memory problems. We suggest that there is no clear contradiction between the description of the CCAS in children and the cognitive findings in children treated for benign or malignant tumors of the cerebellum. An IQ lower than the norm, as well as low processing speed and attention and working memory problems, increases the probability that a child may show low performance, defined by 1.5 SD lower than the mean, in at least one of the four neuropsychologic domains explored by Levisohn et al. (2000). In addition, impaired control of hand and eye movements may contribute to low performances at visuomotor and visual tasks, and hearing problems induced by some drugs used in chemotherapy (e.g., cisplatine) may increase the risk of low performances at language tasks. On the other hand, in children with cerebellar damage, the executive function difficulties described in the CCAS merit further investigations. A more pronounced discrepancy concerns the behavioral and affective problems after damage to the vermis, described in the CCAS, which were found “not significant” in other studies (Mabbott et al., 2005). We may hypothesize that “blunting of affect” and “inappropriate behavior” are more often present in the early after-surgery period than in the long-term. Few children presented more than two abnormalities among the five described in the pediatric CCAS. The term “syndrome” seems to refer not to the cooccurrence of these abnormalities but to problems that may occur after cerebellar damage thought to be related to cerebellocerebral connections and a UCT. This raises an obvious question. Is it necessary to have more than one abnormality to accept the presence of a CCAS, or is one abnormality sufficient? If one abnormality is sufficient, it might be difficult to provide strong evidence for the absence of CCAS in a subject with cerebellar damage. It is always possible to miss a deficient metalinguistic skill, an impaired executive function, or some dysregulation of affect, even after detailed clinical and neuropsychologic examination. Future studies, using for instance the CCAS scale, would contribute to give answers to the questions and problems mentioned earlier.

292

G. DELLATOLAS AND H. CÂMARA-COSTA

UCT and common components in cerebellar motor and neuropsychologic deficits It remains challenging to find a common component in all cerebellar deficits, motor and nonmotor. Schmahmann called this common component “dysmetria,” which can affect motor control, thought, or affect. Dysmertria of movement is a classic cerebellar symptom, dysmetria of thought might be deficits in metalinguistic skills associated with preserved linguistic skills (Guell et al., 2015), and dysmetria of affect, dysregulation of one’s emotions and behavior. For other authors, this common component may be prediction, automatization, sequencing, timing, or perception–action coupling, and all these concepts are possibly related to forward internal models. For Thach (2007), the cerebellum contributes to cognition by linking together cerebral “cognitive units” as a substrate for a coordinated thought, as it links together motor units to coordinate movement. Overall, there are recent efforts to understand cerebellar deficits in terms of common principles, supposed to be related to the intrinsic cellular organization of the cerebellum and internal models. Associative learning, prediction, sequencing, or automatization could depend on the same mechanism. An automatization deficit might explain low processing speed and attention difficulties, the latter related to excessive demand of attention in the absence of automatization. Verbal working memory seems to be associated with motor factors. ASDs might be related to prediction or may have common components with the CMS. Eyeblink conditioning, motor control, and language comprehension could have common computational principles (Moberget and Ivry, 2016). The present review seeks to contribute modestly to this effort, by suggesting similarities between apparently disparate findings, such as cerebellar activation by silent reading and verbal working memory tasks, delayed acquisition of silent reading and counting in children with cerebellar damage, delayed automatization of reading in children with developmental dyslexia, and verbal working memory and subvocal rehearsal deficits in adults with cerebellar damage.

and cognitive learning, and as a consequence motor and cognitive difficulties might be more important in cerebellar dysfunctions occurring early in life than later in life. The consequences of a cerebellar damage could be greater in children than in adults (Alexander et al., 2012). Motor and cognitive deficits in cerebellar malformations bring some support to this idea. The subject with cerebellar agenesis showed massive developmental delay, needing a life-long remediation and rehabilitation program to attend some degree of autonomy. Early age at injury has been associated with more motor and cognitive sequelae in children treated for MB; however, cranial radiotherapy may be a confounding factor in this population. An effect of age at injury has not been consistently reported in cerebellar pilocytic astrocytomas or other acquired cerebellar injuries. On the other hand, the motor of the aeroplane looks useful to the glider, even after completion of the learning process. Cerebellar damage in adults disturbs perfectly learned motor and cognitive skills. To what extent are these learned skills stored in the cerebellum, or elsewhere in the brain? Future studies would investigate further this question, as well as the role of the age at cerebellar injury.

Environment, remediation, rehabilitation Early deprivation affects the development of the cerebellum. An experimental study showed that exposure to an enriched environment accelerates recovery from cerebellar lesion (Foti et al., 2011). There is also evidence of effectiveness of long remediation and rehabilitation programs, even in case of cerebellar agenesis.

CONCLUSION All children with damage to the cerebellum should benefit from remediation programs based on detailed neuropsychologic examination, interpreted in the light of detailed clinical examination, which should include investigation of fine motor skills and eye movements, as well as evaluation of psychologic and environmental factors.

ACKOWLEDGMENT Learning, and cerebellar damage in children and in adults Steinlin (2007) hypothesized that the most important or primary function of the cerebellum is learning during development. Once the function of the cerebrum is well established, the importance of the cerebellum can decline. The cerebrum is like a glider, imagined Steinlin, which needs the cerebellum as the motor aeroplane to bring it up in the air; once there, the glider is able to fly alone. Indeed, the cerebellum is involved in motor

We would like to thank Ippolyti Dellatolas for her comments in a previous version of this chapter.

REFERENCES Aarsen FK, Van Dongen HR, Paquier PF et al. (2004). Longterm sequelae in children after cerebellar astrocytoma surgery. Neurology 62: 1311–1316. Aarsen FK, Paquier PF, Reddingius RE et al. (2006). Functional outcome after low-grade astrocytoma treatment in childhood. Cancer 106: 396–402.

THE ROLE OF CEREBELLUM IN THE CHILD Aarsen FK, Paquier PF, Arts W-F et al. (2009). Cognitive deficits and predictors 3 years after diagnosis of a pilocytic astrocytoma in childhood. J Clin Oncol 27: 3526–3532. Ackermann H (2008). Cerebellar contributions to speech production and speech perception: psycholinguistic and neurobiological perspectives. Trends Neurosci 31: 265–272. Adamaszek M, D’Agata F, Ferrucci R et al. (2017). Consensus paper: cerebellum and emotion. Cerebellum 16: 552–576. Afshar M, Link M, Edwards MSB et al. (2012). Complete ocular paresis in a child with posterior fossa syndrome. Pediatr Neurosurg 48: 51–54. Ahmadian N, van Baarsen K, van Zandvoort M et al. (2019). The cerebellar cognitive affective syndrome—a metaanalysis. Cerebellum 18: 941–950. Ait Khelifa-Gallois N, Puget S, Longaud A et al. (2015). Clinical evidence of the role of the cerebellum in the suppression of overt articulatory movements during reading. A study of reading in children and adolescents treated for cerebellar pilocytic astrocytoma. Cerebellum 14: 97–105. Albus JS (1971). A theory of cerebellar function. Math Biosci 10: 25–61. Alexander MP, Gillingham S, Schweizer T et al. (2012). Cognitive impairments due to focal cerebellar injuries in adults. Cortex 48: 980–990. Allin M, Matsumoto H, Santhouse AM et al. (2001). Cognitive and motor function and the size of the cerebellum in adolescents born very pre-term. Brain 124: 60–66. Alvarez TA, Fiez JA (2018). Current perspectives on the cerebellum and reading development. Neurosci Biobehav Rev 92: 55–66. Amemiya K, Morita T, Saito DN et al. (2019). Local-to-distant development of the cerebrocerebellar sensorimotor network in the typically developing human brain: a functional and diffusion MRI study. Brain Struct Funct 224: 1359–1375. Anderson VA, Godber T, Smibert E et al. (2000). Cognitive and academic outcome following cranial irradiation and chemotherapy in children: a longitudinal study. Br J Cancer 82: 255–262. Apps R, Hawkes R, Aoki S et al. (2018). Cerebellar modules and their role as operational cerebellar processing units. Cerebellum 17: 654–682. Argyropoulos GPD, van Dun K, Adamaszek M et al. (2019). The cerebellar cognitive affective/schmahmann syndrome: a task force paper. Cerebellum 19: 102–125. Aso K, Hanakawa T, Aso T et al. (2010). Cerebro-cerebellar interactions underlying temporal information processing. J Cogn Neurosci 22: 2913–2925. Azevedo FAC, Carvalho LRB, Grinberg LT et al. (2009). Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J Comp Neurol 513: 532–541. Baier B, Stoeter P, Dieterich M (2009). Anatomical correlates of ocular motor deficits in cerebellar lesions. Brain 132: 2114–2124. Baillieux H, Vandervliet EJM, Manto M et al. (2009). Developmental dyslexia and widespread activation across the cerebellar hemispheres. Brain Lang 108: 122–132.

293

Baillieux H, De Smet HJ, Dobbeleir A et al. (2010). Cognitive and affective disturbances following focal cerebellar damage in adults: a neuropsychological and SPECT study. Cortex 46: 869–879. Balsters JH, Cussans E, Diedrichsen J et al. (2010). Evolution of the cerebellar cortex: the selective expansion of prefrontal-projecting cerebellar lobules. Neuroimage 49: 2045–2052. Balsters JH, Laird AR, Fox PT et al. (2014). Bridging the gap between functional and anatomical features of corticocerebellar circuits using meta-analytic connectivity modeling: meta-analytic connectivity modeling of corticocerebellar circuits. Hum Brain Mapp 35: 3152–3169. Barash S, Melikyan A, Sivakov A et al. (1999). Saccadic dysmetria and adaptation after lesions of the cerebellar cortex. J Neurosci Off J Soc Neurosci 19: 10931–10939. Baresˇ M, Apps R, Avanzino L et al. (2019). Consensus paper: decoding the contributions of the cerebellum as a time machine. From neurons to clinical applications. Cerebellum 18: 266–286. Baron-Cohen S (2004). The cognitive neuroscience of autism. J Neurol Neurosurg Psychiatry 75: 945–948. Bauer PM, Hanson JL, Pierson RK et al. (2009). Cerebellar volume and cognitive functioning in children who experienced early deprivation. Biol Psychiatry 66: 1100–1106. Baumann O, Borra RJ, Bower JM et al. (2015). Consensus paper: the role of the cerebellum in perceptual processes. Cerebellum 14: 197–220. Beaton AA (2002). Dyslexia and the cerebellar deficit hypothesis. Cortex 38: 479–490. Beaton A, Marie¨n P (2010). Language, cognition and the cerebellum: grappling with an enigma. Cortex 46: 811–820. Becker EBE, Stoodley CJ (2013). Autism spectrum disorder and the cerebellum. In: International review of neurobiology, Elsevier pp. 1–34. Beebe DW, Ris MD, Armstrong FD et al. (2005). Cognitive and adaptive outcome in low-grade pediatric cerebellar astrocytomas: evidence of diminished cognitive and adaptive functioning in national collaborative research studies (CCG 9891/POG 9130). J Clin Oncol 23: 5198–5204. Bellebaum C, Daum I (2011). Mechanisms of cerebellar involvement in associative learning. Cortex 47: 128–136. Benavides-Varela S, Lorusso R, Baro V et al. (2019). Mathematical skills in children with pilocytic astrocytoma. Acta Neurochir 161: 161–169. Benesch M, Lackner H, Sovinz P et al. (2006). Late sequela after treatment of childhood low-grade gliomas: a retrospective analysis of 69 long-term survivors treated between 1983 and 2003. J Neurooncol 78: 199–205. Benesch M, Spiegl K, Winter A et al. (2009). A scoring system to quantify late effects in children after treatment for medulloblastoma/ependymoma and its correlation with quality of life and neurocognitive functioning. Childs Nerv Syst 25: 173–181. Ben-Yehudah G, Fiez JA (2008). Impact of cerebellar lesions on reading and phonological processing. Ann N Y Acad Sci 1145: 260–274.

294

G. DELLATOLAS AND H. CÂMARA-COSTA

Ben-Yehudah G, Guediche S, Fiez JA (2007). Cerebellar contributions to verbal working memory: beyond cognitive theory. Cerebellum 6: 193–201. Bernard JA, Leopold DR, Calhoun VD et al. (2015). Regional cerebellar volume and cognitive function from adolescence to late middle age: cerebellar volume and cognition. Hum Brain Mapp 36: 1102–1120. Bledsoe J, Semrud-Clikeman M, Pliszka SR (2009). A magnetic resonance imaging study of the cerebellar vermis in chronically treated and treatment-naı¨ve children with attention-deficit/hyperactivity disorder combined type. Biol Psychiatry 65: 620–624. Bodranghien F, Bastian A, Casali C et al. (2016). Consensus paper: revisiting the symptoms and signs of cerebellar syndrome. Cerebellum 15: 369–391. Bolduc M-E, Limperopoulos C (2009). Neurodevelopmental outcomes in children with cerebellar malformations: a systematic review. Dev Med Child Neurol 51: 256–267. Bolduc M-E, Du Plessis AJ, Sullivan N et al. (2011). Spectrum of neurodevelopmental disabilities in children with cerebellar malformations: developmental disabilities in children with cerebellar malformations. Dev Med Child Neurol 53: 409–416. Bolduc M-E, du Plessis AJ, Sullivan N et al. (2012). Regional cerebellar volumes predict functional outcome in children with cerebellar malformations. Cerebellum 11: 531–542. Bolk L (1906). Das Cerebellum der S€augetiere, Fischer, Jena, Germany. Booth JR, Wood L, Lu D et al. (2007). The role of the basal ganglia and cerebellum in language processing. Brain Res 1133: 136–144. Bostan AC, Strick PL (2018). The basal ganglia and the cerebellum: nodes in an integrated network. Nat Rev Neurosci 19: 338–350. Bostan AC, Dum RP, Strick PL (2013). Cerebellar networks with the cerebral cortex and basal ganglia. Trends Cogn Sci 17: 241–254. Boyd CAR (2010). Cerebellar agenesis revisited. Brain 133: 941–944. Boyden ES, Katoh A, Raymond JL (2004). Cerebellumdependent learning: the role of multiple plasticity mechanisms. Annu Rev Neurosci 27: 581–609. Bracha V, Zhao L, Irwin KB et al. (2000). The human cerebellum and associative learning: dissociation between the acquisition, retention and extinction of conditioned eyeblinks. Brain Res 860: 87–94. Braga LW, Souza LN, Najjar YJ et al. (2007). Magnetic resonance imaging (MRI) findings and neuropsychological sequelae in children after severe traumatic brain injury: the role of cerebellar lesion. J Child Neurol 22: 1084–1089. Brandauer B, Timmann D, H€ausler A et al. (2010). Influences of load characteristics on impaired control of grip forces in patients with cerebellar damage. J Neurophysiol 103: 698–708. Breska A, Ivry RB (2018). Double dissociation of singleinterval and rhythmic temporal prediction in cerebellar degeneration and Parkinson’s disease. Proc Natl Acad Sci 115: 12283–12288.

Brinkman TM, Palmer SL, Chen S et al. (2012a). Parentreported social outcomes after treatment for pediatric embryonal tumors: a prospective longitudinal study. J Clin Oncol 30: 4134–4140. Brinkman TM, Reddick WE, Luxton J et al. (2012b). Cerebral white matter integrity and executive function in adult survivors of childhood medulloblastoma. Neuro Oncol 14: iv25–iv36. Brinkman TM, Ness KK, Li Z et al. (2018). Attainment of functional and social independence in adult survivors of pediatric CNS tumors: a report from the St Jude Lifetime Cohort Study. J Clin Oncol 36: 2762–2769. Brooks JX, Carriot J, Cullen KE (2015). Learning to expect the unexpected: rapid updating in primate cerebellum during voluntary self-motion. Nat Neurosci 18: 1310–1317. Buckner RL (2013). The cerebellum and cognitive function: 25 years of insight from anatomy and neuroimaging. Neuron 80: 807–815. Bulgheroni S, D’Arrigo S, Signorini S et al. (2016). Cognitive, adaptive, and behavioral features in Joubert syndrome. Am J Med Genet A 170: 3115–3124. Bull KS, Liossi C, Culliford D et al. (2014). Child-related characteristics predicting subsequent health-related quality of life in 8- to 14-year-old children with and without cerebellar tumors: a prospective longitudinal study. Neurooncol Pract 1: 114–122. Bull KS, Liossi C, Peacock JL et al. (2015). Screening for cognitive deficits in 8 to 14-year old children with cerebellar tumors using self-report measures of executive and behavioral functioning and health-related quality of life. Neuro Oncol 17: 1628–1636. Callu D, Viguier D, Laroussinie F et al. (2009). Cognitive and academic outcome after benign or malignant cerebellar tumor in children. Cogn Behav Neurol 22: 270–278. Cantelmi D, Schweizer TA, Cusimano MD (2008). Role of the cerebellum in the neurocognitive sequelae of treatment of tumours of the posterior fossa: an update. Lancet Oncol 9: 569–576. Cassidy L, Stirling R, May K et al. (2000). Ophthalmic complications of childhood medulloblastoma. Med Pediatr Oncol 34: 43–47. Catsman-Berrevoets CE, Aarsen FK (2010). The spectrum of neurobehavioural deficits in the Posterior Fossa Syndrome in children after cerebellar tumour surgery. Cortex 46: 933–946. Catsman-Berrevoets CE, Van Dongen HR, Mulder PGH et al. (1999). Tumour type and size are high risk factors for the syndrome of “cerebellar” mutism and subsequent dysarthria. J Neurol Neurosurg Psychiatry 67: 755–757. Cerminara NL, Apps R, Marple-Horvat DE (2009). An internal model of a moving visual target in the lateral cerebellum: cerebellar internal model of target motion. J Physiol 587: 429–442. Cerminara NL, Lang EJ, Sillitoe RV et al. (2015). Redefining the cerebellar cortex as an assembly of non-uniform Purkinje cell microcircuits. Nat Rev Neurosci 16: 79–93. Chen SHA, Desmond JE (2005). Temporal dynamics of cerebro-cerebellar network recruitment during a cognitive task. Neuropsychologia 43: 1227–1237.

THE ROLE OF CEREBELLUM IN THE CHILD Chevignard M, C^amara-Costa H, Doz F et al. (2017). Core deficits and quality of survival after childhood medulloblastoma: a review. Neuro Oncol Pract 4: 82–97. Chiricozzi FR, Clausi S, Molinari M et al. (2008). Phonological short-term store impairment after cerebellar lesion: a single case study. Neuropsychologia 46: 1940–1953. Christensen A, Giese MA, Sultan F et al. (2014). An intact action-perception coupling depends on the integrity of the cerebellum. J Neurosci 34: 6707–6716. Chua FHZ, Thien A, Ng LP et al. (2017). Post-operative diffusion weighted imaging as a predictor of posterior fossa syndrome permanence in paediatric medulloblastoma. Childs Nerv Syst 33: 457–465. Coffman KA, Dum RP, Strick PL (2011). Cerebellar vermis is a target of projections from the motor areas in the cerebral cortex. Proc Natl Acad Sci 108: 16068–16073. Copeland DR, deMoor C, Moore BD et al. (1999). Neurocognitive development of children after a cerebellar tumor in infancy: a longitudinal study. J Clin Oncol 17: 3476–3486. Corben LA, Georgiou-Karistianis N, Fahey MC et al. (2006). Towards an understanding of cognitive function in Friedreich ataxia. Brain Res Bull 70: 197–202. Courchesne E, Redcay E, Kennedy DP (2004). The autistic brain: birth through adulthood. Curr Opin Neurol 17: 489–496. Crippa A, Del Vecchio G, Busti Ceccarelli S et al. (2016). Cortico-cerebellar connectivity in autism spectrum disorder: what do we know so far? Front Psych 7: 20. Cuny ML, Pallone M, Piana H et al. (2017). Neuropsychological improvement after posterior fossa arachnoid cyst drainage. Childs Nerv Syst 33: 135–141. D’Mello AM, Stoodley CJ (2015). Cerebro-cerebellar circuits in autism spectrum disorder. Front Neurosci 9: 408. Daszkiewicz P, Maryniak A, Roszkowski M et al. (2009). Long-term functional outcome of surgical treatment of juvenile pilocytic astrocytoma of the cerebellum in children. Childs Nerv Syst 25: 855–860. Daum I, Ackermann H (1995). Cerebellar contributions to cognition. Behav Brain Res 67: 201–210. Davis EE, Pitchford NJ, Jaspan T et al. (2010). Development of cognitive and motor function following cerebellar tumour injury sustained in early childhood. Cortex 46: 919–932. De Smet HJ, Baillieux H, Catsman-Berrevoets C et al. (2007). Postoperative motor speech production in children with the syndrome of ‘cerebellar’ mutism and subsequent dysarthria: a critical review of the literature. Eur J Paediatr Neurol 11: 193–207. De Smet HJ, Catsman-Berrevoets C, Aarsen F et al. (2012). Auditory-perceptual speech analysis in children with cerebellar tumours: a long-term follow-up study. Eur J Paediatr Neurol 16: 434–442. De Zeeuw CI, Ten Brinke MM (2015). Motor learning and the cerebellum. Cold Spring Harb Perspect Biol 7: a021683. Dean P, Porrill J, Ekerot C-F et al. (2010). The cerebellar microcircuit as an adaptive filter: experimental and computational evidence. Nat Rev Neurosci 11: 30–43. Decety J, Sj€oholm H, Ryding E et al. (1990). The cerebellum participates in mental activity: tomographic measurements of regional cerebral blood flow. Brain Res 535: 313–317.

295

Desmond JE, Gabrieli JD, Wagner AD et al. (1997). Lobular patterns of cerebellar activation in verbal working-memory and finger-tapping tasks as revealed by functional MRI. J Neurosci Off J Soc Neurosci 17: 9675–9685. Desmond JE, Gabrieli JDE, Glover GH (1998). Dissociation of frontal and cerebellar activity in a cognitive task: evidence for a distinction between selection and search. Neuroimage 7: 368–376. Desmond JE, Chen SHA, Shieh PB (2005). Cerebellar transcranial magnetic stimulation impairs verbal working memory. Ann Neurol 58: 553–560. Di Rocco C, Chieffo D, Frassanito P et al. (2011). Heralding cerebellar mutism: evidence for pre-surgical language impairment as primary risk factor in posterior fossa surgery. Cerebellum 10: 551–562. Diedrichsen J, King M, Hernandez-Castillo C et al. (2019). Universal transform or multiple functionality? Understanding the contribution of the human cerebellum across task domains. Neuron 102: 918–928. Dimitrov M, Grafman J, Kosseff P et al. (1996). Preserved cognitive processes in cerebellar degeneration. Behav Brain Res 79: 131–135. Dirnberger G, Novak J, Nasel C (2013). Perceptual sequence learning is more severely impaired than motor sequence learning in patients with chronic cerebellar stroke. J Cogn Neurosci 25: 2207–2215. Doron KW, Funk CM, Glickstein M (2010). Fronto-cerebellar circuits and eye movement control: a diffusion imaging tractography study of human cortico-pontine projections. Brain Res 1307: 63–71. Dougherty CC, Evans DW, Myers SM et al. (2016). A comparison of structural brain imaging findings in autism spectrum disorder and attention-deficit hyperactivity disorder. Neuropsychol Rev 26: 25–43. Doyon J, Song AW, Karni A et al. (2002). Experiencedependent changes in cerebellar contributions to motor sequence learning. Proc Natl Acad Sci 99: 1017–1022. Drepper J, Timmann D, Kolb FP et al. (1999). Non-motor associative learning in patients with isolated degenerative cerebellar disease. Brain J Neurol 122: 87–97. Droit-Volet S (2013). Time perception in children: a neurodevelopmental approach. Neuropsychologia 51: 220–234. Droit-Volet S, Z
elanti PS, Dellatolas G et al. (2013). Time perception in children treated for a cerebellar medulloblastoma. Res Dev Disabil 34: 480–494. Dum RP, Strick PL (2003). An unfolded map of the cerebellar dentate nucleus and its projections to the cerebral cortex. J Neurophysiol 89: 634–639. Durisko C, Fiez JA (2010). Functional activation in the cerebellum during working memory and simple speech tasks. Cortex 46: 896–906. Ebner TJ, Pasalar S (2008). Cerebellum predicts the future motor state. Cerebellum 7: 583–588. Edelstein K, Spiegler BJ, Fung S et al. (2011). Early aging in adult survivors of childhood medulloblastoma: longterm neurocognitive, functional, and physical outcomes. Neuro Oncol 13: 536–545.

296

G. DELLATOLAS AND H. CÂMARA-COSTA

Fatemi SH, Aldinger KA, Ashwood P et al. (2012). Consensus paper: pathological role of the cerebellum in autism. Cerebellum 11: 777–807. Fautrelle L, Pichat C, Ricolfi F et al. (2011). Catching falling objects: the role of the cerebellum in processing sensory– motor errors that may influence updating of feedforward commands. An fMRI study. Neuroscience 190: 135–144. Fawcett AJ, Nicolson RI (1999). Performance of dyslexic children on cerebellar and cognitive tests. J Mot Behav 31: 68–78. Fawcett AJ, Nicolson RI, Dean P (1996). Impaired performance of children with dyslexia on a range of cerebellar tasks. Ann Dyslexia 46: 259–283. Fay-McClymont TB, Ploetz DM, Mabbott D et al. (2017). Long-term neuropsychological follow-up of young children with medulloblastoma treated with sequential highdose chemotherapy and irradiation sparing approach. J Neurooncol 133: 119–128. Fiez JA, Petersen SE, Cheney MK et al. (1992). Impaired nonmotor learning and error detection associated with cerebellar damage: a single case study. Brain 115: 155–178. Foti F, Laricchiuta D, Cutuli D et al. (2011). Exposure to an enriched environment accelerates recovery from cerebellar lesion. Cerebellum 10: 104–119. Frange P, Alapetite C, Gaboriaud G et al. (2009). From childhood to adulthood: long-term outcome of medulloblastoma patients. The Institut Curie experience (1980–2000). J Neurooncol 95: 271–279. Frank B, Schoch B, Hein-Kropp C et al. (2007a). Verb generation in children and adolescents with acute cerebellar lesions. Neuropsychologia 45: 977–988. Frank B, Schoch B, Richter S et al. (2007b). Cerebellar lesion studies of cognitive function in children and adolescents— limitations and negative findings. Cerebellum 6: 242–253. Frank B, Schoch B, Hein-Kropp C et al. (2008). Aphasia, neglect and extinction are no prominent clinical signs in children and adolescents with acute surgical cerebellar lesions. Exp Brain Res 184: 511–519. Friston KJ, Frith CD, Passingham RE et al. (1992). Motor practice and neurophysiological adaptation in the cerebellum: a positron tomography study. Proc Biol Sci 248: 223–228. Fukushima K (2003). Roles of the cerebellum in pursuitvestibular interactions. Cerebellum 2: 223–232. Gao Z, van Beugen BJ, De Zeeuw CI (2012). Distributed synergistic plasticity and cerebellar learning. Nat Rev Neurosci 13: 619–635. Gao Z, Davis C, Thomas AM et al. (2018). A cortico-cerebellar loop for motor planning. Nature 563: 113–116. Gebhart AL, Petersen SE, Thach WT (2002). Role of the posterolateral cerebellum in language. Ann N Y Acad Sci 978: 318–333. Gelabert-Gonza´lez M, Ferna´ndez-Villa J (2001). Mutism after posterior fossa surgery. Review of the literature. Clin Neurol Neurosurg 103: 111–114. Gibson TM, Mostoufi-Moab S, Stratton KL et al. (2018). Temporal patterns in the risk of chronic health conditions in survivors of childhood cancer diagnosed 1970–99: a report from the Childhood Cancer Survivor Study cohort. Lancet Oncol 19: 1590–1601.

Gidley Larson JC, Bastian AJ, Donchin O et al. (2008). Acquisition of internal models of motor tasks in children with autism. Brain 131: 2894–2903. Giovannucci A, Badura A, Deverett B et al. (2017). Cerebellar granule cells acquire a widespread predictive feedback signal during motor learning. Nat Neurosci 20: 727–734. Glickstein M (2006). Thinking about the cerebellum. Brain 129: 288–290. Glickstein M, Doron K (2008). Cerebellum: connections and functions. Cerebellum 7: 589–594. Glickstein M, Sultan F, Voogd J (2011). Functional localization in the cerebellum. Cortex 47: 59–80. Gooch CM, Wiener M, Wencil EB et al. (2010). Interval timing disruptions in subjects with cerebellar lesions. Neuropsychologia 48: 1022–1031. Gottwald B (2004). Evidence for distinct cognitive deficits after focal cerebellar lesions. J Neurol Neurosurg Psychiatry 75: 1524–1531. Gottwald B, Mihajlovic Z, Wilde B et al. (2003). Does the cerebellum contribute to specific aspects of attention? Neuropsychologia 41: 1452–1460. Granziera C, Schmahmann JD, Hadjikhani N et al. (2009). Diffusion spectrum imaging shows the structural basis of functional cerebellar circuits in the human cerebellum in vivo. PLoS One 4: e5101. Grill J, Renaux VK, Bulteau C et al. (1999). Long-term intellectual outcome in children with posterior fossa tumors according to radiation doses and volumes. Int J Radiat Oncol Biol Phys 45: 137–145. Grill J, Viguier D, Kieffer V et al. (2004). Critical risk factors for intellectual impairment in children with posterior fossa tumors: the role of cerebellar damage. J Neurosurg 101: 152–158. Grønbæk J, Molinari E, Avula S et al. (2020). The supplementary motor area syndrome and the cerebellar mutism syndrome: a pathoanatomical relationship? Childs Nerv Syst 36: 1197–1204. Grube M, Cooper FE, Chinnery PF et al. (2010a). Dissociation of duration-based and beat-based auditory timing in cerebellar degeneration. Proc Natl Acad Sci 107: 11597–11601. Grube M, Lee K-H, Griffiths TD et al. (2010b). Transcranial magnetic theta-burst stimulation of the human cerebellum distinguishes absolute, duration-based from relative, beat-based perception of subsecond time intervals. Front Psychol 1: 171. Guell X, Hoche F, Schmahmann JD (2015). Metalinguistic deficits in patients with cerebellar dysfunction: empirical support for the dysmetria of thought theory. Cerebellum 14: 50–58. Guell X, Gabrieli JDE, Schmahmann JD (2018). Triple representation of language, working memory, social and emotion processing in the cerebellum: convergent evidence from task and seed-based resting-state fMRI analyses in a single large cohort. Neuroimage 172: 437–449. Gupta T, Jalali R, Goswami S et al. (2012). Early clinical outcomes demonstrate preserved cognitive function in children with average-risk medulloblastoma when treated with hyperfractionated radiation therapy. Int J Radiat Oncol Biol Phys 83: 1534–1540.

THE ROLE OF CEREBELLUM IN THE CHILD Habas C (2012). Functional imaging and the cerebellum: recent developments and challenges. Editorial. Cerebellum 11: 311–313. Hampson DR, Blatt GJ (2015). Autism spectrum disorders and neuropathology of the cerebellum. Front Neurosci 9: 420. Handel B, Thier P, Haarmeier T (2009). Visual motion perception deficits due to cerebellar lesions are paralleled by specific changes in cerebro-cortical activity. J Neurosci 29: 15126–15133. Harding IH, Corben LA, Storey E et al. (2016). Frontocerebellar dysfunction and dysconnectivity underlying cognition in friedreich ataxia: the IMAGE-FRDA study: cognitive networks in Friedreich ataxia. Hum Brain Mapp 37: 338–350. Hardy KK, Bonner MJ, Willard VW et al. (2008). Hydrocephalus as a possible additional contributor to cognitive outcome in survivors of pediatric medulloblastoma. Psychooncology 17: 1157–1161. Hariri AR (2019). The emerging importance of the cerebellum in broad risk for psychopathology. Neuron 102: 17–20. Harrington DL (2003). Does the representation of time depend on the cerebellum?: Effect of cerebellar stroke. Brain 127: 561–574. Hayashi MJ, Kantele M, Walsh V et al. (2014). Dissociable neuroanatomical correlates of subsecond and suprasecond time perception. J Cogn Neurosci 26: 1685–1693. Hayter AL, Langdon DW, Ramnani N (2007). Cerebellar contributions to working memory. Neuroimage 36: 943–954. Heikens J, Michiels EMC, Behrendt H et al. (1998). Long-term neuro-endocrine sequelae after treatment for childhood medulloblastoma. Eur J Cancer 34: 1592–1597. Heitzer AM, Ashford JM, Harel BT et al. (2019). Computerized assessment of cognitive impairment among children undergoing radiation therapy for medulloblastoma. J Neurooncol 141: 403–411. Henrich N, Marra CA, Gastonguay L et al. (2014). De-escalation of therapy for pediatric medulloblastoma: trade-offs between quality of life and survival: treatment preferences for medulloblastoma. Pediatr Blood Cancer 61: 1300–1304. Herzfeld DJ, Kojima Y, Soetedjo R et al. (2015). Encoding of action by the Purkinje cells of the cerebellum. Nature 526: 439–442. Herzfeld DJ, Kojima Y, Soetedjo R et al. (2018). Encoding of error and learning to correct that error by the Purkinje cells of the cerebellum. Nat Neurosci 21: 736–743. Hetherington R, Dennis M, Spiegler B (2000). Perception and estimation of time in long-term survivors of childhood posterior fossa tumors. J Int Neuropsychol Soc 6: 682–692. Hirano T (2018). Regulation and interaction of multiple types of synaptic plasticity in a purkinje neuron and their contribution to motor learning. Cerebellum 17: 756–765. Hoche F, Guell X, Vangel MG et al. (2018). The cerebellar cognitive affective/Schmahmann syndrome scale. Brain 141: 248–270. Hoche F, Daly MP, Chutake YK et al. (2019). The cerebellar cognitive affective syndrome in ataxia-telangiectasia. Cerebellum 18: 225–244.

297

Honda T, Nagao S, Hashimoto Y et al. (2018). Tandem internal models execute motor learning in the cerebellum. Proc Natl Acad Sci 115: 7428–7433. Hoppe-Hirsch E, Brunet L, Laroussinie F et al. (1995). Intellectual outcome in children with malignant tumors of the posterior fossa: influence of the field of irradiation and quality of surgery. Childs Nerv Syst 11: 340–345. Hoppenbrouwers SS, Schutter DJLG, Fitzgerald PB et al. (2008). The role of the cerebellum in the pathophysiology and treatment of neuropsychiatric disorders: a review. Brain Res Rev 59: 185–200. Hopyan T, Laughlin S, Dennis M (2010). Emotions and their cognitive control in children with cerebellar tumors. J Int Neuropsychol Soc 16: 1027–1038. Hoshi E, Tremblay L, F
eger J et al. (2005). The cerebellum communicates with the basal ganglia. Nat Neurosci 8: 1491–1493. Huber JF, Bradley K, Spiegler B et al. (2007). Long-term neuromotor speech deficits in survivors of childhood posterior fossa tumors: effects of tumor type, radiation, age at diagnosis, and survival years. J Child Neurol 22: 848–854. Igelstr€ om KM, Webb TW, Graziano MSA (2017). Functional connectivity between the temporoparietal cortex and cerebellum in autism spectrum disorder. Cereb Cortex 27: 2617–2627. Imamizu H, Kawato M (2009). Brain mechanisms for predictive control by switching internal models: implications for higher-order cognitive functions. Psychol Res Psychol Forsch 73: 527–544. Imamizu H, Kawato M (2012). Cerebellar internal models: implications for the dexterous use of tools. Cerebellum 11: 325–335. Ito M (1993). Movement and thought: identical control mechanisms by the cerebellum. Trends Neurosci 16: 448–450. Ito M (2001). Cerebellar long-term depression: characterization, signal transduction, and functional roles. Physiol Rev 81: 1143–1195. Ito M (2008). Control of mental activities by internal models in the cerebellum. Nat Rev Neurosci 9: 304–313. Ito M, Kano M (1982). Long-lasting depression of parallel fiber-Purkinje cell transmission induced by conjunctive stimulation of parallel fibers and climbing fibers in the cerebellar cortex. Neurosci Lett 33: 253–258. Ivry RB, Keele SW (1989). Timing functions of the cerebellum. J Cogn Neurosci 1: 136–152. Jabarkheel R, Amayiri N, Yecies D et al. (2020). Molecular correlates of cerebellar mutism syndrome in medulloblastoma. Neuro Oncol 22: 290–297. Jissendi P, Baudry S, Bal
eriaux D (2008). Diffusion tensor imaging (DTI) and tractography of the cerebellar projections to prefrontal and posterior parietal cortices: a study at 3T. J Neuroradiol 35: 42–50. Johnson DL, McCabe MA, Nicholson HS et al. (1994). Quality of long-term survival in young children with medulloblastoma. J Neurosurg 80: 1004–1010. Justus T (2004). The cerebellum and english grammatical morphology: evidence from production, comprehension, and grammaticality judgments. J Cogn Neurosci 16: 1115–1130.

298

G. DELLATOLAS AND H. CÂMARA-COSTA

Justus T, Ravizza SM, Fiez JA et al. (2005). Reduced phonological similarity effects in patients with damage to the cerebellum. Brain Lang 95: 304–318. Kamali A, Kramer LA, Frye RE et al. (2010). Diffusion tensor tractography of the human brain cortico-ponto-cerebellar pathways: a quantitative preliminary study. J Magn Reson Imaging 32: 809–817. Kansal K, Yang Z, Fishman AM et al. (2017). Structural cerebellar correlates of cognitive and motor dysfunctions in cerebellar degeneration. Brain 140: 707–720. aww327. Kao GD, Goldwein JW, Schultz DJ et al. (1994). The impact of perioperative factors on subsequent intelligence quotient deficits in children treated for medulloblastoma/posterior fossa primitive neuroectodermal tumors. Cancer 74: 965–971. Karavasilis E, Christidi F, Velonakis G et al. (2019). Ipsilateral and contralateral cerebro-cerebellar white matter connections: a diffusion tensor imaging study in healthy adults. J Neuroradiol 46: 52–60. Kelly RM, Strick PL (2003). Cerebellar loops with motor cortex and prefrontal cortex of a nonhuman primate. J Neurosci Off J Soc Neurosci 23: 8432–8444. Kennedy C, Bull K, Chevignard M et al. (2014). Quality of survival and growth in children and young adults in the PNET4 European controlled trial of hyperfractionated versus conventional radiation therapy for standard-risk medulloblastoma. Int J Radiat Oncol Biol Phys 88: 292–300. Keren-Happuch E, Shen-Hsing Annabel C, Moon-Ho Ringo H et al. (2014). A meta-analysis of cerebellar contributions to higher cognition from PET and fMRI studies: a metaanalysis of cerebellar contributions. Hum Brain Mapp 35: 593–615. Khajuria RK, Blankenburg F, Wuithschick I et al. (2015). Morphological brain lesions of pediatric cerebellar tumor survivors correlate with inferior neurocognitive function but do not affect health-related quality of life. Childs Nerv Syst 31: 569–580. Khalil J, Chaabi S, Oberlin O et al. (2019). Medulloblastoma in childhood: what effects on neurocognitive functions? Cancer Radiother 23: 370–377. Khan AJ, Nair A, Keown CL et al. (2015). Cerebro-cerebellar resting-state functional connectivity in children and adolescents with autism spectrum disorder. Biol Psychiatry 78: 625–634. Kieffer V, Chevignard MP, Dellatolas G et al. (2019). Intellectual, educational, and situation-based social outcome in adult survivors of childhood medulloblastoma. Dev Neurorehabil 22: 19–26. Kieffer-Renaux V, Bulteau C, Grill J et al. (2000). Patterns of neuropsychological deficits in children with medulloblastoma according to craniospatial irradiation doses. Dev Med Child Neurol 42: 741–745. Kieffer-Renaux V, Viguier D, Raquin M-A et al. (2005). Therapeutic schedules influence the pattern of intellectual decline after irradiation of posterior fossa tumors. Pediatr Blood Cancer 45: 814–819. Kiltie AE, Lashford LS, Gattamaneni HR (1997). Survival and late effects in medulloblastoma patients treated with

craniospinal irradiation under three years old. Med Pediatr Oncol 28: 348–354. Kim S, Ugurbil K, Strick P (1994). Activation of a cerebellar output nucleus during cognitive processing. Science 265: 949–951. King AA, Seidel K, Di C et al. (2017). Long-term neurologic health and psychosocial function of adult survivors of childhood medulloblastoma/PNET: a report from the Childhood Cancer Survivor Study. Neuro Oncol 19: 689–698. King M, Hernandez-Castillo CR, Poldrack RA et al. (2019). Functional boundaries in the human cerebellum revealed by a multi-domain task battery. Nat Neurosci 22: 1371–1378. Kirschen MP, Davis-Ratner MS, Milner MW et al. (2008). Verbal memory impairments in children after cerebellar tumor resection. Behav Neurol 20: 39–53. Knolle F, Schr€ oger E, Kotz SA (2013). Cerebellar contribution to the prediction of self-initiated sounds. Cortex 49: 2449–2461. Koh S, Beckwitt Turkel S, Baram TZ (1997). Cerebellar mutism in children: report of six cases and potential mechanisms. Pediatr Neurol 16: 218–219. Konczak J, Timmann D (2007). The effect of damage to the cerebellum on sensorimotor and cognitive function in children and adolescents. Neurosci Biobehav Rev 31: 1101–1113. Konczak J, Schoch B, Dimitrova A et al. (2005). Functional recovery of children and adolescents after cerebellar tumour resection. Brain 128: 1428–1441. Kossorotoff M, Gonin-Flambois C, Gitiaux C et al. (2010). A cognitive and affective pattern in posterior fossa strokes in children: a case series: cognitive and affective deficits in children with posterior fossa strokes. Dev Med Child Neurol 52: 626–631. Kotz SA, Stockert A, Schwartze M (2014). Cerebellum, temporal predictability and the updating of a mental model. Philos Trans R Soc Lond B Biol Sci 369: 20130403. Koustenis E, Herna´iz Driever P, de Sonneville L et al. (2016). Executive function deficits in pediatric cerebellar tumor survivors. Eur J Paediatr Neurol 20: 25–37. Koziol LF, Budding D, Andreasen N et al. (2014). Consensus paper: the cerebellum’s role in movement and cognition. Cerebellum 13: 151–177. Krienen FM, Buckner RL (2009). Segregated frontocerebellar circuits revealed by intrinsic functional connectivity. Cereb Cortex 19: 2485–2497. Kulkarni AV, Piscione J, Shams I et al. (2013). Long-term quality of life in children treated for posterior fossa brain tumors: clinical article. J Neurosurg Pediatr 12: 235–240. Labrell F, C^amara-Costa H, Kieffer V et al. (2018). Time knowledge difficulties following treatment for malignant cerebellar tumors. Child Neuropsychol 24: 524–540. Lafay-Cousin L, Bouffet E, Hawkins C et al. (2009). Impact of radiation avoidance on survival and neurocognitive outcome in infant medulloblastoma. Curr Oncol 16: 21–28. Lafay-Cousin L, Purdy E, Huang A et al. (2013). Early cisplatin induced ototoxicity profile may predict the need for hearing support in children with medulloblastoma: cisplatin related ototoxicity in medulloblastoma. Pediatr Blood Cancer 60: 287–292.

THE ROLE OF CEREBELLUM IN THE CHILD Laidi C, Boisgontier J, Chakravarty MM et al. (2017). Cerebellar anatomical alterations and attention to eyes in autism. Sci Rep 7: 12008. Larsell O (1952). The morphogenesis and adult pattern of the lobules and fissures of the cerebellum of the white rat. J Comp Neurol 97: 281–356. Laughton SJ, Merchant TE, Sklar CA et al. (2008). Endocrine outcomes for children with embryonal brain tumors after risk-adapted craniospinal and conformal primary-site irradiation and high-dose chemotherapy with stem-cell rescue on the SJMB-96 trial. J Clin Oncol 26: 1112–1118. Law N, Greenberg M, Bouffet E et al. (2012). Clinical and neuroanatomical predictors of cerebellar mutism syndrome. Neuro Oncol 14: 1294–1303. Law N, Smith ML, Greenberg M et al. (2017). Executive function in paediatric medulloblastoma: the role of cerebrocerebellar connections. J Neuropsychol 11: 174–200. Leggio MG (2000). Phonological grouping is specifically affected in cerebellar patients: a verbal fluency study. J Neurol Neurosurg Psychiatry 69: 102–106. Leggio MG, Tedesco AM, Chiricozzi FR et al. (2008). Cognitive sequencing impairment in patients with focal or atrophic cerebellar damage. Brain J Neurol 131: 1332–1343. Leggio MG, Chiricozzi FR, Clausi S et al. (2011). The neuropsychological profile of cerebellar damage: the sequencing hypothesis. Cortex 47: 137–144. Leiner HC, Leiner AL, Dow RS (1986). Does the cerebellum contribute to mental skills? Behav Neurosci 100: 443–454. Leiner HC, Leiner AL, Dow RS (1989). Reappraising the cerebellum: what does the hindbrain contribute to the forebrain? Behav Neurosci 103: 998–1008. Leiner HC, Leiner AL, Dow RS (1991). The human cerebrocerebellar system: its computing, cognitive, and language skills. Behav Brain Res 44: 113–128. Leiner HC, Leiner AL, Dow RS (1993). Cognitive and language functions of the human cerebellum. Trends Neurosci 16: 444–447. Lesage E, Hansen PC, Miall RC (2017). Right lateral cerebellum represents linguistic predictability. J Neurosci 37: 6231–6241. Leto K, Arancillo M, Becker EBE et al. (2016). Consensus paper: cerebellar development. Cerebellum 15: 789–828. Levisohn L, Cronin-Golomb A, Schmahmann JD (2000). Neuropsychological consequences of cerebellar tumour resection in children: cerebellar cognitive affective syndrome in a paediatric population. Brain 123: 1041–1050. Limperopoulos C, Robertson RL, Sullivan NR et al. (2009). Cerebellar injury in term infants: clinical characteristics, magnetic resonance imaging findings, and outcome. Pediatr Neurol 41: 1–8. Liu J-F, Dineen RA, Avula S et al. (2018). Development of a pre-operative scoring system for predicting risk of postoperative paediatric cerebellar mutism syndrome. Br J Neurosurg 32: 18–27. Mabbott DJ, Noseworthy MD, Bouffet E et al. (2006). Diffusion tensor imaging of white matter after cranial radiation in children for medulloblastoma: correlation with IQ. Neuro Oncol 8: 244–252.

299

Mabbott DJ, Spiegler BJ, Greenberg ML et al. (2005). Serial evaluation of academic and behavioral outcome after treatment with cranial radiation in childhood. J Clin Oncol 23: 2256–2263. Mabbott DJ, Penkman L, Witol A et al. (2008). Core neurocognitive functions in children treated for posterior fossa tumors. Neuropsychology 22: 159–168. Mabbott DJ, Snyder JJ, Penkman L et al. (2009). The effects of treatment for posterior fossa brain tumors on selective attention. J Int Neuropsychol Soc 15: 205–216. Maddrey AM, Bergeron JA, Lombardo ER et al. (2005). Neuropsychological performance and quality of life of 10 year survivors of childhood medulloblastoma. J Neurooncol 72: 245–253. Manganelli F, Dubbioso R, Pisciotta C et al. (2013). Somatosensory temporal discrimination threshold is increased in patients with cerebellar atrophy. Cerebellum 12: 456–459. Manni E, Petrosini L (2004). A century of cerebellar somatotopy: a debated representation. Nat Rev Neurosci 5: 241–249. Marie¨n P, Manto M (2018). Cerebellum as a master-piece for linguistic predictability. Cerebellum 17: 101–103. Marie¨n P, Ackermann H, Adamaszek M et al. (2014). Consensus paper: language and the cerebellum: an ongoing enigma. Cerebellum 13: 386–410. Marr D (1969). A theory of cerebellar cortex. J Physiol 202: 437–470. Marvel CL, Desmond JE (2010a). The contributions of cerebro-cerebellar circuitry to executive verbal working memory. Cortex 46: 880–895. Marvel CL, Desmond JE (2010b). Functional topography of the cerebellum in verbal working memory. Neuropsychol Rev 20: 271–279. Marvel CL, Morgan OP, Kronemer SI (2019). How the motor system integrates with working memory. Neurosci Biobehav Rev 102: 184–194. Mathiak K, Hertrich I, Grodd W et al. (2002). Cerebellum and speech perception: a functional magnetic resonance imaging study. J Cogn Neurosci 14: 902–912. Mathiak K, Hertrich I, Grodd W et al. (2004). Discrimination of temporal information at the cerebellum: functional magnetic resonance imaging of nonverbal auditory memory. Neuroimage 21: 154–162. Matthews LG, Inder TE, Pascoe L et al. (2018). Longitudinal preterm cerebellar volume: perinatal and neurodevelopmental outcome associations. Cerebellum 17: 610–627. Mauk MD, Medina JF, Nores WL et al. (2000). Cerebellar function: coordination, learning or timing? Curr Biol 10: R522–R525. McAlonan GM (2004). Mapping the brain in autism. A voxelbased MRI study of volumetric differences and intercorrelations in autism. Brain 128: 268–276. McEvoy SD, Lee A, Poliakov A et al. (2016). Longitudinal cerebellar diffusion tensor imaging changes in posterior fossa syndrome. Neuroimage Clin 12: 582–590. Mei C, Morgan AT (2011). Incidence of mutism, dysarthria and dysphagia associated with childhood posterior fossa tumour. Childs Nerv Syst 27: 1129–1136.

300

G. DELLATOLAS AND H. CÂMARA-COSTA

Meola A, Comert A, Yeh F-C et al. (2016). The nondecussating pathway of the dentatorubrothalamic tract in humans: human connectome-based tractographic study and microdissection validation. J Neurosurg 124: 1406–1412. Middleton F, Strick P (1994). Anatomical evidence for cerebellar and basal ganglia involvement in higher cognitive function. Science 266: 458–461. Middleton FA, Strick PL (2001). Cerebellar projections to the prefrontal cortex of the primate. J Neurosci Off J Soc Neurosci 21: 700–712. Miller NG, Reddick WE, Kocak M et al. (2010). Cerebellocerebral diaschisis is the likely mechanism of postsurgical posterior fossa syndrome in pediatric patients with midline cerebellar tumors. Am J Neuroradiol 31: 288–294. Moberget T, Ivry RB (2016). Cerebellar contributions to motor control and language comprehension: searching for common computational principles: cerebellar contributions to motor control and language. Ann N Y Acad Sci 1369: 154–171. Moberget T, Andersson S, Lundar T et al. (2015). Long-term supratentorial brain structure and cognitive function following cerebellar tumour resections in childhood. Neuropsychologia 69: 218–231. Moberget T, Hilland E, Andersson S et al. (2016). Patients with focal cerebellar lesions show reduced auditory cortex activation during silent reading. Brain Lang 161: 18–27. Moore DM, D’Mello AM, McGrath LM et al. (2017). The developmental relationship between specific cognitive domains and grey matter in the cerebellum. Dev Cogn Neurosci 24: 1–11. Morgan AT, Li
egeois F, Liederkerke C et al. (2011). Role of cerebellum in fine speech control in childhood: persistent dysarthria after surgical treatment for posterior fossa tumour. Brain Lang 117: 69–76. Moxon-Emre I, Bouffet E, Taylor MD et al. (2014). Impact of craniospinal dose, boost volume, and neurologic complications on intellectual outcome in patients with medulloblastoma. J Clin Oncol Off J Am Soc Clin Oncol 32: 1760–1768. Moxon-Emre I, Bouffet E, Taylor MD et al. (2016). Vulnerability of white matter to insult during childhood: evidence from patients treated for medulloblastoma. J Neurosurg Pediatr 18: 29–40. Mulhern RK, Kepner JL, Thomas PR et al. (1998). Neuropsychologic functioning of survivors of childhood medulloblastoma randomized to receive conventional or reduced-dose craniospinal irradiation: a Pediatric Oncology Group study. J Clin Oncol 16: 1723–1728. Mulhern RK, Reddick WE, Palmer SL et al. (1999). Neurocognitive deficits in medulloblastoma survivors and white matter loss. Ann Neurol 46: 834–841. Mulhern RK, Palmer SL, Reddick WE et al. (2001). Risks of young age for selected neurocognitive deficits in medulloblastoma are associated with white matter loss. J Clin Oncol Off J Am Soc Clin Oncol 19: 472–479. Mulhern RK, Merchant TE, Gajjar A et al. (2004). Late neurocognitive sequelae in survivors of brain tumours in childhood. Lancet Oncol 5: 399–408.

Mulhern RK, Palmer SL, Merchant TE et al. (2005). Neurocognitive consequences of risk-adapted therapy for childhood medulloblastoma. J Clin Oncol 23: 5511–5519. Murdoch BE (2010). The cerebellum and language: historical perspective and review. Cortex 46: 858–868. Nicolson RI, Fawcett AJ (1990). Automaticity: a new framework for dyslexia research? Cognition 35: 159–182. Nicolson RI, Fawcett AJ (2011). Dyslexia, dysgraphia, procedural learning and the cerebellum. Cortex 47: 117–127. Nicolson RI, Fawcett AJ (2019). Development of dyslexia: the delayed neural commitment framework. Front Behav Neurosci 13: 112. Nicolson RI, Fawcett AJ, Dean P (1995). Time estimation deficits in developmental dyslexia: evidence of cerebellar involvement. Proc R Soc Lond B Biol Sci 259: 43–47. Nicolson RI, Fawcett AJ, Brookes RL et al. (2010). Procedural learning and dyslexia. Dyslexia 16: 194–212. Oh ME, Driever PH, Khajuria RK et al. (2017). DTI fiber tractography of cerebro-cerebellar pathways and clinical evaluation of ataxia in childhood posterior fossa tumor survivors. J Neurooncol 131: 267–276. Ohyama T, Nores WL, Murphy M et al. (2003). What the cerebellum computes. Trends Neurosci 26: 222–227. Oldehinkel M, Mennes M, Marquand A et al. (2019). Altered connectivity between cerebellum, visual, and sensorymotor networks in autism spectrum disorder: results from the EU-AIMS longitudinal European autism project. Biol Psychiatry Cogn Neurosci Neuroimaging 4: 260–270. Olivito G, Lupo M, Iacobacci C et al. (2018). Structural cerebellar correlates of cognitive functions in spinocerebellar ataxia type 2. J Neurol 265: 597–606. Omar D, Ryan T, Carson A et al. (2014). Clinical and methodological confounders in assessing the cerebellar cognitive affective syndrome in adult patients with posterior fossa tumours. Br J Neurosurg 28: 755–764. Orgel E, O’Neil SH, Kayser K et al. (2016). Effect of sensorineural hearing loss on neurocognitive functioning in pediatric brain tumor survivors: hearing loss-associated neurocognitive outcomes. Pediatr Blood Cancer 63: 527–534. Oristaglio J, Hyman West S, Ghaffari M et al. (2013). Children with autism spectrum disorders show abnormal conditioned response timing on delay, but not trace, eyeblink conditioning. Neuroscience 248: 708–718. Oyefiade A, Erdman L, Goldenberg A et al. (2019). PPAR and GST polymorphisms may predict changes in intellectual functioning in medulloblastoma survivors. J Neurooncol 142: 39–48. Ozgur BM, Berberian J, Aryan HE et al. (2006). The pathophysiologic mechanism of cerebellar mutism. Surg Neurol 66: 18–25. Ozimek A, Richter S, Hein-Kropp C et al. (2004). Cerebellar mutism: report of four cases. J Neurol 251: 963–972. Palesi F, Tournier J-D, Calamante F et al. (2015). Contralateral cerebello-thalamo-cortical pathways with prominent involvement of associative areas in humans in vivo. Brain Struct Funct 220: 3369–3384.

THE ROLE OF CEREBELLUM IN THE CHILD Palesi F, Tournier J-D, Calamante F et al. (2016). Reconstructing contralateral fiber tracts: methodological aspects of cerebello-thalamocortical pathway reconstruction. Funct Neurol 31: 229–238. Palmer SL, Goloubeva O, Reddick WE et al. (2001). Patterns of intellectual development among survivors of pediatric medulloblastoma: a longitudinal analysis. J Clin Oncol Off J Am Soc Clin Oncol 19: 2302–2308. Palmer SL, Gajjar A, Reddick WE et al. (2003). Predicting intellectual outcome among children treated with 35-40 Gy craniospinal irradiation for medulloblastoma. Neuropsychology 17: 548–555. Palmer SL, Hassall T, Evankovich K et al. (2010). Neurocognitive outcome 12 months following cerebellar mutism syndrome in pediatric patients with medulloblastoma. Neuro Oncol 12: 1311–1317. Palmer SL, Armstrong C, Onar-Thomas A et al. (2013). Processing speed, attention, and working memory after treatment for medulloblastoma: an international, prospective, and longitudinal study. J Clin Oncol Off J Am Soc Clin Oncol 31: 3494–3500. Paulino AC, Lobo M, Teh BS et al. (2010). Ototoxicity after intensity-modulated radiation therapy and cisplatin-based chemotherapy in children with medulloblastoma. Int J Radiat Oncol Biol Phys 78: 1445–1450. Peeler CE, Edmond JC, Hollander J et al. (2017). Visual and ocular motor outcomes in children with posterior fossa tumors. J AAPOS 21: 375–379. Pelzer EA, Hintzen A, Goldau M et al. (2013). Cerebellar networks with basal ganglia: feasibility for tracking cerebello-pallidal and subthalamo-cerebellar projections in the human brain. Eur J Neurosci 38: 3106–3114. Pernet CR, Poline JB, Demonet JF et al. (2009). Brain classification reveals the right cerebellum as the best biomarker of dyslexia. BMC Neurosci 10: 67. Perreault S, Lober RM, Cheshier S et al. (2014). Timedependent structural changes of the dentatothalamic pathway in children treated for posterior fossa tumor. Am J Neuroradiol 35: 803–807. Peter S, ten Brinke MM, Stedehouder J et al. (2016). Dysfunctional cerebellar Purkinje cells contribute to autism-like behaviour in Shank2-deficient mice. Nat Commun 7: 12627. Peterburs J, Desmond JE (2016). The role of the human cerebellum in performance monitoring. Curr Opin Neurobiol 40: 38–44. Peterburs J, Bellebaum C, Koch B et al. (2010). Working memory and verbal fluency deficits following cerebellar lesions: relation to interindividual differences in patient variables. Cerebellum 9: 375–383. Peterburs J, Blevins LC, Sheu Y-S et al. (2019). Cerebellar contributions to sequence prediction in verbal working memory. Brain Struct Funct 224: 485–499. Petersen SE, van Mier H, Fiez JA et al. (1998). The effects of practice on the functional anatomy of task performance. Proc Natl Acad Sci 95: 853–860. Pieterman K, Batalle D, Dudink J et al. (2017). Cerebellocerebral connectivity in the developing brain. Brain Struct Funct 222: 1625–1634.

301

Piscione PJ, Bouffet E, Mabbott DJ et al. (2014). Physical functioning in pediatric survivors of childhood posterior fossa brain tumors. Neuro Oncol 16: 147–155. Pleger B, Timmann D (2018). The role of the human cerebellum in linguistic prediction, word generation and verbal working memory: evidence from brain imaging, non-invasive cerebellar stimulation and lesion studies. Neuropsychologia 115: 204–210. Pletschko T, Felnhofer A, Lamplmair D et al. (2018). Cerebellar pilocytic astrocytoma in childhood: investigating the long-term impact of surgery on cognitive performance and functional outcome. Dev Neurorehabil 21: 415–422. Pols SYCV, van Veelen MLC, Aarsen FK et al. (2017). Risk factors for development of postoperative cerebellar mutism syndrome in children after medulloblastoma surgery. J Neurosurg Pediatr 20: 35–41. Pompili A, Caperle M, Pace A et al. (2002). Quality-of-life assessment in patients who had been surgically treated for cerebellar pilocytic astrocytoma in childhood. J Neurosurg 96: 229–234. Popa LS, Ebner TJ (2019). Cerebellum, predictions and errors. Front Cell Neurosci 12: 524. Poretti A, Limperopoulos C, Roulet-Perez E et al. (2009). Outcome of severe unilateral cerebellar hypoplasia: outcome of severe unilateral cerebellar hypoplasia. Dev Med Child Neurol 52: 718–724. Poretti A, Snow J, Summers AC et al. (2017). Joubert syndrome: neuroimaging findings in 110 patients in correlation with cognitive function and genetic cause. J Med Genet 54: 521–529. Provasi J, Doye`re V, Z
elanti PS et al. (2014). Disrupted sensorimotor synchronization, but intact rhythm discrimination, in children treated for a cerebellar medulloblastoma. Res Dev Disabil 35: 2053–2068. Puget S, Boddaert N, Viguier D et al. (2009). Injuries to inferior vermis and dentate nuclei predict poor neurological and neuropsychological outcome in children with malignant posterior fossa tumors. Cancer 115: 1338–1347. Rae C, Harasty JA, Dzendrowskyj TE et al. (2002). Cerebellar morphology in developmental dyslexia. Neuropsychologia 40: 1285–1292. Rahmati N, Owens CB, Bosman LWJ et al. (2014). Cerebellar potentiation and learning a whisker-based object localization task with a time response window. J Neurosci Off J Soc Neurosci 34: 1949–1962. Ramnani N (2006). The primate cortico-cerebellar system: anatomy and function. Nat Rev Neurosci 7: 511–522. Ramnani N, Behrens TEJ, Johansen-Berg H et al. (2006). The evolution of prefrontal inputs to the cortico-pontine system: diffusion imaging evidence from macaque monkeys and humans. Cereb Cortex 16: 811–818. Ramos TC, Balardin JB, Sato JR et al. (2019). Abnormal cortico-cerebellar functional connectivity in autism spectrum disorder. Front Syst Neurosci 12: 74. Ravizza SM, McCormick CA, Schlerf JE et al. (2006). Cerebellar damage produces selective deficits in verbal working memory. Brain J Neurol 129: 306–320.

302

G. DELLATOLAS AND H. CÂMARA-COSTA

Reeves CB, Palmer SL, Reddick WE et al. (2005). Attention and memory functioning among pediatric patients with medulloblastoma. J Pediatr Psychol 31: 272–280. Ribi K, Relly C, Landolt MA et al. (2005). Outcome of medulloblastoma in children: long-term complications and quality of life. Neuropediatrics 36: 357–365. Richter S, Schoch B, Kaiser O et al. (2005). Behavioral and affective changes in children and adolescents with chronic cerebellar lesions. Neurosci Lett 381: 102–107. Ris MD, Packer R, Goldwein J et al. (2001). Intellectual outcome after reduced-dose radiation therapy plus adjuvant chemotherapy for medulloblastoma: a Children’s Cancer Group study. J Clin Oncol 19: 3470–3476. Ris MD, Walsh K, Wallace D et al. (2013). Intellectual and academic outcome following two chemotherapy regimens and radiotherapy for average-risk medulloblastoma: COG A9961: neurocognitive outcome in medulloblastoma. Pediatr Blood Cancer 60: 1350–1357. Riva D, Giorgi C (2000). The cerebellum contributes to higher functions during development: evidence from a series of children surgically treated for posterior fossa tumours. Brain 123: 1051–1061. Robertson PL, Muraszko KM, Holmes EJ et al. (2006). Incidence and severity of postoperative cerebellar mutism syndrome in children with medulloblastoma: a prospective study by the Children’s Oncology Group. J Neurosurg 105: 444–451. Roncadin C, Dennis M, Greenberg ML et al. (2008). Adverse medical events associated with childhood cerebellar astrocytomas and medulloblastomas: natural history and relation to very long-term neurobehavioral outcome. Childs Nerv Syst 24: 995–1002. Ronconi L, Casartelli L, Carna S et al. (2017). When one is enough: impaired multisensory integration in cerebellar agenesis. Cereb Cortex 27: 2041–2051. Rønning C, Sundet K, Due-Tønnessen B et al. (2005). Persistent cognitive dysfunction secondary to cerebellar injury in patients treated for posterior fossa tumors in childhood. Pediatr Neurosurg 41: 15–21. Rueckriegel SM, Blankenburg F, Henze G et al. (2009). Loss of fine motor function correlates with ataxia and decline of cognition in cerebellar tumor survivors. Pediatr Blood Cancer 53: 424–431. Rueckriegel SM, Driever PH, Blankenburg F et al. (2010). Differences in supratentorial damage of white matter in pediatric survivors of posterior fossa tumors with and without adjuvant treatment as detected by magnetic resonance diffusion tensor imaging. Int J Radiat Oncol Biol Phys 76: 859–866. Salmi J, Pallesen KJ, Neuvonen T et al. (2010). Cognitive and motor loops of the human cerebro-cerebellar system. J Cogn Neurosci 22: 2663–2676. Sander T, Sprenger A, Neumann G et al. (2009). Vergence deficits in patients with cerebellar lesions. Brain 132: 103–115. Sathyanesan A, Zhou J, Scafidi J et al. (2019). Emerging connections between cerebellar development, behaviour and complex brain disorders. Nat Rev Neurosci 20: 298–313.

Sayah S, Rotg
e J-Y, Francisque H et al. (2018). Personality and neuropsychological profiles in Friedreich ataxia. Cerebellum 17: 204–212. Schmahmann JD (1991). An emerging concept. The cerebellar contribution to higher function. Arch Neurol 48: 1178–1187. Schmahmann JD (2000). The role of the cerebellum in affect and psychosis. J Neurolinguistics 13: 189–214. Schmahmann JD, Pandya DN (1997). Anatomic organization of the basilar pontine projections from prefrontal cortices in rhesus monkey. J Neurosci Off J Soc Neurosci 17: 438–458. Schmahmann J, Pandya D (2008). Disconnection syndromes of basal ganglia, thalamus, and cerebrocerebellar systems. Cortex 44: 1037–1066. Schmahmann JD, Sherman JC (1998). The cerebellar cognitive affective syndrome. Brain J Neurol 121: 561–579. Schmahmann JD, Weilburg JB, Sherman JC (2007). The neuropsychiatry of the cerebellum—insights from the clinic. Cerebellum 6: 254–267. Schmahmann JD, Guell X, Stoodley CJ et al. (2019). The theory and neuroscience of cerebellar cognition. Annu Rev Neurosci 42: 337–364. Schoch B, Gorissen B, Richter S et al. (2004). Do children with focal cerebellar lesions show deficits in shifting attention? J Neurophysiol 92: 1856–1866. Schoch B, Konczak J, Dimitrova A et al. (2006). Impact of surgery and adjuvant therapy on balance function in children and adolescents with cerebellar tumors. Neuropediatrics 37: 350–358. Schreiber JE, Gurney JG, Palmer SL et al. (2014). Examination of risk factors for intellectual and academic outcomes following treatment for pediatric medulloblastoma. Neuro Oncol 16: 1129–1136. Schreiber JE, Palmer SL, Conklin HM et al. (2017). Posterior fossa syndrome and long-term neuropsychological outcomes among children treated for medulloblastoma on a multi-institutional, prospective study. Neuro Oncol 19: 1673–1682. Schutter DJLG (2019). Hemispheric asymmetries in the human cerebellum. Cortex 115: 352–356. Schwalbe EC, Lindsey JC, Nakjang S et al. (2017). Novel molecular subgroups for clinical classification and outcome prediction in childhood medulloblastoma: a cohort study. Lancet Oncol 18: 958–971. Schweren LJS, de Zeeuw P, Durston S (2013). MR imaging of the effects of methylphenidate on brain structure and function in attention-deficit/hyperactivity disorder. Eur Neuropsychopharmacol 23: 1151–1164. Scott RB, Stoodley CJ, Anslow P et al. (2001). Lateralized cognitive deficits in children following cerebellar lesions. Dev Med Child Neurol 43: 685–691. Shaikh AG, Zee DS (2018). Eye movement research in the twenty-first century—a window to the brain, mind, and more. Cerebellum 17: 252–258. Sheu Y-S, Liang Y, Desmond JE (2019). Disruption of cerebellar prediction in verbal working memory. Front Hum Neurosci 13: 61.

THE ROLE OF CEREBELLUM IN THE CHILD Silveri M (1998). Verbal short-term store-rehearsal system and the cerebellum. Evidence from a patient with a right cerebellar lesion. Brain 121: 2175–2187. Sinha P, Kjelgaard MM, Gandhi TK et al. (2014). Autism as a disorder of prediction. Proc Natl Acad Sci 111: 15220–15225. Smaers JB, Vanier DR (2019). Brain size expansion in primates and humans is explained by a selective modular expansion of the cortico-cerebellar system. Cortex 118: 292–305. Smaers JB, Steele J, Zilles K (2011). Modeling the evolution of cortico-cerebellar systems in primates. Ann N Y Acad Sci 1225: 176–190. Smaers JB, Steele J, Case CR et al. (2013). Laterality and the evolution of the prefronto-cerebellar system in anthropoids: laterality and the evolution of the prefrontocerebellar system. Ann N Y Acad Sci 1288: 59–69. Sokolov AA, Erb M, Grodd W et al. (2014). Structural loop between the cerebellum and the superior temporal sulcus: evidence from diffusion tensor imaging. Cereb Cortex 24: 626–632. Spiegler BJ, Bouffet E, Greenberg ML et al. (2004). Change in neurocognitive functioning after treatment with cranial radiation in childhood. J Clin Oncol 22: 706–713. Starowicz-Filip A, Chrobak AA, Milczarek O et al. (2017). The visuospatial functions in children after cerebellar low-grade astrocytoma surgery: a contribution to the pediatric neuropsychology of the cerebellum. J Neuropsychol 11: 201–221. Statton MA, Vazquez A, Morton SM et al. (2018). Making sense of cerebellar contributions to perceptual and motor adaptation. Cerebellum 17: 111–121. Steele CJ, Anwander A, Bazin P-L et al. (2017). Human cerebellar sub-millimeter diffusion imaging reveals the motor and non-motor topography of the dentate nucleus. Cereb Cortex 27: 4537–4548. Steinlin M (2003). Neuropsychological long-term sequelae after posterior fossa tumour resection during childhood. Brain 126: 1998–2008. Steinlin M (2007). The cerebellum in cognitive processes: supporting studies in children. Cerebellum 6: 237–241. Steinlin M (2008). Cerebellar disorders in childhood: cognitive problems. Cerebellum 7: 607–610. Steinlin M, Zangger B, Boltshauser E (1998). Non-progressive congenital ataxia with or without cerebellar hypoplasia: a review of 34 subjects. Dev Med Child Neurol 40: 148–154. Stoodley CJ (2012). The cerebellum and cognition: evidence from functional imaging studies. Cerebellum 11: 352–365. Stoodley CJ (2016). The cerebellum and neurodevelopmental disorders. Cerebellum 15: 34–37. Stoodley CJ, Limperopoulos C (2016). Structure–function relationships in the developing cerebellum: evidence from early-life cerebellar injury and neurodevelopmental disorders. Semin Fetal Neonatal Med 21: 356–364. Stoodley CJ, Schmahmann JD (2009). The cerebellum and language: evidence from patients with cerebellar degeneration. Brain Lang 110: 149–153. Stoodley CJ, Stein JF (2011). The cerebellum and dyslexia. Cortex 47: 101–116.

303

Stoodley CJ, Stein JF (2013). Cerebellar function in developmental dyslexia. Cerebellum 12: 267–276. Stoodley CJ, Fawcett AJ, Nicolson RI et al. (2005). Impaired balancing ability in dyslexic children. Exp Brain Res 167: 370–380. Stoodley CJ, Harrison EPD, Stein JF (2006). Implicit motor learning deficits in dyslexic adults. Neuropsychologia 44: 795–798. Stoodley CJ, MacMore JP, Makris N et al. (2016). Location of lesion determines motor vs. cognitive consequences in patients with cerebellar stroke. Neuroimage Clin 12: 765–775. Stoodley CJ, D’Mello AM, Ellegood J et al. (2017). Altered cerebellar connectivity in autism and cerebellar-mediated rescue of autism-related behaviors in mice. Nat Neurosci 20: 1744–1751. Strick PL, Dum RP, Fiez JA (2009). Cerebellum and nonmotor function. Annu Rev Neurosci 32: 413–434. Summers AC, Snow J, Wiggs E et al. (2017). Neuropsychological phenotypes of 76 individuals with Joubert syndrome evaluated at a single center. Am J Med Genet A 173: 1796–1812. Szentes A, Ero˝s N, Kekecs Z et al. (2018). Cognitive deficits and psychopathological symptoms among children with medulloblastoma. Eur J Cancer Care (Engl) 27: e12912. Takagi M, Zee DS, Tamargo RJ (1998). Effects of lesions of the oculomotor vermis on eye movements in primate: saccades. J Neurophysiol 80: 1911–1931. Tanaka H, Ishikawa T, Kakei S (2019). Neural evidence of the cerebellum as a state predictor. Cerebellum 18: 349–371. Tavano A, Grasso R, Gagliardi C et al. (2007). Disorders of cognitive and affective development in cerebellar malformations. Brain 130: 2646–2660. Tedesco AM, Chiricozzi FR, Clausi S et al. (2011). The cerebellar cognitive profile. Brain 134: 3672–3686. Teki S, Grube M, Kumar S et al. (2011). Distinct neural substrates of duration-based and beat-based auditory timing. J Neurosci 31: 3805–3812. Thach WT (2007). On the mechanism of cerebellar contributions to cognition. Cerebellum 6: 163–167. Thompson RF, Kim JJ (1996). Memory systems in the brain and localization of a memory. Proc Natl Acad Sci 93: 13438–13444. Thompson RF, Steinmetz JE (2009). The role of the cerebellum in classical conditioning of discrete behavioral responses. Neuroscience 162: 732–755. Th€urling M, K€ uper M, Stefanescu R et al. (2011). Activation of the dentate nucleus in a verb generation task: a 7T MRI study. Neuroimage 57: 1184–1191. Th€ urling M, Hautzel H, K€ uper M et al. (2012). Involvement of the cerebellar cortex and nuclei in verbal and visuospatial working memory: a 7T fMRI study. Neuroimage 62: 1537–1550. Tiemeier H, Lenroot RK, Greenstein DK et al. (2010). Cerebellum development during childhood and adolescence: a longitudinal morphometric MRI study. Neuroimage 49: 63–70.

304

G. DELLATOLAS AND H. CÂMARA-COSTA

Timmann D (2002). Motor deficits cannot explain impaired cognitive associative learning in cerebellar patients. Neuropsychologia 40: 788–800. Timmann D, Daum I (2007). Cerebellar contributions to cognitive functions: a progress report after two decades of research. Cerebellum 6: 159–162. Toescu SM, Hettige S, Phipps K et al. (2018). Post-operative paediatric cerebellar mutism syndrome: time to move beyond structural MRI. Childs Nerv Syst 34: 2249–2257. Tomlinson SP, Davis NJ, Morgan HM et al. (2014). Cerebellar contributions to verbal working memory. Cerebellum 13: 354–361. Townsend J, Courchesne E, Covington J et al. (1999). Spatial attention deficits in patients with acquired or developmental cerebellar abnormality. J Neurosci Off J Soc Neurosci 19: 5632–5643. Traut N, Beggiato A, Bourgeron T et al. (2018). Cerebellar volume in autism: literature meta-analysis and analysis of the autism brain imaging data exchange cohort. Biol Psychiatry 83: 579–588. Trouillas P, Takayanagi T, Hallett M et al. (1997). International cooperative ataxia rating scale for pharmacological assessment of the cerebellar syndrome. J Neurol Sci 145: 205–211. Ullrich NJ, Pomeroy SL, Kapur K et al. (2015). Incidence, risk factors, and longitudinal outcome of seizures in long-term survivors of pediatric brain tumors. Epilepsia 56: 1599–1604. Vaquero E, Go´mez CM, Quintero EA et al. (2008). Differential prefrontal-like deficit in children after cerebellar astrocytoma and medulloblastoma tumor. Behav Brain Funct 4: 18. Verly M, Verhoeven J, Zink I et al. (2014). Altered functional connectivity of the language network in ASD: role of classical language areas and cerebellum. Neuroimage Clin 4: 374–382. von Hofsten C, Rosander K (2012). Perception-action in children with ASD. Front Integr Neurosci 6: 115. Voogd J (2003). The human cerebellum. J Chem Neuroanat 26: 243–252. Voogd J (2011). Cerebellar zones: a personal history. Cerebellum 10: 334–350. Walter AW, Mulhern RK, Gajjar A et al. (1999). Survival and neurodevelopmental outcome of young children with

medulloblastoma at St Jude Children’s Research Hospital. J Clin Oncol 17: 3720–3728. Wang D, Buckner RL, Liu H (2013). Cerebellar asymmetry and its relation to cerebral asymmetry estimated by intrinsic functional connectivity. J Neurophysiol 109: 46–57. Wegenschimmel B, Leiss U, Veigl M et al. (2017). Do we still need IQ-scores? Misleading interpretations of neurocognitive outcome in pediatric patients with medulloblastoma: a retrospective study. J Neurooncol 135: 361–369. Wells EM, Khademian ZP, Walsh KS et al. (2010). Postoperative cerebellar mutism syndrome following treatment of medulloblastoma: neuroradiographic features and origin: clinical article. J Neurosurg Pediatr 5: 329–334. Wiener M, Turkeltaub P, Coslett HB (2010). The image of time: a voxel-wise meta-analysis. Neuroimage 49: 1728–1740. Wingeier K, Bigi S, El-Koussy M et al. (2011). Long-term sequelae after acquired pediatric hemorrhagic cerebellar lesions. Childs Nerv Syst 27: 923–931. Wolfe KR, Hunter GR, Madan-Swain A et al. (2012). Cardiorespiratory fitness in survivors of pediatric posterior fossa tumor. J Pediatr Hematol Oncol 34: e222–e227. Wolpert DM, Miall RC, Kawato M (1998). Internal models in the cerebellum. Trends Cogn Sci 2: 338–347. Wu X, Ashe J, Bushara KO (2011). Role of olivocerebellar system in timing without awareness. Proc Natl Acad Sci 108: 13818–13822. Yang Y, Lisberger SG (2014). Purkinje-cell plasticity and cerebellar motor learning are graded by complex-spike duration. Nature 510: 529–532. Yock TI, Yeap BY, Ebb DH et al. (2016). Long-term toxic effects of proton radiotherapy for paediatric medulloblastoma: a phase 2 single-arm study. Lancet Oncol 17: 287–298. Zalesky A, Akhlaghi H, Corben LA et al. (2014). Cerebellocerebral connectivity deficits in Friedreich ataxia. Brain Struct Funct 219: 969–981. Zeffiro T, Eden G (2001). The cerebellum and dyslexia: perpetrator or innocent bystander? Trends Neurosci 24: 512–513. Zuzak TJ, Poretti A, Drexel B et al. (2008). Outcome of children with low-grade cerebellar astrocytoma: long-term complications and quality of life. Childs Nerv Syst 24: 1447–1455.

Section V Etiologies of neurodevelopmental disorders

This page intentionally left blank

Handbook of Clinical Neurology, Vol. 173 (3rd series) Neurocognitive Development: Normative Development A. Gallagher, C. Bulteau, D. Cohen and J.L. Michaud, Editors https://doi.org/10.1016/B978-0-444-64150-2.00024-1 Copyright © 2020 Elsevier B.V. All rights reserved

Chapter 23

Genetic mechanisms of neurodevelopmental disorders P.Y. BILLIE AU1, ALISON EATON2, AND DAVID A. DYMENT3* 1

Department of Medical Genetics, Alberta Children’s Hospital Research Institute, Calgary, AB, Canada 2

Department of Medical Genetics, The Stollery Children’s Hospital, Edmonton, AB, Canada

3

Department of Genetics, Children’s Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada

Abstract Neurodevelopmental disorders encompass a broad range of conditions, which include autism, epilepsy, and intellectual disability. These disorders are relatively common and have associated clinical and genetic heterogeneity. Technology has driven much of our understanding of these diseases and their genetic underlying mechanisms, particularly highlighted by the study of large cohorts with comparative genomic hybridization and the more recent implementation of next-generation sequencing (NGS). The mapping of copy number variants throughout the genome has highlighted the recurrent, highly penetrant, de novo variation in syndromic forms of neurodevelopmental disease. NGS of affected individuals and their parents led to a dramatic shift in our understanding as these studies showed that a significant proportion of affected individuals carry rare, de novo variants within single genes that explain their disease presentation. Deep sequencing studies further implicate mosaicism as another mechanism of disease. However, it has also become clear that while rare variants explain a significant proportion of sporadic neurodevelopmental disease, rare variation still does not fully account for the familial clustering and high heritability observed. Common variants, including those within these known disease genes, are also shown to contribute significantly to overall risk. There is also increasing awareness of the important contribution of epigenetic factors and gene–environment interactions.

INTRODUCTION—HISTORIC PERSPECTIVE AND OVERVIEW Neurodevelopmental disorders encompass a broad range of conditions that include autism and autism spectrum disorder (ASD), attention-deficit hyperactivity disorder (ADHD), syndromic and nonsyndromic forms of intellectual disability (ID), and epilepsies. Additionally, psychiatric conditions such as schizophrenia and anxiety are often found coexisting with neurodevelopmental disorders. A theme among these varied neurologic and psychiatric conditions is that they are common, with the milder forms observed in 1%–3% (or higher) of the population (Hauser et al., 1996; Nassar et al., 2009; Hesdorffer et al., 2011;

Maulik et al., 2011). Another shared theme is that these conditions are frequently comorbid, and further there is significant heterogeneity observed in their clinical presentations and in their etiologies (Morgan et al., 2008; Dias et al., 2013). Each of these neurodevelopmental diseases has forms with differing degrees of genetic and environmental contributions to their pathogenesis. Historic insights into the heritable mechanisms of disease have resulted from observations in the clinic setting. This was particularly important for identification of X-linked ID disorders. The recognition of families with multiple affected males with severe ID, related through a matrilineal pattern of inheritance, allowed discovery of a highly penetrant, monogenic, X-linked mechanism for

*Correspondence to: David A. Dyment, D.Phil., M.D., FRCPC, Department of Genetics, Children’s Hospital of Eastern, Ottawa, ON K1H 8L1, Canada. Tel: +1-613-737-7600x3223, Fax: +1-613-738-4822, E-mail: [email protected]

308

P.Y.B. AU ET AL.

ID (Herbst and Miller, 1980). Expansion mutations in FMR1 were identified early as the most common explanation (Kremer et al., 1991), and further study of similar families has led to identification of over 100 other X-linked ID genes (de Brouwer et al., 2007; Lubs et al., 2012). Insight from clinical observation has also lead to initial understanding of the multifactorial basis for neurodevelopmental disease. Early family studies have consistently described an increased concordance in twins and other family members with autism, mild ID, and the childhood- or adolescent-onset epilepsies (Herbst and Baird, 1982; Sandin et al., 2014; Epi4K Consortium, 2017; Hansen et al., 2019). These observations were consistent with a more complex or multifactorial mode of inheritance that explains a proportion of nonsyndromic neurodevelopmental disease.

Term

Definition

Deletion

Absence of a segment of genetic material. May be as small as one or more nucleotides, or much larger (chromosomal) encompassing one or more genes. Different laboratory methods are required to detect deletions of different sizes The presence of an extra copy of a segment of genetic material. May be as small as one or more nucleotides, or much larger (chromosomal) encompassing one or more genes. Different methods are used to detect duplications of different sizes The presence of one or more extra copies or absence of a copy of a segment of genetic material (eg. Deletions and duplications) Observation of lack of allelic variation at a locus of DNA. Can be copy number neutral (e.g., due to consanguinity, uniparental disomy) or due to a deletion resulting in a single copy of the locus The situation where both copies of a chromosome pair are inherited from a single parent (instead of one copy being inherited from each parent) The process by which maternally and paternally derived chromosomes are chemically differentiated

Duplication

Copy number variant

Absence of heterozygosity

Uniparental disomy

Imprinting

Continued Term

Definition

Mosaicism

The presence of two or more cell lines with different genotypes in an organism as the result of a postzygotic event The scenario whereby contribution from one normal allele is insufficient to prevent disease or atypical phenotype from occurring if there is loss of function of the second allele The percentage of individuals with a pathogenic variant who will manifest symptoms as a result of that variant. Typically used in reference to autosomal dominant conditions Structural rearrangement of chromosomal material without net gain or loss of genetic information (e.g., balanced translocations, inversions) Genome aggregation database. Resource aggregating large-scale exome and genome sequencing data to provide variant level summary data to the wider scientific community Human gene mutation database. A comprehensive database of published variants in genes that are associated with inherited human disease (Sifting intolerant from tolerant) In silico tool for predicting whether an amino acid substitution affects protein function based on the properties of amino acids and sequence homology (Polymorphism phenotyping) In silico tool providing prediction of the impact of an amino acid substitution on the overall function and structure of the protein. Can be used in assessment of the pathogenicity of a specific variant (Combined annotation dependent depletion) In silico tool for scoring the deleteriousness of single nucleotide variants and insertion/ deletion genomic variants. Used in assessment of the pathogenicity of a specific variant

Haploinsufficiency

Penetrance

Balanced rearrangement

gnomAD

HGMD

SIFT

PolyPhen

CADD

GENETIC MECHANISMS OF NEURODEVELOPMENTAL DISORDERS Continued Term

Definition

Exome

The protein-coding portion of the nuclear genome The entire DNA sequence comprised of both protein-coding and noncoding regions An alteration in DNA sequence involving a single base pair Laboratory technique used to identify the presence or absence of specific chromosomal regions. Fluorescently labeled probes targeted to a specific region of DNA will hybridize to that region and subsequently visualized if present The clinically observable characteristics or measurable traits associated with expression of an individual’s genotype The alleles located at a specific locus

Genome

Single nucleotide variant (SNV) Fluorescence in situ hybridization (FISH)

Phenotype

Genotype

In addition to the insights from the clinic setting, the rapid pace of technologic advancement of the last 70 years has been a significant driver in our understanding of the genetics of neurodevelopmental disease. Advances in microscopy and the staining of chromosomes in the 1950s resulted in the identification of an extra chromosome as the basis for Down syndrome (Lejeune et al., 1959) and, later, Edward and Patau syndromes (Edwards et al., 1960; Patau et al., 1960). The field of cytogenetics provided tremendous insights into the genomic basis of disease with observations of other common aneuploidies and the recognition of balanced and unbalanced rearrangements and ring chromosomes (Trask, 2002). Continued advances such as higher resolution staining techniques and the routine use of fluorescence in situ hybridization (FISH) afforded improved (though still somewhat limited) resolution allowing detection of smaller deletions and duplications. The majority of chromosomal diseases identified through microscopic chromosomal analysis involved rearrangements or dosage to several to even hundreds of genes and were consequently associated with multisystem, or syndromic, forms of neurodevelopmental disease. Concurrent with the ongoing development of cytogenetic techniques was the advancement of biochemical genetics. There was an increased recognition of the importance of abnormal metabolic processes as the cause of disease, and that these conditions showed a Mendelian inheritance pattern (Scriver, 2008). Many of these inborn

309

errors of metabolism (IEM) show a progressive course with respect to ID, seizures, and behavioral issues. These conditions are often due to either lack of synthesis of an important metabolite or accumulation of a toxic metabolite, or a combination of those issues. The inheritance pattern is almost always autosomal recessive, as they are typically due to a critically deficient level of enzyme activity. Phenotypes can show significant variation in severity and age of onset (Saudubray et al., 2006). Characterization of these conditions was exceptionally important as it lead to recognition that some forms of ID were actually treatable, with the potential for normal development with early intervention (van Wegberg et al., 2017). Further advances in the 20th century led to the development of various platforms to study these diseases in greater depth. Mass spectroscopy in particular, has enabled biochemical geneticists to study detailed biochemical readouts of their patients and better understand alterations to metabolic pathways. The mass spectroscopy platform is the cornerstone for many biochemical diagnoses of IEMs in metabolic clinics and newborn screening programs (Clarke, 2002; Pandor et al., 2004). By 2003, the completion of the human genome project provided a tremendous leap forward with regard to our understanding of the genomic mechanisms of neurodevelopmental and other heritable diseases (Venter et al., 2001; International Human Genome Sequencing Consortium, 2004). The detailed map of the human genome allowed further advances in cytogenetics with microarrays (de Smith et al., 2007; Wagenstaller et al., 2007) and in molecular genetics with the development of massive parallel sequencing, also known as next-generation sequencing (NGS). NGS, specifically exome (and whole genome) sequencing has been responsible for the identification of hundreds of novel disease genes by providing a comprehensive look at an individual’s or family’s genomic sequence (Boycott et al., 2013). The first NGS successes were identification of the syndromic ID genes KMT2D (Kabuki syndrome) (Ng et al., 2010) and ASXL1 (Bohring–Opitz syndrome) (Hoischen et al., 2011) both of which had previously been unsolvable with traditional gene identification studies given their “sporadic” nature. The broader application of NGS in research and in the clinic highlighted the importance of new dominant mutations as a cause of neurodevelopmental disease. This chapter provides an overview of the genetic mechanisms of neurodevelopmental disease seen in recent decades; in particular, the advances made by chromosomal microarray and NGS.

310

P.Y.B. AU ET AL.

ROLE OF CHROMOSOMAL MICROARRAY Deletions and duplications throughout the genome were recognized as the cause of neurodevelopmental diseases with the widespread uptake of the microarrays in the first decade of the 21st century (Iafrate et al., 2004; Sebat et al., 2004; Hegele, 2007). Chromosomal microarray allowed a marked improvement in resolution, as arrays were able to detect copy number variants (CNVs) as small as 200–400 kb in size (sometimes even less) in comparison to traditional G-banding techniques of metaphase chromosomes that were able to detect deletions or duplications of approximately 5–10 Mb in size. Several studies have shown that for global developmental delays and ID the diagnostic rate by microarray can be high at 10%–30% (Michelson et al., 2011; Vickers and Gibson, 2019) and it is much higher than the traditional G-banded karyotype diagnostic rate of 3% (Vickers and Gibson, 2019). Rates of diagnosis by microarray for epilepsy varies, but ranges from 5% to 16% (Olson et al., 2014; Borlot et al., 2017; Coppola et al., 2019; Sánchez Fernández et al., 2019) and for nonsyndromic ASD, the rate is similar at 9%–12% (Christian et al., 2008; Marshall et al., 2008; Rosenfeld et al., 2010; Tammimies et al., 2015). Two types of microarrays are often employed in the genetics clinical laboratory; the first is the array comparative genomic hybridization and the other is a single nucleotide polymorphism (SNP)-based array. Both microarrays assess the dosage of fluorescent probe(s) hybridized to either oligonucleotides or allelespecific oligonucleotides, representing known loci (and genotypes) that span the genome (Ji et al., 2004; Rosenfeld and Patel, 2017; Zhang et al., 2017; Vickers and Gibson, 2019). A SNP-based microarray has the advantage in that it can detect regions of loss of heterozygosity (LOH). The regions of LOH may represent population inbreeding, consanguinity in the family, or, alternatively, the phenomenon of uniparental disomy (UPD). UPD is observed when a child has 2 copies of the diploid segment of DNA from a single parent and zero copies from the other parent. Though a “balanced” diploid, this still has clinical implications when the region is imprinted, or when there is a pathogenic variant carried by one parent that subsequently is homozygous in the child. Prader–Willi syndrome is a neurodevelopmental syndrome characterized by a recognizable facial gestalt, hypotonia, ID, and behavior issues. It is the result of either a deletion of the paternal copy, or UPD of the maternally imprinted locus at chromosome 15. Temple syndrome, Angelman syndrome, and Beckwith–Weidemann syndromes are other examples of syndromic conditions that can result from UPD. Chromosomal microarrays are also able to detect the presence of mosaicism

(Flore and Milunsky, 2012). Mosaicism is a postmeiotic mutational event and, as such, the deleterious CNV is not present in all cells. It is possible to detect the presence of a variant with as low as 5%–10% mosaicism (Flore and Milunsky, 2012). Mosaicism can also show variation among tissue types (e.g., Pallister Killian syndrome or mosaic tetrasomy 12p, where the variation is not detected in blood but is detected in skin or amniotic fluid). The presence of a mosaic variant not present in all cells may be the explanation for a milder or atypical phenotype seen in a child.

IMPACT AND INSIGHT FROM COPY NUMBER VARIATION The CNVs detected by array vary in size and dosage. The more common, recurrent, and highly penetrant, CNVs that are associated with neurodevelopmental disease are presented in Table 23.1. These are the deletions and duplications that are observed with clinically recognizable syndromes that impact an individual’s cognition, seizure predisposition, and behavior. The type of variation (deletion or duplication) detected also provides insight into underlying disease mechanisms. For example, a pathogenic deletion is consistent with haploinsufficiency of one or more genes within the critical region. While some conditions, such as Williams Syndrome, are now recognized to be due to the effects of haploinsufficiency of multiple genes within the deleted region, other conditions are due to loss of a critical single disease-causing gene within the region. The use of microarrays has facilitated the identification of critical disease-causing genes by comparing the smallest region of overlap in multiple patients presenting with the same syndrome. Examples of successes with this approach include Pitt–Hopkins syndrome due to the loss of TCF4 gene (Zweier et al., 2007) or Koolen–deVries syndrome due to loss of KANSL1 (Koolen et al., 2012) and many others (Kleefstra et al., 2005; Zweier et al., 2010; Talkowski et al., 2011). For some microdeletions, the precise breakpoints given by the microarray can provide certain information regarding genotype phenotype correlation. For example, for the highly variably expressive and common chromosome 22q11.2 deletion syndrome, individuals with the larger 3 Mb deletion that encompasses TBX1 will have characteristic cardiac anomalies (Yagi et al., 2003). For individuals with 5p- (Cri du Chat), more severe ID is associated with deletions encompassing CTNND2 (Zhang et al., 2016). The increased gene dosage of the relevant gene(s) seen in a duplication variant can also result in neurodevelopmental disease (e.g., 22q11 duplication syndrome and Potocki–Lupski syndrome). While increased protein

Table 23.1 Highly penetrant recurrent copy number variants causing recognizable conditions with a neurodevelopmental phenotype

Copy number variant

Genomic coordinates (GRch38)

1p36 deletion

1:0–27,600,000

4p16.3 deletion

4:0–4,500,000

5p deletion

Variable

7q11.2 deletion

7:72,700,000–77,900,000

9q34.3 deletion syndrome

Variable

11p13 deletion

11:31,000,000–36,400,000

11q23 deletion

11:110,600,000–121,300,000

Condition (OMIM#)

Additional clinical features

Comments

1p36 deletion syndrome Typical craniofacial features (straight eyebrows, midface (607872) retrusion, large anterior fontanel), congenital heart defects, seizures in some. ID in all Wolf-Hirschhorn Characteristic facial features “Greek warrior helmet” syndrome (194190) appearance of the nose, high forehead, prominent glabella, hypertelorism, pre- and postnatal growth deficiency, seizures, ID in all Cri du chat syndrome “Cat-like” cry. Microcephaly, hypertelorism, Phenotype may vary with the size of the (123450) micrognathia, epicanthal folds, low set ears. Severe ID deletion Williams syndrome Cardiovascular disease (elastin aortopathy, peripheral (194050) pulmonary stenosis, supravalvular aortic stenosis, hypertension), characteristic facies (broad forehead, periorbital fullness, short nose, broad nasal tip, thick vermillion of upper and lower lips), overfriendliness. ID in the vast majority ranging from mild to severe Kleefstra syndrome Also caused by variants in the EHMT1 Recognizable craniofacial features including (610253) gene, which lies within this deletion brachycephaly, hypertelorism, synophrys or arched eyebrows, midface retrusion, protruding tongue, prognathism. Autistic features, sleep distrubance. Most have moderate to severe ID Wilms Tumor Aniridia Tumor risk (Wilms tumor in 50%, gonadoblastoma), Genitourinary aniridia, genitourinary anomalies (ambiguous anomalies and Mental genitalia, cryptorchidism, hypospadias, uterine Retardation syndrome anomalies, etc.), ID in 70%. Behavioral anomalies (WAGR) (194072) common Jacobsen syndrome Pre- and postnatal growth deficiency, abnormal skull (147791) shape (metopic synostosis, trigonocephaly) hypertelorism, ptosis, camptodactyly, hammer toes, and ID. Thrombocytopenia usually present at birth. Other congenital anomalies common Continued

Table 23.1 Continued

Copy number variant

Genomic coordinates (GRch38)

Condition (OMIM#)

15q11.2 deletion

Prader–Willi syndrome 15: 22,876,632–28,557,186 (176270) (type 1 deletion, BP1–BP3) or 15:23,758,390–28,557,186 (type 2 deletion, BP2–BP3)

17p11.2 deletion

17:16,100,000–22,700,000

17p11.2 duplication

17:16,100,000–22,700,000

17p11.3 deletion

17:0–3,400,000

17q21.31 deletion

Variable

22q11.2 deletion

22: 18627728–18,639,943 in 85% (smaller nested deletion in remainder)

22q13.3 deletion

Variable

Additional clinical features

Comments

Not to be confused with 15q11.2 Severe hypotonia and feeding difficulties in infancy, deletion syndrome, a smaller deletion followed by excessive eating in early childhood leading encompassing four nonimprinted to obesity. Hypogonadism, infertility in most. genes and spanning approximately Distinctive behavioral phenotype. Some degree of ID in 300–500 kb within this same region almost all, ranging from borderline ID to moderate ID (BP1–BP2 deletion, see Table 23.2). Can also be caused by abnormal parent-specific imprinting within the Prader–Willi critical region on chr 15 Smith Magenis Also caused by pathogenic variants in Distinctive facial features including coarse features, syndrome (182290) the RAI1 gene midface retrusion and relative prognathism (progress with age), behavioral issues (including tantrums, polyembolokoilamania, onychotillomania), sleep disturbance. Most have mild to moderate ID Potocki Lupski Infantile hypotonia with oropharyngeal dysphagia and syndrome (610883) failure to thrive. Congenital heart disease (LVOT anomalies common), growth hormone deficiency, behavioral issues Miller–Dieker syndrome Severe lissencephaly, characteristic facial features (247200) (bitemporal narrowing, prominent forehead, upturned nose, small jaw), severe neurodevelopmental abnormalities Also caused by pathogenic variants in Koolen–De Vries Characteristic facial features—abnormal hair color/ the KANSL1 gene, which lies within syndrome (610443) texture, high forehead, blepharophimosis/ptosis, this region upward slanting palpebral fissures, bulbous nose. Genitourinary anomalies. Friendly and amiable personality. Mild to moderate ID in most 22q11.2 microdeletion Congenital heart disease (particularly conotruncal Pathogenic variants in TBX1, which lies syndrome (192430) within this region, can cause clinical malformations), palatal abnormalities, characteristic features of 22q11 facial features, immune deficiency, hypocalcemia. Varying degrees of developmental delay/ID (usually mild or learning differences) Phelan–McDermid Neonatal hypotonia, mild dysmorphisms (full brows, flat Also caused by variants in the SHANK3 syndrome (606232) gene, which lies within this deletion midface, full eyelids, wide nasal bridge, full cheeks, and prominent ears). Delayed to absent speech, dev delay/ID

GENETIC MECHANISMS OF NEURODEVELOPMENTAL DISORDERS

313

expression due to the dose of one or more critical genes may be the underlying cause for disease, another mechanism for a duplication to result in disease is the lossof-function of a critical gene that is bisected by the duplication breakpoint, such as partial duplications involving SCN1A implicated in Dravet syndrome (Marini et al., 2009).

novo CNV, the risk may be considerably less than 1%. Parental testing for the purposes of variant interpretation or for counseling purposes is a common “next step” when a microarray testing is performed.

VARIABLY PENETRANT CNVs

A transformation in the field of genetics has occurred with the advent of massive parallel sequencing. This method allows the sequencing of hundreds to thousands of genes, at the same time, for the same cost and time as the Sanger sequencing of 1–2 genes. The platforms and methods vary for NGS chemistry and platforms but the key element is that DNA sequencing libraries are “enriched” for the target genes by PCR amplification and this is followed by sequencing, in a massively parallel synthesis reaction, that is different from the chain termination reactions of Sanger sequencing. The sequencing can result in billions of “reads” or sequences of varying length that are mapped to the reference genome. Variants are then called and annotated based on information from population databases (e.g., Gnomad, HGMD) regarding allele frequency and previous reports of pathogenicity, in addition to information from conservation and various in silicoprediction programs (e.g., SIFT, POLYPHEN, CADD). The technology can be used in a “panel” approach of 2 to several 100 curated genes, which is now routinely performed for the cases of epilepsies and ID (Sun et al., 2015; Bevilacqua et al., 2017). The technology can also be applied on a broader scale where the whole exome or the entire genome can be sequenced (Hayeems and Boycott, 2018).

As array data was acquired from more individuals, it became apparent that not all neurodevelopmental disorder CNVs were fully penetrant and that there are recurrent CNVs that are incompletely penetrant and therefore present in unaffected individuals. These CNVs represent susceptibility alleles that occur at higher frequency than expected in individuals affected with neurodevelopmental disorders like ASD, epilepsy, and schizophrenia (Coe et al., 2012; Takumi and Tamada, 2018), but can also be present in neurotypical individuals. The more common susceptibility variants include deletions and duplications at 1q21.2, 15q11.2, 16p11.2–p12.2, 16p13.11 and 15q13.3 (Helbig et al., 2009; de Kovel et al., 2010) (Table 23.2). Many are frequently inherited, but many can be de novo as well. In these cases, Gaugler determined that 80% of individuals with a de novo CNV would not have otherwise been affected in the absence of their CNV (Gaugler et al., 2014) stressing the importance of the de novo CNV to an individual’s risk. Conversely, the presence of comorbidities may increase the likelihood of a pathogenic CNV being present. Individuals with ID and epilepsy versus epilepsy alone were found to be at a threefold greater risk of carrying a disease-associated CNV (Mullen et al., 2013). The odds ratios of these CNVs are typically high or intermediate in their risk (see Table 23.2) but there are some that have relatively low risk (i.e., lower penetrance variants) as well.

COUNSELING CONSIDERATIONS FOR CNVs For the highly penetrant syndromic microdeletion/ duplication syndromes the pathogenic CNVs are often the result of a de novo or new event without any family history. Another possibility is that the genomic imbalance may be the result of a balanced rearrangement segregating in the family. As such there may be a family history of recurrent miscarriages, the early demise of a child or a family member with a neurodevelopmental syndrome. The knowledge of the underlying mechanism has important implications for recurrence risk. For an unbalanced rearrangement, the risk of a recurrence can be as high as 50% if a parent is found to be a balanced carrier (Gardner and Sutherland, 2004) while for a de

NEXT-GENERATION SEQUENCING TECHNOLOGY

THE DE NOVO PARADIGM FOR NEURODEVELOPMENTAL DISEASE Some of the early applications of this technology were for cohorts of individuals with severe ID, autism, or epilepsy. An early study of 10 individuals with ID was one of the first to demonstrate the effectiveness of this technology, as the researchers were able to identify a genetic cause in 6 of the 10 cases (Vissers et al., 2010). Another early NGS sequencing study of 100 individuals with severe ID identified explanations in 13 with a potential additional 22 diagnoses (35%) in novel genes (de Ligt et al., 2012). Similarly, 51 patients were enrolled with sporadic, severe ID and 16 were found to carry a causative mutation with another 3 seen in novel genes (37%) (Rauch et al., 2012). These studies leveraged a “trio” approach to analysis, where both parents and the proband were sequenced to facilitate identification of inherited versus de novo variants. All three studies stressed the preponderance of de novo mutation as the cause of ID in affected individuals.

Table 23.2 Copy number variants with variable expressivity/reduced penetrance associated with neurodevelopmental phenotypes

CNV

Genomic coordinates (GRch38)

Associated neurodevelopmental phenoytpes

Additional clinical features

ID (generally mild). ASD, ADHD, autistic features, sleep disturbances, schizophrenia ID, ASD, ADHD

Eye abnormalities, microcephaly, short stature, mild dysmorphic facial features, seizures Macrocephaly, scoliosis

ID (moderate to severe in most), severe language delay, ASD, schizophrenia DD/ID, speech delay, ASD, anxiety, psychosis, schizophrenia

Seizures, hypotonia, nonspecific dysmorphisms

0.02%b

Usually includes disruption of the NRXN1 gene

B
ena et al. (2013) and Dabell et al. (2013)

Feeding problems and FTT, congenital heart defects, dental abnormalities

0.0014%b

Penetrance unclear, but reports of inheritance from unaffected parent suggest it is not 100%

Ballif et al. (2008) and Khan et al. (2019)

DD/ID, ASD, ADHD, schizophrenia DD/ID, ASD, behavioral issues

Seizures

0.38%a

Seizures (particularly infantile spasms), hypotonia, nonspecific dysmorphisms in some Seizures

0.0083b

1q21 deletion (612474)

1:143,200,000– 147,500,000

1q21 duplication (612475) 2p16.3 deletion (614332)

1:143,200,000– 147,500,000 Variable, but disrupt NRXN1

3q29 deletion (609425)

3:195998129–197,623,129

15q11.2 deletion (615656) 15q11q13 duplication (608636)

15:20,500,000–25,500,000

15q13.3 deletion (612001) 16p11.2539-KB deletion (611913)

15:30,900,000–33,400,000 16:28,500,000–35,300,000

DD, ID, ASD, ADHD, schizophrenia, self-harm DD/ID, speech apraxia, ASD, ADHD, schizophrenia

16p11.2220-KB deletion 613,444 16p11.2 duplication (614671)

16:28,500,000–35,300,000

DD/ID, behavioral issues

16:28,500,000–35,300,000

DD/ID, ASD, schizophrenia

15:19,000,000–25,500,000

Seizures, macrocephaly, obesity, Chiari I, vertebral anomalies, nonspecific dysmorphic features. Obesity, seizures in some Short stature, microcephaly, FTT

Frequency in controls

Comments

Selected references

0.03%a

Bernier et al. (2016) and Mefford et al. (2008)

0.03%a

Mefford et al. (2008)

0.019%b 0.03%a

Cox and Butler (2015) Urraca et al. (2013) and Al Ageeli et al. (2014) CHRNA7 is likely causative gene Higher penetrance than the reciprocal duplication

Lowther et al. (2015)

Lower penetrance than reciprocal deletion

Fernandez et al. (2010) and Steinman et al. (2016)

Fedorenko et al. (2016) and Steinman et al. (2016)

0.04%a 0.04%a

17q12 deletion (614527)

17:33,500,000–39,800,000

DD/ID, ASD, schizophrenia, anxiety, bipolar disorder

Maturity onset diabetes of the young (MODY), renal anomalies

0.01%a

17q12 duplication (614526)

17:33,500,000–39,800,000

DD/ID (variable spectrum), ASD, schizophrenia, behavioral anomalies

0.02%a

22q11.2 duplication (608363)

22:17,400,000–25,500,000

LD/ID, ASD, delayed motor development

Seizures, eye or vision problems. Has been associated with esophageal atresia Hypotonia, subtle dysmorphic features in some, velopharyngeal insufficiency in half

a

Rosenfeld et al. (2013). Kirov et al. (2014).

b

0.05%a

Moreno-De-Luca et al. (2010) and Rasmussen et al. (2016) Rasmussen et al. (2016) and Kamath et al. (2018) Wenger et al. (2016) and Wentzel et al. (2008)

316

P.Y.B. AU ET AL.

For epilepsy and autism, the results have been similar. O’Roak et al. sequenced 20 trios with idiopathic autism and identified a de novo pathogenic variant in 25% (O’Roak et al., 2012). The same group later published a study of 2500 families with simplex autism and showed that de novo CNVs and SNV could explain 30% of the simplex cases (Iossifov et al., 2014). Additional cohorts have been sequenced with similar findings (Neale et al., 2012; Sanders et al., 2012; De Rubeis et al., 2014; Kosmicki et al., 2017; Heyne et al., 2018), where CNVs explain approximately 10% and SNVs explain approximately 20%. This technology has resulted in the identification of hundreds of new genes responsible for neurodevelopmental disorders although any individual gene, by itself, is responsible for less than 1% of the respective disease. Interestingly, de novo point variants were often paternal in origin (4:1 bias). This observation was consistent with the known increased risk of autism in children of older fathers (Kong et al., 2009; O’Roak et al., 2012). This is in contrast to aneuploidy, like trisomy 21, which is more likely to have maternal origin from chromosome nondisjunction.

INSIGHTS INTO DISEASE MECHANISM GAINED FROM DE NOVO VARIATION As discussed previously in the context of CNVs, haploinsufficiency of a critical gene is often a mechanism underlying a neurodevelopmental disorder. While many de novo SNVs in neurodevelopmental disease genes do result in a loss-of-function or haploinsufficiency, there are missense variants observed with a likely “gain-offunction” effect. This information has thereby provided insight into the pathobiology of disease, and has been particularly interesting in genes where different presentations may occur depending on the mutation resulting in gain of function versus haploinsufficiency or loss of function. The glutamate receptor genes GRIN2B and GRIN2A provide an example where the variants encode alterations with varying impact on channel function (Swanger et al., 2016). Missense variants in GRIN2A can present with intractable epilepsy syndrome with severe to profound ID while others with gross deletions may show mild ID  seizures that are more easily controlled (Carvill et al., 2013; Lemke et al., 2013; Lesca et al., 2013). SCN2A is another example where the different action of variants may be responsible for seizures of widely varying severity, ID, or ASD (Heron et al., 2002; Ogiwara et al., 2009; Iossifov et al., 2014; Begemann et al., 2019). However, clear genotype to phenotype correlation is not always possible despite some understanding of how mutations may impact function of a gene. One

classic example is the Rett syndrome, which is due to mutation in MECP2. While some mutations in MECP2 are known to cause milder phenotypes with preserved speech, it is still difficult to predict seizure severity and level of disability for other gene variants, despite the fact that all these variants predict loss of function. Variability and overlap in presentation is also seen in genes like SYNGAP1, SETD5, and CHD2 (Carvill and Mefford, 2015). This variability may be due in part to other genetic factors, including contribution from common variation, as discussed below. The large number of genes identified in neurodevelopmental disorders highlights that there is extreme genetic heterogeneity. Many of the genes mentioned thus far can present in highly similar and overlapping ways, with varying combinations of symptoms that include epilepsy, autism, and ID. Clinically, these genes may therefore be difficult to distinguish. The use of NGS sequencing, which allows for assessment of many genes at once, has been critically important for etiologic diagnosis for this reason. The recognition of the hundreds (and potentially thousands) of different genes that determine neurodevelopmental disease has also provided insight into underlying mechanism and common pathways. The genes affected by de novo mutations and CNVs in individuals with neurodevelopmental disorders converge on common pathways and networks related to neuronal development (such as WNT signaling), channel and synaptic function, and chromatin regulation (Krumm et al., 2015). The presence of shared common pathways is not surprising, given that the clinical presentations do overlap greatly (Aldinger et al., 2015). These networks also begin to inform us why many of these genes also cause comorbid disorders like seizure, autism, ID, and psychiatric disease.

RECESSIVE DISEASE While the majority of monogenic neurodevelopmental diseases in outbred populations are the result of new dominant mutations, autosomal recessive homozygous and compound heterozygous mutations are still important mechanisms. The Deciphering Developmental Disorders (DDD) study estimated that autosomal recessive genes account for 11.7% of their diagnoses (Deciphering Developmental Disorders Study, 2017). There exist many populations where recessive forms of neurodevelopmental diseases are enriched (Boycott et al., 2008; Monies et al., 2019). This is often observed within populations that have a high degree of consanguinity. The relative ease of identification of recessive genes has also been highlighted in the NGS era. Rates of homozygous and pathogenic changes can be high

GENETIC MECHANISMS OF NEURODEVELOPMENTAL DISORDERS (Monies et al., 2019) and, as such, the pedigree can provide insight into the inheritance pattern. The clinical presentation can also suggest recessive inheritance. As mentioned earlier, IEMs are typically recessive, and these should be highly suspected in a child with a progressive neurodegenerative disease. Progressive myoclonic epilepsies often represent lysosomal storage disorders and are also autosomal recessive. Joubert syndrome and related other related ciliopathies have highly specific clinical findings (e.g., molar tooth sign on MRI) and can indicate the presence of autosomal recessive inheritance beyond that provided by the family history. Conversely, there are some clinical presentations such as isolated ID, isolated autism, and schizophrenia where the answer is rarely autosomal recessive (Musante and Ropers, 2014).

CURRENT LIMITATIONS OF NGS IN NEURODEVELOPMENTAL DISORDERS While the use of exome sequencing has provided diagnoses for up to 30% for those with multisystem neurodevelopmental disease, there are some types of pathogenic variation that are not detected by commercially available NGS (Table 23.3). Deletions may be missed by both microarray and the typical NGS platform (75–200 base pairs). If an entire exon is deleted, it may be missed by both microarray and NGS and may only be detectable by others methods such as multiplex ligation-dependent probe amplification or FISH. Repeat expansion mutations are also not currently readily detected by NGS sequencing. These associated conditions tend to be progressive and later- onset disorders (e.g., progressive and benign myoclonic epilepsy, DRPLA, and spincocerebellar ataxia). The expansions themselves can be several kb in length and may reside outside of the coding region of the gene (e.g., the regulatory or promoter region). Other “missed” variations may include variants of the mitochondrial genome, if the NGS capture kit does not include this sequence.

ROLE OF MULTIFACTORIAL OR COMPLEX INHERITANCE IN NEURODEVELOPMENTAL DISORDERS As discussed previously, it is now well established that de novo variants and highly penetrant monogenic etiologies have an important role in the etiology of neurodevelopmental disorders. However, multiple studies have demonstrated that most of the heritability (i.e., the proportion of phenotype variation that is due to additive genetic factors) in autism and other neuropsychiatric disorders still resides in common inherited genetic variation (Klei et al., 2012; Gaugler et al., 2014). Rare de novo SNVs and CNVs likely account for 10%–30% of

317

individuals with autism (Iossifov et al., 2014; Vorstman et al., 2017), but it is obvious that familial clustering and the high heritability of autism, epilepsy, and neuropsychiatric disease is not well explained by de novo variation. This missing heritability is likely, in part, due to inherited common variation. In this next section, we discuss the contribution of common variations to neurodevelopmental disorders. The importance of inherited variation has been supported by evidence showing that recurrence risk for neurodevelopmental disorders is higher in families that have affected family members. In the largest study of familial risk for ASD, Sandin et al. (2014) demonstrated that there is increased risk for recurrence of autism within a family if other family members in addition to the proband were also affected (Sandin et al., 2014). They determined from a large population cohort of more than 2 million individuals (of whom 14,516 were diagnosed with autism) that the relative recurrence risk for autism for a full sibling was 10.3. This study also determined that heritability of autism was approximately 50% (Sandin et al., 2014).

INSIGHTS FROM GENOME-WIDE ASSOCIATION STUDIES Despite extensive resources allocated to genome-wide association studies (GWAS), they were largely unsuccessful at identifying specific common variants contributing to neurodevelopmental disorders. There were a few significant findings from these studies, and results were difficult to replicate. Studies were subsequently recognized to be underpowered even when study cohorts included several 1000 individuals and over a million SNPs (Purcell et al., 2009; Wang et al., 2009; Weiss et al., 2009; Kasperaviciute et al., 2010; Anney et al., 2012; Steffens et al., 2012). Replicable and significant associations were identified later using cohorts of tens of thousands of individuals (Lee et al., 2013; International League Against Epilepsy Consortium on Complex Epilepsies, 2014; Grove et al., 2019). However, the handful of significant SNPs identified through GWAS only account for a small proportion of the heritability of neurodevelopmental disorders. Further understanding of how common variants affect disease risk has come from quantitative genetic techniques that allow consideration of all SNPs simultaneously (Yang et al., 2010). Using these approaches, several groups have again estimated that heritability for autism is approximately 50%. While rare and de novo variants have a huge impact on any given individual, most of the variance in liability from these studies appears to be due to common inherited variation (Klei et al., 2012; Gaugler et al., 2014). Therefore, while

318

P.Y.B. AU ET AL.

Table 23.3 Genetic variation not reliably detected by next-generation sequencing Neurodevelopmental disease (OMIM #)

Example of associated disease gene

Fragile X (309550) FRAXE (309548) Myotonic Dystrophy Type 1 (160900) Epilepsy, progressive myoclonic 1A (254800) Huntington disease, juvenile form (613004) PTEN Hamartoma tumor syndrome (Cowden syndrome) (158350) Pelizaeus-Merzbacher-like disease 1 (608804) Any

FMR1 AFF2 DMPK

GJC2 (promoter variants common) Any

Orphan genes

Unknown

Unknown

Gene not previously associated with clinical phenotype

Mosaicism

Megalencephaly capillary malformation (MCAP) syndrome (602501) MPPH (mosaic in some) (603387)

PIK3CA

Read depth inadequate to detect

Variation Repeat expansion

Promoter/regulatory variants

Small deletions or duplications

Why missed

Notes

Repeat regions map poorly to the reference genome. Unable to determine length of expansion due to short reads

CSTB HTT PTEN (promoter variants common)

PIK3R2 (most commonly)

common variants individually typically have relatively tiny effects and have no clinical relevance on their own, they may have substantial impact on an individual collectively.

ROLE OF NORMAL POPULATION VARIATION IN NEURODEVELOPMENTAL DISEASE The concept that common variants are important to autism and other neurodevelopmental disorders like ADHD and psychiatric disease is intuitively not surprising as many of these disorders could be considered as extreme expression of traits that are frequently present in the general population. Using linkage disequilibrium (LD) score regression, Robinson et al. (2016) were able to estimate the genetic correlation between an ASD cohort and a cohort of normal individuals who were assessed using the Social Communication Disorders Checklist (SCDC). The genetic correlation was 0.27,

Noncoding regions not captured by exome sequencing

WGS may be able to detect

Small deletions (larger than the average read length) not easily recognized, as not a reliable “quantitative” tool

Methods for CNV detection improving. WGS may be able to detect May be identified in a research setting Deep sequencing may enable detection

indicating that a quarter of the genetic effects impacting ASD also influence the social traits measured on the SCDC, confirming that genetic variation that predisposes to neurodevelopmental disorders overlaps with the genetic variation that determines social traits in the normal population (Robinson et al., 2016). The Cross Disorder Group of the Psychiatric Genomics Consortium also demonstrated that different psychiatric disorders, including schizophrenia, bipolar disorder, major depressive disorder, ASDs, and ADHD, also show overlap in common SNPs (Lee et al., 2013). Grove et al. recently identified genetic correlation between autism and ADHD and between autism and major depressive disorder (Grove et al., 2019). These relationships are also not surprising given that many of these conditions are comorbid (Aldinger et al., 2015). However, the Brainstorm Consortium reported little genetic overlap between neurologic disorders like epilepsy and psychiatric disorders such as ADHD and schizophrenia, suggesting that the genetic variation

GENETIC MECHANISMS OF NEURODEVELOPMENTAL DISORDERS contributing to epilepsy is likely distinct from the variation contributing to psychiatric disease (The Brainstorm Consortium, 2018). This study interestingly also reported negative correlation between epilepsy and autism. This is somewhat unexpected as autism and epilepsy are frequently comorbid, but suggests that in certain contexts autism may have etiological contributions that are distinct from the continuum of normal population variation. One such context, where common variation is impacting expressivity of a highly penetrant rare monogenic disorder, is discussed further in the following text. The notion that common variation implicated in neurodevelopmental disorders also contributes to social and adaptive traits across the general population is further supported by observations that unaffected family members in multiplex autism families tend to show more broad autism-related phenotype traits than simplex family relatives (Virkud et al., 2009). Interestingly, Klei et al. used the Simons Simplex Collection and Autism Genome Project cohorts to estimate heritability in both simplex and multiplex families and identified a difference in heritability as well, where heritability was approximately 40% for simplex families versus 60% for multiplex families. It is likely that the genetic architecture for autism (and other neurodevelopmental disorders) is different for simplex and multiplex families. One possible difference is there is likely a larger burden of disease associated with de novo SNVs and CNVs in simplex probands, as demonstrated by the aforementioned exome studies in simplex families (Neale et al., 2012; O’Roak et al., 2014) and by CNV studies (Sebat et al., 2007). Similar to the differences between simplex and multiplex families, there is likely also a difference in the genetic architecture of neurodevelopmental disorders, which are associated with normal range intelligence and mild ID, vs those associated with moderate to severe ID. In a study of more than 1 million sibling pairs and 9000 twin pairs, Reichenberg et al. found that siblings of individuals with severe ID had an IQ distribution similar to the general population, but that siblings of individuals with mild ID had a shift toward lower IQ (Reichenberg et al., 2016). This supports that mild ID is influenced by the same factors influencing IQ in the general population and represents the low extreme of the normal distribution of intelligence, akin to what was identified in the genetic correlation studies that demonstrate overlap in the genetic variation impacting ASD and social traits in the general population. In contrast, severe ID (and by association, ASD that presents with severe ID) is not caused by the same genetic or environmental factors that influence IQ variation in the population. One possibility is that severe ID is more

319

likely to be monogenic in etiology, and would therefore encompass the rare de novo variants and rare recessive disease. Epilepsy may be similar to some extent, since severe early-onset epileptic encephalopathy, which is typically due to de novo mutations or rare recessive disorders, does not have the same genetic architecture as milder presentations of epilepsy, which are more likely polygenic.

IMPACT OF COMMON VARIATION ON RARE MONOGENIC NEURODEVELOPMENTAL DISORDERS The study of the genetics of human disease has traditionally been divided between rare monogenic disorders and common complex disorders. However, our evolving understanding of neurodevelopmental conditions has shown this segregation is not completely accurate or useful. Many common conditions, like autism, are now known to have monogenic causes—in some cases. In contrast, many genes implicated in monogenic neurodevelopmental disorders have also been identified to carry common inherited variation that modifies disease risk. For example, de novo variants in SCN1A and SCN2A are known to cause early-onset epileptic encephalopathy, but more common alleles have also been implicated in familial susceptibility to febrile seizures in GWAS studies (Feenstra et al., 2014). In addition, it has also been increasingly recognized that common background genetic variation likely influences penetrance and expressivity of rare pathogenic variants. Recurrent CNVs such as 16p11.2 deletion and duplication or 15q13.3 microdeletion have variable penetrance, and can present differently for different individuals even within a family. This observation is also true for highly penetrant monogenic disorders associated with genes such as PTEN and SCN1A, where some individuals manifest very differently from others. It is not surprising that common variation has been implicated in modifying the expression of these monogenic conditions. Niemi et al. recently provided statistical evidence from a GWAS study on the DDD cohort, which showed that common variants contribute to the risk for rare severe neurodevelopmental disorders. Using LD score regression, this study showed that approximately 8% of the variance in risk for syndromic ID, which is thought to be primarily due to high penetrance monogenic causes, is from common variants (Niemi et al., 2018). Cumulative impact from a combination of rare variants is likely also part of the genetic architecture of neurodevelopmental disorders. For example, individuals with neurodevelopmental conditions are also more likely to harbor a larger burden of CNVs than unaffected controls, suggesting additive effects from multiple rare

320

P.Y.B. AU ET AL.

variants can contribute to disease as well (Cooper et al., 2011). More recently, Pizzo et al. looked at individuals carrying the known variably penetrant pathogenic 16p12.1 deletion and found that affected probands with the deletion had higher burden of rare damaging coding variants, and an excess of rare variants affecting genes expressed in the brain, when compared to their unaffected carrier parents (Pizzo et al., 2019).

GENETIC ARCHITECTURE FOR NEURODEVELOPMENTAL DISEASE IS A COMBINATION OF RARE AND COMMON VARIATION From the evidence and studies discussed so far, it has become apparent that there is contribution from both rare variants and common variation to the development of neurodevelopmental disease. The relative contribution of rare vs common variant can be difficult to predict for any given individual, but may depend on the nature of the disease and the family. Previously, we have discussed the differences between severe ID and simplex families vs mild ID and multiplex families, and that similar concepts may apply to complex syndromic forms of ID vs nonsyndromic presentations. Given the increasing importance of being able to assess the combined contribution of rare and common variants to risk, it is worth discussing the development of polygenic risk scores (Purcell et al., 2009). These polygenic risk scores (also known as genetic risk scores or genome-wide scores) combine the impact of thousands of genetic loci/SNPs and their associated weights to create an estimate for liability for a trait or disease. These scores utilize significant, nearly significant, and nonsignificant SNP associations (Yang et al., 2010). This approach has been made possible from the larger genomic data sets now available, which have allowed polygenic risk scores to have more reliable disease prediction than prediction made from genome-wide statistically significant SNPs alone. While this approach has initially been used in neuropsychiatric diseases like schizophrenia (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014), development of these approaches will likely become very useful for understanding childhood neurodevelopmental disorders like autism (Weiner et al., 2017; Grove et al., 2019), ADHD, and epilepsy as well.

GENE–ENVIRONMENT INTERACTIONS It is recognized that environmental factors also have a significant impact on neurodevelopment. We know that neonatal exposure to teratogens can result in recognizable syndromes (e.g., fetal exposure to alcohol, specific antiepileptic medication, or infections). Early childhood

adverse events and maltreatment can also have lasting effects on adult adaptive functioning and health (Gilbert et al., 2009). However, how these nonspecific, pervasive social/environmental factors interact with underlying genetic liability is not well understood. Much of the interaction between the environment and underlying genetic variation may be through the epigenome, but this understanding is still emerging (Czamara et al., 2019). There are a handful of known conditions where a monogenic disorder is highly influenced by a specific environmental factor. One example is influenza-induced acute necrotizing encephalopathy, where a normally developing child carrying a susceptibility allele in the gene RANBP2 can become severely impacted by an acquired flu infection, resulting in acute encephalopathy and, often, long-term neurologic damage (Neilson, 2010). There are also conditions, like neural tube defects, where common variation likely influences risk in conjunction with environmental factors like nutritional status and folate supplementation (Au et al., 2017). These examples show the complexity and variability of gene–environment interactions. In certain contexts, they likely operate in a highly specific way, while in other contexts, these interactions are likely broad and a combination of many subtle interacting effects.

NEW TECHNOLOGIES AND THE PRACTICAL APPLICATION TO CLINIC TODAY Use of microarrays, comprehensive panels, and now exomes has become a routine aspect of clinical genetic care. However, short of novel gene discovery, these technologies may be attaining a maximum for their respective diagnostic rates. As such, other technologies are on the horizon. Whole genomes are increasingly used by many centers, and the rise in their uptake will continue as costs for sequencing continue to significantly decrease. There are distinct advantages for the use of a whole genomes: whole-genome sequencing obviates the need for a library enrichment step, the process is shorter in duration (Saunders et al., 2012), there is improved potential to detect CNVs, structural rearrangements, and noncoding variation, and there is a uniform coverage compared to exome sequencing (Biesecker and Biesecker, 2014). These latter characteristics enhance the diagnostic potential of a genome over an exome; however, interpretation of whole genomes remains challenging, and novel methods for analysis of the sequence data need to be developed. Whole-genome sequencing has been used as a diagnostic tool in a number of neurodevelopmental cohorts. For a cohort of 50 individuals with ID, a diagnosis was

GENETIC MECHANISMS OF NEURODEVELOPMENTAL DISORDERS made in the coding region of 21/50 (42%) and included 7 CNVs that were originally missed by microarray given limitations with resolution (Gilissen et al., 2014). Similar findings were seen in six cases of epilepsy that had whole-genome sequencing performed. Four were diagnosed with dominant or recessive mutations, plus a structural rearrangement of chromosome 9 (Martin et al., 2014). Specific to autism, a recent study sequenced over 5000 samples from the families of 2620 individuals with ASD and observed a diagnostic rate of 11%, including 10 novel genes and a proportion with CNVs (Yuen et al., 2017). On average they identified 73 de novo variants per sample (vs the average of 1 per exome) (Yuen et al., 2017). While most of the diagnoses were in coding regions, variants in ASD cases have also been shown to be enriched in the regulatory regions of known ASD genes. This provides evidence of the importance of these noncoding variants (Turner et al., 2016) and the potential benefits of a genome approach versus exome sequencing. Genes important for epigenetic regulation (also known as epigenes, and which includes chromatin remodeling and histone modification related genes), have been repeatedly identified as being important in the etiology of neurodevelopmental disorders. This recurrent finding suggests that alteration of the epigenome plays a key role in the pathogenesis of neurodevelopmental conditions. Recent studies evaluating methylation, which is the most stable epigenetic mark, have identified that certain epigene-related neurodevelopmental syndromes have characteristic reproducible DNA methylation signatures. One example is the Sotos syndrome, an overgrowth, ID, and seizure syndrome that is due to mutations in NSD1, a histone methyltransferase. Choufani et al. found that individuals with Sotos syndrome have a specific and sensitive DNA methylation signature that is distinct from controls, and also identified that the altered methylome of these patients frequently impacts genes involved in neuronal differentiation (Choufani et al., 2015). Similar studies have been done for the Kabuki syndrome (of which 75% is due to mutations in the methyltransferase KMT2D) and CHARGE syndrome (which is due to mutations in the chromodomain helicase CHD7). Kabuki and CHARGE syndromes overlap to some degree in their physical manifestations and methylation profiling has identified specific and sensitive methylation patterns for each of these ID and malformation syndromes (Butcher et al., 2017). These studies of the epigenome have begun to elucidate the cellular pathways impacted by mutations in epigenes and how they may potentially be targeted for therapy. As this understanding evolves, changes to the epigenome may eventually even be assessed in patients in the absence of a known epigene mutation as part of the diagnostic and/or management process.

321

Evaluation of the epigenome will be increasingly important for our understanding of gene–environmental interactions and gene–gene interaction. Studies investigating DNA methylation patterns in individuals with fetal alcohol spectrum disorder (Portales-Casamar et al., 2016; Lussier et al., 2018) may provide insight into common cellular pathways impacted in both genetic forms of learning disability and FASD, and also inform us of teratogen–gene effects.

CONCLUSION Knowledge of the underlying mechanisms for neurodevelopmental diseases is integral for clinicians caring for patients with neurodevelopmental disease. An accurate diagnosis can provide improved counseling, facilitate access to resources, and inform health surveillance and long-term prognosis. Ultimately, an understanding of the underlying molecular and cellular pathobiology may provide insights into future therapeutic targets (Beaulieu et al., 2012). Examples of this are already emerging, and there are now instances where the mechanism of disease has led to a new therapy (Mendell et al., 2017). This work will need to continue with further comprehensive clinical studies, the sharing of genetic information for novel gene identification and variant interpretation purposes, and the development of new technologies.

REFERENCES Al Ageeli E, Drunat S, Delanoe¨ C et al. (2014). Duplication of the 15q11-q13 region: clinical and genetic study of 30 new cases. Eur J Med Genet 57: 5–14. Aldinger KA, Lane CJ, Veenstra-VanderWeele J et al. (2015). Patterns of risk for multiple co-occurring medical conditions replicate across distinct cohorts of children with autism spectrum disorder. Autism Res 8: 771–781. Anney R, Klei L, Pinto D et al. (2012). Individual common variants exert weak effects on the risk for autism spectrum disorders. Hum Mol Genet 21: 4781–4792. Au KS, Findley TO, Northrup H (2017). Finding the genetic mechanisms of folate deficiency and neural tube defects—leaving no stone unturned. Am J Med Genet A 173: 3042–3057. Ballif BC, Theisen A, Coppinger J et al. (2008). Expanding the clinical phenotype of the 3q29 microdeletion syndrome and characterization of the reciprocal microduplication. Mol Cytogenet 1: 8. Beaulieu CL, Samuels ME, Ekins S et al. (2012). A generalizable pre-clinical research approach for orphan disease therapy. Orphanet J Rare Dis 7: 39. Begemann A, Acun˜a MA, Zweier M et al. (2019). Further corroboration of distinct functional features in SCN2A variants causing intellectual disability or epileptic phenotypes. Mol Med 25: 6.

322

P.Y.B. AU ET AL.

B
ena F, Bruno DL, Eriksson M et al. (2013). Molecular and clinical characterization of 25 individuals with exonic deletions of NRXN1 and comprehensive review of the literature. Am J Med Genet B Neuropsychiatr Genet 162B: 388–403. Bernier R, Steinman KJ, Reilly B et al. (2016). Clinical phenotype of the recurrent 1q21.1 copy-number variant. Genet Med 18: 341–349. Bevilacqua J, Hesse A, Cormier B et al. (2017). Clinical utility of a 377 gene custom next-generation sequencing epilepsy panel. J Genet 96: 681–685. Biesecker LG, Biesecker BB (2014). An approach to pediatric exome and genome sequencing. Curr Opin Pediatr 26: 639–645. Borlot F, Regan BM, Bassett AS et al. (2017). Prevalence of pathogenic copy number variation in adults with pediatric-onset epilepsy and intellectual disability. JAMA Neurol 74: 1301–1311. Boycott KM, Parboosingh JS, Chodirker BN et al. (2008). Clinical genetics and the Hutterite population: a review of Mendelian disorders. Am J Med Genet A 146A: 1088–1098. Boycott KM, Vanstone MR, Bulman DE et al. (2013). Raredisease genetics in the era of next-generation sequencing: discovery to translation. Nat Rev Genet 14: 681–691. Butcher DT, Cytrynbaum C, Turinsky AL et al. (2017). CHARGE and kabuki syndromes: gene-specific DNA methylation signatures identify epigenetic mechanisms linking these clinically overlapping conditions. Am J Hum Genet 100: 773–788. Carvill GL, Mefford HC (2015). Next-generation sequencing in intellectual disability. J Pediatr Genet 4: 128–135. Carvill GL, Regan BM, Yendle SC et al. (2013). GRIN2A mutations cause epilepsy-aphasia spectrum disorders. Nat Genet 45: 1073–1076. Choufani S, Cytrynbaum C, Chung BH et al. (2015). NSD1 mutations generate a genome-wide DNA methylation signature. Nat Commun 6: 10207. Christian SL, Brune CW, Sudi J et al. (2008). Novel submicroscopic chromosomal abnormalities detected in autism spectrum disorder. Biol Psychiatry 63: 1111–1117. Clarke S (2002). Tandem mass spectrometry: the tool of choice for diagnosing inborn errors of metabolism? Br J Biomed Sci 59: 42–46. Coe BP, Girirajan S, Eichler EE (2012). The genetic variability and commonality of neurodevelopmental disease. Am J Med Genet C Semin Med Genet 160C: 118–129. Cooper GM, Coe BP, Girirajan S et al. (2011). A copy number variation morbidity map of developmental delay. Nat Genet 43: 838–846. Coppola A, Cellini E, Stamberger H et al. (2019). Diagnostic implications of genetic copy number variation in epilepsy plus. Epilepsia 60: 689–706. Cox DM, Butler MG (2015). The 15q11.2 BP1-BP2 microdeletion syndrome: a review. Int J Mol Sci 16: 4068–4082. Czamara D, Eraslan G, Page CM et al. (2019). Integrated analysis of environmental and genetic influences on cord blood DNA methylation in new-borns. Nat Commun 10: 2548.

Dabell MP, Rosenfeld JA, Bader P et al. (2013). Investigation of NRXN1 deletions: clinical and molecular characterization. Am J Med Genet A 161A: 717–731. de Brouwer AP, Yntema HG, Kleefstra T et al. (2007). Mutation frequencies of X-linked mental retardation genes in families from the EuroMRX consortium. Hum Mutat 28: 207–208. de Kovel CG, Trucks H, Helbig I et al. (2010). Recurrent microdeletions at 15q11.2 and 16p13.11 predispose to idiopathic generalized epilepsies. Brain 133: 23–32. de Ligt J, Willemsen MH, van Bon BWM et al. (2012). Diagnostic exome sequencing in persons with severe intellectual disability. N Engl J Med 367: 1921–1929. De Rubeis S, He X, Goldberg AP et al. (2014). Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515: 209–215. de Smith AJ, Tsalenko A, Sampas N et al. (2007). Array CGH analysis of copy number variation identifies 1284 new genes variant in healthy white males: implications for association studies of complex diseases. Hum Mol Genet 16: 2783–2794. Deciphering Developmental Disorders Study (2017). Prevalence and architecture of de novo mutations in developmental disorders. Nature 542: 433–438. Dias S, Ware RS, Kinner SA et al. (2013). Co-occurring mental disorder and intellectual disability in a large sample of Australian prisoners. Aust N Z J Psychiatry 47: 938–944. Edwards JH, Harnden DG, Cameron AH et al. (1960). A new trisomic syndrome. Lancet 1: 787–790. Epi4K Consortium (2017). Phenotypic analysis of 303 multiplex families with common epilepsies. Brain 140: 2144–2156. Fedorenko E, Morgan A, Murray E et al. (2016). A highly penetrant form of childhood apraxia of speech due to deletion of 16p11.2. Eur J Hum Genet 24: 302–306. Feenstra B, Pasternak B, Geller F et al. (2014). Common variants associated with general and MMR vaccine-related febrile seizures. Nat Genet 46: 1274–1282. Fernandez BA, Roberts W, Chung B et al. (2010). Phenotypic spectrum associated with de novo and inherited deletions and duplications at 16p11.2 in individuals ascertained for diagnosis of autism spectrum disorder. J Med Genet 47: 195–203. Flore LA, Milunsky JM (2012). Updates in the genetic evaluation of the child with global developmental delay or intellectual disability. Semin Pediatr Neurol 19: 173–180. Gardner R, Sutherland G (2004). Chromosome abnormalities and genetic counseling, Oxford University Press, Oxford. Gaugler T, Klei L, Sanders SJ et al. (2014). Most genetic risk for autism resides with common variation. Nat Genet 46: 881–885. Gilbert R, Widom CS, Browne K et al. (2009). Burden and consequences of child maltreatment in high-income countries. Lancet 373: 68–81. Gilissen C, Hehir-Kwa JY, Thung DT et al. (2014). Genome sequencing identifies major causes of severe intellectual disability. Nature 511: 344–347.

GENETIC MECHANISMS OF NEURODEVELOPMENTAL DISORDERS Grove J, Ripke S, Als TD et al. (2019). Identification of common genetic risk variants for autism spectrum disorder. Nat Genet 51: 431–444. Hansen SN, Schendel DE, Francis RW et al. (2019). Recurrence risk of autism in siblings and cousins: a multi-national, population-based study. J Am Acad Child Adolesc Psychiatry 58: 866–875. Hauser WA, Annegers JF, Rocca WA (1996). Descriptive epidemiology of epilepsy: contributions of population-based studies from Rochester, Minnesota. Mayo Clin Proc 71: 576–586. Hayeems RZ, Boycott KM (2018). Genome-wide sequencing technologies: a primer for paediatricians. Paediatr Child Health 23: 191–197. Hegele RA (2007). Copy-number variations add a new layer of complexity in the human genome. CMAJ 176: 441–442. Helbig I, Mefford HC, Sharp AJ et al. (2009). 15q13.3 microdeletions increase risk of idiopathic generalized epilepsy. Nat Genet 41: 160–162. Herbst DS, Baird PA (1982). Sib risks for nonspecific mental retardation in British Columbia. Am J Med Genet 13: 197–208. Herbst DS, Miller JR (1980). Nonspecific X-linked mental retardation II: the frequency in British Columbia. Am J Med Genet 7: 461–469. Heron SE, Crossland KM, Andermann E et al. (2002). Sodium-channel defects in benign familial neonatalinfantile seizures. Lancet 360: 851–852. Hesdorffer DC, Logroscino G, Benn EK et al. (2011). Estimating risk for developing epilepsy: a population-based study in Rochester, Minnesota. Neurology 76: 23–27. Heyne HO, Singh T, Stamberger H et al. (2018). De novo variants in neurodevelopmental disorders with epilepsy. Nat Genet 50: 1048–1053. Hoischen A, van Bon BW, Rodrı´guez-Santiago B et al. (2011). De novo nonsense mutations in ASXL1 cause BohringOpitz syndrome. Nat Genet 43: 729–731. Iafrate AJ, Feuk L, Rivera MN et al. (2004). Detection of large-scale variation in the human genome. Nat Genet 36: 949–951. International Human Genome Sequencing Consortium (2004). Finishing the euchromatic sequence of the human genome. Nature 431: 931–945. International League Against Epilepsy Consortium on Complex Epilepsies (2014). Genetic determinants of common epilepsies: a meta-analysis of genome-wide association studies. Lancet Neurol 13: 893–903. Iossifov I, O’Roak BJ, Sanders SJ et al. (2014). The contribution of de novo coding mutations to autism spectrum disorder. Nature 515: 216–221. Ji M, Hou P, Li S et al. (2004). Microarray-based method for genotyping of functional single nucleotide polymorphisms using dual-color fluorescence hybridization. Mutat Res 548: 97–105. Kamath A, Linden SC, Evans FM et al. (2018). Chromosome 17q12 duplications: further delineation of the range of psychiatric and clinical phenotypes. Am J Med Genet B Neuropsychiatr Genet 177: 520–528.

323

Kasperavici ute D, Catarino CB, Heinzen EL et al. (2010). Common genetic variation and susceptibility to partial epilepsies: a genome-wide association study. Brain 133: 2136–2147. Khan WA, Cohen N, Scott SA et al. (2019). Familial inheritance of the 3q29 microdeletion syndrome: case report and review. BMC Med Genomics 12: 51. Kirov G, Rees E, Walters JTR et al. (2014). The penetrance of copy number variations for schizophrenia and developmental delay. Biol Psychiatry 75 (5): 378–385. Kleefstra T, Smidt M, Banning MJ et al. (2005). Disruption of the gene euchromatin histone methyl transferase1 (Eu-HMTase1) is associated with the 9q34 subtelomeric deletion syndrome. J Med Genet 42: 299–306. Klei L, Sanders SJ, Murtha MT et al. (2012). Common genetic variants, acting additively, are a major source of risk for autism. Mol Autism 3: 9. Kong A, Steinthorsdottir V, Masson G et al. (2009). Parental origin of sequence variants associated with complex diseases. Nature 462: 868–874. Koolen DA, Kramer JM, Neveling K et al. (2012). Mutations in the chromatin modifier gene KANSL1 cause the 17q21.31 microdeletion syndrome. Nat Genet 44: 639–641. Kosmicki JA, Samocha KE, Howrigan DP et al. (2017). Refining the role of de novo protein-truncating variants in neurodevelopmental disorders by using population reference samples. Nat Genet 49: 504–510. Kremer EJ, Pritchard M, Lynch M et al. (1991). Mapping of DNA instability at the fragile X to a trinucleotide repeat sequence p(CCG)n. Science 252: 1711–1714. Krumm N, Turner TN, Baker C et al. (2015). Excess of rare, inherited truncating mutations in autism. Nat Genet 47: 582–588. Lee SH, Ripke S, Neale BM et al. (2013). Genetic relationship between five psychiatric disorders estimated from genomewide SNPs. Nat Genet 45: 984–994. Lejeune J, Turpin R, Gautier M (1959). Chromosomic diagnosis of mongolism. Arch Fr Pediatr 16: 962–963. Lemke JR, Lal D, Reinthaler EM et al. (2013). Mutations in GRIN2A cause idiopathic focal epilepsy with rolandic spikes. Nat Genet 45: 1067–1072. Lesca G, Rudolf G, Bruneau N et al. (2013). GRIN2A mutations in acquired epileptic aphasia and related childhood focal epilepsies and encephalopathies with speech and language dysfunction. Nat Genet 45: 1061–1066. Lowther C, Costain G, Stavropoulos DJ et al. (2015). Delineating the 15q13.3 microdeletion phenotype: a case series and comprehensive review of the literature. Genet Med 17: 149–157. Lubs HA, Stevenson RE, Schwartz CE (2012). Fragile X and X-linked intellectual disability: four decades of discovery. Am J Hum Genet 90: 579–590. Lussier AA, Morin AM, MacIsaac JL et al. (2018). DNA methylation as a predictor of fetal alcohol spectrum disorder. Clin Epigenetics 10: 5. Marini C, Scheffer IE, Nabbout R et al. (2009). SCN1A duplications and deletions detected in Dravet syndrome: implications for molecular diagnosis. Epilepsia 50: 1670–1678.

324

P.Y.B. AU ET AL.

Marshall CR, Noor A, Vincent JB et al. (2008). Structural variation of chromosomes in autism spectrum disorder. Am J Hum Genet 82: 477–488. Martin HC, Kim GE, Pagnamenta AT et al. (2014). Clinical whole-genome sequencing in severe early-onset epilepsy reveals new genes and improves molecular diagnosis. Hum Mol Genet 23: 3200–3211. Maulik PK, Mascarenhas MN, Mathers CD et al. (2011). Prevalence of intellectual disability: a meta-analysis of population-based studies. Res Dev Disabil 32: 419–436. Mefford HC, Sharp AJ, Baker C et al. (2008). Recurrent rearrangements of chromosome 1q21.1 and variable pediatric phenotypes. N Engl J Med 359: 1685–1699. Mendell JR, Al-Zaidy S, Shell R et al. (2017). Single-dose gene-replacement therapy for spinal muscular atrophy. N Engl J Med 377: 1713–1722. Michelson DJ, Shevell MI, Sherr EH et al. (2011). Evidence report: genetic and metabolic testing on children with global developmental delay: report of the Quality Standards Subcommittee of the American Academy of Neurology and the Practice Committee of the Child Neurology Society. Neurology 77: 1629–1635. Monies D, Abouelhoda M, Assoum M et al. (2019). Lessons learned from large-scale, first-tier clinical exome sequencing in a highly consanguineous population. Am J Hum Genet 104: 1182–1201. Moreno-De-Luca D, Mulle JG, Kaminsky EB et al. (2010). Deletion 17q12 is a recurrent copy number variant that confers high risk of autism and schizophrenia. Am J Hum Genet 87: 618–630. Morgan VA, Leonard H, Bourke J et al. (2008). Intellectual disability co-occurring with schizophrenia and other psychiatric illness: population-based study. Br J Psychiatry 193: 364–372. Mullen SA, Carvill GL, Bellows S et al. (2013). Copy number variants are frequent in genetic generalized epilepsy with intellectual disability. Neurology 81: 1507–1514. Musante L, Ropers HH (2014). Genetics of recessive cognitive disorders. Trends Genet 30: 32–39. Nassar N, Dixon G, Bourke J et al. (2009). Autism spectrum disorders in young children: effect of changes in diagnostic practices. Int J Epidemiol 38: 1245–1254. Neale BM, Kou Y, Liu L et al. (2012). Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485: 242–245. Neilson DE (2010). The interplay of infection and genetics in acute necrotizing encephalopathy. Curr Opin Pediatr 22: 751–757. Ng SB, Bigham AW, Buckingham KJ et al. (2010). Exome sequencing identifies MLL2 mutations as a cause of Kabuki syndrome. Nat Genet 42: 790–793. Niemi MEK, Martin HC, Rice DL et al. (2018). Common genetic variants contribute to risk of rare severe neurodevelopmental disorders. Nature 562: 268–271. Ogiwara I, Ito K, Sawaishi Y et al. (2009). De novo mutations of voltage-gated sodium channel alphaII gene SCN2A in intractable epilepsies. Neurology 73: 1046–1053.

Olson H, Shen Y, Avallone J et al. (2014). Copy number variation plays an important role in clinical epilepsy. Ann Neurol 75: 943–958. O’Roak BJ, Vives L, Girirajan S et al. (2012). Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485: 246–250. O’Roak BJ, Stessman HA, Boyle EA et al. (2014). Recurrent de novo mutations implicate novel genes underlying simplex autism risk. Nat Commun 5: 5595. Pandor A, Eastham J, Beverley C et al. (2004). Clinical effectiveness and cost-effectiveness of neonatal screening for inborn errors of metabolism using tandem mass spectrometry: a systematic review. Health Technol Assess 8: 1–121, iii. Patau K, Smith DW, Therman E et al. (1960). Multiple congenital anomaly caused by an extra autosome. Lancet 1: 790–793. Pizzo L, Jensen M, Polyak A et al. (2019). Rare variants in the genetic background modulate cognitive and developmental phenotypes in individuals carrying disease-associated variants. Genet Med 21: 816–825. Portales-Casamar E, Lussier AA, Jones MJ et al. (2016). DNA methylation signature of human fetal alcohol spectrum disorder. Epigenetics Chromatin 9: 25. Purcell SM, Wray NR, Stone JL et al. (2009). Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460: 748–752. Rasmussen M, Vestergaard EM, Graakjaer J et al. (2016). 17q12 deletion and duplication syndrome in Denmark—a clinical cohort of 38 patients and review of the literature. Am J Med Genet A 170: 2934–2942. Rauch A, Wieczorek D, Graf E et al. (2012). Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study. Lancet 380: 1674–1682. Reichenberg A, Cederl€ of M, McMillan A et al. (2016). Discontinuity in the genetic and environmental causes of the intellectual disability spectrum. Proc Natl Acad Sci U S A 113: 1098–1103. Robinson EB, St Pourcain B, Anttila V et al. (2016). Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population. Nat Genet 48: 552–555. Rosenfeld JA, Patel A (2017). Chromosomal microarrays: understanding genetics of neurodevelopmental disorders and congenital anomalies. J Pediatr Genet 6: 42–50. Rosenfeld JA, Ballif BC, Torchia BS et al. (2010). Copy number variations associated with autism spectrum disorders contribute to a spectrum of neurodevelopmental disorders. Genet Med 12: 694–702. Rosenfeld JA, Coe BP, Eichler EE et al. (2013). Estimates of penetrance for recurrent pathogenic copy-number variations. Genet Med 15: 478–481. Sa´nchez Ferna´ndez I, Loddenkemper T, Gaı´nza-Lein M et al. (2019). Diagnostic yield of genetic tests in epilepsy: a meta-analysis and cost-effectiveness study. Neurology 92: e418–e428.

GENETIC MECHANISMS OF NEURODEVELOPMENTAL DISORDERS Sanders SJ, Murtha MT, Gupta AR et al. (2012). De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485: 237–241. Sandin S, Lichtenstein P, Kuja-Halkola R et al. (2014). The familial risk of autism. JAMA 311: 1770–1777. Saudubray J-M, Fernandes J, Walter JH et al. (2006). Inborn metabolic diseases, Springer Medizan Verlag, Germany. Saunders CJ, Miller NA, Soden SE et al. (2012). Rapid whole-genome sequencing for genetic disease diagnosis in neonatal intensive care units. Sci Transl Med 4: 154ra135. Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014). Biological insights from 108 schizophrenia-associated genetic loci. Nature 511: 421–427. Scriver CR (2008). Garrod’s Croonian lectures (1908) and the charter ’inborn errors of metabolism’: albinism, alkaptonuria, cystinuria, and pentosuria at age 100 in 2008. J Inherit Metab Dis 31: 580–598. Sebat J, Lakshmi B, Troge J et al. (2004). Large-scale copy number polymorphism in the human genome. Science 305: 525–528. Sebat J, Lakshmi B, Malhotra D et al. (2007). Strong association of de novo copy number mutations with autism. Science 316: 445–449. Steffens M, Leu C, Ruppert AK et al. (2012). Genome-wide association analysis of genetic generalized epilepsies implicates susceptibility loci at 1q43, 2p16.1, 2q22.3 and 17q21.32. Hum Mol Genet 21: 5359–5372. Steinman KJ, Spence SJ, Ramocki MB et al. (2016). 16p11.2 deletion and duplication: characterizing neurologic phenotypes in a large clinically ascertained cohort. Am J Med Genet A 170: 2943–2955. Sun Y, Ruivenkamp CA, Hoffer MJ et al. (2015). Nextgeneration diagnostics: gene panel, exome, or whole genome? Hum Mutat 36: 648–655. Swanger SA, Chen W, Wells G et al. (2016). Mechanistic insight into NMDA receptor dysregulation by rare variants in the GluN2A and GluN2B agonist binding domains. Am J Hum Genet 99: 1261–1280. Takumi T, Tamada K (2018). CNV biology in neurodevelopmental disorders. Curr Opin Neurobiol 48: 183–192. Talkowski ME, Mullegama SV, Rosenfeld JA et al. (2011). Assessment of 2q23.1 microdeletion syndrome implicates MBD5 as a single causal locus of intellectual disability, epilepsy, and autism spectrum disorder. Am J Hum Genet 89: 551–563. Tammimies K, Marshall CR, Walker S et al. (2015). Molecular diagnostic yield of chromosomal microarray analysis and whole-exome sequencing in children with autism spectrum disorder. JAMA 314: 895–903. The Brainstorm Consortium (2018). Analysis of shared heritability in common disorders of the brain. Science 360: pii: eaap8757. Trask BJ (2002). Human cytogenetics: 46 chromosomes, 46 years and counting. Nat Rev Genet 3: 769–778.

325

Turner TN, Hormozdiari F, Duyzend MH et al. (2016). Genome sequencing of autism-affected families reveals disruption of putative noncoding regulatory DNA. Am J Hum Genet 98: 58–74. Urraca N, Cleary J, Brewer V et al. (2013). The interstitial duplication 15q11.2-q13 syndrome includes autism, mild facial anomalies and a characteristic EEG signature. Autism Res 6: 268–279. van Wegberg AMJ, MacDonald A, Ahring K et al. (2017). The complete European guidelines on phenylketonuria: diagnosis and treatment. Orphanet J Rare Dis 12: 162. Venter JC, Adams MD, Myers EW et al. (2001). The sequence of the human genome. Science 291: 1304–1351. Vickers RR, Gibson JS (2019). A review of the genomic analysis of children presenting with developmental delay/intellectual disability and associated dysmorphic features. Cureus 11: e3873. Virkud YV, Todd RD, Abbacchi AM et al. (2009). Familial aggregation of quantitative autistic traits in multiplex versus simplex autism. Am J Med Genet B Neuropsychiatr Genet 150B: 328–334. Vissers LE, de Ligt J, Gilissen C et al. (2010). A de novo paradigm for mental retardation. Nat Genet 42: 1109–1112. Vorstman JAS, Parr JR, Moreno-De-Luca D et al. (2017). Autism genetics: opportunities and challenges for clinical translation. Nat Rev Genet 18: 362–376. Wagenstaller J, Spranger S, Lorenz-Depiereux B et al. (2007). Copy-number variations measured by singlenucleotide-polymorphism oligonucleotide arrays in patients with mental retardation. Am J Hum Genet 81: 768–779. Wang K, Zhang H, Ma D et al. (2009). Common genetic variants on 5p14.1 associate with autism spectrum disorders. Nature 459: 528–533. Weiner DJ, Wigdor EM, Ripke S et al. (2017). Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders. Nat Genet 49: 978–985. Weiss LA, Arking DE, Daly MJ (2009). A genome-wide linkage and association scan reveals novel loci for autism. Nature 461: 802–808. Wenger TL, Miller JS, DePolo LM et al. (2016). 22q11.2 duplication syndrome: elevated rate of autism spectrum disorder and need for medical screening. Mol Autism 7: 27. Wentzel C, Fernstr€ om M, Ohrner Y et al. (2008). Clinical variability of the 22q11.2 duplication syndrome. Eur J Med Genet 51: 501–510. Yagi H, Furutani Y, Hamada H et al. (2003). Role of TBX1 in human del22q11.2 syndrome. Lancet 362: 1366–1373. Yang J, Benyamin B, McEvoy BP et al. (2010). Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42: 565–569. Yuen RKC, Merico D, Bookman M et al. (2017). Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder. Nat Neurosci 20: 602–611.

326

P.Y.B. AU ET AL.

Zhang B, Willing M, Grange DK et al. (2016). Multigenerational autosomal dominant inheritance of 5p chromosomal deletions. Am J Med Genet A 170: 583–593. Zhang C, Cerveira E, Romanovitch M et al. (2017). Array-based comparative genomic hybridization (aCGH). Methods Mol Biol 1541: 167–179. Zweier C, Peippo MM, Hoyer J et al. (2007). Haploinsufficiency of TCF4 causes syndromal mental

retardation with intermittent hyperventilation (Pitt-Hopkins syndrome). Am J Hum Genet 80: 994–1001. Zweier M, Gregor A, Zweier C et al. (2010). Mutations in MEF2C from the 5q14.3q15 microdeletion syndrome region are a frequent cause of severe mental retardation and diminish MECP2 and CDKL5 expression. Hum Mutat 31: 722–733.

Handbook of Clinical Neurology, Vol. 173 (3rd series) Neurocognitive Development: Normative Development A. Gallagher, C. Bulteau, D. Cohen and J.L. Michaud, Editors https://doi.org/10.1016/B978-0-444-64150-2.00025-3 Copyright © 2020 Elsevier B.V. All rights reserved

Chapter 24

The effects of sex on prevalence and mechanisms underlying neurodevelopmental disorders
SABRINA NOWAK AND SEBASTIEN JACQUEMONT* Department of Pediatrics, University of Montreal, Montreal, QC, Canada

Abstract Neurodevelopmental disorders occur more frequently in boys than in girls and often differ in presentation between the sexes. The sex differences in prevalence and presentation of autism spectrum disorder, intellectual disability, communication disorders, specific learning disabilities, attention deficit/hyperactivity disorder, Tourette’s syndrome, and epilepsy are discussed, as well as sex differences in the patterns of comorbidities between these disorders. Prominent theories have been proposed to explain sex biases. These include genetic factors, sex hormones, sociological factors, cognitive differences between the sexes, and environmental insult. Despite the large body of research reviewed in this chapter, many aspects of sex-related effects in neurodevelopmental disorders remain poorly understood.

INTRODUCTION Sex is a major factor influencing the way disorders present and develop, but it remains undervalued in medical research. Various sociological and biological hypotheses have been investigated to explain the highly skewed sex ratios observed in neurodevelopmental disorders. When pooled, neurodevelopmental disorders (NDDs) are almost twice as likely to occur in males (Boyle et al., 2011), and many show differences in presentation, age of onset, symptom severity, and prevalence based on sex. There has been understandable concern about separately considering male and female forms of some disorders. Boys and girls with these disorders are demonstrably more similar to each other than they are to the general population. However, understanding the mechanisms underlying sex differences in prevalence and presentation may bring us one step closer to understanding the etiologies of these disorders. In this chapter, we examine sex differences in seven neurodevelopmental conditions and explore the biological

and sociological factors that may underlie differences in diagnosis and presentation. These disorders were selected due to (1) onset in the developmental period, (2) abundant literature on sex bias, (3) their frequent comorbidity in patients, and (4) significant overlap between genetic contribution. We first review the level and nature of the sex bias in these conditions and then discuss mechanisms that may explain why the sexes are differently impacted. Ratios are presented as boys: girls throughout the chapter.

SEX DIFFERENCES WITHIN DISORDERS Autism spectrum disorder Autism spectrum disorder (ASD) occurs in about 0.76%–1.5% of the population (American Psychiatric Association, 2013; Baxter et al., 2015; Christensen et al., 2016). Its core symptoms are social and communication deficits as well as repetitive behaviors and restricted interests. Sex differences in ASD have been

*Correspondence to: Sebastien Jacquemont, M.D., Department of Pediatrics, University of Montreal, CHU Sainte-Justine Research Center, 3175 chemin de la C^ote-Sainte-Catherine, Montreal QC, H3T 1C5, Canada. Tel: +1-514-922-5949, E-mail: [email protected]

328

S. NOWAK AND S. JACQUEMONT

researched extensively because clinical samples present with a sex ratio of 4.2:1. The sex bias is milder (3.25:1) in population-based samples (Loomes et al., 2017). Girls with a diagnosis of ASD have more cognitive and behavioral deficits than boys, as well as worse adaptive skills and executive functions (Frazier et al., 2014; White et al., 2017; Ratto et al., 2018). Accordingly, the male-to-female ratio in ASD is positively correlated with IQ. In the presence of intellectual disability (ID; IQ < 70), the sex bias is approximately 2:1 whereas for individuals with ASD and normal intellectual functioning, it is close to 6:1 (Fombonne, 2003). Whether this is due to a camouflaging of autism symptoms in girls or a genuine difference in the impairment of girls with higher IQ remains unknown (Gould and AshtonSmith, 2011; Ratto et al., 2018). In the general population, above-average VIQ is protective for social and communication deficits in girls (Skuse et al., 2009), and girls with ASD use more complex social strategies (i.e., imitation, social scripts) and rely on closer friendships than boys (Hiller et al., 2016; Dean et al., 2017). This suggests that girls with autistic traits without ID may be less likely to be impaired enough to meet the criteria for a full diagnosis (Dworzynski et al., 2012). However, girls with a diagnosis of ASD are also more likely than boys to have never come to the attention of their teachers and may simply be better at “camouflaging” their deficits (Mandy et al., 2012; Hiller et al., 2016). A delayed or missed diagnosis in girls is associated with significant distress and morbidity (Bargiela et al., 2016); therefore the extent to which camouflaging prevents accurate diagnosis remains an important research topic. Children diagnosed before age 4 have a sex ratio of about 3–4:1, both with and without ID. At this stage, there are no sex differences in core symptoms, cognitive measures, or adaptive behavior (Andersson et al., 2013; Postorino et al., 2015; Fulton et al., 2017). In fact, sex is not associated with earlier diagnosis (Petrou et al., 2018; Zwaigenbaum et al., 2019). While there are some findings of later diagnosis in girls (Shattuck et al., 2009; Begeer et al., 2013; Petrou et al., 2018), the most common finding is no difference in age at diagnosis in boys and girls (Daniels and Mandell, 2014; Loomes et al., 2017). Overall, the age trajectory of core symptoms in children diagnosed with ASD does not vary by sex (Fountain et al., 2012; Lord et al., 2015), except for repetitive behaviors and restricted interests, which are more common in boys after 6 years of age (Van WijngaardenCremers et al., 2014). There is some evidence that girls may present with female-specific restricted and repetitive behaviors and interests. The majority of boys have a fascination with wheeled toys or screen time

(e.g., video games), while girls mostly exhibit obsessions with random objects (e.g., stickers, rocks, pens, animals) and obsessive and repetitive play with other toys (e.g., dolls, teddy bears, figurines). These behaviors may be less likely to be identified by standard diagnostic tools, which may explain the difference (Hiller et al., 2014, 2016). The frequency of regression (i.e., the loss of previously learned language, motor, or other skills, which occurs around the age of 12 months) is the same in boys and girls with ASD (Barger et al., 2013; Pearson et al., 2018). Symptoms commonly associated with ASD, such as sleep and eating problems, motor delays, and sensory issues, are equally common in both genders, although males have more hyperactivity symptoms and display more aggression (Giarelli et al., 2010).

Intellectual disability Intellectual disability (ID) occurs in about 1%–2% of the pediatric population and about 0.5–1% of the adult population (Maulik et al., 2011; Olzenak McGuire et al., 2019). Pediatric samples have a sex ratio of around 1.6–2.2:1 (Bourke et al., 2016; Maenner et al., 2016; Hughes-McCormack et al., 2017; Westerinen et al., 2017; Olzenak McGuire et al., 2019), but adult samples show a ratio of 1.1–1.4:1 (Maulik et al., 2011). The core criteria of ID are deficits in cognitive functioning (usually IQ < 70) and adaptive skills (i.e., the ability to carry out age-appropriate daily living activities). The sex bias in ID is thought to be less pronounced in severe ID (IQ < 35) than in moderate (IQ 36–49) and mild (IQ 55–69) ID (Leonard and Wen, 2002), although a population-based study of a large catchment area found no difference in sex ratios between severe and mild ID (1.6–1.7:1) (Bourke et al., 2016). Studies on sex-related differences in adaptive skills in ID are scarce. No significant effects have been reported in children (Tremblay et al., 2010) or adults (Su et al., 2008; Belva and Matson, 2013; Lin et al., 2013). Similarly, challenging behaviors (i.e., self-injury, aggressive-destructive behavior, stereotyped behavior) do not seem to differ between sexes (Lundqvist, 2013; Bowring et al., 2017). Females with ID have significantly poorer health than males at all ages (Hughes-McCormack et al., 2017). Although women with ID live longer than men with ID, the sex gap in life expectancy is smaller than in the general population (i.e., average years of life lost is 20–30 years for women vs. 17–22 years for men) (McCarron et al., 2015; Arvio et al., 2017; Glover et al., 2017). Girls with ID have higher mortality relative to normative data at all ages (McCarron et al., 2015; Glover et al., 2017).

SEX BIAS AND NEURODEVELOPMENTAL DISORDERS

Communication disorders Communication disorders encompass deficits of speech (i.e., the production of sounds), language (i.e., the symbols of communication and their use), and other forms of communication. The normal process of language development is variable, and diagnoses are not considered stable before 3–5 years of age.

LANGUAGE DISORDER OR SPECIFIC LANGUAGE IMPAIRMENT

Specific language impairment (SLI) is an impairment in language that is not caused by hearing impairment, ID, or ASD. It occurs in about 7.5% of 4- to 6-year-olds (Norbury et al., 2016) and has a sex ratio of 1.64:1 (Rudolph, 2017). After school entry (5–6 years), relative language ability is very stable through adolescence (Conti-Ramsden et al., 2012; Rice and Hoffman, 2015; Norbury et al., 2017), and 70% of those diagnosed at age 5 continue to meet the criteria for language impairment by age 18, with persistence not affected by sex (Johnson et al., 1999). Severity of SLI in childhood is not impacted by gender (Norbury et al., 2016). However, affected girls begin to fall behind boys around age 10, and by age 21, affected girls are about 3 years behind affected boys in language skills (Rice and Hoffman, 2015).

SPEECH SOUND DISORDER Speech sound disorder (SSD) involves difficulties in articulation or phonological processing that are not otherwise explained by IQ. It has a population prevalence of 3.4%–3.6% and occurs with a sex ratio of 1.6–1.8:1 (Eadie et al., 2015; Wren et al., 2016). Remission occurs frequently in adolescence and is not affected by sex (Morgan et al., 2017). Boys with SSD are more likely to have comorbid SLI, and at age 4, both disorders co-occur in children with a sex ratio of 1.9:1 (Eadie et al., 2015). The severity of both disorders occurring together is higher in boys. A third of boys and no girls with both disorders do not know the names of any letters by age 4, an important preliteracy measure (Eadie et al., 2015).

CHILDHOOD-ONSET FLUENCY DISORDER (STUTTERING) About 5% of children have a history of stuttering (Boyle et al., 2011; Yairi and Ambrose, 2013). It exhibits a sex bias that increases with age: 1.3–1.6:1 before 4 years, 2:1 at 7 years, and 4:1 after the age of 8 (Craig et al., 2002; Craig and Tran, 2005; Howell et al., 2008; Reilly et al., 2013; Yairi and Ambrose, 2013). The increasing sex ratio is due in part to recovery, which is more common in girls (Yairi and Ambrose, 2013; Walsh et al., 2018).

329

Some studies have found less severe stuttering in girls; however, others have found no difference (Månsson, 2000; Yairi and Ambrose, 2013). Motor analyses show that boys who stutter have more variable and smaller amplitudes of articulatory movement than controls, while girls do not, which may indicate a difference in underlying speech motor processes that may explain the sex differences in recovery (Walsh et al., 2015). Boys who stutter are also more likely than girls to have comorbid speech and language disorders, as well as other developmental and learning disorders (Blood et al., 2003).

Specific learning disability The diagnosis of Specific learning disability (SLD) requires “persistent difficulties in learning keystone academic skills” for at least 6 months. The individual’s performance of these skills must be well below the average for his or her age group ( 1.5 to 2 SD has been suggested), in the absence of co-occurring diagnoses that may better explain underachievement, such as ID or hearing or visual impairments (American Psychiatric Association, 2013). Sex ratios vary between the impairment types in SLD. The highest sex ratios are found in reading and writing impairments and the lowest in mathematics impairment (Moll et al., 2014). The prevalence of reading impairment is about 7%, with a sex ratio that slightly increases with age (1.44:1 at 8 years and 2.0:1 at 10 years) and severity (2.0:1 at 1.5 SD and 2.3:1 at 2 SD) (Moll et al., 2014; Fortes et al., 2016). The overall pediatric sex ratio is 2:1 (Rutter et al., 2004; Arnett et al., 2017). Of note, even when children with attentional disturbances or high activity levels are removed, sex ratios remain essentially unchanged (Flannery et al., 2000). The prevalence of mathematics impairment is about 5%–6% (Moll et al., 2014; Fortes et al., 2016; Morsanyi et al., 2018). Although older studies have reported an excess in females, other investigations show a 1:1 sex ratio regardless of the severity of the deficit (Devine et al., 2013; Morsanyi et al., 2018; Soares et al., 2018). There are also no sex differences in the rate of remission (Nelson, 2018).

Attention deficit/hyperactivity disorder Attention deficit/hyperactivity disorder (ADHD) is among the most common NDDs, with a prevalence of 6%–7% in pediatric samples (Boyle et al., 2011; Willcutt, 2012; Thomas et al., 2015; Wang et al., 2017). The overall sex ratio of ADHD is 3.2:1 (Willcutt, 2012), but this is reduced in adult samples, where it is about 2:1 (Williamson and Johnston, 2015).

330

S. NOWAK AND S. JACQUEMONT

ADHD presents with symptoms in two domains: hyperactivity/impulsivity and inattention. There are three presentations of ADHD depending on symptom counts in each domain: predominately inattentive, predominately hyperactive/impulsive, and combined type. Sex ratios differ by presentation as girls are less likely to exhibit hyperactivity/impulsivity symptoms. The sex ratio is 1.8:1 in the inattentive type, 3.5:1 in the hyperactive/impulsive type, and 2.7:1 in the combined type (Willcutt, 2012). Girls are also more likely to experience hyperactivity as subjective restlessness rather than inappropriate movement (Williamson and Johnston, 2015). In measures of symptom severity and impairment, boys and girls diagnosed with ADHD do not differ on parent and teacher rating scales (Williamson and Johnston, 2015), although in the general population levels of both inattention and hyperactivity/impulsivity are higher in boys than in girls (Arnett et al., 2015). Girls with ADHD subjectively rate their symptoms more severely than boys, but this appears to reflect a bias in self-perception or gendered expectations rather than a true difference in presentation (Williamson and Johnston, 2015). Sex ratios in ADHD also differ with age. Male excess is highest in school-aged samples (6–18 years) and lower in preschool and adult samples (Willcutt, 2012). ADHD is persistent into adulthood in about 40%–50% of cases but whether this persistence is sex-biased is unknown (Caye, 2016; Sibley et al., 2017; Sibley et al., 2016). Prospective and birth cohort studies have suggested the onset of ADHD in late adolescence, and this group may have an equal sex ratio or female majority (Moffitt et al., 2015; Agnew-Blais et al., 2016; Caye et al., 2016; Sibley et al., 2018). In other investigations, the majority of potential late-onset cases were explained by missed or borderline diagnoses in childhood, other psychiatric comorbidity, drug use, or study design (Sibley et al., 2018). Girls are more likely than boys to remain undiagnosed until adolescence or adulthood (Nussbaum, 2012; Quinn and Madhoo, 2014; Williamson and Johnston, 2015). The perception of ADHD as a disorder mostly affecting boys may contribute to the underdiagnosis of girls as depictions of children with ADHD symptoms are less often identified as needing referral by parents and teachers when female names are used (Ohan and Visser, 2009). Girls may also be less likely to be referred because they are less hyperactive and disruptive. Mood disorders, which affect more girls than boys with ADHD, further complicate diagnosis because symptoms can include inattention (Quinn and Madhoo, 2014). Overall, studying sex differences in ADHD presentation is difficult as it is likely that girls who are diagnosed in childhood are the most severe cases or those that present with more externalizing symptoms.

Epilepsy Overall, epilepsies demonstrate a slight male bias, and the worldwide sex ratio is about 1.1:1 (Kotsopoulos et al., 2002; Aaberg et al., 2017; Mao et al., 2018). About 0.6% of children will have a history of epilepsy (Aaberg et al., 2017; Fiest et al., 2017). Epilepsies most frequently occur in the first year of life, where the male bias is also higher at 1.3–1.5:1 (Cowan et al., 1989; Christensen et al., 2007; Eltze et al., 2013; Aaberg et al., 2017). This declines to female excess in epilepsies with onset in adolescence (Christensen et al., 2007). Only about a third of children with epilepsy have a specific epilepsy syndrome, many of which have low incidence rates (e.g., West syndrome occurs in 2–7/10,000 live births, Lennox–Gastaut syndrome affects 2/100,000 children) (Wirrell et al., 2012; Behr et al., 2016). Sex ratios for these syndromes are necessarily derived from small numbers of cases and as such often differ substantially between publications. When epilepsies are categorized more broadly, epilepsy with focal seizures is the most common form of epilepsy and occurs with a male excess (Savic, 2014). Boys with focal seizures are also more likely to have lesions visible on a brain magnetic resonance imaging than girls (Ortiz-Gonzalez et al., 2013; Savic, 2014). Idiopathic epilepsies with generalized-onset seizures are thought to be largely genetic (Gardiner, 2005; Berg et al., 2010) and occur with a female predominance (Savic, 2014). Epilepsy outcomes, both remission and response to drugs, are more affected by epilepsy type and seizure semiology than sex (Abramovici and Bagi
c, 2016), although epilepsy-related mortality is higher in males than in females (Greenlund et al., 2017).

Gilles de la Tourette syndrome Gilles de la Tourette syndrome (GTS) is a syndrome that consists of verbal and/or physical tics. It presents in about 0.52%–0.77% of the pediatric population with a sex ratio of 3–4:1 (Scharf et al., 2015; Robertson et al., 2017), which increases when tics occur in both domains, from 2.3:1 in GTS with either verbal or physical tics to 3.6:1 in GTS with both types of tics (Scharf et al., 2012). The sex ratio in GTS declines to 2.3:1 in adulthood (Levine et al., 2019). Of note, 30%–50% of patients experience remission of tics to subclinical levels (Robertson et al., 2017).

Comorbidity In neurodevelopmental disorders, comorbidity is the rule rather than the exception (Moreno-De-Luca et al., 2013), but the type of comorbid disorder can vary by sex. In general, girls experience more multimorbidity than boys (Cooper et al., 2015).

SEX BIAS AND NEURODEVELOPMENTAL DISORDERS In children with ID, comorbidity with any psychiatric or neurodevelopmental disorder is higher in girls (Polyak et al., 2015). A girl diagnosed with ID is almost twice as likely as a boy to be subsequently or previously diagnosed with another NDD and more likely to be diagnosed with schizophrenia or epilepsy (Polyak et al., 2015; Robertson, 2015; Plana-Ripoll et al., 2019). An exception to these observations is comorbidity with ADHD, which is more common in boys than in girls with ID (Polyak et al., 2015). Comorbidity is also higher in girls than in boys with ASD (Polyak et al., 2015), and a girl diagnosed with ASD is about twice as likely as a boy to have a previous or subsequent diagnosis of another NDD (Plana-Ripoll et al., 2019). ASD comorbid with epilepsy or ID is more common in girls. An exception to this rule is ASD comorbid with schizophrenia or bipolar disorder being more common in boys (sex ratio 8:1) (Polyak et al., 2015). In children with ADHD, girls are more likely to have learning disorders, coordination disorders, affective disorders, obsessive–compulsive disorder, personality disorders, ID, and eating disorders than boys but less likely to demonstrate oppositional-defiant disorder, ASD, tic disorders, or conduct disorder than boys (Jensen and Steinhausen, 2015; Williamson and Johnston, 2015; Joelsson et al., 2016; Plana-Ripoll et al., 2019).

FACTORS UNDERLYING SEX DIFFERENCES Many studies have investigated potential factors underlying sex differences in NDDs. These may be divided into two categories: (1) differences in typical development between boys and girls, with boys inherently displaying more symptoms more likely to lead to referral; and (2) differential effects of genetic and environmental factors in boys and girls, with the latter being less susceptible.

Dimorphism in typical development COGNITION In typically developing children, sex differences are present in some domains of cognition. The magnitude and direction of these differences have often been subject to change over recent decades, and even relatively stable sex differences are often task-specific, transient, or of low magnitude. A metasynthesis has found that 85% of sex differences in cognitive domains are small or very small (Zell et al., 2015). A larger exploration of sex differences in cognition is a topic that extends far beyond the scope of this chapter, but it should be noted that

331

despite male predominance in NDDs and intellectual disability, there is little evidence that this is due to lower ability or IQ in the male sex than in the female sex. There are two major areas where cognitive differences between the sexes may contribute to the sex bias in rates of neurodevelopmental disorders: in the early development of language skills, and in social domains. In both domains, girls show an early advantage. Since language, communication, and social development are impacted in multiple NDDs, delayed boys may stand out more from their peers as their absolute delay would be higher than that of similarly delayed girls. Girls reach milestones earlier than boys in many language skills. Boys are at higher risk of having expressive vocabulary below the 10th percentile at 2 years (Collisson et al., 2016) and are outperformed by girls on measures of expressive vocabulary and semantic fluency, phonological processing, verbal short-term memory, and comprehension and execution of oral instructions from 2 to 4 years (Peyre et al., 2019). These differences diminish to parity by 5–6 years, although girls tend to perform better than boys in language-related domains such as reading fluency, verbal learning, and literacy into early adulthood (Wallentin, 2009; Loveless, 2015; Peyre et al., 2019). Girls are also more socially oriented than boys from an early age. Female infants gaze longer at images of human faces (Connellan et al., 2000; Lutchmaya and Baron-Cohen, 2002) and make more eye contact with strangers and parents (Lutchmaya et al., 2002; Leeb and Rejskind, 2004) than do male infants. They also imitate social gestures more accurately (Nagy et al., 2007). In childhood, girls develop more social and more structured forms of play at younger ages than boys (e.g., associative play versus parallel play), but boys catch up by 5–6 years (Barbu et al., 2011). Girls at all ages are better at recognizing emotions from visual and/or auditory cues (Thompson and Voyer, 2014).

PROBLEMATIC BEHAVIORS Children with externalizing behaviors, such as hyperactivity, aggression, and delinquency, are likely to be referred to services. Boys exhibit consistently higher levels of externalizing behaviors than girls (King et al., 2018). They have higher levels of self-reported nonviolent and violent delinquency and are more likely to have chronic levels of delinquency from childhood to adulthood (Miller et al., 2010; Zheng and Cleveland, 2013). By the mid- to late teens, the difference in outcomes is striking, with boys 2.6–4 times as likely to have been arrested for a crime (Junger-Tas et al., 2004). Boys display more aggression than girls (Lansford et al., 2013) and are also more likely to be both victims and perpetrators of bullying (Cook et al., 2010) and to experience out-of-school suspensions (Skiba et al., 2014).

332

S. NOWAK AND S. JACQUEMONT

Internalizing behaviors, such as anxiety and guilt, are less likely to result in a referral and are more common in girls (Pearcy et al., 1993; Bradshaw et al., 2008; Papandrea and Winefield, 2011). This phenomenon has been dubbed “the squeaky wheel gets the grease” (Papandrea and Winefield, 2011).

HORMONAL FACTORS A prominent theory of the effect of hormones on psychiatric illness is the “organizational vs. activational” theory. This theory suggests that boys may be more vulnerable to the “organizational” effects of prenatal hormones and thus to prenatal insult and NDDs, while girls may be more vulnerable to the “activational” effect of hormones during puberty, leading to liability to disorders with onset during adolescence (i.e., mood disorders) (McCarthy and Arnold, 2011). Therefore there is interest in the effect of maternal androgens on NDDs and in how endogenous hormone differences between boys and girls might result in different disease course or prevalence. In the prenatal period, elevated maternal blood androgen levels have been suggested to increase ASD rates, as well as scores on the autism quotient (AQ) test. This has led to the development of the “extreme male brain theory” of autism, i.e., the idea that autism is an extreme expression of male cognition (Baron-Cohen, 2002). However, in longitudinal studies of pregnant women, androgen/estrogen ratios in pregnancy did not influence offspring subscale or total scores on the AQ (Jamnadass et al., 2015), and androgen levels gathered from amniotic fluid were not associated with scores on the Childhood Autism Spectrum Test (CAST) (Kung et al., 2016). Also, while girls with ASD are more likely to have diseases associated with hormone dysfunction in adulthood (Ingudomnukul et al., 2007), girls with congenital adrenal hyperplasia, who are exposed to high levels of prenatal testosterone, score higher than typically developing girls on the AQ (Knickmeyer et al., 2006) but not the CAST (Kung et al., 2016). Similarly, while boys are more prone to seizures in the neonatal period (Vasudevan and Levene, 2013), female twins with male co-twins, who are exposed to higher levels of testosterone in utero, do not have higher rates of epilepsy than female twins with female co-twins (Mao et al., 2018). Observations supporting the “activational” effect of hormones on NDD in girls are usually the worsening or onset of symptoms in girls during puberty or the worsening of symptoms during the menstrual cycle. ADHD symptoms worsen in both boys and girls around 12 (Murray et al., 2019), and the excess of boys in epilepsy cases reduces to parity in this period (Christensen et al., 2007). These symptoms may vary

in girls across the menstrual cycle (Roberts et al., 2018), and one-third of women with epilepsy exhibit catamenial patterns, i.e., twofold or more increase in seizures during the perimenstrual period, periovulatory period, or the luteal phase in anovulatory cycles (Herzog, 2008). Overall, while there is some evidence that sex hormones play a role in the development or progression of NDDs, the extent of their influence is still to be determined as is the molecular mechanism by which they have their effect.

Potential interactions between risk factors and sex GENETIC FACTORS Sex chromosome aneuploidies are associated with several NDDs. Mild to moderate decreases in general cognitive abilities have been reported in XO, XXX, XXY, XYY, and XXYY populations (Lee et al., 2012; Printzlau et al., 2017). Social-communication deficits have been identified in all sex chromosome aneuploidies, with 20% of XO girls (Wolstencroft et al., 2018), 10% of XXY boys, 38% of XYY boys, and 52% of XXYY boys meeting criteria for an ASD (Tartaglia et al., 2017). ADHD is also relatively common and occurs in 24% of XO girls (Russell et al., 2006), 52% of XXX girls, 36% of XXY boys, 76% of XYY boys, and 72% of XXYY boys (Tartaglia et al., 2012). These observations show an important dosage-dependent role for both the X and Y chromosomes in NDDs and suggest that boys may be more vulnerable to NDDs partly because of their Y chromosome or their lack of a second X chromosome. Because of the unbalanced sex ratio observed in ID and the identification of large ID-affected families showing X-linked segregation, much attention has been focused on X-linked ID (XLID). Mutations causing monogenic XLID have now been reported in over 100 genes (Piton et al., 2011). The identification of XLID genes has been very successful but mutations in these genes account for less than 10% of male ID and are insufficient to explain the sex bias (Lubs et al., 2012). There is evidence that girls with ASD or ID have more mutations than boys, even when controlling for IQ in ASD individuals (Jacquemont et al., 2014) or when comorbidities are considered (Polyak et al., 2015). This suggests that it requires more genetic “hits” for a girl to develop ASD or ID than it would for a boy, a theory that has been dubbed the “female protective effect” (Skuse, 2000; Robinson et al., 2013). In ADHD too, while diagnosed girls do not have higher genetic risk using common variants, ADHD is observed at a higher frequency in relatives when the ADHD proband is a female, suggesting that girls with ADHD may also carry more hits

SEX BIAS AND NEURODEVELOPMENTAL DISORDERS than boys (Martin et al., 2018). More male than female first-degree relatives of SSD/SLI children have language impairment as well, which suggests that fewer hits are necessary for boys in communication disorders (Lewis et al., 2006; Whitehouse, 2010). However, studies investigating groups of individuals with autosomal mutations strongly associated with NDDs have not been able to identify sex-specific effects. 16p11.2 (Martin-Brevet et al., 2018) and 22q11.2 (Sun et al., 2018) deletions and duplications, which are linked to autism, are associated with the same impact on cognition, language, and structural brain alterations in both sexes. The penetrance of SCN1A mutations, which are associated with epilepsy, is not related to sex (Cetica et al., 2017).

ENVIRONMENTAL INSULT AND INJURY Boys and girls have been reported to be differently susceptible to environmental toxicity resulting in NDDs. Male sex is a risk factor for long-term adverse outcomes related to prematurity (Spinillo et al., 2009), extreme prematurity (Ski€ old et al., 2014), low birth weight (Spinillo et al., 2009), and preeclampsia (Spinillo et al., 2009). It is also an independent risk factor for seizures in the neonatal period (Vasudevan and Levene, 2013). Other prenatal and perinatal adversities, including stress, infection, malnutrition, and maternal diabetes, have been linked to more severe outcomes in boys. The effect of exposure to some substances that contribute to the development of NDDs is also attenuated in girls. The sex ratio of live births is normally around 1.06:1, but it falls as low as 0.88:1 for mothers who consume alcohol heavily (six or more standard drinks per day) (May et al., 2017). Boys exposed to lead show more attentional and behavioral difficulties than girls with similar blood lead levels (Lasley, 2018) and are diagnosed with ADHD more frequently (Ji et al., 2018). Boys also demonstrate a decline in cognitive function at lower levels of lead exposure than girls (Jedrychowski et al., 2009). Boys are more likely to experience epilepsy due to environmental risk factors such as head injury, stroke, and central nervous system infection (Savic, 2014; Abramovici and Bagi
c, 2016). However, some studies have also shown that boys are more exposed to these risk factors. More research is needed to determine whether these risk factors interact with sex (Perucca et al., 2014).

FUTURE DIRECTIONS The research reviewed in this chapter highlights the many aspects of sex bias across all neurodevelopmental disorders. While these findings have been well replicated, factors and mechanisms underlying these observations

333

have not been identified clearly. There are likely many factors involved in sex bias observed in each condition. The combined contribution of sexual dimorphism and an interaction between risk factors and sex makes deciphering the impact of individual factors particularly difficult. Recognizing the influence of a factor like sex, which affects both who develops an NDD and how that disorder progresses, has the potential to help us understand these conditions better and identify new avenues of research.

REFERENCES Aaberg KM, Gunnes N, Bakken IJ et al. (2017). Incidence and prevalence of childhood epilepsy: a nationwide cohort study. Pediatrics 139: e20163908. https://doi.org/10.1542/ peds.2016-3908. Abramovici S, Bagi
c A (2016). Chapter 10: epidemiology of epilepsy. In: C Rosano, M Ganguli, MA Ikram (Eds.), Handbook of clinical neurology, neuroepidemiology. Elsevier p. 13. Agnew-Blais JC, Polanczyk GV, Danese A et al. (2016). Persistence, remission and emergence of ADHD in Young adulthood: results from a longitudinal, prospective population-based cohort. JAMA Psychiatry 73: 713–720. https://doi.org/10.1001/jamapsychiatry.2016.0465. American Psychiatric Association (Ed.) (2013). Diagnostic and statistical manual of mental disorders: DSM-5, fifth edn. American Psychiatric Association, Washington, D.C. Andersson GW, Gillberg C, Miniscalco C (2013). Pre-school children with suspected autism spectrum disorders: do girls and boys have the same profiles? Res Dev Disabil 34: 413–422. https://doi.org/10.1016/j.ridd.2012.08.025. Arnett AB, Pennington BF, Willcutt EG et al. (2015). Sex differences in ADHD symptom severity. J Child Psychol Psychiatry 56: 632–639. https://doi.org/10.1111/ jcpp.12337. Arnett AB, Pennington BF, Peterson RL et al. (2017). Explaining the sex difference in dyslexia. J Child Psychol Psychiatry 58: 719–727. https://doi.org/10.1111/ jcpp.12691. Arvio M, Salokivi T, Bjelogrlic-Laakso N (2017). Age at death in individuals with intellectual disabilities. J Appl Res Intellect Disabil 30: 782–785. https://doi.org/10.1111/ jar.12269. Barbu S, Cabanes G, Le Maner-Idrissi G (2011). Boys and girls on the playground: sex differences in social development are not stable across early childhood. PLoS One 6: e16407. https://doi.org/10.1371/journal.pone.0016407. Barger BD, Campbell JM, McDonough JD (2013). Prevalence and onset of regression within autism spectrum disorders: a meta-analytic review. J Autism Dev Disord 43: 817–828. https://doi.org/10.1007/s10803-012-1621-x. Bargiela S, Steward R, Mandy WPL (2016). The experiences of late-diagnosed women with autism Spectrum conditions: an investigation of the female autism phenotype. J Autism Dev Disord 46: 3281–3294. https://doi.org/ 10.1007/s10803-016-2872-8.

334

S. NOWAK AND S. JACQUEMONT

Baron-Cohen S (2002). The extreme male brain theory of autism. Trends Cogn Sci 6: 248–254. https://doi.org/ 10.1016/S1364-6613(02)01904-6. Baxter AJ, Brugha TS, Erskine HE et al. (2015). The epidemiology and global burden of autism spectrum disorders. Psychol Med 45: 601–613. https://doi.org/10.1017/ S003329171400172X. Begeer S, Mandell D, Wijnker-Holmes B et al. (2013). Sex differences in the timing of identification among children and adults with autism Spectrum disorders. J Autism Dev Disord 43: 1151–1156. https://doi.org/10.1007/s10803012-1656-z. Behr C, Goltzene MA, Kosmalski G et al. (2016). Epidemiology of epilepsy. Rev Neurol (Paris)172: 27–36. https://doi.org/10.1016/j.neurol.2015.11.003. Belva BC, Matson JL (2013). An examination of specific daily living skills deficits in adults with profound intellectual disabilities. Res Dev Disabil 34: 596–604. https://doi.org/ 10.1016/j.ridd.2012.09.021. Berg AT, Berkovic SF, Brodie MJ et al. (2010). Revised terminology and concepts for organization of seizures and epilepsies: report of the ILAE commission on classification and terminology, 2005–2009. Epilepsia 51: 676–685. https://doi.org/10.1111/j.1528-1167.2010.02522.x. Blood GW, Jr VJR, Qualls CD et al. (2003). Co-occurring disorders in children who stutter. J Commun Disord 36: 427–448. https://doi.org/10.1016/S0021-9924(03)00023-6. Bourke J, de Klerk N, Smith T et al. (2016). Populationbased prevalence of intellectual disability and autism Spectrum disorders in Western Australia. Medicine (Baltimore) 95: e3737. https://doi.org/10.1097/MD.00000 00000003737. Bowring DL, Totsika V, Hastings RP et al. (2017). Challenging behaviours in adults with an intellectual disability: a total population study and exploration of risk indices. Br J Clin Psychol 56: 16–32. https://doi.org/ 10.1111/bjc.12118. Boyle CA, Boulet S, Schieve LA et al. (2011). Trends in the prevalence of developmental disabilities in US children, 1997-2008. Pediatrics 127: 1034–1042. https://doi.org/ 10.1542/peds.2010-2989. Bradshaw CP, Buckley JA, Ialongo NS (2008). School-based service utilization among urban children with early onset educational and mental health problems: the squeaky wheel phenomenon. Sch Psychol Q 23: 169–186. https://doi.org/ 10.1037/1045-3830.23.2.169. Caye A (2016). Predictors of persistence of ADHD into adulthood: a systematic review of the literature and metaanalysis. Eur Child Adolesc Psychiatry 25: 1151–1159. Caye A, Botter-Maio Rocha T, Anselmi L et al. (2016). Attention-deficit/hyperactivity disorder trajectories from childhood to Young adulthood: evidence from a birth cohort supporting a late-onset syndrome. JAMA Psychiatry 73: 705–712. https://doi.org/10.1001/jamapsychiatry. 2016.0383. Cetica V, Chiari S, Mei D et al. (2017). Clinical and genetic factors predicting Dravet syndrome in infants with SCN1A mutations. Neurology 88: 1037–1044. https://doi. org/10.1212/WNL.0000000000003716.

Christensen J, Vestergaard M, Pedersen MG et al. (2007). Incidence and prevalence of epilepsy in Denmark. Epilepsy Res 76: 60–65. https://doi.org/10.1016/j. eplepsyres.2007.06.012. Christensen DL, Baio J, Van Naarden Braun K et al. (2016). Prevalence and characteristics of autism Spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, 2012. MMWR Surveill Summ 65: 1–23. https://doi.org/ 10.15585/mmwr.ss6503a1. Collisson BA, Graham SA, Preston JL et al. (2016). Risk and protective factors for late talking: an epidemiologic investigation. J Pediatr 172: 168–174.e1. https://doi.org/ 10.1016/j.jpeds.2016.02.020. Connellan J, Baron-Cohen S, Wheelwright S et al. (2000). Sex differences in human neonatal social perception. Infant Behav Dev 23: 113–118. https://doi.org/10.1016/S01636383(00)00032-1. Conti-Ramsden G, Clair MCS, Pickles A et al. (2012). Developmental trajectories of verbal and nonverbal skills in individuals with a history of specific language impairment: from childhood to adolescence. J Speech Lang Hear Res 55: 1716–1735. https://doi.org/10.1044/10924388(2012/10-0182. Cook CR, Williams KR, Guerra NG et al. (2010). Predictors of bullying and victimization in childhood and adolescence: a meta-analytic investigation. Sch Psychol Q 25: 65–83. https://doi.org/10.1037/a0020149. Cooper S-A, McLean G, Guthrie B et al. (2015). Multiple physical and mental health comorbidity in adults with intellectual disabilities: population-based cross-sectional analysis. BMC Fam Pract 16: 110. https://doi.org/10.1186/ s12875-015-0329-3. Cowan LD, Bodensteiner JB, Leviton A et al. (1989). Prevalence of the epilepsies in children and adolescents. Epilepsia 30: 94–106. https://doi.org/10.1111/j.1528-1157. 1989.tb05289.x Craig A, Tran Y (2005). The epidemiology of stuttering: the need for reliable estimates of prevalence and anxiety levels over the lifespan. Adv Speech Lang Pathol 7: 41–46. https://doi.org/10.1080/14417040500055060. Craig A, Hancock K, Tran Y et al. (2002). Epidemiology of stuttering in the community across the entire life span. J Speech Lang Hear Res 45: 1097–1105. https://doi.org/ 10.1044/1092-4388(2002/088). Daniels AM, Mandell DS (2014). Explaining differences in age at autism spectrum disorder diagnosis: a critical review. Autism 18: 583–597. https://doi.org/10.1177/ 1362361313480277. Dean M, Harwood R, Kasari C (2017). The art of camouflage: gender differences in the social behaviors of girls and boys with autism spectrum disorder. Autism 21: 678–689. https://doi.org/10.1177/1362361316671845. Devine A, Solt
esz F, Nobes A et al. (2013). Gender differences in developmental dyscalculia depend on diagnostic criteria. Learn Instr 27: 31–39. https://doi.org/ 10.1016/j.learninstruc.2013.02.004. Dworzynski K, Ronald A, Bolton P et al. (2012). How different are girls and boys above and below the diagnostic threshold

SEX BIAS AND NEURODEVELOPMENTAL DISORDERS for autism Spectrum disorders? J Am Acad Child Adolesc Psychiatry 51: 788–797. https://doi.org/10.1016/ j.jaac.2012.05.018. Eadie P, Morgan A, Ukoumunne OC et al. (2015). Speech sound disorder at 4 years: prevalence, comorbidities, and predictors in a community cohort of children. Dev Med Child Neurol 57: 578–584. https://doi.org/10.1111/ dmcn.12635. Eltze CM, Chong WK, Cox T et al. (2013). 2013 Eltze newly diagnosed epilepsy young children london. pdf. Epilepsia 54: 437–445. https://doi.org/10.1111/epi.12046. Fiest KM, Sauro KM, Wiebe S et al. (2017). Prevalence and incidence of epilepsy. Neurology 88: 1–8. https://doi.org/ 10.1212/WNL.0000000000003509. Flannery KA, Liederman J, Daly L et al. (2000). Male prevalence for reading disability is found in a large sample of black and White children free from ascertainment bias. J Int Neuropsychol Soc 6: 433–442. https://doi.org/ 10.1017/S1355617700644016. Fombonne E (2003). Epidemiological surveys of autism and other pervasive developmental disorders: an update. J Autism Dev Disord 33: 365–382. https://doi.org/10.1023/ a:1025054610557. Fortes IS, Paula CS, Oliveira MC et al. (2016). A crosssectional study to assess the prevalence of DSM-5 specific learning disorders in representative school samples from the second to sixth grade in Brazil. Eur Child Adolesc Psychiatry 25: 195–207. https://doi.org/10.1007/s00787015-0708-2. Fountain C, Winter AS, Bearman PS (2012). Six developmental trajectories characterize children with autism. Pediatrics 129: e1112–e1120. https://doi.org/10.1542/ peds.2011-1601. Frazier TW, Georgiades S, Bishop SL et al. (2014). Behavioral and cognitive characteristics of females and males with autism in the Simons simplex collection. J Am Acad Child Adolesc Psychiatry 53: 329–340.e3. https://doi.org/ 10.1016/j.jaac.2013.12.004. Fulton AM, Paynter JM, Trembath D (2017). Gender comparisons in children with ASD entering early intervention. Res Dev Disabil 68: 27–34. https://doi.org/10.1016/j.ridd. 2017.07.009. Gardiner M (2005). Genetics of idiopathic generalized epilepsies. Epilepsia 46: 15–20. https://doi.org/10.1111/j.15281167.2005.00310.x. Giarelli E, Levy SE, Wiggins LD et al. (2010). Sex differences in the evaluation and diagnosis of autism spectrum disorders among children. Disabil Health J 3: 107–116. https://doi.org/10.1016/j.dhjo.2009.07.001. Glover G, Williams R, Heslop P et al. (2017). Mortality in people with intellectual disabilities in England: mortality in people with ID in England. J Intellect Disabil Res 61: 62–74. https://doi.org/10.1111/jir.12314. Gould J, Ashton-Smith J (2011). Missed diagnosis or misdiagnosis? Girls and women on the autism spectrum. Good Autism Pract 12: 34–41. Greenlund SF, Croft JB, Kobau R (2017). Epilepsy by the numbers: epilepsy deaths by age, race/ethnicity, and gender in the United States significantly increased from 2005 to

335

2014. Epilepsy Behav 69: 28–30. https://doi.org/10.1016/ j.yebeh.2017.01.016. Herzog AG (2008). Catamenial epilepsy: definition, prevalence pathophysiology and treatment. Seizure 17: 151–159. https://doi.org/10.1016/j.seizure.2007.11.014. Hiller RM, Young RL, Weber N (2014). Sex differences in autism Spectrum disorder based on DSM-5 criteria: evidence from clinician and teacher reporting. J Abnorm Child Psychol 42: 1381–1393. https://doi.org/10.1007/ s10802-014-9881-x. Hiller RM, Young RL, Weber N (2016). Sex differences in pre-diagnosis concerns for children later diagnosed with autism spectrum disorder. Autism 20: 75–84. https://doi. org/10.1177/1362361314568899. Howell P, Davis S, Williams R (2008). Late childhood stuttering. J Speech Lang Hear Res 51: 669–687. https://doi.org/ 10.1044/1092-4388(2008/048). Hughes-McCormack LA, Rydzewska E, Henderson A et al. (2017). Prevalence and general health status of people with intellectual disabilities in Scotland: a total population study. J Epidemiol Community Health 72: 78–85. https:// doi.org/10.1136/jech-2017-209748. Ingudomnukul E, Baron-Cohen S, Wheelwright S et al. (2007). Elevated rates of testosterone-related disorders in women with autism spectrum conditions. Horm Behav 51: 597–604. https://doi.org/10.1016/j.yhbeh. 2007.02.001. Jacquemont S, Coe BP, Hersch M et al. (2014). A higher mutational burden in females supports a “female protective model” in neurodevelopmental disorders. Am J Hum Genet 94: 415–425. https://doi.org/10.1016/j.ajhg.2014. 02.001. Jamnadass ESL, Keelan JA, Hollier LP et al. (2015). The perinatal androgen to estrogen ratio and autistic-like traits in the general population: a longitudinal pregnancy cohort study. J Neurodev Disord 7: 17. https://doi.org/10.1186/ s11689-015-9114-9. Jedrychowski W, Perera F, Jankowski J et al. (2009). Gender specific differences in neurodevelopmental effects of prenatal exposure to very low-lead levels: the prospective cohort study in three-year olds. Early Hum Dev 85: 503–510. https://doi.org/10.1016/j.earlhumdev. 2009.04.006. Jensen CM, Steinhausen H-C (2015). Comorbid mental disorders in children and adolescents with attention-deficit/ hyperactivity disorder in a large nationwide study. ADHD Atten Deficit Hyperact Disord 7: 27–38. https://doi.org/ 10.1007/s12402-014-0142-1. Ji Y, Hong X, Wang G et al. (2018). A prospective birth cohort study on early childhood lead levels and attention deficit hyperactivity disorder: new insight on sex differences. J Pediatr 199: 124–131. https://doi.org/10.1016/j.jpeds. 2018.03.076. Joelsson P, Chudal R, Gyllenberg D et al. (2016). Demographic characteristics and psychiatric comorbidity of children and adolescents diagnosed with ADHD in specialized healthcare. Child Psychiatry Hum Dev 47: 574–582. https://doi.org/10.1007/s10578-0150591-6.

336

S. NOWAK AND S. JACQUEMONT

Johnson CJ, Beitchman JH, Young A et al. (1999). Fourteenyear follow-up of children with and without speech/ language impairments: speech/language stability and outcomes. J Speech Lang Hear Res 42: 744–760. https://doi. org/10.1044/jslhr.4203.744. Junger-Tas J, Ribeaud D, Cruyff MJLF (2004). Juvenile delinquency and gender. Eur J Criminol 1: 333–375. https://doi. org/10.1177/1477370804044007. King KM, Luk JW, Witkiewitz K et al. (2018). Externalizing behavior across childhood as reported by parents and teachers: a partial measurement invariance model. Assessment 25: 744–758. https://doi.org/10.1177/1073191 116660381. Knickmeyer R, Baron-Cohen S, Fane BA et al. (2006). Androgens and autistic traits: a study of individuals with congenital adrenal hyperplasia. Horm Behav 50: 148–153. https://doi.org/10.1016/j.yhbeh.2006.02.006. Kotsopoulos IAW, Van Merode T, Kessels FGH et al. (2002). Systematic review and meta-analysis of incidence studies of epilepsy and unprovoked seizures. Epilepsia 43: 1402–1409. https://doi.org/10.1046/j.1528-1157.2002.t011-26901.x. Kung KTF, Spencer D, Pasterski V et al. (2016). No relationship between prenatal androgen exposure and autistic traits: convergent evidence from studies of children with congenital adrenal hyperplasia and of amniotic testosterone concentrations in typically developing children. J Child Psychol Psychiatry 57: 1455–1462. https://doi.org/10.1111/ jcpp.12602. Lansford JE, Skinner AT, Sorbring E (2013). Boys’ and girls’ relational and physical aggression in nine countries. Aggress Behav 38: 298–308. https://doi.org/10.1002/ab.21433. Lasley SM (2018). Chapter 37—developmental neurotoxicology of lead: neurobehavioral and neurological impacts. In: Handbook of developmental neurotoxicology, 413–425. Elsevier. Lee NR, Wallace GL, Adeyemi EI et al. (2012). Dosage effects of X and Y chromosomes on language and social functioning in children with supernumerary sex chromosome aneuploidies: implications for idiopathic language impairment and autism spectrum disorders. J Child Psychol Psychiatry 53: 1072–1081. https://doi.org/10.1111/j.14697610.2012.02573.x. Leeb RT, Rejskind FG (2004). Here’s looking at you, kid! A longitudinal study of perceived gender differences in mutual gaze behavior in young infants. Sex Roles 50: 1–14. https://doi.org/10.1023/B:SERS.0000011068.42663.ce. Leonard H, Wen X (2002). The epidemiology of mental retardation: challenges and opportunities in the new millennium. Ment Retard Dev Disabil Res Rev 8: 117–134. https://doi.org/10.1002/mrdd.10031. Levine JLS, Szejko N, Bloch MH (2019). Meta-analysis: adulthood prevalence of Tourette syndrome. Prog Neuropsychopharmacol Biol Psychiatry 95: 109675. https://doi.org/10.1016/j.pnpbp.2019.109675. Lewis BA, Freebairn LA, Hansen AJ et al. (2006). Dimensions of early speech sound disorders: a factor analytic study. J Commun Disord 39: 139–157. https://doi.org/10.1016/j. jcomdis.2005.11.003.

Lin L-P, Hsia Y-C, Hsu S-W et al. (2013). Caregivers’ reported functional limitations in activities of daily living among middle-aged adults with intellectual disabilities. Res Dev Disabil 34: 4559–4564. https://doi.org/10.1016/j. ridd.2013.09.038. Loomes R, Hull L, Mandy WPL (2017). What is the male-tofemale ratio in autism Spectrum disorder? A systematic review and meta-analysis. J Am Acad Child Adolesc Psychiatry 56: 466–474. https://doi.org/10.1016/j.jaac. 2017.03.013. Lord C, Bishop S, Anderson D (2015). Developmental trajectories as autism phenotypes. Am J Med Genet C Semin Med Genet 169: 198–208. https://doi.org/10.1002/ajmg.c.31440. Loveless T (2015). The 2015 Brown Center report on American education: how well are American students learning? With sections on the gender gap in reading, effects of the common Core, and student engagement, Brown Center report on American education, Brookings Institution. Lubs HA, Stevenson RE, Schwartz CE (2012). Fragile X and X-linked intellectual disability: four decades of discovery. Am J Hum Genet 90: 579–590. https://doi.org/10.1016/j. ajhg.2012.02.018. Lundqvist L-O (2013). Prevalence and risk markers of behavior problems among adults with intellectual disabilities: a € total population study in Orebro County, Sweden. Res Dev Disabil 34: 1346–1356. https://doi.org/10.1016/j. ridd.2013.01.010. Lutchmaya S, Baron-Cohen S (2002). Human sex differences in social and non-social looking preferences, at 12 months of age. Infant Behav Dev 25: 319–325. https://doi.org/ 10.1016/S0163-6383(02)00095-4. Lutchmaya S, Baron-Cohen S, Raggatt P (2002). Foetal testosterone and eye contact in 12-month-old human infants. Infant Behav Dev 25: 327–335. https://doi.org/10.1016/ S0163-6383(02)00094-2. Maenner MJ, Blumberg SJ, Kogan MD et al. (2016). Prevalence of cerebral palsy and intellectual disability among children identified in two U.S. National Surveys, 2011-2013. Ann Epidemiol 26: 222–226. https://doi.org/ 10.1016/j.annepidem.2016.01.001. Mandy WPL, Chilvers R, Chowdhury U et al. (2012). Sex differences in autism Spectrum disorder: evidence from a large sample of children and adolescents. J Autism Dev Disord 42: 1304–1313. https://doi.org/10.1007/s10803011-1356-0. Ma˚nsson H (2000). Childhood stuttering: incidence and development. J Fluency Disord 25: 47–57. https://doi.org/ 10.1016/S0094-730X(99)00023-6. Mao Y, Ahrenfeldt LJ, Christensen K et al. (2018). Risk of epilepsy in opposite-sex and same-sex twins: a twin cohort study. Biol Sex Differ 9: 21. https://doi.org/10.1186/ s13293-018-0179-5. Martin J, Walters RK, Demontis D et al. (2018). A genetic investigation of sex bias in the prevalence of attention-deficit/ hyperactivity disorder. Biol Psychiatry 83: 1044–1053. https://doi.org/10.1016/j.biopsych.2017.11.026. Martin-Brevet S, Rodrı´guez-Herreros B, Nielsen JA et al. (2018). Quantifying the effects of 16p11.2 copy number

SEX BIAS AND NEURODEVELOPMENTAL DISORDERS variants on brain structure: a multisite genetic-first study. Biol Psychiatry 84: 253–264. https://doi.org/10.1016/j. biopsych.2018.02.1176. Maulik PK, Mascarenhas MN, Mathers CD et al. (2011). Prevalence of intellectual disability: a meta-analysis of population-based studies. Res Dev Disabil 32: 419–436. https://doi.org/10.1016/j.ridd.2010.12.018. May PA, Tabachnick B, Hasken JM et al. (2017). Who is most affected by prenatal alcohol exposure: boys or girls? Drug Alcohol Depend 177: 258–267. https://doi.org/10.1016/j. drugalcdep.2017.04.010. McCarron M, Carroll R, Kelly C et al. (2015). Mortality rates in the general irish population compared to those with an intellectual disability from 2003 to 2012. J Appl Res Intellect Disabil 28: 406–413. https://doi.org/10.1111/jar.12194. McCarthy MM, Arnold AP (2011). Reframing sexual differentiation of the brain. Nat Neurosci 14: 677–683. https://doi. org/10.1038/nn.2834. Miller S, Malone PS, Dodge KA et al. (2010). Developmental trajectories of boys’ and girls’ delinquency: sex differences and links to later adolescent outcomes. J Abnorm Child Psychol 38: 1021–1032. https://doi.org/10.1007/s10802-0109430-1. Moffitt TE, Houts R, Asherson P et al. (2015). Is adult ADHD a childhood-onset neurodevelopmental disorder? Evidence from a four-decade longitudinal cohort study. Am J Psychiatry 172: 967–977. https://doi.org/10.1176/appi. ajp.2015.14101266. Moll K, Kunze S, Neuhoff N et al. (2014). Specific learning disorder: prevalence and gender differences. PLoS One 9: e103537. https://doi.org/10.1371/journal.pone.0103537. Moreno-De-Luca A, Myers SM, Challman TD et al. (2013). Developmental brain dysfunction: revival and expansion of old concepts based on new genetic evidence. Lancet Neurol 12: 406–414. https://doi.org/10.1016/S1474-4422 (13)70011-5. Morgan A, Ttofari Eecen K, Pezic A et al. (2017). Who to refer for speech therapy at 4 years of age versus who to “watch and wait”? J Pediatr 185: 200–204.e1. https://doi.org/ 10.1016/j.jpeds.2017.02.059. Morsanyi K, van Bers BMCW, McCormack T et al. (2018). The prevalence of specific learning disorder in mathematics and comorbidity with other developmental disorders in primary school-age children. Br J Psychol 109: 917–940. https://doi.org/10.1111/bjop.12322. Murray AL, Booth T, Eisner M et al. (2019). Sex differences in ADHD trajectories across childhood and adolescence. Dev Sci 22: e12721. https://doi.org/10.1111/desc.12721. Nagy E, Kompagne H, Orvos H et al. (2007). Gender-related differences in neonatal imitation. Infant Child Dev 16: 267–276. https://doi.org/10.1002/icd.497. Nelson G (2018). A systematic review of longitudinal studies of mathematics difficulty. J Learn Disabil 51: 523–539. https://doi.org/10.1177/00222194177147. Norbury CF, Gooch D, Wray C et al. (2016). The impact of nonverbal ability on prevalence and clinical presentation of language disorder: evidence from a population study. J Child Psychol Psychiatry 57: 1247–1257. https://doi. org/10.1111/jcpp.12573.

337

Norbury CF, Vamvakas G, Gooch D et al. (2017). Language growth in children with heterogeneous language disorders: a population study. J Child Psychol Psychiatry 58: 1092–1105. https://doi.org/10.1111/jcpp.12793. Nussbaum NL (2012). ADHD and female specific concerns. J Atten Disord 16: 87–100. https://doi.org/10.1177/10870 54711416909. Ohan JL, Visser TAW (2009). Why is there a gender gap in children presenting for attention deficit/hyperactivity disorder services? J Clin Child Adolesc Psychol 38: 650–660. https://doi.org/10.1080/15374410903103627. Olzenak McGuire D, Tian LH, Yeargin-Allsopp M et al. (2019). Prevalence of cerebral palsy, intellectual disability, hearing loss, and blindness, National Health Interview Survey, 2009-2016. Disabil Health J 12: 443–451. https://doi.org/10.1016/j.dhjo.2019.01.005. Ortiz-Gonzalez XR, Poduri A, Roberts CM et al. (2013). Focal cortical dysplasia is more common in boys than in girls. Epilepsy Behav 27: 121–123. https://doi.org/10.1016/ j.yebeh.2012.12.035. Papandrea K, Winefield H (2011). It’s not just the squeaky wheels that need the oil: examining teachers’ views on the disparity between referral rates for students with internalizing versus externalizing problems. School Ment Health 3: 222–235. https://doi.org/10.1007/s12310-0119063-8. Pearcy MT, Clopton JR, Pope AW (1993). Influences on teacher referral of children to mental health services: gender, severity, and internalizing versus externalizing problems. J Emot Behav Disord 1: 165–169. https://doi.org/ 10.1177/106342669300100304. Pearson N, Charman T, Happ
e F et al. (2018). Regression in autism spectrum disorder: reconciling findings from retrospective and prospective research: pearson et al./regression in ASD-reconciling findings. Autism Res 11: 1602–1620. https://doi.org/10.1002/aur.2035. Perucca P, Camfield P, Camfield C (2014). Does gender influence susceptibility and consequences of acquired epilepsies? Neurobiol Dis 72: 125–130. https://doi.org/10.1016/ j.nbd.2014.05.016. Petrou AM, Parr JR, McConachie H (2018). Gender differences in parent-reported age at diagnosis of children with autism spectrum disorder. Res Autism Spectr Disord 50: 32–42. https://doi.org/10.1016/j.rasd. 2018.02.003. Peyre H, Hoertel N, Bernard JY et al. (2019). Sex differences in psychomotor development during the preschool period: a longitudinal study of the effects of environmental factors and of emotional, behavioral, and social functioning. J Exp Child Psychol 178: 369–384. https://doi.org/10.1016/j. jecp.2018.09.002. Piton A, Gauthier J, Hamdan F et al. (2011). Systematic resequencing of X-chromosome synaptic genes in autism spectrum disorder and schizophrenia. Mol Psychiatry 16: 867–880. https://doi.org/10.1038/mp.2010.54. Plana-Ripoll O, Pedersen CB, Holtz Y et al. (2019). Exploring comorbidity within mental disorders among a Danish national population. JAMA Psychiatry 76: 259–270. https://doi.org/10.1001/jamapsychiatry.2018.3658.

338

S. NOWAK AND S. JACQUEMONT

Polyak A, Rosenfeld JA, Girirajan S (2015). An assessment of sex bias in neurodevelopmental disorders. Genome Med 7: 94. https://doi.org/10.1186/s13073-015-0216-5. Postorino V, Fatta LM, De Peppo L et al. (2015). Longitudinal comparison between male and female preschool children with autism spectrum disorder. J Autism Dev Disord 45: 2046–2055. https://doi.org/10.1007/s10803-015-2366-0. Printzlau F, Wolstencroft J, Skuse DH (2017). Cognitive, behavioral, and neural consequences of sex chromosome aneuploidy. J Neurosci Res 95: 311–319. https://doi.org/ 10.1002/jnr.23951. Quinn PO, Madhoo M (2014). A review of attention deficit/ hyperactivity disorder in women and girls: uncovering this hidden diagnosis. Prim Care Companion CNS Disord 16: PCC.13r01596. https://doi.org/10.4088/PCC.13r01596. Ratto AB, Kenworthy L, Yerys BE et al. (2018). What about the girls? Sex-based differences in autistic traits and adaptive skills. J Autism Dev Disord 48: 1698–1711. https://doi. org/10.1007/s10803-017-3413-9. Reilly S, Onslow M, Packman A et al. (2013). Natural history of stuttering to 4 years of age: a prospective community-based study. Pediatrics 132: 460–467. https://doi.org/10.1542/peds. 2012-3067. Rice ML, Hoffman L (2015). Predicting vocabulary growth in children with and without specific language impairment: a longitudinal study from 2;6 to 21 years of age. J Speech Lang Hear Res 58: 345–359. https://doi.org/10.1044/ 2015_JSLHR-L-14-0150. Roberts B, Eisenlohr-Moul T, Martel MM (2018). Reproductive steroids and ADHD symptoms across the menstrual cycle. Psychoneuroendocrinology 88: 105–114. https://doi.org/10.1016/j.psyneuen.2017.11.015. Robertson MM (2015). A personal 35 year perspective on Gilles de la Tourette syndrome: prevalence, phenomenology, comorbidities, and coexistent psychopathologies. Lancet Psychiatry 2: 68–87. https://doi.org/10.1016/ S2215-0366(14)00132-1. Robertson MM, Eapen V, Singer HS et al. (2017). Gilles de la Tourette syndrome. Nat Rev Dis Primers 3: 16097. https:// doi.org/10.1038/nrdp.2016.97. Robinson EB, Lichtenstein P, Anckarsater H et al. (2013). Examining and interpreting the female protective effect against autistic behavior. Proc Natl Acad Sci 110: 5258–5262. https://doi.org/10.1073/pnas.1211070110. Rudolph JM (2017). Case history risk factors for specific language impairment: a systematic review and meta-analysis. Am J Speech Lang Pathol 26: 991–1010. https://doi.org/ 10.1044/2016_AJSLP-15-0181. Russell HF, Wallis D, Mazzocco MMM et al. (2006). Increased prevalence of ADHD in Turner syndrome with no evidence of imprinting effects. J Pediatr Psychol 31: 945–955. https://doi.org/10.1093/jpepsy/jsj106. Rutter M, Caspi A, Fergusson D et al. (2004). Sex differences in developmental reading disability: new findings from 4 epidemiological studies. JAMA 291: 2007. https://doi. org/10.1001/jama.291.16.2007. Savic I (2014). Sex differences in human epilepsy. Exp Neurol 259: 38–43. https://doi.org/10.1016/j.expneurol.2014.04.009.

Scharf JM, Miller LL, Mathews CA et al. (2012). Prevalence of Tourette syndrome and chronic tics in the population-based Avon longitudinal study of parents and children cohort. J Am Acad Child Adolesc Psychiatry 51: 192–201.e5. https://doi.org/10.1016/j.jaac.2011.11.004. Scharf JM, Miller LL, Gauvin CA et al. (2015). Population prevalence of Tourette syndrome: a systematic review and meta-analysis: meta-analysis of TS prevalence. Mov Disord 30: 221–228. https://doi.org/10.1002/mds.26089. Shattuck PT, Durkin M, Maenner M et al. (2009). Timing of identification among children with an autism Spectrum disorder: findings from a population-based surveillance study. J Am Acad Child Adolesc Psychiatry 48: 474–483. https:// doi.org/10.1097/CHI.0b013e31819b3848. Sibley MH, Mitchell JT, Becker SP (2016). Method of adult diagnosis influences estimated persistence of childhood ADHD: a systematic review of longitudinal studies. Lancet Psychiatry 3: 1157–1165. https://doi.org/10.1016/ S2215-0366(16)30190-0. Sibley MH, Swanson JM, Arnold LE et al. (2017). Defining ADHD symptom persistence in adulthood: optimizing sensitivity and specificity. J Child Psychol Psychiatry 58: 655–662. https://doi.org/10.1111/jcpp.12620. Sibley MH, Rohde LA, Swanson JM et al. (2018). Late-onset ADHD reconsidered with comprehensive repeated assessments between ages 10 and 25. Am J Psychiatry 175: 140–149. https://doi.org/10.1176/appi.ajp.2017.170 30298. Skiba RJ, Chung C-G, Trachok M et al. (2014). Parsing disciplinary disproportionality. Am Educ Res J 51: 640–670. https://doi.org/10.3102/0002831214541670. Ski€ old B, Alexandrou G, Padilla N et al. (2014). Sex differences in outcome and associations with neonatal brain morphology in extremely preterm children. J Pediatr 164: 1012–1018. https://doi.org/10.1016/j.jpeds.2013.12.051. Skuse DH (2000). Imprinting, the X-chromosome, and the male brain: explaining sex differences in the liability to autism. Pediatr Res 47: 9. https://doi.org/10.1203/ 00006450-200001000-00006. Skuse DH, Mandy WPL, Steer C et al. (2009). Social communication competence and functional adaptation in a general population of children: preliminary evidence for sex-byverbal IQ differential risk. J Am Acad Child Adolesc Psychiatry 48: 128–137. https://doi.org/10.1097/CHI.0b013 e31819176b8. Soares N, Evans T, Patel DR (2018). Specific learning disability in mathematics: a comprehensive review. Transl Pediatr 7: 48–62. https://doi.org/10.21037/tp.2017.08.03. Spinillo A, Montanari L, Gardella B et al. (2009). Infant sex, obstetric risk factors, and 2-year neurodevelopmental outcome among preterm infants. Dev Med Child Neurol 51: 518–525. https://doi.org/10.1111/j.1469-8749.2009. 03273.x. Su CY, Chen C-C, Wuang Y-P et al. (2008). Neuropsychological predictors of everyday functioning in adults with intellectual disabilities. J Intellect Disabil Res 52: 18–28. https://doi.org/10.1111/j.1365-2788.2007. 00969.x.

SEX BIAS AND NEURODEVELOPMENTAL DISORDERS Sun D, Ching CRK, Lin A et al. (2018). Large-scale mapping of cortical alterations in 22q11.2 deletion syndrome: convergence with idiopathic psychosis and effects of deletion size. Mol Psychiatry. https://doi.org/10.1038/s41380-0180078-5. Tartaglia NR, Ayari N, Hutaff-Lee C et al. (2012). Attentiondeficit hyperactivity disorder symptoms in children and adolescents with sex chromosome aneuploidy: XXY, XXX, XYY, and XXYY. J Dev Behav Pediatr 33: 309–318. https://doi.org/10.1097/DBP.0b013e31824501c8. Tartaglia NR, Wilson R, Miller JS et al. (2017). Autism Spectrum disorder in males with sex chromosome aneuploidy: XXY/Klinefelter syndrome, XYY, and XXYY. J Dev Behav Pediatr 38: 197–207. https://doi.org/10.1097/ DBP.0000000000000429. Thomas R, Sanders S, Doust J et al. (2015). Prevalence of attention-deficit/hyperactivity disorder: a systematic review and meta-analysis. Pediatrics 135: e994–e1001. https://doi.org/10.1542/peds.2014-3482. Thompson AE, Voyer D (2014). Sex differences in the ability to recognise non-verbal displays of emotion: a meta-analysis. Cognit Emot 28: 1164–1195. https://doi.org/10.1080/02699 931.2013.875889. Tremblay KN, Richer L, Lachance L (2010). Research in developmental disabilities. Res Dev Disabil 31: 57–69. https://doi.org/10.1016/j.ridd.2009.07.016. Van Wijngaarden-Cremers PJM, van Eeten E, Groen WB et al. (2014). Gender and age differences in the Core triad of impairments in autism Spectrum disorders: a systematic review and meta-analysis. J Autism Dev Disord 44: 627–635. https://doi.org/10.1007/s10803-013-1913-9. Vasudevan C, Levene M (2013). Epidemiology and aetiology of neonatal seizures. Semin Fetal Neonatal Med 18: 185–191. https://doi.org/10.1016/j.siny.2013.05.008. Wallentin M (2009). Putative sex differences in verbal abilities and language cortex: a critical review. Brain Lang 108: 175–183. https://doi.org/10.1016/j.bandl.2008.07.001. Walsh B, Mettel KM, Smith A (2015). Speech motor planning and execution deficits in early childhood stuttering. J Neurodev Disord 7: 27. https://doi.org/10.1186/s11689015-9123-8. Walsh B, Usler E, Bostian A et al. (2018). What are predictors for persistence in childhood stuttering? Semin Speech Lang 39: 299–312. https://doi.org/10.1055/s-0038-1667159. Wang T, Liu K, Li Z et al. (2017). Prevalence of attention deficit/hyperactivity disorder among children and adolescents in China: a systematic review and meta-analysis. BMC Psychiatry 17: 32. https://doi.org/10.1186/s12888016-1187-9.

339

Westerinen H, Kaski M, Virta LJ et al. (2017). The nationwide register-based prevalence of intellectual disability during childhood and adolescence. J Intellect Disabil Res 61: 802–809. https://doi.org/10.1111/jir.12351. White EI, Wallace GL, Bascom J et al. (2017). Sex differences in parent-reported executive functioning and adaptive behavior in children and Young adults with autism Spectrum disorder. Autism Res 10: 1653–1662. https:// doi.org/10.1002/aur.1811. Whitehouse AJO (2010). Is there a sex ratio difference in the familial aggregation of specific language impairment? A meta-analysis. J Speech Lang Hear Res 53: 1015–1025. Willcutt EG (2012). The prevalence of DSM-IV attentiondeficit/hyperactivity disorder: a meta-analytic review. Neurotherapeutics 9: 490–499. https://doi.org/10.1007/ s13311-012-0135-8. Williamson D, Johnston C (2015). Gender differences in adults with attention-deficit/hyperactivity disorder: a narrative review. Clin Psychol Rev 40: 15–27. https://doi. org/10.1016/j.cpr.2015.05.005. Wirrell EC, Grossardt BR, Wong-Kisiel LC-L et al. (2012). Incidence and classification of new-onset epilepsy and epilepsy syndromes in children in Olmsted county, Minnesota from 1980–2004: A population-based study. Epilepsy Res 95: 110–118. https://doi.org/10.1016/j.eplepsyres. 2011.03.009. Wolstencroft J, Mandy WPL, Skuse DH (2018). 040 Autism spectrum disorders in girls and women with Turner syndrome. Arch Dis Child A16. https://doi.org/10.1136/goshabs.40. Wren Y, Miller LL, Peters TJ et al. (2016). Prevalence and predictors of persistent speech sound disorder at eight years old: findings from a population cohort study. J Speech Lang Hear Res 59: 647–673. https://doi.org/ 10.1044/2015_JSLHR-S-14-0282. Yairi E, Ambrose N (2013). Epidemiology of stuttering: 21st century advances. J Fluency Disord 38: 66–87. https://doi. org/10.1016/j.jfludis.2012.11.002. Zell E, Krizan Z, Teeter SR (2015). Evaluating gender similarities and differences using Metasynthesis. Am Psychol 70: 10–20. https://doi.org/10.1037/a0038208. Zheng Y, Cleveland HH (2013). Identifying gender-specific developmental trajectories of nonviolent and violent delinquency from adolescence to young adulthood. J Adolesc 36: 371–381. https://doi.org/10.1016/j.adolescence.2012.12.007. Zwaigenbaum L, Duku E, Fombonne E et al. (2019). Developmental functioning and symptom severity influence age of diagnosis in Canadian preschool children with autism. Paediatr Child Health 24: e57–e65. https:// doi.org/10.1093/pch/pxy076.

This page intentionally left blank

Handbook of Clinical Neurology, Vol. 173 (3rd series) Neurocognitive Development: Normative Development A. Gallagher, C. Bulteau, D. Cohen and J.L. Michaud, Editors https://doi.org/10.1016/B978-0-444-64150-2.00026-5 Copyright © 2020 Elsevier B.V. All rights reserved

Chapter 25

Impact of prematurity on neurodevelopment FABRICE WALLOIS1,2*, LAURA ROUTIER1,2, AND EMILIE BOUREL-PONCHEL1,2 1

Research Group on Multimodal Analysis of Brain Function, Jules Verne Picardie University, Amiens, France

2

Department of Pediatric Functional Exploration of the Nervous System, University Hospital, Picardie, Amiens, France

Abstract The consequences of prematurity on brain functional development are numerous and diverse, and impact all brain functions at different levels. Prematurity occurs between 22 and 36 weeks of gestation. This period is marked by extreme dynamics in the physiologic maturation, structural, and functional processes. These different processes appear sequentially or simultaneously. They are dependent on genetic and/or environmental factors. Disturbance of these processes or of the fine-tuning between them, when caring for premature children, is likely to induce disturbances in the structural and functional development of the immature neural networks. These will appear as impairments in learning skills progress and are likely to have a lasting impact on the development of children born prematurely. The level of severity depends on the initial alteration, whether structural or functional. In this chapter, after having briefly reviewed the neurodevelopmental, structural, and functional processes, we describe, in a nonexhaustive manner, the impact of prematurity on the different brain, motor, sensory, and cognitive functions.

INTRODUCTION According to the World Health Organization, prematurity is defined as birth occurring before 37 weeks and is considered extremely preterm (5%, 300 mv

200 mv

0.3–1 Hz

0.5–1.5 Hz

100 mv

Amplitude

4 Hz

Dominante frequency Reactivity RR: Rapid rythms O: occipital, TO: temporooccipital QS: quiet sleep, AS: active sleep SAD: Slow anterior dysrithmia

–

+– 26

28

+ 30

++ 32

34

36

38

40

F. wallois, GRAMFC, Amiens

Fig. 25.4. Synopsis of maturation of specific features in EEG of premature neonates. Adapted from Wallois, F., 2010. Synopsis of maturation of specific features in EEG of premature neonates. Neurophysiol Clin 40, 125–126. https://doi.org/10.1016/j.neucli.2010.02.001.

In parallel to neurogenesis, with determined time and spatial dynamics (Dubois et al., 2008), myelination begins at about 20 wGA in the medulla, then successively extends to commissural structures and the primary sensory and motor areas. The last areas to be myelinated are in the prefrontal cortex (Fuster, 2002). Myelination is dependent on genetic factors and neuronal activity. It increases the speed of transmission of information and optimizes early connectivity between different structures and occurs gradually and inhomogeneously. Myelination between the different cortical areas follows a specific spatiotemporal course. Disturbances in the fine-tuning of interactions between myelination and functional connectivity maturation could disrupt all maturation processes, compromising neural network functionality and inducing long-term neurodevelopmental disorders. In the late fetal period (after 31 wGA), gyrification occurs as the consequence of all the preceding processes, particularly neuronal proliferation and migration, emergence of thalamocortical connections, and myelination. The migration of neurons from the subplate to the cortex triggers successive waves of primary, secondary, and tertiary folding (20, 32, and 38 wGA, respectively) (Dubois et al., 2016) and the explosive development of corticocortical fiber connections (Kostovic and Rakic, 1990;

Van Essen and Drury, 1997; Kostovi
c and JovanovMiloševi
c, 2006; Huang et al., 2009; Takahashi et al., 2012; Mitter et al., 2015). Similarly, the cerebellum shows exponential growth in foliation and grows fivefold between 27 and 40 wGA and increases more than 30-fold in surface area. In parallel with the neuronal system, the vascular system undergoes complex maturation processes. With gestational age, vascular density gradually increases, particularly in the brain matrix zone, and the vessel walls mature. Neurovascular coupling reaches maturity from 28 wGA (J€obsis, 1977; Buxton et al., 2004; Mahmoudzadeh et al., 2013, 2017a,b). Fine-tuning between the two networks allows metabolic means to match the energy demand solicited by immature networks involved in sensory processing (mostly auditory processing) (Mahmoudzadeh et al., 2017a,b). Maturation of vascular networks progresses in close relation with neuronal networks, notably, the building of the columnar organization of the cortical plate (Patel, 1983; Cox et al., 1993). Maturation of brain neuronal and vascular networks partly relies on genetic programming. Neural activity promotes the formation of vascular networks (Lacoste et al., 2014; Lacoste and Gu, 2015) and is necessary for vascular patterning. This effect is reciprocal between the networks. Perturbations

Fig. 25.5. Specific electrical activities recorded on low definition electroencephalography (EEG) with a bipolar montage. Eleven EEG electrodes, ECG, and respiratory channels. Filter: 0,53–70 Hz, Notch: 50 Hz. (A) Theta temporal activities in coalescence with a slow wave (TTA-SW) in a preterm infant recorded at 28 weeks of gestational age (wGA), (B) delta brush recorded at 32 wGA, (C) frontal transients recorded at 40 wGA, and (D) slow anterior dysrythmia recorded at 41 wGA.

Fig. 25.6. Theta temporal activities in coalescence with a slow wave (TTA-SW) recorded on high density electroencephalography and their source localization in the superior temporal sulcus using two methods of source localization. Adapted from Routier, L., Mahmoudzadeh, M., Panzani, M., et al., 2017. Plasticity of neonatal neuronal networks in very premature infants: source localization of temporal theta activity, the first endogenous neural biomarker, in temporoparietal areas: networks plasticity in very premature infants. Hum Brain Mapp 38, 2345–2358.

348

F. WALLOIS ET AL.

in neural activity are likely to trigger alterations in vascular network maturation (Lacoste et al., 2014). Disruptions in vascular networks, by intraventricular hemorrhage (IVH) for example, could compromise neurovascular coupling. The vascular network can provide an adapted hemodynamic response to cortical areas activated by IVH, inducing neural activity disturbances (Mahmoudzadeh et al., 2018). Because premature birth drastically modifies the neuronal environment, both endogenous and exogenous processes and their complex interactions are likely to have a lasting impact on the subsequent functional development of neural networks.

Electroencephalographic monitoring of functional brain development The electroencephalogram of the premature newborn reflects the shaping of both structural and functional maturation. Different features that rely either on the property of endogenous sensory or nonsensory-driven activities and/or on the property of the developing network have been described.

PROPERTIES OF DEVELOPING NETWORKS The EEG of preterm newborns is characterized by discontinuity, alternating between high amplitude activity (bursts of activity) and periods of low voltage activity defined as quiescent periods (from Latin quiescens from quiescere: to rest, to be quiet) (interburst intervals). Discontinuous activity might reflect the spontaneously generated activity of subplate neurons and/or the output of synchronized interactions between subplate neuronal networks and migrating pyramidal cells. Promoting functional interactions within cortical neural networks tends to shorten the length of quiescent periods, and EEG activity becomes continuous near term. In parallel, a progressive decrease in EEG amplitude and an increase in frequency content are likely to result from the establishment of local and remote intra- and interhemispheric connectivity. Similarly, the interhemispheric synchronization of the burst of activity causing transient disruptions between 34 and 36 wGA might result from different connectivity processes (e.g., interthalamic, callosal) that occur sequentially in the course of development (Figs. 25.4 and 25.5).

ENDOGENOUS GENERATORS Bursts of activity are of long but varying duration, consisting of complex age-specific EEG activity. These age-specific characteristics reflect the activation of endogenous nonsensory-driven generators before

28 wGA (Tritsch et al., 2007; Clause et al., 2014; Babola et al., 2018). At about 28 wGA, the relocation of thalamic afferents from the subplate to the cortical plate provides sensory inputs to the immature network. The immature network progressively becomes sensory driven in addition to being spontaneous and endogenous (Milh et al., 2007; Minlebaev et al., 2007; Colonnese et al., 2010; Wess et al., 2017). The generators display hierarchically nested oscillatory activity, consisting of SW with faster activity nested within them (Vanhatalo et al., 2005; Routier et al., 2017; Kaminska et al., 2018; Moghimi et al., 2020). These features have different spatiotemporal and frequency characteristics, appearing sequentially during neurodevelopment. They are described as: frontal activity (24–28 wGA), TTA-SW (24–32 wGA) (Figs. 25.5 and 25.6), delta brushes (29–36 wGA), and frontal transient and slow anterior dysrhythmia (36–42 wGA). The establishment of neural networks and the sequential activation of age-related endogenous generators imply precise timing for full development (temporal and spatial) of these successive generators in close relation with the progressive development of the mature network. Here, the slightest disturbance, “a grain of sand” in these elegant interactions might be the first step in a long-term neurodevelopmental disorder. Structural and functional disorganization of developing networks might induce disturbed EEG activity shown as a disorganized or dysmature pattern, predictive of a neurodevelopmental disorder (Hayakawa et al., 1997a; Watanabe et al., 1999; Nguyen The Tich et al., 2007).

IMPACT OF PREMATURITY ON STRUCTURAL AND FUNCTIONAL BRAIN DEVELOPMENT AND MATURATION Premature birth could have an impact on all the processes of proliferation, differentiation, migration, and cerebral/cerebellar growth, leading to structural and functional neurodevelopmental disorders. Brain (cerebral and cerebellar) anomalies acquired during the premature period result from pathologic mechanisms (inflammation, hemorrhage/ischemia, and excitotoxicity). Such mechanisms seem to be mainly clastic and are destructive to the brain tissue. Brain anomalies can also be related to disorders that alter the programming and outcome of the complex processes of cerebral maturation and development. These two major mechanisms are entangled and are involved in neurologic disorders linked to prematurity (Volpe, 2009a).

IMPACT OF PREMATURITY ON NEURODEVELOPMENT

Hemorrhagic damage Hemorrhagic damage during the period of prematurity mainly includes IVH and hemorrhagic parenchymal infarction. They result from injuries of immature vessel walls presenting increased vulnerability to hemorrhages highly distributed in the brain matrix zones. Vascular lesions induce hemorrhages in the choroid plexus and in the germinal matrix zone, destroying neuronal precursors. When IVH spreads to the cerebral parenchyma, it leads to venous hemorrhagic infarction. The parenchymal infarction induces lesion in the periventricular white matter, which could disturb the thalamocortical axons and destroy preoligodendrocytes in the premyelination phase. In addition, IVH decreases hemodynamic availability to functioning structures (Mahmoudzadeh et al., 2018). In the same way, cerebellar lesion due to hemorrhage, usually localized in one of the cerebellar hemispheres, might destroy neuronal progenitors, particularly the external granular layer, and induce local or more extensive parenchymal lesion. Cerebellar lesions with a vascular origin are frequently associated with supratentorial lesion, primarily IVH. Both cerebellar and cerebral hemorrhages are associated with high morbidity and mortality rates. Cerebellar hemorrhages are associated with a high mortality rate (38%). Survivors of large cerebellar hemorrhages are described as having microcephaly, severe developmental delays, and hypotonia (Dyet et al., 2006; Bednarek et al., 2008). Isolated cerebellar hemorrhaging might be associated with motor impairment and/or cognitive and language dysfunctions (Limperopoulos et al., 2007) (see specific sections).

Periventricular leukomalacia Periventrivular leukomalacia (PVL) refers to focal or diffuse cerebral white matter damage due to ischemia and inflammatory mechanisms (Volpe, 2009a,c). Focal PLV consists of localized cell necrosis in periventricular white matter. The extent of cell necrosis could be limited and develop into glial scars. When necrosis is more extensive, it could progress to multiple cystic lesions (cystic PVL) (Pierson et al., 2007). Diffuse PVL is characterized by diffuse white matter damage associating gliosis and hypomyelination. Diffuse PLV is the most common and severe form of injury in preterm newborns (Volpe, 2009a,c; Buser et al., 2012; Back, 2017). The frequency of PVL reaches 5% for all stages of prematurity, with risk declining with GA (below 10% after 27 wGA and 2% at 32 wGA) (Larroque et al., 2003). In PVL, inflammatory and ischemic mechanisms of penetration through the blood–brain barrier are

349

achieved through proinflammatory cytokines and activated leukocytes causing activation of microglia and astrocytes, which cause cell death by apoptosis and/or necrosis. Excitatory neurons of the cortical subplate are particularly vulnerable to these mechanisms (McQuillen et al., 2003; Volpe, 2009a), inducing neuronal loss and gliosis of the subplate with functional consequences on endogenous generators (Ranasinghe et al., 2015). Progenitors of oligodendrocytes could also be affected by these mechanisms. In parallel to cell effects, inflammation and ischemia affect axon growth, which is maximal at this period of prematurity (Volpe, 2001, 2009c; Counsell et al., 2003, 2006; Ment et al., 2009; Miller and Ferriero, 2009). Neuronal, glial, and axonal lesions are not limited to periventricular white matter (axons and the subplate neurons), but involve the thalamus, basal ganglia, cerebral cortex, brainstem relay nuclei, and cerebellum (Pierson et al., 2007; Volpe, 2009a), causing widespread dysfunction of the nervous system. Functionally, during the neonatal (28–32 wGA) period, the presence of white matter damage is associated with the presence of positive rolandic spike waves on the EEG. The recording of more than two positive rolandic spike waves per minute is predictive of unfavorable motor outcomes (Marret et al., 1997) (Fig. 25.7).

Trophic and maturation disturbances Secondary or not to initial destructive processes, subsequent trophic/maturation disturbances can be observed. Abnormal proliferation and/or apoptosis of the neural and glial precursor cells might be associated with defective neuroblast migration and abnormal cortical organization (Barkovich et al., 2001; Sztriha et al., 2004). At the same time, the initial destruction of glial cells is followed by a glial reaction that forms a screen for the migration of glial progenitors and astrocytes intended to be used in the cortex. The decreased number of neocortical astrocytes, which have a trophic role in neuronal function, contributes to the reduction in the neural population in the cortical subplate and the cortex. In parallel, axonal fiber injury and vulnerability of oligodendrocytes (necrosis/apoptosis of oligodendrocyte precursors) are the cause of secondary myelination disorders. All these maturation disturbances are associated with a greater risk for reducing both cerebral white and gray matter volumes and alteration in gyrification, particularly in the insula, pre- and postcentral sulci, and temporal regions (Engelhardt and Liebner, 2014) are associated with adverse cognitive development (Rakic, 1988; Peterson et al., 2000; Loeliger et al., 2006; Anderson and Doyle, 2008; Kesler et al., 2008;

Fig. 25.7. Electroencephalographic abnormalities and brain clastic injuries related to prematurity. (A) Cystic periventricular leucomalacia (PVL) in a preterm infant, (B) grade IV intraventricular hemorrhage (IVH) in a preterm infant, (C) positive rolandic spike waves PRSW recorded on low density electroencephalography (EEG). Bipolar montage. (D) PRSW on high density EEG PRSW, which are observed at 26–32 weeks of gestational age, are a precocious pathologic neurobiomarker of the presence of white matter damage (PVL and high-grade IVH) and a deletorious motor outcomes. Panel A: From Saliba, E., 2015. L
esions c
er
ebrales du nouveau-n
e pr
ematur
e. Contraste 1, 85–105. https://doi.org/10.3917/ cont.041.0085; https://www.cairn.info/revue-contraste-2015-1-page-85.htm; Panel B: adapted from Whitelaw, A., 2011. Core concepts: intraventricular hemorrhage. NeoReviews 12, e94–e101. https://doi.org/10.1542/neo.12-2-e94.

IMPACT OF PREMATURITY ON NEURODEVELOPMENT Nosarti et al., 2008; Soria-Pastor et al., 2008, 2009; Aarnoudse-Moens et al., 2009; Delobel-Ayoub et al., 2009; Scafidi et al., 2009; Vasung et al., 2016; Back, 2017). The synaptogenesis and the establishment of neural circuits could be disrupted. Subplate neuronal dysfunction might disrupt axonal growth and maturation of cortical and thalamic neurons, inducing disruption of interactions between cortical subplate neurons and cortical/thalamic neurons with long-term consequences on functional cortical connectivity (Nagode et al., 2017). Myelination disorders could also have an impact on connectivity and neuronal network function. Myelination could influence temporal relations, oscillations, and synchrony in the interactions of distant brain regions, causing activity-dependent plasticity. Even slight changes in conduction velocity resulting from small changes in myelin thickness or nodal structure can have profound effects on neuronal network function in terms of spike-time arrival, oscillation frequency, oscillator coupling, and propagation of brain waves (Pajevic et al., 2014). Functional connectivity network perturbation could contribute to the neurocognitive prognosis of the premature infant but is only beginning to be investigated, and further studies are needed (see Rogers et al., 2018 for a recent review on connectivity in preterm infants). In parallel to cerebral maturation disturbances, acquired cerebellar lesions and disturbance of cerebellar development in premature infants could contribute to neurodevelopmental impairments. Cerebellar hypoplasia is most frequently observed in infants before 32 wGA and decreases with age. Volume loss is accompanied by neuronal loss, gliosis, and disruption of the cerebellar microarchitecture (Miller et al., 2002; Volpe, 2009a,c; Hart et al., 2010; Haldipur et al., 2011). The mechanisms of cerebellar hypoplasia are complex. A strong factor associated with cerebellar hypoplasia is cerebral brain injury. Severe cerebral brain injury could induce a loss of excitatory input from the cerebellum via corticopontine tracts synapsing in the pontine nuclei, then crossing the pons to innervate the contralateral cerebellar hemisphere (Limperopoulos et al., 2005; Shah et al., 2006; Volpe, 2009a). Supratentorial hemorrhage can also result in obstructive hydrocephalus. The dilatation of the fourth ventricle could result in direct injury to the brainstem and cerebellum due to mechanical factors, with a risk of proliferation disturbance in the external granular layers (ventricular germinal matrix zone). These proliferative cells are potentially directly exposed to the neurotoxic effects of blood from IVH (Agyemang et al., 2017). Other factors such as glucocorticoids (Bohn and Lauder, 1978; Aden et al., 2008;

351

Noguchi et al., 2008) opioids, pain exposure (Golalipour and Ghafari, 2012; McPherson et al., 2015; Ranger et al., 2015; Zwicker et al., 2016), cardiorespiratory factors (Argyropoulou et al., 2003; Padilla et al., 2015), nutrition, and somatic growth have been associated with cerebellar hypoplasia. We now discuss the major brain systems and see how their maturation and functionality can be affected by prematurity.

IMPACT OF PREMATURITY ON THE MOTOR SYSTEM Structural maturation of the motor system Cortical-mediated movements are mediated by the corticospinal system. The corticospinal tract reaches the caudal medulla at 10 wGA. Between 16 and 19 wGA, the number of pyramidal tract fibers increases as they move from the cervical to the lumbar spinal cord (Humphrey, 1964; M€uller and O’Rahilly, 1990; Borsani et al., 2019). The pyramidal decussation of the corticospinal tract is completed by 17 wGA. At 20 wGA, the spinothalamic tract is formed (Guimarães Filho et al., 2013). At about 29 wGA, the functional connection between the periphery and functioning cortex is fully developed (Bartocci et al., 2001; Slater et al., 2006; Vanhatalo and Lauronen, 2006; Kostovi
c and Judas, 2007; Guimarães Filho et al., 2013; Thomason et al., 2015; Borsani et al., 2019). The fetal brain demonstrates cerebral–cerebellar and cortical–subcortical connectivity (Borsani et al., 2019). Between 20 and 28 wGA, mature myelin is detected, first in subcortical regions and later in cortical regions (Tau and Peterson, 2010).

Functional maturation of the motor system As early as 7 wGA, whole embryonic body movements that reflect the development of subcortical systems can be observed (Schr€oder and Young, 1995; Kurjak et al., 2005, 2012; Borsani et al., 2019). From 16 wGA, almost the entire range of movements characterizing the full-term infant is already present (Humphrey, 1964; Ianniruberto, 1981; de Vries and Fong, 2006; Guimarães Filho et al., 2013; Borsani et al., 2019), including startle and twitch movements, isolated limb or head movements, breathing movements, hiccups, yawning, sucking, and swallowing (de Vries and Fong, 2006). Between 17 and 20 wGA, the supraspinal structures start to influence fetal motor behavior (Andonotopo et al., 2005; Borsani et al., 2019). At the third trimester, the motor development is well established.

352

F. WALLOIS ET AL.

Impact of the prematurity on motor functions Preterm infants have an increased risk of motor disabilities including higher rates of cerebral palsy (CP) and non-CP motor impairments (Spittle et al., 2018a,b). The overall rate of CP decreases with gestational age from 14.6% (12.7%–17%) between 22 and 27 wGA, 6.2% (4.9%–7.8%) between 28 and 31 wGA, and 0.7% in moderate to late preterm (32–36 wGA) (Himpens et al., 2010; Spittle et al., 2018b). Minor neuromotor dysfunctions are observed at 5 years old in children born before 33 wGA (41%) and those born between 33 and 37 wGA (31%) (Arnaud et al., 2007; Pascal et al., 2018). Motor impairments, whether mild or severe, impact education outcomes and participation in social activities and sports in children, compared to those without motor impairment (Wu and Colford, 2000; Stavsky et al., 2017).

CEREBRAL PALSY CP is defined as “a group of permanent disorders of the development of movement and posture, causing activity limitation, that are attributed to nonprogressive disturbances that occurred in the developing fetal or infant brain” (Rosenbaum et al., 2007; Spittle et al., 2018b) and is diagnosed by standard motor neurologic examination. Prematurity is the most frequent cause of CP. In the neonatal period, one of the best predictive factors of CP in preterm children is the visualization of brain injury on neuroimaging (IVH, periventricular leukomalacia, or cerebellar hemorrhage) (Linsell et al., 2016; Spittle et al., 2018b). The risk for CP also increases with posthemorrhagic ventricular dilatation (IVH grade 3) or parenchyma hemorrhage infarction (IVH grade 4) (Sherlock et al., 2005; Nongena et al., 2010; Spittle et al., 2018b). In periventricular leukomalacia, the severity of CP is highly associated with the presence of extensive cysts, particularly when they are in specific locations such as in the occipital periventricular region (Bassan et al., 2007; van Haastert et al., 2008). These brain injuries are associated with microstructure abnormalities (e.g., lower fractional anisotropy), notably in strategic regions such as the posterior limb of the internal capsule and the centrum semiovale (De Bruïne et al., 2013). CP is also associated with cerebellar lesions. Up to 58% of children with CP have both cerebral and cerebellar damage (Zayek et al., 2012; Kitai et al., 2015; Spittle et al., 2018b). The clinical correlation between cerebellar lesions and the outcome remains difficult because of the presence of supratentorial injury. Nevertheless, the association of cerebral with

cerebellar lesions is responsible for worse motor and cognitive outcomes with mixed CP, including spastic–ataxic, spastic–dyskinetic, and dyskinetic–ataxic subtypes (Messerschmidt et al., 2008; Anderson et al., 2017).

NONCEREBRAL PALSY MOTOR IMPAIRMENT Even in the absence of CP, preterm infants are at risk of minor motor impairments, including minor neurologic dysfunction (MND), developmental coordination disorder, and fine motor skills, visual–motor, and sensorimotor deficits. MND includes subtle motor disabilities such as difficulty with posture, muscle tone regulation, motor coordination, and cranial nerve function (HaddersAlgra, 2002; Brostr€om et al., 2018), associated or not with cognitive or behavioral dysfunctions. MND seems to be associated with disorganization of complex networks, including cortico-striato-thalamo-cortical and cerebello-thalamo-cortical circuits (Bolk et al., 2018b; Brostr€om et al., 2018). Fine motor deficits, which refer to the impairment of control and coordination of the hands and fingers, are three times more frequent than CP (Duncan et al., 2019). The association between fine motor skill deficits and visual perceptional integration deficits induce visual–motor integration disturbances associated with decreased academic performance in spelling, writing, reading, and mathematics (Weil and Amundson, 1994; Daly et al., 2003; Sortor and Kulp, 2003; Volman et al., 2006; Bolk et al., 2018a,b; Duncan and Matthews, 2018; Duncan et al., 2019). Poor motor and visual–motor integration performance is related to impaired development of both supra- and infratentorial structures. A reduction in cerebral structure volume (the precentral gyrus, the caudate, and globus pallidus) and in the cerebellum has long been known to be involved in movement modulation of balance and coordination and has been reported to be correlated with poor visual-motor integration performance and with fine motor skills (Inder et al., 2003; Volpe, 2009c; Hintz et al., 2015; Bolk et al., 2018b). Predictive biomarkers of motor impairment have been described in neonatal EEG. Positive rolandic spike waves are very specific markers of PVL (Baud et al., 1998) and are highly sensitive and specific for motor disability (including mild distal hypertonia and spastic diplegia or tetraplegia) at 2 years old (Marret et al., 1997). A frequency above 2/min is a specific (92%) sign of severe spastic diplegia (Marret et al., 1997; Baud et al., 1998). Their appearance always precedes ultrasonic detection of cysts in PLV (Baud et al., 1998). Dysmature or disorganized patterns, consisting notably of asymmetric frequencies, deformed generators, or reduced/

IMPACT OF PREMATURITY ON NEURODEVELOPMENT absent or persistent specific generators at a given GA have also been described (Hayakawa et al., 1997a,b; Watanabe et al., 1999; Nguyen The Tich et al., 2007). These patterns seem to be associated with motor impairment (including CP for 2 out of 36 preterm newborns who developed sequelae) and/or cognitive developmental delays (Le Bihannic et al., 2012; Nunes et al., 2014). Large studies are needed to confirm these findings. The integrity of motor pathways has been studied indirectly, using somatosensory evoked potential (SEP). Studies have described the value of altered neonatal SEP to predict the outcome at school age, particularly for sensorimotor abilities, including spastic diplegia and attention-deficit disorder (Majnemer and Rosenblatt, 2000), but their predictive value remain controversial.

IMPACT OF PREMATURITY ON THE NEUROSENSORY SYSTEMS Neurosensory systems develop following a fixed sequence: the somesthetic system (tactile and nociceptive sensitivity), the chemosensory system (smell and taste), the vestibular and the auditory systems, and the visual system (Borsani et al., 2019). Functionality occurs at the end of the second trimester and develops during the third trimester. In the late gestational period, the immature networks are able to detect and discriminate environmental stimuli, integrate varied information, and learn and recognize different stimuli (James, 2010; Mahmoudzadeh et al., 2013; Krueger and Garvan, 2014; Borsani et al., 2019). The development of neuronal functional networks in the fetus that participate in integrating sensory information make up a key element of neuronal development and depend on intimate interactions between genetically determined guidance and activitydependent refinement of synaptic connections (O’Leary et al., 2007; Tritsch and Bergles, 2010). Premature birth could directly induce brain lesions and interfere with the complex maturation processes with subsequent long-lasting functional and structural consequences that vary with gestational age. In addition, environmental factors in the NICU could compromise normal brain maturation. Premature infants are exposed to unexpected sensory experiences, which are both quantitatively and/or qualitatively modified. It has been suggested that sensory understimulation (e.g., tactile and kinesthesic, vestibular, gustatory) causes apoptotic damage (Anand and Scalzo, 2000; Maroney, 2003; Als et al., 2004; Grunau, 2013; Adams et al., 2015; Br€ oring et al., 2018), whereas sensory overstimulation (e.g., noise, lights, nursery

353

handling, noxious odors, repetitive pain) may cause excitotoxic neural damage (Grunau, 2002; Roofthooft et al., 2014; Adams et al., 2015; Br€oring et al., 2018). Unexpected sensory stimulation could interfere with functional network maturation and contribute to atypical sensory processing in children born preterm. Sensory processing difficulties are important to consider not only because of their impact on daily activities but also because they are related to neurocognitive and academic functioning (Bart et al., 2011).

The somatosensory system STRUCTURAL MATURATION The first primitive sensitive and proprioceptive nerve receptor endings are observed at 7–8 wGA. In the second trimester, somatosensory system development is complete. Neurons for nociception are observed in the dorsal root ganglion at 19 wGA (Konstantinidou et al., 1995). Thalamic afferents reach the subplate zone at 20–22 wGA and are relocated to the cortical plate from 26 to 28 wGA, consistent with observations of a “waiting period” in animals, when thalamocortical axon synapses in the subplate remain for days or weeks before entering the cortical plate (Kostovic and Rakic, 1984, 1990).

FUNCTIONAL MATURATION The somatosensory system (tactile and nociceptive sensitivity) is the first functional sensory system to develop during the neonatal period (Humphrey, 1964; Lee et al., 2005; Borsani et al., 2019). The skin area demonstrates sensitivity to stimulation from 7.5 wGA (mouth) (Hooker, 1952; Humphrey, 1964). A spinal reflex arc in response to stimuli is observed at 8 wGA (Okado and Oppenheim, 1984). Somatosensory evoked responses (SER) can be observed individually in human neonates as early as 28 wGA (Hrbek et al., 1973; Milh et al., 2007; Vanhatalo et al., 2009; Leikos et al., 2019). The earliest cortical reactions to sensory stimulation occur in the subplate and deeper cortical layers (Wess et al., 2017; Luhmann et al., 2018). SER evolve with increasing GA toward faster and higher amplitudes, reflecting changes in subplate and cortex interaction and cortical maturation (Luhmann et al., 2018). Moreover, cortical responses become more spatially dispersed with increasing integration of the ipsilateral hemisphere and with sensorimotor areas (Allievi et al., 2016).

IMPACT OF PREMATURITY Premature birth could compromise both somatosensory registration and sensory modulation. The risk of

354

F. WALLOIS ET AL.

somatosensory processing impairment decreases with GA (Adams et al., 2015). Children born preterm could exhibit hyposensitivity (threshold) in thermal (Walker et al., 2009) and tactile perception, in kinesthesia, and graphesthesia (DeMaio-Feldman, 1994; Wickremasinghe et al., 2013). Somatosensory modulation could be impaired as children born prematurely have been described as being highly responsive and defensive to their external environmental stimuli (Case-Smith et al., 1998; Walker et al., 2009; Eeles et al., 2013; Wickremasinghe et al., 2013). Somatosensory impairment could be explained by brain injuries (severe IVH [grade 3 and 4, Papille classification]) and cystic PLV (Wickremasinghe et al., 2013), particularly cerebral white matter disturbances. Atypical sensory processing could also be affected by neonatal frequent and painful somatosensory stimuli and early deprivation of parental stimulation (Case-Smith et al., 1998; Walker et al., 2009; Bart et al., 2011; Wickremasinghe et al., 2013). SEPs have been used to study the integrity of somatosensory pathways from the peripheral nerves (median or tibial nerves) to the somatosensory cortex (via the dorsal column tract of the spinal cord and the thalamus) (Majnemer and Rosenblatt, 1996; Leikos et al., 2019). The predictive value of SEP during the neonatal period to predict somatosensory deficit is controversial. Nevertheless, neonatal SEP might be predictive of outcomes at school age, notably for sensorimotor abilities and intellectual performance (Majnemer and Rosenblatt, 2000). Concordant results between neuroimaging studies and evoked potentials are highly predictive of sensorimotor outcomes (Klimach and Cooke, 1988; Willis et al., 1989; de Vries et al., 1992; Pierrat et al., 2017).

The chemosensory systems The olfactory and taste systems are closely related functionally because of their ability to discriminate specific molecules in water and air. These senses play a major role in eating behavior, enjoyment, and maintaining social relationships. Some of the psychobiologic effects of olfaction begin very early and depend on the fetal experience (Schaal et al., 2004). These systems are rarely examined by pediatric neurologists, and very few study reports are available on the potential dysfunctions of olfactory and taste systems due to prematurity.

STRUCTURAL MATURATION OF THE OLFACTORY AND TASTE SYSTEMS

The primary olfactory receptors are formed by 8 wGA (Chuah and Zheng, 1987; Johnson et al., 1995) and have a mature appearance by the end of the second

trimester of pregnancy. The Olfactory marker protein, considered to correlate with neuroreceptor functionality and connectivity in the main olfactory bulb, is expressed in the olfactory mucosa by 28–30 wGA (Chuah and Zheng, 1987; Bloomfield et al., 2017). At 28 wGA, the olfactory nerve is present in almost the whole length of the inferior surface. Olfactory bulbs connect with the limbic forebrain, olfactory paleocortex, and frontal cortex during this early period of development (Carney et al., 2006; García-Moreno et al., 2008). Taste cells begin to form at 7–8 wGA and look like mature receptor cells at 13–15 wGA (Lipchock et al., 2011). They connect to the subplate and the cortical plate between 24 and 28 wGA.

FUNCTIONAL MATURATION OF THE OLFACTORY AND TASTE SYSTEMS

Inhalation and swallowing of amniotic fluid are the first chemosensory experiences of the fetus that induce, in utero, the first flavor perceptions of the prenatal environment to the olfactory and taste neural networks (Schaal, 2015; Bloomfield et al., 2017). From 28 to 30 wGA, the fetal organism acquires the ability to detect, discriminate, and respond to odors (Chuah and Zheng, 1987; Schaal et al., 2004; Bloomfield et al., 2017). After food ingestion by the mother, changes in respiratory pattern, increases in fetal movements, modification of facial expression, and lingual movements are observed (Sarnat et al., 2017). The fetal responses differ with distinct odorants, indicating the abilities to encode and, probably, discriminate olfactive information (Schaal et al., 1995, 1998, 2004; Varendi et al., 1996; Marlier et al., 1998). Preterm infants, as early as 31 wGA, may prefer certain odor stimuli over others, and perinatally acquired odors generate long-term memory (24 months) (Bloomfield et al., 2017). The neonatal cortical ability to process odor inputs is suggested by odor-induced variation in oxygenated hemoglobin (HbO), through near-infrared spectroscopy (NIRS) recordings, detected over the orbitofrontal regions in premature infants (Bartocci et al., 2000, 2001). Taste receptors are considered functional by about 17 wGA. Fetal swallowing begins at approximately 12 wGA. At about 18 wGA, gestational nonnutritive suckling begins and sucking and swallowing actions are coordinated at 35–40 wGA. At the end of the second trimester, the fetus can detect and discriminate tastes: fetal swallowing frequency increases in response to the introduction of sweet solutions into the amniotic fluid, and decreases in response to a bitter solution.

IMPACT OF PREMATURITY ON NEURODEVELOPMENT

IMPACT OF PREMATURITY Regarding the olfactory and taste systems, there are very few reports on the potential impact of prematurity (in particular HIV and periventricular leukomalacia) on the development and structural maturation of the olfactory pathways. In a model of acute placental insufficiency, long-lasting injury to neuronal tracts of the olfactory system, including the olfactory epithelium, has been demonstrated (Drobyshevsky et al., 2006). In the case of IVH, evidence of bleeding into a transient fetal olfactory structure (the olfactory recess) has been observed, but no hemorrhage within the olfactory bulb was demonstrated (Sarnat et al., 2017). No data are available on the potential functional consequences of these structural lesions on olfactory and taste systems. Premature infants are nevertheless at risk of atypical sensory processing, comprising smell and taste sensitivity symptoms (Bart et al., 2011). The NICU environment and resuscitation methods could interfere with their development and maturation. Premature birth, particularly with tube feeding, induces a depletion of taste quantitative stimuli and a major disruption of the odor/ flavor expectations established in fetal life (Lipchock et al., 2011, 2012). These disruptions could modify synaptic organization and connections to the olfactory and taste cortices (Sarnat et al., 2017) and modify the development of hypothalamic pathways that regulate appetite, contributing to increased risk of metabolic syndrome and cardiovascular disease in adult life (Markopoulou et al., 2019). Odors modulate many homeostatic functions: stress and energy expenditure (motor activity and crying) (Sarnat et al., 2017), sleep organization (Schaal et al., 2004), respiratory control that could negatively impact the infant outcome during the neonatal period with long-lasting effects on neurodevelopment, social skills, attachment (Schaal et al., 2004), and oral skills (Yildiz et al., 2011).

The vestibular and auditory systems STRUCTURAL MATURATION OF THE AUDITORY SYSTEM The inner ear and vestibular organs start to differentiate as soon as 4 wGA (Zimmerman and Lahav, 2013; Borsani et al., 2019). The middle ear starts its development at 8wGA. The auditory receptors appear at 10 wGA with the first hair cells visible by 12 wGA and the first synapses by 15 wGA (Lecanuet et al., 2000). The cochlear structures are functional by 20 wGA (Pujol and Lavigne-Rebillard, 1992) together with vestibular (18–20 wGA) and startle reflexes (23–24 wGA) (Lim and Brichta, 2016). The auditory

355

nerve pathways are active at 24–25 wGA. From about 24 wGA, thalamocortical axons grow into the auditory subplate, then into the cortex (Vanhatalo and Kaila, 2006). The afferent thalamic fibers supporting the peripheral auditory information are located in the subplate opposite the future auditory cortex at 20–23 wGA (Krmpoti
c-Nemani
c et al., 1980). From 26 to 28 wGA, these auditory afferents penetrate the cortical plate (Krmpoti
c-Nemani
c et al., 1980; Dubois et al., 2015). During the last trimester, long-distance fibers connect the temporal, parietal, and frontal lobes according to two “dorsal” and “ventral” pathways that play a major role in linguistic processing (Leroy et al., 2011; Mahmoudzadeh et al., 2013; Dubois et al., 2016). The “dorsal pathway” mainly contributes to phonologic processing, whereas the “ventral pathway” supports semantic processing (Rolheiser et al., 2011; Dick and Tremblay, 2012; Vandermosten et al., 2012). In premature infants, maturation of the ventral pathway is more advanced than that of the dorsal pathway (Dubois et al., 2016). Moreover, asymmetries between hemisphere in perisylvian areas have been described. Many sulci appear 1 or 2 weeks before on the right than on the left side (Chi et al., 1977; Dubois et al., 2008); the cerebral blood flow at rest (Lin et al., 2013) and the EEG power are higher (Myers et al., 2012) in several right hemisphere regions, compared to the contralateral left regions. Asymmetry is also observed in the arcuate fasciculus in prematurity (Dubois et al., 2016). Because anatomical properties of the arcuate fasciculus predict phonologic and reading skills in children (Yeatman et al., 2011), any disruptions in their maturation profiles are likely to affect language abilities in the course of development. Such early structural specificities already present in the premature cortex are likely to provide early indicators of cortical functional specialization induced by genetic and environmental factors (Dubois et al., 2008).

FUNCTIONAL MATURATION OF THE AUDITORY SYSTEM At 25 wGA, the structural features necessary for audition are functional (Zimmerman and Lahav, 2013). From 25 wGA, short latency auditory evoked potentials (brainstem) are recorded in premature infants and confirm the early functionality of the auditory pathway to the brainstem. At this stage, the hair cells in the cochlea are fine-tuned for specific frequencies and can translate vibratory acoustic stimuli into an electrical signal that is sent to the brainstem (McMahon et al., 2012). At 25 wGA, brainstem responses are consistent and reproducible but with very high thresholds (Busnel et al., 1992). Around this GA, premature infants seem to be able to perceive and react to auditory information

356

F. WALLOIS ET AL.

(Ruben, 1992). Middle and late latency auditory eventrelated potentials appear progressively to become consistently detected from 28 wGA (Busnel et al., 1992; McMahon et al., 2012; Mahmoudzadeh et al., 2013). Late latency auditory event-related potentials recorded from 28 wGA demonstrate an immature early functionality of the auditory cortex from 28 wGA (Fig. 25.8). The functionality of the preterm infant’s auditory cortex is not limited to the detection of auditory events (Draganova et al., 2007, 2008; Sheridan et al., 2008; Hartkopf et al., 2016). From 28 wGA, premature infants are able to achieve complex processing of sensory information using strategies such as synchronization, habituation, and mismatch responses (MMRs) for discrimination of voice and phonemes (Mahmoudzadeh et al., 2017b), involving specific perisylvian areas already with leftward asymmetry for phonemes and rightward asymmetry for voice discrimination (Mahmoudzadeh et al., 2017b) (Fig. 25.9). Because this occurs as early as 28 wGA, this sophisticated functional organization of the perisylvian regions observed at the very onset of cortical circuitry emphasizes the influence of genetic factors on regions involved in linguistic processing and social communication in humans (Mahmoudzadeh et al., 2013) (Figs. 25.8 and 25.9). Therefore, brain injuries such as IVH in preterm neonates are likely to have a long-lasting impact on the functionality of this still immature network (Mahmoudzadeh et al., 2018).

IMPACT OF PREMATURITY Auditory impairment in prematurely born infants could be due to an inner dysfunction and/or injury to the auditory pathways, from the cochlea to the auditory cortical areas (primary auditory cortex and associative areas). Deafness complicates the outcome of premature infants, with a frequency inversely proportional to GA (Jarjour, 2015). Severe deafness is infrequent and affects fewer than 3% of extremely preterm-born children (Doyle et al., 2012; Synnes et al., 2017). Most preterm infants with severe hearing loss can be detected by early newborn hearing screening. However, some very premature infants with normal neonatal hearing screening may nevertheless experience sensorineural hearing loss in the toddler period (van Noort-van der Spek et al., 2017; Burnett et al., 2018). Other hearing deficits, such as figure-ground perceptual loss and poor short-term auditory memory have been reported in premature infants, which can interfere with classroom learning at school age (Burnett et al., 2018). Concerning the hearing loss management, hearing thresholds of preterm infants can improve during the first year of life with sometimes normalization with the auditory pathway maturation (Hof et al., 2013; Frezza et al., 2019). Audiologic

monitoring of children up to 80 wGA and a measured indication of cochlear implantation are recommended (Hof et al., 2013). Independently of direct injury of auditory pathways, the development and maturation of the auditory system are influenced by the acoustic environment. Exposing infants to loud noises in the NICU and depriving them of biologic maternal sounds, premature birth can contribute to auditory impairments (McMahon et al., 2012). Changes in sensory input can have profound effects on the functional organization of the developing cortex (McMahon et al., 2012). Environmental factors such as treatments or exposure to high noise levels in the NICU could contribute to hearing difficulties in genetically predisposed preterm infants (Burnett et al., 2018). For example, antibiotics such as aminoglycosides, which are widely used in the NICU, have ototoxic side effects such as hair cell death in the cochlea, particularly in genetic predisposed premature infants (Zimmerman and Lahav, 2013). Similarly, medically or surgically treated infants with patent ductus arteriosus are at increased risk of hearing impairment in toddlerhood (5%–6% vs 2% in infants with no or mild patent ductus arteriosus) (Janz-Robinson et al., 2015). The integrity of auditory pathways from the middle ear to the brainstem nerve can be assessed by auditory brainstem-evoked response (ABR). In ABR, waves I–III are generated by the auditory nerve and the cochlear nucleus. Waves IV–VII are generated successively by the superior olivary complex, the region of the lateral lemniscus, the inferior colliculus, and the medial geniculate body. These regions have been described to be vulnerable to hypoxoischemia (Hall, 1964; Majnemer and Rosenblatt, 1996). ABR is widely used for assessing hearing loss in premature infants (Taylor et al., 1996). In normal-hearing infants, preterm birth is associated with significantly delayed auditory conduction from the cochlea to the brainstem at term age, compared to healthy term-born infants (Pasman et al., 1996; Ribeiro and Carvallo, 2008; Hasani and Jafari, 2013), suggesting that prematurity, including earlier exposure to the extrauterine environment, interfere with the brainstem auditory pathways (Stipdonk et al., 2016). However, during the premature period, ABR is not a reliable predictor of neurologic sequelae, particularly in terms of language impairment (Stockard et al., 1983; Taylor et al., 1996; Ols
en et al., 2002), which could be assessed by cortical auditory evoked potential (see following sections).

The visual system STRUCTURAL MATURATION OF THE VISUAL SYSTEM The early corneal endothelium is formed by the end of 8 wGA (Wulle, 1972; Borsani et al., 2019). Retinal ganglion cells generate an axon directed toward the inner

IMPACT OF PREMATURITY ON NEURODEVELOPMENT

357

Fig. 25.8. High density electroencephalography (HD EEG) (64 channels) and high density near-infrared spectroscopy responses to deviant syllabic presentation (ba vs ga or male vs female voice). In HD EEG, two MMRs can be identified in response to phoneme or voice deviant with a leftward lateralization for phonemes and a rightward lateralization for voice (Mahmoudzadeh et al., 2013, The`se de Sciences; Mahmoudzadeh et al., 2017a, b). HD NIRS show specific cortical areas involved in the treatment of deviant phonemes or deviant voice stimulations (Mahmoudzadeh et al., 2013). All together this suggests a strong temporal and facial genetic fingerprint allowing the premature neonatal networks at 28–32 wGA to distinguish between phonemes and voices. Adapted from Mahmoudzadeh, M., Dehaene-Lambertz, G., Fournier, M., et al., 2013. Syllabic discrimination in premature human infants prior to complete formation of cortical layers. Proc Natl Acad Sci 110, 4846–4851; Mahmoudzadeh, M., DehaeneLambertz, G., Wallois, F., 2017a. Electrophysiological and hemodynamic mismatch responses in rats listening to human speech syllables. PLoS One 12, e0173801; Mahmoudzadeh, M., Wallois, F., Kongolo, G., et al., 2017b. Functional maps at the onset of auditory inputs in very early preterm human neonates. Cereb Cortex 27, 2500–2512, these de Sciences.

358

F. WALLOIS ET AL.

Syllable

S1

S2

S3

S4

Latency to each syllable (ms)

344 344

336 936

310 1510

343 2143

O

O

Latency to onset of trial (ms)

O

O

Topoplot of peaks in anterior L

R

L

R

L

R

L

R

Fz

Oz

+1 µV O : Occipital L : Left R : Right

S1

S2

S3

200 ms

S4

Topoplot of troughs in posterior L

O

L

R

O

L

R

O

R

L

Topoplot of peaks in posterior L

O

R

L

O

R

L

O

R

O

R

L

O

R

Fig. 25.9. High density electroencephalography at 28–32 wGA showing event-related responses to repetitive phonemes stimulations (Mahmoudzadeh et al., 2017a, b). This shows that the premature neonatal network is able to synchronize and habituate with longer latencies than in adults in response to auditory stimulation.

surface of the retina and form the optic nerves. Fibers in the optic tract reach the LGN by about 7 wGA (Gilbert, 1935; Cooper, 1945; Hevner, 2000). Synapses between optic fibers and LGN cells are formed by about 13–14 wGA (Cooper, 1945; Dekaban, 1954; Hitchcock and Hickey, 1980; Hevner, 2000). Thalamic projections reach the VC at 24–27 wGA (Krmpoti
c-Nemani
c et al., 1983; Kostovic and Rakic, 1984).

FUNCTIONAL MATURATION OF THE VISUAL SYSTEM Spontaneous (i.e., without visual stimulation) firing of ganglion cells, resulting in synchronous retinal waves,

begins at about 24 wGA (Graven, 2004). This endogenous activity participates in cell alignment in the LGN, and is involved in topographic layer organization of the retina (Graven, 2004). Sensory input from exogenous sources is secondarily involved in visual development. From 26 wGA, the striate and extrastriate cortex might be able to detect visual stimulation (Spekreijse et al., 1985; Dagnelie et al., 1986; Birch and O’Connor, 2001). In a comprehensive work in rodents by Murata and Colonnese (2016), the authors simultaneously recorded activity in the VC and the LGN. They then silenced the retina, the LGN, and the VC by optogenetics. The VC, demonstrated a complex functional circuit in

IMPACT OF PREMATURITY ON NEURODEVELOPMENT which: (1) most (80%) of the spindle-burst oscillations recorded in the VC require the thalamus and the retina, (2) the transmission of retinal waves requires corticothalamic feedback, (3) the role of the VC in this feedback loop is first limited to amplification, then is responsible for amplification and transformation of retinal input through the LGN, (4) the role of corticothalamic feedback is limited to the period of retinal waves, and (5) this excitatory feedback specifically amplifies spindle-burst oscillations (Murata and Colonnese, 2016). Slow and rapid eye movements (REMs) appear at 16 wGA and 23 wGA, respectively. In the third trimester, from 28 wGA, the fetus reacts to upright and inverted stimuli projected through the uterine wall by rotating its head (Reid et al., 2017). Visual preferences have been measured from 34 wGA (Brown and Yamamoto, 1986). At birth, at 38–40 wGA, the visual system is still immature and requires appropriate visual stimulation experience to continue its development.

IMPACT OF PREMATURITY ON THE VISUAL SYSTEM Vision impairment in prematurely born infants could be due to both retinopathy of prematurity (ROP) and, more frequently, injury to the visual pathways from the retina to the visual cortical areas (primary VC and associative areas) (Rosenberg et al., 1996). The risk of visual impairment or blindness decreases with GA, from 3.63% (born before 32 wGA) to 1.96% (32–33wGA), and 1.19% (33–36 wGA) (Hirvonen et al., 2018). ROP is a multifactorial disease that involves abnormal development of the retinal vasculature, affecting preferentially extreme and very preterm premature infants. ROP generally resolves without causing blindness (Burnett et al., 2018). However, reduced retinal sensitivity could induce a decrease in afferent information to the visual cortices and contribute to poor cortical retinotopy and dysfunction of associative visual areas. Visual impairment could be directly due to disruption of visual pathways. Cystic PVL might interrupt optical radiation or induce thalamic lesions, affecting visual function with cortical blindness in the most severe cases (Good et al., 1994; Uggetti et al., 1996; Lanzi et al., 1998; Huo et al., 1999; Ricci et al., 2006). Severe IVH of grade III and IV might alter or even interrupt the functioning of thalamocortical fibers (Marín-Padilla, 1997). Severe IVH may also result in hydrocephalus with associated abnormalities of optic radiations (O’Keefe et al., 2001) that might be associated with visual impairment (33%) and optic strabismus, atrophy, and refractive errors (80%) (Arroyo et al., 1985). Even low grade HIV (I and II of Papille classification), has been linked to deficits in visual cortical function (O’Keefe et al., 2001; Madan et al., 2012).

359

Visual impairment could result in visual sensory processing disturbances. Visual–spatial (Geldof et al., 2012), visual-attention (Atkinson et al., 2008; Ricci et al., 2010), and visual–motor abilities (Goyen et al., 1998) are the most frequent visual deficits related to prematurity. Impairment of neural networks linking the occipital and posterior parietal cortices and their connections with the prefrontal and premotor cortex and hippocampal regions have been hypothesized to underlie visual processing impairment and behavioral responses (Hoyt, 2007). Basal ganglia injury and cerebellar lesion might be involved in visual information processing disorders. Developmental impairment or lesion of the cerebellar lateral lobes in preterm children could disrupt cerebellar connections to the posterior parietal cortex and contribute to visual–spatial and visual–motor impairments in preterm children (Van Braeckel and Taylor, 2013). To explore the functionality of visual pathways, visual-evoked potentials (VEPs) could be recorded as early as 26 wGA in EEG and 28 wGA in magnetoencephalography (MEG) (Eswaran et al., 2004). VEP evolve with gestational age in parallel with cerebral maturation (Ellingson et al., 1973; Taylor et al., 1987; Birch and O’Connor, 2001; Eswaran et al., 2004). VEP may help by reflecting the degree of cerebral involvement and aid in determining a prognosis (Taylor et al., 1992; Whyte, 1993). The predictive value of VEP for neurodevelopmental outcome in preterm infants is, however, controversial (Beverley et al., 1990; Ekert et al., 1997; Shepherd et al., 1999; Pike and Marlow, 2000; Kato and Watanabe, 2006). VEP seems to be predictive of neurodevelopmental outcome, particularly for CP, with a relatively high specificity but a lower sensitivity (Beverley et al., 1990; Ekert et al., 1997; Shepherd et al., 1999; Pike and Marlow, 2000). In preterm infants with periventricular leukomalacia, VEP maturation seems to be delayed compared to full-term infants, but these results need to be confirmed (Placzek et al., 1985). Sensory integration disturbances combine difficulties from multiple sensory modalities (Miller et al., 2007; Dionne-Dostie et al., 2015; Br€oring et al., 2018). Perturbation of integration of multisensory information impacts the construction of representation of the environment and effective interaction with the actual environment. This could interfere with social activities and has been found to be involved in ASDs and attentiondeficit hyperactivity disorder (ADHD) (Shum et al., 2008; Spittle et al., 2009; Burnett et al., 2011). Multisensory integration capacity also relates to neurocognition, particularly executive function and academic performance (Wolke et al., 2008; Pritchard et al., 2014; Soleimani et al., 2014) in school-age children (Eeles et al., 2013; Adams et al., 2015; Br€oring et al., 2017).

360

F. WALLOIS ET AL.

IMPACT OF PREMATURITY ON THE COGNITIVE SYSTEM Maturation of the cognitive system Cognitive and behavioral functions are mainly housed in the frontal lobe and its association areas, principally in the prefrontal cortex. The prefrontal cortex constitutes the largest part of the human cerebral mantle, covering one-third of the total cortical surface. It is highly connected with association cortices of cortical and subcortical structures, in particular the thalamus, hypothalamus, and other limbic structures (Fuster, 2002). The prefrontal region is the key element in higher integrative cognitive functions such as memory, linguistic, cognitive, and behavioral functions (Fuster, 2002; Molnár et al., 2019). The prefrontal cortex houses a large part of temporal integration, working memory, planning, and inhibitory control. The prefrontal–limbic connections are involved in the control of emotional behavior, whereas the prefrontal–striatal connections are involved in the coordination of motor behavior (Fuster, 2002). The cognitive capacities are housed in large neural networks, including connections of the lateral prefrontal cortex with the hippocampus. After birth, with maturation, the volume of gray matter in the prefrontal cortex increases, mostly between 4 and 12 years of age. There is a concomitant decrease in synaptic density suggesting a process of specialization of cognitive networks relating to learning processes. In the neocortex, the prefrontal areas are the last to develop and take the longest time to progress (Fuster, 2002). Its ventromedial areas are involved in the expression and control of emotional and instinctive behaviors and develop earlier, compared to the later-maturing zones of the lateral prefrontal convexity mainly involved in executive functions (Fuster, 2002). Frontal lobe development and maturation during fetal life is largely overlooked. All of the maturation processes develop later in the frontal lobe than in the other structures (cell differentiation, cell maturation, axonal growth, synaptogenesis, and myelination) (Fuster, 2002). To date, the maturation of cerebral structures, in particular the frontal structures, during the fetal period, has been poorly evaluated.

Impact of prematurity on cognitive functions All of the functional networks underlying cognitive function can be altered by prematurity. Impairment of cognitive functions results from deleterious interactions between disturbances in endogenous developmental processes, including proliferation, synaptogenesis, and

myelination. Additionally, disturbances in exogenousdependent processes could be disrupted by neonatal complications, such as NICU management and exposure to nonecologic stimuli. The disruption of the fine-tuning between these different events leads to structural and functional disorganization. Delayed regional microstructural organization in the prefrontal region was found in very preterm infants, compared to full-term controls (Bouyssi-Kobar et al., 2018). Likewise, a recent meta-analysis showed a reduction of approximately one standard deviation in the total brain volume of very premature infants, compared to term-born infants. Brain volume reduction specifically affects gray and white matter of the hippocampus, the cerebellum, and the corpus callosum (de Kieviet et al., 2012). Associated with structural disorganization, functional network pervasive maturation disorders could lead to the cognitive processing disabilities of coding, transmission, integration, and interpretation of endogenous and exogenous stimuli-messages. Functional neural network disorganization during the neonatal periods could lead to alterations in higher functions that will occur during development, varying with learning periods. Unlike major motor deficits, which can be predicted and diagnosed quickly in the months following birth, the deleterious cognitive effects of prematurity can be screened and diagnosed only from preschool age and, often, not until school age if they are minor or moderate in severity. This reinforces the need for regular medical evaluations throughout childhood for children born prematurely. Children born prematurely are at increased risk for disorders of higher functions, including intellectual efficiency, language (Van Lierde et al., 2009; Guarini et al., 2010; Sansavini et al., 2010), memory (Mouron et al., 2010), and attention (Anderson et al., 2011; Anderson and Dewey, 2011) as well as behavioral disorders (Larroque et al., 2008) and emotional difficulties (de Kieviet et al., 2012; Hall and Wolke, 2012). Cognitive deficits without major motor or neurosensory deficits are now the dominant neurodevelopmental sequelae in survivors of early preterm birth (Volpe, 2009a). Impaired cognitive development affects 30%– 40% of premature infants born before 27 wGA (Jacobs et al., 2000; Raz et al., 2010). Cognitive delay affects 16.9% of very preterm infants, with a higher prevalence of mild impairment vs severe impairment (14.3% and 8.2%, respectively) (Pascal et al., 2018). The prevalence of cognitive delay is inversely proportional to the GA at birth (Raz et al., 2010; Pascal et al., 2018). Intellectual disability concerns 5%–36% of children born before 28 wGA (Jarjour, 2015). Full-scale IQ is on average 0.7 SD lower, compared to children born full term,

IMPACT OF PREMATURITY ON NEURODEVELOPMENT tending to worsen with decreasing GA (Bhutta et al., 2002; Johnson, 2007; Kerr-Wilson et al., 2012; Synnes and Hicks, 2018; Twilhaar et al., 2018). The largest deleterious effects are observed in performance ( 0.67 SD), compared to verbal abilities ( 0.53 SD) (Synnes and Hicks, 2018). Smaller total brain volume, specifically smaller volume of white and gray matter, the cerebellum, the hippocampus, and the corpus callosum have been found to be associated with lower IQ (de Kieviet et al., 2012). The cortical thickness of the frontal lobe has been proved to be related to specific cognitive functions in premature infants (Kostovi
c Srzenti
c et al., 2019). A frontal white matter reduction is related to performance IQ (Kostovi
c Srzenti
c et al., 2019). Indeed, children born very preterm consistently perform worse than term-born children on executive function tasks assessing planning, fluency, working memory, and response inhibition (Anderson and Doyle, 2004; Woodward et al., 2011; Aarnoudse-Moens et al., 2012). Extremely preterm and very preterm neonates present selective, sustained, and executive attention disorders as well as executive shifting and divided attention disorders (62% and 41%, respectively) (Bayless and Stevenson, 2007; Anderson et al., 2011; Murray et al., 2014; Delane et al., 2017; Lean et al., 2017). Working memory and processing speed are approximately 0.5 SD lower in preterm than term-born cohorts (Synnes and Hicks, 2018). Language function is essential in all aspects of social and academic life. Preterm birth coincides with higher rates of language dysfunction (Wolke and Meyer, 1999; van Noort-van der Spek et al., 2010; Barre et al., 2011). Approximately one-third of children born prematurely show a significant delay in language acquisition at age 3 (Sansavini et al., 2010) and language impairment at preschool age, with rates as high as 48% for children born before 30 wGA (Vieira and Linhares, 2011). Deficits in both receptive and expressive language domains persist into school age, mainly affecting word finding, perception, grammar, dialog, and linguistics (Wolke et al., 2008; Foster-Cohen et al., 2010; Vieira and Linhares, 2011; Reidy et al., 2013; Soleimani et al., 2014). van Noort-van der Spek et al. performed a meta-analysis, reporting language dysfunctions in preterm-born children who scored significantly lower, compared to term-born children on simple (vocabulary) and complex (total language, receptive, and expressive language scores) language function tests, even in the absence of major disabilities and independent of socioeconomic status (van Noort-van der Spek et al., 2012). For complex language function, group differences between preterm- and term-born children increased significantly from 3 to 12 years of age (van Noort-van der Spek et al., 2012). Even without brain injury, preterm

361

birth continues to affect linguistic development up to the end of the preschool years, mostly marked by vocabulary and grammar difficulties accompanied by weak phonologic awareness skills (Guarini et al., 2009). Language development can be correlated with the initial metrics of the brain in preterm-born children (Beauchemin et al., 2011; Dubois et al., 2016). Cortical folding in preterm infants at birth correlates with neurobehavioral development at term-equivalent age. Thus, at 2 years of age, language abilities are negatively correlated with the mean diffusivity in the left superior temporal gyrus in preterm infants, highlighting the key role of the left superior temporal gyrus in the development of language abilities (Aeby et al., 2013). Apart from cognitive deficit, preterm birth is associated with learning disorders. 10% to 15% of learning disorders, apart from cognitive deficit, are attributable to preterm birth. Children born preterm had lower scores in reading, mathematics, and spelling assessments by primary school age than those born at term (Allotey et al., 2018). Preterm children are about three times more likely than term-born children to receive special education and score significantly worse in arithmetic, reading, and spelling (Synnes and Hicks, 2018). Children born preterm are also at higher risk of social–emotional deficits and psychiatric disorders (Johnson and Marlow, 2011), including inattention, anxiety, and social communication deficits (Montagna and Nosarti, 2016). These comorbid symptoms, along with ADHD, anxiety, and ASD are two to four times more common among preterm children (Elgen et al., 2002; Indredavik et al., 2005; Shum et al., 2008; Hack et al., 2009; Spittle et al., 2009; Johnson et al., 2010; Burnett et al., 2011; Breeman et al., 2016; Allotey et al., 2018).

AN EXAMPLE OF HOW THE PERTURBATIONS OF BUILDING OF LINGUISTIC NETWORKS LEADS TO CONSEQUENT DEFICIENCIES IN PREMATURITY

Shortly after birth, human infants already exhibit a variety of sophisticated linguistic abilities, from discriminating syllables and human languages to remembering short stories. These abilities, housed in the perisylvian cerebral zones (in particular the temporal and frontal zone), require early auditory functionality (Rotteveel et al., 1987). Mahmoudzadeh et al. demonstrated that, as early as 28 wGA, preterm infants discriminate phonemes (Figs. 25.8 and 25.9). This ability, present from the beginning of the building of the cortical circuit involved in auditory perception, supports the idea of prior genetic endogenous activity that would prepare the auditory network to process auditory information at the onset of thalamocortical connectivity (Mahmoudzadeh et al., 2017b). These initial functionalities rely on the

362

F. WALLOIS ET AL.

functional prewiring of the neural networks that rely on a structural connectivity already in place (Mahmoudzadeh et al., 2013). Functional left- or rightward asymmetry for phonemes and voice discrimination (Mahmoudzadeh et al., 2013) might rely on structural asymmetry described early in prematurity (Dubois et al., 2008, 2016). The functional maturation processes behind the early establishment of these early “linguistic” abilities raise questions about the implications of the electrical activity recorded on EEG in the very preterm infant. Routier et al. studied TTA-SW, which is the earliest well-known transient electrical activity occurring spontaneously on EEG recordings (for more information, see the section “A brief overview of the timeline of the maturation processes”) (Routier et al., 2017). These nonsensory-driven activities constitute a specific signature of the early development of the perisylvian networks (Routier et al., 2017). TTA-SW results from an endogenous generator whose cortical expression is located in the cortical subplate at the superior temporal sulcus. Although the role of TTA-SW in development and its precise location remain to be described, these results reinforce the idea of a prior genetic, structural, or activity-dependent fingerprint that would prepare the auditory network for auditory information processing and have an essential role in the establishment of future cortical functionality (Fig. 25.7). TTA-SW might be involved in the implementation of auditory function and language processing, which, at term, allows the newborn to recognize its mother’s voice (Beauchemin et al., 2011). Mahmoudzadeh et al. demonstrated that the presence of a high-grade IVH (grades III and IV of the Papille classification; Larroque et al., 2003) in premature infants leads to a clear disconnection between neural and vascular responses studied on high density EEG and NIRS. In healthy preterm controls, the hemodynamic response consists of an increase in HbO and a decrease in Hb that peaks at around 7 s after the onset of auditive and linguistic stimulation (listening to speech syllables) in the perisylvian areas. In premature infants with IVH, the neural response to auditive stimulation was not different from the control infants, but no hemodynamic response was found. The cerebral vascular network in IVH preterm neonates was unable to compensate for the increased metabolism resulting from neuronal activation in response to external stimulation (Mahmoudzadeh et al., 2018). This could cause the disruption of the integration of sensory and cognitive information involved in neurodevelopmental disorders: e.g., language impairment. The establishment of cortical functions is not limited to the development and the maturation of the neural network. The vascular network and the fine interactions

between electrical, hemodynamic, and metabolic activities are essential for future cortical functionality. Considering the different features in cortical evokedrelated potential (ERP), the latencies and/or the amplitudes of the different components, including MMR to deviant stimulations, preterm children performed worse than full-term children. Measured at different equivalent ages in children born preterm, different publications report consistently longer latencies and/or decreased amplitudes in response to both auditoryspeech and nonspeech stimuli (Pasman et al., 1996; Cheour et al., 1998a,b; Jansson-Verkasalo et al., 2003, 2010; Fellman et al., 2004; Friedrich et al., 2004; Mikkola et al., 2007; Ribeiro and Carvallo, 2008). Preterm infants as young as 3 months old had a delayed MMN response to speech stimuli, compared to full-term infants. In a recent and detailed paper (Paquette et al., 2015) that only included prematurely born children without major neonatal brain injury, a delayed P150 response was observed in prematurely born children aged 3, 12, and 36 months. A significant negative correlation was also found between MMN latency to speech sounds and the BSID-III expressive language subscale. However, no significant differences between full-terms and preterm-born children were found on the MMN for nonspeech stimuli. These alterations in ERP components in response to auditory-speech and nonspeech stimuli in prematurely born children suggest alterations in cortical processing of auditory information (Lavoie et al., 1997; L
eveill
e et al., 2002; Bisiacchi et al., 2009) in speed (resulting in increased latencies) and in the ability to synchronize (resulting in decreased amplitude), which are both involved in processing. Alterations in the process of myelination and/or in cerebral connectivity might account for these defects (Fuchino et al., 2013; Paquette et al., 2015). This might support the idea of a deficit in the ability to integrate auditory information arriving at the target cortical area in asynchronized form. This shows that even the slightest disorganization in one of the steps of building this finely tuned circuitry can lead to deficits in the system that normally processes linguistic skills.

IMPACT OF PREMATURITY ON THE NEUROVEGETATIVE SYSTEM The neurovegetative system allows neural regulation of the cardiorespiratory functions, thermoregulation, and states of consciousness, in particular the sleep–wake cyclicity. The brainstem is the key structure for regulating these functions. Sleep, the predominant behavioral state in newborns, is an important regulatory function throughout life. The sleep state differentiation appears early in human

IMPACT OF PREMATURITY ON NEURODEVELOPMENT ontogenesis. As early as 24–26 wGA, preterm neonates show sleep state cyclicity (Scher et al., 2008). States with REMs and non-REM periods can be differentiated and are associated with changes in EEG characteristics (Vecchierini et al., 2003). Stable concordance between EEG patterns and presence or absence of REM allows differentiation of active (precursor of REM sleep) and quiet (precursor of non-REM sleep) sleep from 27 wGA in healthy infants. Active paradoxical sleep (REM) is involved in the development of sensory and motor functions, with twitches capable of triggering neuronal oscillations, and in the building of cerebral connectivity (Kurth et al., 2015). Quiet slow sleep (non-REM), in which sleep spindles appear at about 6 weeks after full-term birth, is involved in the formation of myelin and structural connectivity (Kurth et al., 2015; Clawson et al., 2016; Del Rio-Bermudez and Blumberg, 2018). REM sleep enhances procedural learning tasks, while non-REM sleep is involved in declarative memory. Different factors in the NICU (theophylline, caffeine, high light levels, acoustic stimuli, temperature) as well as medical conditions (type of respiratory support, bronchopulmonary dysplasia, hypoxic ischemic encephalopathy, IVH, and seizure with sedative medication) are likely to affect the quality of sleep in preterm infants (for a review, see Dereymaeker et al., 2017; Gogou et al., 2019). Premature birth induces sleep macroarchitecture alteration during infancy. Children born preterm (before 32 wGA) have a longer ultradian sleep cycle, more abundant quiet sleep and less abundant active sleep, and fewer body and REMs (Scher et al., 1992), compared to matched full-term infants at the same age (Scher et al., 1994). They also present some characteristics: arousals from sleep are less frequent and shorter with a lower threshold (Horne et al., 2000) and persist in school-age children (Hagmann-von Arx et al., 2014; Hibbs et al., 2014; Huang et al., 2014). They also have more irregular sleep schedules (i.e., advanced sleep phase) (Mirmiran, 1995; Biagioni et al., 2005; Hibbs et al., 2014; Biggs et al., 2016) and longer sleep latency. At 5 years, they present a higher prevalence of sleep disorders, such as problems falling asleep, waking up frequently during the night, and waking up early in the morning. This worsens with neurodevelopmental delay (Stangenes et al., 2017). Early and later alterations of the sleep functions could have a lasting impact on the maturation of neural networks, resulting in functional disorders (Andr
e et al., 2010). Cognitive functions with problems of attention, orienting, and distractibility have been reported in premature infants (Scher et al., 1996; Geva et al., 2016). In the same way, night-time sleep disorders in very

363

preterm-born children have been found to be associated with executive function disorders, in particular with a poor verbal working memory (McCann et al., 2018). Vegetative functions could be impacted by sleep disorders. For example, prematurity has been demonstrated to be a significant predictor of sleep breathing disorders such as obstructive sleep apnea syndrome (Rosen et al., 2003; Manuel et al., 2013; Tapia et al., 2016). Apart from sleep, the impact of prematurity on the neural regulation of other vegetative functions as cardiorespiratory functions, thermoregulation, or the control of states of consciousness have not been reported.

CONCLUSION Prematurity, through its complications directly affecting the brain and through the environmental effects of ex utero life, has a deleterious impact on the neurodevelopmental outcomes of children. This period is crucial in brain maturation and development that relies on precise overlapping mechanisms in time and space (e.g., neurogenesis, synaptogenesis, neural migration, myelination). These mechanisms are at the origin of the establishment of the brain structures and functions, marked by the creation of short- and long-distance connections between the different structures and the improvement of neural networks. The pathologic processes induced by a premature birth are varied and can lead to the destruction of existing structures in development (LVP, IVH, parenchymal infarction). Moreover, they affect the step-by-step developmental processing of brain structure and/or function (e.g., cerebellar hypoplasia). The deleterious consequences of prematurity are not limited to the impairment of motor and sensory functions, but can also affect their integration and processing. All brain functions, including association functions, can be affected by premature birth and are often the subject of a difficult and late diagnosis. These neurologic dysfunctions have a negative impact on brain development and ultimately on the quality of life of prematurely born children; hence the need to follow these children throughout childhood for search of both major and minor neurologic and psychiatric consequences.

REFERENCES Aarnoudse-Moens CSH, Weisglas-Kuperus N, van Goudoever JB et al. (2009). Meta-analysis of neurobehavioral outcomes in very preterm and/or very low birth weight children. Pediatrics 124: 717–728. Aarnoudse-Moens CSH, Duivenvoorden HJ, WeisglasKuperus N et al. (2012). The profile of executive function in very preterm children at 4 to 12 years. Dev Med Child Neurol 54: 247–253.

364

F. WALLOIS ET AL.

Adams JN, Feldman HM, Huffman LC et al. (2015). Sensory processing in preterm preschoolers and its association with executive function. Early Hum Dev 91: 227–233. Adebimpe A, Routier L, Wallois F (2019). Preterm modulation of connectivity by endogenous generators: the theta temporal activities in coalescence with slow waves. Brain Topogr 32: 762–772. Aden P, Goverud I, Liestøl K et al. (2008). Low-potency glucocorticoid hydrocortisone has similar neurotoxic effects as high-potency glucocorticoid dexamethasone on neurons in the immature chicken cerebellum. Brain Res 1236: 39–48. Aeby A, De Tie`ge X, Creuzil M et al. (2013). Language development at 2 years is correlated to brain microstructure in the left superior temporal gyrus at term equivalent age: a diffusion tensor imaging study. Neuroimage 78: 145–151. Agyemang AA, Sveinsdo´ttir K, Vallius S et al. (2017). Cerebellar exposure to cell-free hemoglobin following preterm intraventricular hemorrhage: causal in cerebellar damage? Transl Stroke Res 8: 461–473. Allievi AG, Arichi T, Tusor N et al. (2016). Maturation of sensori-motor functional responses in the preterm brain. Cereb Cortex 26: 402–413. Allotey J, Zamora J, Cheong-See F et al. (2018). Cognitive, motor, behavioural and academic performances of children born preterm: a meta-analysis and systematic review involving 64 061 children. BJOG Int J Obstet Gy 125: 16–25. Als H, Duffy FH, McAnulty GB et al. (2004). Early experience alters brain function and structure. Pediatrics 113: 846–857. Anand KJ, Scalzo FM (2000). Can adverse neonatal experiences alter brain development and subsequent behavior? Biol Neonate 77: 69–82. Ancel P-Y, Goffinet F, Kuhn P et al. (2015). Survival and morbidity of preterm children born at 22 through 34 weeks’ gestation in France in 2011: results of the EPIPAGE-2 cohort study. JAMA Pediatr 169: 230. Anderson PJ, Dewey D (2011). Introduction: the consequences of being born very early or very small. Dev Neuropsychol 36: 1–4. Anderson PJ, Doyle LW (2004). Victorian infant collaborative study group. Executive functioning in school-aged children who were born very preterm or with extremely low birth weight in the 1990s. Pediatrics 114: 50–57. Anderson PJ, Doyle LW (2008). Cognitive and educational deficits in children born extremely preterm. Semin Perinatol 32: 51–58. Anderson PJ, Luca CRD, Hutchinson E et al. (2011). Attention problems in a representative sample of extremely preterm/ extremely low birth weight children. Dev Neuropsychol 36: 57–73. Anderson PJ, Treyvaud K, Neil JJ et al. (2017). Associations of newborn brain magnetic resonance imaging with long-term neurodevelopmental impairments in very preterm children. J Pediatr 187: 58–65.e1. Andonotopo W, Kurjak A, Kosuta MI (2005). Behavior of an anencephalic fetus studied by 4D sonography. J Matern Fetal Neonatal Med 17: 165–168.

Andr
e M, Lamblin M-D, d’Allest AM et al. (2010). Electroencephalography in premature and full-term infants. Developmental features and glossary. Neurophysiol Clin 40: 59–124. Argyropoulou MI, Xydis V, Drougia A et al. (2003). MRI measurements of the pons and cerebellum in children born preterm; associations with the severity of periventricular leukomalacia and perinatal risk factors. Neuroradiology 45: 730–734. Arnaud C, Daubisse-Marliac L, White-Koning M et al. (2007). Prevalence and associated factors of minor neuromotor dysfunctions at age 5 years in prematurely born children: the EPIPAGE study. Arch Pediatr Adolesc Med 161: 1053–1061. Arroyo HA, Jan JE, McCormick AQ et al. (1985). Permanent visual loss after shunt malfunction. Neurology 35: 25–29. Atkinson J, Braddick O, Anker S et al. (2008). Cortical vision, MRI and developmental outcome in preterm infants. Arch Dis Child Fetal Neonatal Ed 93: F292–F297. Ayoub AE, Kostovic I (2009). New horizons for the subplate zone and its pioneering neurons. Cereb Cortex 19: 1705–1707. Babola TA, Li S, Gribizis A et al. (2018). Homeostatic control of spontaneous activity in the developing auditory system. Neuron 99: 511–524.e5. Back SA (2017). White matter injury in the preterm infant: pathology and mechanisms. Acta Neuropathol 134: 331–349. Barkovich AJ, Kuzniecky RI, Jackson GD et al. (2001). Classification system for malformations of cortical development: update 2001. Neurology 57: 2168–2178. Barre N, Morgan A, Doyle LW et al. (2011). Language abilities in children who were very preterm and/or very low birth weight: a meta-analysis. J Pediatr 158 (5): 766.e1–774.e1. https://doi.org/10.1016/j.jpeds.2010. 10.032. Bart O, Shayevits S, Gabis LV et al. (2011). Prediction of participation and sensory modulation of late preterm infants at 12 months: a prospective study. Res Dev Disabil 32: 2732–2738. Bartocci M, Winberg J, Ruggiero C et al. (2000). Activation of olfactory cortex in newborn infants after odor stimulation: a functional near-infrared spectroscopy study. Pediatr Res 48: 18–23. Bartocci M, Winberg J, Papendieck G et al. (2001). Cerebral hemodynamic response to unpleasant odors in the preterm newborn measured by near-infrared spectroscopy. Pediatr Res 50: 324–330. Bassan H, Limperopoulos C, Visconti K et al. (2007). Neurodevelopmental outcome in survivors of periventricular hemorrhagic infarction. Pediatrics 120: 785–792. Baud O, Nedelcoux H, Boithias C et al. (1998). TThe early diagnosis of periventricular leukomalacia in premature infants with positive rolandic sharp waves on serial electroencephalography. J Pediatr 132: 5. Bayless S, Stevenson J (2007). Executive functions in schoolage children born very prematurely. Early Hum Dev 83: 247–254.

IMPACT OF PREMATURITY ON NEURODEVELOPMENT Beauchemin M, Gonza´lez-Frankenberger B, Tremblay J et al. (2011). Mother and stranger: an electrophysiological study of voice processing in newborns. Cereb Cortex 21: 1705–1711. Bednarek N, Akhavi A, Pietrement C et al. (2008). Outcome of cerebellar injury in very low birth-weight infants: 6 case reports. J Child Neurol 23: 906–911. Beverley DW, Smith IS, Beesley P et al. (1990). Relationship of cranial ultrasonography, visual and auditory evoked responses with neurodevelopmental outcome. Dev Med Child Neurol 32: 210–222. Bhutta AT, Cleves MA, Casey PH et al. (2002). Cognitive and behavioral outcomes of school-aged children who were born preterm: a meta-analysis. JAMA 288: 728–737. Biagioni E, Boldrini A, Giganti F et al. (2005). Distribution of sleep and wakefulness EEG patterns in 24-h recordings of preterm and full-term newborns. Early Hum Dev 81: 333–339. Biggs SN, Meltzer LJ, Tapia IE et al. (2016). Sleep/wake patterns and parental perceptions of sleep in children born preterm. J Clin Sleep Med 12: 711–717. Birch EE, O’Connor AR (2001). Preterm birth and visual development. Semin Neonatol 6: 487–497. Bisiacchi PS, Mento G, Suppiej A (2009). Cortical auditory processing in preterm newborns: an ERP study. Biol Psychol 82: 176–185. Blencowe H, Lee AC, Cousens S et al. (2013). Preterm birth– associated neurodevelopmental impairment estimates at regional and global levels for 2010. Pediatr Res 74: 17–34. Bloomfield FH, Alexander T, Muelbert M et al. (2017). Smell and taste in the preterm infant. Early Hum Dev 114: 31–34. Bohn MC, Lauder JM (1978). The effects of neonatal hydrocortisone on rat cerebellar development. An autoradiographic and light-microscopic study. Dev Neurosci 1: 250–266. Bolk J, Fredriksson Kaul Y, Hellstr€om-Westas L et al. (2018a). National population-based cohort study found that visualmotor integration was commonly affected in extremely preterm born children at six-and-a-half years. Acta Paediatr 107: 831–837. Bolk J, Padilla N, Forsman L et al. (2018b). Visual-motor integration and fine motor skills at 6½ years of age and associations with neonatal brain volumes in children born extremely preterm in Sweden: a population-based cohort study. BMJ Open 8: e020478. Borsani E, Della Vedova AM, Rezzani R et al. (2019). Correlation between human nervous system development and acquisition of fetal skills: an overview. Brain Dev 41: 225–233. Bouyssi-Kobar M, Brossard-Racine M, Jacobs M et al. (2018). Regional microstructural organization of the cerebral cortex is affected by preterm birth. Neuroimage Clin 18: 871–880. Breeman LD, Jaekel J, Baumann N et al. (2016). Attention problems in very preterm children from childhood to adulthood: the Bavarian longitudinal study. J Child Psychol Psychiatry 57: 132–140. Br€ oring T, Oostrom KJ, Lafeber HN et al. (2017). Sensory modulation in preterm children: theoretical perspective

365

and systematic review. Key A, editor, PLoS One 12: e0170828. Br€ oring T, K€ onigs M, Oostrom KJ et al. (2018). Sensory processing difficulties in school-age children born very preterm: an exploratory study. Early Hum Dev 117: 22–31. Brostr€ om L, Vollmer B, Bolk J et al. (2018). Minor neurological dysfunction and associations with motor function, general cognitive abilities, and behaviour in children born extremely preterm. Dev Med Child Neurol 60: 826–832. Brown AM, Yamamoto M (1986). Visual acuity in newborn and preterm infants measured with grating acuity cards. Am J Ophthalmol 102: 245–253. Burnett AC, Anderson PJ, Cheong J et al. (2011). Prevalence of psychiatric diagnoses in preterm and full-term children, adolescents and young adults: a meta-analysis. Psychol Med 41: 2463–2474. Burnett AC, Cheong JLY, Doyle LW (2018). Biological and social influences on the neurodevelopmental outcomes of preterm infants. Clin Perinatol 45: 485–500. Buser JR, Maire J, Riddle A et al. (2012). Arrested preoligodendrocyte maturation contributes to myelination failure in premature infants. Ann Neurol 71: 93–109. Busnel MC, Granier-Deferre C, Lecanuet JP (1992). Fetal audition. Ann N Y Acad Sci 662: 118–134. Buxton RB, Uludag˘ K, Dubowitz DJ et al. (2004). Modeling the hemodynamic response to brain activation. Neuroimage 23: S220–S233. Bystron I, Blakemore C, Rakic P (2008). Development of the human cerebral cortex: Boulder committee revisited. Nat Rev Neurosci 9: 110–122. Carney RSE, Alfonso TB, Cohen D et al. (2006). Cell migration along the lateral cortical stream to the developing basal telencephalic limbic system. J Neurosci 26: 11562–11574. Case-Smith J, Butcher L, Reed D (1998). Parents’ report of sensory responsiveness and temperament in preterm infants. Am J Occup Ther 52: 547–555. Cheour M, Ceponiene R, Lehtokoski A et al. (1998a). Development of language-specific phoneme representations in the infant brain. Nat Neurosci 1: 351–353. Cheour M, Haapanen ML, Ceponiene R et al. (1998b). Mismatch negativity (MMN) as an index of auditory sensory memory deficit in cleft-palate and CATCH syndrome children. Neuroreport 9: 2709–2712. Chi JG, Dooling EC, Gilles FH (1977). Gyral development of the human brain. Ann Neurol 1 (1): 86–93. https://doi.org/ 10.1002/ana.410010109. Chuah MI, Zheng DR (1987). Olfactory marker protein is present in olfactory receptor cells of human fetuses. Neuroscience 23: 363–370. Clause A, Kim G, Sonntag M et al. (2014). The precise temporal pattern of prehearing spontaneous activity is necessary for tonotopic map refinement. Neuron 82: 822–835. Clawson BC, Durkin J, Aton SJ (2016). Form and function of sleep spindles across the lifespan. Neural Plast 2016: 6936381. Colonnese MT, Phillips MA (2018). Thalamocortical function in developing sensory circuits. Curr Opin Neurobiol 52: 72–79.

366

F. WALLOIS ET AL.

Colonnese MT, Kaminska A, Minlebaev M et al. (2010). A conserved switch in sensory processing prepares developing neocortex for vision. Neuron 67: 480–498. Cooper ERA (1945). The development of the human lateral geniculate body. Brain J Neurol 68: 222–239. Counsell SJ, Allsop JM, Harrison MC et al. (2003). Diffusionweighted imaging of the brain in preterm infants with focal and diffuse white matter abnormality. Pediatrics 112: 1–7. Counsell SJ, Shen Y, Boardman JP et al. (2006). Axial and radial diffusivity in preterm infants who have diffuse white matter changes on magnetic resonance imaging at termequivalent age. Pediatrics 117: 376–386. Cox SB, Woolsey TA, Rovainen CM (1993). Localized dynamic changes in cortical blood flow with whisker stimulation corresponds to matched vascular and neuronal architecture of rat barrels. J Cereb Blood Flow Metab 13: 899–913. Dagnelie G, de Vries MJ, Maier J et al. (1986). Pattern reversal stimuli: motion or contrast? Doc Ophthalmol 61: 343–349. Daly CJ, Kelley GT, Krauss A (2003). Relationship between visual-motor integration and handwriting skills of children in kindergarten: a modified replication study. Am J Occup Ther 57: 459–462. De Bruı¨ne FT, Van Wezel-Meijler G, Leijser LM et al. (2013). Tractography of white-matter tracts in very preterm infants: a 2-year follow-up study. Dev Med Child Neurol 55: 427–433. de Kieviet JF, Zoetebier L, van Elburg RM et al. (2012). Brain development of very preterm and very low-birthweight children in childhood and adolescence: a meta-analysis: review. Dev Med Child Neurol 54: 313–323. de Vries JIP, Fong BF (2006). Normal fetal motility: an overview. Ultrasound Obstet Gynecol 27: 701–711. de Vries LS, Eken P, Pierrat V et al. (1992). Prediction of neurodevelopmental outcome in the preterm infant: short latency cortical somatosensory evoked potentials compared with cranial ultrasound. Arch Dis Child 67: 1177–1181. Dekaban A (1954). Human thalamus; an anatomical, developmental and pathological study. II. Development of the human thalamic nuclei. J Comp Neurol 100: 63–97. Del Rio-Bermudez C, Blumberg MS (2018). Active sleep promotes functional connectivity in developing sensorimotor networks. Bioessays 40: e1700234. Delane L, Campbell C, Bayliss DM et al. (2017). Poorer divided attention in children born very preterm can be explained by difficulty with each component task, not the executive requirement to dual-task. Child Neuropsychol 23: 510–522. Delobel-Ayoub M, Arnaud C, White-Koning M et al. (2009). Behavioral problems and cognitive performance at 5 years of age after very preterm birth: the EPIPAGE study. Pediatrics 123: 1485–1492. DeMaio-Feldman D (1994). Somatosensory processing abilities of very low–birth weight infants at school age. Am J Occup Ther 48: 639–645. Dereymaeker A, Pillay K, Vervisch J et al. (2017). Review of sleep-EEG in preterm and term neonates. Early Hum Dev 113: 87–103.

Dick AS, Tremblay P (2012). Beyond the arcuate fasciculus: consensus and controversy in the connectional anatomy of language. Brain J Neurol 135: 3529–3550. Dionne-Dostie E, Paquette N, Lassonde M et al. (2015). Multisensory integration and child neurodevelopment. Brain Sci 5: 32–57. Doyle LW (2010). Regionalized perinatal care systems and very low-birth-weight and very preterm infants. JAMA 304: 2696. Doyle LW, Davis PG, Schmidt B et al. (2012). Cognitive outcome at 24 months is more predictive than at 18 months for IQ at 8-9 years in extremely low birth weight children. Early Hum Dev 88: 95–98. Draganova R, Eswaran H, Murphy P et al. (2007). Serial magnetoencephalographic study of fetal and newborn auditory discriminative evoked responses. Early Hum Dev 83: 199–207. Draganova R, Ross B, Wollbrink A et al. (2008). Cortical steady-state responses to central and peripheral auditory beats. Cereb Cortex 18: 1193–1200. Drobyshevsky A, Robinson AM, Derrick M et al. (2006). Sensory deficits and olfactory system injury detected by novel application of MEMRI in newborn rabbit after antenatal hypoxia–ischemia. Neuroimage 32: 1106–1112. Dubois J, Benders M, Cachia A et al. (2008). Mapping the early cortical folding process in the preterm newborn brain. Cereb Cortex 18: 1444–1454. Dubois J, Kostovic I, Judas M (2015). Development of structural and functional connectivity. In: Brain mapping: an encyclopedic reference, 423–437 [Internet]. [cited 2020 Feb 21]. Available from: https://hal.archives-ouvertes.fr/ hal-02436274. Dubois J, Poupon C, Thirion B et al. (2016). Exploring the early organization and maturation of linguistic pathways in the human infant brain. Cereb Cortex 26: 2283–2298. Duncan AF, Matthews MA (2018). Neurodevelopmental outcomes in early childhood. Clin Perinatol 45: 377–392. Duncan AF, Bann CM, Dempsey AG et al. (2019). Neuroimaging and Bayley-III correlates of early hand function in extremely preterm children. J Perinatol 39: 488–496. Dyet LE, Kennea N, Counsell SJ et al. (2006). Natural history of brain lesions in extremely preterm infants studied with serial magnetic resonance imaging from birth and neurodevelopmental assessment. Pediatrics 118: 536–548. Eeles AL, Anderson PJ, Brown NC et al. (2013). Sensory profiles of children born < 30 weeks’ gestation at 2 years of age and their environmental and biological predictors. Early Hum Dev 89: 727–732. Ekert PG, Keenan NK, Whyte HE et al. (1997). Visual evoked potentials for prediction of neurodevelopmental outcome in preterm infants. Biol Neonate 71: 148–155. Elgen I, Sommerfelt K, Markestad T (2002). Population based, controlled study of behavioural problems and psychiatric disorders in low birthweight children at 11 years of age. Arch Dis Child Fetal Neonatal Ed 87: F128–F132. Elias LAB, Kriegstein AR (2008). Gap junctions: multifaceted regulators of embryonic cortical development. Trends Neurosci 31: 243–250.

IMPACT OF PREMATURITY ON NEURODEVELOPMENT Elias LAB, Wang DD, Kriegstein AR (2007). Gap junction adhesion is necessary for radial migration in the neocortex. Nature 448: 901–907. Ellingson RJ, Lathrop GH, Danahy T et al. (1973). Variability of visual evoked potentials in human infants and adults. Electroencephalogr Clin Neurophysiol 34: 113–124. Engelhardt B, Liebner S (2014). Novel insights into the development and maintenance of the blood-brain barrier. Cell Tissue Res 355: 687–699. Eswaran H, Lowery CL, Wilson JD et al. (2004). Functional development of the visual system in human fetus using magnetoencephalography. Exp Neurol 190: S52–S58. Feller M (2012). Cortical development: the sources of spontaneous patterned activity. Curr Biol 22: R89–R91. Fellman V, Kushnerenko E, Mikkola K et al. (2004). Atypical auditory event-related potentials in preterm infants during the first year of life: a possible sign of cognitive dysfunction? Pediatr Res 56: 291–297. Foster-Cohen SH, Friesen MD, Champion PR et al. (2010). High prevalence/low severity language delay in preschool children born very preterm. J Dev Behav Pediatr 31: 658–667. Frezza S, Catenazzi P, Gallus R et al. (2019). Hearing loss in very preterm infants: should we wait or treat? Acta Otorhinolaryngol Ital 39: 257–262. Friedrich M, Weber C, Friederici AD (2004). Electrophysiological evidence for delayed mismatch response in infants at-risk for specific language impairment. Psychophysiology 41: 772–782. Fuchino Y, Naoi N, Shibata M et al. (2013). Effects of preterm birth on intrinsic fluctuations in neonatal cerebral activity examined using optical imaging. PLoS One 8: e67432. Fuster JM (2002). Frontal lobe and cognitive development. J Neurocytol 31: 373–385. Garcı´a-Moreno F, Lo´pez-Mascaraque L, de Carlos JA (2008). Early telencephalic migration topographically converging in the olfactory cortex. Cereb Cortex 18: 1239–1252. Geldof CJA, van Wassenaer AG, de Kieviet JF et al. (2012). Visual perception and visual-motor integration in very preterm and/or very low birth weight children: a metaanalysis. Res Dev Disabil 33: 726–736. Geva R, Yaron H, Kuint J (2016). Neonatal sleep predicts attention orienting and distractibility. J Atten Disord 20 (2): 138–150. https://doi.org/10.1177/1087054713491493. Gilbert MS (1935). The early development of the human diencephalon. J Comp Neurol 62: 81–115. Gogou M, Haidopoulou K, Pavlou E (2019). Sleep and prematurity: sleep outcomes in preterm children and influencing factors. World J Pediatr 15: 209–218. Golalipour MJ, Ghafari S (2012). Purkinje cells loss in off spring due to maternal morphine sulfate exposure: a morphometric study. Anat Cell Biol 45: 121–127. Good WV, Jan JE, DeSa L et al. (1994). Cortical visual impairment in children. Surv Ophthalmol 38: 351–364. Goyen TA, Lui K, Woods R (1998). Visual-motor, visual-perceptual, and fine motor outcomes in very-lowbirthweight children at 5 years. Dev Med Child Neurol 40: 76–81.

367

Graven SN (2004). Early neurosensory visual development of the fetus and newborn. Clin Perinatol 31: 199–216. Grunau R (2002). Early pain in preterm infants. A model of long-term effects. Clin Perinatol 29: 373–394, vii–viii. Grunau RE (2013). Neonatal pain in very preterm infants: long-term effects on brain, neurodevelopment and pain reactivity. Rambam Maimonides Med J 4: e0025. Guarini A, Sansavini A, Fabbri C et al. (2009). Reconsidering the impact of preterm birth on language outcome. Early Hum Dev 85: 639–645. Guarini A, Sansavini A, Fabbri C et al. (2010). Long-term effects of preterm birth on language and literacy at eight years. J Child Lang 37: 865–885. Guimara˜es Filho HA, Araujo Ju´nior E, de Mello Ju´nior CF et al. (2013). Assessment of fetal behavior using fourdimensional ultrasonography: current knowledge and perspectives. Rev Assoc Med Bras (1992) 59: 507–513. Hack M, Taylor HG, Schluchter M et al. (2009). Behavioral outcomes of extremely low birth weight children at age 8 years. J Dev Behav Pediatr 30: 122–130. Hadders-Algra M (2002). Two distinct forms of minor neurological dysfunction: perspectives emerging from a review of data of the Groningen perinatal project. Dev Med Child Neurol 44: 561–571. Hagmann-von Arx P, Perkinson-Gloor N, Brand S et al. (2014). In school-age children who were born very preterm sleep efficiency is associated with cognitive function. Neuropsychobiology 70: 244–252. Haldipur P, Bharti U, Alberti C et al. (2011). Preterm delivery disrupts the developmental program of the cerebellum. PLoS One 6: e23449. Hall JG (1964). On the neuropathological changes in the central nervous system following neonatal asphyxia: with special reference to the auditory system in man. Acta Otolaryngol 57: 331–339. Hall J, Wolke D (2012). A comparison of prematurity and small for gestational age as risk factors for age 6-13 year emotional problems. Early Hum Dev 88: 797–804. Harrison MS, Eckert LO, Cutland C et al. (2016). Pathways to preterm birth: case definition and guidelines for data collection, analysis, and presentation of immunization safety data. Vaccine 34: 6093–6101. Hart AR, Whitby EH, Clark SJ et al. (2010). Diffusionweighted imaging of cerebral white matter and the cerebellum following preterm birth. Dev Med Child Neurol 52: 652–659. Hartkopf J, Schleger F, Weiss M et al. (2016). Neuromagnetic signatures of syllable processing in fetuses and infants provide no evidence for habituation. Early Hum Dev 100: 61–66. Hasani S, Jafari Z (2013). Effect of infant prematurity on auditory brainstem response at preschool age. Iran J Otorhinolaryngol 25: 107–114. Hayakawa F, Okumura A, Kato T et al. (1997a). Disorganized patterns: chronic-stage EEG abnormality of the late neonatal period following severely depressed EEG activities in early preterm infants. Neuropediatrics 28: 272–275.

368

F. WALLOIS ET AL.

Hayakawa F, Okumura A, Kato T et al. (1997b). Dysmature EEG pattern in EEGs of preterm infants with cognitive impairment: maturation arrest caused by prolonged mild CNS depression. Brain Dev 19: 122–125. Hevner RF (2000). Development of connections in the human visual system during fetal mid-gestation: a DiI-tracing study. J Neuropathol Exp Neurol 59: 8. Hibbs AM, Storfer-Isser A, Rosen C et al. (2014). Advanced sleep phase in adolescents born preterm. Behav Sleep Med 12: 412–424. Himpens E, Oostra A, Franki I et al. (2010). Predictability of cerebral palsy in a high-risk NICU population. Early Hum Dev 86: 413–417. Hintz SR, Barnes PD, Bulas D et al. (2015). Neuroimaging and neurodevelopmental outcome in extremely preterm infants. Pediatrics 135: e32–e42. Hirvonen M, Ojala R, Korhonen P et al. (2018). Visual and hearing impairments after preterm birth. Pediatrics 142: e20173888. Hitchcock PF, Hickey TL (1980). Prenatal development of the human lateral geniculate nucleus. J Comp Neurol 194: 395–411. Hoerder-Suabedissen A, Molna´r Z (2015). Development, evolution and pathology of neocortical subplate neurons. Nat Rev Neurosci 16: 133–146. Hof JR, Stokroos RJ, Wix E et al. (2013). Auditory maturation in premature infants. Laryngoscope 123: 2013–2018. Hooker D (1952). Early human fetal activity. Anat Rec 113: 503–504. Horne RS, Sly DJ, Cranage SM et al. (2000). Effects of prematurity on arousal from sleep in the newborn infant. Pediatr Res 47: 468–474. Hoyt CS (2007). Brain injury and the eye. Eye (Lond) 21: 1285–1289. Hrbek A, Karlberg P, Olsson T (1973). Development of visual and somatosensory evoked responses in pre-term newborn infants. Electroencephalogr Clin Neurophysiol 34: 225–232. Huang H, Xue R, Zhang J et al. (2009). Anatomical characterization of human fetal brain development with diffusion tensor magnetic resonance imaging. J Neurosci 29: 4263–4273. Huang Y-S, Paiva T, Hsu J-F et al. (2014). Sleep and breathing in premature infants at 6 months post-natal age. BMC Pediatr 14: 303. Humphrey T (1964). Some correlations between the appearance of human fetal reflexes and the development of the nervous system. In: Progress in brain research, Elsevier 93–135 [Internet]. [cited 2020 Feb 12]. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0079612308 61273X. Huo R, Burden SK, Hoyt CS et al. (1999). Chronic cortical visual impairment in children: aetiology, prognosis, and associated neurological deficits. Br J Ophthalmol 83: 670–675. Ianniruberto A (1981). Ultrasonographic study of fetal movements. Semin Perinatol 5: 175–181. Inder TE, Wells SJ, Mogridge NB et al. (2003). Defining the nature of the cerebral abnormalities in the premature infant:

a qualitative magnetic resonance imaging study. J Pediatr 143: 171–179. Indredavik MS, Vik T, Heyerdahl S et al. (2005). Psychiatric symptoms in low birth weight adolescents, assessed by screening questionnaires. Eur Child Adolesc Psychiatry 14: 226–236. Jacobs SE, O’Brien K, Inwood S et al. (2000). Outcome of infants 23-26 weeks’ gestation pre and post surfactant. Acta Paediatr 89: 959–965. James DK (2010). Fetal learning: a critical review. Infant Child Dev 19: 45–54. Jansson-Verkasalo E, Ceponiene R, Valkama M et al. (2003). Deficient speech-sound processing, as shown by the electrophysiologic brain mismatch negativity response, and naming ability in prematurely born children. Neurosci Lett 348: 5–8. Jansson-Verkasalo E, Ruusuvirta T, Huotilainen M et al. (2010). Atypical perceptual narrowing in prematurely born infants is associated with compromised language acquisition at 2 years of age. BMC Neurosci 11: 88. Janz-Robinson EM, Badawi N, Walker K et al. (2015). Neurodevelopmental outcomes of premature infants treated for patent ductus arteriosus: a population-based cohort study. J Pediatr 167: 1025–1032.e3. Jarjour IT (2015). Neurodevelopmental outcome after extreme prematurity: a review of the literature. Pediatr Neurol 52: 143–152. J€ obsis FF (1977). Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters. Science 198: 1264–1267. Johnson S (2007). Cognitive and behavioural outcomes following very preterm birth. Semin Fetal Neonatal Med 12: 363–373. Johnson S, Marlow N (2011). Preterm birth and childhood psychiatric disorders. Pediatr Res 69: 11R–8R. Johnson EW, Eller PM, Jafek BW (1995). Distribution of OMP-, PGP 9.5- and CaBP-like immunoreactive chemoreceptor neurons in the developing human olfactory epithelium. Anat Embryol 191: 311–317. Johnson S, Hollis C, Kochhar P et al. (2010). Psychiatric disorders in extremely preterm children: longitudinal finding at age 11 years in the EPICure study. J Am Acad Child Adolesc Psychiatry 49: 453–463.e1. Kaminska A, Delattre V, Laschet J et al. (2018). Cortical auditory-evoked responses in preterm neonates: revisited by spectral and temporal analyses. Cereb Cortex 28: 3429–3444. Kato T, Watanabe K (2006). Visual evoked potential in the newborn: does it have predictive value? Semin Fetal Neonatal Med 11: 459–463. Kerr-Wilson CO, Mackay DF, Smith GCS et al. (2012). Metaanalysis of the association between preterm delivery and intelligence. J Public Health 34: 209–216. Kesler SR, Reiss AL, Vohr B et al. (2008). Brain volume reductions within multiple cognitive systems in male preterm children at age twelve. J Pediatr 152: 513, 520.e1.

IMPACT OF PREMATURITY ON NEURODEVELOPMENT Kitai Y, Hirai S, Ohmura K et al. (2015). Cerebellar injury in preterm children with cerebral palsy after intraventricular hemorrhage: prevalence and relationship to functional outcomes. Brain Dev 37: 758–763. Klimach VJ, Cooke RWI (1988). Short-latency cortical somatosensory evoked responses of preterm infants with ultrasound abnormality of the brain. Dev Med Child Neurol 30: 215–221. Konstantinidou AD, Silos-Santiago I, Flaris N et al. (1995). Development of the primary afferent projection in human spinal cord. J Comp Neurol 354: 11–12. Kostovic I, Goldman-Rakic PS (1983). Transient cholinesterase staining in the mediodorsal nucleus of the thalamus and its connections in the developing human and monkey brain. J Comp Neurol 219: 431–447. Kostovi
c I, Jovanov-Milosˇevi
c N (2006). The development of cerebral connections during the first 20–45 weeks’ gestation. Semin Fetal Neonatal Med 11: 415–422. Kostovi
c I, Judas M (2002). Correlation between the sequential ingrowth of afferents and transient patterns of cortical lamination in preterm infants. Anat Rec 267: 1–6. Kostovi
c I, Judas M (2007). Transient patterns of cortical lamination during prenatal life: do they have implications for treatment? Neurosci Biobehav Rev 31: 1157–1168. Kostovi
c I, Judas M (2010). The development of the subplate and thalamocortical connections in the human foetal brain. Acta Paediatr 99: 1119–1127. Kostovic I, Rakic P (1984). Development of prestriate visual projections in the monkey and human fetal cerebrum revealed by transient cholinesterase staining. J Neurosci 4: 25–42. Kostovic I, Rakic P (1990). Developmental history of the transient subplate zone in the visual and somatosensory cortex of the macaque monkey and human brain. J Comp Neurol 297: 441–470. Kostovi
c Srzenti
c M, Raguzˇ M, Ozreti
c D (2019). Specific cognitive deficits in preschool age correlated with qualitative and quantitative MRI parameters in prematurely born children. Pediatr Neonatol 61: 160–167. S187595721 9304991. Kostovi
c I, Judasˇ M, Sedmak G (2011). Developmental history of the subplate zone, subplate neurons and interstitial white matter neurons: relevance for schizophrenia. Int J Dev Neurosci 29: 193–205. Kostovi
c I, Sedmak G, Vuksˇi
c M et al. (2015). The relevance of human fetal subplate zone for developmental neuropathology of neuronal migration disorders and cortical dysplasia. CNS Neurosci Ther 21: 74–82. Kostovi
c I, Sedmak G, Judasˇ M (2019). Neural histology and neurogenesis of the human fetal and infant brain. Neuroimage 188: 743–773. Krmpoti
c-Nemani
c J, Kostovi
c I, Kelovi
c Z et al. (1980). Development of acetylcholinesterase (AChE) staining in human fetal auditory cortex. Acta Otolaryngol 89: 388–392. Krmpoti
c-Nemani
c J, Kostovi
c I, Kelovi
c Z et al. (1983). Development of the human fetal auditory cortex: growth of afferent fibres. Acta Anat (Basel) 116: 69–73.

369

Krueger C, Garvan C (2014). Emergence and retention of learning in early fetal development. Infant Behav Dev 37 (2): 162–173. Kurjak A, Stanojevic M, Andonotopo W et al. (2005). Fetal behavior assessed in all three trimesters of normal pregnancy by four-dimensional ultrasonography. Croat Med J 46: 772–780. Kurjak A, Predojevic M, Stanojevic M et al. (2012). Intrauterine growth restriction and cerebral palsy. Acta Inform Med 18: 64–82. Kurth S, Olini N, Huber R et al. (2015). Sleep and early cortical development. Curr Sleep Med Rep 1: 64–73. Lacoste B, Gu C (2015). Control of cerebrovascular patterning by neural activity during postnatal development. Mech Dev 138: 43–49. Lacoste B, Comin CH, Ben-Zvi A et al. (2014). Sensoryrelated neural activity regulates the structure of vascular networks in the cerebral cortex. Neuron 83: 1117–1130. Lanzi G, Fazzi E, Uggetti C et al. (1998). Cerebral visual impairment in periventricular leukomalacia. Neuropediatrics 29: 145–150. Larroque B, Marret S, Ancel P-Y et al. (2003). White matter damage and intraventricular hemorrhage in very preterm infants: the EPIPAGE study. J Pediatr 143: 477–483. Larroque B, Ancel P-Y, Marret S et al. (2008). Neurodevelopmental disabilities and special care of 5-year-old children born before 33 weeks of gestation (the EPIPAGE study): a longitudinal cohort study. Lancet 371: 813–820. Lavoie ME, Robaey P, Stauder JE et al. (1997). A topographical ERP study of healthy premature 5-year-old children in the auditory and visual modalities. Electroencephalogr Clin Neurophysiol 104: 228–243. Le Bihannic A, Beauvais K, Busnel A et al. (2012). Prognostic value of EEG in very premature newborns. Arch Dis Child Fetal Neonatal Ed 97: F106–F109. Lean RE, Melzer TR, Bora S et al. (2017). Attention and regional gray matter development in very preterm children at age 12 years. J Int Neuropsychol Soc 23: 539–550. Lecanuet JP, Graniere-Deferre C, Jacquet AY et al. (2000). Fetal discrimination of low-pitched musical notes. Dev Psychobiol 36: 29–39. Lee SJ, Ralston HJP, Drey EA et al. (2005). Fetal pain: a systematic multidisciplinary review of the evidence. JAMA 294: 947–954. Leikos S, Tokariev A, Koolen N et al. (2019). Cortical responses to tactile stimuli in preterm infants. Eur J Neurosci 51: 1059–1073, ejn.14613. Leroy F, Glasel H, Dubois J et al. (2011). Early maturation of the linguistic dorsal pathway in human infants. J Neurosci 31: 1500–1506. L
eveill
e J, Robaey P, Ge Y-L et al. (2002). Auditory ERP in extremely premature 5-year-old children. Brain Cogn 48: 437–441. Lim R, Brichta AM (2016). Anatomical and physiological development of the human inner ear. Hear Res 338: 9–21.

370

F. WALLOIS ET AL.

Limperopoulos C, Soul J, Haidar H et al. (2005). Impaired trophic interactions between the cerebellum and the cerebrum among preterm infants. Pediatrics 116: 844–850. Limperopoulos C, Bassan H, Gauvreau K et al. (2007). Does cerebellar injury in premature infants contribute to the high prevalence of long-term cognitive, learning, and behavioral disability in survivors? Pediatrics 120: 584–593. Lin P-Y et al. (2013). Regional and hemispheric asymmetries of cerebral hemodynamic and oxygen metabolism in newborns. Cereb Cortex 23 (2): 339–348. Linsell L, Malouf R, Morris J et al. (2016). Prognostic factors for cerebral palsy and motor impairment in children born very preterm or very low birthweight: a systematic review. Dev Med Child Neurol 58: 554–569. Lipchock SV, Reed DR, Mennella JA (2011). The gustatory and olfactory systems during infancy: implications for development of feeding behaviors in the high-risk neonate. Clin Perinatol 38: 627–641. Lipchock SV, Reed DR, Mennella JA (2012). Relationship between bitter-taste receptor genotype and solid medication formulation usage among young children: a retrospective analysis. Clin Ther 34: 728–733. Loeliger M, Inder T, Cain S et al. (2006). Cerebral outcomes in a preterm baboon model of early versus delayed nasal continuous positive airway pressure. Pediatrics 118: 1640–1653. Luhmann HJ, Kirischuk S, Kilb W (2018). The superior function of the subplate in early neocortical development. Front Neuroanat 12: 97. Madan A, Norcia AM, Hou C et al. (2012). Effect of grade I and II intraventricular hemorrhage on visuocortical function in very low birth weight infants. Seeing Perceiving 25: 143–154. Mahmoudzadeh M, Dehaene-Lambertz G, Fournier M et al. (2013). Syllabic discrimination in premature human infants prior to complete formation of cortical layers. Proc Natl Acad Sci 110: 4846–4851. Mahmoudzadeh M, Dehaene-Lambertz G, Wallois F (2017a). Electrophysiological and hemodynamic mismatch responses in rats listening to human speech syllables. PLoS One 12: e0173801. Mahmoudzadeh M, Wallois F, Kongolo G et al. (2017b). Functional maps at the onset of auditory inputs in very early preterm human neonates. Cereb Cortex 27: 2500–2512. Mahmoudzadeh M, Dehaene-Lambertz G, Kongolo G et al. (2018). Consequence of intraventricular hemorrhage on neurovascular coupling evoked by speech syllables in preterm neonates. Dev Cogn Neurosci 30: 60–69. Majnemer A, Rosenblatt B (1996). Evoked potentials as predictors of outcome in neonatal intensive care unit survivors: review of the literature. Pediatr Neurol 14: 189–195. Majnemer A, Rosenblatt B (2000). Prediction of outcome at school age in neonatal intensive care unit graduates using neonatal neurologic tools. J Child Neurol 15: 645–651. Manuel A, Witmans M, El-Hakim H (2013). Children with a history of prematurity presenting with snoring and sleep-disordered breathing: a cross-sectional study. Laryngoscope 123: 2030–2034.

Marı´n-Padilla M (1997). Developmental neuropathology and impact of perinatal brain damage. II: white matter lesions of the neocortex. J Neuropathol Exp Neurol 56: 219–235. Markopoulou P, Papanikolaou E, Analytis A et al. (2019). Preterm birth as a risk factor for metabolic syndrome and cardiovascular disease in adult life: a systematic review and meta-analysis. J Pediatr 210: 69–80.e5. Marlier L, Schaal B, Soussignan R (1998). Neonatal responsiveness to the odor of amniotic and lacteal fluids: a test of perinatal chemosensory continuity. Child Dev 69: 611–623. Maroney DI (2003). Recognizing the potential effect of stress and trauma on premature infants in the NICU: how are outcomes affected? J Perinatol 23: 679–683. Marret S, Parain D, M
enard J-F et al. (1997). Prognostic value of neonatal electroencephalography in premature newborns less than 33 weeks of gestational age. Electroencephalogr Clin Neurophysiol 102: 178–185. McCann M, Bayliss DM, Anderson M et al. (2018). The relationship between sleep problems and working memory in children born very preterm. Child Neuropsychol 24: 124–144. McMahon E, Wintermark P, Lahav A (2012). Auditory brain development in premature infants: the importance of early experience: McMahon et al. Ann N Y Acad Sci 1252: 17–24. McPherson C, Haslam M, Pineda R et al. (2015). Brain injury and development in preterm infants exposed to fentanyl. Ann Pharmacother 49: 1291–1297. McQuillen PS, Sheldon RA, Shatz CJ et al. (2003). Selective vulnerability of subplate neurons after early neonatal hypoxia-ischemia. J Neurosci 23: 3308–3315. Ment LR, Kesler S, Vohr B et al. (2009). Longitudinal brain volume changes in preterm and term control subjects during late childhood and adolescence. Pediatrics 123: 503–511. Messerschmidt A, Prayer D, Brugger PC et al. (2008). Preterm birth and disruptive cerebellar development: assessment of perinatal risk factors. Eur J Paediatr Neurol 12: 455–460. Mikkola K, Kushnerenko E, Partanen E et al. (2007). Auditory event-related potentials and cognitive function of preterm children at five years of age. Clin Neurophysiol 118: 1494–1502. Milh M, Kaminska A, Huon C et al. (2007). Rapid cortical oscillations and early motor activity in premature human neonate. Cereb Cortex 17: 1582–1594. Miller SP, Ferriero DM (2009). From selective vulnerability to connectivity: insights from newborn brain imaging. Trends Neurosci 32: 496–505. Miller SP, Vigneron DB, Henry RG et al. (2002). Serial quantitative diffusion tensor MRI of the premature brain: development in newborns with and without injury. J Magn Reson Imaging 16: 621–632. Miller LJ, Anzalone ME, Lane SJ et al. (2007). Concept evolution in sensory integration: a proposed nosology for diagnosis. Am J Occup Ther 61: 135–140. Minlebaev M, Ben-Ari Y, Khazipov R (2007). Network mechanisms of spindle-burst oscillations in the neonatal rat barrel cortex in vivo. J Neurophysiol 97: 692–700.

IMPACT OF PREMATURITY ON NEURODEVELOPMENT Mirmiran M (1995). The function of fetal/neonatal rapid eye movement sleep. Behav Brain Res 69: 13–22. Mitter C, Jakab A, Brugger PC et al. (2015). Validation of in utero tractography of human fetal commissural and internal capsule fibers with histological structure tensor analysis. Front Neuroanat 9: 164. Moghimi S, Shadkam A, Mahmoudzadeh M et al. (2020). The intimate relationship between coalescent generators in very premature human newborn brains: quantifying the coupling of nested endogenous oscillations. (submitted). Molliver ME, Kostovi
c I, Van Der Loos H (1973). The development of synapses in cerebral cortex of the human fetus. Brain Res 50: 403–407. Molna´r Z, Clowry GJ, Sˇestan N et al. (2019). New insights into the development of the human cerebral cortex. J Anat 235: 432–451. Montagna A, Nosarti C (2016). Socio-emotional development following very preterm birth: pathways to psychopathology. Front Psychol 7: 80. Moore AR, Zhou W-L, Jakovcevski I et al. (2011). Spontaneous electrical activity in the human fetal cortex in vitro. J Neurosci 31: 2391–2398. Moore T, Hennessy EM, Myles J et al. (2012). Neurological and developmental outcome in extremely preterm children born in England in 1995 and 2006: the EPICure studies. BMJ 345: e7961. Moore GP, Lemyre B, Barrowman N et al. (2013). Neurodevelopmental outcomes at 4 to 8 years of children born at 22 to 25 weeks’ gestational age: a meta-analysis. JAMA Pediatr 167: 967. Mouron V, Hays S, Gonzalez-Monge S (2010). Developmental amnesia in the premature infant. Arch Pediatr 17: 154–156. M€ uller F, O’Rahilly R (1990). The human brain at stages 21-23, with particular reference to the cerebral cortical plate and to the development of the cerebellum. Anat Embryol 182: 375–400. Murata Y, Colonnese MT (2016). An excitatory cortical feedback loop gates retinal wave transmission in rodent thalamus. eLife 11: 5. Murray AL, Scratch SE, Thompson DK et al. (2014). Neonatal brain pathology predicts adverse attention and processing speed outcomes in very preterm and/or very low birth weight children. Neuropsychology 28: 552–562. Myers MM, Grieve PG, Izraelit A et al. (2012). Developmental profiles of infant EEG: overlap with transient cortical circuits. Clin Neurophysiol 123: 1502–1511. Nagode DA, Meng X, Winkowski DE et al. (2017). Abnormal development of the earliest cortical circuits in a mouse model of autism spectrum disorder. Cell Rep 18: 1100–1108. Nguyen The Tich S, d’Allest A-M, Touzery de Villepin A et al. (2007). Pathological patterns in neonatal EEG before 30 weeks of gestational age. Neurophysiol Clin 37: 177–221. Noguchi KK, Walls KC, Wozniak DF et al. (2008). Acute neonatal glucocorticoid exposure produces selective and rapid cerebellar neural progenitor cell apoptotic death. Cell Death Differ 15: 1582–1592.

371

Nongena P, Ederies A, Azzopardi DV et al. (2010). Confidence in the prediction of neurodevelopmental outcome by cranial ultrasound and MRI in preterm infants. Arch Dis Child Fetal Neonatal Ed 95: F388–F390. Nosarti C, Giouroukou E, Healy E et al. (2008). Grey and white matter distribution in very preterm adolescents mediates neurodevelopmental outcome. Brain 131: 205–217. Nunes ML, Khan RL, Gomes Filho I et al. (2014). Maturational changes of neonatal electroencephalogram: a comparison between intra uterine and extra uterine development. Clin Neurophysiol 125: 1121–1128. O’Keefe M, Kafil-Hussain N, Flitcroft I et al. (2001). Ocular significance of intraventricular haemorrhage in premature infants. Br J Ophthalmol 85: 357–359. O’Leary DDM, Chou S-J, Hamasaki T et al. (2007). Regulation of laminar and area patterning of mammalian neocortex and behavioural implications. Novartis Found Symp 288: 141–159; discussion 159–164, 276–281. Okado N, Oppenheim RW (1984). Cell death of motoneurons in the chick embryo spinal cord. IX. The loss of motoneurons following removal of afferent inputs. J Neurosci 4: 1639–1652. Ols
en P, Yliherva A, P€a€akk€ o E et al. (2002). Brainstem auditory-evoked potentials of 8-year-old preterm children in relation to their psycholinguistic abilities and MRI findings. Early Hum Dev 70: 25–34. Padilla N, Alexandrou G, Blennow M et al. (2015). Brain growth gains and losses in extremely preterm infants at term. Cereb Cortex 25: 1897–1905. Pajevic S, Basser PJ, Fields RD (2014). Role of myelin plasticity in oscillations and synchrony of neuronal activity. Neuroscience 276: 135–147. Paquette N, Vannasing P, Tremblay J et al. (2015). Early electrophysiological markers of atypical language processing in prematurely born infants. Neuropsychologia 79: 21–32. Pascal A, Govaert P, Oostra A et al. (2018). Neurodevelopmental outcome in very preterm and very-low-birthweight infants born over the past decade: a meta-analytic review. Dev Med Child Neurol 60: 342–355. Pasman JW, Rotteveel JJ, de Graaf R et al. (1996). The effects of early and late preterm birth on brainstem and middle-latency auditory evoked responses in children with normal neurodevelopment. J Clin Neurophysiol 13: 234–241. Patel U (1983). Non-random distribution of blood vessels in the posterior region of the rat somatosensory cortex. Brain Res 289: 65–70. Peterson BS, Vohr B, Staib LH et al. (2000). Regional brain volume abnormalities and long-term cognitive outcome in preterm infants. JAMA 284: 1939–1947. Pierrat V, Marchand-Martin L, Arnaud C et al. (2017). Neurodevelopmental outcome at 2 years for preterm children born at 22 to 34 weeks’ gestation in France in 2011: EPIPAGE-2 cohort study. BMJ 358: j3448. Pierson CR, Folkerth RD, Billiards SS et al. (2007). Gray matter injury associated with periventricular leukomalacia in the premature infant. Acta Neuropathol 114: 619–631.

372

F. WALLOIS ET AL.

Pike AA, Marlow N (2000). The role of cortical evoked responses in predicting neuromotor outcome in very preterm infants. Early Hum Dev 57: 123–135. Placzek M, Mushin J, Dubowitz LM (1985). Maturation of the visual evoked response and its correlation with visual acuity in preterm infants. Dev Med Child Neurol 27: 448–454. Pritchard VE, Bora S, Austin NC et al. (2014). Identifying very preterm children at educational risk using a school readiness framework. Pediatrics 134: e825–e832. Pujol R, Lavigne-Rebillard M (1992). Development of neurosensory structures in the human cochlea. Acta Otolaryngol 112: 259–264. Quigley MA, Poulsen G, Boyle E et al. (2012). Early term and late preterm birth are associated with poorer school performance at age 5 years: a cohort study. Arch Dis Child Fetal Neonatal Ed 97: F167–F173. Rakic P (1988). Specification of cerebral cortical areas. Science 241: 170–176. Ranasinghe S, Or G, Wang EY et al. (2015). Reduced cortical activity impairs development and plasticity after neonatal hypoxia ischemia. J Neurosci 35: 11946–11959. Ranger M, Zwicker JG, Chau CMY et al. (2015). Neonatal pain and infection relate to smaller cerebellum in very preterm children at school age. J Pediatr 167: 292–298.e1. Raz S, Debastos AK, Newman JB et al. (2010). Extreme prematurity and neuropsychological outcome in the preschool years. J Int Neuropsychol Soc 16: 169–179. Reid VM, Dunn K, Young RJ et al. (2017). The human fetus preferentially engages with face-like visual stimuli. Curr Biol 27: 2052. Reidy N, Morgan A, Thompson DK et al. (2013). Impaired language abilities and white matter abnormalities in children born very preterm and/or very low birth weight. J Pediatr 162: 719–724. Ribeiro FM, Carvallo RM (2008). Tone-evoked ABR in fullterm and preterm neonates with normal hearing. Int J Audiol 47: 21–29. Ricci D, Cowan F, Pane M et al. (2006). Neurological examination at 6 to 9 months in infants with cystic periventricular leukomalacia. Neuropediatrics 37: 247–252. Ricci D, Cesarini L, Gallini F et al. (2010). Cortical visual function in preterm infants in the first year. J Pediatr 156: 550–555. Rogers CE, Lean RE, Wheelock MD et al. (2018). Aberrant structural and functional connectivity and neurodevelopmental impairment in preterm children. J Neurodev Disord 10: 38. Rolheiser T, Stamatakis EA, Tyler LK (2011). Dynamic processing in the human language system: synergy between the arcuate fascicle and extreme capsule. J Neurosci 31: 16949–16957. Roofthooft DWE, Simons SHP, Anand KJS et al. (2014). Eight years later, are we still hurting newborn infants? Neonatology 105: 218–226. Rosen CL, Larkin EK, Kirchner HL et al. (2003). Prevalence and risk factors for sleep-disordered breathing in 8- to 11-year-old children: association with race and prematurity. J Pediatr 142: 383–389.

Rosenbaum P, Paneth N, Leviton A et al. (2007). A report: the definition and classification of cerebral palsy April 2006. Dev Med Child Neurol Suppl 109: 8–14. Rosenberg T, Flage T, Hansen E et al. (1996). Incidence of registered visual impairment in the Nordic child population. Br J Ophthalmol 80: 49–53. Rotteveel JJ, de Graaf R, Stegeman DF et al. (1987). The maturation of the central auditory conduction in preterm infants until three months post term. V. The auditory cortical response (ACR). Hear Res 27: 95–110. Routier L, Mahmoudzadeh M, Panzani M et al. (2017). Plasticity of neonatal neuronal networks in very premature infants: source localization of temporal theta activity, the first endogenous neural biomarker, in temporoparietal areas: networks plasticity in very premature infants. Hum Brain Mapp 38: 2345–2358. Ruben RJ (1992). The ontogeny of human hearing. Acta Otolaryngol 112: 192–196. Saigal S, Hoult LA, Streiner DL et al. (2000). School difficulties at adolescence in a regional cohort of children who were extremely low birth weight. Pediatrics 105: 325–331. Saigal S, Ferro MA, Van Lieshout RJ et al. (2016). Healthrelated quality of life trajectories of extremely low birth weight survivors into adulthood. J Pediatr 179: 68–73.e1. Sansavini A, Guarini A, Justice LM et al. (2010). Does preterm birth increase a child’s risk for language impairment? Early Hum Dev 86: 765–772. Sarnat HB, Flores-Sarnat L, Wei X-C (2017). Olfactory development, part 1: function, from fetal perception to adult wine-tasting. J Child Neurol 32: 566–578. Scafidi J, Fagel DM, Ment LR et al. (2009). Modeling premature brain injury and recovery. Int J Dev Neurosci 27: 863–871. Schaal B (2015). Prenatal and postnatal human olfactory development: influences on cognition and behavior. In: RL Doty (Ed.), Handbook of olfaction and gustation. John Wiley & Sons, Inc, Hoboken, NJ, pp. 305–336 [Internet]. [cited 2020 Feb 12]. Available from: http:// doi.wiley.com/10.1002/9781118971758.ch14. Schaal B, Marlier L, Soussignan R (1995). Responsiveness to the odour of amniotic fluid in the human neonate. Biol Neonate 67: 397–406. Schaal B, Marlier L, Soussignan R (1998). Olfactory function in the human fetus: evidence from selective neonatal responsiveness to the odor of amniotic fluid. Behav Neurosci 112: 1438–1449. Schaal B, Hummel T, Soussignan R (2004). Olfaction in the fetal and premature infant: functional status and clinical implications. Clin Perinatol 31: 261–285. Scher MS, Steppe DA, Dahl RE et al. (1992). Comparison of EEG sleep measures in healthy full-term and preterm infants at matched conceptional ages. Sleep 15: 442–448. Scher MS, Sun M, Steppe DA et al. (1994). Comparisons of EEG sleep state-specific spectral values between healthy full-term and preterm infants at comparable postconceptional ages. Sleep 17: 47–51.

IMPACT OF PREMATURITY ON NEURODEVELOPMENT Scher MS, Steppe DA, Banks DL (1996). Prediction of lower developmental performances of healthy neonates by neonatal EEG-sleep measures. Pediatr Neurol 14 (2): 137–144. https://doi.org/10.1016/0887-8994(96)00013-6. Scher A, Tse L, Hayes VE et al. (2008). Sleep difficulties in infants at risk for developmental delays: a longitudinal study. J Pediatr Psychol 33: 396–405. Schr€oder H, Young M (1995). Classics revisited: Wilhelm (William) Th. Preyer: Specielle Physiologie des Embryo. pp. 644 (Grieben, Leipzig 1885). Placenta 16: 105–108. Serenius F, Kallen K, Blennow M et al. (2013). Neurodevelopmental outcome in extremely preterm infants at 2.5 years after active perinatal care in Sweden. JAMA 309: 1810–1820. Serenius F, Ewald U, Farooqi A et al. (2016). Neurodevelopmental outcomes among extremely preterm infants 6.5 years after active perinatal care in Sweden. JAMA Pediatr 170: 954. Shah DK, Anderson PJ, Carlin JB et al. (2006). Reduction in cerebellar volumes in preterm infants: relationship to white matter injury and neurodevelopment at two years of age. Pediatr Res 60: 97–102. Shepherd AJ, Saunders KJ, McCulloch DL et al. (1999). Prognostic value of flash visual evoked potentials in preterm infants. Dev Med Child Neurol 41: 9–15. Sheridan CJ, Preissl H, Siegel ER et al. (2008). Neonatal and fetal response decrement of evoked responses: a MEG study. Clin Neurophysiol 119: 796–804. Sherlock RL, Anderson PJ, Doyle LW (2005). Victorian infant collaborative study group. Neurodevelopmental sequelae of intraventricular haemorrhage at 8 years of age in a regional cohort of ELBW/very preterm infants. Early Hum Dev 81: 909–916. Shum D, Neulinger K, O’Callaghan M et al. (2008). Attentional problems in children born very preterm or with extremely low birth weight at 7-9 years. Arch Clin Neuropsychol 23: 103–112. Slater R, Cantarella A, Gallella S et al. (2006). Cortical pain responses in human infants. J Neurosci 26: 3662–3666. Soleimani F, Zaheri F, Abdi F (2014). Long-term neurodevelopmental outcomes after preterm birth. Iran Red Crescent Med J 16: e17965. Soria-Pastor S, Gimenez M, Narberhaus A et al. (2008). Patterns of cerebral white matter damage and cognitive impairment in adolescents born very preterm. Int J Dev Neurosci 26: 647–654. Soria-Pastor S, Padilla N, Zubiaurre-Elorza L et al. (2009). Decreased regional brain volume and cognitive impairment in preterm children at low risk. Pediatrics 124: e1161–e1170. Sortor JM, Kulp MT (2003). Are the results of the beeryBuktenica developmental test of visual-motor integration and its subtests related to achievement test scores? Optom Vis Sci 80: 758–763. Spekreijse H, Dagnelie G, Maier J et al. (1985). Flicker and movement constituents of the pattern reversal response. Vision Res 25: 1297–1304.

373

Spittle AJ, Treyvaud K, Doyle LW et al. (2009). Early emergence of behavior and social-emotional problems in very preterm infants. J Am Acad Child Adolesc Psychiatry 48: 909–918. Spittle AJ, Cameron K, Doyle LW et al. (2018a). Motor impairment trends in extremely preterm children: 1991–2005. Pediatrics 141: e20173410. Spittle AJ, Morgan C, Olsen JE et al. (2018b). Early diagnosis and treatment of cerebral palsy in children with a history of preterm birth. Clin Perinatol 45: 409–420. Stangenes KM, Fevang SK, Grundt J et al. (2017). Children born extremely preterm had different sleeping habits at 11 years of age and more childhood sleep problems than term-born children. Acta Paediatr 106: 1966–1972. Stavsky M, Mor O, Mastrolia SA et al. (2017). Cerebral palsytrends in epidemiology and recent development in prenatal mechanisms of disease, treatment, and prevention. Front Pediatr 5: 21. Stipdonk LW, Weisglas-Kuperus N, Franken M-CJ et al. (2016). Auditory brainstem maturation in normal-hearing infants born preterm: a meta-analysis. Dev Med Child Neurol 58: 1009–1015. Stockard JE, Stockard JJ, Kleinberg F et al. (1983). Prognostic value of brainstem auditory evoked potentials in neonates. Arch Neurol 40: 360–365. Synnes A, Hicks M (2018). Neurodevelopmental outcomes of preterm children at school age and beyond. Clin Perinatol 45: 393–408. Synnes A, Luu TM, Moddemann D et al. (2017). Determinants of developmental outcomes in a very preterm Canadian cohort. Arch Dis Child Fetal Neonatal Ed 102: F235–F234. Sztriha L, Dawodu A, Gururaj A et al. (2004). Microcephaly associated with abnormal gyral pattern. Neuropediatrics 35: 346–352. Takahashi E, Folkerth RD, Galaburda AM et al. (2012). Emerging cerebral connectivity in the human fetal brain: an MR tractography study. Cereb Cortex 22: 455–464. Tapia IE, Shults J, Doyle LW et al. (2016). Perinatal risk factors associated with the obstructive sleep apnea syndrome in school-aged children born preterm. Sleep 39: 737–742. Tau GZ, Peterson BS (2010). Normal development of brain circuits. Neuropsychopharmacology 35: 147–168. Taylor MJ, Menzies R, MacMillan LJ et al. (1987). VEP’s in normal full-term and premature neonates: longitudinal versus cross-sectional data. Electroencephalogr Clin Neurophysiol 68: 20–27. Taylor MJ, Murphy WJ, Whyte HE (1992). Prognostic reliability of somatosensory and visual evoked potentials of asphyxiated term infants. Dev Med Child Neurol 34: 507–515. Taylor MJ, Saliba E, Laugier J (1996). Use of evoked potentials in preterm neonates. Arch Dis Child Fetal Neonatal Ed 74: F70–F76. Thomason ME, Grove LE, Lozon TA et al. (2015). Age-related increases in long-range connectivity in fetal functional neural connectivity networks in utero. Dev Cogn Neurosci 11: 96–104.

374

F. WALLOIS ET AL.

Tritsch NX, Bergles DE (2010). Developmental regulation of spontaneous activity in the mammalian cochlea. J Neurosci 30: 1539–1550. Tritsch NX, Yi E, Gale JE et al. (2007). The origin of spontaneous activity in the developing auditory system. Nature 450: 50–55. Twilhaar ES, de Kieviet JF, Aarnoudse-Moens CS et al. (2018). Academic performance of children born preterm: a meta-analysis and meta-regression. Arch Dis Child Fetal Neonatal Ed 103: F322–F330. Uggetti C, Egitto MG, Fazzi E et al. (1996). Cerebral visual impairment in periventricular leukomalacia: MR correlation. Am J Neuroradiol 17: 979–985. Van Braeckel KNJA, Taylor HG (2013). Visuospatial and visuomotor deficits in preterm children: the involvement of cerebellar dysfunctioning. Dev Med Child Neurol 55: 19–22. Van Essen DC, Drury HA (1997). Structural and functional analyses of human cerebral cortex using a surface-based atlas. J Neurosci 17: 7079–7102. van Haastert IC, de Vries LS, Eijsermans MJC et al. (2008). Gross motor functional abilities in preterm-born children with cerebral palsy due to periventricular leukomalacia. Dev Med Child Neurol 50: 684–689. Van Lierde KM, Roeyers H, Boerjan S et al. (2009). Expressive and receptive language characteristics in three-year-old preterm children with extremely low birth weight. Folia Phoniatr Logop 61: 296–299. van Noort-van der Spek IL, Franken M-CJP, Weisglas-Kuperus N (2012). Language functions in preterm-born children: a systematic review and meta-analysis. Pediatrics 129: 745–754. van Noort-van der Spek IL, Goedegebure A, Hartwig NG et al. (2017). Normal neonatal hearing screening did not preclude sensorineural hearing loss in two-year-old very preterm infants. Acta Paediatrica (Oslo, Norway: 1992) 106: 1569–1575. Vandermosten M, Boets B, Poelmans H et al. (2012). A tractography study in dyslexia: neuroanatomic correlates of orthographic, phonological and speech processing. Brain J Neurol 135: 935–948. Vanhatalo S, Kaila K (2006). Development of neonatal EEG activity: from phenomenology to physiology. Semin Fetal Neonatal Med 11: 471–478. Vanhatalo S, Lauronen L (2006). Neonatal SEP—back to bedside with basic science. Semin Fetal Neonatal Med 11: 464–470. Vanhatalo S, Palva JM, Andersson S et al. (2005). Slow endogenous activity transients and developmental expression of K +-Cl- cotransporter 2 in the immature human cortex. Eur J Neurosci 22: 2799–2804. Vanhatalo S, Jousm€aki V, Andersson S et al. (2009). An easy and practical method for routine, bedside testing of somatosensory systems in extremely low birth weight infants. Pediatr Res 66: 710–713. van Noort-van der Spek IL, Franken M-CJP, Wieringa MH et al. (2010). Phonological development in very-low-birthweight children: an exploratory study. Dev Med Child

Neurol 52 (6): 541–546. https://doi.org/10.1111/j.14698749.2009.03507.x. Varendi H, Porter RH, Winberg J (1996). Attractiveness of amniotic fluid odor: evidence of prenatal olfactory learning? Acta Paediatr 85: 1223–1227. Vasung L, Lepage C, Radosˇ M et al. (2016). Quantitative and qualitative analysis of transient fetal compartments during prenatal human brain development. Front Neuroanat 10: 11. Vecchierini M-F, d’Allest A-M, Verpillat P (2003). EEG patterns in 10 extreme premature neonates with normal neurological outcome: qualitative and quantitative data. Brain Dev 25: 330–337. Vieira MEB, Linhares MBM (2011). Developmental outcomes and quality of life in children born preterm at preschool- and school-age. J Pediatr (Rio J) 87: 281–291. Vohr BR (2014). Neurodevelopmental outcomes of extremely preterm infants. Clin Perinatol 41: 241–255. Volman MJM, van Schendel BM, Jongmans MJ (2006). Handwriting difficulties in primary school children: a search for underlying mechanisms. Am J Occup Ther 60: 451–460. Volpe JJ (2001). Neurobiology of periventricular leukomalacia in the premature infant. Pediatr Res 50: 553–562. Volpe JJ (2009a). Brain injury in premature infants: a complex amalgam of destructive and developmental disturbances. Lancet Neurol 8: 110–124. Volpe JJ (2009b). Cerebellum of the premature infant: rapidly developing, vulnerable, clinically important. J Child Neurol 24: 1085–1104. Volpe JJ (2009c). The encephalopathy of prematurity—brain injury and impaired brain development inextricably intertwined. Semin Pediatr Neurol 16: 167–178. Walker SM, Franck LS, Fitzgerald M et al. (2009). Long-term impact of neonatal intensive care and surgery on somatosensory perception in children born extremely preterm. Pain 141: 79–87. Watanabe K, Hayakawa F, Okumura A (1999). Neonatal EEG: a powerful tool in the assessment of brain damage in preterm infants. Brain Dev 21: 361–372. Weil MJ, Amundson SJ (1994). Relationship between visuomotor and handwriting skills of children in kindergarten. Am J Occup Ther 48: 982–988. Wess JM, Isaiah A, Watkins PV et al. (2017). Subplate neurons are the first cortical neurons to respond to sensory stimuli. Proc Natl Acad Sci U S A 114: 12602–12607. Whyte HE (1993). Visual-evoked potentials in neonates following asphyxia. Clin Perinatol 20: 451–461. Wickremasinghe AC, Rogers EE, Johnson BC et al. (2013). Children born prematurely have atypical sensory profiles. J Perinatol 33: 631–635. Willis J, Duncan MC, Bell R et al. (1989). Somatosensory evoked potentials predict neuromotor outcome after periventricular hemorrhage. Dev Med Child Neurol 31: 435–439. Wolke D, Meyer R (1999). Cognitive status, language attainment, and prereading skills of 6-year-old very preterm children and their peers: the Bavarian Longitudinal Study. Dev Med Child Neurol 41 (2): 94–109. https://doi.org/10.1017/ s0012162299000201.

IMPACT OF PREMATURITY ON NEURODEVELOPMENT Wolke D, Samara M, Bracewell M et al. (2008). Specific language difficulties and school achievement in children born at 25 weeks of gestation or less. J Pediatr 152: 256–262. Woodward LJ, Clark CAC, Pritchard VE et al. (2011). Neonatal white matter abnormalities predict global executive function impairment in children born very preterm. Dev Neuropsychol 36: 22–41. Wu YW, Colford JM (2000). Chorioamnionitis as a risk factor for cerebral palsy: a meta-analysis. JAMA 284: 1417–1424. Wulle KG (1972). Electron microscopy of the fetal development of the corneal endothelium and Descemet’s membrane of the human eye. Invest Ophthalmol 11: 897–904. Yeatman JD, Dougherty RF, Rykhlevskaia E et al. (2011). Anatomical properties of the arcuate fasciculus predict phonological and reading skills in children. J Cogn Neurosci 23: 3304–3317.

375

Yildiz A, Arikan D, G€ oz€ um S et al. (2011). The effect of the odor of breast milk on the time needed for transition from gavage to total oral feeding in preterm infants. J Nurs Scholarsh 43: 265–273. Younge N, Goldstein RF, Bann CM et al. (2017). Survival and neurodevelopmental outcomes among periviable infants. N Engl J Med 376: 617–628. Zayek MM, Benjamin JT, Maertens P et al. (2012). Cerebellar hemorrhage: a major morbidity in extremely preterm infants. J Perinatol 32: 699–704. Zimmerman E, Lahav A (2013). Ototoxicity in preterm infants: effects of genetics, aminoglycosides, and loud environmental noise. J Perinatol 33: 3–8. Zwicker JG, Miller SP, Grunau RE et al. (2016). Smaller cerebellar growth and poorer neurodevelopmental outcomes in very preterm infants exposed to neonatal morphine. J Pediatr 172: 81–87.e2.

This page intentionally left blank

Handbook of Clinical Neurology, Vol. 173 (3rd series) Neurocognitive Development: Normative Development A. Gallagher, C. Bulteau, D. Cohen and J.L. Michaud, Editors https://doi.org/10.1016/B978-0-444-64150-2.00027-7 Copyright © 2020 Elsevier B.V. All rights reserved

Chapter 26

Pregnant women, prescription, and fetal risk 1
ELISABETH ELEFANT *, CYRIL HANIN2, AND DAVID COHEN2,3 1

Centre de R
ef
erence sur les Agents T
eratogènes, H^ opital Armand-Trousseau, Paris, France

2

Service de Psychiatrie de l’Enfant et de l’Adolescent, APHP.Sorbonne Universit
e, Groupe Hospitalier Piti
e-Salpêtrière, Paris, France 3

Institut des Systèmes Intelligents et Robotiques, Sorbonne Universit
e, Paris, France

Abstract Since the historical scandal of thalidomide in the 1960s, practitioners and future mothers are fearful of drugs during pregnancy. In-uterine exposure to drugs can induce major malformation of the fetus or even intrauterine fetal death. Prescribing drugs to a pregnant woman requires particular attention, and it is necessary to consider both the maternal needs and the proven and potential fetal risks. In this chapter, we review the mechanisms for medication transfer from mother to fetus, fetal risk according to pregnancy timeline, and the main dangerous drugs during pregnancy. We also focus on three prescription debates, which are relevant for neurodevelopmental disorder, because they each point to a paradigmatic situation—diethylstilbestrol, which shows transgenerational adversary effects; valproate, which impacts neurodevelopment as a whole; and antidepressants for which the adverse impact on neurodevelopment is still controversial given the impact of depression itself. Finally, we consider the implications for practice and toxicologic research to promote risk prevention.

INTRODUCTION Physicians, practitioners, and the general population are fearful of drugs during pregnancy. The historical scandal of thalidomide in the 1960s has been followed by several other warnings that in utero exposure to drugs can induce major malformation of the fetus, or even intrauterine fetal death. Diethylstilbestrol in the 1980s and valproic acid, more recently, are drugs that have given rise to similar occurrences. These events highlight the need for a careful approach in this critical life stage. Prescribing drugs to a pregnant woman requires particular attention and must consider both the maternal needs and the proven and potential fetal risks. Drug treatment of pregnant women is becoming more common: in occidental countries, about 30% of pregnancies are not anticipated, and a woman can find out she is pregnant while she is already under medical treatment. Moreover, first-time mothers are getting older and so are more at risk

of being already diagnosed with a chronic disease. The situation where women undergoing medication wish to have children is now more common.

WHY WOULD THE FETUS BE AT RISK? During pregnancy, normally there is no mixing of fetal and maternal blood. Vascular systems are separated by the placenta, a temporary tissue interface that allows exchanges between both blood compartments. As the pregnancy progresses, the surface area of the placenta increases and it becomes thinner. This transformation allows a larger quantity of nutrients to be transported and so facilitates the transit of drugs to the growing fetus. Some enzymatic activity is present in the placenta (e.g., prednisolone can be metabolized into prednisone), but its impact on drug metabolism is small. The term placental “barrier” is inappropriate. Except for high-weight molecules such as insulin, heparins, interferons, or

*Correspondence to: Elisabeth Elefant, Centre de r
ef
erences sur les agents t
eratogènes (CRAT), h^ opital Armand-Trousseau, DMU ESPRIT, AP-HP, Paris, France, E-mail: [email protected]


E. ELEFANT ET AL.

378

anatoxins, most drugs can get through the placenta to various degrees (Elefant and Beghin, 2009). This means that the fetus can be exposed to exogenous substances with potentially dramatic consequences. In this chapter, we describe how the placenta functions, its transfer mechanisms, and the associated risks.

Function and histology The placenta is the unique link between the mother and the fetus and is essential to the fetus’ appropriate development. It is responsible for the nutrient supply and the removal of waste products from the fetus’ blood. During the embryonic phase, the chorion is not perfused by maternal blood but by an extracellular fluid extracted from the plasma (Burton and Jaunaiux, 2001). Drugs and viruses may easily diffuse during organogenesis. From 10 weeks of pregnancy until delivery, maternal and fetal blood are separated by the so-called “barrier” composed of the fetal endothelium, the surrounding mesenchyma, and the trophoblast cells (discontinued cytotrophoblast and syncytiotrophoblast). From this point, molecules have to cross the barrier to get to the fetal blood compartment (Gude et al., 2004). With this barrier in place, the mother’s and child’s compartments are sealed and do not mix. However, some xenobiotics (such as drugs) can still pass through, using different mechanisms.

Transfer mechanisms PASSIVE DIFFUSION Passive diffusion is the main mechanism of exchanges through the placenta. It does not require any energy, is not overloaded, or prone to competitive inhibition. According to Fick’s law, diffusion rate is proportional to both the surface and concentration, and inversely proportional to the placenta’s thickness. Drugs with low molecular weight (