Development of Brain Network in Children with Autism from Early Childhood to Late Childhood

Extensive studies have indicated brain function connectivity abnormalities in autism spectrum disorder (ASD). However, there is a lack of longitudinal or cross-sectional research focused on tracking age-related developmental trends of autistic children at an early stage of brain development or based on a relatively large sample. The present study examined brain network changes in a total of 186 children both with and without ASD from 3 to 11 years, an early and key development period when significant changes are expected. The study aimed to investigate possible abnormal connectivity patterns and topological properties of children with ASD from early childhood to late childhood by using resting-state electroencephalographic (EEG) data. The main findings of the study were as follows: (1) From the connectivity analysis, several inter-regional synchronizations with reduction were identified in the younger and older ASD groups, and several intra-regional synchronization increases were observed in the older ASD group. (2) From the graph analysis, a reduced clustering coefficient and enhanced mean shortest path length in specific frequencies was observed in children with ASD. (3) Results suggested an age-related decrease of the mean shortest path length in the delta and theta bands in TD children, whereas atypical age-related alteration was observed in the ASD group. In addition, graph measures were correlated with ASD symptom severity in the alpha band. These results demonstrate that abnormal neural communication is already present at the early stages of brain development in autistic children and this may be involved in the behavioral deficits associated with ASD.

[1]  Sam M. Doesburg,et al.  Reduced beta band connectivity during number estimation in autism , 2014, NeuroImage: Clinical.

[2]  John Suckling,et al.  Brain anatomy and its relationship to behavior in adults with autism spectrum disorder: a multicenter magnetic resonance imaging study. , 2012, Archives of general psychiatry.

[3]  Vicente L. Malave,et al.  Autism as a neural systems disorder: A theory of frontal-posterior underconnectivity , 2012, Neuroscience & Biobehavioral Reviews.

[4]  O. Sporns,et al.  White matter maturation reshapes structural connectivity in the late developing human brain , 2010, Proceedings of the National Academy of Sciences.

[5]  Scott P. Johnson,et al.  Electrophysiological evidence of heterogeneity in visual statistical learning in young children with ASD. , 2015, Developmental science.

[6]  Cornelis J. Stam,et al.  Disrupted Functional Brain Networks in Autistic Toddlers , 2013, Brain Connect..

[7]  C. Stam,et al.  r Human Brain Mapping 32:413–425 (2011) r Network Analysis of Resting State EEG in the Developing Young Brain: Structure Comes With Maturation , 2022 .

[8]  R. Barry,et al.  EEG power and coherence in autistic spectrum disorder , 2008, Clinical Neurophysiology.

[9]  G. Dawson,et al.  Resting State Cortical Connectivity Reflected in EEG Coherence in Individuals With Autism , 2007, Biological Psychiatry.

[10]  G. Dawson,et al.  Defining the broader phenotype of autism: Genetic, brain, and behavioral perspectives , 2002, Development and Psychopathology.

[11]  C J Stam,et al.  The trees and the forest: Characterization of complex brain networks with minimum spanning trees. , 2014, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[12]  Mark A. Kramer,et al.  Robust disruptions in electroencephalogram cortical oscillations and large-scale functional networks in autism , 2015, BMC Neurology.

[13]  J. M. Moran,et al.  Local and long-range functional connectivity is reduced in concert in autism spectrum disorders , 2013, Proceedings of the National Academy of Sciences.

[14]  J. Sweeney,et al.  Resting state EEG abnormalities in autism spectrum disorders , 2013, Journal of Neurodevelopmental Disorders.

[15]  L. Schieve,et al.  Estimated Prevalence of Autism and Other Developmental Disabilities Following Questionnaire Changes in the 2014 National Health Interview Survey. , 2015, National health statistics reports.

[16]  C. Stam,et al.  Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources , 2007, Human brain mapping.

[17]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[18]  Hiroki Sayama,et al.  Developmental changes in spontaneous electrocortical activity and network organization from early to late childhood , 2015, NeuroImage.

[19]  Eugenio Rodriguez,et al.  The development of neural synchrony reflects late maturation and restructuring of functional networks in humans , 2009, Proceedings of the National Academy of Sciences.

[20]  L. Eaves,et al.  Screening for Autism Spectrum Disorders With the Social Communication Questionnaire , 2006, Journal of developmental and behavioral pediatrics : JDBP.

[21]  Piotr J. Durka,et al.  A simple system for detection of EEG artifacts in polysomnographic recordings , 2003, IEEE Transactions on Biomedical Engineering.

[22]  E. Bullmore,et al.  Annual Research Review: Growth connectomics – the organization and reorganization of brain networks during normal and abnormal development , 2014, Journal of child psychology and psychiatry, and allied disciplines.

[23]  Isaac Meilijson,et al.  Synaptic Pruning in Development: A Computational Account , 1998, Neural Computation.

[24]  J. Helen Cross,et al.  Functional brain network organisation of children between 2 and 5years derived from reconstructed activity of cortical sources of high-density EEG recordings , 2013, NeuroImage.

[25]  Danielle Smith Bassett,et al.  Small-World Brain Networks , 2006, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[26]  A. Irimia,et al.  Trajectory of frequency stability in typical development , 2015, Brain Imaging and Behavior.

[27]  C. Stam,et al.  Alzheimer's disease: connecting findings from graph theoretical studies of brain networks , 2013, Neurobiology of Aging.

[28]  R. Kahn,et al.  Efficiency of Functional Brain Networks and Intellectual Performance , 2009, The Journal of Neuroscience.

[29]  C. Stam.  Modern network science of neurological disorders , 2014, Nature Reviews Neuroscience.

[30]  Margot J. Taylor,et al.  Review of neuroimaging in autism spectrum disorders: what have we learned and where we go from here , 2011, Molecular autism.

[31]  J. Suckling,et al.  Distinct patterns of grey matter abnormality in high-functioning autism and Asperger's syndrome. , 2008, Journal of child psychology and psychiatry, and allied disciplines.

[32]  Koushik Maharatna,et al.  Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates , 2014, Journal of neural engineering.

[33]  V. Menon.  Large-scale brain networks and psychopathology: a unifying triple network model , 2011, Trends in Cognitive Sciences.

[34]  C. Gillberg,et al.  Autism spectrum symptoms in children with neurological disorders , 2012, Child and Adolescent Psychiatry and Mental Health.

[35]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[36]  N. Minshew,et al.  Autism as a selective disorder of complex information processing and underdevelopment of neocortical systems , 2002, Molecular Psychiatry.

[37]  Richard Coppola,et al.  Graph theoretical analysis of resting magnetoencephalographic functional connectivity networks , 2013, Front. Comput. Neurosci..

[38]  Yasser Ghanbari,et al.  Joint Analysis of Band-Specific Functional Connectivity and Signal Complexity in Autism , 2015, Journal of autism and developmental disorders.

[39]  S. Baron-Cohen,et al.  What is available for case identification in autism research in mainland China , 2013 .

[40]  O. Sporns.  Contributions and challenges for network models in cognitive neuroscience , 2014, Nature Neuroscience.

[41]  M. Sigman,et al.  A big-world network in ASD: Dynamical connectivity analysis reflects a deficit in long-range connections and an excess of short-range connections , 2010, Neuropsychologia.

[42]  Janet B W Williams,et al.  Diagnostic and Statistical Manual of Mental Disorders , 2013 .

[43]  W. Singer,et al.  Impaired Gamma-Band Activity during Perceptual Organization in Adults with Autism Spectrum Disorders: Evidence for Dysfunctional Network Activity in Frontal-Posterior Cortices , 2012, The Journal of Neuroscience.

[44]  F. Volkmar,et al.  An evaluation of the autism behavior checklist , 1988, Journal of autism and developmental disorders.

[45]  Natasa Kovacevic,et al.  Maturation of EEG power spectra in early adolescence: a longitudinal study. , 2011, Developmental science.

[46]  Matti S. Hämäläinen,et al.  Altered Development and Multifaceted Band-Specific Abnormalities of Resting State Networks in Autism , 2015, Biological Psychiatry.

[47]  Lindsay Walker,et al.  Modeling healthy male white matter and myelin development: 3 through 60 months of age , 2014, NeuroImage.

[48]  Lei Wang,et al.  Complex network analysis of resting state EEG in amnestic mild cognitive impairment patients with type 2 diabetes , 2015, Front. Comput. Neurosci..

[49]  H. Clancy,et al.  The Diagnosis of Infantile Autism , 1969, Developmental medicine and child neurology.

[50]  J. Palva,et al.  Functional Roles of Alpha-Band Phase Synchronization in Local and Large-Scale Cortical Networks , 2011, Front. Psychology.

[51]  T. Prescott,et al.  The brainstem reticular formation is a small-world, not scale-free, network , 2006, Proceedings of the Royal Society B: Biological Sciences.