Different brain networks underlying intelligence in autism spectrum disorders

There has been sustained clinical and cognitive neuroscience research interest in how network correlates of brain‐behavior relationships might be altered in Autism Spectrum Disorders (ASD) and other neurodevelopmental disorders. As previous work has mostly focused on adults, the nature of whole‐brain connectivity networks underlying intelligence in pediatric cohorts with abnormal neurodevelopment requires further investigation. We used network‐based statistics (NBS) to examine the association between resting‐state functional Magnetic Resonance Imaging (fMRI) connectivity and fluid intelligence ability in male children (n = 50) with Autism Spectrum Disorders (ASD; M = 10.45, SD = 1.58 years and in controls (M = 10.38, SD = 0.96 years) matched on fluid intelligence performance, age and sex. Repeat analyses were performed in independent sites for validation and replication. Despite being equivalent on fluid intelligence ability to strictly matched neurotypical controls, boys with ASD displayed a subnetwork of significantly increased associations between functional connectivity and fluid intelligence. Between‐group differences remained significant at higher edge thresholding, and results were validated in independent‐site replication analyses in an equivalent age and sex‐matched cohort with ASD. Regions consistently implicated in atypical connectivity correlates of fluid intelligence in ASD were the angular gyrus, posterior middle temporal gyrus, occipital and temporo‐occipital regions. Development of fluid intelligence neural correlates in young ASD males is aberrant, with an increased strength in intrinsic connectivity association during childhood. Alterations in whole‐brain network correlates of fluid intelligence in ASD may be a compensatory mechanism that allows equal task performance to neurotypical peers.

[1]  Penny A. MacDonald,et al.  Subcortical regional morphology correlates with fluid and spatial intelligence , 2014, Human brain mapping.

[2]  Kaustubh Supekar,et al.  Reconceptualizing functional brain connectivity in autism from a developmental perspective , 2013, Front. Hum. Neurosci..

[3]  S. Bowden,et al.  Different brain networks underlying intelligence in Autism Spectrum Disorders and Typically Developing Children , 2017, bioRxiv.

[4]  Jason B. Mattingley,et al.  Functional brain networks related to individual differences in human intelligence at rest , 2016, Scientific Reports.

[5]  Vanessa Sluming,et al.  Voxel-based morphometry and stereology provide convergent evidence of the importance of medial prefrontal cortex for fluid intelligence in healthy adults , 2005, NeuroImage.

[6]  S. Petersen,et al.  Brain Networks and Cognitive Architectures , 2015, Neuron.

[7]  F. Krueger,et al.  Fluid Intelligence Allows Flexible Recruitment of the Parieto-Frontal Network in Analogical Reasoning , 2011, Front. Hum. Neurosci..

[8]  T. Zeffiro,et al.  Enhanced visual processing contributes to matrix reasoning in autism , 2009, Human brain mapping.

[9]  S. Gotts,et al.  A theoretical rut: revisiting and critically evaluating the generalized under/over-connectivity hypothesis of autism. , 2016, Developmental science.

[10]  Arvid Lundervold,et al.  General fluid-type intelligence is related to indices of white matter structure in middle-aged and old adults , 2013, NeuroImage.

[11]  Dimitris Samaras,et al.  Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example , 2016, NeuroImage.

[12]  C. Chabris,et al.  Neural mechanisms of general fluid intelligence , 2003, Nature Neuroscience.

[13]  Damien A. Fair,et al.  Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature , 2017, NeuroImage.

[14]  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.

[15]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

[16]  Gian Luca Romani,et al.  Common and unique neuro-functional basis of induction, visualization, and spatial relationships as cognitive components of fluid intelligence , 2012, NeuroImage.

[17]  A. Pickles,et al.  Prenatal anxiety, maternal stroking in infancy, and symptoms of emotional and behavioral disorders at 3.5 years , 2016, European Child & Adolescent Psychiatry.

[18]  Sven Bölte,et al.  Brief Report: The Level and Nature of Autistic Intelligence Revisited , 2009, Journal of Autism and Developmental Disorders.

[19]  Vince D. Calhoun,et al.  Modern Methods for Interrogating the Human Connectome , 2016, Journal of the International Neuropsychological Society.

[20]  Dennis Velakoulis,et al.  MRI correlates of general intelligence in neurotypical adults , 2016, Journal of Clinical Neuroscience.

[21]  Laurent Mottron,et al.  The Level and Nature of Autistic Intelligence , 2007, Psychological science.

[22]  Thomas Bourgeron,et al.  Neuroanatomical Diversity of Corpus Callosum and Brain Volume in Autism: Meta-analysis, Analysis of the Autism Brain Imaging Data Exchange Project, and Simulation , 2015, Biological Psychiatry.

[23]  Daniel P. Kennedy,et al.  The Autism Brain Imaging Data Exchange: Towards Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism , 2013, Molecular Psychiatry.

[24]  K. McGrew,et al.  The Cattell-Horn-Carroll model of intelligence. , 2012 .

[25]  Ulrike Basten,et al.  Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence , 2015 .

[26]  Susan L. Whitfield-Gabrieli,et al.  Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks , 2012, Brain Connect..

[27]  J. Burack,et al.  Neuroscience and Biobehavioral Reviews Veridical Mapping in the Development of Exceptional Autistic Abilities , 2022 .

[28]  Peter C. Hansen,et al.  Functional neural correlates of fluid and crystallized analogizing , 2010, NeuroImage.

[29]  James K. Kroger,et al.  Recruitment of anterior dorsolateral prefrontal cortex in human reasoning: a parametric study of relational complexity. , 2002, Cerebral cortex.

[30]  Marcel Adam Just,et al.  Functional connectivity in an fMRI working memory task in high-functioning autism , 2005, NeuroImage.

[31]  L. Williams,et al.  Abnormal Structural Networks Characterize Major Depressive Disorder: A Connectome Analysis , 2014, Biological Psychiatry.

[32]  Danielle S. Bassett,et al.  Multi-scale brain networks , 2016, NeuroImage.

[33]  Gian Luca Romani,et al.  Differential patterns of cortical activation as a function of fluid reasoning complexity , 2009, Human brain mapping.

[34]  L Penke,et al.  Brain white matter tract integrity as a neural foundation for general intelligence , 2012, Molecular Psychiatry.

[35]  Jessica A. Turner,et al.  Behavioral Interpretations of Intrinsic Connectivity Networks , 2011, Journal of Cognitive Neuroscience.

[36]  E. Bullmore,et al.  Disrupted Axonal Fiber Connectivity in Schizophrenia , 2011, Biological Psychiatry.

[37]  A. Laird,et al.  Developmental meta-analysis of the functional neural correlates of autism spectrum disorders. , 2013, Journal of the American Academy of Child and Adolescent Psychiatry.

[38]  L. Cocchi,et al.  Decreased Functional Brain Connectivity in Adolescents with Internet Addiction , 2013, PloS one.

[39]  Laurent Mottron,et al.  Autistic fluid intelligence: Increased reliance on visual functional connectivity with diminished modulation of coupling by task difficulty , 2015, NeuroImage: Clinical.

[40]  E. Bullmore,et al.  Opportunities and Challenges for Psychiatry in the Connectomic Era. , 2017, Biological psychiatry. Cognitive neuroscience and neuroimaging.

[41]  J. Belliveau,et al.  Neuroimaging of the functional and structural networks underlying visuospatial vs. linguistic reasoning in high-functioning autism , 2010, Neuropsychologia.

[42]  F. Happé,et al.  Wechsler IQ profile and theory of mind in autism: a research note. , 1994, Journal of child psychology and psychiatry, and allied disciplines.

[43]  T. Keith,et al.  Multi-group and hierarchical confirmatory factor analysis of the Wechsler Intelligence Scale for Children—Fifth Edition: What does it measure? , 2017 .

[44]  S. Bowden,et al.  Autism spectrum disorders: Neuroimaging findings from systematic reviews , 2017 .

[45]  Dawn P. Flanagan,et al.  Contemporary intellectual assessment : theories, tests, and issues , 1997 .

[46]  Ahmad Shojaei,et al.  A statistical approach in human brain connectome of Parkinson Disease in elderly people using Network Based Statistics , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[47]  Edward T. Bullmore,et al.  Fundamentals of Brain Network Analysis , 2016 .

[48]  Edward T. Bullmore,et al.  Network-based statistic: Identifying differences in brain networks , 2010, NeuroImage.

[49]  Timothy Edward John Behrens,et al.  Task-free MRI predicts individual differences in brain activity during task performance , 2016, Science.

[50]  E. Bullmore,et al.  Reconciling abnormalities of brain network structure and function in schizophrenia , 2015, Current Opinion in Neurobiology.

[51]  Stephen M. Smith,et al.  Temporally-independent functional modes of spontaneous brain activity , 2012, Proceedings of the National Academy of Sciences.

[52]  Li Yao,et al.  Gray matter abnormalities in pediatric autism spectrum disorder: a meta-analysis with signed differential mapping , 2017, European Child & Adolescent Psychiatry.

[53]  R. Haier,et al.  The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence , 2007, Behavioral and Brain Sciences.

[54]  C. Gillberg,et al.  Asperger syndrome, autism and attention disorders: a comparative study of the cognitive profiles of 120 children. , 1997, Journal of child psychology and psychiatry, and allied disciplines.

[55]  Gary King,et al.  MatchIt: Nonparametric Preprocessing for Parametric Causal Inference , 2011 .

[56]  Motoichiro Kato,et al.  Superior Fluid Intelligence in Children with Asperger's Disorder , 2007 .

[57]  Rex E. Jung,et al.  Gray matter correlates of fluid, crystallized, and spatial intelligence: Testing the P-FIT model , 2009 .

[58]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[59]  S. Kosslyn,et al.  Bridging psychology and biology. The analysis of individuals in groups. , 2002, The American psychologist.

[60]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[61]  J. Desmond,et al.  Neural Substrates of Fluid Reasoning: An fMRI Study of Neocortical Activation during Performance of the Raven's Progressive Matrices Test , 1997, Cognitive Psychology.

[62]  Ravindra K. Ahuja,et al.  Network Flows: Theory, Algorithms, and Applications , 1993 .

[63]  Thomas T. Liu,et al.  A component based noise correction method (CompCor) for BOLD and perfusion based fMRI , 2007, NeuroImage.

[64]  R. Hashimoto,et al.  Functional Alterations in Neural Substrates of Geometric Reasoning in Adults with High-Functioning Autism , 2012, PloS one.

[65]  Albert Hofman,et al.  Functional connectivity between parietal and frontal brain regions and intelligence in young children: The Generation R study , 2013, Human brain mapping.