Developmental changes of cortical white–gray contrast as predictors of autism diagnosis and severity

Recent studies suggest that both cortical gray and white-matter microstructural characteristics are distinct for subjects with autism. There is a lack of evidence regarding how these characteristics change in a developmental context. We analysed a longitudinal/cross-sectional dataset of 402 magnetic resonance imaging (MRI) scans (171 subjects with autism and 231 with typical development) from the Autism Brain Imaging Data Exchange, cohorts I–II (ABIDE-I-II). In the longitudinal sample, we computed the rate of change in the white–gray contrast, a measure which has been related to age and cognitive performance, at the boundary of the cerebral cortex. Then, we devised an analogous metric for the cross-sectional sample of the ABIDE dataset to measure age-related differences in cortical contrast. Further, we developed a probabilistic model to predict the diagnostic group in the longitudinal sample of the cortical contrast change data, using results obtained from the cross-sectional sample. In both subsets, we observed a similar overall pattern of greater decrease within the autistic population in intensity contrast for most cortical regions (81%), with occasional increases, mostly in primary sensory regions. This pattern correlated well with raw and calibrated behavioural scores. The prediction results show 76% accuracy for the whole-cortex diagnostic prediction and 86% accuracy in prediction using the motor system alone. Our results support a contrast change analysis strategy that appears sensitive in predicting diagnostic outcome and symptom severity in autism spectrum disorder, and is readily extensible to other MRI-based studies of neurodevelopmental cohorts.

[1]  André J. W. van der Kouwe,et al.  Age-associated alterations in cortical gray and white matter signal intensity and gray to white matter contrast , 2009, NeuroImage.

[2]  Wendy J. Ungar,et al.  National Database for Autism Research (NDAR): Big Data Opportunities for Health Services Research and Health Technology Assessment , 2016, PharmacoEconomics.

[3]  H. Yamasue,et al.  Comparison of white matter integrity between autism spectrum disorder subjects and typically developing individuals: a meta-analysis of diffusion tensor imaging tractography studies , 2013, Molecular Autism.

[4]  Nikolaus Weiskopf,et al.  Apparent thinning of visual cortex during childhood is associated with myelination, not pruning , 2018, bioRxiv.

[5]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[6]  Hrishikesh Deshpande,et al.  White Matter Diffusion of Major Fiber Tracts Implicated in Autism Spectrum Disorder , 2016, Brain Connect..

[7]  Vasily A. Vakorin,et al.  Developmental changes in neuromagnetic rhythms and network synchrony in autism , 2017, Annals of neurology.

[8]  Alan C. Evans,et al.  Fast and robust parameter estimation for statistical partial volume models in brain MRI , 2004, NeuroImage.

[9]  Seraphina Solders,et al.  Regional specificity of aberrant thalamocortical connectivity in autism , 2015, Human brain mapping.

[10]  D. Le Bihan,et al.  Structural Asymmetries in the Infant Language and Sensori-motor Networks , 2022 .

[11]  Huafu Chen,et al.  Increased Gray Matter Volume and Resting-State Functional Connectivity in Somatosensory Cortex and their Relationship with Autistic Symptoms in Young Boys with Autism Spectrum Disorder , 2017, Front. Physiol..

[12]  Baxter Rogers,et al.  Thalamocortical dysconnectivity in autism spectrum disorder: An analysis of the Autism Brain Imaging Data Exchange. , 2017, Biological psychiatry. Cognitive neuroscience and neuroimaging.

[13]  Jared A. Nielsen,et al.  Abnormal lateralization of functional connectivity between language and default mode regions in autism , 2014, Molecular Autism.

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

[15]  J. Hutsler,et al.  Abnormal cell patterning at the cortical gray–white matter boundary in autism spectrum disorders , 2010, Brain Research.

[16]  J. Iverson,et al.  The development of autism spectrum disorders: variability and causal complexity. , 2017, Wiley interdisciplinary reviews. Cognitive science.

[17]  S. Baron-Cohen,et al.  Developmental white matter microstructure in autism phenotype and corresponding endophenotype during adolescence , 2015, Translational Psychiatry.

[18]  Ralph-Axel Müller,et al.  Editorial: Time to give up on Autism Spectrum Disorder? , 2017, Autism research : official journal of the International Society for Autism Research.

[19]  D. V. van Essen,et al.  Mapping Human Cortical Areas In Vivo Based on Myelin Content as Revealed by T1- and T2-Weighted MRI , 2011, The Journal of Neuroscience.

[20]  A. R. McIntosh,et al.  Spatiotemporal analysis of event-related fMRI data using partial least squares , 2004, NeuroImage.

[21]  Do P. M. Tromp,et al.  Multivariate characterization of white matter heterogeneity in autism spectrum disorder , 2017, NeuroImage: Clinical.

[22]  Ceri H. Davies,et al.  Neurodevelopmental disorders , 2013, Neuropharmacology.

[23]  M. Dylan Tisdall,et al.  Head motion during MRI acquisition reduces gray matter volume and thickness estimates , 2015, NeuroImage.

[24]  John Suckling,et al.  On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance‐weighted principal component analysis , 2016, Human brain mapping.

[25]  M. Taylor,et al.  Widespread White Matter Differences in Children and Adolescents with Autism Spectrum Disorder , 2016, Journal of autism and developmental disorders.

[26]  C. Lord,et al.  Standardizing ADOS Scores for a Measure of Severity in Autism Spectrum Disorders , 2009, Journal of autism and developmental disorders.

[27]  Guido Gerig,et al.  The Emergence of Network Inefficiencies in Infants With Autism Spectrum Disorder , 2017, Biological Psychiatry.

[28]  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.

[29]  Alan C. Evans,et al.  Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data , 2016, NeuroImage.

[30]  Christian O'Reilly,et al.  Is functional brain connectivity atypical in autism? A systematic review of EEG and MEG studies , 2017, PloS one.

[31]  A. Franco,et al.  NeuroImage: Clinical , 2022 .

[32]  Dardo Tomasi,et al.  Reduced Local and Increased Long-Range Functional Connectivity of the Thalamus in Autism Spectrum Disorder , 2019, Cerebral cortex.

[33]  Alan C. Evans,et al.  Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis , 2002, IEEE Transactions on Medical Imaging.

[34]  Milos Judas,et al.  The development of the subplate and thalamocortical connections in the human foetal brain , 2010, Acta paediatrica.

[35]  Edward T. Bullmore,et al.  Synaptic and transcriptionally downregulated genes are associated with cortical thickness differences in autism , 2017, bioRxiv.

[36]  Alan C. Evans,et al.  Cortical Thickness Abnormalities in Autism Spectrum Disorders Through Late Childhood, Adolescence, and Adulthood: A Large‐Scale MRI Study , 2017, Cerebral cortex.

[37]  Alan C. Evans,et al.  Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification , 2005, NeuroImage.

[38]  G Pearlson,et al.  Magnetic resonance imaging evidence for a defect of cerebral cortical development in autism. , 1990, The American journal of psychiatry.

[39]  John Suckling,et al.  Association Between the Probability of Autism Spectrum Disorder and Normative Sex-Related Phenotypic Diversity in Brain Structure , 2017, JAMA psychiatry.

[40]  Sigal Berman,et al.  Anatomical Abnormalities in Autism? , 2016, Cerebral cortex.

[41]  E. Rolls,et al.  Autism: reduced connectivity between cortical areas involved in face expression, theory of mind, and the sense of self , 2015, Brain : a journal of neurology.

[42]  John Suckling,et al.  In Vivo Evidence of Reduced Integrity of the Gray–White Matter Boundary in Autism Spectrum Disorder , 2017, Cerebral cortex.

[44]  Brain Development Cooperative Group,et al.  The NIH MRI study of normal brain development , 2006, NeuroImage.

[45]  M. Casanova,et al.  Review: Cortical construction in autism spectrum disorder: columns, connectivity and the subplate , 2016, Neuropathology and applied neurobiology.

[46]  Hirotaka Kosaka,et al.  Sex Differences in the Default Mode Network with Regard to Autism Spectrum Traits: A Resting State fMRI Study , 2015, PloS one.

[47]  Jason S. Nomi,et al.  Intrinsic functional connectivity variance and state‐specific under‐connectivity in autism , 2017, Human brain mapping.

[48]  C. Lord,et al.  The Autism Diagnostic Observation Schedule: Revised Algorithms for Improved Diagnostic Validity , 2007, Journal of autism and developmental disorders.

[49]  Nicholas Lange,et al.  Longitudinal changes in cortical thickness in autism and typical development. , 2014, Brain : a journal of neurology.

[50]  Lucina Q. Uddin,et al.  Idiosyncratic connectivity in autism: developmental and anatomical considerations , 2015, Trends in Neurosciences.

[51]  Kaustubh Supekar,et al.  Sex differences in cortical volume and gyrification in autism , 2015, Molecular Autism.

[52]  S. Bookheimer,et al.  Reduced modulation of thalamocortical connectivity during exposure to sensory stimuli in ASD , 2017, Autism research : official journal of the International Society for Autism Research.

[53]  Steven Robbins,et al.  An unbiased iterative group registration template for cortical surface analysis , 2007, NeuroImage.

[54]  C. Keysers,et al.  Increased Functional Connectivity Between Subcortical and Cortical Resting-State Networks in Autism Spectrum Disorder. , 2015, JAMA psychiatry.

[55]  Jennifer Fedor,et al.  Cortical and subcortical brain morphometry differences between patients with autism spectrum disorders (ASD) and healthy individuals across the lifespan: results from the ENIGMA-ASD working group , 2017 .

[56]  Tobias Bonhoeffer,et al.  Neuronal Plasticity: Beyond the Critical Period , 2014, Cell.

[57]  J. Lainhart Brain imaging research in autism spectrum disorders: in search of neuropathology and health across the lifespan , 2015, Current opinion in psychiatry.

[58]  O. Andreassen,et al.  Probing developmental patterns of intracortical myelination using gray/white matter contrast and associations with cognitive abilities and psychopathology in youth , 2018 .

[59]  D. Collins,et al.  Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space , 1994, Journal of computer assisted tomography.

[60]  Krzysztof J. Gorgolewski,et al.  MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites , 2016, bioRxiv.

[61]  Alan C. Evans,et al.  T1 white/gray contrast as a predictor of chronological age, and an index of cognitive performance , 2017, NeuroImage.

[62]  Daniel P. Kennedy,et al.  Enhancing studies of the connectome in autism using the autism brain imaging data exchange II , 2017, Scientific Data.

[63]  L. Wing The autistic spectrum , 1997, The Lancet.

[64]  B. Leventhal,et al.  The Autism Diagnostic Observation Schedule—Generic: A Standard Measure of Social and Communication Deficits Associated with the Spectrum of Autism , 2000, Journal of autism and developmental disorders.

[65]  J. Robins,et al.  Explaining discrepancies between longitudinal and cross-sectional models. , 1986, Journal of chronic diseases.

[66]  Vince D. Calhoun,et al.  Transient increased thalamic-sensory connectivity and decreased whole-brain dynamism in autism , 2019, NeuroImage.

[67]  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.