Multimodal neuroimaging based classification of autism spectrum disorder using anatomical, neurochemical, and white matter correlates

Neuroimaging techniques, such as fMRI, structural MRI, diffusion tensor imaging (DTI), and proton magnetic resonance spectroscopy (1H-MRS) have uncovered evidence for widespread functional and anatomical brain abnormalities in autism spectrum disorder (ASD) suggesting it to be a system-wide neural systems disorder. Nevertheless, most previous studies have focused on examining one index of neuropathology through a single neuroimaging modality, and seldom using multiple modalities to examine the same cohort of individuals. The current study aims to bring together multiple brain imaging modalities (structural MRI, DTI, and 1H-MRS) to investigate the neural architecture in the same set of individuals (19 high-functioning adults with ASD and 18 typically developing (TD) peers). Morphometry analysis revealed increased cortical thickness in ASD participants, relative to typical controls, across the left cingulate, left pars opercularis of the inferior frontal gyrus, left inferior temporal cortex, and right precuneus, and reduced cortical thickness in right cuneus and right precentral gyrus. ASD adults also had reduced fractional anisotropy (FA) and increased radial diffusivity (RD) for two clusters on the forceps minor of the corpus callosum, revealed by DTI analyses. 1H-MRS results showed a reduction in the N-acetylaspartate/Creatine ratio in dorsal anterior cingulate cortex (dACC) in ASD participants. A decision tree classification analysis across the three modalities resulted in classification accuracy of 91.9% with FA, RD, and cortical thickness as key predictors. Examining the same cohort of adults with ASD and their TD peers, this study found alterations in cortical thickness, white matter (WM) connectivity, and neurochemical concentration in ASD. These findings underscore the potential for multimodal imaging to better inform on the neural characteristics most relevant to the disorder.

[1]  Catherine Lord,et al.  Autism Diagnostic Interview-Revised , 2016, Encyclopedia of Autism Spectrum Disorders.

[2]  Suzanne E. Welcome,et al.  Longitudinal Mapping of Cortical Thickness and Brain Growth in Normal Children , 2022 .

[3]  W. McMahon,et al.  The Ritvo Autism Asperger Diagnostic Scale-Revised (RAADS-R): A Scale to Assist the Diagnosis of Autism Spectrum Disorder in Adults: An International Validation Study , 2010, Journal of autism and developmental disorders.

[4]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[5]  Karl J. Friston,et al.  Generative and recognition models for neuroanatomy , 2004, NeuroImage.

[6]  Robert T. Schultz,et al.  Multi-voxel pattern analysis of fMRI data predicts clinical symptom severity , 2011, NeuroImage.

[7]  J. Hutsler,et al.  Histological and Magnetic Resonance Imaging Assessment of Cortical Layering and Thickness in Autism Spectrum Disorders , 2007, Biological Psychiatry.

[8]  Roberto Toro,et al.  Cortical anatomy in autism spectrum disorder: an in vivo MRI study on the effect of age. , 2010, Cerebral cortex.

[9]  Kartini Rahmat,et al.  A decision tree for differentiating multiple system atrophy from Parkinson’s disease using 3-T MR imaging , 2013, European Radiology.

[10]  A. Dale,et al.  High‐resolution intersubject averaging and a coordinate system for the cortical surface , 1999, Human brain mapping.

[11]  J. Piven,et al.  Magnetic resonance imaging and head circumference study of brain size in autism: birth through age 2 years. , 2005, Archives of general psychiatry.

[12]  John Suckling,et al.  Brain surface anatomy in adults with autism: the relationship between surface area, cortical thickness, and autistic symptoms. , 2013, JAMA psychiatry.

[13]  Andrew L. Alexander,et al.  Diffusion tensor imaging of the corpus callosum in Autism , 2007, NeuroImage.

[14]  S. Lawrie,et al.  Towards a neuroanatomy of autism: A systematic review and meta-analysis of structural magnetic resonance imaging studies , 2008, European Psychiatry.

[15]  A M Dale,et al.  Measuring the thickness of the human cerebral cortex from magnetic resonance images. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Alan C. Evans,et al.  Positive association between cognitive ability and cortical thickness in a representative US sample of healthy 6 to 18 year-olds , 2009, NeuroImage.

[17]  Jan K Buitelaar,et al.  Pervasive microstructural abnormalities in autism: a DTI study. , 2011, Journal of psychiatry & neuroscience : JPN.

[18]  Randy L. Gollub,et al.  Reproducibility of quantitative tractography methods applied to cerebral white matter , 2007, NeuroImage.

[19]  Jared A. Nielsen,et al.  Functional connectivity magnetic resonance imaging classification of autism. , 2011, Brain : a journal of neurology.

[20]  Anders M. Dale,et al.  Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer , 2006, NeuroImage.

[21]  Eric Courchesne,et al.  Patches of disorganization in the neocortex of children with autism. , 2014, The New England journal of medicine.

[22]  A. Dale,et al.  Thinning of the cerebral cortex in aging. , 2004, Cerebral cortex.

[23]  Christian Scheel,et al.  Imaging derived cortical thickness reduction in high-functioning autism: Key regions and temporal slope , 2011, NeuroImage.

[24]  A. Dale,et al.  Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach , 1993, Journal of Cognitive Neuroscience.

[25]  Moo K. Chung,et al.  Diffusion tensor imaging of white matter in the superior temporal gyrus and temporal stem in autism , 2007, Neuroscience Letters.

[26]  Lauren E. Libero,et al.  Identification of neural connectivity signatures of autism using machine learning , 2013, Front. Hum. Neurosci..

[27]  John O. Willis,et al.  Wechsler Abbreviated Scale of Intelligence , 2014 .

[28]  R. Schiffmann,et al.  Invited Article: An MRI-based approach to the diagnosis of white matter disorders , 2009, Neurology.

[29]  L. Lotspeich,et al.  White matter structure in autism: preliminary evidence from diffusion tensor imaging , 2004, Biological Psychiatry.

[30]  A. M. Dale,et al.  A hybrid approach to the skull stripping problem in MRI , 2004, NeuroImage.

[31]  Vanhamme,et al.  Improved method for accurate and efficient quantification of MRS data with use of prior knowledge , 1997, Journal of magnetic resonance.

[32]  P. Huttenlocher Morphometric study of human cerebral cortex development , 1990, Neuropsychologia.

[33]  M. Just,et al.  A developmental study of the structural integrity of white matter in autism , 2007, Neuroreport.

[34]  N. Hadjikhani,et al.  Anatomical differences in the mirror neuron system and social cognition network in autism. , 2006, Cerebral cortex.

[35]  Alan C. Evans,et al.  Neuroanatomical differences in brain areas implicated in perceptual and other core features of autism revealed by cortical thickness analysis and voxel‐based morphometry , 2009, Human brain mapping.

[36]  D. Graveron-Demilly,et al.  Java-based graphical user interface for the MRUI quantitation package , 2001, Magnetic Resonance Materials in Physics, Biology and Medicine.

[37]  Do P. M. Tromp,et al.  Diffusion Tensor Imaging in Autism Spectrum Disorder: A Review , 2012, Autism research : official journal of the International Society for Autism Research.

[38]  Jonathan R. M. Hosking,et al.  Partitioning Nominal Attributes in Decision Trees , 1999, Data Mining and Knowledge Discovery.

[39]  E. Halpern,et al.  Quantitative neuropathologic correlates of changes in ratio of N-acetylaspartate to creatine in macaque brain. , 2005, Radiology.

[40]  J. Stanley,et al.  In vivo Magnetic Resonance Spectroscopy and its Application to Neuropsychiatric Disorders , 2002, Canadian journal of psychiatry. Revue canadienne de psychiatrie.

[41]  Anand R. Kumar,et al.  Multimodal Brain Connectivity Analysis in Unmedicated Late-Life Depression , 2014, PloS one.

[42]  H. Engeland,et al.  Minicolumnar abnormalities in autism , 2006, Acta Neuropathologica.

[43]  E. Courchesne,et al.  Brain growth across the life span in autism: Age-specific changes in anatomical pathology , 2011, Brain Research.

[44]  J. Piven,et al.  Early brain overgrowth in autism associated with an increase in cortical surface area before age 2 years. , 2011, Archives of general psychiatry.

[45]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[46]  B. Wandell,et al.  Tract Profiles of White Matter Properties: Automating Fiber-Tract Quantification , 2012, PloS one.

[47]  Alan L. Yuille,et al.  Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief , 2011, NeuroImage.

[48]  Steven C. R. Williams,et al.  Describing the Brain in Autism in Five Dimensions—Magnetic Resonance Imaging-Assisted Diagnosis of Autism Spectrum Disorder Using a Multiparameter Classification Approach , 2010, The Journal of Neuroscience.

[49]  Vince D. Calhoun,et al.  Altered Small-World Brain Networks in Temporal Lobe in Patients with Schizophrenia Performing an Auditory Oddball Task , 2011, Front. Syst. Neurosci..

[50]  H. Isoda,et al.  Metabolite alterations in the hippocampus of high-functioning adult subjects with autism. , 2010, The international journal of neuropsychopharmacology.

[51]  Manuel F. Casanova,et al.  Clinical and Macroscopic Correlates of Minicolumnar Pathology in Autism , 2002, Journal of child neurology.

[52]  Andrew E. Switala,et al.  Minicolumnar pathology in autism , 2002, Neurology.

[53]  Derek K. Jones,et al.  White matter integrity in Asperger syndrome: a preliminary diffusion tensor magnetic resonance imaging study in adults , 2010, Autism research : official journal of the International Society for Autism Research.

[54]  Lauren E. Libero,et al.  Disrupted cortical connectivity theory as an explanatory model for autism spectrum disorders. , 2011, Physics of life reviews.

[55]  P. Basser,et al.  Comprehensive approach for correction of motion and distortion in diffusion‐weighted MRI , 2004, Magnetic resonance in medicine.

[56]  Yun Jiao,et al.  Predictive models of autism spectrum disorder based on brain regional cortical thickness , 2010, NeuroImage.

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

[58]  Marianna D. Eddy,et al.  Regionally Localized Thinning of the Cerebral Cortex in Schizophrenia , 2003 .

[59]  Pearl H. Chiu,et al.  Self Responses along Cingulate Cortex Reveal Quantitative Neural Phenotype for High-Functioning Autism , 2008, Neuron.

[60]  P. Basser,et al.  Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. 1996. , 1996, Journal of magnetic resonance.

[61]  R. Adolphs The neurobiology of social cognition , 2001, Current Opinion in Neurobiology.

[62]  Anders M. Dale,et al.  Reliability in multi-site structural MRI studies: Effects of gradient non-linearity correction on phantom and human data , 2006, NeuroImage.

[63]  F. Volkmar,et al.  Structural Neural Phenotype of Autism: Preliminary Evidence from a Diffusion Tensor Imaging Study Using Tract-Based Spatial Statistics , 2011, American Journal of Neuroradiology.

[64]  Janaina Mourão Miranda,et al.  Investigating the predictive value of whole-brain structural MR scans in autism: A pattern classification approach , 2010, NeuroImage.

[65]  D. van Ormondt,et al.  SVD-based quantification of magnetic resonance signals , 1992 .

[66]  D. Shukla,et al.  White matter compromise of callosal and subcortical fiber tracts in children with autism spectrum disorder: a diffusion tensor imaging study. , 2010, Journal of the American Academy of Child and Adolescent Psychiatry.

[67]  E H Herskovits,et al.  Predictive models for subtypes of autism spectrum disorder based on single-nucleotide polymorphisms and magnetic resonance imaging. , 2011, Advances in medical sciences.

[68]  Nagesh Adluru,et al.  Atypical diffusion tensor hemispheric asymmetry in autism , 2010, Autism research : official journal of the International Society for Autism Research.

[69]  A. Dale,et al.  Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.

[70]  Thomas E. Nichols,et al.  Nonparametric permutation tests for functional neuroimaging: A primer with examples , 2002, Human brain mapping.

[71]  Lauren E. Libero,et al.  Advancing our understanding of the brain in autism: contribution of functional MRI and diffusion tensor imaging , 2013 .

[72]  Yun Jiao,et al.  Structural MRI in Autism Spectrum Disorder , 2011, Pediatric Research.

[73]  J. Hutsler,et al.  Increased dendritic spine densities on cortical projection neurons in autism spectrum disorders , 2010, Brain Research.

[74]  Alex Martin,et al.  Age-related temporal and parietal cortical thinning in autism spectrum disorders. , 2010, Brain : a journal of neurology.

[75]  Kenji Mori,et al.  Function of the frontal lobe in autistic individuals: a proton magnetic resonance spectroscopic study. , 2010, The journal of medical investigation : JMI.

[76]  E. Courchesne,et al.  N-acetyl aspartate in autism spectrum disorders: Regional effects and relationship to fMRI activation , 2007, Brain Research.

[77]  Marcel Adam Just,et al.  Inter-Regional Brain Communication and Its Disturbance in Autism , 2011, Front. Syst. Neurosci..

[78]  P. Rakic Specification of cerebral cortical areas. , 1988, Science.

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

[80]  Antonio Y Hardan,et al.  An MRI study of increased cortical thickness in autism. , 2006, The American journal of psychiatry.

[81]  Margot J. Taylor,et al.  Measures of Cortical Grey Matter Structure and Development in Children with Autism Spectrum Disorder , 2011, Journal of Autism and Developmental Disorders.

[82]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[83]  R. C. Oldfield The assessment and analysis of handedness: the Edinburgh inventory. , 1971, Neuropsychologia.

[84]  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.

[85]  Jing Wang,et al.  Differential Deactivation during Mentalizing and Classification of Autism Based on Default Mode Network Connectivity , 2012, PloS one.

[86]  C Ecker,et al.  Translational approaches to the biology of Autism: false dawn or a new era? , 2012, Molecular Psychiatry.

[87]  Lucina Q. Uddin,et al.  Multivariate Searchlight Classification of Structural Magnetic Resonance Imaging in Children and Adolescents with Autism , 2011, Biological Psychiatry.

[88]  S. Olmos,et al.  Physical Basis of Magnetic Resonance Spectroscopy and its Application to Central Nervous System Diseases , 2006 .

[89]  P. Renshaw,et al.  Mitochondrial dysfunction in bipolar disorder: evidence from magnetic resonance spectroscopy research , 2005, Molecular Psychiatry.

[90]  A. Dale,et al.  Regional and progressive thinning of the cortical ribbon in Huntington’s disease , 2002, Neurology.

[91]  Anders M. Dale,et al.  Sequence-independent segmentation of magnetic resonance images , 2004, NeuroImage.

[92]  R. Adolphs,et al.  The social brain: neural basis of social knowledge. , 2009, Annual review of psychology.

[93]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[94]  Alex Alves Freitas,et al.  A Survey of Evolutionary Algorithms for Decision-Tree Induction , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[95]  Peter A. Calabresi,et al.  Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract-specific quantification , 2008, NeuroImage.

[96]  L. Soorya,et al.  Impaired Structural Connectivity of Socio-Emotional Circuits in Autism Spectrum Disorders: A Diffusion Tensor Imaging Study , 2011, PloS one.

[97]  Naomi B. Pitskel,et al.  Neural signatures of autism , 2010, Proceedings of the National Academy of Sciences.

[98]  J. Soares,et al.  Brain anatomy and development in autism: review of structural MRI studies , 2003, Brain Research Bulletin.

[99]  Moo K. Chung,et al.  Cortical thickness analysis in autism with heat kernel smoothing , 2005, NeuroImage.

[100]  Rajesh K. Kana,et al.  The Implications of Brain Connectivity in the Neuropsychology of Autism , 2014, Neuropsychology Review.

[101]  1H-MRS in autism spectrum disorders: a systematic meta-analysis , 2012, Metabolic Brain Disease.

[102]  D. Amaral,et al.  Neuroanatomy of autism , 2008, Trends in Neurosciences.

[103]  M. Buonocore,et al.  MR spectroscopic studies of the brain in psychiatric disorders. , 2012, Current topics in behavioral neurosciences.

[104]  Bruce Fischl,et al.  Geometrically Accurate Topology-Correction of Cortical Surfaces Using Nonseparating Loops , 2007, IEEE Transactions on Medical Imaging.

[105]  Kevin A. Pelphrey,et al.  Developmental Cognitive Neuroscience Disrupted Action Perception in Autism: Behavioral Evidence, Neuroendophenotypes, and Diagnostic Utility , 2022 .

[106]  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.

[107]  Nikos Makris,et al.  Automatically parcellating the human cerebral cortex. , 2004, Cerebral cortex.

[108]  Luke Bloy,et al.  Diffusion based abnormality markers of pathology: Toward learned diagnostic prediction of ASD , 2011, NeuroImage.

[109]  Simon B Eickhoff,et al.  Brain structure anomalies in autism spectrum disorder—a meta‐analysis of VBM studies using anatomic likelihood estimation , 2012, Human brain mapping.

[110]  Anders M. Dale,et al.  Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex , 2001, IEEE Transactions on Medical Imaging.

[111]  E T Bullmore,et al.  A novel functional brain imaging endophenotype of autism: the neural response to facial expression of emotion , 2011, Translational Psychiatry.

[112]  G. Fein,et al.  Reduced brain N‐acetylaspartate suggests neuronal loss in cognitively impaired human immunodeficiency virus‐seropositive individuals , 1993, Neurology.

[113]  André J. W. van der Kouwe,et al.  Reliability of MRI-derived cortical and subcortical morphometric measures: Effects of pulse sequence, voxel geometry, and parallel imaging , 2009, NeuroImage.

[114]  Peter M. G. Munro,et al.  Inhibition of N-acetylaspartate production: implications for 1H MRS studies in vivo. , 1996, Neuroreport.

[115]  Eric Courchesne,et al.  Outcome classification of preschool children with autism spectrum disorders using MRI brain measures. , 2004, Journal of the American Academy of Child and Adolescent Psychiatry.