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Autism is associated with inter-individual variations of gray and white matter morphology

  • Ting Mei
    Correspondence
    Corresponding author: Ting Mei,
    Affiliations
    Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
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  • Natalie J. Forde
    Affiliations
    Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
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  • Dorothea L. Floris
    Affiliations
    Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands

    Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland
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  • Flavio Dell’Acqua
    Affiliations
    Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
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  • Richard Stones
    Affiliations
    Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
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  • Iva Ilioska
    Affiliations
    Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
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  • Sarah Durston
    Affiliations
    Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
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  • Carolin Moessnang
    Affiliations
    Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany

    Department of Applied Psychology, SRH University, Heidelberg, Germany
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  • Tobias Banaschewski
    Affiliations
    Department of Child and Adolescent Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
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  • Rosemary J. Holt
    Affiliations
    Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
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  • Simon Baron-Cohen
    Affiliations
    Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
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  • Annika Rausch
    Affiliations
    Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
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  • Eva Loth
    Affiliations
    Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
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  • Bethany Oakley
    Affiliations
    Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
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  • Tony Charman
    Affiliations
    Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
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  • Christine Ecker
    Affiliations
    Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
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  • Declan G.M. Murphy
    Affiliations
    Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
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  • the EU-AIMS LEAP group
  • Christian F. Beckmann
    Affiliations
    Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands

    Centre for Functional MRI of the Brain, University of Oxford, Oxford, UK
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  • Author Footnotes
    # JKB and AL contributed equally to this work.
    Alberto Llera
    Footnotes
    # JKB and AL contributed equally to this work.
    Affiliations
    Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands

    Karakter Child and Adolescent Psychiatry University Centre, Nijmegen, Netherlands
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  • Author Footnotes
    # JKB and AL contributed equally to this work.
    Jan K. Buitelaar
    Correspondence
    Corresponding author: Jan Buitelaar,
    Footnotes
    # JKB and AL contributed equally to this work.
    Affiliations
    Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands

    Karakter Child and Adolescent Psychiatry University Centre, Nijmegen, Netherlands
    Search for articles by this author
  • Author Footnotes
    # JKB and AL contributed equally to this work.
Open AccessPublished:September 05, 2022DOI:https://doi.org/10.1016/j.bpsc.2022.08.011

      Abstract

      Background

      Although many studies have explored atypicalities in gray and white matter (GM, WM) morphology of autism, most of them rely on unimodal analyses that do not benefit from the likelihood that different imaging modalities may reflect common neurobiology. We aimed to establish brain patterns of modalities that differentiate between autism and controls and explore associations between these brain patterns and clinical measures.

      Methods

      We studied 183 individuals with autism and 157 non-autistic individuals (6-30 years) in a large deeply phenotyped autism dataset (EU-AIMS LEAP). Linked Independent Component Analysis was utilized to link all participants’ GM volume and WM diffusion tensor images, and group comparisons of modality shared variances were examined. Subsequently, we performed univariate and multivariate brain-behavior correlation analyses to separately explore the relations between brain patterns and clinical profiles.

      Results

      One multimodal pattern was significantly related to autism. This pattern was primarily associated with GM volume in bilateral insula, frontal, pre- and post-central, cingulate, and caudate areas, and co-occurred with altered WM features in the superior longitudinal fasciculus. The brain-behavior correlation analyses showed a significant multivariate association primarily between brain patterns that involved variation of WM, and symptoms of restricted and repetitive behavior in the autism group.

      Conclusions

      Our findings demonstrate the assets of integrated analyses of GM and WM alterations to study the brain mechanisms that underpin autism, and show that the complex clinical autism phenotype can be interpreted by brain covariation patterns that are spread across the brain involving both cortical and subcortical areas.

      Key words

      Introduction

      Autism Spectrum Disorder (autism) is a heterogeneous condition characterized by difficulties with social and communicative behaviors, repetitive, rigid behaviors and altered sensory processes (

      American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Arlington2013.

      ). In search of the brain basis of autism, the condition has been associated with multiple morphological differences in gray matter (GM) and white matter (WM) (
      • Aoki Y.
      • Yoncheva Y.N.
      • Chen B.
      • Nath T.
      • Sharp D.
      • Lazar M.
      • et al.
      Association of White Matter Structure With Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder.
      ,
      • van Rooij D.
      • Anagnostou E.
      • Arango C.
      • Auzias G.
      • Behrmann M.
      • Busatto G.F.
      • et al.
      Cortical and Subcortical Brain Morphometry Differences Between Patients With Autism Spectrum Disorder and Healthy Individuals Across the Lifespan: Results From the ENIGMA ASD Working Group.
      ), as reported by magnetic resonance imaging (MRI) studies. However, former studies have shown heterogeneous findings of the alterations in both cortical (e.g., cortical thickness, surface area, volume) and subcortical (e.g., volume) morphometry in multiple brain regions making it difficult to define the neural correlates of autism (
      • van Rooij D.
      • Anagnostou E.
      • Arango C.
      • Auzias G.
      • Behrmann M.
      • Busatto G.F.
      • et al.
      Cortical and Subcortical Brain Morphometry Differences Between Patients With Autism Spectrum Disorder and Healthy Individuals Across the Lifespan: Results From the ENIGMA ASD Working Group.
      ,
      • Bedford S.A.
      • Park M.T.M.
      • Devenyi G.A.
      • Tullo S.
      • Germann J.
      • Patel R.
      • et al.
      Large-scale analyses of the relationship between sex, age and intelligence quotient heterogeneity and cortical morphometry in autism spectrum disorder.
      ,
      • Zhang W.
      • Groen W.
      • Mennes M.
      • Greven C.
      • Buitelaar J.
      • Rommelse N.
      Revisiting subcortical brain volume correlates of autism in the ABIDE dataset: effects of age and sex.
      ). Additionally, voxel-wise GM volume analyses revealed divergent results, for instance, in temporal areas in autism (
      • DeRamus T.P.
      • Kana R.K.
      Anatomical likelihood estimation meta-analysis of grey and white matter anomalies in autism spectrum disorders.
      ,
      • Carlisi C.O.
      • Norman L.J.
      • Lukito S.S.
      • Radua J.
      • Mataix-Cols D.
      • Rubia K.
      Comparative Multimodal Meta-analysis of Structural and Functional Brain Abnormalities in Autism Spectrum Disorder and Obsessive-Compulsive Disorder.
      ,
      • Mei T.
      • Llera A.
      • Floris D.L.
      • Forde N.J.
      • Tillmann J.
      • Durston S.
      • et al.
      Gray matter covariations and core symptoms of autism: the EU-AIMS Longitudinal European Autism Project.
      ). Studies of WM microstructural associations in autism are similarly heterogenous in their findings (
      • Aoki Y.
      • Yoncheva Y.N.
      • Chen B.
      • Nath T.
      • Sharp D.
      • Lazar M.
      • et al.
      Association of White Matter Structure With Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder.
      ,
      • Naigles L.R.
      • Johnson R.
      • Mastergeorge A.
      • Ozonoff S.
      • Rogers S.J.
      • Amaral D.G.
      • et al.
      Neural correlates of language variability in preschool-aged boys with autism spectrum disorder.
      ,
      • Tung Y.H.
      • Lin H.Y.
      • Chen C.L.
      • Shang C.Y.
      • Yang L.Y.
      • Hsu Y.C.
      • et al.
      Whole Brain White Matter Tract Deviation and Idiosyncrasy From Normative Development in Autism and ADHD and Unaffected Siblings Link With Dimensions of Psychopathology and Cognition.
      ). One explanation for discrepant and heterogeneous findings is that the studies differ widely in data analytic strategy - i.e., these studies rely on unimodal analyses techniques that ignores the signal of interest probably present in more than one modality (
      • Groves A.R.
      • Beckmann C.F.
      • Smith S.M.
      • Woolrich M.W.
      Linked independent component analysis for multimodal data fusion.
      ). Additionally, when integrated together these modalities might provide additional analytical sensitivity.
      This prompted research to move beyond unimodality and incorporate and connect data from different imaging modalities. For example, (
      • Cauda F.
      • Costa T.
      • Palermo S.
      • D'Agata F.
      • Diano M.
      • Bianco F.
      • et al.
      Concordance of white matter and gray matter abnormalities in autism spectrum disorders: a voxel-based meta-analysis study.
      ) suggested that GM variation in autism is generally accompanied by WM variation; (
      • Ecker C.
      • Andrews D.
      • Dell'Acqua F.
      • Daly E.
      • Murphy C.
      • Catani M.
      • et al.
      Relationship Between Cortical Gyrification, White Matter Connectivity, and Autism Spectrum Disorder.
      ) showing higher axial diffusivity (L1) in the WM fiber tracts originating and/or terminating in the GM clusters with increased local gyrification in adults with autism. Despite the progress away from unimodal approaches, in essence, these MRI studies which correlate GM and WM measures do so after separate unimodal statistical analyses. This likely has less sensitivity to assess the biological variance than fully integrating multimodal data analysis across participants.
      It is assumed that a relatively high level of co-occurring neurobiology underlying different aspects of brain morphology due to the complicated natures of autism. Therefore, efficient modeling of this potential shared variance would increase the chances to produce a more complete picture of autism in a specific perspective (i.e., brain morphology in our study). Here, we aim to utilize an integrative multimodal approach, linked independent component analysis (LICA), to simultaneously incorporate several imaging modalities allowing the investigation of inter-subject variability across modalities in one analysis (
      • Groves A.R.
      • Smith S.M.
      • Fjell A.M.
      • Tamnes C.K.
      • Walhovd K.B.
      • Douaud G.
      • et al.
      Benefits of multi-modal fusion analysis on a large-scale dataset: life-span patterns of inter-subject variability in cortical morphometry and white matter microstructure.
      ,
      • Llera A.
      • Wolfers T.
      • Mulders P.
      • Beckmann C.F.
      Inter-individual differences in human brain structure and morphology link to variation in demographics and behavior.
      ), which equips the ability to isolate the artifacts and may increase the sensitivity to correlate the remaining signals with variables of interest (
      • Groves A.R.
      • Smith S.M.
      • Fjell A.M.
      • Tamnes C.K.
      • Walhovd K.B.
      • Douaud G.
      • et al.
      Benefits of multi-modal fusion analysis on a large-scale dataset: life-span patterns of inter-subject variability in cortical morphometry and white matter microstructure.
      ). So far, studies that highlight the underlying shared variance between modalities using LICA in autism remain scarce. Previous studies revealed case-control differences between adults with autism and typically developing individuals in linked patterns of voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) measures in several brain regions (
      • Itahashi T.
      • Yamada T.
      • Nakamura M.
      • Watanabe H.
      • Yamagata B.
      • Jimbo D.
      • et al.
      Linked alterations in gray and white matter morphology in adults with high-functioning autism spectrum disorder: a multimodal brain imaging study.
      ,
      • Mueller S.
      • Keeser D.
      • Samson A.C.
      • Kirsch V.
      • Blautzik J.
      • Grothe M.
      • et al.
      Convergent Findings of Altered Functional and Structural Brain Connectivity in Individuals with High Functioning Autism: A Multimodal MRI Study.
      ). However, these studies focused exclusively on adult autistic individuals without intellectual disability and were comprised of relatively small sample sizes (<100 individuals) (
      • Itahashi T.
      • Yamada T.
      • Nakamura M.
      • Watanabe H.
      • Yamagata B.
      • Jimbo D.
      • et al.
      Linked alterations in gray and white matter morphology in adults with high-functioning autism spectrum disorder: a multimodal brain imaging study.
      ,
      • Mueller S.
      • Keeser D.
      • Samson A.C.
      • Kirsch V.
      • Blautzik J.
      • Grothe M.
      • et al.
      Convergent Findings of Altered Functional and Structural Brain Connectivity in Individuals with High Functioning Autism: A Multimodal MRI Study.
      ). Autism is a highly diverse condition; we therefore investigate brain patterns in a broader more representative autism sample which might help better characterize brain patterns of autism - one of the aims of the current study. We hypothesized that the model analysis would reveal the autism-related regional correspondence between GM and WM or modality-specific effect.
      In addition to identifying categorical group differences, dimensional analyses, i.e., analyses of continuous scores of autism symptoms might capture more of the heterogeneity of autism compared to categorical diagnostic labels. Many studies have demonstrated the univariate connections between GM or WM patterns and the core symptoms of autism (e.g., (
      • Aoki Y.
      • Yoncheva Y.N.
      • Chen B.
      • Nath T.
      • Sharp D.
      • Lazar M.
      • et al.
      Association of White Matter Structure With Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder.
      ,
      • van Rooij D.
      • Anagnostou E.
      • Arango C.
      • Auzias G.
      • Behrmann M.
      • Busatto G.F.
      • et al.
      Cortical and Subcortical Brain Morphometry Differences Between Patients With Autism Spectrum Disorder and Healthy Individuals Across the Lifespan: Results From the ENIGMA ASD Working Group.
      )). Nonetheless, the relationships between brain substrates and clinical phenotypes are potentially the outcome of integrative effects across multiple autism symptom domains and brain areas, and therefore the multidimensional associations between brain covariations and core symptoms of autism need to be clarified. We therefore performed univariate analyses to identify the one-to-one dimensional associations and additionally implemented a multivariate analysis using canonical correlation analysis (CCA) to learn the integrated associations (
      • Hotelling H.
      Relations between two sets of variates.
      ). Similarly, we furthermore expected CCA would help to elucidate the potential correlation between brain and behavior.
      This study was designed to overcome the aforementioned limitations of previous work by applying LICA to the Longitudinal European Autism Project (LEAP) dataset (
      • Loth E.
      • Charman T.
      • Mason L.
      • Tillmann J.
      • Jones E.J.H.
      • Wooldridge C.
      • et al.
      The EU-AIMS Longitudinal European Autism Project (LEAP): design and methodologies to identify and validate stratification biomarkers for autism spectrum disorders.
      ) to link the sources of variance of voxel-wise GM volume and WM diffusion tensor measures. The LEAP dataset provides a deeply phenotyped and comprehensively biologically assessed multisite sample of individuals with/without autism that allows relating the results of LICA to clinical characteristics of the participants. More specifically, we applied (a) a univariate approach to identify categorical group difference of linking brain patterns, and subsequently their one-to-one relations to continuous measures of autism symptoms; (b) a multivariate method (i.e., CCA) to further quantify the association between two datasets of brain patterns and autistic traits in the autism group.

      Methods and Materials

      Participants

      The participants were part of the EU-AIMS and AIMS-2-TRIALS Longitudinal European Autism Project (LEAP) dataset - a large multicenter study aimed at identifying and validating biomarkers in autism (
      • Loth E.
      • Charman T.
      • Mason L.
      • Tillmann J.
      • Jones E.J.H.
      • Wooldridge C.
      • et al.
      The EU-AIMS Longitudinal European Autism Project (LEAP): design and methodologies to identify and validate stratification biomarkers for autism spectrum disorders.
      ,
      • Charman T.
      • Loth E.
      • Tillmann J.
      • Crawley D.
      • Wooldridge C.
      • Goyard D.
      • et al.
      The EU-AIMS Longitudinal European Autism Project (LEAP): clinical characterisation.
      ). Individuals with autism were included based on an existing clinical diagnosis according to DSM-IV, DSM-IV-TR, DSM-5, or ICD-10. Each participant underwent clinical, cognitive, and MRI assessment at one of six collaborative centers. We refer to (
      • Loth E.
      • Charman T.
      • Mason L.
      • Tillmann J.
      • Jones E.J.H.
      • Wooldridge C.
      • et al.
      The EU-AIMS Longitudinal European Autism Project (LEAP): design and methodologies to identify and validate stratification biomarkers for autism spectrum disorders.
      ,
      • Charman T.
      • Loth E.
      • Tillmann J.
      • Crawley D.
      • Wooldridge C.
      • Goyard D.
      • et al.
      The EU-AIMS Longitudinal European Autism Project (LEAP): clinical characterisation.
      ) for further details on experimental design and clinical characterization. In the present study, diffusion-weighted image (DWI) data at timepoint 1 were only available from participants in three centers. Therefore, the participants were selected who had both T1-weighted and DWI data available from the following centers: Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom; Radboud University Medical Centre, Nijmegen, the Netherlands; Central Institute of Mental Health, Mannheim, Germany (Supplementary Section 1).
      The final sample comprised 344 participants between 6 and 30 years, including 185 autistic individuals (133 male and 52 female, IQ≥40), and 159 non-autistic individuals (99 male and 60 female, IQ≥50). The demographic and clinical information of the final sample is summarized in Table 1. For the details on exclusion criteria, please see Supplementary Section 2.
      Table 1Demographic information of participants
      IQ and symptoms profiles reported are the imputed data (30).
      DemographicAutism, n=185Controls, n=159t/X2p value
      MeanSDMeanSD
      Age, years
      Statistical differences were assessed by two-sample t-test. Degrees of freedom of the two t-tests were 342.
      17.305.2217.515.190.3690.712
      IQ
      Statistical differences were assessed by two-sample t-test. Degrees of freedom of the two t-tests were 342.
      ,
      IQ ranged from 40 to 148 in the ASD group and from 50 to 142 in the control group.
      98.9020.44102.6819.101.7690.079
      n=154n=142
      IQ≥75105.4715.30107.3214.041.0830.028
      n=31n=172.620
      The differences were examined by the chi-square test.
      0.106
      IQ<7566.286.9563.888.55-0.9940.329
      n%n%
      Sex, male/female
      The differences were examined by the chi-square test.
      133/5271.9/28.199/6062.3/37.73.6100.057
      Symptom ProfilesMeanSDMeanSD
      ADI
       Social Interaction16.546.95
       Communication13.355.57
       RRB4.072.58
      ADOS CSS
       Total5.402.75
       Social Affect6.062.64
       RRB4.702.77
      SRS raw score
      In SRS, RBS and SSP questionnaires, we used parent-rated report.
      70.8011.5555.7811.89
      RBS
      In SRS, RBS and SSP questionnaires, we used parent-rated report.
      15.5413.545.306.05
      SSP
      In SRS, RBS and SSP questionnaires, we used parent-rated report.
      142.1623.63166.6617.76
      SD, standard deviation; IQ, full-scale intelligence quotient; ADHD, Attention Deficit Hyperactivity Disorder; ADI, Autism Diagnostic Interview-Revised; RRB, restricted, repetitive behaviors; ADOS, Autism Diagnostic Observational Schedule 2; CSS, calibrated severity scores; SRS, Social Responsiveness Scale 2nd Edition; RBS, Repetitive Behavior Scale-Revised; SSP, Short Sensory Profile.
      a IQ and symptoms profiles reported are the imputed data (30).
      b Statistical differences were assessed by two-sample t-test. Degrees of freedom of the two t-tests were 342.
      c IQ ranged from 40 to 148 in the ASD group and from 50 to 142 in the control group.
      d The differences were examined by the chi-square test.
      e In SRS, RBS and SSP questionnaires, we used parent-rated report.

      Clinical measures

      The Autism Diagnostic Interview-Revised (ADI) (

      Rutter M, Le Couteur A, Lord C. Autism Diagnostic Interview-Revised. Los Angeles: Western Psychological Services; 2003.

      ) and the Autism Diagnostic Observational Schedule 2 (ADOS) (

      Lord C, Rutter M, DiLavore PC, Risi S, Gotham K, Bishop S. Autism DiagnosticObservation Schedule, Second Edition (ADOS-2) manual (part I): modules 1–4. Torrance: Western Psychological Services; 2012.

      ) were used to measure the past (ever and previous 4-to-5 years) and current core symptom severities of autism from social interaction, communication, and restricted repetitive behaviors (RRB) domains. Specifically, the calibrated severity scores (CSS) for subscales and total of ADOS were calculated to use in the following analyses (
      • Gotham K.
      • Pickles A.
      • Lord C.
      Standardizing ADOS scores for a measure of severity in autism spectrum disorders.
      ,
      • Hus V.
      • Gotham K.
      • Lord C.
      Standardizing ADOS domain scores: separating severity of social affect and restricted and repetitive behaviors.
      ). Additionally, we used several parent-reported scales to assess autism symptoms, including the Social Responsiveness Scale 2nd Edition (SRS) (

      Constantino JN, Gruber CP. Social Responsiveness Scale. 2nd ed. Los Angeles: Western Psychological Services; 2012.

      ) capturing the social-communication variations, the Repetitive Behavior Scale-Revised (RBS) (
      • Bodfish J.W.
      • Symons F.J.
      • Parker D.E.
      • Lewis M.H.
      Varieties of repetitive behavior in autism: comparisons to mental retardation.
      ) identifying the repetitive and rigid behaviors, and the Short Sensory Profile (SSP) (
      • Tomchek S.D.
      • Dunn W.
      Sensory processing in children with and without autism: a comparative study using the short sensory profile.
      ) evaluating the sensory processing variations. Concerning the potential effect of Attention Deficit Hyperactivity Disorder (ADHD), anxiety and depression co-occurrence, we separately included the scores from the ADHD DSM-5 rating scale (

      DuPaul GJ, Power TJ, Anastopoulos AD, Reid R. ADHD Rating Scale—5 for Children and Adolescents Checklists, Norms, and Clinical Interpretation. New York: Guilford Publications; 2016.

      ), anxiety and depression from the Development and Well-Being Assessment (DAWBA) (
      • Goodman R.
      • Ford T.
      • Richards H.
      • Gatward R.
      • Meltzer H.
      The Development and Well-Being Assessment: description and initial validation of an integrated assessment of child and adolescent psychopathology.
      ) as additional covariates in the post-hoc analyses. There was a substantial amount of missing clinical data, which could greatly reduce the power of our analysis. To tackle the missing clinical data and fully harness the large LEAP sample size we used imputed clinical data (

      Llera A, Brammer M, Oakley B, Tillmann J, Zabihi M, Mei T, et al. Evaluation of data imputation strategies in complex, deeply-phenotyped data sets: the case of the EU-AIMS Longitudinal European Autism Project. arXiv pre-print server. 2022.

      ). The imputation procedure developed by our colleagues considering the potential non-randomness of missing data, who developed quantitative measures to assess the quality of the imputations, and finally imputed data adopting a non-parametric tree regression model embedded in an iterative round Robin iterative schedule (

      Llera A, Brammer M, Oakley B, Tillmann J, Zabihi M, Mei T, et al. Evaluation of data imputation strategies in complex, deeply-phenotyped data sets: the case of the EU-AIMS Longitudinal European Autism Project. arXiv pre-print server. 2022.

      ). The details of missingness of the current sample can be found in Supplementary Section 3.

      MRI data acquisition

      All participants were scanned on 3T MRI scanners. High-resolution structural T1-weighted images were acquired using magnetization-prepared rapid gradient-echo sequence with full head coverage, at 1.2 mm thickness with 1.1×1.1 mm in-plane resolution. Diffusion-weighted imaging (DWI) scans were acquired using echo-planar imaging sequence, at 2 mm thickness with 2.0x2.0 mm in-plane resolution.
      MRI data acquisition parameters can be found in the Supplementary Section 4.

      Image processing

      GM volume estimation

      Structural T1 images were preprocessed according to CAT12 toolbox (https://dbm.neuro.uni-jena.de/cat/) pipeline in SPM12 (Wellcome Department of Imaging Neuroscience, London, UK) to obtain VBM data, which is a spatially-unbiased whole-brain approach extracting voxel-wise GM volume estimations. T1-weighted images were automatically segmented into GM, WM, and cerebrospinal fluid and affine registered to the MNI template. A high-dimensional, nonlinear diffeomorphic registration algorithm (DARTEL) (
      • Ashburner J.
      A fast diffeomorphic image registration algorithm.
      ) was used to generate a study-specific template from GM and WM tissue segments of all participants, and then to normalize all segmented GM maps to MNI space with 2mm isotropic resolution. All GM images were smoothed with a 4mm full-width half-max (FWHM) isotropic Gaussian kernel.

      Diffusion parameters

      DWI images from all sites were preprocessed using the same pipeline. De-noising was performed using the Marchenko-Pastur principal component analysis (MP-PCA) method (
      • Veraart J.
      • Fieremans E.
      • Novikov D.S.
      Diffusion MRI noise mapping using random matrix theory.
      ). Subsequently, Gibbs-ringing artifacts were removed according to (
      • Kellner E.
      • Dhital B.
      • Kiselev V.G.
      • Reisert M.
      Gibbs-ringing artifact removal based on local subvoxel-shifts.
      ). FSL eddy was employed to correct the eddy-current induced distortions and subject motion (
      • Andersson J.L.R.
      • Sotiropoulos S.N.
      An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging.
      ). To improve the final quality of data and recover most of the motion artifacts, we utilized intra-volume slice motion correction (
      • Andersson J.L.R.
      • Graham M.S.
      • Drobnjak I.
      • Zhang H.
      • Filippini N.
      • Bastiani M.
      Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement.
      ). Quality control reports were then generated for each subject and each site (
      • Bastiani M.
      • Cottaar M.
      • Fitzgibbon S.P.
      • Suri S.
      • Alfaro-Almagro F.
      • Sotiropoulos S.N.
      • et al.
      Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction.
      ).
      Individual voxel-wise FA, mean diffusivity (MD), mode of anisotropy (MO), L1 and radial diffusivity (RA) maps were derived using dtifit in FSL (
      • Smith S.M.
      • Jenkinson M.
      • Woolrich M.W.
      • Beckmann C.F.
      • Behrens T.E.
      • Johansen-Berg H.
      • et al.
      Advances in functional and structural MR image analysis and implementation as FSL.
      ). These five DTI features were selected on account of the different aspects of white matter microstructure, for example, FA measures the degree of anisotropic movement of water molecules and L1 represents the magnitude of the diffusion in the primary direction, which are related to myelin structure or myelination. FA images were processed using Tract-Based Spatial Statistics (TBSS) pipeline including registration of all images to FMRIB58_FA standard space, skeletonization of the mean group white matter and projection of each individual’s data onto the skeleton (
      • Smith S.M.
      • Jenkinson M.
      • Johansen-Berg H.
      • Rueckert D.
      • Nichols T.E.
      • Mackay C.E.
      • et al.
      Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data.
      ). The mean skeleton image was thresholded at FA 0.2. Other DTI measures (MD, MO, L1, RA) were projected onto the FA skeleton using the tbss_non_FA option. All DTI data had 1mm isotropic resolution when entering the following data fusion model.
      A full quality control report and additional preprocessing details of the GM and WM images are included in the Supplementary Section 2.

      Modalities fusing analysis

      The shared inter-participant variations across six features (i.e., VBM, FA, MD, MO, L1, RA) were explored using LICA (
      • Groves A.R.
      • Beckmann C.F.
      • Smith S.M.
      • Woolrich M.W.
      Linked independent component analysis for multimodal data fusion.
      ). LICA is able to factorize the multiple input modalities simultaneously into modality-wise independent components (ICs) while importantly constraining all decompositions to be linked through a shared participant-loading matrix, which describes the amount of contribution of each participant to a specific IC. In addition to the participant-loading matrix, this method provides, per IC, a vector reflecting the contribution (weight) of each modality and a spatial map per modality showing the extent of the spatial variation. All mathematical algorithms of LICA are detailed in (
      • Groves A.R.
      • Beckmann C.F.
      • Smith S.M.
      • Woolrich M.W.
      Linked independent component analysis for multimodal data fusion.
      ). As the model order is recommended to be less than 25% of the sample size (
      • Groves A.R.
      • Beckmann C.F.
      • Smith S.M.
      • Woolrich M.W.
      Linked independent component analysis for multimodal data fusion.
      ,
      • Groves A.R.
      • Smith S.M.
      • Fjell A.M.
      • Tamnes C.K.
      • Walhovd K.B.
      • Douaud G.
      • et al.
      Benefits of multi-modal fusion analysis on a large-scale dataset: life-span patterns of inter-subject variability in cortical morphometry and white matter microstructure.
      ), 80-dimensional factorization was chosen to perform LICA. A multimodal index (MMI) (
      • Francx W.
      • Llera A.
      • Mennes M.
      • Zwiers M.P.
      • Faraone S.V.
      • Oosterlaan J.
      • et al.
      Integrated analysis of gray and white matter alterations in attention-deficit/hyperactivity disorder.
      ) (Supplementary Information Section 5) was calculated to present the contribution uniformity of the modalities in each IC. This results in a scalar value where 0 would equate to 100% unimodal contribution and 1 would mean equal contributions from all modalities.

      Statistical approach

      The participant-loadings characterize the inter-individual variations of the uni/multimodal effects, and in the current study, they were used for the analyses of group differences between autistic and non-autistic individuals, and for associations with behavioral measures. Results reported in the main text are performed using imputed data to maximize the statistical power. All analyses were replicated using the original non-imputed data.

      Case-control difference

      A generalized linear model (GLM) was utilized to examine group differences of the brain’s inter-participant variations in LICA outputs while controlling for age, sex, IQ and scanner site. Multiple comparison (number of tests=80) correction was implemented using false discovery rate (FDR) (p<0.05) (
      • Benjamini Y.
      • Hochberg Y.
      Controlling the false discovery rate: A practical and powerful approach to multiple testing.
      ). In addition to considering the effects of co-occurring conditions on case-control difference, we separately investigated the age-by-group, IQ-by-group, sex-by-group and site-by-group interactions, and medication use effects on brain pattern(s) with the case-control difference.

      Brain-behavior associations

      Similarly, we used a GLM to explore the univariate associations between each IC and subscales of ADI and ADOS, SRS, RBS, and SSP in the autism group while controlling for age, sex, IQ and scanner site. We corrected for multiple comparisons (number of tests = 80 x number of (sub)scale(s)) with FDR (p<0.05).
      Subsequently, we utilized one CCA (
      • Hotelling H.
      Relations between two sets of variates.
      ) to better picture the overall association between all brain ICs and subscales of ADI and ADOS, and total scores of SRS, RBS, and SSP in the autism group. CCA is a multivariate approach to simultaneously learn two sets of linear projections corresponding to the brain ICs and the behavioral profiles, which maximizes the correlation between two sets of variables at the participant level. In such maximized correlation, the evaluation of brain-behavior relationships is on the basis of the respective contribution of each IC and each behavioral profile to the correlation, which can be measured by the loading of each variable (transformed from canonical coefficient) described previously (
      • Haufe S.
      • Meinecke F.
      • Gorgen K.
      • Dahne S.
      • Haynes J.D.
      • Blankertz B.
      • et al.
      On the interpretation of weight vectors of linear models in multivariate neuroimaging.
      ). Additionally, the canonical variates are calculated respectively for the brain and the behavioral sets according to the product of the canonical coefficients and the original sets. In this study, we referred to each pair of canonical variates as CCA mode. Before entering the CCA model, age, sex, IQ and scanning site were controlled for both brain and behavior profile datasets using Huh-Jhun residualization method (
      • Huh M.
      • Jhun M.
      Random permutation testing in multiple linear regression.
      ,
      • Winkler A.M.
      • Renaud O.
      • Smith S.M.
      • Nichols T.E.
      Permutation inference for canonical correlation analysis.
      ). The statistical significance of CCA modes was assessed by a complete permutation inference algorithm proposed by (
      • Winkler A.M.
      • Renaud O.
      • Smith S.M.
      • Nichols T.E.
      Permutation inference for canonical correlation analysis.
      ), where both brain and behavior data were permuted separately across all participants with 10,000 iterations. For each CCA mode’s multiple testing correction, we used stepwise cumulative maximum approach, p<0.05, see details in (
      • Winkler A.M.
      • Renaud O.
      • Smith S.M.
      • Nichols T.E.
      Permutation inference for canonical correlation analysis.
      ). We further tested the reliability of the CCA findings and the stability of each loading of the significant CCA mode(s) using a leave-one-subject-out approach.

      Results

      Group effect of brain components

      We obtained 80 ICs from the multimodal integration analysis in our study. The modality contributions (for 80 ICs) and MMI of each IC can be found in Supplementary Section 5. We subsequently used the participant-loadings of the 80 ICs to test for group differences and found one component (IC58) with a significant case-control difference (β=0.192, t(337)=-3.595, FDR corrected p=0.030; Figure 1). The respective contributions of the modalities in IC58 are 26.0% from VBM, 18.4% from FA, 17.9% from MO, 13.8% from L1, 13.7% from RA, and 10.2% from MD, indicating that various MRI features share variance associated with autism. In Figure 1, we present the summarized images of each modality’s spatial map of IC58. The spatial patterns show autism-related smaller GM volume in the bilateral insula, inferior frontal gyrus (IFG), orbitofrontal cortex (OFC), precentral, postcentral gyrus, lateral occipital cortex (LOC), inferior temporal gyrus (ITG), angular gyrus (AG), posterior division of cingulate gyrus (PCC), and precuneus cortex, and larger GM volume in calcarine cortex, bilateral middle frontal gyrus (MFG), caudate and anterior division of cingulate gyrus (ACC). Correspondingly, autism-related DTI features were found in bilateral superior longitudinal fasciculus (SLF), corticospinal tract (CST), and inferior fronto-occipital fasciculus (IFOF). In addition to these fasciculi, RA and MD in the cingulum and anterior thalamic radiation were also implicated. Taken together, the implication of SLF and their adjacent GM volumes; frontal, precentral, and postcentral areas (Supplementary Section 6) in autism indicate that variations of GM volumes and WM microstructure are linked in these brain locations, rather than modality or tissue dependent.
      Figure thumbnail gr1
      Figure 1The multimodal component shows significant case-control difference. The relative contribution of each feature is displayed in brackets. The VBM spatial map is thresholded at 5<|Z|<10. Clusters of DTI features were filled and thresholded at 3<|Z|<10, then smoothed using a 0.3mm Gaussian kernel in FSL for visualization purposes. VBM, voxel-based morphometry; FA, fractional anisotropy; MO, mode of anisotropy; L1, axial diffusivity; RA, radial diffusivity; MD, mean diffusivity.
      Post-hoc, to assess the respective influences of co-occurring conditions, interactions of diagnosis-by -age, -sex or -IQ, and medication use on the multimodal IC found significantly associated with group, we additionally included them as separate covariates in the GLM of IC58. The analysis showed that the group effect of IC58 was robust to the inclusion of these additional covariates in the model (p<0.01). However, we found a significant moderate site-by-diagnostic group interaction effect on the current result (G2(
      • Aoki Y.
      • Yoncheva Y.N.
      • Chen B.
      • Nath T.
      • Sharp D.
      • Lazar M.
      • et al.
      Association of White Matter Structure With Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder.
      )=6.860, p=0.032). This was driven by having significant effects in 2 of the 3 sites with no significant differences at the third. Details are in Supplementary Section 7.

      Relating brain patterns to behavior profiles

      We conducted the univariate (GLM) and multivariate (CCA) correlation analyses on brain and behavior data in the autism group only. No significant univariate brain-behavior relationship in the autism group was found (FDR corrected p>0.120). We did however find a significant multivariate association pattern of CCA (r=0.823, corrected p=0.006, Figure 2). The proportion of total variance explained by this multivariate pattern was 20.8% for brain ICs and 14.2% for behaviors. In this multivariate associated pattern, multimodal IC7 (canonical loading: -0.334) and IC78 (canonical loading: 0.283) showed the strong contributions to the correlation with autism core symptoms, and from a phenotypic perspective this multivariate pattern demonstrated a strong association with the ADI RRB and ADOS RRB subscales. WM microstructure mainly dominated in IC7 and IC78. IC7 mainly included right inferior longitudinal fasciculus (ILF), IFOF, and CST, and IC78 primarily involved bilateral anterior thalamic radiation and SLF. These two predominant ICs highlight the involvement of WM in autism symptoms. The loadings of each brain component of this CCA mode can be found in the Supplementary Section 8. The leave-one-subject-out analysis indicated that the significant CCA mode of CCA analysis was reliably estimated (Supplementary Section 9). We additionally ran a CCA model excluding SSP to probe the effect the imputed SSP scores (42%) specifically may be having and found the entire structure of the output did not differ greatly (Supplementary Section 10).
      Figure thumbnail gr2
      Figure 2The multivariate association pattern (i.e., CCA mode) was found significant between the two sets of brain components and all behavioral profiles. A displays the scatterplot of this correlation (between the CCA mode), and x, y axes are the pair of CCA variates. One dot in each participant is coded with gradient color regarding to the RRB subscale of ADI. B demonstrates the loading of each behavioral (sub)scale in this CCA mode. C shows the modality contributions to the components displayed in D. D exhibits the two multimodal components with the strong contribution to the correlation with autism core symptoms, where the top two loading modalities in each component are shown in the figure. The canonical loading of each component is shown in the brackets. The modality spatial maps are thresholded at 3<|Z|<10. The CCA was only performed in autism group. CCA, canonical correlation analysis; ADI, Autism Diagnostic Interview-Revised; ADOS, Autism Diagnostic Observational Schedule 2; SA, social affect; RRB, restricted repetitive behavior; IC, independent component; MO, mode of anisotropy; RA, radial diffusivity; MD, mean diffusivity; FA, fractional anisotropy; L1, axial diffusivity; VBM, voxel-based morphometry.
      The results using non-imputed data of group effect and univariate brain-behavior association were similar to the main results. The different CCA patterns using non-imputed data were reasonable owing to the large amount of missingness (Supplementary Section 3).

      Discussion

      We examined autism-related inter-individual variance of integrated GM-WM morphology in a large European sample of individuals with and without autism across a broad age and IQ range. Analyses showed a significant diagnostic-group effect of the linked GM-WM pattern that supports our hypothesis of the link between GM and WM morphology alterations in autistic individuals. In particular, the GM volume variation in pre- and post-central areas converged with the WM microstructural variation in the SLF. This spotlights the shared variances between GM and WM morphology in these brain areas in autism, and suggests the structural associations in autism are not only limited to localized regions but also involve the WM pathways connecting these brain areas. In a next set of analyses, we found a significant integrative association between brain patterns and autism core symptoms using CCA in the autism group, where the identified brain multimodal patterns underline the important role of WM morphology.
      Notably, the autism-specific VBM pattern on this multimodal analysis replicates our previous unimodal GM volume covariation study in a larger overlapped sample of the EU-AIMS project to a certain extent (
      • Mei T.
      • Llera A.
      • Floris D.L.
      • Forde N.J.
      • Tillmann J.
      • Durston S.
      • et al.
      Gray matter covariations and core symptoms of autism: the EU-AIMS Longitudinal European Autism Project.
      ). The areas of bilateral insula, IFG, OFC, and caudate form a steady autism-related covariation pattern in previous and current studies. These areas were demonstrated previously to relate to repetitive behaviors and reward-based decision-making abilities in autism (
      • Carlisi C.O.
      • Norman L.
      • Murphy C.M.
      • Christakou A.
      • Chantiluke K.
      • Giampietro V.
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      Shared and Disorder-Specific Neurocomputational Mechanisms of Decision-Making in Autism Spectrum Disorder and Obsessive-Compulsive Disorder.
      ,
      • Langen M.
      • Durston S.
      • Kas M.J.
      • van Engeland H.
      • Staal W.G.
      The neurobiology of repetitive behavior: ...and men.
      ). The covariation of insula and frontal areas in our studies indicates the consistency and stability of the co-occurring GM morphological alterations in autism. Benefitting from multimodal/multivariate approaches suggested by previous studies (
      • Mulders P.
      • Llera A.
      • Tendolkar I.
      • van Eijndhoven P.
      • Beckmann C.
      Personality Profiles Are Associated with Functional Brain Networks Related to Cognition and Emotion.
      ,
      • Smith S.M.
      • Nichols T.E.
      Statistical Challenges in "Big Data" Human Neuroimaging.
      ), the application of the LICA approach modeling the shared variances across modalities, extends identified autism-related GM associations to pre-central, post-central, occipital and temporal areas and additionally links with significant WM findings of DTI measures.
      Our results indicated one covarying set of brain GM and WM areas associated with autism diagnosis. In this multimodal set, GM volume in cortical and subcortical regions and microstructure in WM tracts (mainly SLF, CST and IFOF) were implicated and these regions/tracts have previously been identified in unimodal analyses (
      • Tung Y.H.
      • Lin H.Y.
      • Chen C.L.
      • Shang C.Y.
      • Yang L.Y.
      • Hsu Y.C.
      • et al.
      Whole Brain White Matter Tract Deviation and Idiosyncrasy From Normative Development in Autism and ADHD and Unaffected Siblings Link With Dimensions of Psychopathology and Cognition.
      ,
      • Carlisi C.O.
      • Norman L.
      • Murphy C.M.
      • Christakou A.
      • Chantiluke K.
      • Giampietro V.
      • et al.
      Shared and Disorder-Specific Neurocomputational Mechanisms of Decision-Making in Autism Spectrum Disorder and Obsessive-Compulsive Disorder.
      ,
      • Jung M.
      • Tu Y.
      • Lang C.A.
      • Ortiz A.
      • Park J.
      • Jorgenson K.
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      Decreased structural connectivity and resting-state brain activity in the lateral occipital cortex is associated with social communication deficits in boys with autism spectrum disorder.
      ,
      • Lukito S.
      • Norman L.
      • Carlisi C.
      • Radua J.
      • Hart H.
      • Simonoff E.
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      Comparative meta-analyses of brain structural and functional abnormalities during cognitive control in attention-deficit/hyperactivity disorder and autism spectrum disorder.
      ,
      • Thompson A.
      • Shahidiani A.
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      • O'Muircheartaigh J.
      • Walker L.
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      Age-related differences in white matter diffusion measures in autism spectrum condition.
      ). This broad range of brain regions along with large WM bundles associated with autism is in accord with the notion that the neural correlates of autism are widespread in brain regions and connectivity patterns (
      • Ecker C.
      • Bookheimer S.Y.
      • Murphy D.G.
      Neuroimaging in autism spectrum disorder: brain structure and function across the lifespan.
      ,
      • Philip R.C.
      • Dauvermann M.R.
      • Whalley H.C.
      • Baynham K.
      • Lawrie S.M.
      • Stanfield A.C.
      A systematic review and meta-analysis of the fMRI investigation of autism spectrum disorders.
      ,

      Ilioska I, Oldehinkel M, Llera A, Chopra S, Looden T, Chauvin R, et al. Connectome-wide mega-analysis reveals robust patterns of atypical functional connectivity in autism. 2022.

      ). This also corresponds with another multimodal autism study reporting extensive autism-related brain areas (
      • Itahashi T.
      • Yamada T.
      • Nakamura M.
      • Watanabe H.
      • Yamagata B.
      • Jimbo D.
      • et al.
      Linked alterations in gray and white matter morphology in adults with high-functioning autism spectrum disorder: a multimodal brain imaging study.
      ). The areas of this IC have been linked previously to both social and non-social cognitive difficulties in individuals with autism, varying from visual, sensory and motor processing to high-order cognitive abilities (
      • Tung Y.H.
      • Lin H.Y.
      • Chen C.L.
      • Shang C.Y.
      • Yang L.Y.
      • Hsu Y.C.
      • et al.
      Whole Brain White Matter Tract Deviation and Idiosyncrasy From Normative Development in Autism and ADHD and Unaffected Siblings Link With Dimensions of Psychopathology and Cognition.
      ,
      • Kana R.K.
      • Keller T.A.
      • Minshew N.J.
      • Just M.A.
      Inhibitory control in high-functioning autism: decreased activation and underconnectivity in inhibition networks.
      ,
      • McGrath J.
      • Johnson K.
      • O'Hanlon E.
      • Garavan H.
      • Gallagher L.
      • Leemans A.
      White matter and visuospatial processing in autism: a constrained spherical deconvolution tractography study.
      ,
      • Nomi J.S.
      • Molnar-Szakacs I.
      • Uddin L.Q.
      Insular function in autism: Update and future directions in neuroimaging and interventions.
      ,
      • Sato W.
      • Kochiyama T.
      • Uono S.
      • Yoshimura S.
      • Kubota Y.
      • Sawada R.
      • et al.
      Reduced Gray Matter Volume in the Social Brain Network in Adults with Autism Spectrum Disorder.
      ). For example, pre-central, post-central gyrus, SLF and CST are related to (sensory-)motor processing and have been implicated in autism (
      • Tung Y.H.
      • Lin H.Y.
      • Chen C.L.
      • Shang C.Y.
      • Yang L.Y.
      • Hsu Y.C.
      • et al.
      Whole Brain White Matter Tract Deviation and Idiosyncrasy From Normative Development in Autism and ADHD and Unaffected Siblings Link With Dimensions of Psychopathology and Cognition.
      ,
      • Thompson A.
      • Shahidiani A.
      • Fritz A.
      • O'Muircheartaigh J.
      • Walker L.
      • D'Almeida V.
      • et al.
      Age-related differences in white matter diffusion measures in autism spectrum condition.
      ,
      • Thompson A.
      • Murphy D.
      • Dell'Acqua F.
      • Ecker C.
      • McAlonan G.
      • Howells H.
      • et al.
      Impaired Communication Between the Motor and Somatosensory Homunculus Is Associated With Poor Manual Dexterity in Autism Spectrum Disorder.
      ).These adjacent affected areas (grouped areas of pre-, post-central areas and SLF, CST; grouped areas of LOC and IFOF occipital section) in our findings logically is in line with the brain organization principles during development, which states that nearby areas tend to be more interconnected (
      • Beul S.F.
      • Grant S.
      • Hilgetag C.C.
      A predictive model of the cat cortical connectome based on cytoarchitecture and distance.
      ,
      • Oligschlager S.
      • Xu T.
      • Baczkowski B.M.
      • Falkiewicz M.
      • Falchier A.
      • Linn G.
      • et al.
      Gradients of connectivity distance in the cerebral cortex of the macaque monkey.
      ). In summary, the autism diagnosis-related co-varying GM-WM pattern reflect that autism is a complex condition associated with neural morphology. However, we did not find any significant univariate relationship between behavioral phenotypes and brain patterns. This is probably a result of the diverse phenotypes in our sample (i.e., complex and heterogenous nature of autism), therefore, the compound variances of the symptom profiles cannot be explained by single uni/multimodal brain patterns. Additionally, imaging studies suggested that individuals with autism develop alternative processing strategies (
      • Philip R.C.
      • Dauvermann M.R.
      • Whalley H.C.
      • Baynham K.
      • Lawrie S.M.
      • Stanfield A.C.
      A systematic review and meta-analysis of the fMRI investigation of autism spectrum disorders.
      ) that might mix or neutralize the manifestations of behavioral phenotypes in autism moderating detection of well-established brain-behavior relations. Furthermore, non-significant univariate but one remarkable multivariate brain-behavior relationship in current study may relate to the relatively mild autism traits in LEAP cohort, for example, the average score of ADOS CSS total is lower than the clinical cutoffs, which was reported in the larger LEAP sample compared to other cohorts (
      • Charman T.
      • Loth E.
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      • Crawley D.
      • Wooldridge C.
      • Goyard D.
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      The EU-AIMS Longitudinal European Autism Project (LEAP): clinical characterisation.
      ).
      The significant multivariate brain-behavior relationship in the current study is one prominent WM dominated multivariate relation between all brain patterns and all behavioral profiles. The top two ranking ICs emphasize the importance of WM connection to the core traits of autism. Multivariate/multimodal analysis increases the difficulty in interpreting findings, as it’s challenging to clarify the direction of each association. Nonetheless, coinciding with previous studies (
      • Aoki Y.
      • Yoncheva Y.N.
      • Chen B.
      • Nath T.
      • Sharp D.
      • Lazar M.
      • et al.
      Association of White Matter Structure With Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder.
      ,
      • Tung Y.H.
      • Lin H.Y.
      • Chen C.L.
      • Shang C.Y.
      • Yang L.Y.
      • Hsu Y.C.
      • et al.
      Whole Brain White Matter Tract Deviation and Idiosyncrasy From Normative Development in Autism and ADHD and Unaffected Siblings Link With Dimensions of Psychopathology and Cognition.
      ,
      • Thompson A.
      • Shahidiani A.
      • Fritz A.
      • O'Muircheartaigh J.
      • Walker L.
      • D'Almeida V.
      • et al.
      Age-related differences in white matter diffusion measures in autism spectrum condition.
      ,
      • Prigge M.B.D.
      • Lange N.
      • Bigler E.D.
      • King J.B.
      • Dean 3rd, D.C.
      • Adluru N.
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      ,
      • Ohta H.
      • Aoki Y.Y.
      • Itahashi T.
      • Kanai C.
      • Fujino J.
      • Nakamura M.
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      White matter alterations in autism spectrum disorder and attention-deficit/hyperactivity disorder in relation to sensory profile.
      ), there are associations of ILF, IFOF, CST, SLF and SLF microstructural measures with core symptoms/traits in autism. In line with previous findings our work also shows laterality effects with much of the contribution from IC7 being right lateralized in autism (
      • McGrath J.
      • Johnson K.
      • O'Hanlon E.
      • Garavan H.
      • Gallagher L.
      • Leemans A.
      White matter and visuospatial processing in autism: a constrained spherical deconvolution tractography study.
      ,
      • Boets B.
      • Van Eylen L.
      • Sitek K.
      • Moors P.
      • Noens I.
      • Steyaert J.
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      ). Significantly, GM volume contributed only by a small amount, which implicates WM morphology has a stronger connection to the autism behavioral phenotypes compared to GM in this multivariate correlation. In our previous GM work a multivariate correlation pattern exhibited a strong association between RRB scores of ADI and ADOS and GM covariations in autism, while here when including WM microstructural measures, the brain patterns demonstrated a strong association with RRB domains of the ADI and ADOS. This multivariate brain-behavior association needs further investigation to determine the relationship between the development of WM microstructure and behaviors, the generalizability beyond the current sample, and to explore how different behavioral scales capture behavioral phenotypes in autism, which might expand our knowledge of current brain-behavior association patterns.
      Our findings should be interpreted with regard to several limitations. First, to generalize our pattern of brain alterations associated with autism requires replication in other large-scale datasets. Second, the current multimodal dataset included fewer participants than our previous work (
      • Mei T.
      • Llera A.
      • Floris D.L.
      • Forde N.J.
      • Tillmann J.
      • Durston S.
      • et al.
      Gray matter covariations and core symptoms of autism: the EU-AIMS Longitudinal European Autism Project.
      ), which may have lowered statistical power when detecting the group effects and brain-behavior associations in autism group. Despite that, this is still the largest multimodal MRI study of autism to date and includes a diverse sample of autistic and non-autistic participants. Third, limited to the cross-sectional nature of the current study, our findings are deficient to address the developmental effects on these brain patterns and their relations to the behavior profiles, as the structures of the brain (especially WM) change remarkably over puberty and with aging.
      In current study, we demonstrate autism-related inter-individual covariations of GM volume in frontal, pre-central, post-central and occipital areas and microstructure in associated WM fasciculi. Together, these GM and WM alterations are part of the underlying neural substrates of the phenotypes in autism. Subsequently, we highlight the potential role of WM, in the relation to the core symptoms of autism. Further studies may link our GM-WM morphometric findings with brain function acquired from cognitive assessments and/or functional MRI data.

      Acknowledgments

      This work is primarily supported by the EU-AIMS consortium (European Autism Interventions), which receives support from Innovative Medicines Initiative Joint Undertaking Grant No.115300, the resources of which are composed of financial contributions from the European Union’s Seventh Framework Programme (Grant No. FP7/2007-2013), from the European Federation of Pharmaceutical Industries and Associations companies’ in-kind contributions; and by the AIMS-2-TRIALS consortium (Autism Innovative Medicine Studies-2-Trials), which has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 777394, and this Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA and AUTISM SPEAKS, Autistica, SFARI. This work reflects the authors’ views and neither IMI nor the European Union, EFPIA or any Associated Partners are responsible for any use that may be made of the information contained therein.
      TM is supported by a China Scholarship Council grant (No 201806010408). DLF is supported by funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101025785. This work has been further supported by the European Union Seventh Framework Programme Grant Nos. 602805 (AGGRESSOTYPE) (to JKB), 603016 (MATRICS) (to JKB), and 278948 (TACTICS) (to JKB); European Community’s Horizon 2020 Programme (H2020/2014-2020) Grant Nos. 643051 (MiND) (to JKB), 642996 (BRAINVIEW) (to JKB) and 847818 (CANDY) (to JKB and CFB); the Netherlands Organization for Scientific Research VICI Grant No. 2020/TTW/00836465 (to CFB); Wellcome Trust Collaborative Award Grant No. 215573/Z/19/Z (to CFB); the Autism Research Trust (to SBC).
      This manuscript has been posted on bioRxiv on February 17, 2022, doi: https://doi.org/10.1101/2022.02.16.480649.
      We thank all participants and their families for participating in this study. We gratefully acknowledge the contributions of all members of the EU-AIMS LEAP group: Jan K. Buitelaar (primary contact/principal investigator), Jumana Ahmad, Sara Ambrosino, Bonnie Auyeung, Tobias Banaschewski, Simon Baron-Cohen, Sarah Baumeister, Christian F. Beckmann, Sven Bölte, Thomas Bourgeron, Carsten Bours, Michael Brammer, Daniel Brandeis, Claudia Brogna, Yvette de Bruijn, Bhismadev Chakrabarti, Tony Charman, Ineke Cornelissen, Daisy Crawley, Flavio Dell’Acqua, Guillaume Dumas, Sarah Durston, Christine Ecker, Jessica Faulkner, Vincent Frouin, Pilar Garcés, David Goyard, Lindsay Ham, Hannah Hayward, Joerg Hipp, Rosemary Holt, Mark H. Johnson, Emily J.H. Jones, Prantik Kundu, Meng-Chuan Lai, Xavier Liogier D’ardhuy, Michael V. Lombardo, Eva Loth, David J. Lythgoe, René Mandl, Andre Marquand, Luke Mason, Maarten Mennes, Andreas Meyer-Lindenberg, Carolin Moessnang, Nico Mueller, Declan G.M. Murphy, Bethany Oakley, Laurence O’Dwyer, Marianne Oldehinkel, Bob Oranje, Gahan Pandina, Antonio M. Persico, Annika Rausch, Barbara Ruggeri, Amber Ruigrok, Jessica Sabet, Roberto Sacco, Antonia San José Cáceres, Emily Simonoff, Will Spooren, Julian Tillmann, Roberto Toro, Heike Tost, Jack Waldman, Steve C.R. Williams, Caroline Wooldridge, Iva Ilioska, Ting Mei and Marcel P. Zwiers.
      Disclosures
      TC has received consultancy from Roche and Servier and received book royalties from Guildford Press and Sage. DGM has been a consultant to, and advisory board member, for Roche and Servier. He is not an employee of any of these companies, and not a stock shareholder of any of these companies. CFB is director and shareholder in SBGNeuro Ltd. TB served in an advisory or consultancy role for ADHS digital, Infectopharm, Lundbeck, Medice, Neurim Pharmaceuticals, Oberberg GmbH, Roche, and Takeda. He received conference support or speaker’s fee by Medice and Takeda. He received royalities from Hogrefe, Kohlhammer, CIP Medien, and Oxford University Press. JKB has been a consultant to, advisory board member of, and a speaker for Janssen Cilag BV, Eli Lilly, Shire, Lundbeck, Roche, and Servier. He is not an employee of any of these companies, and not a stock shareholder of any of these companies. He has no other financial or material support, including expert testimony, patents or royalties. The present work is unrelated to the above grants and relationships. The other authors report no biomedical financial interests or potential conflicts of interest.

      Supplementary Material

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