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Robust, Generalizable, and Interpretable Artificial Intelligence–Derived Brain Fingerprints of Autism and Social Communication Symptom Severity

  • Author Footnotes
    1 KS and SR contributed equally to this work.
    Kaustubh Supekar
    Correspondence
    Address correspondence to Kaustubh Supekar, Ph.D.
    Footnotes
    1 KS and SR contributed equally to this work.
    Affiliations
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
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  • Author Footnotes
    1 KS and SR contributed equally to this work.
    Srikanth Ryali
    Footnotes
    1 KS and SR contributed equally to this work.
    Affiliations
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
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  • Rui Yuan
    Affiliations
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
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  • Devinder Kumar
    Affiliations
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
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  • Carlo de los Angeles
    Affiliations
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
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  • Vinod Menon
    Correspondence
    Vinod Menon, Ph.D.
    Affiliations
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California

    Department of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, California

    Stanford Neurosciences Institute, Stanford University School of Medicine, Stanford, California
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  • Author Footnotes
    1 KS and SR contributed equally to this work.
Published:February 15, 2022DOI:https://doi.org/10.1016/j.biopsych.2022.02.005

      Abstract

      Background

      Autism spectrum disorder (ASD) is among the most pervasive neurodevelopmental disorders, yet the neurobiology of ASD is still poorly understood because inconsistent findings from underpowered individual studies preclude the identification of robust and interpretable neurobiological markers and predictors of clinical symptoms.

      Methods

      We leverage multiple brain imaging cohorts and exciting recent advances in explainable artificial intelligence to develop a novel spatiotemporal deep neural network (stDNN) model, which identifies robust and interpretable dynamic brain markers that distinguish ASD from neurotypical control subjects and predict clinical symptom severity.

      Results

      stDNN achieved consistently high classification accuracies in cross-validation analysis of data from the multisite ABIDE (Autism Brain Imaging Data Exchange) cohort (n = 834). Crucially, stDNN also accurately classified data from independent Stanford (n = 202) and GENDAAR (Gender Exploration of Neurogenetics and Development to Advanced Autism Research) (n = 90) cohorts without additional training. stDNN could not distinguish attention-deficit/hyperactivity disorder from neurotypical control subjects, highlighting the model’s specificity. Explainable artificial intelligence revealed that brain features associated with the posterior cingulate cortex and precuneus, dorsolateral and ventrolateral prefrontal cortex, and superior temporal sulcus, which anchor the default mode network, cognitive control, and human voice processing systems, respectively, most clearly distinguished ASD from neurotypical control subjects in the three cohorts. Furthermore, features associated with the posterior cingulate cortex and precuneus nodes of the default mode network emerged as robust predictors of the severity of core social and communication deficits but not restricted/repetitive behaviors in ASD.

      Conclusions

      Our findings, replicated across independent cohorts, reveal robust individualized functional brain fingerprints of ASD psychopathology, which could lead to more objective and precise phenotypic characterization and targeted treatments.

      Keywords

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