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Most current neuroimaging analyses in studies of brain disorders assume a homogenous presentation of the disorder such that traditional statistical analysis methods based on Gaussian distributions can be applied. Yet, most brain disorders present with a heterogeneous spectrum of cognitive, behavioral, morphometric as well as functional manifestations. In this paper, we introduce a novel approach called PRADA (Phenotype Representation and Analysis via Discriminant Atypicality) that embraces the heterogeneity of both typical and atypical brain morphometry. This approach employs Multiscale Score Matching Analysis (MSMA), a global and local multiscale out-of-distribution analysis via the gradients of the log density (scores). Combining MSMA and manifold-mapping, we compute a morphospace of brain phenotypes representing deviations from a population of typical subjects. Using these brain phenotypes, disorder-related subtyping can be performed. Furthermore, subject-specific profiles of atypicality can be extracted via Spatial-MSMA and summarized per subtype. We show the application of PRADA to structural MRI data in a study of Autism Spectrum Disorder (ASD). The resulting analysis detects disorder-related subtypes and reveals that subtype-specific structural atypicality correlates with cognitive and behavioral outcomes. These results highlight the potential of PRADA to discover disorder relevant phenotypes.

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Phenotype Representation and Analysis via Discriminative Atypicality (PRADA) to Capture the Structural Heterogeneity of Autism Spectrum Disorder

  • 会议论文

  • 发表日期：

    2026年

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  • Emre Onemli,
  • Ahsan Mahmood,
  • Omar Azrak,
  • Dea Garic,
  • Meghan R. Swanson,
  • Rebecca Grzadzinski,
  • Kattia Mata,
  • Mark D. Shen,
  • Jessica B. Girault,
  • Tanya St. John,
  • Juhi Pandey,
  • Lonnie Zwaigenbaum,
  • Annette M. Estes,
  • Audrey M. Shen,
  • Stephen R. Dager,
  • Robert T. Schultz,
  • Kelly N. Botteron,
  • Alan C. Evans,
  • Jed T. Elison,
  • Essa Yacoub,
  • Sun Hyung Kim,
  • Robert C. McKinstry,
  • Guido Gerig,
  • Heather C. Hazlett,
  • Natasha Marrus,
  • Joseph Piven,
  • John R. Pruett Jr.,
  • Martin Styner

摘要

Most current neuroimaging analyses in studies of brain disorders assume a homogenous presentation of the disorder such that traditional statistical analysis methods based on Gaussian distributions can be applied. Yet, most brain disorders present with a heterogeneous spectrum of cognitive, behavioral, morphometric as well as functional manifestations. In this paper, we introduce a novel approach called PRADA (Phenotype Representation and Analysis via Discriminant Atypicality) that embraces the heterogeneity of both typical and atypical brain morphometry. This approach employs Multiscale Score Matching Analysis (MSMA), a global and local multiscale out-of-distribution analysis via the gradients of the log density (scores). Combining MSMA and manifold-mapping, we compute a morphospace of brain phenotypes representing deviations from a population of typical subjects. Using these brain phenotypes, disorder-related subtyping can be performed. Furthermore, subject-specific profiles of atypicality can be extracted via Spatial-MSMA and summarized per subtype. We show the application of PRADA to structural MRI data in a study of Autism Spectrum Disorder (ASD). The resulting analysis detects disorder-related subtypes and reveals that subtype-specific structural atypicality correlates with cognitive and behavioral outcomes. These results highlight the potential of PRADA to discover disorder relevant phenotypes.