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Linear discriminant analysis of phenotypic data for classifying autism spectrum disorder by diagnosis and sex

  • Jacokes, Zachary1
  • Jack, Allison2
  • Sullivan, Catherine A. W.3
  • Aylward, Elizabeth4
  • Bookheimer, Susan Y.5
  • Dapretto, Mirella5
  • Bernier, Raphael A.4
  • Geschwind, Daniel H.5, 6
  • Sukhodolsky, Denis G.7
  • McPartland, James C.7
  • Webb, Sara J.4, 8
  • Torgerson, Carinna M.9
  • Eilbott, Jeffrey7
  • Kenworthy, Lauren10
  • Pelphrey, Kevin A.11
  • Van Horn, John D.1
  • 1 Laboratory of Brain and Data Science, Department of Psychology, School of Data Science, University of Virginia, Charlottesville, VA , (United States)
  • 2 Department of Psychology, George Mason University, Fairfax, VA , (United States)
  • 3 Department of Pediatrics, Yale School of Medicine, New Haven, CT , (United States)
  • 4 Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA , (United States)
  • 5 Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA , (United States)
  • 6 Center for Neurobehavioral Genetics, University of California, Los Angeles, Los Angeles, CA , (United States)
  • 7 Child Study Center, Yale School of Medicine, New Haven, CT , (United States)
  • 8 Center on Child Health, Behavior, and Development, Seattle Children’s Research Institute, Seattle, WA , (United States)
  • 9 Neuroscience Graduate Program, University of Southern California, Los Angeles, CA , (United States)
  • 10 Center for Autism Spectrum Disorders, Children’s National Hospital, Washington, DC , (United States)
  • 11 Department of Neurology, University of Virginia, Charlottesville, VA , (United States)
Published Article
Frontiers in Neuroscience
Frontiers Media SA
Publication Date
Nov 16, 2022
DOI: 10.3389/fnins.2022.1040085
  • Neuroscience
  • Original Research


Autism Spectrum Disorder (ASD) is a developmental condition characterized by social and communication differences. Recent research suggests ASD affects 1-in-44 children in the United States. ASD is diagnosed more commonly in males, though it is unclear whether this diagnostic disparity is a result of a biological predisposition or limitations in diagnostic tools, or both. One hypothesis centers on the ‘female protective effect,’ which is the theory that females are biologically more resistant to the autism phenotype than males. In this examination, phenotypic data were acquired and combined from four leading research institutions and subjected to multivariate linear discriminant analysis. A linear discriminant model was trained on the training set and then deployed on the test set to predict group membership. Multivariate analyses of variance were performed to confirm the significance of the overall analysis, and individual analyses of variance were performed to confirm the significance of each of the resulting linear discriminant axes. Two discriminant dimensions were identified between the groups: a dimension separating groups by the diagnosis of ASD (LD1: 87% of variance explained); and a dimension reflective of a diagnosis-by-sex interaction (LD2: 11% of variance explained). The strongest discriminant coefficients for the first discriminant axis divided the sample in domains with known differences between ASD and comparison groups, such as social difficulties and restricted repetitive behavior. The discriminant coefficients for the second discriminant axis reveal a more nuanced disparity between boys with ASD and girls with ASD, including executive functioning and high-order behavioral domains as the dominant discriminators. These results indicate that phenotypic differences between males and females with and without ASD are identifiable using parent report measures, which could be utilized to provide additional specificity to the diagnosis of ASD in female patients, potentially leading to more targeted clinical strategies and therapeutic interventions. The study helps to isolate a phenotypic basis for future empirical work on the female protective effect using neuroimaging, EEG, and genomic methodologies.

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