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Linear discriminant analysis of phenotypic data for classifying autism spectrum disorder by diagnosis and sex
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 o...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9709432/ https://www.ncbi.nlm.nih.gov/pubmed/36466170 http://dx.doi.org/10.3389/fnins.2022.1040085 |
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author | Jacokes, Zachary Jack, Allison Sullivan, Catherine A. W. Aylward, Elizabeth Bookheimer, Susan Y. Dapretto, Mirella Bernier, Raphael A. Geschwind, Daniel H. Sukhodolsky, Denis G. McPartland, James C. Webb, Sara J. Torgerson, Carinna M. Eilbott, Jeffrey Kenworthy, Lauren Pelphrey, Kevin A. Van Horn, John D. |
author_facet | Jacokes, Zachary Jack, Allison Sullivan, Catherine A. W. Aylward, Elizabeth Bookheimer, Susan Y. Dapretto, Mirella Bernier, Raphael A. Geschwind, Daniel H. Sukhodolsky, Denis G. McPartland, James C. Webb, Sara J. Torgerson, Carinna M. Eilbott, Jeffrey Kenworthy, Lauren Pelphrey, Kevin A. Van Horn, John D. |
author_sort | Jacokes, Zachary |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9709432 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97094322022-12-01 Linear discriminant analysis of phenotypic data for classifying autism spectrum disorder by diagnosis and sex Jacokes, Zachary Jack, Allison Sullivan, Catherine A. W. Aylward, Elizabeth Bookheimer, Susan Y. Dapretto, Mirella Bernier, Raphael A. Geschwind, Daniel H. Sukhodolsky, Denis G. McPartland, James C. Webb, Sara J. Torgerson, Carinna M. Eilbott, Jeffrey Kenworthy, Lauren Pelphrey, Kevin A. Van Horn, John D. Front Neurosci Neuroscience 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. Frontiers Media S.A. 2022-11-16 /pmc/articles/PMC9709432/ /pubmed/36466170 http://dx.doi.org/10.3389/fnins.2022.1040085 Text en Copyright © 2022 Jacokes, Jack, Sullivan, Aylward, Bookheimer, Dapretto, Bernier, Geschwind, Sukhodolsky, McPartland, Webb, Torgerson, Eilbott, Kenworthy, Pelphrey, Van Horn and The GENDAAR Consortium. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Jacokes, Zachary Jack, Allison Sullivan, Catherine A. W. Aylward, Elizabeth Bookheimer, Susan Y. Dapretto, Mirella Bernier, Raphael A. Geschwind, Daniel H. Sukhodolsky, Denis G. McPartland, James C. Webb, Sara J. Torgerson, Carinna M. Eilbott, Jeffrey Kenworthy, Lauren Pelphrey, Kevin A. Van Horn, John D. Linear discriminant analysis of phenotypic data for classifying autism spectrum disorder by diagnosis and sex |
title | Linear discriminant analysis of phenotypic data for classifying autism spectrum disorder by diagnosis and sex |
title_full | Linear discriminant analysis of phenotypic data for classifying autism spectrum disorder by diagnosis and sex |
title_fullStr | Linear discriminant analysis of phenotypic data for classifying autism spectrum disorder by diagnosis and sex |
title_full_unstemmed | Linear discriminant analysis of phenotypic data for classifying autism spectrum disorder by diagnosis and sex |
title_short | Linear discriminant analysis of phenotypic data for classifying autism spectrum disorder by diagnosis and sex |
title_sort | linear discriminant analysis of phenotypic data for classifying autism spectrum disorder by diagnosis and sex |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9709432/ https://www.ncbi.nlm.nih.gov/pubmed/36466170 http://dx.doi.org/10.3389/fnins.2022.1040085 |
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