Assessment of Neuroanatomical Endophenotypes of Autism Spectrum Disorder and Association With Characteristics of Individuals With Schizophrenia and the General Population.

Autor:	Hwang G; AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia., Wen J; AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia.; Laboratory of AI & Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey., Sotardi S; Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania., Brodkin ES; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia., Chand GB; AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia.; Department of Radiology, School of Medicine, Washington University in St Louis, St Louis, Missouri., Dwyer DB; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany., Erus G; AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia., Doshi J; AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia., Singhal P; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia., Srinivasan D; AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia., Varol E; AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia.; Department of Statistics, Zuckerman Institute, Columbia University, New York, New York., Sotiras A; AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia.; Department of Radiology, School of Medicine, Washington University in St Louis, St Louis, Missouri., Dazzan P; Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK., Kahn RS; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York., Schnack HG; Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands., Zanetti MV; Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil.; Hospital Sírio-Libanês, São Paulo, Brazil., Meisenzahl E; LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany., Busatto GF; Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil., Crespo-Facorro B; University Hospital Virgen del Rocio, Department of Psychiatry, School of Medicine, IBiS-CIBERSAM, University of Sevilla, Seville, Spain., Pantelis C; Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia., Wood SJ; Orygen, Melbourne, Victoria, Australia.; Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia.; School of Psychology, University of Birmingham, Edgbaston, UK., Zhuo C; Department of Psychiatric-Neuroimaging-Genetics and Co-morbidity Laboratory, Tianjin Anding Hospital, Tianjin, China.; Department of Psychiatry, Tianjin Medical University, Tianjin, China., Shinohara RT; AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia.; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia., Shou H; AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia.; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia., Fan Y; AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia., Di Martino A; Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience at the New York University Child Study Center, New York., Koutsouleris N; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany., Gur RE; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia., Gur RC; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia., Satterthwaite TD; AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia.; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia., Wolf DH; AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia.; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia., Davatzikos C; AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia.
Jazyk:	angličtina
Zdroj:	JAMA psychiatry [JAMA Psychiatry] 2023 May 01; Vol. 80 (5), pp. 498-507.
DOI:	10.1001/jamapsychiatry.2023.0409
Abstrakt:	Importance: Autism spectrum disorder (ASD) is associated with significant clinical, neuroanatomical, and genetic heterogeneity that limits precision diagnostics and treatment. Objective: To assess distinct neuroanatomical dimensions of ASD using novel semisupervised machine learning methods and to test whether the dimensions can serve as endophenotypes also in non-ASD populations. Design, Setting, and Participants: This cross-sectional study used imaging data from the publicly available Autism Brain Imaging Data Exchange (ABIDE) repositories as the discovery cohort. The ABIDE sample included individuals diagnosed with ASD aged between 16 and 64 years and age- and sex-match typically developing individuals. Validation cohorts included individuals with schizophrenia from the Psychosis Heterogeneity Evaluated via Dimensional Neuroimaging (PHENOM) consortium and individuals from the UK Biobank to represent the general population. The multisite discovery cohort included 16 internationally distributed imaging sites. Analyses were performed between March 2021 and March 2022. Main Outcomes and Measures: The trained semisupervised heterogeneity through discriminative analysis models were tested for reproducibility using extensive cross-validations. It was then applied to individuals from the PHENOM and the UK Biobank. It was hypothesized that neuroanatomical dimensions of ASD would display distinct clinical and genetic profiles and would be prominent also in non-ASD populations. Results: Heterogeneity through discriminative analysis models trained on T1-weighted brain magnetic resonance images of 307 individuals with ASD (mean [SD] age, 25.4 [9.8] years; 273 [88.9%] male) and 362 typically developing control individuals (mean [SD] age, 25.8 [8.9] years; 309 [85.4%] male) revealed that a 3-dimensional scheme was optimal to capture the ASD neuroanatomy. The first dimension (A1: aginglike) was associated with smaller brain volume, lower cognitive function, and aging-related genetic variants (FOXO3; Z = 4.65; P = 1.62 × 10-6). The second dimension (A2: schizophrenialike) was characterized by enlarged subcortical volumes, antipsychotic medication use (Cohen d = 0.65; false discovery rate-adjusted P = .048), partially overlapping genetic, neuroanatomical characteristics to schizophrenia (n = 307), and significant genetic heritability estimates in the general population (n = 14 786; mean [SD] h2, 0.71 [0.04]; P < 1 × 10-4). The third dimension (A3: typical ASD) was distinguished by enlarged cortical volumes, high nonverbal cognitive performance, and biological pathways implicating brain development and abnormal apoptosis (mean [SD] β, 0.83 [0.02]; P = 4.22 × 10-6). Conclusions and Relevance: This cross-sectional study discovered 3-dimensional endophenotypic representation that may elucidate the heterogeneous neurobiological underpinnings of ASD to support precision diagnostics. The significant correspondence between A2 and schizophrenia indicates a possibility of identifying common biological mechanisms across the 2 mental health diagnoses.
Databáze:	MEDLINE
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