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Diagnostic Classification for Human Autism and Obsessive-Compulsive Disorder Based on Machine Learning From a Primate Genetic Model
Zhan, Yafeng1,2; Wei, Jianze3,4; Liang, Jian4; Xu, Xiu5; He, Ran4; Robbins, Trevor W.6,7; Wang, Zheng1,2,8,9
Source PublicationAMERICAN JOURNAL OF PSYCHIATRY
ISSN0002-953X
2021
Volume178Issue:1Pages:65-76
Abstract

Objective: Psychiatric disorders commonly comprise comorbid symptoms, such as autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD), and attention deficit hyperactivity disorder (ADHD), raising controversies over accurate diagnosis and overlap of their neural underpinnings. The authors used non invasive neuroimaging in humans and nonhuman primates to identify neural markers associated with DSM-5 diagnoses and quantitative measures of symptom severity. Methods: Resting-state functional connectivity data obtained from both wild-type and methyl-CpG binding protein 2 (MECP2) transgenic monkeys were used to construct monkey-derived classifiers for diagnostic classification in four human data sets (ASD: Autism Brain Imaging Data Exchange [ABIDE-I], N=1,112; ABIDE-II, N=1,114; ADHD-200 sample: N=776; OCD local institutional database: N=186). Stepwise linear regression models were applied to examine associations between functional connections of monkey-derived classifiers and dimensional symptom severity of psychiatric disorders. Results: Nine core regions prominently distributed in frontal and temporal cortices were identified in monkeys and used as seeds to construct the monkey-derived classifier that informed diagnostic classification in human autism. This same set of core regions was useful for diagnostic classification in the OCD cohort but not the ADHD cohort. Models based on functional connections of the right ventrolateral prefrontal cortex with the left thalamus and right prefrontal polar cortex predicted communication scores of ASD patients and compulsivity scores of OCD patients, respectively. Conclusions: The identified core regions may serve as a basis for building markers for ASD and OCD diagnoses, as well as measures of symptom severity. These findings may inform future development of machine-learning models for psychiatric disorders and may improve the accuracy and speed of clinical assessments.

DOI10.1176/appi.ajp.2020.19101091
WOS KeywordSPECTRUM DISORDERS ; BRAIN ; ANXIETY ; BEHAVIOR ; CONNECTIVITY ; IMPULSIVITY ; REGRESSION ; SELECTION ; SYMPTOMS ; CHILDREN
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2017YFC1310400] ; National Key R&D Program of China[2018YFC1313803] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32030000] ; Shanghai Municipal Science and Technology Major Project[2018SHZDZX05] ; National Natural Science Foundation[81571300] ; National Natural Science Foundation[81527901] ; National Natural Science Foundation[31771174] ; Key Realm R&D Program of Guangdong Province[2019B030335001] ; Wellcome Trust[104631/Z/12/Z]
Funding OrganizationNational Key R&D Program of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Shanghai Municipal Science and Technology Major Project ; National Natural Science Foundation ; Key Realm R&D Program of Guangdong Province ; Wellcome Trust
WOS Research AreaPsychiatry
WOS SubjectPsychiatry
WOS IDWOS:000604750700010
PublisherAMER PSYCHIATRIC PUBLISHING, INC
Sub direction classification机器学习
Citation statistics
Cited Times:28[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/42570
Collection智能感知与计算研究中心
Corresponding AuthorHe, Ran; Wang, Zheng
Affiliation1.Chinese Acad Sci, State Key Lab Neurosci, Key Lab Primate Neurobiol, Inst Neurosci,Ctr Excellence Brain Sci & Intellig, Shanghai, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Automat, Ctr Excellence Brain Sci & Intelligence Technol, Natl Lab Pattern Recognit, Beijing, Peoples R China
5.Fudan Univ, Dept Child Hlth Care, Childrens Hosp, Shanghai, Peoples R China
6.Univ Cambridge, Dept Psychol, Behav & Clin Neurosci Inst, Cambridge, England
7.Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Shanghai, Peoples R China
8.Shanghai Ctr Brain Sci & Brain Inspired Intellige, Shanghai, Peoples R China
9.Chinese Acad Sci, Kunming Inst Zool, Kunming, Yunnan, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhan, Yafeng,Wei, Jianze,Liang, Jian,et al. Diagnostic Classification for Human Autism and Obsessive-Compulsive Disorder Based on Machine Learning From a Primate Genetic Model[J]. AMERICAN JOURNAL OF PSYCHIATRY,2021,178(1):65-76.
APA Zhan, Yafeng.,Wei, Jianze.,Liang, Jian.,Xu, Xiu.,He, Ran.,...&Wang, Zheng.(2021).Diagnostic Classification for Human Autism and Obsessive-Compulsive Disorder Based on Machine Learning From a Primate Genetic Model.AMERICAN JOURNAL OF PSYCHIATRY,178(1),65-76.
MLA Zhan, Yafeng,et al."Diagnostic Classification for Human Autism and Obsessive-Compulsive Disorder Based on Machine Learning From a Primate Genetic Model".AMERICAN JOURNAL OF PSYCHIATRY 178.1(2021):65-76.
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