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Mapping relationships among schizophrenia, bipolar and schizoaffective disorders: A deep classification and clustering framework using fMRI time series
Yan, Weizheng1,2; Zhao, Min2,3; Fu, Zening1; Pearlson, Godfrey D.4; Sui, Jing1,2,3,6; Calhoun, Vince D.1,5
发表期刊SCHIZOPHRENIA RESEARCH
ISSN0920-9964
2022-07-01
卷号245页码:141-150
通讯作者Sui, Jing(jing.sui@nlpr.ia.ac.cn) ; Calhoun, Vince D.(vcalhoun@gsu.edu)
摘要Background: Psychiatric disorders are categorized using self-report and observational information rather than biological data. There is also considerable symptomatic overlap between different types of psychiatric disorders, which makes diagnostic categorization and multi-class classification challenging.Methods: In this work, we propose a unified framework for supervised classification and unsupervised clustering of psychotic disorders using brain imaging data. A new multi-scale recurrent neural network (MsRNN) model was developed and applied to fMRI time courses (TCs) for multi-class classification. The high-level representations of the original TCs were then submitted to a tSNE clustering model for visualizing the group differences between disorders. A leave-one-feature-out approach was used for disorder-related biomarker identification.Results: When studying fMRI from schizophrenia, psychotic bipolar disorder, schizoaffective disorder, and healthy individuals, the accuracy of a 4-class classification reached 46%, significantly above chance. The hippo campus, supplementary motor area and paracentral lobule were discovered as the most contributing regional TCs in the multi-class classification. Beyond this, visualization of the tSNE clustering suggested that the disease severity can be captured and schizoaffective disorder (SAD) may be separated into two subtypes. SAD cluster1 has significantly higher Positive And Negative Syndrome Scale (PANSS) scores than SAD cluster2 in PANSS negative2 (emotional withdrawal), general2 (anxiety), general3 (guilt feelings), general4 (tension).Conclusions: The proposed deep classification and clustering framework is not only able to identify psychiatric disorders with high accuracy, but also interpret the correlation between brain networks and specific psychiatric disorders, and reveal the relationship between them. This work provides a promising way to investigate a spectrum of similar disorders using neuroimaging-based measures.(c) 2021 Elsevier B.V. All rights reserved.
关键词Deep learning FMRI Schizophrenia Bipolar disorder Schizoaffective disorder
DOI10.1016/j.schres.2021.02.007
关键词[WOS]INTERMEDIATE PHENOTYPES ; GROUP ICA ; CONNECTIVITY ; BRAIN ; PSYCHOSIS ; NETWORK ; CEREBELLUM ; DEPRESSION ; BIOMARKERS ; ARTIFACT
收录类别SCI
语种英语
资助项目Natural Science Foundation of China[82022035] ; Natural Science Foundation of China[61773380] ; National Institute of Health[R01MH11710] ; National Institute of Health[R01MH118695] ; National Institute of Health[R01EB020407] ; NIMH[R01MH077851] ; NIMH[MH078113] ; NIMH[MH077945] ; NIMH[MH096942] ; NIMH[MH096957] ; Beijing Municipal Science and Technology Commission[Z181100001518005]
项目资助者Natural Science Foundation of China ; National Institute of Health ; NIMH ; Beijing Municipal Science and Technology Commission
WOS研究方向Psychiatry
WOS类目Psychiatry
WOS记录号WOS:000815955100013
出版者ELSEVIER
引用统计
被引频次:20[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/49171
专题脑网络组研究
通讯作者Sui, Jing; Calhoun, Vince D.
作者单位1.Emory Univ, Georgia State Univ, Georgia Inst Technol, Triinst Ctr Translat Res Neuroimaging & Data Sci T, Atlanta, GA 30303 USA
2.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Natl Lab Pattern Recognit, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Sch Arti fi cial Intelligence, Beijing 100049, Peoples R China
4.Yale Univ Sch Med, Dept Psychiat & Neurobiol, New Haven, CT USA
5.Emory Univ, Georgia State Univ, Georgia Inst Technol, Triinst Ctr Translat Res Neuroimaging, Atlanta, GA 30303 USA
6.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
第一作者单位模式识别国家重点实验室
通讯作者单位模式识别国家重点实验室;  中国科学院自动化研究所
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GB/T 7714
Yan, Weizheng,Zhao, Min,Fu, Zening,et al. Mapping relationships among schizophrenia, bipolar and schizoaffective disorders: A deep classification and clustering framework using fMRI time series[J]. SCHIZOPHRENIA RESEARCH,2022,245:141-150.
APA Yan, Weizheng,Zhao, Min,Fu, Zening,Pearlson, Godfrey D.,Sui, Jing,&Calhoun, Vince D..(2022).Mapping relationships among schizophrenia, bipolar and schizoaffective disorders: A deep classification and clustering framework using fMRI time series.SCHIZOPHRENIA RESEARCH,245,141-150.
MLA Yan, Weizheng,et al."Mapping relationships among schizophrenia, bipolar and schizoaffective disorders: A deep classification and clustering framework using fMRI time series".SCHIZOPHRENIA RESEARCH 245(2022):141-150.
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