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Mapping relationships among schizophrenia, bipolar and schizoaffective disorders: A deep classification and clustering framework using fMRI time series

文献类型：期刊论文

作者Yan, Weizheng5,6; Zhao, Min4,5; Fu, Zening6; Pearlson, Godfrey D.3; Sui, Jing1,4,5,6; Calhoun, Vince D.2,6
刊名SCHIZOPHRENIA RESEARCH
出版日期2022-07-01
卷号245页码:141-150
ISSN号0920-9964
关键词Deep learning FMRI Schizophrenia Bipolar disorder Schizoaffective disorder
DOI10.1016/j.schres.2021.02.007
通讯作者Sui, Jing(jing.sui@nlpr.ia.ac.cn) ; Calhoun, Vince D.(vcalhoun@gsu.edu)
英文摘要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.
WOS关键词INTERMEDIATE PHENOTYPES ; GROUP ICA ; CONNECTIVITY ; BRAIN ; PSYCHOSIS ; NETWORK ; CEREBELLUM ; DEPRESSION ; BIOMARKERS ; ARTIFACT
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]
WOS研究方向Psychiatry
语种英语
出版者ELSEVIER
WOS记录号WOS:000815955100013
资助机构Natural Science Foundation of China ; National Institute of Health ; NIMH ; Beijing Municipal Science and Technology Commission
源URL[http://ir.ia.ac.cn/handle/173211/49170]  
专题自动化研究所_脑网络组研究中心
通讯作者Sui, Jing; Calhoun, Vince D.
作者单位1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
2.Emory Univ, Georgia State Univ, Georgia Inst Technol, Triinst Ctr Translat Res Neuroimaging, Atlanta, GA 30303 USA
3.Yale Univ Sch Med, Dept Psychiat & Neurobiol, New Haven, CT USA
4.Univ Chinese Acad Sci, Sch Arti fi cial Intelligence, Beijing 100049, Peoples R China
5.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Natl Lab Pattern Recognit, Beijing, Peoples R China
6.Emory Univ, Georgia State Univ, Georgia Inst Technol, Triinst Ctr Translat Res Neuroimaging & Data Sci T, Atlanta, GA 30303 USA
推荐引用方式
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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来源：自动化研究所

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