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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
Source PublicationSCHIZOPHRENIA RESEARCH
ISSN0920-9964
2022-07-01
Volume245Pages:141-150
Corresponding AuthorSui, Jing(jing.sui@nlpr.ia.ac.cn) ; Calhoun, Vince D.(vcalhoun@gsu.edu)
AbstractBackground: 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.
KeywordDeep learning FMRI Schizophrenia Bipolar disorder Schizoaffective disorder
DOI10.1016/j.schres.2021.02.007
WOS KeywordINTERMEDIATE PHENOTYPES ; GROUP ICA ; CONNECTIVITY ; BRAIN ; PSYCHOSIS ; NETWORK ; CEREBELLUM ; DEPRESSION ; BIOMARKERS ; ARTIFACT
Indexed BySCI
Language英语
Funding ProjectNatural 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]
Funding OrganizationNatural Science Foundation of China ; National Institute of Health ; NIMH ; Beijing Municipal Science and Technology Commission
WOS Research AreaPsychiatry
WOS SubjectPsychiatry
WOS IDWOS:000815955100013
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49171
Collection脑网络组研究
Corresponding AuthorSui, Jing; Calhoun, Vince D.
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China;  Institute of Automation, Chinese Academy of Sciences
Recommended Citation
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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