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 |
ISSN | 0920-9964 |
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 |
DOI | 10.1016/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 | |
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 |
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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