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A group ICA based framework for evaluating resting fMRI markers when disease categories are unclear: application to schizophrenia, bipolar, and schizoaffective disorders
Du, Yuhui1,2,3; Pearlson, Godfrey D.4,5,6; Liu, Jingyu1,2,7; Sui, Jing1,2,8,9; Yu, Qingbao1,2; He, Hao1,2,7; Castro, Eduardo1,2; Calhoun, Vince D.1,2,4,7
Source PublicationNEUROIMAGE
2015-11-15
Volume122Pages:272-280
SubtypeArticle
AbstractSchizophrenia (SZ), bipolar disorder (BP) and schizoaffective disorder (SAD) share some common symptoms, and there is still a debate about whether SAD is an independent category. To the best of our knowledge, no study has been done to differentiate these three disorders or to investigate the distinction of SAD as an independent category using fMRI data. This study is aimed to explore biomarkers from resting-state fMRI networks for differentiating these disorders and investigate the relationship among these disorders based on fMRI networks with an emphasis on SAD. Firstly, a novel group ICA method, group information guided independent component analysis (GIG-ICA), was applied to extract subject-specific brain networks from fMRI data of 20 healthy controls (HC), 20 SZ patients, 20 BP patients, 20 patients suffering from SAD with manic episodes (SADM), and 13 patients suffering from SAD with depressive episodes exclusively (SADD). Then, five-level one-way analysis of covariance and multiclass support vector machine recursive feature elimination were employed to identify discriminative regions from the networks. Subsequently, the t-distributed stochastic neighbor embedding (t-SNE) projection and the hierarchical clustering were implemented to investigate the relationship among those groups. Finally, to evaluate the generalization ability, 16 new subjects were classified based on the found regions and the trained model using original 93 subjects. Results show that the discriminative regions mainly included frontal, parietal, precuneus, cingulate, supplementary motor, cerebellar, insula and supramarginal cortices, which performed well in distinguishing different groups. SADM and SADD were the most similar to each other, although SADD had greater similarity to SZ compared to other groups, which indicates that SAD may be an independent category. BP was closer to HC compared with other psychotic disorders. In summary, resting-state fMRI brain networks extracted via GIG-ICA provide a promising potential to differentiate SZ, BP, and SAD. (C) 2015 Elsevier Inc. All rights reserved.
KeywordSchizophrenia Bipolar Disorder Schizoaffective Disorder Resting-state Brain Intrinsic Networks Independent Component Analysis Functional Magnetic Resonance Imaging
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
DOI10.1016/j.neuroimage.2015.07.054
WOS KeywordDEFAULT MODE NETWORK ; PHENOTYPES B-SNIP ; FUNCTIONAL CONNECTIVITY ; STATE FMRI ; MOOD DISORDERS ; PSYCHOSIS ; CLASSIFICATION ; DSM-5 ; VISUALIZATION ; DYSFUNCTION
Indexed BySCI
Language英语
Funding OrganizationNational Institutes of Health(R01EB006841) ; National Sciences Foundation(1016619) ; Centers of Biomedical Research Excellence (COBRE)(5P20RR021938/P20GM103472) ; National Institute of Mental Health (NIMH)(R37MH43775) ; "100 Talents Plan" of the Chinese Academy of Sciences ; Chinese Natural Science Foundation(81471367) ; State High-Tech Development Plan of China(2015AA020513)
WOS Research AreaNeurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectNeurosciences ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000363125200027
Citation statistics
Cited Times:41[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10489
Collection脑网络组研究中心
Affiliation1.Mind Res Network, Albuquerque, NM 87131 USA
2.LBERI, Albuquerque, NM USA
3.North Univ China, Sch Informat & Commun Engn, Taiyuan, Peoples R China
4.Yale Univ, Dept Psychiat, New Haven, CT 06520 USA
5.Yale Univ, Dept Neurobiol, New Haven, CT USA
6.Inst Living, Olin Neuropsychiat Res Ctr, Hartford, CT USA
7.Univ New Mexico, Dept Elect & Comp Engn, Albuquerque, NM 87131 USA
8.Chinese Acad Sci, Brainnetome Ctr, Beijing, Peoples R China
9.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Du, Yuhui,Pearlson, Godfrey D.,Liu, Jingyu,et al. A group ICA based framework for evaluating resting fMRI markers when disease categories are unclear: application to schizophrenia, bipolar, and schizoaffective disorders[J]. NEUROIMAGE,2015,122:272-280.
APA Du, Yuhui.,Pearlson, Godfrey D..,Liu, Jingyu.,Sui, Jing.,Yu, Qingbao.,...&Calhoun, Vince D..(2015).A group ICA based framework for evaluating resting fMRI markers when disease categories are unclear: application to schizophrenia, bipolar, and schizoaffective disorders.NEUROIMAGE,122,272-280.
MLA Du, Yuhui,et al."A group ICA based framework for evaluating resting fMRI markers when disease categories are unclear: application to schizophrenia, bipolar, and schizoaffective disorders".NEUROIMAGE 122(2015):272-280.
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