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Machine learning in major depression: From classification to treatment outcome prediction
Gao, Shuang1,2,3; Calhoun, Vince D.4,5; Sui, Jing1,2,3,6
发表期刊CNS NEUROSCIENCE & THERAPEUTICS
ISSN1755-5930
2018-11-01
卷号24期号:11页码:1037-1052
通讯作者Sui, Jing(kittysj@gmail.com)
摘要Aims: Major depression disorder (MDD) is the single greatest cause of disability and morbidity, and affects about 10% of the population worldwide. Currently, there are no clinically useful diagnostic biomarkers that are able to confirm a diagnosis of MDD from bipolar disorder (BD) in the early depressive episode. Therefore, exploring translational biomarkers of mood disorders based on machine learning is in pressing need, though it is challenging, but with great potential to improve our understanding of these disorders. Discussions: In this study, we review popular machine-learning methods used for brain imaging classification and predictions, and provide an overview of studies, specifically for MDD, that have used magnetic resonance imaging data to either (a) classify MDDs from controls or other mood disorders or (b) investigate treatment outcome predictors for individual patients. Finally, challenges, future directions, and potential limitations related to MDD biomarker identification are also discussed, with a goal of offering a comprehensive overview that may help readers to better understand the applications of neuroimaging data mining in depression. Conclusions: We hope such efforts may highlight the need for an urgently needed paradigm shift in treatment, to guide personalized optimal clinical care.
关键词classification machine learning magnetic resonance imaging major depressive disorder review
DOI10.1111/cns.13048
关键词[WOS]LATE-LIFE DEPRESSION ; FUNCTIONAL CONNECTIVITY PATTERNS ; TREATMENT-RESISTANT DEPRESSION ; SUPPORT VECTOR MACHINE ; 2 INDEPENDENT SAMPLES ; BRAIN IMAGING DATA ; BIPOLAR DISORDER ; DISCRIMINANT-ANALYSIS ; CORTICAL THICKNESS ; FEATURE-SELECTION
收录类别SCI
语种英语
资助项目National High-Tech Development Plan (863)[2015AA020513] ; NIH[1R01MH094524] ; NIH[P20GM103472] ; NIH[R01EB005846] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDBS01000000] ; 100 Talents Plan of Chinese Academy of Sciences ; Chinese Natural Science Foundation[61773380] ; Chinese Natural Science Foundation[81471367]
项目资助者National High-Tech Development Plan (863) ; NIH ; Strategic Priority Research Program of the Chinese Academy of Sciences ; 100 Talents Plan of Chinese Academy of Sciences ; Chinese Natural Science Foundation
WOS研究方向Neurosciences & Neurology ; Pharmacology & Pharmacy
WOS类目Neurosciences ; Pharmacology & Pharmacy
WOS记录号WOS:000447199600005
出版者WILEY
引用统计
被引频次:1[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/173211/23044
专题脑网络组研究中心
通讯作者Sui, Jing
作者单位1.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing, Peoples R China
2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Mind Res Network, Albuquerque, NM USA
5.Univ New Mexico, Dept Elect & Comp Engn, Albuquerque, NM 87131 USA
6.Chinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing, Peoples R China
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Gao, Shuang,Calhoun, Vince D.,Sui, Jing. Machine learning in major depression: From classification to treatment outcome prediction[J]. CNS NEUROSCIENCE & THERAPEUTICS,2018,24(11):1037-1052.
APA Gao, Shuang,Calhoun, Vince D.,&Sui, Jing.(2018).Machine learning in major depression: From classification to treatment outcome prediction.CNS NEUROSCIENCE & THERAPEUTICS,24(11),1037-1052.
MLA Gao, Shuang,et al."Machine learning in major depression: From classification to treatment outcome prediction".CNS NEUROSCIENCE & THERAPEUTICS 24.11(2018):1037-1052.
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