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 |
ISSN | 1755-5930 |
2018-11-01 | |
卷号 | 24期号:11页码:1037-1052 |
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 |
DOI | 10.1111/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 | Chinese Natural Science Foundation[81471367] ; Chinese Natural Science Foundation[61773380] ; 100 Talents Plan of Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDBS01000000] ; National High-Tech Development Plan (863)[2015AA020513] ; NIH[1R01MH094524] ; NIH[R01EB005846] ; NIH[P20GM103472] ; NIH[P20GM103472] ; NIH[R01EB005846] ; NIH[1R01MH094524] ; National High-Tech Development Plan (863)[2015AA020513] ; 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] |
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 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/23044 |
专题 | 脑图谱与类脑智能实验室_脑网络组研究 |
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 |
第一作者单位 | 中国科学院自动化研究所; 模式识别国家重点实验室 |
通讯作者单位 | 中国科学院自动化研究所; 模式识别国家重点实验室; 中国科学院分子影像重点实验室 |
推荐引用方式 GB/T 7714 | 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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