TY - JOUR
T1 - Machine learning in major depression
T2 - From classification to treatment outcome prediction
AU - Gao, Shuang
AU - Calhoun, Vince D.
AU - Sui, Jing
N1 - Funding Information:
of Sciences, the Chinese Natural Science Foundation Number 81471367, 61773380, the Strategic Priority Research Program of the Chinese Academy of Sciences (No.XDBS01040100) and NIH Grant
Funding Information:
This work was supported by the National High-Tech Development Plan (863, No. 2015AA020513), ?100 Talents Plan? of Chinese Academy of Sciences, the Chinese Natural Science Foundation Number 81471367, 61773380, the Strategic Priority Research Program of the Chinese Academy of Sciences (No.XDBS01040100) and NIH Grant R56MH117107, R01EB005846, 1R01MH094524, and P20GM103472.
Funding Information:
National High‐Tech Development Plan (863), Grant/Award Number: 2015AA020513; NIH Grant, Grant/Award Number: 1R01MH094524, P20GM103472 and R01EB005846; Strategic Priority Research Program of the Chinese Academy of Sciences, Grant/Award Number: XDBS01000000; “100 Talents Plan” of Chinese Academy of Sciences, the Chinese Natural Science Foundation, Grant/Award Number: 61773380 and 81471367
Funding Information:
This work was supported by the National High‐Tech Development Plan
Publisher Copyright:
© 2018 John Wiley & Sons Ltd
PY - 2018/11
Y1 - 2018/11
N2 - 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.
AB - 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.
KW - classification
KW - machine learning
KW - magnetic resonance imaging
KW - major depressive disorder
KW - review
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U2 - 10.1111/cns.13048
DO - 10.1111/cns.13048
M3 - Review article
C2 - 30136381
AN - SCOPUS:85052475384
VL - 24
SP - 1037
EP - 1052
JO - CNS Neuroscience and Therapeutics
JF - CNS Neuroscience and Therapeutics
SN - 1755-5930
IS - 11
ER -