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The Application of a Machine Learning-Based Brain Magnetic Resonance Imaging Approach in Major Depression

Authors
Na, Kyoung-SaeKim, Yong-Ku
Issue Date
2021
Publisher
SPRINGER-VERLAG SINGAPORE PTE LTD
Keywords
Machine learning; Neuroimaging; Depression; MRI
Citation
MAJOR DEPRESSIVE DISORDER: RETHINKING AND UNDERSTANDING RECENT DISCOVERIES, v.1305, pp.57 - 69
Indexed
SCIE
SCOPUS
Journal Title
MAJOR DEPRESSIVE DISORDER: RETHINKING AND UNDERSTANDING RECENT DISCOVERIES
Volume
1305
Start Page
57
End Page
69
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/130275
DOI
10.1007/978-981-33-6044-0_4
ISSN
0065-2598
Abstract
Major depressive disorder (MDD) shows a high prevalence and is associated with increased disability. While traditional studies aimed to investigate global characteristic neurobiological substrates of MDD, machine learning-based approaches focus on individual people rather than a group. Therefore, machine learning has been increasingly conducted and applied to clinical practice. Several previous neuroimaging studies used machine learning for stratifying MDD patients from healthy controls as well as in differentially diagnosing MDD apart from other psychiatric disorders. Also, machine learning has been used to predict treatment response using magnetic resonance imaging (MRI) results. Despite the recent accomplishments of machine learning-based MRI studies, small sample sizes and the heterogeneity of the depression group limit the generalizability of a machine learning-based predictive model. Future neuroimaging studies should integrate various materials such as genetic, peripheral, and clinical phenotypes for more accurate predictability of diagnosis and treatment response.
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