Probing the clinical and brain structural boundaries of bipolar and major depressive disorder

Bipolar disorder (BD) and major depressive disorder (MDD) have both common and distinct clinical features, that pose both conceptual challenges in terms of their diagnostic boundaries and practical difficulties in optimizing treatment. Multivariate machine learning techniques offer new avenues for e...

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Bibliographic Details
Published in:Translational psychiatry 2021-01, Vol.11 (1), p.48-48, Article 48
Main Authors: Yang, Tao, Frangou, Sophia, Lam, Raymond W., Huang, Jia, Su, Yousong, Zhao, Guoqing, Mao, Ruizhi, Zhu, Na, Zhou, Rubai, Lin, Xiao, Xia, Weiping, Wang, Xing, Wang, Yun, Peng, Daihui, Wang, Zuowei, Yatham, Lakshmi N., Chen, Jun, Fang, Yiru
Format: Article
Language:English
Subjects:
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Behavioral Sciences
Biological Psychology
Bipolar disorder
Discriminant analysis
Emotional disorders
Medicine
Medicine & Public Health
Mental depression
Mood disorders
Neurosciences
Pharmacotherapy
Psychiatry
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Summary:Bipolar disorder (BD) and major depressive disorder (MDD) have both common and distinct clinical features, that pose both conceptual challenges in terms of their diagnostic boundaries and practical difficulties in optimizing treatment. Multivariate machine learning techniques offer new avenues for exploring these boundaries based on clinical neuroanatomical features. Brain structural data were obtained at 3 T from a sample of 90 patients with BD, 189 patients with MDD, and 162 healthy individuals. We applied sparse partial least squares discriminant analysis (s-PLS-DA) to identify clinical and brain structural features that may discriminate between the two clinical groups, and heterogeneity through discriminative analysis (HYDRA) to detect patient subgroups with reference to healthy individuals. Two clinical dimensions differentiated BD from MDD (area under the curve: 0.76, P  
ISSN:2158-3188
2158-3188
DOI:10.1038/s41398-020-01169-7