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

Tao Yang, Sophia Frangou, Raymond W. Lam, Jia Huang, Yousong Su, Guoqing Zhao, Ruizhi Mao, Na Zhu, Rubai Zhou, Xiao Lin, Weiping Xia, Xing Wang, Yun Wang, Daihui Peng, Zuowei Wang, Lakshmi N. Yatham, Jun Chen, Yiru Fang

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

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 < 0.001); one dimension emphasized disease severity as well as irritability, agitation, anxiety and flight of ideas and the other emphasized mostly elevated mood. Brain structural features could not distinguish between the two disorders. HYDRA classified patients in two clusters that differed in global and regional cortical thickness, the distribution proportion of BD and MDD and positive family history of psychiatric disorders. Clinical features remain the most reliable discriminant attributed of BD and MDD depression. The brain structural findings suggests that biological partitions of patients with mood disorders are likely to lead to the identification of subgroups, that transcend current diagnostic divisions into BD and MDD and are more likely to be aligned with underlying genetic variation. These results set the foundation for future studies to enhance our understanding of brain–behavior relationships in mood disorders.

Original languageEnglish
Article number48
JournalTranslational Psychiatry
Volume11
Issue number1
DOIs
StatePublished - Jun 2021

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