| Autor: |
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 |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Translational Psychiatry, Vol 11, Iss 1, Pp 1-8 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2158-3188 |
| DOI: |
10.1038/s41398-020-01169-7 |
| Popis: |
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 |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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