Individualized diagnosis of major depressive disorder via multivariate pattern analysis of thalamic sMRI features

BMC Psychiatry

Zhonglin Tan¹  Hanxiaoran Li²  Zhenzhen Lian²  Xin Zhou²  Chenyuan Pan²  Yan Wang³  Donglin Wang³  Sutao Song⁴ 

[1] Department of Psychiatry, Hangzhou Seventh People’s Hospital, 310013, Hangzhou, China;Institutes of Psychological Sciences, College of Education, Hangzhou Normal University, 2318#, Yuhangtang Rd, 311121, Hangzhou, China;Center for Cognition and Brain Disorders, Hangzhou Normal University, 311121, Hangzhou, China;Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, 311121, Hangzhou, China;Institutes of Psychological Sciences, College of Education, Hangzhou Normal University, 2318#, Yuhangtang Rd, 311121, Hangzhou, China;Center for Cognition and Brain Disorders, Hangzhou Normal University, 311121, Hangzhou, China;Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, 311121, Hangzhou, China;Department of Psychiatry, The Affiliated Hospital, Hangzhou Normal University, 310015, Hangzhou, China;School of Information Science and Engineering, Shandong Normal University, 1#, University Rd, Changqing District, 250358, Jinan, China;

关键词: Major depressive disorder (MDD);    Thalamus;    Multivariate pattern analysis (MVPA);    Individualized diagnosis;   

DOI  :  10.1186/s12888-021-03414-9

来源: Springer

【 摘 要 】

BackgroundMagnetic resonance imaging (MRI) studies have found thalamic abnormalities in major depressive disorder (MDD). Although there are significant differences in the structure and function of the thalamus between MDD patients and healthy controls (HCs) at the group level, it is not clear whether the structural and functional features of the thalamus are suitable for use as diagnostic prediction aids at the individual level. Here, we were to test the predictive value of gray matter density (GMD), gray matter volume (GMV), amplitude of low-frequency fluctuations (ALFF), and fractional amplitude of low-frequency fluctuations (fALFF) in the thalamus using multivariate pattern analysis (MVPA).MethodsSeventy-four MDD patients and 44 HC subjects were recruited. The Gaussian process classifier (GPC) was trained to separate MDD patients from HCs, Gaussian process regression (GPR) was trained to predict depression scores, and Multiple Kernel Learning (MKL) was applied to explore the contribution of each subregion of the thalamus.ResultsThe primary findings were as follows: [1] The balanced accuracy of the GPC trained with thalamic GMD was 96.59% (P < 0.001). The accuracy of the GPC trained with thalamic GMV was 93.18% (P < 0.001). The correlation between Hamilton Depression Scale (HAMD) score targets and predictions in the GPR trained with GMD was 0.90 (P < 0.001, r2 = 0.82), and in the GPR trained with GMV, the correlation between HAMD score targets and predictions was 0.89 (P < 0.001, r2 = 0.79). [2] The models trained with ALFF and fALFF in the thalamus failed to discriminate MDD patients from HC participants. [3] The MKL model showed that the left lateral prefrontal thalamus, the right caudal temporal thalamus, and the right sensory thalamus contribute more to the diagnostic classification.ConclusionsThe results suggested that GMD and GMV, but not functional indicators of the thalamus, have good potential for the individualized diagnosis of MDD. Furthermore, the thalamus shows the heterogeneity in the structural features of thalamic subregions for predicting MDD. To our knowledge, this is the first study to focus on the thalamus for the prediction of MDD using machine learning methods at the individual level.

【 授权许可】

CC BY