Identifying Depressed Essential Tremor Using Resting-State Voxel-Wise Global Brain Connectivity: A Multivariate Pattern Analysis - Li, Yufen; Tao, Li; Chen, Huiyue; Wang, Hansheng; Zhang, Xiaoyu; Zhang, Xueyan; Duan, Xiyue; Fang, Zhou; Li, Qin; He, Wanlin; Lv, Fajin; Luo, Jin; et al. (Frontiers in Human Neuroscience, 2021)
Background and objective: Although depression is one of the most common non-motor symptoms in essential tremor (ET), its pathogenesis and diagnosis biomarker are still unknown. Recently, machine learning multivariate pattern analysis (MVPA) combined with connectivity mapping of resting-state fMRI has provided a promising way to identify depressed ET patients at the individual level, and help to reveal the brain network pathogenesis of depression in ET patients. Methods: Based on global brain connectivity (GBC) mapping from 41 depressed ET, 49 non-depressed ET, 45 primary depression and 43 healthy controls (HCs), multiclass Gaussian Process Classification (GPC) and binary support vector machine (SVM) algorithms were used to identify depressed ET patients from non-depressed ET, primary depression and HCs, the accuracy and permutations test were used to assess the classification performance. Results: The four-class GPC and binary SVM could be used to discriminate depressed ET from non-depressed ET, primary depression and HCs with accuracy of 70.73%, 73.17%, 80.49% and 75.61%, (P < 0.001), the significant discriminative features mainly located in cerebellar-motor-prefrontal cortices circuits (P < 0.001), and a further correlation analysis showed the GBC values of significant discriminative features in right middle prefrontal gyrus and bilateral cerebellum VI and Crus 1 were correlated with clinical depression severity in depressed ET patients. Conclusions: Our findings demonstrated that GBC mapping combined with machine learning MVPA could be used to identify depressed ET patients, and the GBC changes in cerebellar-prefrontal cortices circuits not only posed as the significant discriminative features but also helped to understand the network pathogenesis underlying depression in ET patients.