Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor - Zhang, Xueyan; Tao, Li; Chen, Huiyue; Zhang, Xiaoyu; Wang, Hansheng; He, Wanlin; Li, Qin; Lv, Fajin; Luo, Tianyou; Luo, Jin; Man, Yun; Xiao, Zheng; et al. (Frontiers in Neurology, 2022)
Background: Although depression is one of the most common neuropsychiatric symptoms in essential tremor (ET), the diagnosis biomarker and intrinsic brain activity remain unclear. We aimed to combine multivariate pattern analysis (MVPA) with local brain functional connectivity to identify depressed ET. Methods: Based on individual voxel-level local brain functional connectivity (regional homogeneity, ReHo) mapping from 41 depressed ET, 43 non-depressed ET and 45 healthy controls (HCs), the binary support vector machine (BSVM) and multiclass Gaussian Process Classification (MGPC) algorithms were used to identify depressed ET patients from non-depressed ET and HCs, the accuracy and permutations test were used to assess the classification performance. Results: The MGPC algorithm was able to classify the three groups (depressed ET, non-depressed ET and HCs) with a total accuracy of 84.5%. The BSVM algorithm achieved a better classification performance with total accuracy at 90.7%, 88.64%, and 90.48% for depressed ET vs HCs, non-depressed ET vs HCs, and depressed ET vs non-depressed ET, and the sensitivity for them at 80.49%, 76.64% and 80.49%, respectively. The significant discriminative features of depressed ET vs HCs were primarily located in the cerebellar-motor-prefrontal gyrus-anterior cingulate cortex pathway, and it for depressed ET vs non-depressed ET located in cerebellar-prefrontal gyrus-anterior cingulate cortex circuits. The partial correlation showed that the ReHo values in the bilateral middle prefrontal gyrus (positive) and the bilateral cerebellum XI (negative) were significantly correlated with clinical depression severity. Conclusion: Our findings suggested that combined individual ReHo maps with MVPA not only could be used to identify depressed ET but also help to reveal the intrinsic brain activity changes and further act as the potential diagnosis biomarker in depressed ET patients.