Identifying Depressed Essential Tremor Using Resting-State Voxel-Wise Global Brain Connectivity: A Multivariate Pattern Analysis

Identifying Depressed Essential Tremor Using Resting-State Voxel-Wise Global Brain Connectivity: A Multivariate Pattern Analysis

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 ha...

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Bibliographic Details
Main Authors: Cao, J. (Author), Chen, H. (Author), Duan, X. (Author), Fang, W. (Author), Fang, Z. (Author), He, W. (Author), Li, Q. (Author), Li, Y. (Author), Luo, J. (Author), Lv, F. (Author), Tao, L. (Author), Wang, H. (Author), Xiao, Z. (Author), Zhang, X. (Author)
Format: Article
Language:English
Published: Frontiers Media S.A. 2021
Subjects:
adult
Article
brain mapping
cerebellum
controlled study
depression
disease severity
essential tremor
female
functional connectivity
functional magnetic resonance imaging
global brain connectivity
head movement
human
machine learning
major clinical study
male
motor control
multivariate pattern analysis
neuroimaging
pathogenesis
prefrontal cortex
resting state network
resting-state functional magnetic resonance imaging
support vector machine
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