Combined brain network topological metrics with machine learning algorithms to identify essential tremor.

ABSTRACT:

Background and objective

Essential tremor (ET) is a common movement syndrome, and the pathogenesis mechanisms, especially the brain network topological changes in ET are still unclear. The combination of graph theory (GT) analysis with machine learning (ML) algorithms provides a promising way to identify ET from healthy controls (HCs) at the individual level, and further help to reveal the topological pathogenesis in ET.

Methods

Resting-state functional magnetic resonance imaging (fMRI) data were obtained from 101 ET and 105 HCs. The topological properties were analyzed by using GT analysis, and the topological metrics under every single threshold and the area under the curve (AUC) of all thresholds were used as features. Then a Mann-Whitney U-test and least absolute

SUBMITTER: Li Q 

PROVIDER: S-EPMC9667093 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature