Classification of bipolar disorders using the multilayer modularity in dynamic minimum spanning tree from resting state fMRI.

ABSTRACT: The diagnosis of bipolar disorders (BD) mainly depends on the clinical history and behavior observation, while only using clinical tools often limits the diagnosis accuracy. The study aimed to create a novel BD diagnosis framework using multilayer modularity in the dynamic minimum spanning tree (MST). We collected 45 un-medicated BD patients and 47 healthy controls (HC). The sliding window approach was utilized to construct dynamic MST via resting-state functional magnetic resonance imaging (fMRI) data. Firstly, we used three null models to explore the effectiveness of multilayer modularity in dynamic MST. Furthermore, the module allegiance exacted from dynamic MST was applied to train a classifier to discriminate BD patients. Finally, we explored the influence of the FC estimator and MST

SUBMITTER: Wang H 

PROVIDER: S-EPMC10640554 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature