Tri-Clustering Dynamic Functional Network Connectivity Identifies Significant Schizophrenia Effects Across Multiple States in Distinct Subgroups of Individuals.

ABSTRACT: Background: Brain imaging data collected from individuals are highly complex with unique variation; however, such variation is typically ignored in approaches that focus on group averages or even supervised prediction. State-of-the-art methods for analyzing dynamic functional network connectivity (dFNC) subdivide the entire time course into several (possibly overlapping) connectivity states (i.e., sliding window clusters). However, such an approach does not factor in the homogeneity of underlying data and may result in a less meaningful subgrouping of the data set. Methods: Dynamic-N-way tri-clustering (dNTiC) incorporates a homogeneity benchmark to approximate clusters that provide a more "apples-to-apples" comparison between groups within analogous subsets of

SUBMITTER: Rahaman MA 

PROVIDER: S-EPMC8867091 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature