Explainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophrenia.

ABSTRACT:

Introduction

Dynamic functional network connectivity (dFNC) analysis of resting state functional magnetic resonance imaging data has yielded insights into many neurological and neuropsychiatric disorders. A common dFNC analysis approach uses hard clustering methods like k-means clustering to assign samples to states that summarize network dynamics. However, hard clustering methods obscure network dynamics by assuming (1) that all samples within a cluster are equally like their assigned centroids and (2) that samples closer to one another in the data space than to their centroids are well-represented by their centroids. In addition, it can be hard to compare subjects, as in some cases an individual may not manifest a state strongly enough to enter a hard cluster. Approaches that all

SUBMITTER: Ellis CA 

PROVIDER: S-EPMC10941842 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature