Partial entropy decomposition reveals higher-order information structures in human brain activity.

ABSTRACT: The standard approach to modeling the human brain as a complex system is with a network, where the basic unit of interaction is a pairwise link between two brain regions. While powerful, this approach is limited by the inability to assess higher-order interactions involving three or more elements directly. In this work, we explore a method for capturing higher-order dependencies in multivariate data: the partial entropy decomposition (PED). Our approach decomposes the joint entropy of the whole system into a set of nonnegative atoms that describe the redundant, unique, and synergistic interactions that compose the system's structure. PED gives insight into the mathematics of functional connectivity and its limitation. When applied to resting-state fMRI data, we find robust evidence of high

SUBMITTER: Varley TF 

PROVIDER: S-EPMC10372615 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature