Analyzing asymmetry in brain hierarchies with a linear state-space model of resting-state fMRI data.

ABSTRACT: This study challenges the traditional focus on zero-lag statistics in resting-state functional magnetic resonance imaging (rsfMRI) research. Instead, it advocates for considering time-lag interactions to unveil the directionality and asymmetries of the brain hierarchy. Effective connectivity (EC), the state matrix in dynamical causal modeling (DCM), is a commonly used metric for studying dynamical properties and causal interactions within a linear state-space system description. Here, we focused on how time-lag statistics are incorporated within the framework of DCM resulting in an asymmetric EC matrix. Our approach involves decomposing the EC matrix, revealing a steady-state differential cross-covariance matrix that is responsible for modeling information flow and introducing time-irrever

SUBMITTER: Benozzo D 

PROVIDER: S-EPMC11424037 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature