Computational modelling reveals neurobiological contributions to static and dynamic functional connectivity patterns.

ABSTRACT: Functional connectivity (FC) is a widely used indicator of brain function in health and disease, yet its neurobiological underpinnings still need to be firmly established. Recent advances in computational modelling allow us to investigate the relationship of both static FC (sFC) and dynamic FC (dFC) with neurobiology non-invasively. In this study, we modelled the brain activity of 200 healthy individuals based on empirical resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data. Simulations were conducted using a group-averaged structural connectome and four parameters guiding global integration and local excitation-inhibition balance: (i) G, a global coupling scaling parameter; (ii) J _i , an inhibitory coupling parameter; (iii)

SUBMITTER: Hoheisel L 

PROVIDER: S-EPMC12339445 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature