Structurally informed resting-state effective connectivity recapitulates cortical hierarchy.

ABSTRACT: Neuronal communication relies on the anatomy of the brain, yet it remains unclear whether, at the macroscale, structural (or anatomical) connectivity provides useful constraints for modeling effective connectivity. Here, we assess a hierarchical empirical Bayes model that builds on a well-established dynamic causal model by integrating structural connectivity into resting-state effective connectivity via priors. In silico analyses show that the model successfully recovers ground-truth effective connectivity and compares favorably with a popular alternative. Analyses of empirical data reveal that a positive, monotonic relationship between structural connectivity and the prior variance of group-level effective connectivity generalizes across sessions and samples. Finally, attesting to

SUBMITTER: Greaves MD 

PROVIDER: S-EPMC11014588 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature