Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks.

ABSTRACT: A Bayesian model for sparse, hierarchical, inver-covariance estimation is presented, and applied to multi-subject functional connectivity estimation in the human brain. It enables simultaneous inference of the strength of connectivity between brain regions at both subject and population level, and is applicable to fMRI, MEG and EEG data. Two versions of the model can encourage sparse connectivity, either using continuous priors to suppress irrelevant connections, or using an explicit description of the network structure to estimate the connection probability between each pair of regions. A large evaluation of this model, and thirteen methods that represent the state of the art of inverse covariance modelling, is conducted using both simulated and resting-state functional imaging datasets.

SUBMITTER: Colclough GL 

PROVIDER: S-EPMC6565932 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature