Characterising group-level brain connectivity: A framework using Bayesian exponential random graph models.

ABSTRACT: The brain can be modelled as a network with nodes and edges derived from a range of imaging modalities: the nodes correspond to spatially distinct regions and the edges to the interactions between them. Whole-brain connectivity studies typically seek to determine how network properties change with a given categorical phenotype such as age-group, disease condition or mental state. To do so reliably, it is necessary to determine the features of the connectivity structure that are common across a group of brain scans. Given the complex interdependencies inherent in network data, this is not a straightforward task. Some studies construct a group-representative network (GRN), ignoring individual differences, while other studies analyse networks for each individual independently, ignoring inform

SUBMITTER: Lehmann BCL 

PROVIDER: S-EPMC7613122 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature