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

摘要: Abstract The brain can be modelled as a network with nodes and edges derived from range of imaging modalities: the correspond to spatially distinct regions interactions between them. Whole-brain connectivity studies typically seek determine how properties change given categorical phenotype such age-group, disease condition or mental state. To do so reliably, it is necessary features structure that are common across group scans. Given complex interdependencies inherent in data, this not straightforward task. Some construct group-representative (GRN), ignoring individual differences, while other analyse networks for each independently, information shared individuals. We propose Bayesian framework based on exponential random graph models (ERGM) extended multiple characterise distribution entire population networks. Using resting-state fMRI data Cam-CAN project, study healthy ageing, we demonstrate our method used compare brain’s functional young individuals old