A two-part mixed-effects modeling framework for analyzing whole-brain network data.

ABSTRACT: Whole-brain network analyses remain the vanguard in neuroimaging research, coming to prominence within the last decade. Network science approaches have facilitated these analyses and allowed examining the brain as an integrated system. However, statistical methods for modeling and comparing groups of networks have lagged behind. Fusing multivariate statistical approaches with network science presents the best path to develop these methods. Toward this end, we propose a two-part mixed-effects modeling framework that allows modeling both the probability of a connection (presence/absence of an edge) and the strength of a connection if it exists. Models within this framework enable quantifying the relationship between an outcome (e.g., disease status) and connectivity patterns in the brain whi

SUBMITTER: Simpson SL 

PROVIDER: S-EPMC4433821 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature