Thresholding functional connectomes by means of mixture modeling.

ABSTRACT: Functional connectivity has been shown to be a very promising tool for studying the large-scale functional architecture of the human brain. In network research in fMRI, functional connectivity is considered as a set of pair-wise interactions between the nodes of the network. These interactions are typically operationalized through the full or partial correlation between all pairs of regional time series. Estimating the structure of the latent underlying functional connectome from the set of pair-wise partial correlations remains an open research problem though. Typically, this thresholding problem is approached by proportional thresholding, or by means of parametric or non-parametric permutation testing across a cohort of subjects at each possible connection. As an alternative, we propose

SUBMITTER: Bielczyk NZ 

PROVIDER: S-EPMC5981009 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature