Partial covariance based functional connectivity computation using Ledoit-Wolf covariance regularization.

ABSTRACT: Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit-Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We inves

SUBMITTER: Brier MR 

PROVIDER: S-EPMC4604032 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature