Using Edge Voxel Information to Improve Motion Regression for rs-fMRI Connectivity Studies.

ABSTRACT: Recent fMRI studies have outlined the critical impact of in-scanner head motion, particularly on estimates of functional connectivity. Common strategies to reduce the influence of motion include realignment as well as the inclusion of nuisance regressors, such as the 6 realignment parameters, their first derivatives, time-shifted versions of the realignment parameters, and the squared parameters. However, these regressors have limited success at noise reduction. We hypothesized that using nuisance regressors consisting of the principal components (PCs) of edge voxel time series would be better able to capture slice-specific and nonlinear signal changes, thus explaining more variance, improving data quality (i.e., lower DVARS and temporal SNR), and reducing the effect of motion on default-m

SUBMITTER: Patriat R 

PROVIDER: S-EPMC4652211 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature