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

Brain Connect. 2015 Jun 24;

Authors: Patriat R, Molloy EK, Birn R

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 square these 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-mode network connectivity. Functional MRI data from 22 healthy adult subjects were pre-processed using typical motion regression approaches, as well as nuisance regression derived from edge voxel time courses. Results were evaluated in the presence and absence of both global signal regression and motion censoring. Nuisance regressors derived from signal intensity time courses at the edge of the brain significantly improved motion correction as compared to using only the realignment parameters and their derivatives. Of the models tested, only the edge-voxel regression models were able to eliminate significant differences in DMN connectivity between high- and low-motion subjects regardless of the use of GSR or censoring.

PMID: 26107049 [PubMed - as supplied by publisher]