Evaluation of Denoising Strategies To Address Motion-Correlated Artifact in Resting State fMRI Data from the Human Connectome Project.

Brain Connect. 2016 Aug 29;

Authors: Burgess GC, Kandala S, Nolan D, Laumann TO, Power J, Adeyemo B, Harms MP, Petersen SE, Barch DM

Abstract
Like all resting-state functional connectivity data, the data from the Human Connectome Project (HCP) are adversely affected by structured noise artifact arising from head motion and physiological processes. Functional connectivity estimates (Pearson's correlation coefficients) were inflated for high-motion time points and for high-motion participants. This inflation occurred across the brain, suggesting the presence of globally-distributed artifact. The degree of inflation was further increased for connections between nearby regions compared to distant regions, suggesting the presence of distance-dependent, spatially-specific artifact. We evaluated several denoising methods: censoring high-motion time points, motion regression, FMRIB's ICA-based X-noiseifier (FIX), and mean grayordinate time series regression (MGTR; as a proxy for global signal regression). The results suggest that FIX-denoising reduced both types of artifact, but left substantial global artifact behind. MGTR significantly reduced global artifact, but left substantial spatially-specific artifact behind. Censoring high-motion time points resulted in a small reduction of distance-dependent and global artifact, eliminating neither type. All denoising strategies left differences between high- and low-motion participants, but only MGTR substantially reduced those differences. Ultimately, functional connectivity estimates from HCP data showed spatially-specific and globally-distributed artifact, and the most effective approach to address both types of motion-correlated artifact was a combination of FIX and MGTR.

PMID: 27571276 [PubMed - as supplied by publisher]