Evaluation of Denoising Strategies to Address Motion-Correlated Artifacts in Resting-State Functional Magnetic Resonance Imaging Data from the Human Connectome Project

Gregory C. Burgess, Sridhar Kandala, Dan Nolan, Timothy O. Laumann, Jonathan D. Power, Babatunde Adeyemo, Michael P. Harms, Steven E. Petersen, Deanna M. Barch

Research output: Contribution to journalArticle

78 Scopus citations

Abstract

Like all resting-state functional connectivity data, the data from the Human Connectome Project (HCP) are adversely affected by structured noise artifacts 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 artifacts. The degree of inflation was further increased for connections between nearby regions compared with distant regions, suggesting the presence of distance-dependent spatially specific artifacts. We evaluated several denoising methods: censoring high-motion time points, motion regression, the FMRIB independent component analysis-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 artifacts, but left substantial global artifacts behind. MGTR significantly reduced global artifacts, but left substantial spatially specific artifacts behind. Censoring high-motion time points resulted in a small reduction of distance-dependent and global artifacts, 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 artifacts, and the most effective approach to address both types of motion-correlated artifacts was a combination of FIX and MGTR.

Original languageEnglish
Pages (from-to)669-680
Number of pages12
JournalBrain connectivity
Volume6
Issue number9
DOIs
StatePublished - Nov 2016

Keywords

  • Human Connectome Project
  • artifact
  • denoising
  • fMRI
  • functional connectivity
  • independent component analysis
  • motion
  • resting state

Fingerprint Dive into the research topics of 'Evaluation of Denoising Strategies to Address Motion-Correlated Artifacts in Resting-State Functional Magnetic Resonance Imaging Data from the Human Connectome Project'. Together they form a unique fingerprint.

  • Cite this