An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI

Estimates of functional connectivity derived from resting-state functional magnetic resonance imaging (rs-fMRI) are sensitive to artefacts caused by in-scanner head motion. This susceptibility has motivated the development of numerous denoising methods designed to mitigate motion-related artefacts. Here, we compare popular retrospective rs-fMRI denoising methods, such as regression of head motion parameters and mean white matter (WM) and cerebrospinal fluid (CSF) (with and without expansion terms), aCompCor, volume censoring (e.g., scrubbing and spike regression), global signal regression and ICA-AROMA, combined into 19 different pipelines. These pipelines were evaluated across five different quality control…

• MU
  Monash University
• AR
  Australian Research Council

  Award: FT130100589
• NH
  National Health and Medical Research Council

  Awards: 1089718, 3251250, 3251213, 1117188, 3251392