Motion-corrected independent component analysis for robust functional magnetic resonance imaging

Journal Article

Head movement during fMRI data collection can result in confusing artifacts when estimating task-related brain activations. In this paper, we propose an improved version of Motion-Corrected Independent Component Analysis (MCICA), which mitigates motion effects of fMRI time-series by maximizing the entropy difference between the observed fMRI data and a nonlinear function of the derived ICA components. Specifically, the improved MCICA algorithm operates on all timepoints, removing the requirement on the existence of enough motionless timepoints in the time-series and the need to detect motion-corrupted timepoints. Simulations demonstrate that MCICA was robust to activation level and the results were more accurate than cubic interpolation even when the displacement was known. In a real data from a motor fMRI experiment, preprocessing the data with MCICA resulted in the emergence of activation in the primary motor and supplementary motor cortices, and the Mutual Information between all subsequent volumes and the first one was increased.

Duke Authors

Cited Authors

  • Liao, R; McKeown, MJ; Krolik, JL

Published Date

  • October 7, 2004

Published In

Volume / Issue

  • 2 /

International Standard Serial Number (ISSN)

  • 1520-6149

Citation Source

  • Scopus