Denoising the speaking brain: toward a robust technique for correcting artifact-contaminated fMRI data under severe motion.

ABSTRACT: A comprehensive set of methods based on spatial independent component analysis (sICA) is presented as a robust technique for artifact removal, applicable to a broad range of functional magnetic resonance imaging (fMRI) experiments that have been plagued by motion-related artifacts. Although the applications of sICA for fMRI denoising have been studied previously, three fundamental elements of this approach have not been established as follows: 1) a mechanistically-based ground truth for component classification; 2) a general framework for evaluating the performance and generalizability of automated classifiers; and 3) a reliable method for validating the effectiveness of denoising. Here we perform a thorough investigation of these issues and demonstrate the power of our technique by resolv

SUBMITTER: Xu Y 

PROVIDER: S-EPMC4312243 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature