Reducing the Effects of Motion Artifacts in fMRI: A Structured Matrix Completion Approach.

ABSTRACT: Functional MRI (fMRI) is widely used to study the functional organization of normal and pathological brains. However, the fMRI signal may be contaminated by subject motion artifacts that are only partially mitigated by motion correction strategies. These artifacts lead to distance-dependent biases in the inferred signal correlations. To mitigate these spurious effects, motion-corrupted volumes are censored from fMRI time series. Censoring can result in discontinuities in the fMRI signal, which may lead to substantial alterations in functional connectivity analysis. We propose a new approach to recover the missing entries from censoring based on structured low rank matrix completion. We formulated the artifact-reduction problem as the recovery of a super-resolved matrix from unprocessed fMR

SUBMITTER: Balachandrasekaran A 

PROVIDER: S-EPMC8934405 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature