A Multi-Dataset Evaluation of Frame Censoring for Motion Correction in Task-Based fMRI.

ABSTRACT: Subject motion during fMRI can affect our ability to accurately measure signals of interest. In recent years, frame censoring-that is, statistically excluding motion-contaminated data within the general linear model using nuisance regressors-has appeared in several task-based fMRI studies as a mitigation strategy. However, there have been few systematic investigations quantifying its efficacy. In the present study, we compared the performance of frame censoring to several other common motion correction approaches for task-based fMRI using open data and reproducible workflows. We analyzed eight publicly available datasets representing 11 distinct tasks in child, adolescent, and adult participants. Performance was quantified using maximum t-values in group analyses, and region of inte

SUBMITTER: Jones MS 

PROVIDER: S-EPMC9506314 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature