Improving the use of principal component analysis to reduce physiological noise and motion artifacts to increase the sensitivity of task-based fMRI

摘要

BackgroundFunctional magnetic resonance imaging (fMRI) time series are subject to corruption by many noise sources, especially physiological noise and motion. Researchers have developed many methods to reduce physiological noise, including RETROICOR, which retroactively removes cardiac and respiratory waveforms collected during the scan, and CompCor, which applies principal components analysis (PCA) to remove physiological noise components without any physiological monitoring during the scan.