Correction of Structured Noise in fMRI Using Spatial Independent Component Analysis: Corsica

Abstract

The physiological fluctuations (breathing and heartbeat) and brain movements are the main sources of confounds in activation and functional connectivity studies in functional magnetic resonance imaging (fMRI). The main difficulty to cope with these effects is the aliasing of cardiac and possible respiration signals for acquisitions with long TR (typically TR > 1s). We proposed a method of structured noise correction based on spatial Independent Component Analysis, able to extract components linked to cardio-respiratory activity and brain movements. The automatic selection of noise-related components was based on a stepwise regression procedure using true” physiological noise time courses as reference (extracted from regions of interest in the cerebro-spinal fluid and near major blood vessels). We evaluated the sensitivity of the selection on long-TR and short-TR datasets and we showed that our method was efficient even for long-TR datasets.