Preprocessing of fMR datasets

Abstract

When studying complex cognitive tasks using functional magnetic resonance (fMR) imaging one often encounters weak signal responses. These weak responses are corrupted by noise and artifacts of various sources. Preprocessing of the raw data before the application of test statistics helps to extract the signal and thus can vastly improve signal detection. The authors discuss artifact sources and algorithms to handle them. Experiments with simulated and real data underline the usefulness of this preprocessing sequence.