Components of brain activity-data analysis for fMRI

Functional magnetic resonance imaging (fMRI) is a promising method to determine noninvasively the spatial distribution of brain activity in a given situation, e.g. in response to a stimulus or during task solving. The fMRI signal is very small and often cannot be identified from the anatomical images. Thus data analysis methods are required to localize the activity. We discuss different data analysis methods, a simple correlation analysis, principal component analysis (PCA) and independent component analysis (ICA), in the context of a motor task experiment with predefined stimulus time course. We show how it is possible to detect even weak activity without prior knowledge about the stimulus time course with PCA and ICA. The stimulus time course is extracted and major components of the signal, e.g. head movements are also identified.