Explorative signal processing in functional MR imaging

Identification and separation of artifacts as well as quantification of exp ected, i.e., stimulus-correlated, and novel information on brain activity a re important for both new insights in neuroscience and future developments in functional magnetic resonance imaging (MRI) of the human brain. Here, we present several examples in which gross head motion or physiologic motion (e.g., pulsation, respiration, large veins) could be identified and separat ed by using fuzzy cluster analysis of fMRI time series. Furthermore, our ex perience with single- and multislice fMRI (FLASH and EPI; 1.5 and 3 T) data analysis is summarized and several examples, including long echo time and high-resolution fMRI of the motor cortex, are discussed. Explorative signal processing in fMRI, based on fuzzy clustering, represents a robust and pow erful tool for screening large fMRI data sets, extracting expected and nove l functional activity of the human brain, and obtaining improved reproducib ility of fMRI results. Finally, it may help to improve or develop functiona l brain models which can then be tested by applying statistical models. (C) 1999 John Wiley & Sons, Inc.