Quantification in functional magnetic resonance imaging: fuzzy clustering vs. correlation analysis

Abstract

The potential of functional MRI (fMRI) data analysis using the paradigm independent fuzzy cluster analysis (FCA) applied in the time domain compared to frequently used paradigm based correlation analysis (CA) was studied with simulated and in vivo fMRI data. The performance of FCA and CA was investigated in a typical contrast-to-noise range for fMRI, ranging from 1.33 to 3.33. Using simulated fMRI data the methods were quantitatively compared in terms of generation of true positives, false positives, and the corresponding signal enhancement. Even without prior knowledge about the stimulation paradigm and the actual hemodynamic response function the performance of FCA was comparable to that of CA where extensive prior knowledge has to be added. Furthermore, discrimination of nonanticipated hemodynamic responses by FCA, such as different levels of activation and delayed response, are demonstrated in simulated and in vivo fMRI data. We demonstrate that using CA one cannot differentiate between these responses at least without extensive prior knowledge, i.e., FCA yields a more particular description of fMRI data. This may be worthwhile for analysis and optimization of data quality in fMRI as well as in the final data analysis.