A multistep unsupervised fuzzy clustering analysis of fMRI time series

A paradigm independent multistage strategy based on the Unsupervised Fuzzy Clustering Analysis (UFCA) and its potential for fMRI data analysis are presented. The influence of the fuzziness index is studied using Receiver Operating Characteristics (ROC) methodology and an interval of choice, around the widely used value 2, is shown to yield the best performance. The ill-balanced data problem is also overcome using a pre-processing step to reduce the number of voxels presented to the method. Statistical and anatomical criteria are proposed to exclude some voxels and enhance the UFCA sensitivity. An original postprocessing step aiming at statistically characterizing the obtained clusters is also developed. Two similarity criteria are used: the correlation coefficient on temporal profiles and a novel fuzzy overlap coefficient on membership degree maps. This final step provides a useful analysis tool to study intra-individual reproducibility of the classes across series (stimulation vs. stimulation, noise vs. noise or stimulation vs. noise). Finally, a comparison between this technique and some existing or locally developed postprocessing algorithms is presented using ROC methods. Its sensitivity and robustness is compared to the classical FCA or other techniques as a function of several parameters such as Contrast-to-Noise Ratio (CNR) and noise amplitude. Even without knowledge about the paradigm, the hemodynamic response function and the number of clusters, the performances of the proposed strategy are comparable to those of the classical approaches where extensive prior knowledge has to be added.