Novel approach to control false positive rate in fuzzy cluster analysis of fMRI

Abstract

Fuzzy c-means (FCM) suffers from some limitations such as the need for a priori knowledge of the number of clusters, and unknown statistical significance and instability of the results, when it is applied to the raw fMRI time series. Based on randomization, we developed a method to control the false positive detection rate in FCM and estimate the statistical significance of the results. Using this novel approach, we proposed an fMRI activation detection method which uses FCM with controlled false positive rate. The ability of the method in controlling the false positive rate is shown by an analysis of false positives in activation maps of resting-state fMRI data. Controlling the false positive rate allows comparison of different feature spaces and fuzzy clustering methods. A new feature space, in multi and scalar wavelet domain, is proposed for activation detection in fMRI to address the stability problem. Finally, using the proposed method for controlling the false positive rate, the proposed feature space is compared to the cross-correlation feature space.