Controling the False Positive Detection Rate in Fuzzy Clustering of Fmri Data
نویسندگان
چکیده
Despite its potential advantages for fMRI analysis, fuzzy C-means (FCM) clustering suffers from limitations such as the need for a priori knowledge of the number of clusters, and unknown statistical significance and instability of the results. We propose a randomizationbased method to control the false positive rate and estimate statistical significance of the FCM results. Using this novel approach, we develop an fMRI activation detection method. The ability of the method in controlling the false positive rate is shown by analysis of false positives in activation maps of resting-state fMRI data. Controlling the false positive rate in FCM allows comparison of different fuzzy clustering methods, using different feature spaces, to other fMRI detection methods. In this paper, using simulation and real fMRI data, we compare a novel feature space that takes the variability of the hemodynamic response function into account (HRFbased feature space) to the conventional cross-correlation analysis and FCM using the cross-correlation feature space.
منابع مشابه
Novel approach to control false positive rate in fuzzy cluster analysis of fMRI
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 t...
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