Detecting regions of interest in fMRI: an application on exploratory-based data analysis

Cluster validity has been mainly used to evaluate the quality of individual clusters, and compare the whole partitions resulting from different or same (using different parameters) clustering algorithms. However, depending on the application, the demands for a validity measure may differ, resulting the necessity of introducing new measures which will suit to the problem under investigation. We introduce a new statistical concept for quantitative validation of fMRI analysis methods based on the exploratory data analysis algorithms. A "greedy" algorithm which combines partitions on a sequential basis is applied on 100 runs of a clustering algorithm with randomized cluster initialization. As a result, a fuzzy partition is derived where every data point is assigned to a cluster with a certain degree of "support". The purpose of this measure is to check the stability of the resulting clustering structures and improve the diagnostic reliability of fMRI post-processing strategies