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 whole partitions resulting from different or same (using different parameters) clustering algorithms [13]. However, depending on the application, the demands for a validity measure may differ, inducing 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 exploratory data analysis (EDA) 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.