Automated clustering of ICA results for fMRI data analysis

Abstract

While independent component analysis can be a fruitful method to analyze fMRI data, the manual work that is usually necessary in viewing the results is complex and time consuming and thus limits its clinical application. In this article we try to solve this problem by presenting a new way to cluster the results of an ICA into few, easy to classify activation maps by using incomplete ICA. These maps are then the basis for a further in-deep analysis of the fMRI data. We demonstrate our approach on a real world WCST example.