Clustering Functional MRI Patterns with Fuzzy and Competitive Algorithms

Abstract

We used model-free methods to explore the brain’s functional properties adopting a partitioning procedure based on cross-clustering. We selected Fuzzy C-Means (FCM) and Neural Gas (NG) algorithms to find spatial patterns with temporal features and temporal patterns with spatial features. We applied these algorithms to a shared fMRI repository of face recognition tasks. We matched the classes found and our results of functional connectivity analysis with partitioning of BOLD signal signatures. We compared the outcomes using the just acquired model-based knowledge as likely ground truth, confirming the role of Fusiform Brain Regions. In general, partitioning results show a better spatial clustering than temporal clustering for both algorithms. In the case of temporal clustering, FCM outperforms Neural Gas. The relevance of brain subregions related to face recognition were correctly distinguished by the algorithms and the results are in agreement with the current neuroscientific literature.