Clustering of dynamic functional connectivity features obtained from functional Magnetic Resonance Imaging data

Abstract

Clustering is one of the most important methods for organizing database into groups. In this paper, Ordering Points To Identify Clustering Structure (OPTICS) algorithm has been used to perform clustering of functional Magnetic Resonance Imaging (fMRI) data. Dynamic functional connectivity features on fMRI data (ADNI database) obtained from subjects with early mild cognitive impairment (E-MCI), late mild cognitive impairment (L-MCI), Alzheimer's disease and healthy controls has been used for the study. On performing clustering, it has been observed that OPTICS is able to cluster the subjects into four inherent groups with a very high success rate. This result gives rise to applications in determining latent groups indicating various brain disorders.