Resting state fMRI analysis using unsupervised learning algorithms

RS-fMRI data analysis for functional connectivity explorations is a challenging topic in computational neuroimaging. Several approaches have been investigated to discover whole-brain data features. Among these, clustering techniques based on Competitive Learning (CL) and Spectral Methods (SM) have been shown effective in providing useful information in various contexts. We selected three clustering algorithms and two spectral methods, i.e the clustering algorithm are Self-organising Maps (SOM), Neural Gas (NG) and Growing Neural Gas (GNG), whereas the spectral methods are the classic Principal Component Analysis (PCA) and the Nonlinear Robust Fuzzy Principal Component Analysis (NRFPCA). We validated clustering with Davies–Bouldin Index (DBI) and we selected informative principal components using Random Matrix Theory (RMT). tools. We adopted these techniques to study the intrinsic functional properties of images coming from a shared repository of resting state fMRI experiments (1000 Functional Connectome Project)