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 Soft Competitive Learning (SCL) have been shown effective in providing useful information in various contexts. However, although significant achievements have been reached, these techniques still present critical aspects that require further investigations. We selected three clustering algorithms, i.e. Self-Organizing Maps (SOM), Neural Gas (NG) and Growing Neural Gas (GNG), to study the intrinsic functional properties of images coming from a shared repository of resting state fMRI experiments (1000 Functional Connectome Project, i.e. Oxford dataset). To compare the functional connectivity based on soft clustering, we calculated the Seed Based Linear Correlation (SBLC) to study the Default Mode Network (DMN) functionality, i.e. we found that Precuneus L/R has the higher Correlations Coefficients with its controlateral part and with the posterior division of Cingulate Gyrus. The differences among the three soft clustering algorithms adopted were measured basing on Jaccard Similarity Coefficient (JSC), whereas the quality of clusters has been evaluated with Davies-Bouldin Index (DBI). The optimal clustering computation was with 2 partitions for all the algorithms. We obtained the following results: a) clusters differentiated the amplitude of BOLD signals for both Males and Females, i.e. low level signal vs high level signal; b) clusters also differentitated the quality of seedbased correlations, i.e. strong (positive) associations vs weakly associations. These multivariate outcomes highlighted the complementarty usage of clustering algorithms with statistical signal processing: the first made the partions, the last explain the partions. Alberto A. Vergani, Elisabetta Binaghi, Samuele Martinelli, Sabina Strocchi
分享
引用
