Group-wise functional community detection through joint Laplacian diagonalization

View at Publisher| Export | Download | Add to List | More... Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 8674 LNCS, Issue PART 2, 2014, Pages 708-715 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014; Boston, MA; United States; 14 September 2014 through 18 September 2014; Code 107426 Group-wise functional community detection through joint Laplacian diagonalization (Conference Paper) Dodero, L.a, Gozzi, A.b, Liska, A.b, Murino, V.a, Sona, D.a a Pattern Analysis and Computer Vision (PAVIS), Istituto Italiano di Tecnologia, Genova, Italy b Center for Neuroscience and Cognitive Systems at UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy View references (20) Abstract There is a growing conviction that the understanding of the brain function can come through a deeper knowledge of the network connectivity between different brain areas. Resting state Functional Magnetic Resonance Imaging (rs-fMRI) is becoming one of the most important imaging modality widely used to understand network functionality. However, due to the variability at subject scale, mapping common networks across individuals is by now a real challenge. In this work we present a novel approach to group-wise community detection, i.e. identification of functional coherent sub-graphs across multiple subjects. This approach is based on a joint diagonalization of two or more graph Laplacians, aiming at finding a common eigenspace across individuals, over which clustering in fewer dimension can then be applied. This allows to identify common sub-networks across different graphs. We applied our method to rs-fMRI dataset of mouse brain finding most important sub-networks recently described in literature.