Group-wise functional community detection through joint Laplacian diagonalization

• Published in: Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Vol. 17; no. Pt 2; p. 708
• Main Authors: Dodero, Luca, Gozzi, Alessandro, Liska, Adam, Murino, Vittorio, Sona, Diego
• Format: Journal Article
• Language: English
• Published: Germany 2014
• Subjects: Algorithms; Animals; Brain - physiology; Connectome - methods; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Male; Mice; Mice, Inbred C57BL; Nerve Net - physiology; Pattern Recognition, Automated - methods; Reproducibility of Results; Rest - physiology; Sensitivity and Specificity

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.