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Group-Wise Functional Community Detection through Joint Laplacian Diagonalization

  • Luca Dodero
  • Alessandro Gozzi
  • Adam Liska
  • Vittorio Murino
  • Diego Sona
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

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.

Keywords

Joint Diagonalization fMRI Laplacian Spectral Clustering Community Detection 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Luca Dodero
    • 1
  • Alessandro Gozzi
    • 2
  • Adam Liska
    • 2
  • Vittorio Murino
    • 1
  • Diego Sona
    • 1
  1. 1.Pattern Analysis and Computer Vision (PAVIS)Istituto Italiano di TecnologiaGenovaItaly
  2. 2.Center for Neuroscience and Cognitive Systems @UniTnIstituto Italiano di TecnologiaRoveretoItaly

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