Joint laplacian diagonalization for multi-modal brain community detection

In this paper we present a novel approach to group-wise multi-modal community detection, i.e. identification of coherent sub-graphs across multiple subjects with strong correlation across modalities. This approach is based on joint diagonalization of two or more graph Laplacians aiming at finding a...

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Bibliographic Details
Published in2014 International Workshop on Pattern Recognition in Neuroimaging pp. 1 - 4
Main Authors Dodero, Luca, Murino, Vittorio, Sona, Diego
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2014
Subjects
Clustering
Coherence
Communities
Community detection
Diffusion tensor imaging
Eigenvalues and eigenfunctions
fMRI DTI
Graph Laplacian
Joint diagonalization
Joints
Laplace equations
Multi-modal connectivity
Symmetric matrices
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Summary:In this paper we present a novel approach to group-wise multi-modal community detection, i.e. identification of coherent sub-graphs across multiple subjects with strong correlation across modalities. This approach is based on joint diagonalization of two or more graph Laplacians aiming at finding a common eigenspace across individuals, over which spectral clustering in fewer dimension is then applied. The method allows to identify common sub-networks across different graphs. We applied our method on 40 multi-modal structural and functional healthy subjects, finding well known sub-networks described in literature. Our experiments revealed that detected multi-modal brain sub-networks improve the consistency of group-wise unimodal community detection.
DOI:10.1109/PRNI.2014.6858515