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Communication Dans Un Congrès Année : 2015

SimNet: A new algorithm for measuring brain networks similarity

Résumé

Measuring similarity among graphs is recognized as a non-trivial problem. Most of the algorithms proposed so far ignore the spatial location of vertices, which is a crucial factor in the context of brain networks. In this paper, we present a novel algorithm, called "SimNet", for measuring the similarity between two graphs whose vertices represent the position of sources over the cortex. The novelty is to account for differences at the level of spatially-registered vertices and edges. Simulated graphs are used to evaluate the algorithm performance and to compare it with methods reported elsewhere. Results show that SimNet is able to quantify the similarity between two graphs under a spatial constraint based on the 3D location of edges. The application of SimNet on real data (dense EEG) reveals the presence of spatially-different brain networks modules activating during cognitive activity.
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Dates et versions

hal-01375613 , version 1 (03-10-2016)

Identifiants

Citer

Ahmad Mheich, Mahmoud Hassan, Fabrice Wendling, Mohamad Khalil, Olivier Dufor, et al.. SimNet: A new algorithm for measuring brain networks similarity. ICABME 2015 : 3rd International Conference on Advances in Biomedical Engineering Advances in Biomedical Engineering, Sep 2015, Beyrouth, Lebanon. pp.119 - 122, ⟨10.1109/ICABME.2015.7323266⟩. ⟨hal-01375613⟩
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