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Communication Dans Un Congrès Année : 2022

Pruning Graph Convolutional Networks to Select Meaningful Graph Frequencies for FMRI Decoding

Résumé

Graph Signal Processing is a promising framework to manipulate brain signals as it allows to encompass the spatial dependencies between the activity in regions of interest in the brain. In this work, we are interested in better understanding what are the graph frequencies that are the most useful to decode fMRI signals. To this end, we introduce a deep learning architecture and adapt a pruning methodology to automatically identify such frequencies. We experiment with various datasets, architectures and graphs, and show that low graph frequencies are consistently identified as the most important for fMRI decoding, with a stronger contribution for the functional graph over the structural one. We believe that this work provides novel insights on how graph-based methods can be deployed to increase fMRI decoding accuracy and interpretability.

Dates et versions

hal-04173125 , version 1 (28-07-2023)

Identifiants

Citer

Yassine El Ouahidi, Hugo Tessier, Giulia Lioi, Nicolas Farrugia, Bastien Pasdeloup, et al.. Pruning Graph Convolutional Networks to Select Meaningful Graph Frequencies for FMRI Decoding. EUSIPCO 2022: 30th European Signal Processing Conference, Aug 2022, Belgrade, Serbia. pp.937-941, ⟨10.23919/EUSIPCO55093.2022.9909966⟩. ⟨hal-04173125⟩
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