  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.
