2022/03/09 by Yassine El Ouahidi, Hugo Tessier, Ouahidi, Yassine El +9
Biochemistry, Genetics and Molecular Biology · Computer Science · Neuroscience · #Advanced Graph Neural Networks #Bioinformatics and Genomic Networks #FOS: Computer and information sciences #FOS: Electrical engineering #Functional Brain Connectivity Studies #Machine Learning (cs.LG) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2203.04455
openalex publication_date 2022/03/09 · openalex created_date 2022/04/03 · openalex updated_date 2026/07/28
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.