Deep learning models of cognitive processes constrained by human brain connectomes [article]

Yu Zhang, Nicolas et Farrugia, Pierre Bellec
2021 bioRxiv   pre-print
Brain decoding aims to infer cognitive states from recordings of brain activity. Current literature has mainly focused on isolated brain regions engaged in specific experimental conditions, but ignored the integrative nature of cognitive processes recruiting distributed brain networks. To tackle this issue, we propose a connectome-based graph neural network to integrate distributed patterns of brain activity in a multiscale manner, ranging from localized brain regions, to a specific brain
more » ... t/network and towards the full brain. We evaluate the decoding model using a large task-fMRI database from the human connectome project. By implementing connectomic constraints and multiscale interactions in deep graph convolutions, the model achieves high accuracy of decoding 21 cognitive states (93%, chancel level: 4.8%) and shows high robustness against adversarial attacks on the graph architecture. Our study bridges human connectomes with deep learning techniques and provides new avenues to study the underlying neural substrates of human cognition at scale.
doi:10.1101/2021.10.12.464145 fatcat:vhgq5lpdbvhsdlrcker3efxthq