AI RESEARCH
Decoding Functional Networks for Visual Categories via GNNs
arXiv CS.CV
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ArXi:2603.28931v1 Announce Type: new Understanding how large-scale brain networks represent visual categories is fundamental to linking perception and cortical organization. Using high-resolution 7T fMRI from the Natural Scenes Dataset, we construct parcel-level functional graphs and train a signed Graph Neural Network that models both positive and negative interactions, with a sparse edge mask and class-specific saliency.