Decoding Functional Networks for Visual Categories via GNNs

• URL: http://arxiv.org/abs/2603.28931v1
• Date: Mon, 30 Mar 2026 19:10:28 GMT
• Title: Decoding Functional Networks for Visual Categories via GNNs
• Abstract summary: Understanding how large-scale brain networks represent visual categories is fundamental to linking perception and cortical organization.<n>We construct parcel-level functional graphs and train a signed Graph Neural Network that models both positive and negative interactions.<n>This framework bridges machine learning and neuroscience by extending voxel-level category selectivity to a connectivity-based representation of visual processing.
• Score: 5.301721673195314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. The model accurately decodes category-specific functional connectivity states (sports, food, vehicles) and reveals reproducible, biologically meaningful subnetworks along the ventral and dorsal visual pathways. This framework bridges machine learning and neuroscience by extending voxel-level category selectivity to a connectivity-based representation of visual processing.

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