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arXiv · 2603.28931

Decoding Functional Networks for Visual Categories via GNNs

Shira Karmi·Galia Avidan·Tammy Riklin Raviv
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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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Shira Karmi, Galia Avidan, Tammy Riklin Raviv. 2026-03-30. Decoding Functional Networks for Visual Categories via GNNs. https://arxiv.org/abs/2603.28931

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