Decoding Functional Networks for Visual Categories via GNNs

Shira Karmi, Galia Avidan, Tammy Riklin Raviv
3/30/2026
cs.CV

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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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Cite this paper

@article{karmi2026decoding,
  title  = {Decoding Functional Networks for Visual Categories via GNNs},
  author = {Shira Karmi and Galia Avidan and Tammy Riklin Raviv},
  year   = {2026},
  eprint = {2603.28931},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2603.28931v1}
}

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