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.
@article{arxiv.2603.28931,
title = {Decoding Functional Networks for Visual Categories via GNNs},
author = {Shira Karmi and Galia Avidan and Tammy Riklin Raviv},
journal= {arXiv preprint arXiv:2603.28931},
year = {2026}
}
Comments
Accepted for publication in IEEE International Symposium on Biomedical Imaging (ISBI) 2026