Decoding Functional Networks for Visual Categories via GNNs

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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.

Comments: Accepted for publication in IEEE International Symposium on Biomedical Imaging (ISBI) 2026

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2603.28931 [cs.CV]

(or arXiv:2603.28931v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2603.28931

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Shira Karmi [view email] [v1] Mon, 30 Mar 2026 19:10:28 UTC (3,862 KB)