Decoding Functional Networks for Visual Categories via GNNs

TL;DR

This paper introduces a GNN-based framework to decode and interpret large-scale brain network representations of visual categories using high-resolution fMRI data.

Contribution

It develops a signed Graph Neural Network that models positive and negative brain interactions to classify visual categories and identify meaningful subnetworks.

Findings

Accurately decodes category-specific functional connectivity states

Reveals reproducible subnetworks along visual pathways

Bridges machine learning with neuroscience for connectivity-based visual processing

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.