用图神经网络解码视觉类别在脑网络中的连接模式。
Decoding Functional Networks for Visual Categories via GNNs
- 构建脑区功能图，用带符号的GNN建模正负连接关系。
- 准确识别运动、食物、车辆等类别的功能连接状态。
- 发现沿腹侧和背侧通路的可重复生物意义子网络，适合脑科学与AI交叉研究者。
理解大规模脑网络如何表征视觉类别，是连接感知与皮层组织的基础。基于自然场景数据集的7T高分辨率fMRI，我们构建了区域级别的功能图，并训练了一种带符号的图神经网络，该网络同时建模正负交互关系，采用稀疏边掩码和类别特定显著性。模型能准确解码运动、食物、车辆等类别的特异性功能连接状态，并揭示沿腹侧和背侧视觉通路的可重复、具有生物学意义的子网络。该框架通过将体素级类别选择性扩展为基于连接性的视觉加工表示，实现了机器学习与神经科学的融合。
原文摘要 · Abstract (English)
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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