用谱图神经网络分析脑连接组，准确识别认知任务
Spectral Graph Neural Networks for Cognitive Task Classification in fMRI Connectomes
- 基于图傅里叶变换设计谱卷积模型，捕捉脑区拓扑关系
- 在HCPTask数据集上达到96.25%分类准确率，优于传统方法
- 代码开源，适合脑科学与图神经网络交叉研究者使用
利用机器学习进行认知任务分类是解码神经影像数据中脑状态的核心手段。通过结合机器学习与脑网络分析，可从功能磁共振成像连接组中提取复杂的连接模式，将原始血氧水平依赖（BOLD）信号转化为可解释的认知过程表征。图神经网络（GNN）进一步推进该范式，将脑区视为节点、功能连接视为边，捕捉传统方法常忽略的拓扑依赖和多尺度交互。本文提出SpectralBrainGNN模型，一种基于归一化拉普拉斯特征分解计算图傅里叶变换的谱卷积框架。在人类连接组计划-任务（HCPTask）数据集上的实验表明，该方法有效，分类准确率达96.25%。实现代码已公开于https://github.com/gnnplayground/SpectralBrainGNN，以支持可复现性与后续研究。
原文摘要 · Abstract (English)
Cognitive task classification using machine learning plays a central role in decoding brain states from neuroimaging data. By integrating machine learning with brain network analysis, complex connectivity patterns can be extracted from functional magnetic resonance imaging connectomes. This process transforms raw blood-oxygen-level-dependent (BOLD) signals into interpretable representations of cognitive processes. Graph neural networks (GNNs) further advance this paradigm by modeling brain regions as nodes and functional connections as edges, capturing topological dependencies and multi-scale interactions that are often missed by conventional approaches. Our proposed SpectralBrainGNN model, a spectral convolution framework based on graph Fourier transforms (GFT) computed via normalized Laplacian eigendecomposition. Experiments on the Human Connectome Project-Task (HCPTask) dataset demonstrate the effectiveness of the proposed approach, achieving a classification accuracy of 96.25\%. The implementation is publicly available at https://github.com/gnnplayground/SpectralBrainGNN to support reproducibility and future research.
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