复杂图神经网络在脑连接组分析中效果不如传统方法，新模型提升预测与可解释性。
Rethinking Functional Brain Connectome Analysis: Do Graph Deep Learning Models Help
- 设计双路径混合模型，融合线性模型与图注意力网络
- 实验表明消息聚合机制会降低预测性能，而非提升
- 适合关注脑连接组分析可解释性的研究者
图深度学习模型凭借消息聚合机制，在功能脑连接组分析中备受青睐。然而其实际有效性尚不明确。本研究基于四项大规模神经影像学研究，重新评估图深度学习与经典机器学习模型的表现。出人意料的是，消息聚合机制并未提升预测性能，反而持续降低表现。为此，我们提出一种双路径混合模型，将线性模型与图注意力网络结合，实现了稳健的预测能力，并通过揭示局部与全局神经连接模式，显著增强模型可解释性。研究警示：在功能脑连接组分析中应谨慎采用复杂深度学习模型，强调需通过严谨实验验证性能提升，更应重视模型可解释性改进。
原文摘要 · Abstract (English)
Graph deep learning models, a class of AI-driven approaches employing a message aggregation mechanism, have gained popularity for analyzing the functional brain connectome in neuroimaging. However, their actual effectiveness remains unclear. In this study, we re-examine graph deep learning versus classical machine learning models based on four large-scale neuroimaging studies. Surprisingly, we find that the message aggregation mechanism, a hallmark of graph deep learning models, does not help with predictive performance as typically assumed, but rather consistently degrades it. To address this issue, we propose a hybrid model combining a linear model with a graph attention network through dual pathways, achieving robust predictions and enhanced interpretability by revealing both localized and global neural connectivity patterns. Our findings urge caution in adopting complex deep learning models for functional brain connectome analysis, emphasizing the need for rigorous experimental designs to establish tangible performance gains and perhaps more importantly, to pursue improvements in model interpretability.
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