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.

翻译：图深度学习模型作为一类采用信息聚合机制的人工智能驱动方法，在神经影像学中分析功能性脑连接组时已广受欢迎。然而，其实际有效性仍不明确。在本研究中，我们基于四项大规模神经影像研究，重新审视了图深度学习模型与经典机器学习模型的对比。令人惊讶的是，我们发现作为图深度学习模型标志的信息聚合机制，并未如通常假设的那样有助于预测性能，反而持续削弱了该性能。为解决此问题，我们提出了一种混合模型，通过双路径将线性模型与图注意力网络相结合，实现了稳健的预测，并通过揭示局部与全局神经连接模式增强了可解释性。我们的研究结果警示在功能性脑连接组分析中采用复杂深度学习模型需谨慎，强调需要严格的实验设计以确立实质性的性能提升，并且或许更重要的是，应致力于提高模型的可解释性。