标题： 重新思考功能脑连接组分析：图深度学习模型是否有帮助？ 显示英文标题

标题： Rethinking Functional Brain Connectome Analysis: Do Graph Deep Learning Models Help?

摘要： 功能脑连接组对于解码认知功能和神经疾病背后的神经机制至关重要。 图深度学习模型最近在这一领域获得了极大的流行。 然而，它们在建模脑连接组方面的实际效果仍不明确。 在本研究中，我们基于四项涵盖多种认知和临床结果的大规模神经影像学研究重新审视了图深度学习模型。 令人惊讶的是，我们发现图深度学习模型的标志性信息聚合机制并不像通常假设的那样有助于预测性能，而是持续地降低它。 为了解决这个问题，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结合，通过揭示局部和全局神经连接模式，实现了稳健的预测和增强的可解释性。 我们的研究结果呼吁在采用复杂的深度学习模型进行功能脑连接组分析时要谨慎，强调需要严格的实验设计来确立实际的性能提升，也许更重要的是，要追求模型可解释性的改进。 显示英文摘要

摘要： Functional brain connectome is crucial for deciphering the neural mechanisms underlying cognitive functions and neurological disorders. Graph deep learning models have recently gained tremendous popularity in this field. However, their actual effectiveness in modeling the brain connectome remains unclear. In this study, we re-examine graph deep learning models based on four large-scale neuroimaging studies encompassing diverse cognitive and clinical outcomes. 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.