用3D CNN和注意力图分析海马功能连接，发现与年龄相关的关键脑区连接变化。
Age Sensitive Hippocampal Functional Connectivity: New Insights from 3D CNNs and Saliency Mapping
- 构建3D CNN结合注意力图，可解释性预测脑龄。
- 识别出海马与后扣带回等6个关键脑区的年龄敏感连接。
- 分离前后海马功能连接，揭示其不同衰老机制，适合神经影像研究者。
海马灰质流失是神经生物学衰老的标志，但其功能连接的变化仍不清晰。基于种子点的功能连接（FC）分析可实现海马与皮层区域同步活动的体素级映射，为衰老过程中的功能重组提供窗口。本研究提出一种可解释的深度学习框架，利用三维卷积神经网络（3D CNN）结合LayerCAM显著性映射，从海马功能连接中预测脑龄。该方法定位到海马与楔前叶、楔叶、后扣带皮层、旁海马皮层、左侧上顶叶和右侧上颞沟等关键脑区之间的高龄敏感连接。关键发现是，将海马前后部分别分析后，其功能连接模式分别与已知功能特化高度一致。这些结果揭示了海马衰老的功能机制，并展示了可解释深度学习在神经影像数据中挖掘生物意义模式的强大能力。
原文摘要 · Abstract (English)
Grey matter loss in the hippocampus is a hallmark of neurobiological aging, yet understanding the corresponding changes in its functional connectivity remains limited. Seed-based functional connectivity (FC) analysis enables voxel-wise mapping of the hippocampus's synchronous activity with cortical regions, offering a window into functional reorganization during aging. In this study, we develop an interpretable deep learning framework to predict brain age from hippocampal FC using a three-dimensional convolutional neural network (3D CNN) combined with LayerCAM saliency mapping. This approach maps key hippocampal-cortical connections, particularly with the precuneus, cuneus, posterior cingulate cortex, parahippocampal cortex, left superior parietal lobule, and right superior temporal sulcus, that are highly sensitive to age. Critically, disaggregating anterior and posterior hippocampal FC reveals distinct mapping aligned with their known functional specializations. These findings provide new insights into the functional mechanisms of hippocampal aging and demonstrate the power of explainable deep learning to uncover biologically meaningful patterns in neuroimaging data.
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