标题： 年龄敏感的海马功能连接：来自3D卷积神经网络和显著性映射的新见解 显示英文标题

标题： Age Sensitive Hippocampal Functional Connectivity: New Insights from 3D CNNs and Saliency Mapping

摘要： 海马灰质丢失是神经生物学衰老的标志，但对其功能连接相应变化的理解仍然有限。基于种子点的功能连接（FC）分析能够对海马与皮层区域的同步活动进行体素级映射，为了解衰老过程中的功能重组提供了一个窗口。在本研究中，我们开发了一个可解释的深度学习框架，使用三维卷积神经网络（3D CNN）结合LayerCAM显著性映射来预测脑年龄。这种方法映射了关键的海马-皮层连接，特别是与楔前叶、楔叶、后扣带皮层、海马旁回、左侧上顶叶和右侧上颞沟的高度敏感于年龄的连接。关键的是，分离前部和后部海马功能连接揭示了与其已知功能特化的不同映射。这些发现为海马衰老的功能机制提供了新的见解，并展示了可解释深度学习在神经影像数据中揭示生物学意义模式的力量。 显示英文摘要

摘要： 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.