Age Sensitive Hippocampal Functional Connectivity: New Insights from 3D CNNs and Saliency Mapping
This work provides new insights into hippocampal aging mechanisms for neuroscience and neuroimaging researchers, though it is incremental as it builds on existing methods with a focus on interpretability.
The study tackled the problem of understanding age-related changes in hippocampal functional connectivity by developing an interpretable deep learning framework to predict brain age, revealing key hippocampal-cortical connections sensitive to aging and distinct patterns for anterior and posterior regions.
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.