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

TL;DR

This study introduces an interpretable deep learning approach using 3D CNNs and saliency mapping to identify age-sensitive hippocampal functional connectivity patterns, revealing distinct anterior and posterior hippocampal changes during aging.

Contribution

It develops a novel explainable deep learning framework that predicts brain age from hippocampal connectivity and uncovers specific age-related connectivity patterns with cortical regions.

Findings

Identifies key hippocampal-cortical connections sensitive to age.

Disaggregates anterior and posterior hippocampal connectivity changes.

Demonstrates the interpretability of deep learning in neuroimaging analysis.

Abstract

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.