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

2025-07-02

AI Summary

This study investigates region-specific alterations in hippocampal functional connectivity during neuroaging, focusing on age-related declines in synchrony between anterior/posterior hippocampus and cortical regions—including the precuneus and posterior cingulate cortex (PCC). We propose an interpretable 3D convolutional neural network (CNN) framework that jointly integrates seed-based functional connectivity features and LayerCAM-based saliency mapping, enabling end-to-end decoding of anterior versus posterior hippocampal connectivity differences for the first time. Results demonstrate significant age-related reductions in functional coupling between the hippocampus and core default mode network (DMN) nodes—particularly the precuneus and PCC—and reveal heightened vulnerability of posterior hippocampal–cortical coupling to aging. The model enhances the interpretability of fMRI-derived biomarkers and provides novel empirical evidence linking hippocampal functional parcellation to mechanisms underlying cognitive aging.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Reasoning under Uncertainty: Relational Probabilistic Models
• Computer Vision: Multi-modal Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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.

Problem

• Understand age-related hippocampal functional connectivity changes
• Predict brain age using interpretable 3D CNN and saliency mapping
• Identify key hippocampal-cortical connections sensitive to aging

Innovation

• 3D CNN for hippocampal functional connectivity analysis
• LayerCAM saliency mapping for interpretable deep learning
• Disaggregating anterior and posterior hippocampal FC