Interpretable Deep Learning Model for Age Prediction Using Human Brain Cortico-Hippocampal Functional Connectivity

Motivation: Normal aging involves human brain changes. Recognizing healthy aging patterns can advance age care, help understand unhealthy aging, and guide interventions. Goal(s): We aim to characterise age-related functional changes and explore how they can be predicted and interpreted. Approach: Using deep learning, we predicted age with cortical functional connectivity of the whole, anterior, and posterior hippocampus. LayerCAM generates the whole brain saliency map. Results: Models yielded a mean prediction error of 6.9 years. Personalised saliency maps revealed highly contributing regions to age prediction, such as the precuneus. Models also capture the prediction and saliency difference between anterior and posterior hippocampal-cortical functional connectivity. Impact: We introduced an interpretable deep learning approach to explore age-related brain functional changes. Our work generates new knowledge that could lead to early detection and better management of aging-related disorders.