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Brain Age (BA) estimation via Deep Learning has become a strong and reliablebio-marker for brain health, but the black-box nature of Neural Networks doesnot easily allow insight into the features of brain ageing.We trained a ResNetmodel as a BA regressor on T1 structural MRI volumes from a smallcross-sectional cohort of 524 individuals. Using Layer-wise RelevancePropagation (LRP) and DeepLIFT saliency mapping techniques, we analysed thetrained model to determine the most relevant structures for brain ageing forthe network, and compare these between the saliency mapping techniques. We showthe change in attribution of relevance to different brain regions through thecourse of ageing. A tripartite pattern of relevance attribution to brainregions emerges. Some regions increase in relevance with age (e.g. the rightTransverse Temporal Gyrus); some decrease in relevance with age (e.g. the rightFourth Ventricle); and others are consistently relevant across ages. We alsoexamine the effect of the Brain Age Gap (BAG) on the distribution of relevancewithin the brain volume. It is hoped that these findings will provideclinically relevant region-wise trajectories for normal brain ageing, and abaseline against which to compare brain ageing trajectories.

Brain Structural Saliency Over The Ages