  Brain Age (BA) estimation via Deep Learning has become a strong and reliable
bio-marker for brain health, but the black-box nature of Neural Networks does
not easily allow insight into the features of brain ageing.We trained a ResNet
model as a BA regressor on T1 structural MRI volumes from a small
cross-sectional cohort of 524 individuals. Using Layer-wise Relevance
Propagation (LRP) and DeepLIFT saliency mapping techniques, we analysed the
trained model to determine the most relevant structures for brain ageing for
the network, and compare these between the saliency mapping techniques. We show
the change in attribution of relevance to different brain regions through the
course of ageing. A tripartite pattern of relevance attribution to brain
regions emerges. Some regions increase in relevance with age (e.g. the right
Transverse Temporal Gyrus); some decrease in relevance with age (e.g. the right
Fourth Ventricle); and others are consistently relevant across ages. We also
examine the effect of the Brain Age Gap (BAG) on the distribution of relevance
within the brain volume. It is hoped that these findings will provide
clinically relevant region-wise trajectories for normal brain ageing, and a
baseline against which to compare brain ageing trajectories.
