Saliency Mapping in Convolutional Neural Networks to Determine Brain Age Trajectories

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 causal features of brain ageing. In this work, a ResNet model was trained as a BA regressor on T1 structural brain MRI volumes from a small cross-sectional cohort of 524 individuals. Using Layer-wise Relevance Propagation (LRP) and DeepLIFT saliency mapping techniques, analyses were performed on the trained model to determine the most revealing structures over the course of brain ageing for the network, and compare these between the saliency mapping techniques. This work shows the change in attribution of relevance to di erent 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, known to be a ected by healthy ageing); some decrease in relevance with age (e.g. the right Fourth Ventricle, known to dilate with age); and others remained consistently relevant across ages. This work also examines the e ect of Brain Age Delta (DBA) on the distribution of relevance within the brain volume, for both older and younger individuals. It is hoped that these ndings will provide clinically relevant region-wise trajectories for normal brain ageing, and a baseline against which to compare brain ageing trajectories.

Grantor

Department of Mathematics and Applied Mathematics

Year dc:date.issued

2022

Author dc:creator

• Taylor, Daniel

Advisors dc:contributor.advisor

• Shock, Jonathan
• Moodley, Deshendran

• Mathematics And Applied Mathematics

Handle dc:identifier.uri

http://hdl.handle.net/11427/37373

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/37373