Brain Structural Saliency Over The Ages
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 49 references that are in the map. First posted on arXiv 12 Jan 2022.
Builds on
17 of 49 references.
References · 17
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2017Axiomatic Attribution for Deep Networkscited by 3,350
- 2017Learning Important Features Through Propagating Activation Differencescited by 1,454
- 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,430
- 2017SmoothGrad: removing noise by adding noisecited by 1,097
- 2016Not Just a Black Box: Learning Important Features Through Propagating Activation Differencescited by 295
- 2015Explaining NonLinear Classification Decisions with Deep Taylor Decompositioncited by 248
- 2019Explanations can be manipulated and geometry is to blamecited by 215
- 2019When Explanations Lie: Why Many Modified BP Attributions Failcited by 92
- 2016Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarkercited by 72
- 2019Towards Best Practice in Explaining Neural Network Decisions with LRPcited by 70
5 more
- 2017Tensor Regression Networkscited by 44
- 2016Gradients of Counterfactualscited by 31
- 2019Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagationcited by 21
- 2019Parkinson's Disease Recognition Using SPECT Image and Interpretable AI: A Tutorialcited by 1
- 2019Interpretable Convolutional Neural Networks for Preterm Birth Classificationcited by 1
Built on by
Citing papers per year: 2023: 1.
1 paper.