Soft Brain Ageing Indicators Based on Light-Weight LeNet-Like Neural Networks and Localized 2D Brain Age Biomarkers

In recent years, there have been several proposed applications based on Convolutional Neural Networks (CNN) to neuroimaging data analysis and explanation. Traditional pipelines require several processing steps for feature extraction and ageing biomarker detection. However, modern deep learning strategies based on transfer learning and gradient-based explanations (e.g., Grad-Cam++) can provide a more powerful and reliable framework for automatic feature mapping, further identifying 3D ageing biomarkers. Despite the existence of several 3D CNN methods, we show that a LeNet-like 2D-CNN model trained on T1-weighted MRI images can be used to predict brain biological age in a classification task and, by transfer learning, in a regression task. In addition, automatic averaging and aligning of 2D-CNN gradient-based images is applied and shown to improve its biological meaning. The proposed model predicts soft biological brain ageing indicators with a six-class-balanced accuracy of $${\approx }70\%$$ by using the anagraphic age of 1100 healthy subjects in comparison to their brain scans.