Abstract
Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging biomarker for brain health; however, the underlying neural features have remained unclear. We combined ensembles of convolutional neural networks with Layer-wise Relevance Propagation (LRP) to detect which brain features contribute to BA. Trained on magnetic resonance imaging (MRI) data of a population-based study (n=2637, 18-82 years), our models estimated age accurately based on single and multiple modalities, regionally restricted and whole-brain images (mean absolute errors 3.37-3.86 years). We find that BA estimates capture aging at both small and large-scale changes, revealing gross enlargements of ventricles and subarachnoid spaces, as well as lesions, iron accumulations and atrophies that appear throughout the brain. Divergence from expected aging reflected cardiovascular risk factors and accelerated aging was more pronounced in the frontal lobe. Applying LRP, our study demonstrates how superior deep learning models detect brain-aging in healthy and at-risk individuals throughout adulthood.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Via a 10-fold cross validation approach, the initial models were trained such that all subjects lie once in the test set. This provided age-estimates for each subject, in contrast to the initial small (test-set) subset. Consequently, DBA-correlation analyses could be expended and are more robust due to the greater number of subject in these analyses. Result and Discussion sections were adapted accordingly.
