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Brain age prediction using neuroimaging data has shown great potential as anindicator of overall brain health and successful aging, as well as a diseasebiomarker. Deep learning models have been established as reliable and efficientbrain age estimators, being trained to predict the chronological age of healthysubjects. In this paper, we investigate the impact of a pre-training step ondeep learning models for brain age prediction. More precisely, instead of thecommon approach of pre-training on natural imaging classification, we proposepre-training the models on brain-related tasks, which led to state-of-the-artresults in our experiments on ADNI data. Furthermore, we validate the resultingbrain age biomarker on images of patients with mild cognitive impairment andAlzheimer's disease. Interestingly, our results indicate that better-performingdeep learning models in terms of brain age prediction on healthy patients donot result in more reliable biomarkers.

Does pre-training on brain-related tasks results in better deep-learning-based brain age biomarkers?