Does pre-training on brain-related tasks results in better
  deep-learning-based brain age biomarkers?
Bruno Machado Pacheco Victor Hugo Rocha de Oliveira Augusto Braga Fernandes Antunes Saulo Domingos de Souza Pedro Danilo Silva
Brain age prediction using neuroimaging data has shown great potential as an
indicator of overall brain health and successful aging, as well as a disease
biomarker. Deep learning models have been established as reliable and efficient
brain age estimators, being trained to predict the chronological age of healthy
subjects. In this paper, we investigate the impact of a pre-training step on
deep learning models for brain age prediction. More precisely, instead of the
common approach of pre-training on natural imaging classification, we propose
pre-training the models on brain-related tasks, which led to state-of-the-art
results in our experiments on ADNI data. Furthermore, we validate the resulting
brain age biomarker on images of patients with mild cognitive impairment and
Alzheimer's disease. Interestingly, our results indicate that better-performing
deep learning models in terms of brain age prediction on healthy patients do
not result in more reliable biomarkers.