  Brain age is a critical measure that reflects the biological ageing process
of the brain. The gap between brain age and chronological age, referred to as
brain PAD (Predicted Age Difference), has been utilized to investigate
neurodegenerative conditions. Brain age can be predicted using MRIs and machine
learning techniques. However, existing methods are often sensitive to
acquisition-related variabilities, such as differences in acquisition
protocols, scanners, MRI sequences, and resolutions, significantly limiting
their application in highly heterogeneous clinical settings. In this study, we
introduce Synthetic Brain Age (SynthBA), a robust deep-learning model designed
for predicting brain age. SynthBA utilizes an advanced domain randomization
technique, ensuring effective operation across a wide array of
acquisition-related variabilities. To assess the effectiveness and robustness
of SynthBA, we evaluate its predictive capabilities on internal and external
datasets, encompassing various MRI sequences and resolutions, and compare it
with state-of-the-art techniques. Additionally, we calculate the brain PAD in a
large cohort of subjects with Alzheimer's Disease (AD), demonstrating a
significant correlation with AD-related measures of cognitive dysfunction.
SynthBA holds the potential to facilitate the broader adoption of brain age
prediction in clinical settings, where re-training or fine-tuning is often
unfeasible. The SynthBA source code and pre-trained models are publicly
available at https://github.com/LemuelPuglisi/SynthBA.
