As we age, our brains undergo substantial structural and functional changes, which can be either accelerated or slowed by various factors. In this talk, I will demonstrate how these processes can be modeled using non-invasive neuroimaging data. I will present results obtained through machine learning to calibrate brain-age estimation models, which can then be used to investigate how different diseases and environmental factors disrupt healthy aging trajectories. Finally, I will discuss how biophysically grounded models based on coupled dynamical systems can shed light on the mechanisms underlying these accelerated aging processes.