Neural network changes during aging may contribute to vulnerability and resilience to brain lesions in age-related neurological disorders, such as stroke. However, the relationship between age-related neural network features and stroke outcome is unknown. Therefore, we assessed structural and functional network status in young adult and aged rat brain, and measured the effects of simulated stroke lesions.Eleven rats underwent diffusion-weighted MRI and resting-state functional MRI at young adult age (post-natal day 88) and old age (between post-natal day 760 and 880). Structural and functional brain network features were calculated from graph-based network analysis. We performed three lesion simulations based on the brain injury pattern in frequently applied rodent stroke models, i.e. a small cortical lesion, a subcortical lesion, or a large cortical plus subcortical lesion, for which we computationally removed the involved network regions.Global network characteristics, i.e. integration and segregation, were not significantly different between the two age groups. However, we detected local differences in structural and functional networks between young adult and old rats, mainly reflected by shifts of hub regions. Stroke lesion simulations induced significant global and local network changes, characterized by lower efficiency and shifts of hub regions in structural and functional networks, which was most evident after a large cortical plus subcortical lesion. Functional and structural hub region shifts after lesion simulations differed between young adult and aged rats.Our lesion simulation study demonstrates that age-dependent brain network status affects structural and functional network reorganization after stroke, particularly involving hub shifts, which may influence functional outcome. Computational lesion studies offer a cheap and simple alternative to empirical studies and can complement or guide more complicated experimental studies in animal models and patients.