ABSTRACT The functional and cognitive effects of traumatic brain injury (TBI) are poorly understood, as even mild injuries (concussion) can lead to long-lasting, untreatable symptoms. Simplified brain dynamics models may help researchers better understand the relationship between brain injury patterns and functional outcomes. Properly developed, these computational models provide an approach to investigate the effects of both computational and in vivo injury on simulated dynamics and cognitive function, respectively, for model organisms. In this study, we apply the Kuramoto model and an existing mesoscale mouse brain structural network to develop a simplified computational model of mouse brain dynamics. We explore how to optimize our initial model to predict existing mouse brain functional connectivity collected from mice under various anesthetic protocols. Finally, to determine how strongly the changes in our optimized models’ dynamics can predict the extent of a brain injury, we investigate how our simulations respond to varying levels of structural network damage. Results predict a mixture of hypo- and hyperconnectivity after experimental TBI, similar to results in TBI survivors, and also suggest a compensatory remodeling of connections that may have an impact on functional outcomes after TBI. AUTHOR SUMMARY Recent research has investigated the consequences of traumatic brain injuries by combining computational models of human brain activity and structural models of the whole human brain or cortex. As experimental injury research can only be conducted using animal models, we apply a simplified computational model of whole-brain dynamics, the Kuramoto model, to a mouse brain structural network. We tune our model to best predict measurements of functional connectivity recorded from 58 fMRI scans of mice and lesion the network model to explore the effects of injury. Our findings predict that functional connectivity may increase or decrease in various regions of the brain, even at a high injury level, which may aid in future predictions of cognitive impairments after brain injury.