Author Summary Significant advances in nonlinear dynamics and computational modeling have opened up the possibility of studying how whole-brain dynamics may be impacted by traumatic brain injury (TBI). One hypothesis is that whole-brain dynamics may show differential spatiotemporal patterns during the recovery trajectory. We used a novel turbulence framework to examine how several measures related to turbulent dynamics differ between healthy controls and TBI patients at 3, 6, and 12 months post-injury. This framework revealed a significant reduction in these empirical measures after TBI differentially affecting long distances, with the largest change at 6 months post-injury. The alterations observed at network level, however, showed a certain degree of recovery after 1 year. In addition, the Hopf whole-brain model demonstrated decreased susceptibility and information-encoding capability after TBI. The clinical implications of this work are discussed. Abstract It has been previously shown that traumatic brain injury (TBI) is associated with reductions in metastability in large-scale networks in resting-state fMRI (rsfMRI). However, little is known about how TBI affects the local level of synchronization and how this evolves during the recovery trajectory. Here, we applied a novel turbulent dynamics framework to investigate whole-brain dynamics using an rsfMRI dataset from a cohort of moderate to severe TBI patients and healthy controls (HCs). We first examined how several measures related to turbulent dynamics differ between HCs and TBI patients at 3, 6, and 12 months post-injury. We found a significant reduction in these empirical measures after TBI, with the largest change at 6 months post-injury. Next, we built a Hopf whole-brain model with coupled oscillators and conducted in silico perturbations to investigate the mechanistic principles underlying the reduced turbulent dynamics found in the empirical data. A simulated attack was used to account for the effect of focal lesions. This revealed a shift to lower coupling parameters in the TBI dataset and, critically, decreased susceptibility and information-encoding capability. These findings confirm the potential of the turbulent framework to characterize longitudinal changes in whole-brain dynamics and in the reactivity to external perturbations after TBI.