Connectome reorganization and precision neurorehabilitation after traumatic brain injury

Traumatic brain injury (TBI) is a leading cause of long-term disability. The connectome paradigm reframes TBI as a disconnection syndrome, in which disruption of large-scale brain networks underlies cognitive and behavioral sequelae. This narrative review synthesizes the current evidence on connectome alterations after TBI using diffusion magnetic resonance imaging (dMRI), resting-state functional magnetic resonance imaging (rsfMRI), electroencephalography (EEG), and graph theory analyses. TBI severity correlates with reductions in global efficiency and modularity, and connectomic reorganization is shaped by neuroinflammation, glymphatic dysfunction, and neurovascular uncoupling—processes that can leave network nodes functionally impaired despite structural preservation. Reorganization follows a biphasic pattern, characterized by acute hypoconnectivity within integrative networks, such as the default mode network (DMN), followed by compensatory hyperconnectivity. These disruptions are associated with deficits in memory, executive function, and emotional regulation. Emerging connectome-guided approaches enable neuromodulation targeting dysfunctional hubs, and machine learning (ML)-based recovery prediction has achieved accuracies of up to 92% in stroke connectome models. However, comparable TBI-specific frameworks remain at a considerably earlier stage of development and, where tested across independent cohorts, have demonstrated more modest and heterogeneous performance. TBI is best understood as a dynamic network disorder in which clinical outcomes depend on the trajectory of connectomic reorganization. Future research should prioritize biomarker validation, mechanistic studies linking neuroinflammation to network remodeling, pro-glymphatic therapies, and the development of individualized precision neuroengineering interventions.