Abstract
ObjectiveTraumatic brain injury (TBI) is a heterogeneous disease with multiple neurological deficits that evolve over time. It is also associated with an increased incidence of neurodegenerative diseases. Accordingly, clinicians need better tools to predict a patient’s long‐term prognosis.MethodsDiffusion‐weighted and anatomical MRI data were collected from 17 adolescents (mean age = 15y8mo) with moderate‐to‐severe TBI and 19 healthy controls. Using a network diffusion model (NDM), we examined the effect of progressive deafferentation and gray matter thinning in young TBI patients. Moreover, using a novel automated inference method, we identified several injury epicenters in order to determine the neural degenerative patterns in each TBI patient.ResultsWe were able to identify the subject‐specific patterns of degeneration in each patient. In particular, the hippocampus, temporal cortices, and striatum were frequently found to be the epicenters of degeneration across the TBI patients. Orthogonal transformation of the predicted degeneration, using principal component analysis, identified distinct spatial components in the temporal–hippocampal network and the cortico‐striatal network, confirming the vulnerability of these networks to injury. The NDM model, best predictive of the degeneration, was significantly correlated with time since injury, indicating that NDM can potentially capture the pathological progression in the chronic phase of TBI.InterpretationThese findings suggest that network spread may help explain patterns of distant gray matter thinning, which would be consistent with Wallerian degeneration of the white matter connections (i.e., “diaschisis”) from diffuse axonal injuries and multifocal contusive injuries, and the neurodegenerative patterns of abnormal protein aggregation and transmission, which are hallmarks of brain changes in TBI. NDM approaches could provide highly subject‐specific biomarkers relevant for disease monitoring and personalized therapies in TBI.
Highlights
The risk of neurodegenerative diseases (e.g., Parkinson’s disease, Alzheimer’s disease) is increased when traumatic brain injury (TBI) is sustained at an early age.[1,2] This observation is of particular concern, given the high annual incidence rates of childhood brain injuries (765 per 100.000 population) resulting from motor vehicle accidents, falls, sports, and abuse.[3]
The pattern of degeneration predicted by these injury seeds across individuals comprised principal modes of atrophy with distinct anatomical distribution
These findings demonstrate the potential utility of network spread models in predicting the progression of neural degeneration in future longitudinal studies in TBI patients
Summary
The risk of neurodegenerative diseases (e.g., Parkinson’s disease, Alzheimer’s disease) is increased when traumatic brain injury (TBI) is sustained at an early age.[1,2] This observation is of particular concern, given the high annual incidence rates of childhood brain injuries (765 per 100.000 population) resulting from motor vehicle accidents, falls, sports, and abuse.[3]. This view is supported by recent studies using graph theoretical analyses demonstrating alterations in network measures, such as global efficiency, clustering coefficient, and betweenness-centrality in TBI patients compared to healthy controls.[6,7] these changes in network metrics are unable to determine the patterns of degeneration within the brain networks.[8] It is essential to understand how the initial brain trauma relates to future patterns of degeneration in TBI patients Achieving this understanding will lead to more appropriate head injury management and reduce the risk of TBI-initiated neurodegenerative diseases
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