Network diffusion modeling predicts neurodegeneration in traumatic brain injury

Abstract

Objective: Traumatic 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.

Methods: Diffusion-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.

Results: We 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.

Interpretation: These 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.

Figure 1

Overview of the workflow.

Figure 2

Visual representation of injury epicenters in 17 TBI individuals, mapped on the Desikan‐Killiany atlas (available in FreeSurfer). The red regions correspond to the brain regions within the injury epicenters. Modeling the network diffusion from these seeds achieved the highest correlation between measured and predicted atrophy.

Figure 3

Scatterplots showing a linear association between predicted and measured atrophy in 17 subjects. Subject‐specific (represented by Subject ID (SID)) Pearson correlation coefficient values (R) and associated P values are provided within each scatterplot.

Figure 4

Patterns of injury epicenters. The first five principal components, explaining 68% of the variance, in the atrophy maps predicted by network diffusion modeling. Spatial maps corresponding to the coefficient of the corresponding eigenvectors (first to fifth), sorted from top (first eigenvector) to bottom (fifth eigenvector) are overlaid on a surface brain.

Figure 5

Scatter plot of the relationship between time since injury and coefficient of determination (r‐squared) between the measured atrophy and the predicted atrophy. The scatterplot represents the residuals obtained after controlling for the effect of age.