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Network diffusion modeling predicts neurodegeneration in traumatic brain injury

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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.
Keywords
HEAD-INJURY, PROGRESSION, SYSTEM, RISK

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MLA
Poudel, Govinda R., et al. “Network Diffusion Modeling Predicts Neurodegeneration in Traumatic Brain Injury.” ANNALS OF CLINICAL AND TRANSLATIONAL NEUROLOGY, vol. 7, no. 3, 2020, pp. 270–79, doi:10.1002/acn3.50984.
APA
Poudel, G. R., Dominguez D, J. F., Verhelst, H., Vander Linden, C., Deblaere, K., Jones, D. K., … Caeyenberghs, K. (2020). Network diffusion modeling predicts neurodegeneration in traumatic brain injury. ANNALS OF CLINICAL AND TRANSLATIONAL NEUROLOGY, 7(3), 270–279. https://doi.org/10.1002/acn3.50984
Chicago author-date
Poudel, Govinda R., Juan F. Dominguez D, Helena Verhelst, Catharine Vander Linden, Karel Deblaere, Derek K. Jones, Ester Cerin, Guy Vingerhoets, and Karen Caeyenberghs. 2020. “Network Diffusion Modeling Predicts Neurodegeneration in Traumatic Brain Injury.” ANNALS OF CLINICAL AND TRANSLATIONAL NEUROLOGY 7 (3): 270–79. https://doi.org/10.1002/acn3.50984.
Chicago author-date (all authors)
Poudel, Govinda R., Juan F. Dominguez D, Helena Verhelst, Catharine Vander Linden, Karel Deblaere, Derek K. Jones, Ester Cerin, Guy Vingerhoets, and Karen Caeyenberghs. 2020. “Network Diffusion Modeling Predicts Neurodegeneration in Traumatic Brain Injury.” ANNALS OF CLINICAL AND TRANSLATIONAL NEUROLOGY 7 (3): 270–279. doi:10.1002/acn3.50984.
Vancouver
1.
Poudel GR, Dominguez D JF, Verhelst H, Vander Linden C, Deblaere K, Jones DK, et al. Network diffusion modeling predicts neurodegeneration in traumatic brain injury. ANNALS OF CLINICAL AND TRANSLATIONAL NEUROLOGY. 2020;7(3):270–9.
IEEE
[1]
G. R. Poudel et al., “Network diffusion modeling predicts neurodegeneration in traumatic brain injury,” ANNALS OF CLINICAL AND TRANSLATIONAL NEUROLOGY, vol. 7, no. 3, pp. 270–279, 2020.
@article{8649945,
  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.}},
  author       = {{Poudel, Govinda R. and Dominguez D, Juan F. and Verhelst, Helena and Vander Linden, Catharine and Deblaere, Karel and Jones, Derek K. and Cerin, Ester and Vingerhoets, Guy and Caeyenberghs, Karen}},
  issn         = {{2328-9503}},
  journal      = {{ANNALS OF CLINICAL AND TRANSLATIONAL NEUROLOGY}},
  keywords     = {{HEAD-INJURY,PROGRESSION,SYSTEM,RISK}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{270--279}},
  title        = {{Network diffusion modeling predicts neurodegeneration in traumatic brain injury}},
  url          = {{http://dx.doi.org/10.1002/acn3.50984}},
  volume       = {{7}},
  year         = {{2020}},
}

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