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Exploring personalized structural connectomics for moderate to severe traumatic brain injury

Graph theoretical analysis of the structural connectome has been employed successfully to characterize brain network alterations in patients with traumatic brain injury (TBI). However, heterogeneity in neuropathology is a well-known issue in the TBI population, such that group comparisons of patient...

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Autores principales: Imms, Phoebe, Clemente, Adam, Deutscher, Evelyn, Radwan, Ahmed M., Akhlaghi, Hamed, Beech, Paul, Wilson, Peter H., Irimia, Andrei, Poudel, Govinda, Domínguez Duque, Juan F., Caeyenberghs, Karen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MIT Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10270710/
https://www.ncbi.nlm.nih.gov/pubmed/37334004
http://dx.doi.org/10.1162/netn_a_00277
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author Imms, Phoebe
Clemente, Adam
Deutscher, Evelyn
Radwan, Ahmed M.
Akhlaghi, Hamed
Beech, Paul
Wilson, Peter H.
Irimia, Andrei
Poudel, Govinda
Domínguez Duque, Juan F.
Caeyenberghs, Karen
author_facet Imms, Phoebe
Clemente, Adam
Deutscher, Evelyn
Radwan, Ahmed M.
Akhlaghi, Hamed
Beech, Paul
Wilson, Peter H.
Irimia, Andrei
Poudel, Govinda
Domínguez Duque, Juan F.
Caeyenberghs, Karen
author_sort Imms, Phoebe
collection PubMed
description Graph theoretical analysis of the structural connectome has been employed successfully to characterize brain network alterations in patients with traumatic brain injury (TBI). However, heterogeneity in neuropathology is a well-known issue in the TBI population, such that group comparisons of patients against controls are confounded by within-group variability. Recently, novel single-subject profiling approaches have been developed to capture inter-patient heterogeneity. We present a personalized connectomics approach that examines structural brain alterations in five chronic patients with moderate to severe TBI who underwent anatomical and diffusion magnetic resonance imaging. We generated individualized profiles of lesion characteristics and network measures (including personalized graph metric GraphMe plots, and nodal and edge-based brain network alterations) and compared them against healthy reference cases (N = 12) to assess brain damage qualitatively and quantitatively at the individual level. Our findings revealed alterations of brain networks with high variability between patients. With validation and comparison to stratified, normative healthy control comparison cohorts, this approach could be used by clinicians to formulate a neuroscience-guided integrative rehabilitation program for TBI patients, and for designing personalized rehabilitation protocols based on their unique lesion load and connectome.
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spelling pubmed-102707102023-06-16 Exploring personalized structural connectomics for moderate to severe traumatic brain injury Imms, Phoebe Clemente, Adam Deutscher, Evelyn Radwan, Ahmed M. Akhlaghi, Hamed Beech, Paul Wilson, Peter H. Irimia, Andrei Poudel, Govinda Domínguez Duque, Juan F. Caeyenberghs, Karen Netw Neurosci Research Article Graph theoretical analysis of the structural connectome has been employed successfully to characterize brain network alterations in patients with traumatic brain injury (TBI). However, heterogeneity in neuropathology is a well-known issue in the TBI population, such that group comparisons of patients against controls are confounded by within-group variability. Recently, novel single-subject profiling approaches have been developed to capture inter-patient heterogeneity. We present a personalized connectomics approach that examines structural brain alterations in five chronic patients with moderate to severe TBI who underwent anatomical and diffusion magnetic resonance imaging. We generated individualized profiles of lesion characteristics and network measures (including personalized graph metric GraphMe plots, and nodal and edge-based brain network alterations) and compared them against healthy reference cases (N = 12) to assess brain damage qualitatively and quantitatively at the individual level. Our findings revealed alterations of brain networks with high variability between patients. With validation and comparison to stratified, normative healthy control comparison cohorts, this approach could be used by clinicians to formulate a neuroscience-guided integrative rehabilitation program for TBI patients, and for designing personalized rehabilitation protocols based on their unique lesion load and connectome. MIT Press 2023-01-01 /pmc/articles/PMC10270710/ /pubmed/37334004 http://dx.doi.org/10.1162/netn_a_00277 Text en © 2022 Massachusetts Institute of Technology https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/.
spellingShingle Research Article
Imms, Phoebe
Clemente, Adam
Deutscher, Evelyn
Radwan, Ahmed M.
Akhlaghi, Hamed
Beech, Paul
Wilson, Peter H.
Irimia, Andrei
Poudel, Govinda
Domínguez Duque, Juan F.
Caeyenberghs, Karen
Exploring personalized structural connectomics for moderate to severe traumatic brain injury
title Exploring personalized structural connectomics for moderate to severe traumatic brain injury
title_full Exploring personalized structural connectomics for moderate to severe traumatic brain injury
title_fullStr Exploring personalized structural connectomics for moderate to severe traumatic brain injury
title_full_unstemmed Exploring personalized structural connectomics for moderate to severe traumatic brain injury
title_short Exploring personalized structural connectomics for moderate to severe traumatic brain injury
title_sort exploring personalized structural connectomics for moderate to severe traumatic brain injury
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10270710/
https://www.ncbi.nlm.nih.gov/pubmed/37334004
http://dx.doi.org/10.1162/netn_a_00277
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