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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MIT Press
2023
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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. |
format | Online Article Text |
id | pubmed-10270710 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MIT Press |
record_format | MEDLINE/PubMed |
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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