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Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction()
Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomar...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3757727/ https://www.ncbi.nlm.nih.gov/pubmed/24179732 http://dx.doi.org/10.1016/j.nicl.2012.08.002 |
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author | Irimia, Andrei Wang, Bo Aylward, Stephen R. Prastawa, Marcel W. Pace, Danielle F. Gerig, Guido Hovda, David A. Kikinis, Ron Vespa, Paul M. Van Horn, John D. |
author_facet | Irimia, Andrei Wang, Bo Aylward, Stephen R. Prastawa, Marcel W. Pace, Danielle F. Gerig, Guido Hovda, David A. Kikinis, Ron Vespa, Paul M. Van Horn, John D. |
author_sort | Irimia, Andrei |
collection | PubMed |
description | Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome. |
format | Online Article Text |
id | pubmed-3757727 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-37577272013-10-31 Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction() Irimia, Andrei Wang, Bo Aylward, Stephen R. Prastawa, Marcel W. Pace, Danielle F. Gerig, Guido Hovda, David A. Kikinis, Ron Vespa, Paul M. Van Horn, John D. Neuroimage Clin Review Article Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome. Elsevier 2012-08-24 /pmc/articles/PMC3757727/ /pubmed/24179732 http://dx.doi.org/10.1016/j.nicl.2012.08.002 Text en © 2012 The Authors http://creativecommons.org/licenses/by-nc-sa/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike License, which permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Review Article Irimia, Andrei Wang, Bo Aylward, Stephen R. Prastawa, Marcel W. Pace, Danielle F. Gerig, Guido Hovda, David A. Kikinis, Ron Vespa, Paul M. Van Horn, John D. Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction() |
title | Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction() |
title_full | Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction() |
title_fullStr | Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction() |
title_full_unstemmed | Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction() |
title_short | Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction() |
title_sort | neuroimaging of structural pathology and connectomics in traumatic brain injury: toward personalized outcome prediction() |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3757727/ https://www.ncbi.nlm.nih.gov/pubmed/24179732 http://dx.doi.org/10.1016/j.nicl.2012.08.002 |
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