Cargando…
Identification of predictive MRI and functional biomarkers in a pediatric piglet traumatic brain injury model
Traumatic brain injury (TBI) at a young age can lead to the development of long-term functional impairments. Severity of injury is well demonstrated to have a strong influence on the extent of functional impairments; however, identification of specific magnetic resonance imaging (MRI) biomarkers tha...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer - Medknow
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7896230/ https://www.ncbi.nlm.nih.gov/pubmed/32859794 http://dx.doi.org/10.4103/1673-5374.290915 |
_version_ | 1783653512598519808 |
---|---|
author | Wang, Hongzhi Baker, Emily W. Mandal, Abhyuday Pidaparti, Ramana M. West, Franklin D. Kinder, Holly A. |
author_facet | Wang, Hongzhi Baker, Emily W. Mandal, Abhyuday Pidaparti, Ramana M. West, Franklin D. Kinder, Holly A. |
author_sort | Wang, Hongzhi |
collection | PubMed |
description | Traumatic brain injury (TBI) at a young age can lead to the development of long-term functional impairments. Severity of injury is well demonstrated to have a strong influence on the extent of functional impairments; however, identification of specific magnetic resonance imaging (MRI) biomarkers that are most reflective of injury severity and functional prognosis remain elusive. Therefore, the objective of this study was to utilize advanced statistical approaches to identify clinically relevant MRI biomarkers and predict functional outcomes using MRI metrics in a translational large animal piglet TBI model. TBI was induced via controlled cortical impact and multiparametric MRI was performed at 24 hours and 12 weeks post-TBI using T1-weighted, T2-weighted, T2-weighted fluid attenuated inversion recovery, diffusion-weighted imaging, and diffusion tensor imaging. Changes in spatiotemporal gait parameters were also assessed using an automated gait mat at 24 hours and 12 weeks post-TBI. Principal component analysis was performed to determine the MRI metrics and spatiotemporal gait parameters that explain the largest sources of variation within the datasets. We found that linear combinations of lesion size and midline shift acquired using T2-weighted imaging explained most of the variability of the data at both 24 hours and 12 weeks post-TBI. In addition, linear combinations of velocity, cadence, and stride length were found to explain most of the gait data variability at 24 hours and 12 weeks post-TBI. Linear regression analysis was performed to determine if MRI metrics are predictive of changes in gait. We found that both lesion size and midline shift are significantly correlated with decreases in stride and step length. These results from this study provide an important first step at identifying relevant MRI and functional biomarkers that are predictive of functional outcomes in a clinically relevant piglet TBI model. This study was approved by the University of Georgia Institutional Animal Care and Use Committee (AUP: A2015 11-001) on December 22, 2015. |
format | Online Article Text |
id | pubmed-7896230 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Wolters Kluwer - Medknow |
record_format | MEDLINE/PubMed |
spelling | pubmed-78962302021-02-24 Identification of predictive MRI and functional biomarkers in a pediatric piglet traumatic brain injury model Wang, Hongzhi Baker, Emily W. Mandal, Abhyuday Pidaparti, Ramana M. West, Franklin D. Kinder, Holly A. Neural Regen Res Research Article Traumatic brain injury (TBI) at a young age can lead to the development of long-term functional impairments. Severity of injury is well demonstrated to have a strong influence on the extent of functional impairments; however, identification of specific magnetic resonance imaging (MRI) biomarkers that are most reflective of injury severity and functional prognosis remain elusive. Therefore, the objective of this study was to utilize advanced statistical approaches to identify clinically relevant MRI biomarkers and predict functional outcomes using MRI metrics in a translational large animal piglet TBI model. TBI was induced via controlled cortical impact and multiparametric MRI was performed at 24 hours and 12 weeks post-TBI using T1-weighted, T2-weighted, T2-weighted fluid attenuated inversion recovery, diffusion-weighted imaging, and diffusion tensor imaging. Changes in spatiotemporal gait parameters were also assessed using an automated gait mat at 24 hours and 12 weeks post-TBI. Principal component analysis was performed to determine the MRI metrics and spatiotemporal gait parameters that explain the largest sources of variation within the datasets. We found that linear combinations of lesion size and midline shift acquired using T2-weighted imaging explained most of the variability of the data at both 24 hours and 12 weeks post-TBI. In addition, linear combinations of velocity, cadence, and stride length were found to explain most of the gait data variability at 24 hours and 12 weeks post-TBI. Linear regression analysis was performed to determine if MRI metrics are predictive of changes in gait. We found that both lesion size and midline shift are significantly correlated with decreases in stride and step length. These results from this study provide an important first step at identifying relevant MRI and functional biomarkers that are predictive of functional outcomes in a clinically relevant piglet TBI model. This study was approved by the University of Georgia Institutional Animal Care and Use Committee (AUP: A2015 11-001) on December 22, 2015. Wolters Kluwer - Medknow 2020-08-24 /pmc/articles/PMC7896230/ /pubmed/32859794 http://dx.doi.org/10.4103/1673-5374.290915 Text en Copyright: © 2021 Neural Regeneration Research http://creativecommons.org/licenses/by-nc-sa/4.0 This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms. |
spellingShingle | Research Article Wang, Hongzhi Baker, Emily W. Mandal, Abhyuday Pidaparti, Ramana M. West, Franklin D. Kinder, Holly A. Identification of predictive MRI and functional biomarkers in a pediatric piglet traumatic brain injury model |
title | Identification of predictive MRI and functional biomarkers in a pediatric piglet traumatic brain injury model |
title_full | Identification of predictive MRI and functional biomarkers in a pediatric piglet traumatic brain injury model |
title_fullStr | Identification of predictive MRI and functional biomarkers in a pediatric piglet traumatic brain injury model |
title_full_unstemmed | Identification of predictive MRI and functional biomarkers in a pediatric piglet traumatic brain injury model |
title_short | Identification of predictive MRI and functional biomarkers in a pediatric piglet traumatic brain injury model |
title_sort | identification of predictive mri and functional biomarkers in a pediatric piglet traumatic brain injury model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7896230/ https://www.ncbi.nlm.nih.gov/pubmed/32859794 http://dx.doi.org/10.4103/1673-5374.290915 |
work_keys_str_mv | AT wanghongzhi identificationofpredictivemriandfunctionalbiomarkersinapediatricpiglettraumaticbraininjurymodel AT bakeremilyw identificationofpredictivemriandfunctionalbiomarkersinapediatricpiglettraumaticbraininjurymodel AT mandalabhyuday identificationofpredictivemriandfunctionalbiomarkersinapediatricpiglettraumaticbraininjurymodel AT pidapartiramanam identificationofpredictivemriandfunctionalbiomarkersinapediatricpiglettraumaticbraininjurymodel AT westfranklind identificationofpredictivemriandfunctionalbiomarkersinapediatricpiglettraumaticbraininjurymodel AT kinderhollya identificationofpredictivemriandfunctionalbiomarkersinapediatricpiglettraumaticbraininjurymodel |