Neural Regeneration Research ›› 2021, Vol. 16 ›› Issue (2): 338-344.doi: 10.4103/1673-5374.290915

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Identification of predictive MRI and functional biomarkers in a pediatric piglet traumatic brain injury model 

Hongzhi Wang1, Emily W. Baker2, 3, Abhyuday Mandal1, Ramana M. Pidaparti4, Franklin D. West2, 3, *, Holly A. Kinder2, 3, *   

  1. 1 Department of Statistics, University of Georgia, Athens, GA, USA;  2 Regenerative Bioscience Center, University of Georgia, Athens, GA, USA;  3 Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA;   4 College of Engineering, University of Georgia, Athens, GA, USA 
  • Online:2021-02-15 Published:2020-12-04
  • Contact: Holly A. Kinder, PhD, HollyK17@uga.edu; Franklin D. West, PhD, westf@uga.edu.
  • Supported by:
    Financial support was provided by the University of Georgia Office of the Vice President for Research to FDW.

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Abstract: 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. 

Key words: controlled cortical impact, gait analysis, linear regression, magnetic resonance imaging, motor function, pediatric pig model, principal component analysis, traumatic brain injury