Sports-related concussion is a growing health and socio-economical problem and yet, its biomechanical mechanisms are still poorly understood. Accurate quantification of how mechanical energy from an external impact is transferred into brain mechanical responses is critical for understanding the mechanisms of traumatic brain injury (TBI). We propose to correlate white matter (WM) fiber strain and strain rate accumulated from repetitive head impacts with changes in diffusion tensor imaging (DTI) parameters to assess brain regional susceptibility to diffuse axonal injury (DAI) using thresholds established from in vivo animal and in vitro studies, and to evaluate their performance in assessing the risk of concussion as well as neuropsychological and neurocognitive impairment. These efforts will be facilitated by a large cohort of concussed and matched control athletes with actual on-field head impacts, neuroimaging finding, and clinical symptomatic measures. There are three specific aims. First, we will develop a multi-scale simulation strategy to improve the spatial resolution of the model-estimated responses. In the second aim, we will evaluate the correlation between regional WM fiber strain/strain rate with longitudinal changes in DTI for the targeted athletes based on 1) neuroanatomical regions and 2) prominent WM fiber tracts or neural pathways. In the third aim, we will correlate accumulated WM fiber strain/strain rate in generic and targeted brain regions and neural pathways with neurocognitive alterations as well as clinical diagnosis of concussion. Through these efforts, we will compare the relative performances of WM fiber strain vs. maximum principal strain in generic vs. targeted regions from single vs. repetitive head impacts in injury prediction. We will derive injury thresholds through statistical analysis based on simulation results on concussed and matched control athletes. In addition, we will define, quantify, and rank injury severity index for each prominent WM fiber tract to identify the most vulnerable ones during head impact. This proposal will leverage the established and validated Dartmouth Head Injury Model (DHIM) and an existing large database of actual on-field head kinematics, neuroimaging findings, and clinical outcomes for helmeted athletes generated from previous NIH- and CDC-funded research efforts. The proposed research will accelerate exploration of the biomechanics involved in sports-related concussion on an individual and a group basis which is expected to advance our understanding of which brain regions and/or neural pathways are most susceptible to concussive injury as well as to characterize the head impacts that are more likely to cause such concussive injury and associated neurocognitive impairment.

Public Health Relevance

Sports-related concussion is a growing health and socio-economical problem and yet, its biomechanical mechanisms are still poorly understood. We propose to improve the understanding by correlating accumulated brain mechanical responses along white matter fiber tracts from repetitive head impacts and changes in diffusion tensor imaging parameters, clinical diagnosis of concussion, as well as neuropsychological and neurocognitive alterations in order to assess injury susceptibility using thresholds established from in vivo animal and in vitro studies. These efforts will be facilitated by a large cohort of contact sports athletes with actual on-field head impacts, neuroimaging finding, and clinical symptomatic measures.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
1R01NS092853-01
Application #
8943993
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Bellgowan, Patrick S F
Project Start
2015-06-01
Project End
2016-05-31
Budget Start
2015-06-01
Budget End
2016-05-31
Support Year
1
Fiscal Year
2015
Total Cost
Indirect Cost
Name
Dartmouth College
Department
Engineering (All Types)
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
041027822
City
Hanover
State
NH
Country
United States
Zip Code
Zhao, Wei; Choate, Bryan; Ji, Songbai (2018) Material properties of the brain in injury-relevant conditions - Experiments and computational modeling. J Mech Behav Biomed Mater 80:222-234
Zhao, Wei; Ji, Songbai (2018) Mesh Convergence Behavior and the Effect of Element Integration of a Human Head Injury Model. Ann Biomed Eng :
Cai, Yunliang; Wu, Shaoju; Zhao, Wei et al. (2018) Concussion classification via deep learning using whole-brain white matter fiber strains. PLoS One 13:e0197992
Beckwith, Jonathan G; Zhao, Wei; Ji, Songbai et al. (2018) Estimated Brain Tissue Response Following Impacts Associated With and Without Diagnosed Concussion. Ann Biomed Eng 46:819-830
Kuo, Calvin; Wu, Lyndia; Zhao, Wei et al. (2018) Propagation of errors from skull kinematic measurements to finite element tissue responses. Biomech Model Mechanobiol 17:235-247
Feng, Yuan; Qiu, Suhao; Xia, Xiaolong et al. (2017) A computational study of invariant I5 in a nearly incompressible transversely isotropic model for white matter. J Biomech 57:146-151
Zhao, Wei; Kuo, Calvin; Wu, Lyndia et al. (2017) Performance Evaluation of a Pre-computed Brain Response Atlas in Dummy Head Impacts. Ann Biomed Eng 45:2437-2450
Zhao, Wei; Cai, Yunliang; Li, Zhigang et al. (2017) Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter. Biomech Model Mechanobiol 16:1709-1727
Lytton, William W; Arle, Jeff; Bobashev, Georgiy et al. (2017) Multiscale modeling in the clinic: diseases of the brain and nervous system. Brain Inform 4:219-230
Zhao, Wei; Ji, Songbai (2017) Brain strain uncertainty due to shape variation in and simplification of head angular velocity profiles. Biomech Model Mechanobiol 16:449-461

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