Sports-related concussion is a growing health and socio-economical problem and yet, its biomechanical mechanisms are still poorly understood. Computational models of the human head are important tools to understand how mechanical energy from an external impact is transferred into regional brain mechanical responses sufficient to cause injury. However, current computational schemes require hours on a modern multi-CPU computer or even a supercomputer to simulate a single head impact, and are impractical to handle the amount of computations involved when studying a large athletic population on a large scale in the number of head impacts sustained. We propose to establish a pre-computed model response atlas to specifically address the computational challenges to allow efficient estimates of model responses without significant loss of accuracy. There are two specific aims. First, we will establish and evaluate the performance of a pre-computed model response atlas using a sophisticated finite element model of the human head developed in our group based on a large database of actual on-field head impact exposure. In the second aim, we will utilize the pre- computed atlas to investigate the significance of cumulative effects of repetitive head impacts and evaluate whether accumulated brain responses improve the sensitivity and specificity in concussion prediction and result in stronger and more significant correlations with neurocognitive alterations. These efforts will provide an important research tool to allow rapid exploration of brain biomechanics involved in sports-related concussion and offer an initial understanding on the significance of cumulative effects of repetitive head impacts. This proposal will leverage an existing large database of 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 that occur in individual brains subjected to actual on-field impacts which are expected to advance our understanding of which brain regions and are most susceptible to concussive injury.

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 establish a pre-computed model response atlas to address the computational challenges in studying a large athletic population on a large scale in order to facilitate our goal of identifying key brain regions that are most susceptible to repetitive head impacts and associated with cognitive impairment in contact sports. This effort will be facilitated by a large database of on- field head impact exposure as well as clinical outcomes for a targeted athletic population.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21NS088781-02
Application #
9001382
Study Section
Acute Neural Injury and Epilepsy Study Section (ANIE)
Program Officer
Bellgowan, Patrick S F
Project Start
2015-03-01
Project End
2016-08-15
Budget Start
2016-03-01
Budget End
2016-08-15
Support Year
2
Fiscal Year
2016
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
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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

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