A Mechanistic End-to-End Concussion Model That Translates Head Kinematics to Neurologic Injury.

Laurel J. Ng,Darrell J. Swenson,James H. Stuhmiller,Vladislav Volman,Jianxia Cui,Pi Phohomsiri,Melissa M. Gibbons

doi:10.3389/fneur.2017.00269

Laurel J. Ng, Darrell J. Swenson + Show 5 more

Open Access

https://doi.org/10.3389/fneur.2017.00269

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Abstract

Past concussion studies have focused on understanding the injury processes occurring on discrete length scales (e.g., tissue-level stresses and strains, cell-level stresses and strains, or injury-induced cellular pathology). A comprehensive approach that connects all length scales and relates measurable macroscopic parameters to neurological outcomes is the first step toward rationally unraveling the complexity of this multi-scale system, for better guidance of future research. This paper describes the development of the first quantitative end-to-end (E2E) multi-scale model that links gross head motion to neurological injury by integrating fundamental elements of tissue and cellular mechanical response with axonal dysfunction. The model quantifies axonal stretch (i.e., tension) injury in the corpus callosum, with axonal functionality parameterized in terms of axonal signaling. An internal injury correlate is obtained by calculating a neurological injury measure (the average reduction in the axonal signal amplitude) over the corpus callosum. By using a neurologically based quantity rather than externally measured head kinematics, the E2E model is able to unify concussion data across a range of exposure conditions and species with greater sensitivity and specificity than correlates based on external measures. In addition, this model quantitatively links injury of the corpus callosum to observed specific neurobehavioral outcomes that reflect clinical measures of mild traumatic brain injury. This comprehensive modeling framework provides a basis for the systematic improvement and expansion of this mechanistic-based understanding, including widening the range of neurological injury estimation, improving concussion risk correlates, guiding the design of protective equipment, and setting safety standards.

Highlights

Concussion is the result of a cascade of events with violent head motion as the initiator
We focused on the axons of the corpus callosum for injury quantification because: [1] imaging studies suggest reduced integrity of corpus callosum axons following injury [21, 81]; [2] finite element model (FEM) simulations predict that head kinematics associated with concussive outcomes yield the highest strain concentration in the corpus callosum [10,11,12,13]; and [3] the corpus callosum plays an important role in interhemispheric communications
The E2E model is the first to establish a quantitative framework linking head motion to neurological outcomes by linking the gross mechanics of motion to key pathophysiological processes that result in injury

Summary

Introduction

Concussion is the result of a cascade of events with violent head motion as the initiator. Head kinematics has previously served as the basis of concussion correlates because it yields readily measurable external parameters, such as peak linear or rotational head acceleration, which are assumed to be related to a tissue response and injury. These correlates, are usually limited in applicability to the conditions in which the data are collected [1,2,3,4,5,6], and are restrictive in nature. Correlates developed from internal injury measures are applicable under a broad range of conditions, do not require scaling, and provide insight into the injury mechanism. The development of a robust injury correlate relies on the quantification of an internal injury measure, requiring a mechanistic understanding of the entire injury pathway

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