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Future Technologies Conference (FTC) 2018
Vancouver, BC, Canada

Predicting Concussion Symptoms Using Computer Simulations

Milan Toma
Department of Mechanical Engineering, School of Engineering & Computing Sciences, New York Institute of Technology,
Old Westbury Campus - HSH 116A, Northern Boulevard, Old Westbury, New York 11568-8000, USA

Abstract

The reported rate of concussion is smaller than the actual rate. Less than half of concussion cases in high school football players is reported. The ultimate concern associated with unreported concussion is increased risk of cumulative effects from recurrent injury. This can, partially, be attributed to the fact that the signs and symptoms of a concussion can be subtle and may not show up immediately. Common symptoms after a concussive traumatic brain injury are headache, amnesia and confusion. Computer simulations, based on the impact force magnitude, location and direction, are able to predict these symptoms and their severity. When patients are aware of what to expect in the coming days after head trauma, they are more likely to report the signs of concussion, which decreases the potential risks of unreported injury. In this work, the first ever fluid-structure interaction analysis is used to simulate the interaction between cerebrospinal fluid and comprehensive brain model to assess the concussion symptoms when exposed to head trauma conditions.

I. Introduction

In 1981, Goldsmith's letter to the editor states, "The state of knowledge concerning trauma of the human head is so scant that the community cannot agree on new and improved criteria even though it is generally admitted that present designations are not satisfactory" [1]. Even decades later, this assessment can still be considered reasonable to a degree.

The head model presented here is the only model currently incorporating cerebrospinal fluid (CSF) flow. Other reported head models treat CSF as a solid part incapable of flowing around the brain when exposed to head trauma conditions [2-6]. The CSF flows even on its own when the head is at rest, albeit slowly. Obviously, when the head is exposed to a sudden stop, e.g. in a car accident, the CSF flow around the brain has a significant contribution to the head injury mechanism. Without the flow the simulated cushioning effect of CSF can not be considered realistic.

The most common reasons for concussion not being reported include a player not thinking the injury is serious enough to warrant medical attention (66.4% of unreported injuries), motivation not to be withheld from competition (41.0%), and lack of awareness of probable concussion (36.1%) [7]. Regardless of the reason, as McCrea et al. state, "Future prevention initiatives should focus on education to improve athlete awareness of the signs of concussion and potential risks of unreported injury.", [7]. Needless to say, there is an unlimited number of trauma situations that can occur, and the concussion symptoms can vary from one case to another.

In some cases, the skull is dented inward and it presses against the surface of the brain. These types of fractures occur in 11% of severe head injuries. In impact sports, the skull dentation rarely occurs. Most sport-related brain injuries result from coup-contrecoup type of injury. Coup-contrecoup injury is dual impacting of the brain into the skull; coup injury occurs at the point of impact; countercoup injury occurs on the opposite side of impact, as the brain rebounds, see Fig. 1. Most common causes of coup-contrecoup brain injury include circumstances when the head jerks violently, e.g. during motor vehicle accidents, when baseball players are colliding during the chase for a ball, football players tackling, boxers punching, and so on.

Figure 1: Coup-contrecoup injuries, brain shifts inside the skull resulting in injuries at point of impact and away from point of impact, e.g. forehead injury can result in additional injury to occipital area.

The brain can be structurally divided into the cerebrum, cerebellum, and brainstem. The cerebrum is divided into two roughly equal hemispheres connected by the corpus callosum and a shared ventricular system. The brainstem is further divided into the midbrain, pons, and medulla oblongata. The CSF fills a system of cavities at the center of the brain, known as ventricles, and the subarachnoid space surrounding the brain and spinal cord (Fig. 2). The CSF cushions the brain within the skull and serves as a shock absorber for the central nervous system [8,9].

Figure 2: The schematic of the cerebrospinal fluid in which the brain is submerged. The 3D computational model used is designed based on this schematic.

II. Methods

The methods section describes the creation of the head model, loading conditions used for its validation, and numerical and computational methods used.

A. Head Model

The five distinct anatomical structures used in this model are shown in Fig. 3. The skull, cerebrum, cerebellum, pituitary gland, and brainstem each have unique material properties. The patient-specific model is based on the Digital Imaging and Communications in Medicine (DICOM) images acquired from an online database. Anatomical features missing in this model include the skin, spinal cord, meninges, and the arachnoid granulation, Fig. 2. When compared to the very short impact impulse time history used in these simulations, the CSF flow in the head can be neglected, too. The CSF flow is relatively slow, 0.05 - 0.08 m·s-1, i.e. during the impact impulse time history the CSF flows by 0.2 - 0.3 mm. These assumptions also make the presence of the granulations negligible.

Figure 3: The depiction of the entire head model with skull, cerebrum, cerebellum, pituitary gland and brainstem, respectively. The subarachnoid space and other cavities are filled with fluid particles (blue dots surrounding the brain model, in the lower right corner). The entire model with half the skull is also shown (lower left).

B. Loading Conditions

Based on whether the head is stationary and struck by a moving object, or is moving and strikes a stationary object, the type of brain injury differs, according to [10]. The stationary head is usually hit by objects which are of similar mass to the head. In this study, the scenario in Fig. 1 is used and it is assumed that the impacting object does not penetrate the skull. Thus, local deformation of the skull in the frontal area is not resulting in direct contact injury to the underlying brain tissue. It has been estimated that for a contact area of approximately 6.5 cm2 the force required to produce a clinically significant skull fracture in the frontal area of the cadaver skull is twice that required in the temporoparietal area [11].

Corresponding loading conditions from cadaveric experiments in [12] are used to perform the computational analysis of a frontal impact. The experiments examined the blow to the head of a seated human cadaver. The impact pulse history applied to the skull of the computational model is shown in Fig. 4.

Figure 4: Impact impulse time history used to simulate the cadaveric experiments in [12] and applied to the skull in the current model.

C. Computer Simulations

The model is comprised of 5 parts. The skull part is assigned rigid material properties with density 1900 kg·m-3 [13]. Data from studies characterizing the macroscopic physical characteristics of the brain show that it is a viscoelastic material [14]. The cerebrum, cerebellum, pituitary gland, and brainstem are simulated using a non-linear elastic constitutive material model with varying material properties based from the literature [15-19]. The cerebrum, cerebellum, brainstem, and pituitary gland are each composed of a different number of tetrahedral elements; 96385, 40808, 18634, 310, respectively. The CSF is modeled using the smoothed-particle hydrodynamics (SPH) method with the bulk modulus of 21.9 GPa [3] and density 1000 kg·m-3 [20]. The number of fluid particles filling the subarachnoid space between the skull and brain, and other cavities, is 94,690.

Fluid motion and boundary interaction calculations were solved with the IMPETUS Afea SPH Solver® (IMPETUS Afea AS, Norway), while large deformations in the solid parts were simultaneously solved with the IMPETUS Afea Solver®. Both the solvers use a commodity GPU for parallel processing. All solid elements were fully integrated, removing the possibility of hourglass modes and element inversion that plagues the classic under-integrated elements. Both fluid and solid domains and their interaction were solved with an explicit integration scheme. All simulations were solved on a standard workstation. Parallel acceleration is achieved with a Tesla K40 GPU with 12 GB of Graphic DDR memory and 2880 CUDA Cores. To confirm that convergence is reached, h-refinement of the finite element mesh is performed, and the solution is found to yield same results. Similarly, smaller number of fluid particles is used to obtain results within 5% of the values obtained with the higher number of particles. The above stated number of particles, 94,690, is high considering that the volume of the cavities and subarachnoid space is much smaller than rest of the model. This confirmed that the results are converged. Our prior publication describes the SPH equations in greater detail [21]. The SPH method is chosen for this study because traditional FSI techniques can be computationally expensive and challenging regarding their parallelization [22]. In order to use traditional FSI techniques, geometrical simplifications would need to occur, and the anatomical accuracy would have to be sacrificed. Moreover, in recent years the SPH has been increasingly used in biomedical applications [23].

III. Results

TO BE CONTINUED

REFERENCES

[1] W. Goldsmith. Current controversies in the stipulation of head injury criteria - letter to the editor. Journal of Biomechanics, 14(12):883-884, 1981.
[2] Y. Luo, Z. Li, and H. Chen. Finite-element study of cerebrospinal fluid in mitigating closed head injuries. J. Engineering in Medicine, 226(7):499-509, 2012.
[3] M.S. Chafi, V. Dirisala, G. Karami, and M. Ziejewski. A finite element method parametric study of the dynamic response of the human brain with different cerebrospinal fluid constitutive properties. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 223(8):1003-1019, 2009.
[4] Z. Liang and Y. Luo. A QCT-based nonsegmentation finite element head model for studying traumatic brain injury. Applied Bionics and Biomechanics, 2015:1-8, 2015.
[5] M.D. Gilchrist and D. O'Donoghue. Simulation of the development of the frontal head impact injury. Computational Mechanics, 26:229-235, 2000.
[6] M. Ghajari, P.J. Hellyer, and D.J. Sharp. Computational modelling of traumatic brain injury predicts the location of chronic traumatic encephalopathy pathology. Brain, 140(2):333-343, 2017.
[7] M. McCrea, T. Hammeke, G. Olsen, P. Leo, and Guskiewicz, K. Unreported concussion in high school football players: Implications for prevention. Clin J Sport Med., 14(1):13-7, 2004.
[8] S.S. Rengachary and R.G. Ellenbogen. Principles of Neurosurgery. Elsevier Mosby, 2005.
[9] M. Toma, P. Nguyen, Fluid-Structure Interaction Analysis of Cerebral Spinal Fluid with a Comprehensive Head Model Subject to a Car Crash-Related Whiplash. 5th International Conference on Computational and Mathematical Biomedical Engineering - CMBE2017. University of Pittsburgh, Pittsburgh, PA, United States, 10-12 April 2017.
[10] Y. Yanagida, S. Fujiwara, and Y. Mizoi. Differences in the intracranial pressure caused by a blow and/or a fall - experimental study using physical models of the head and neck. Forensic Science International, 41:135-145, 1989.
[11] A.M. Nahum, J.D. Gatts, C.W. Gadd, and J. Danforth. Impact tolerance of the skull and face. In 12th Stapp Car Crash Conference, pages 302-316, Warrendale, PA, 1968. Society of Automotive Engineers.
[12] A.M. Nahum, R.W. Smith, and C.C. Ward. Intracranial pressure dynamics during head impact. In 21st Stapp Car Crash Cnference, 1977.
[13] F.J. Fry and J.E. Barger. Acoustical properties of the human skull. J. Acoust. Soc. Am., 63(5):1576-1590, 1978.
[14] W.J. Tyler. The mechanobiology of brain function. Nature Reviews Neuroscience, 13:867-878, 2012.
[15] T.W. Barser, J.A. Brockway, and L.S. Higgins. The density of tissues in and about the head. Acta Neurol. Scandinav., 46:85-92, 1970.
[16] B.S. Elkin, E.U. Azeloglu, K.D. Costa, and B. Morrison. Mechanical heterogeneity of the rat hippicampus measured by atomic force microscope indentation. J. Neurotrauma, 24(812-822), 2007.
[17] A. Gefen, N. Gefen, Q. Zhu, R. Raghupathi, and S.S. Margulies. Age-dependent changes in material properties of the brain and braincase of the rat. J. Neurotrauma, 20:1163-1177, 2003.
[18] S.A. Kruse, G.H. Rose, K.J. Glaser, A. Manduca, J.P. Felmlee, C.R. Jack Jr., and R.L. Ehman. Magnetic resonance elastography of the brain. Neuroimage, 39:231-237, 2008.
[19] S.W. Moore and M.P. Sheetz. Biophysics of substrate interaction: influence on neutral motility, differentiation, and repair. Dev. Neurobiol., 71:1090-1101, 2011.
[20] A.C. Lui, T.Z. Polis, and N.J. Cicutti. Densities of cerebrospinal fluid and spinal anaesthetic solutions in surgical patients at body temperature. Canadian Journal Anaesthesia, 45(4):297-303, 1998.
[21] M. Toma, D.R. Einstein, C.H. Bloodworth, R.P. Cochran, A.P. Yoganathan, and K.S. Kunzelman. Fluid-structure interaction and structural analyses using a comprehensive mitral valve model with 3D chordal structure. Int. J. Numer. Meth. Biomed. Engng., 33(4), 2017.
[22] M. Toma, M. Oshima, and S. Takagi. Decomposition and parallelization of strongly coupled fluid-structure interaction linear subsystems based on the Q1/P0 discretization. Computers & Structures, 173:84-94, 2016.
[23] M. Toma. The emerging use of SPH in biomedical applications. Significances of Bioengineering & Biosciences, 1(1):SBB.000502, 2017.


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Dept. of Mechanical Engineering
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New York Institute of Technology
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