Technique and preliminary findings for in vivo quantification of brain motion during injurious head impacts. (11th October 2019)

• Record Type: Journal Article
• Authors: Whyte, T.; Liu, J.; Chung, V.; McErlane, S.A.; Abebe, Z.A.; McInnes, K.A.; Wellington, C.L.; Cripton, P.A.
• Is Part Of: Journal of biomechanics. Volume 95(2019)
• Publication Date: 2019-10-11
• Subjects: Brain motion -- Mechanical properties -- Head impact -- Traumatic brain injury; Animal mechanics -- Periodicals; Biomechanics -- Periodicals; Biomechanics -- Periodicals; Mécanique animale -- Périodiques; Biomécanique -- Périodiques; Electronic journals; 571.4305
• DOI: 10.1016/j.jbiomech.2019.07.023
• Languages: English
• ISSNs: 0021-9290

Abstract

Abstract: Computational models of the human brain are widely used in the evaluation and development of helmets and other protective equipment. These models are often attempted to be validated using cadaver tissue displacements despite studies showing neural tissue degrades quickly after death. Addressing this limitation, this study aimed to develop a technique for quantifying living brain motion in vivo using a closed head impact animal model of traumatic brain injury (TBI) called CHIMERA. We implanted radiopaque markers within the brain of three adult ferrets and resealed the skull while the animals were anesthetized. We affixed additional markers to the skull to track skull kinematics. The CHIMERA device delivered controlled, repeatable head impacts to the head of the animals while the impacts were fluoroscopically stereo-visualized. We observed that 1.5 mm stainless steel fiducials (∼8 times the density of the brain) migrated from their implanted positions while neutral density targets remained in their implanted position post-impact. Brain motion relative to the skull was quantified in neutral density target tests and showed increasing relative motion at higher head impact severities. We observed the motion of the brain lagged behind that of the skull, similar to previous studies. This technique can be used to obtain a comprehensive dataset of in vivo brain motion to validate computational models reflecting the mechanical properties of the living brain. The technique would also allow the mechanical response of in vivo brain tissue to be compared to cadaveric preparations for investigating the fidelity of current human computational brain models.