Improved prediction of direction‐dependent, acute axonal injury in piglets

Abstract

To guide development of safety equipment that reduces sports-related head injuries, we sought to enhance predictive relationships between head movement and acute axonal injury severity. The severity of traumatic brain injury (TBI) is influenced by the magnitude and direction of head kinematics. Previous studies have demonstrated correlation between rotational head kinematics and symptom severity in the adult. More recent studies have demonstrated brain injury age- and direction- dependence, relating head kinematics to white matter tract-oriented strains (TOS). Now, we have developed and assessed novel rotational head kinematic parameters as predictors of white matter damage in the female, immature piglet. We show that many previously published rotational kinematic injury predictor metrics poorly predict acute axonal pathology induced by rapid, non-impact head rotations; and that inclusion of cerebral moments of inertia (MOI) in rotational head injury metrics refines prediction of diffuse axonal injury following rapid head rotations for two immature age groups. Rotational Work (RotWork) was the best significant predictor of traumatic axonal injury in both newborn and pre-adolescent piglets following head rotations in the axial, coronal and sagittal planes. An improvement over current metrics, we find that RotWork, which incorporates head rotation rate, direction and brain shape, significantly enhanced acute traumatic axonal injury prediction. For similar injury extent, the RotWork threshold is lower for the newborn piglet than the pre-adolescent.

Reproducibility and Characterization of Head Kinematics During a Large Animal Acceleration Model of Traumatic Brain Injury

“…However, these studies intentionally varied head kinematics either experimentally (6) or through retrospective data analyses (2), purposefully employing a much larger range of putative inertial load (i.e., measured at the machine level). Additional differences in sample size and statistical power (N = 49 in Atlan [2] vs. N = 13 in the current study) are also present across experiments. In contrast, a primary focus of experiments 1 and 2 was to examine reproducibility and thus minimize injury variation (e.g., a targeted machine exposure of 250 rad/s in coronal plane for all animals).…”

“…Although heterogeneous in nature, most human traumatic brain injuries (TBI) are caused by the transmission of energy from an external force to the head that subsequently results in rapid acceleration/deceleration of the brain with or without deformation of the skull (1). Head kinematics have therefore been used to predict TBI pathology in both human and animal models, design safety equipment, and assess the risk of brain injury (2)(3)(4). However, to our knowledge, there have only been a handful of large animal studies that have used sensors (5)(6)(7)(8)(9) and/or high-speed cameras [see Table 1; (8,10,11)] to directly measure the magnitude of head kinematics during acceleration models of injury.…”

Developing Brain-Strain-Based Scaling to Inform the Clinical Relevance of Mouse Models of Concussion Induced by Rotation

“…First, we found that peak rotational velocity could serve as an efficient metric for scaling. Second, we developed direction-specific scaling laws, as the same rotational kinematics could result in various degrees of brain injury when applied to different rotational directions (49,50). Moreover, the geometry difference between human and mouse brain complicated the process of finding human-to-mouse scaling parameters.…”

“…Scaling studies have been conducted with the understanding of various degrees of brain injury (49,50) and the acknowledgement of the geometry difference between human and animal brain. One example is to use natural frequency of the brain through a single-degreeof-freedom mechanical model (40).…”

Relationships between biomechanical parameters, neurological recovery, and neuropathology following concussion in swine

“…Implications for developing explanatory relationships between injury kinematics and recovery outcomes are immense. Injury kinematic data can be utilized to develop computational models of brain movement during injury (Post et al , 2014; Atlan et al , 2018; Hajiaghamemar et al , 2020). Many kinematic parameters are related to one another and computational modeling may be able to enhance predictive accuracy by informing which parameters to prioritize.…”