Investigation on an Injury Criterion Related to Traumatic Brain Injury Primarily Induced by Head Rotation

Yanaoka, Toshiyuki; Dokko, Yasuhiro; Takahashi, Yukou

doi:10.4271/2015-01-1439

Cited by 27 publications

(21 citation statements)

References 19 publications

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“…For each animal ( n = 32 newborn piglets, n = 17 pre‐adolescent piglets), the filtered angular velocity‐time profiles were used to calculate eight previously published rotational head injury prediction metrics: peak angular velocity (VEL i ), peak angular acceleration (ACCEL i ), Rotational Injury Criterion (RIC36; Kimpara & Iwamoto, ), Head Impact Power (HIP; Newman et al, ), Brain Rotational Injury Criteria (BRIC; Takhounts, et al, ), Revised Brain Rotational Injury Criteria (Revised BrIC; Takhounts et al, ), Power Rotational Head Injury Criterion (PRHIC36; Kimpara & Iwamoto, ) and Rotational Velocity Change Index (RVCI; Yanaoka, Dokko, & Takahashi, ).…”

Section: Methodsmentioning

confidence: 99%

“…Rotational Velocity Change Index (RVCI):

R V C I = {[\sqrt{R_{x} {({true \int}_{t_{2}}^{t_{1}} α_{x} d t)}^{2} + R_{y} {({true \int}_{t_{2}}^{t_{1}} α_{y} d t)}^{2} + R_{z} {({true \int}_{t_{2}}^{t_{1}} α_{z} d t)}^{2}}]}_{m a x}

Where R x , R y, and R z are weighting factors about the x, y, and z axes respectively and t 1 and t 2 are the initial and final times over a 10 ms duration (Yanaoka et al, ). The cumulative strain damage measure weighting factors, R x = 1, R y = 2.29, and R z = 1.98, were used because they were more appropriate for predicting diffuse injuries than weighting factors determined from correlation with max principal strain (Yanaoka et al, ).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

Atlan

Smith

Margulies

2017

J of Neuroscience Research

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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.

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Section: Methodsmentioning

confidence: 99%

“…Rotational Velocity Change Index (RVCI):

R V C I = {[\sqrt{R_{x} {({true \int}_{t_{2}}^{t_{1}} α_{x} d t)}^{2} + R_{y} {({true \int}_{t_{2}}^{t_{1}} α_{y} d t)}^{2} + R_{z} {({true \int}_{t_{2}}^{t_{1}} α_{z} d t)}^{2}}]}_{m a x}

Section: Methodsmentioning

confidence: 99%

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

Atlan

Smith

Margulies

2017

J of Neuroscience Research

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“…Thus, the most important is to establish robust injury criteria to comprehensively evaluate head injuries including both skull and brain structures. The previous studies related to head biomechanics have been worldwide carried out but injury criteria of brain remain controversial (Yanaoka et al 2015). Due to the lack of human test data, real-world accident data is useful for head injury related studies.…”

Section: Introductionmentioning

confidence: 99%

Investigation on Risk Prediction of Pedestrian Head Injury by Real-World Accidents

et al. 2019

Transport

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Head injury is the most common and fatal injury in car-pedestrian accidents. Due to the lack of human test data, real-world accident data is useful for the research on the mechanism and tolerance of head injuries. The objective of the present work is to investigate pedestrian head-brain injuries through real car-pedestrian accidents and evaluate the existed injury criteria. Seven car-to-pedestrian accidents in China were selected from the IVAC (Investigation of Vehicle Accident in Changsha) database. Accident reconstructions using multi-body models were conducted to determine the kinematic parameters associated with the injury and were used to measure head injury criteria. Kinematic parameters were input into a finite element model to run simulations on the head-brain and car interface to determine levels of brain tissue stress, strain, and brain tissue injury criteria. A binary logistic regression model was used to determine the probability of head injury risk associated with AIS3+ injuries (Abbreviated Injury Scale). The results showed that head injury criteria using kinematic parameters can effectively predict injury risk of a pedestrians’ head skull. Regarding brain injuries, physical parameters like coup/countercoup pressure are more effective predictors. The results of this study can be used as the background knowledge for pedestrian friendly car design.

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“…These metrics were often based on a combination of resultant linear and resultant angular head acceleration (Newman, 1986;Newman et al, 2000;Rowson and Duma, 2013), while some included empirically derived combinations of rotational parameters and HIC (Greenwald et al, 2008;Kleiven, 2007). As experimental and computational evidence mounted supporting rotational head motion as a brain injury mechanism, translational kinematic parameters were excluded from the mathematical form of the metric, and brain injury criteria were based solely on rotational kinematics (Kimpara and Iwamoto, 2012;Takhounts et al, 2013;Yanaoka et al, 2015).…”

Section: Rotational Kinematic Metricsmentioning

confidence: 99%

“…Recently, several studies have leveraged FE brain models to develop kinematicbased metric-formulations for brain injury criteria (Kleiven, 2007;Takhounts et al, 2013;Yanaoka et al, 2015). Most notably was the development of the Brain Injury Criterion (BrIC) by Takhounts et al, 2013.…”

Section: Brain Injury Criterion (Bric)mentioning

confidence: 99%

Development of Improved Metrics for Predicting Brain Strain in Diverse Impacts

Gabler

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An estimated 3.8 million concussions are believed to occur annually in the United States, resulting in a serious concern for helmet and automotive safety manufacturers. Tissue-level deformation is believed to be the primary mechanism for concussion; however, existing dummies used in helmet and crash testing do not directly measure brain strain. Instead, brain injury assessments are made using head kinematics. Kinematic brain injury criteria utilize a metric which relates head impact severity to a mathematical function of the velocity and/or acceleration components of translational and/or rotational head motion. Existing metrics used in helmet and crash testing are based on translational kinematics which were developed for skull fracture assessment; however, rotational motion of the head is believed to be the primary mechanism for brain strain. Although numerous rotational metrics have been proposed, many were developed using empirical methods based on limited datasets, and do not represent the mechanical principles that govern brain deformation. As a result, this renders most rotational brain injury criteria ineffective for application in a broad range of head impacts. This dissertation presents the development of two new metrics for brain injury criteria. These metrics were developed through several steps: First, existing kinematicbased metrics were compared with finite element (FE) model brain strains from simulations of head kinematics from football impacts and car crash tests. Correlations between brain strain and rotational metrics were highest, while translational metrics were least correlative. The Brain Injury Criterion (BrIC), an angular velocity-based metric proposed for government crashworthiness assessments had the highest overall correlation with FE iv strains; however, its performance was limited in longer duration events. Results from this study suggest that brain injury metrics use only on rotational head kinematics. Based on the correlation analysis, a parametric study was performed using idealized head motions and an FE model to study physics of brain deformation to rotational head motion. Results from this study revealed a resonance behavior of the brain, which was adequately described using a second order system: For short duration head motions, brain deformation was proportional to maximum angular velocity, for motions of long duration brain deformation was proportional to maximum angular acceleration. For head motions near system resonance (30-40) ms, where typical head impacts occur, brain deformations depended on both velocity and acceleration. These results explain limitations with existing rotational metrics, and were used as a basis for formulating improved metrics. The first metric presented in this dissertation is the Universal Brain Injury Criterion (UBrIC). UBrIC was formulated based on deformation response from a second order system and uses the magnitudes of head angular velocity and acceleration to predict strainbased brain injury indicators (MPS and CSDM). Relative to existing m...

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Investigation on an Injury Criterion Related to Traumatic Brain Injury Primarily Induced by Head Rotation

Cited by 27 publications

References 19 publications

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

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

Investigation on Risk Prediction of Pedestrian Head Injury by Real-World Accidents

Development of Improved Metrics for Predicting Brain Strain in Diverse Impacts

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