scholarly journals Evaluation of brain injury criteria based on reliability analysis

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Máté Hazay ◽  
Imre Bojtár
Keyword(s):  
Brain Injury ◽  
Reliability Analysis ◽  
Diffuse Axonal Injury ◽  
Tissue Level ◽  
Frontal Crash ◽  
Injury Criteria ◽  
Cumulative Strain ◽  
Injury Metrics ◽  
Risk Curves ◽  
Crash Tests

Purpose: Among the proposed brain injury metrics, Brain Injury Criteria (BrIC) is a promising tool for performing safety assessment of vehicles in the future. In this paper, the available risk curves of BrIC were re-evaluated with the use of reliability analysis and new risk curves were constructed for different injury types based on literature data of tissue-level tolerances. Moreover, the comparison of different injury metrics and their corresponding risk curves were performed. Methods: Tissue-level uncertainties of the effect and resistance were considered by random variables. The variability of the tissue-level predictors was quantified by the finite element reconstruction of 100 frontal crash tests which were performed in Simulated Injury Monitor environment. The applied tests were scaled to given BrIC magnitudes and the injury probabilities were calculated by Monte Carlo simulations. New risk curves were fitted to the observed results using Weibull and Lognormal distribution functions. Results: The available risk curves of diffuse axonal injury (DAI) could be slightly improved, and combined AIS 4+ risk curves were obtained by considering subdural hematoma and contusion as well. The performance of several injury metrics and their risk curves were evaluated based on the observed correlations with the tissue-level predictors. Conclusions: The cumulative strain damage measure and the BrIC provide the highest correlation (R2 = 0.61) and the most reliable risk curve for the evaluation of DAI. Although the observed correlation is smaller for other injury types, the BrIC and the associated reliability analysis-based risk curves seem to provide the best available method for estimating the brain injury risk for frontal crash tests.

Evaluation of Tissue-Level Brain Injury Metrics Using Species-Specific Simulations

2021 ◽  
Author(s):  
Taotao Wu ◽  
Marzieh Hajiaghamemar ◽  
J. Sebastian Giudice ◽  
Ahmed Alshareef ◽  
Susan S. Margulies ◽  
...  
Keyword(s):  
Brain Injury ◽  
Tissue Level ◽  
Injury Metrics ◽  
Species Specific

scholarly journals Added Value of Tissue Level Brain Injury Criteria

2019 ◽  
Vol 10 (2) ◽  
pp. 191-196
Author(s):  
Caroline Deck ◽  
Rémy Willinger
Keyword(s):  
Brain Injury ◽  
Tissue Level ◽  
Added Value ◽  
Injury Criteria

The Effects of Directionality of Blunt Impacts on Mechanical Response of the Brain

2014 ◽  
Author(s):  
Hesam Sarvghad-Moghaddam ◽  
Ghodrat Karami ◽  
Mariusz Ziejewski
Keyword(s):  
Shear Stress ◽  
Mechanical Response ◽  
Diffuse Axonal Injury ◽  
Linear Acceleration ◽  
Human Head ◽  
Tissue Level ◽  
Injury Criteria ◽  
Local Shear ◽  
The Brain ◽  
Local Shear Stress

The intrinsic complexity of the human head and brain lies within the non-uniformity of their constitutive components in terms of shape, material, function, and tolerance. Due to this complexity, the directionality of impact, when the head is exposed to an assault, is a major concern as different responses are anticipated based on the location of impact. The main objective of the study was to show that while most studies propose the injury criteria as based on the kinematical parameters, the tissue-level brain features are more substantiated injury indicators. Accordingly, a finite element (FE) approach was employed to elucidate the injury-related behavior of the head for front, back, and side impacts against a rigid wall. To this end, a 50th percentile FE head-neck model, including most anatomical features, was used. The kinematics of the head in terms of the linear acceleration, as well as the biomechanical response of the brain at the tissue level in terms of intracranial pressure (ICP) and maximum local shear stress, were evaluated as the main injury criteria. Ls-Dyna, a transient, nonlinear, and explicit FE code, was employed to carry out all the simulations. To verify the influence of impact directionality, identical boundary conditions were enforced in all impact scenarios. While brain responses showed similar patterns in all three directions, different peak values were predicted. The highest peak values for the local shear stress, ICP gradient, and the center mass linear acceleration of brain were observed for the frontal impact. These threshold values are of great significance in predicting injuries such as diffuse axonal injury (DAI) resulting from the shear deformation of brain axons. It is believed that directionality considerations could greatly help to improve the design of protective headgears which are considered to be the most effective tools in mitigating a TBI.

scholarly journals The relationship between brain injury criteria and brain strain across different types of head impacts can be different

2021 ◽  
Vol 18 (179) ◽  
pp. 20210260
Author(s):  
Xianghao Zhan ◽  
Yiheng Li ◽  
Yuzhe Liu ◽  
August G. Domel ◽  
Hossein Vahid Alizadeh ◽  
...  
Keyword(s):  
Brain Injury ◽  
Injury Risk ◽  
Risk Estimation ◽  
Head Impact ◽  
Principal Strain ◽  
Estimation Accuracy ◽  
Head Impacts ◽  
Injury Criteria ◽  
Maximum Principal Strain ◽  
Cumulative Strain

Multiple brain injury criteria (BIC) are developed to quickly quantify brain injury risks after head impacts. These BIC originated from different head impact types (e.g. sports and car crashes) are widely used in risk evaluation. However, the accuracy of using the BIC on brain injury risk estimation across head impact types has not been evaluated. Physiologically, brain strain is often considered the key parameter of brain injury. To evaluate the BIC's risk estimation accuracy across five datasets comprising different head impact types, linear regression was used to model 95% maximum principal strain, 95% maximum principal strain at the corpus callosum and cumulative strain damage (15%) on 18 BIC. The results show significantly different relationships between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain across head impact types. The accuracy of brain strain regression is generally decreasing if the BIC regression models are fitted on a dataset with a different type of head impact rather than on the dataset with the same type. Given this finding, this study raises concerns for applying BIC to estimate the brain injury risks for head impacts different from the head impacts on which the BIC was developed.

Risk Prediction of Traumatic Brain Injury From Car Accidents

2017 ◽  
Author(s):  
Seyed Saeed Ahmadisoleymani ◽  
Samy Missoum
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Adaptive Sampling ◽  
Risk Model ◽  
Diffuse Axonal Injury ◽  
Impact Angle ◽  
Support Vector ◽  
Fe Simulations ◽  
Cumulative Strain ◽  
The Brain

The purpose of this study is to build a risk model to predict the probability of Traumatic Brain Injury (TBI). The focus is on the occurrence of one of TBI outcomes, Diffuse Axonal Injury (DAI), due to car crashes. This goal is achieved by developing a multilevel framework, which includes vehicle crash Finite Element (FE) simulations with a dummy along with FE simulations of the brain using loading conditions derived from the crash simulations. The framework is used to propagate uncertainties and obtain probabilities of DAI based on certain injury criteria such as Cumulative Strain Damage Measure (CSDM). The risk model is constructed from a support vector machine classifier, adaptive sampling, and Monte-Carlo simulations. In contrast to previous risk models, it includes the uncertainty of explicit parameters such as impact conditions (e.g., velocity, impact angle), and material properties of the brain model. This risk model can provide, for instance, the probability of DAI for a given assumed velocity.

Development of Tissue Level Brain Injury Criteria by Finite Element Analysis

1995 ◽  
Vol 12 (4) ◽  
pp. 695-706 ◽  
Author(s):  
KAZUNARI UENO ◽  
JOHN W. MELVIN ◽  
LINDA LI ◽  
JAMES W. LIGHTHALL
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Brain Injury ◽  
Tissue Level ◽  
Element Analysis ◽  
Injury Criteria

scholarly journals Age, Sex and Cerebral Microbleed Effects On White Matter Degradation After Traumatic Brain Injury

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 886-887
Author(s):  
Andrei Irimia ◽  
Ammar Dharani ◽  
Van Ngo ◽  
David Robles ◽  
Kenneth Rostowsky
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
White Matter ◽  
Diffusion Tensor ◽  
Diffuse Axonal Injury ◽  
Principal Component ◽  
Older Age ◽  
Analysis Of Covariance ◽  
Future Research ◽  
Cerebral Microbleed

Abstract Mild traumatic brain injury (mTBI) affects white matter (WM) integrity and accelerates neurodegeneration. This study assesses the effects of age, sex, and cerebral microbleed (CMB) load as predictors of WM integrity in 70 subjects aged 18-77 imaged acutely and ~6 months after mTBI using diffusion tensor imaging (DTI). Two-tensor unscented Kalman tractography was used to segment and cluster 73 WM structures and to map changes in their mean fractional anisotropy (FA), a surrogate measure of WM integrity. Dimensionality reduction of mean FA feature vectors was implemented using principal component (PC) analysis, and two prominent PCs were used as responses in a multivariate analysis of covariance. Acutely and chronically, older age was significantly associated with lower FA (F2,65 = 8.7, p < .001, η2 = 0.2; F2,65 = 12.3, p < .001, η2 = 0.3, respectively), notably in the corpus callosum and in dorsolateral temporal structures, confirming older adults’ WM vulnerability to mTBI. Chronically, sex was associated with mean FA (F2,65 = 5.0, p = 0.01, η2 = 0.1), indicating males’ greater susceptibility to WM degradation. Acutely, a significant association was observed between CMB load and mean FA (F2,65 = 5.1, p = 0.009, η2 = 0.1), suggesting that CMBs reflect the acute severity of diffuse axonal injury. Together, these findings indicate that older age, male sex, and CMB load are risk factors for WM degeneration. Future research should examine how sex- and age-mediated WM degradation lead to cognitive decline and connectome degeneration after mTBI.

scholarly journals Traumatic Brain Injury Caused by Motor Vehicle Collision and Alcoholism in Piauí

2016 ◽  
Vol 37 (03) ◽  
pp. 174-181
Author(s):  
Benjamim Vale ◽  
Juçara Castro ◽  
Marx Araújo ◽  
Herb Morais ◽  
Lívio Macêdo
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Motor Vehicle ◽  
Care Service ◽  
Diffuse Axonal Injury ◽  
Total Sample ◽  
Motor Vehicle Collision ◽  
Alcoholic Beverages ◽  
Trauma Patients ◽  
Vehicle Collision

Objectives To determine the relationship between alcohol consumption and the incidence of traumatic brain injury (TBI) with diffuse axonal injury (DAI), determining these indices, checking acquired comorbidities and characterizing the patients by gender, age and race/color, as well as describing the characteristics of the motor vehicle collision (vehicle, period of the day, day of the week and site) in people admitted to an emergency hospital in the city of Teresina, in the state of Piauí, Brazil. Methods We have analyzed the data contained in the medical records of patients admitted with a history of motor vehicle collision and severe TBI in intensive care units, based on the forms provided by the Mobile Emergency Care Service (SAMU, in the Portuguese acronym) in the period between February 28 and November 28, 2013. Results In the period covered by the present study, 200 individuals were analyzed, and 54 (27%) had consumed alcohol; of these 11 had DAI. Of the total sample, 17% (34) presented DAI, however, with unknown data regarding the consumption of alcoholic beverages. Conclusion Considering the data, we observed that the profile of the head trauma patients are brown men, mostly (53.5%) aged between 15 and 30 years. The collisions occurred mostly on weekends and at night (55%), and 89.5% of the crashes involved motorcycles.

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