Modeling of the Brain for Injury Prevention

Yang, King H.; Mao, Haojie; Wagner, Christina; Zhu, Feng; Chou, Clifford C.; King, Albert I.

doi:10.1007/8415_2010_62

Cited by 26 publications

(23 citation statements)

References 112 publications

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“…In part, this may explain why no consensus has been reached on the most appropriate metric for injury prediction. To estimate impact-induced responses, finite element (FE) models of the human head are important tools (Yang et al 2011). Model-estimated brain responses have been shown to be more effective in predicting injury than kinematic metrics alone (Zhang et al 2004; Marjoux et al 2008; Takhounts et al 2008; Giordano and Kleiven 2014a; Hernandez et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter

Zhao

Cai

et al. 2017

Biomech Model Mechanobiol

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Reliable prediction and diagnosis of concussion is important for its effective clinical management. Previous model- based studies largely employ peak responses from a single element in a pre-selected anatomical region of interest (ROI) and utilize a single training dataset for injury prediction. A more systematic and rigorous approach is necessary to scrutinize the entire white matter (WM) ROIs as well as ROI-constrained neural tracts. To this end, we evaluated injury prediction performances of the 50 deep WM regions using predictor variables based on strains obtained from simulating the 58 reconstructed American National Football League (NFL) head impacts. To objectively evaluate performance, repeated random subsampling was employed to split the impacts into independent training and testing datasets (39 and 19 cases, respectively, with 100 trials). Univariate logistic regressions were conducted based on training datasets to compute the area under the receiver operating characteristic curve (AUC), while accuracy, sensitivity, and specificity were reported based on testing datasets. Two tract-wise injury susceptibilities were identified as the best overall via pair-wise permutation test. They had comparable AUC, accuracy, and sensitivity, with the highest values occurring in SLF (superior longitudinal fasciculus; 0.867–0.879, 84.4–85.2%, and 84.1–84.6%, respectively). Using metrics based on WM fiber strain, the most vulnerable ROIs included genu of corpus callosum, cerebral peduncle, and uncinate fasciculus, while genu and main body of corpus callosum, and SLF were among the most vulnerable tracts. Even for one un-concussed athlete, injury susceptibility of the cingulum (hippocampus) right was elevated. These findings highlight the unique injury discriminatory potentials of computational models, and may provide important insight into how best to incorporate WM structural anisotropy for investigation of brain injury.

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Section: Introductionmentioning

confidence: 99%

Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter

Zhao

Cai

et al. 2017

Biomech Model Mechanobiol

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“…Regrettably, development of hexahedral meshes typically takes more time to generate compared to many other element types. As such, many published FE head models use more easily generated tetrahedral meshes which may significantly compromise simulation results [see review by Yang et al (2011) for human and animal head models]. Furthermore, the increased demand in using subject/patient-specific FE Biomechanics and Biomedical Engineering, 2013Vol.…”

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confidence: 98%

“…In the automotive industry, detailed car-specific and component-specific models have been successfully used in the product development stage, evaluation of structure responses, design of safety countermeasures, and noisevibration -harshness reduction. On the contrary, in biomedical research, numerous FE models of anatomically accurate human bodies have also been developed (see review by Yang et al 2011), but their practical application in medical industry, forensic science or safety countermeasure development is still in the early stage. Among numerous reasons proposed by many researchers, a key difficulty is that accurate and efficient subject/patientspecific models are much more difficult to develop compared to mechanical components for which computeraided design information is available.…”

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confidence: 99%

Development of high-quality hexahedral human brain meshes using feature-based multi-block approach

Mao

Gao

Cao

et al. 2013

Computer Methods in Biomechanics and Biomedical Engineering

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The finite element (FE) method is a powerful tool to study brain injury that remains to be a critical health concern. Subject/patient-specific FE brain models have the potential to accurately predict a specific subject/patient's brain responses during computer-assisted surgery or to design subject-specific helmets to prevent brain injury. Unfortunately, efforts required in the development of high-quality hexahedral FE meshes for brain, which consists of complex intracranial surfaces and varying internal structures, are daunting. Using multi-block techniques, an efficient meshing process to develop all-hexahedral FE brain models for an adult and a paediatric brain (3-year old) was demonstrated in this study. Furthermore, the mesh densities could be adjusted at ease using block techniques. Such an advantage can facilitate a mesh convergence study and allows more freedom for choosing an appropriate brain mesh density by balancing available computation power and prediction accuracy. The multi-block meshing approach is recommended to efficiently develop 3D all-hexahedral high-quality models in biomedical community to enhance the acceptance and application of numerical simulations.

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“…Over the past three decades, several versions of FE models have been developed and validated. [1][2][3][4][5][6][7][8][9][10] To precisely reflect and predict head biomechanical responses and injury mechanisms, the parameters and characteristics of biological tissues should be properly selected. When establishing the FE model of a human head, the simulation of cerebrospinal fluid (CSF) is always a focus of research.…”

Section: Introductionmentioning

confidence: 99%

Effect of Cerebrospinal Fluid Modeled With Different Material Properties on a Human Finite Element Head Model

Jin

Zhang

Song

et al. 2015

J. Mech. Med. Biol.

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The aim of this study was to enhance head-injury prediction, this paper investigated the behavior of cerebrospinal fluid (CSF) in finite element (FE) modeling. Nine different material properties selected according to material definitions and property values were used to represent CSF in FE head models. To evaluate the influence of CSF material parameters on brain mechanical responses, the models were validated against available cadaver experiment data. Results showed that coup pressure increased whereas contrecoup pressure decreased when the head sustained purely translational impact with increased bulk modulus when CSF was modeled as fluid. However, with increased bulk modulus, coup pressure, contrecoup pressure and relative skull-brain motions decreased under rotational impact. When CSF was assumed to be an elastic material, coup pressure increased whereas contrecoup pressure decreased with increased elastic modulus when the head was subjected to purely translational impact. However, the variation trend was not obvious during head rotation. Results also indicated that when subjected to brain strain and von Mises stress, the model was prone to underestimate brain injury when CSF was modeled as an elastic material, especially during purely translational impact to the head. The model with CSF as fluid reduced the strain rate of brain, which was more likely to be realistic than the model with CSF as a viscoelastic material. These findings suggested that significantly higher values of the bulk modulus of CSF modeled as fluid were needed to predict intracranial dynamic responses and brain injury during head impact.

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Modeling of the Brain for Injury Prevention

Cited by 26 publications

References 112 publications

Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter

Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter

Development of high-quality hexahedral human brain meshes using feature-based multi-block approach

Effect of Cerebrospinal Fluid Modeled With Different Material Properties on a Human Finite Element Head Model

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