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  • 1.
    Giordano, Chiara
    et al.
    KTH, School of Technology and Health (STH).
    Kleiven, Svein
    KTH, School of Technology and Health (STH), Medical Engineering, Neuronic Engineering.
    Development of an Unbiased Validation Protocol to Assess the Biofidelity of Finite Element Head Models used in Prediction of Traumatic Brain Injury2016In: Stapp Car Crash Journal, ISSN 1532-8546, Vol. 60, p. 363-471Article in journal (Refereed)
    Abstract [en]

    This study describes a method to identify laboratory test procedures and impact response requirements suitablefor assessing the biofidelity of finite element head models used in prediction of traumatic brain injury. The selection of theexperimental data and the response requirements were result of a critical evaluation based on the accuracy, reproducibility andrelevance of the available experimental data. A weighted averaging procedure was chosen in order to consider differentcontributions from the various test conditions and target measurements based on experimental error. According to the qualitycriteria, 40 experimental cases were selected to be a representative dataset for validation. Based on the evaluation of responsecurves from four head finite element models, CORA was chosen as a quantitative method to compare the predicted time historyresponse to the measured data. Optimization of the CORA global settings led to the recommendation of performing curvecomparison on a fixed time interval of 0-30 ms for intracranial pressure and at least 0-40 ms for brain motion and deformation.The allowable maximum time shift was adjusted depending on the shape of the experimental curves (􀜦􀯆􀮺􀯑􀀃􀀃= 0.12 forintracranial pressure, 􀜦􀯆􀮺􀯑 = 0.40 for brain motion and 􀜦􀯆􀮺􀯑 = 0.25 for brain deformation). Finally, bigger penalization ofratings was assigned to curves with fundamentally incorrect shape compared to those having inaccuracies in amplitude or timeshift (cubic vs linear). This rigorous approach is necessary to ensure confidence in the model results and progress in the usage offinite element head models for traumatic brain injury prediction.

  • 2.
    Giordano, Chiara
    et al.
    KTH, School of Technology and Health (STH), Medical Engineering, Neuronic Engineering.
    Kleiven, Svein
    KTH, School of Technology and Health (STH), Medical Engineering, Neuronic Engineering.
    Evaluation of Axonal Strain as a Predictor for Mild Traumatic Brain Injuries Using Finite Element Modeling2014In: Stapp Car Crash Journal, ISSN 1532-8546, Vol. 58Article in journal (Refereed)
    Abstract [en]

    Finite element (FE) models are often used to study the biomechanical effects of traumatic brain injury (TBI). Measures based on mechanical responses, such as principal strain or invariants of the strain tensor, are used as a metric to predict the risk of injury. However, the reliability of inferences drawn from these models depends on the correspondence between the mechanical measures and injury data, as well as the establishment of accurate thresholds of tissue injury. In the current study, a validated anisotropic FE model of the human head is used to evaluate the hypothesis that strain in the direction of fibers (axonal strain) is a better predictor of TBI than maximum principal strain (MPS), anisotropic equivalent strain (AESM) and cumulative strain damage measure (CSDM). An analysis of head kinematics-based metrics, such as head injury criterion (HIC) and brain injury criterion (BrIC), is also provided. Logistic regression analysis is employed to compare binary injury data (concussion/no concussion) with continuous strain/kinematics data. The threshold corresponding to 50% of injury probability is determined for each parameter. The predictive power (area under the ROC curve, AUC) is calculated from receiver operating characteristic (ROC) curve analysis. The measure with the highest AUC is considered to be the best predictor of mTBI.Logistic regression shows a statistical correlation between all the mechanical predictors and injury data for different regions of the brain. Peaks of axonal strain have the highest AUC and determine a strain threshold of 0.07 for corpus callosum and 0.15 for the brainstem, in agreement with previously experimentally derived injury thresholds for reversible axonal injury. For a data set of mild TBI from the national football league, the strain in the axonal direction is found to be a better injury predictor than MPS, AESM, CSDM, BrIC and HIC.

  • 3.
    Halldin, Peter
    et al.
    KTH, School of Technology and Health (STH), Neuronic Engineering (Closed 20130701).
    Jakobsson, Lotta
    Chalmers tekniska högskola School of Mechanical Engineering. Institutionen för tillämpad mekanik. .
    Brolin, Karin
    Chalmers tekniska högskola School of Mechanical Engineering. Institutionen för tillämpad mekanik. .
    Palmertz, Camilla
    Kleiven, Svein
    KTH, School of Technology and Health (STH), Neuronic Engineering (Closed 20130701).
    von Holst, Hans
    KTH, School of Technology and Health (STH), Neuronic Engineering (Closed 20130701).
    Investigations of Conditions that Affect Neck Compression-Flexion Injuries Using Numerical Techniques2000In: Stapp Car Crash Journal, ISSN 1532-8546Article in journal (Refereed)
  • 4.
    Kleiven, Svein
    KTH, School of Technology and Health (STH), Neuronic Engineering (Closed 20130701).
    Predictors for Traumatic Brain Injuries Evaluated through Accident Reconstructions2007In: Stapp Car Crash Journal, ISSN 1532-8546, Vol. 51, p. 81-114Article in journal (Refereed)
    Abstract [en]

    The aim of this study is to evaluate all the 58 available NFL cases and compare various predictors for mild traumatic brain injuries using a detailed and extensively validated finite element model of the human head. Global injury measures such as magnitude in angular and translational acceleration, change in angular velocity, head impact power (HIP) and HIC were also investigated with regard to their ability to predict the intracranial pressure and strains associated with injury. The brain material properties were modeled using a hyperelastic and viscoelastic constitutive law. Also, three different stiffness parameters, encompassing a range of published brain tissue properties, were tested. 8 tissue injury predictors were evaluated for 6 different regions, covering the entire cerebrum, as well as for the whole brain. In addition, 10 head kinematics based predictors were evaluated both for correlation with injury as well as with strain and pressure. When evaluating the results, a statistical correlation between strain, strain rate, product of strain and strain rate, Cumulative Strain Damage Measure (CSDM), strain energy density, maximum pressure, magnitude of minimum pressure, as well as von Mises effective stress, with injury was found when looking into specific regions of the brain. However, the maximal pressure in the gray matter showed a higher correlation with injury than other evaluated measures. On the other hand, it was possible, through the reconstruction of a motocross accident, to re-create the injury pattern in the brain of the injured rider using maximal principal strain. It was also found that a simple linear combination of peak change in rotational velocity and HIC showed a high correlation (R=0.98) with the maximum principal strain in the brain, in addition to being a significant predictor of injury. When applying the rotational and translational kinematics separately for one of the cases, it was found that the translational kinematics contribute very little to the intracranial distortional strains while the rotational kinematics contributes insignificantly to the pressure response. This study underlines that the strain based brain tissue injury predictors are very sensitive to the choice of stiffness for the brain tissue.

  • 5.
    Strömbäck Alvarez, Victor
    et al.
    KTH, School of Technology and Health (STH), Medical Engineering, Neuronic Engineering.
    Halldin, Peter
    KTH, School of Technology and Health (STH), Medical Engineering, Neuronic Engineering.
    Kleiven, Svein
    KTH, School of Technology and Health (STH), Medical Engineering, Neuronic Engineering.
    The Influence of Neck Muscle Tonus and Posture on Brain Tissue Strain in Pedestrian Head Impacts2014In: Stapp Car Crash Journal, ISSN 1532-8546, Vol. 58, p. ​63-101Article in journal (Refereed)
    Abstract [en]

    Pedestrians are one of the least protected groups in urban traffic and frequently suffer fatal head injuries. An important boundary condition for the head is the cervical spine, and it has previously been demonstrated that neck muscle activation is important for head kinematics during inertial loading. It has also been shown in a recent numerical study that a tensed neck musculature also has some influence on head kinematics during a pedestrian impact situation. The aim of this study was to analyze the influence on head kinematics and injury metrics during the isolated time of head impact by comparing a pedestrian with relaxed neck and a pedestrian with increased tonus. The human body Finite Element model THUMS Version 1.4 was connected to head and neck models developed at KTH and used in pedestrian-to-vehicle impact simulations with a generalized hood, so that the head would impact a surface with an identical impact response in all simulations. In order to isolate the influence of muscle tonus, the model was activated shortly before head impact so the head would have the same initial position prior to impact among different tonus. A symmetric and asymmetric muscle activation scheme that used high level of activation was used in order to create two extremes to investigate. It was found that for the muscle tones used in this study, the influence on the strain in the brain was very minor, in general about 1-14% change. A relatively large increase was observed in a secondary peak in maximum strains in only one of the simulated cases.

  • 6.
    Zhou, Zhou
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Neuronic Engineering.
    Li, Xiaogai
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Neuronic Engineering.
    Kleiven, Svein
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Neuronic Engineering.
    Shah, C.S.
    Hardy, W.N.
    A reanalysis of experimental brain strain data: implication for finite element head model validation2018In: Stapp Car Crash Journal, ISSN 1532-8546, Vol. 62, p. 293-318Article in journal (Refereed)
    Abstract [en]

    Relative motion between the brain and skull and brain deformation are biomechanics aspects associated with many types of traumatic brain injury (TBI). Thus far, there is only one experimental endeavor (Hardy et al., 2007) reported brain strain under loading conditions commensurate with levels that were capable of producing injury. Most of the existing finite element (FE) head models are validated against brain-skull relative motion and then used for TBI prediction based on strain metrics. However, the suitability of using a model validated against brain-skull relative motion for strain prediction remains to be determined. To partially address the deficiency of experimental brain deformation data, this study revisits the only existing dynamic experimental brain strain data and updates the original calculations, which reflect incremental strain changes. The brain strain is recomputed by imposing the measured motion of neutral density target (NDT) to the NDT triad model. The revised brain strain and the brain-skull relative motion data are then used to test the hypothesis that an FE head model validated against brainskull relative motion does not guarantee its accuracy in terms of brain strain prediction. To this end, responses of brain strain and brain-skull relative motion of a previously developed FE head model (Kleiven, 2007) are compared with available experimental data. CORrelation and Analysis (CORA) and Normalized Integral Square Error (NISE) are employed to evaluate model validation performance for both brain strain and brain-skull relative motion. Correlation analyses (Pearson coefficient) are conducted between average cluster peak strain and average cluster peak brain-skull relative motion, and also between brain strain validation scores and brain-skull relative motion validation scores. The results show no significant correlations, neither between experimentally acquired peaks nor between computationally determined validation scores. These findings indicate that a head model validated against brain-skull relative motion may not be sufficient to assure its strain prediction accuracy. It is suggested that a FE head model with intended use for strain prediction should be validated against the experimental brain deformation data and not just the brain-skull relative motion.

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