Embedded Finite Elements for Modeling Axonal Injury

Garimella, Harsha T.; Menghani, Ritika; Gerber, Jesse I.; Sridhar, Srikumar; Kraft, Reuben H.

doi:10.1007/s10439-018-02166-0

Cited by 46 publications

(33 citation statements)

References 26 publications

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“…They can provide estimates of tissue responses such as strain, stress, and their rates during an event, using the kinematics experienced by the head as input. FEM can be used either to determine tissue injury thresholds or to predict the possible presence and degree of brain injury following a head trauma incident, or both (Coats et al 2012;Colgan et al 2010;Garimella et al 2018;Giordano and Kleiven 2014a, b;Giordano et al 2017;Hajiaghamemar et al 2019;King et al 2003;Sahoo et al 2014Sahoo et al , 2016Sullivan et al 2015;Wright et al 2013;Wright and Ramesh 2012;Zhao et al 2017). Tissue injury thresholds are determined by simulating a set of rapid head rotation and/ or biomechanical head impact events, with known injury status.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, a method was developed that can explicitly embed axonal tractography at a mesoscopic spatial resolution comparable to diffusion tensor imaging (DTI). The embedded element method, which has been recently introduced to TBI research community for human models, includes multiple fiber paths in each brain element and explicitly incorporates the axonal fiber structural network into the brain FE models using tractography data (Garimella and Kraft 2017;Garimella et al 2018;Wu et al 2019). In this paper, we improve the biofidelity of our pig FEM using this method, which to our knowledge has not been used in any animal TBI studies.…”

Section: Introductionmentioning

confidence: 99%

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Embedded axonal fiber tracts improve finite element model predictions of traumatic brain injury

Hajiaghamemar

Panzer

et al. 2019

Biomech Model Mechanobiol

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With the growing rate of traumatic brain injury (TBI), there is an increasing interest in validated tools to predict and prevent brain injuries. Finite element models (FEM) are valuable tools to estimate tissue responses, predict probability of TBI, and guide the development of safety equipment. In this study, we developed and validated an anisotropic pig brain multi-scale FEM by explicitly embedding the axonal tract structures and utilized the model to simulate experimental TBI in piglets undergoing dynamic head rotations. Binary logistic regression, survival analysis with Weibull distribution, and receiver operating characteristic curve analysis, coupled with repeated k-fold cross-validation technique, were used to examine 12 FEM-derived metrics related to axonal/brain tissue strain and strain rate for predicting the presence or absence of traumatic axonal injury (TAI). All 12 metrics performed well in predicting of TAI with prediction accuracy rate of 73-90%. The axonal-based metrics outperformed their rival brain tissue-based metrics in predicting TAI. The best predictors of TAI were maximum axonal strain times strain rate (MASxSR) and its corresponding optimal fraction-based metric (AF-MASxSR 7.5) that represents the fraction of axonal fibers exceeding MASxSR of 7.5 s −1. The thresholds compare favorably with tissue tolerances found in in-vitro/in-vivo measurements in the literature. In addition, the damaged volume fractions (DVF) predicted using the axonal-based metrics, especially MASxSR (DVF = 0.05-4.5%), were closer to the actual DVF obtained from histopathology (AIV = 0.02-1.65%) in comparison with the DVF predicted using the brain-related metrics (DVF = 0.11-41.2%). The methods and the results from this study can be used to improve model prediction of TBI in humans.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Embedded axonal fiber tracts improve finite element model predictions of traumatic brain injury

Hajiaghamemar

Panzer

et al. 2019

Biomech Model Mechanobiol

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“…A discussion regarding the relationship between axonal strain and brain tissue deformation has not been provided here as it is not directly connected to the main aims of this experiment. Additional information on this issue has been presented in the literature [60,61].…”

Section: Discussionmentioning

confidence: 99%

Material and Structural Modeling Aspects of Brain Tissue Deformation under Dynamic Loads

et al. 2019

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The aim of this work was to assess the numerous approaches to structural and material modeling of brain tissue under dynamic loading conditions. The current technological improvements in material modeling have led to various approaches described in the literature. However, the methods used for the determination of the brain’s characteristics have not always been stated or clearly defined and material data are even more scattered. Thus, the research described in this paper explicitly underlines directions for the development of numerical brain models. An important element of this research is the development of a numerical model of the brain based on medical imaging methods. This approach allowed the authors to assess the changes in the mechanical and geometrical parameters of brain tissue caused by the impact of mechanical loads. The developed model was verified through comparison with experimental studies on post-mortem human subjects described in the literature, as well as through numerical tests. Based on the current research, the authors identified important aspects of the modeling of brain tissue that influence the assessment of the actual biomechanical response of the brain for dynamic analyses.

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“…The embedded element capability available in Abaqus [2,3,6] allows the two different materials (Implant and the matrix material holding it) to be meshed using finite elements independently, with no connectivity. The matrix material holding the implant is modeled as if there is no insert in it.…”

Section: Methods Detailsmentioning

confidence: 99%

Implant and submodeling techniques for detailed finite element study of inserts in composites

Gattineni

Nathi

2019

MethodsX

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Embedded Finite Elements for Modeling Axonal Injury

Cited by 46 publications

References 26 publications

Embedded axonal fiber tracts improve finite element model predictions of traumatic brain injury

Embedded axonal fiber tracts improve finite element model predictions of traumatic brain injury

Material and Structural Modeling Aspects of Brain Tissue Deformation under Dynamic Loads

Implant and submodeling techniques for detailed finite element study of inserts in composites

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