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

Wu, Taotao; Hajiaghamemar, Marzieh; Giudice, J. Sebastian; Alshareef, Ahmed; Margulies, Susan S.; Panzer, Matthew B.

doi:10.1089/neu.2020.7445

Cited by 50 publications

(34 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To verify the calibrated heterogeneous material, the remaining 11 rotation cases for each subject were simulated (36 total simulations including case used for calibration), and the nodal displacements of the calibrated subject-specific models were compared to the corresponding experimental brain displacement data using wcCORA. To assess the calibrated model performance relative to other state-of-the-art and widely used FE brain models, wcCORA values obtained in this study were compared to those reported for the Global Human Body Models Consortium (GHBMC) brain model (Mao et al, 2013) and the UVA embedded axon model (UVA-EAM) (Wu et al, 2019(Wu et al, , 2020(Wu et al, , 2021. These models were morphed to the anatomy of the three subjects using surface-based morphing (Wu et al, 2019) and simulated under identical boundary conditions, resulting in 36 simulations per model.…”

Section: Calibration and Verification Of Non-linear Coefficientmentioning

confidence: 99%

Calibration of a Heterogeneous Brain Model Using a Subject-Specific Inverse Finite Element Approach

Giudice

Alshareef

et al. 2021

Front. Bioeng. Biotechnol.

Self Cite

View full text Add to dashboard Cite

Central to the investigation of the biomechanics of traumatic brain injury (TBI) and the assessment of injury risk from head impact are finite element (FE) models of the human brain. However, many existing FE human brain models have been developed with simplified representations of the parenchyma, which may limit their applicability as an injury prediction tool. Recent advances in neuroimaging techniques and brain biomechanics provide new and necessary experimental data that can improve the biofidelity of FE brain models. In this study, the CAB-20MSym template model was developed, calibrated, and extensively verified. To implement material heterogeneity, a magnetic resonance elastography (MRE) template image was leveraged to define the relative stiffness gradient of the brain model. A multi-stage inverse FE (iFE) approach was used to calibrate the material parameters that defined the underlying non-linear deviatoric response by minimizing the error between model-predicted brain displacements and experimental displacement data. This process involved calibrating the infinitesimal shear modulus of the material using low-severity, low-deformation impact cases and the material non-linearity using high-severity, high-deformation cases from a dataset of in situ brain displacements obtained from cadaveric specimens. To minimize the geometric discrepancy between the FE models used in the iFE calibration and the cadaveric specimens from which the experimental data were obtained, subject-specific models of these cadaveric brain specimens were developed and used in the calibration process. Finally, the calibrated material parameters were extensively verified using independent brain displacement data from 33 rotational head impacts, spanning multiple loading directions (sagittal, coronal, axial), magnitudes (20–40 rad/s), durations (30–60 ms), and severity. Overall, the heterogeneous CAB-20MSym template model demonstrated good biofidelity with a mean overall CORA score of 0.63 ± 0.06 when compared to in situ brain displacement data. Strains predicted by the calibrated model under non-injurious rotational impacts in human volunteers (N = 6) also demonstrated similar biofidelity compared to in vivo measurements obtained from tagged magnetic resonance imaging studies. In addition to serving as an anatomically accurate model for further investigations of TBI biomechanics, the MRE-based framework for implementing material heterogeneity could serve as a foundation for incorporating subject-specific material properties in future models.

show abstract

Section: Calibration and Verification Of Non-linear Coefficientmentioning

confidence: 99%

Calibration of a Heterogeneous Brain Model Using a Subject-Specific Inverse Finite Element Approach

Giudice

Alshareef

et al. 2021

Front. Bioeng. Biotechnol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…As reflected by our modeling choice that no relative motion between the truss elements and their master elements were permitted, 54 these analytical solutions in Table 1 were attained based on the assumption that the displacements across the fiber tracts and surrounding matrix was continuous, similar to the assumption in the aforementioned in vitro models [56][57][58] and other computational studies. 34,35,45,46,51,82,100,101 This assumption was partially verified by one in vitro model, in which no evident movement of soma/neurite with respect to the surrounding matrix was observed in the rat. 58 However, the authors acknowledge that a systematic examination of the coupling response between the WM fiber tracts and surrounding matrix in humans is deemed necessary.…”

Section: Discussionmentioning

confidence: 83%

“…When focusing on MPS and MTOS, enhanced performance on injury discrimination was noted for MTOS with respect to MPS, correlating well with the findings in previous computational studies. 30,36,43,45,46,55 However, substantial differences existed among the previous studies, particularly on the brain material modeling (e.g., isotropic vs. anisotropic, heterogeneous vs.…”

Section: Discussionmentioning

confidence: 99%

Towards a comprehensive delineation of white matter tract-related deformation

Zhou

Liu

et al. 2021

Preprint

View full text Add to dashboard Cite

Finite element (FE) models of the human head are valuable instruments to explore the mechanobiological pathway from external loading, localized brain response, and resultant injury risks. The injury predictability of these models depends on the use of effective criteria as injury predictors. The FE-derived normal deformation along white matter (WM) fiber tracts (i.e., tract-oriented strain) has recently been suggested as an appropriate predictor for axonal injury. However, the tract-oriented strain only represents a partial depiction of the WM fiber tract deformation. A comprehensive delineation of tract-related deformation may improve the injury predictability of the FE head model by delivering new tract-related criteria as injury predictors. Thus, the present study performed a theoretical strain analysis to comprehensively characterize the WM fiber tract deformation by relating the strain tensor of the WM element to its embedded fiber tracts. Three new tract-related strains were proposed, measuring the normal deformation vertical to the fiber tracts (i.e., tract-vertical strain), and shear deformation along and vertical to the fiber tracts (i.e., axial-shear strain and lateral-shear strain, respectively). The injury predictability of these three newly-proposed strain peaks along with the previously-used tract-oriented strain peak and maximum principal strain (MPS) were evaluated by simulating 151 impacts with known outcome (concussion or no-concussion). The results showed that four tract-related strain peaks exhibit superior performance compared to MPS in discriminating concussion and non-concussion cases. This study presents a comprehensive quantification of WM tract-related deformation and advocates the use of orientation-dependent strains as criteria for injury prediction, which may ultimately contribute to an advanced mechanobiological understanding and enhanced computational predictability of brain injury.

show abstract

“…Computational models of the head have been extensively used to quantify in mechanical terms the intracranial response to TBI by analyzing the relationship between the functional/structural damage and the brain tissue's stress and strain fields (Chen et al, 2014). In particular, finite element (FE) models have been regarded as valuable tools to determine tissue-level local injury thresholds for specific types of TBI (Kleiven, 2007;Scott et al, 2016;Giordano et al, 2017;Zhao et al, 2017;Horstemeyer et al, 2019;Zhou et al, 2019;Hajiaghamemar et al, 2020;Trotta et al, 2020;Wu et al, 2021). Regarding CC, several injury metrics have been proposed based on different quantities, with lack of agreement on which criterion outperforms the others.…”

Section: Introductionmentioning

confidence: 99%

“…In the case of focal injury patterns resulting from, for example, CCI or dynamic vacuum pressure tests, the use of strain and strain rate injury criteria is preferred over other metrics assessed in the literature, such as those based on stress, intracranial pressure, or strain energy (Shreiber et al, 1997;Mao and Yang, 2011). While several FE models of the pig brain have been developed to investigate different diffuse TBI scenarios (Coats et al, 2012;Zhu et al, 2013;Yates and Untaroiu, 2016;Hajiaghamemar and Margulies, 2021;Wu et al, 2021), no computational model has specifically targeted the localized brain response to CCI experiments.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Investigate the Uncertainty of Tissue-Level Injury Metrics for Cerebral Contusion

Menichetti

Bartsoen

Depreitere

et al. 2021

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

Controlled cortical impact (CCI) on porcine brain is often utilized to investigate the pathophysiology and functional outcome of focal traumatic brain injury (TBI), such as cerebral contusion (CC). Using a finite element (FE) model of the porcine brain, the localized brain strain and strain rate resulting from CCI can be computed and compared to the experimentally assessed cortical lesion. This way, tissue-level injury metrics and corresponding thresholds specific for CC can be established. However, the variability and uncertainty associated with the CCI experimental parameters contribute to the uncertainty of the provoked cortical lesion and, in turn, of the predicted injury metrics. Uncertainty quantification via probabilistic methods (Monte Carlo simulation, MCS) requires a large number of FE simulations, which results in a time-consuming process. Following the recent success of machine learning (ML) in TBI biomechanical modeling, we developed an artificial neural network as surrogate of the FE porcine brain model to predict the brain strain and the strain rate in a computationally efficient way. We assessed the effect of several experimental and modeling parameters on four FE-derived CC injury metrics (maximum principal strain, maximum principal strain rate, product of maximum principal strain and strain rate, and maximum shear strain). Next, we compared the in silico brain mechanical response with cortical damage data from in vivo CCI experiments on pig brains to evaluate the predictive performance of the CC injury metrics. Our ML surrogate was capable of rapidly predicting the outcome of the FE porcine brain undergoing CCI. The now computationally efficient MCS showed that depth and velocity of indentation were the most influential parameters for the strain and the strain rate-based injury metrics, respectively. The sensitivity analysis and comparison with the cortical damage experimental data indicate a better performance of maximum principal strain and maximum shear strain as tissue-level injury metrics for CC. These results provide guidelines to optimize the design of CCI tests and bring new insights to the understanding of the mechanical response of brain tissue to focal traumatic brain injury. Our findings also highlight the potential of using ML for computationally efficient TBI biomechanics investigations.

show abstract

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

Cited by 50 publications

References 58 publications

Calibration of a Heterogeneous Brain Model Using a Subject-Specific Inverse Finite Element Approach

Calibration of a Heterogeneous Brain Model Using a Subject-Specific Inverse Finite Element Approach

Towards a comprehensive delineation of white matter tract-related deformation

A Machine Learning Approach to Investigate the Uncertainty of Tissue-Level Injury Metrics for Cerebral Contusion

Contact Info

Product

Resources

About