Mesh Convergence Behavior and the Effect of Element Integration of a Human Head Injury Model

Numerous head injury models exist that vary in mesh density by orders of magnitude. A careful study of the mesh convergence behavior is necessary, especially in terms of strain most relevant to brain injury. To this end, as well as to investigate the effect of element integration scheme on simulated strains, we re-meshed the Worcester Head Injury Model at five mesh densities (~ 7.2-1000 k high-quality hexahedral elements of the brain). Results from explicit dynamic simulations of three cadaveric impacts and an in vivo head rotation were compared. First, scalar metrics of the whole brain only considering magnitude were used, including peak maximum principal strain and population-based median strain. They were further extended to deep white matter regions and the entire brain elements, respectively, to form two "response vectors" to account for both magnitude and distribution. Using benchmark enhanced full-integration elements (C3D8I), a minimum of 202.8 k brain elements were necessary to converge for response vectors of the deep white matter regions. This model was further used to simulate with reduced integration (C3D8R). We found that hourglass energy higher than the common rule of thumb (e.g., up to 44.38% vs. < 10% of internal energy) was necessary to maintain comparable strain relative to C3D8I. Based on these results, it is recommended that a human head injury model should have a minimum number of 202.8 k elements, or an average element size of no larger than 1.8 mm, for the brain. C3D8R formulation with relax stiffness hourglass control using a high scaling factor is also recommended to achieve sufficient accuracy without substantial computational cost.

T2 mapping in dermatomyositis/polymyositis and correlation with clinical parameters

AIM

To explore the T2-mapping signal characteristics of the thigh muscles in patients with dermatomyositis/polymyositis (DM/PM) and to investigate the correlation between thigh muscle T2 values, clinical parameters, and serum creatinine kinase (CK).

MATERIALS AND METHODS

Forty-two patients with DM/PM proven by diagnostic criteria were enrolled in the study along with 13 healthy control subjects. Both T2-mapping and conventional magnetic resonance imaging (MRI) images were obtained in the thigh musculature of all subjects. The T2 values of thigh muscles were compared between the DM/PM patients and control groups. Thirty-one DM/PM patients were evaluated with manual muscle testing (MMT) and serum CK levels. A Spearman correlation coefficient model was used to correlate the mean T2 values and clinical assessments. The Kruskal-Wallis test and receiver operating characteristic (ROC) curves were also utilised. p-Values <0.05 reflected statistical significance.

RESULTS

The T2 value of all oedematous muscles was greater on average than that of the unaffected muscles of the DM/PM patients (p<0.05) and the muscles of healthy volunteers (p<0.05). The T2 value of unaffected muscles in DM/PM patients was also greater than that of the normal muscles in healthy volunteers (p<0.05). The area under the curves (AUCs) for T2 relaxation time values was 0.72 with respective sensitivity and specificity of 72.6% and 65.4%. The mean T2 relaxation time of the 31 patients group and the MMTs (p<0.05) was correlated without serum CK levels (p>0.05).

CONCLUSION

T2 mapping is not only quantitatively used for subclinical muscle involvement in DM/PM, but also be used to demonstrate severity of damaged muscles in DM/PM.

White Matter Anisotropy for Impact Simulation and Response Sampling in Traumatic Brain Injury

Advanced neuroimaging provides new opportunities to enhance head injury models, including the incorporation of white matter (WM) structural anisotropy. Information from high-resolution neuroimaging, however, usually has to be "down-sampled" to match a typically coarse brain mesh. To understand how this mesh-image resolution mismatch affects impact simulation and subsequent response sampling, we compared three competing anisotropy implementations (using either voxels, tractography, or a multiscale submodeling) and two response sampling strategies (element-wise or tractography-based, using brain mesh or neuroimaging for region segmentation, respectively). Using the combination of high resolution options as a baseline, we studied how the choice in each individual category affected the resulting injury metrics. By simulating a recorded loss of consciousness head impact, we found that injury metrics including peak strain and injury susceptibility in the deep WM regions based on fiber strain, but not on maximum principal strain, were sensitive to the anisotropy implementation, response sampling, and region segmentation. Overall, it was recommended to use tractography for anisotropy implementation and response sampling, and to employ neuroimaging for region segmentation, because they led to more accurate injury metrics. Further refining mesh locally via submodeling was unnecessary. Brain strain responses were also parametrically found to be closer to that from minimum fiber reinforcement, consistent with the fact that the majority of WM had a rather high degree of fiber dispersion. Finally, the upgraded Worcester Head Injury Model incorporating WM anisotropy was successfully re-validated against cadaveric impacts and an in vivo head rotation ("good" to "excellent" validation with an average Correlation Analysis score of 0.437 and 0.509, respectively). These investigations may facilitate further continual development of head injury models to better study traumatic brain injury.

Concussion classification via deep learning using whole-brain white matter fiber strains

Developing an accurate and reliable injury predictor is central to the biomechanical studies of traumatic brain injury. State-of-the-art efforts continue to rely on empirical, scalar metrics based on kinematics or model-estimated tissue responses explicitly pre-defined in a specific brain region of interest. They could suffer from loss of information. A single training dataset has also been used to evaluate performance but without cross-validation. In this study, we developed a deep learning approach for concussion classification using implicit features of the entire voxel-wise white matter fiber strains. Using reconstructed American National Football League (NFL) injury cases, leave-one-out cross-validation was employed to objectively compare injury prediction performances against two baseline machine learning classifiers (support vector machine (SVM) and random forest (RF)) and four scalar metrics via univariate logistic regression (Brain Injury Criterion (BrIC), cumulative strain damage measure of the whole brain (CSDM-WB) and the corpus callosum (CSDM-CC), and peak fiber strain in the CC). Feature-based machine learning classifiers including deep learning, SVM, and RF consistently outperformed all scalar injury metrics across all performance categories (e.g., leave-one-out accuracy of 0.828-0.862 vs. 0.690-0.776, and .632+ error of 0.148-0.176 vs. 0.207-0.292). Further, deep learning achieved the best cross-validation accuracy, sensitivity, AUC, and .632+ error. These findings demonstrate the superior performances of deep learning in concussion prediction and suggest its promise for future applications in biomechanical investigations of traumatic brain injury.

Routes of transmission of influenza A H1N1, SARS CoV, and norovirus in air cabin: Comparative analyses

Identifying the exact transmission route(s) of infectious diseases in indoor environments is a crucial step in developing effective intervention strategies. In this study, we proposed a comparative analysis approach and built a model to simulate outbreaks of 3 different in-flight infections in a similar cabin environment, that is, influenza A H1N1, severe acute respiratory syndrome (SARS) coronavirus (CoV), and norovirus. The simulation results seemed to suggest that the close contact route was probably the most significant route (contributes 70%, 95% confidence interval [CI]: 67%-72%) in the in-flight transmission of influenza A H1N1 transmission; as a result, passengers within 2 rows of the index case had a significantly higher infection risk than others in the outbreak (relative risk [RR]: 13.4, 95% CI: 1.5-121.2, P = .019). For SARS CoV, the airborne, close contact, and fomite routes contributed 21% (95% CI: 19%-23%), 29% (95% CI: 27%-31%), and 50% (95% CI: 48%-53%), respectively. For norovirus, the simulation results suggested that the fomite route played the dominant role (contributes 85%, 95% CI: 83%-87%) in most cases; as a result, passengers in aisle seats had a significantly higher infection risk than others (RR: 9.5, 95% CI: 1.2-77.4, P = .022). This work highlighted a method for using observed outbreak data to analyze the roles of different infection transmission routes.

Estimated Brain Tissue Response Following Impacts Associated With and Without Diagnosed Concussion

Kinematic measurements of head impacts are sensitive to sports concussion, but not highly specific. One potential reason is these measures reflect input conditions only and may have varying degrees of correlation to regional brain tissue deformation. In this study, previously reported head impact data recorded in the field from high school and collegiate football players were analyzed using two finite element head models (FEHM). Forty-five impacts associated with immediately diagnosed concussion were simulated along with 532 control impacts without identified concussion obtained from the same players. For each simulation, intracranial response measures (max principal strain, strain rate, von Mises stress, and pressure) were obtained for the whole brain and within four regions of interest (ROI; cerebrum, cerebellum, brain stem, corpus callosum). All response measures were sensitive to diagnosed concussion; however, large inter-athlete variability was observed and sensitivity strength depended on measure, ROI, and FEHM. Interestingly, peak linear acceleration was more sensitive to diagnosed concussion than all intracranial response measures except pressure. These findings suggest FEHM may provide unique and potentially important information on brain injury mechanisms, but estimations of concussion risk based on individual intracranial response measures evaluated in this study did not improve upon those derived from input kinematics alone.

Material properties of the brain in injury-relevant conditions - Experiments and computational modeling

Material properties of the brain have been extensively studied but remain poorly characterized. The vast variations in constitutive models and material constants are well documented. However, no study exists to translate the variations into disparities in impact-induced brain strains most relevant to brain injury. Here, we reviewed a subset of injury-relevant brain material properties either characterized in experiments or adopted in recent head injury models. To highlight how variations in measured brain material properties manifested in simulated brain strains, we selected six experiments that have provided a complete set of brain material model and constants to implement a common head injury model. Responses resulting from two extreme events representing a high-rate cadaveric head impact and a low-rate in vivo head rotation, respectively, varied substantially. We hypothesized, and further confirmed, that the time-varying shear moduli at the appropriate time scales (e.g., ~5 ms and ~40 ms corresponding to the impulse durations of the major acceleration peaks for the two impacts, respectively), rather than the initial or long-term shear moduli, were the most indicative of impact-induced brain strains. These results underscored the need to implement measured brain material properties into an actual head injury model for evaluation. They may also provide guidelines to better characterize brain material properties in future experiments and head injury models. Finally, our finding provided a practical solution to satisfy head injury model validation requirements at both ends of the impact severity spectrum. This would improve the confidence in model simulation performance across a range of time scales relevant to concussion and sub-concussion in the real-world.

Multiscale modeling in the clinic: diseases of the brain and nervous system

Computational neuroscience is a field that traces its origins to the efforts of Hodgkin and Huxley, who pioneered quantitative analysis of electrical activity in the nervous system. While also continuing as an independent field, computational neuroscience has combined with computational systems biology, and neural multiscale modeling arose as one offshoot. This consolidation has added electrical, graphical, dynamical system, learning theory, artificial intelligence and neural network viewpoints with the microscale of cellular biology (neuronal and glial), mesoscales of vascular, immunological and neuronal networks, on up to macroscales of cognition and behavior. The complexity of linkages that produces pathophysiology in neurological, neurosurgical and psychiatric disease will require multiscale modeling to provide understanding that exceeds what is possible with statistical analysis or highly simplified models: how to bring together pharmacotherapeutics with neurostimulation, how to personalize therapies, how to combine novel therapies with neurorehabilitation, how to interlace periodic diagnostic updates with frequent reevaluation of therapy, how to understand a physical disease that manifests as a disease of the mind. Multiscale modeling will also help to extend the usefulness of animal models of human diseases in neuroscience, where the disconnects between clinical and animal phenomenology are particularly pronounced. Here we cover areas of particular interest for clinical application of these new modeling neurotechnologies, including epilepsy, traumatic brain injury, ischemic disease, neurorehabilitation, drug addiction, schizophrenia and neurostimulation.

Performance Evaluation of a Pre-computed Brain Response Atlas in Dummy Head Impacts

A pre-computed brain response atlas (pcBRA) may have the potential to accelerate the investigation of the biomechanical mechanisms of traumatic brain injury on a large-scale. In this study, we further enhance the technique and evaluate its performance using six degree-of-freedom angular velocity profiles from dummy head impacts. Using the pcBRA to simplify profiles into acceleration-only shapes, sufficiently accurate strain estimates were obtained for impacts with a major dominating velocity peak. However, they were largely under-estimated when substantial deceleration occurred that reversed the direction of the angular velocity. For these impacts, estimation accuracy was substantially improved with a biphasic profile simplification (average correlation coefficient and linear regression slope of 0.92 ± 0.03 and 0.95 ± 0.07 for biphasic shapes, respectively, vs. 0.80 ± 0.06 and 0.80 ± 0.08 for acceleration-only shapes). Peak maximum principal strain (ɛ p) and cumulative strain damage measure (CSDM) from the estimated strains consistently correlated stronger than kinematic metrics with respect to the baseline ɛ p and CSDM from the directly simulated responses, regardless of the brain region, and by a large margin (e.g., correlation of 0.93 vs. 0.75 compared to Brain Injury Criterion (BrIC) for ɛ p in the whole-brain, and 0.91 vs. 0.47 compared to BrIC for CSDM in the corpus callosum). These findings further support the pre-computation technique for accurate, real-time strain estimation, which could be important to accelerate model-based brain injury studies in the future.

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

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 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 superior longitudinal fasciculus (SLF; 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.

Complex Intratissue Microbiota Forms Biofilms in Periodontal Lesions

Periodontitis is caused by dysbiotic subgingival bacterial communities that may lead to increased bacterial invasion into gingival tissues. Although shifts in community structures associated with transition from health to periodontitis have been well characterized, the nature of bacteria present within the gingival tissue of periodontal lesions is not known. To characterize microbiota within tissues of periodontal lesions and compare them with plaque microbiota, gingival tissues and subgingival plaques were obtained from 7 patients with chronic periodontitis. A sequencing analysis of the 16S rRNA gene revealed that species richness and diversity were not significantly different between the 2 groups. However, intersubject variability of intratissue communities was smaller than that of plaque communities. In addition, when compared with the plaque communities, intratissue communities were characterized by decreased abundance of Firmicutes and increased abundance of Fusobacteria and Chloroflexi. In particular, Fusobacterium nucleatum and Porphyromonas gingivalis were highly enriched within the tissue, composing 15% to 40% of the total bacteria. Furthermore, biofilms, as visualized by alcian blue staining and atomic force microscopy, were observed within the tissue where the degradation of connective tissue fibers was prominent. In conclusion, very complex bacterial communities exist in the form of biofilms within the gingival tissue of periodontal lesions, which potentially serve as a reservoir for persistent infection. This novel finding may prompt new research on therapeutic strategies to treat periodontitis.

Propagation of errors from skull kinematic measurements to finite element tissue responses

Real-time quantification of head impacts using wearable sensors is an appealing approach to assess concussion risk. Traditionally, sensors were evaluated for accurately measuring peak resultant skull accelerations and velocities. With growing interest in utilizing model-estimated tissue responses for injury prediction, it is important to evaluate sensor accuracy in estimating tissue response as well. Here, we quantify how sensor kinematic measurement errors can propagate into tissue response errors. Using previous instrumented mouthguard validation datasets, we found that skull kinematic measurement errors in both magnitude and direction lead to errors in tissue response magnitude and distribution. For molar design instrumented mouthguards susceptible to mandible disturbances, 150-400% error in skull kinematic measurements resulted in 100% error in regional peak tissue response. With an improved incisor design mitigating mandible disturbances, errors in skull kinematics were reduced to <50%, and several tissue response errors were reduced to <10%. Applying 30[Formula: see text] rotations to reference kinematic signals to emulate sensor transformation errors yielded below 10% error in regional peak tissue response; however, up to 20% error was observed in peak tissue response for individual finite elements. These findings demonstrate that kinematic resultant errors result in regional peak tissue response errors, while kinematic directionality errors result in tissue response distribution errors. This highlights the need to account for both kinematic magnitude and direction errors and accurately determine transformations between sensors and the skull.

A computational study of invariant I5 in a nearly incompressible transversely isotropic model for white matter

The aligned axonal fiber bundles in white matter make it suitable to be modeled as a transversely isotropic material. Recent experimental studies have shown that a minimal form, nearly incompressible transversely isotropic (MITI) material model, is capable of describing mechanical anisotropy of white matter. Here, we used a finite element (FE) computational approach to demonstrate the significance of the fifth invariant (I₅) when modeling the anisotropic behavior of white matter in the large-strain regime. We first implemented and validated the MITI model in an FE simulation framework for large deformations. Next, we applied the model to a plate-hole structural problem to highlight the significance of the invariant I₅ by comparing with the standard fiber reinforcement (SFR) model. We also compared the two models by fitting the experiment data of asymmetric indentation, shear test, and uniaxial stretch of white matter. Our results demonstrated the significance of I₅ in describing shear deformation/anisotropy, and illustrated the potential of the MITI model to characterize transversely isotropic white matter tissues in the large-strain regime.

Effect of epigallocatechin-3-gallate on acrylamide-induced oxidative stress and apoptosis in PC12 cells

Acrylamide (ACR) is a chemical intermediate utilized in industry. ACR is also formed during heating of foods containing carbohydrates and amino acids. Therefore, humans are widely exposed to ACR, and ACR neurotoxicity in humans is a significant public health issue attracting wide attention. In this study, we investigated the potential neuroprotective effects of epigallocatechin-3-gallate (EGCG), the most abundant polyphenolic compound in green tea, in PC12 cells treated with ACR. ACR-treated PC12 cells pretreated with various concentrations of EGCG (2.5, 5 and 10 μM) for 24 h had increased viability and acetylcholinesterase activity and reduced apoptosis and necrosis compared to cells exposed to ACR alone. EGCG reduced the expression of bax mRNA, decreased cytochrome c release, reduced intracellular calcium levels, inactivated caspase 3 and increased mitochondrial membrane potential, suggesting that EGCG prevents ACR-induced apoptosis through a mitochondrial-mediated pathway. In addition, EGCG inhibited the formation of reactive oxygen species and lipid peroxidation while enhancing superoxide dismutase activity and glutathione levels, thereby reducing oxidative stress. Our results indicate that pretreatment of PC12 cells with EGCG attenuates ACR-induced apoptosis by reducing oxidative stress. Therefore, drinking green tea may reduce nerve injury induced by ACR.

Characterizing white matter tissue in large strain via asymmetric indentation and inverse finite element modeling

Characterizing the mechanical properties of white matter is important to understand and model brain development and injury. With embedded aligned axonal fibers, white matter is typically modeled as a transversely isotropic material. However, most studies characterize the white matter tissue using models with a single anisotropic invariant or in a small-strain regime. In this study, we combined a single experimental procedure - asymmetric indentation - with inverse finite element (FE) modeling to estimate the nearly incompressible transversely isotropic material parameters of white matter. A minimal form comprising three parameters was employed to simulate indentation responses in the large-strain regime. The parameters were estimated using a global optimization procedure based on a genetic algorithm (GA). Experimental data from two indentation configurations of porcine white matter, parallel and perpendicular to the axonal fiber direction, were utilized to estimate model parameters. Results in this study confirmed a strong mechanical anisotropy of white matter in large strain. Further, our results suggested that both indentation configurations are needed to estimate the parameters with sufficient accuracy, and that the indenter-sample friction is important. Finally, we also showed that the estimated parameters were consistent with those previously obtained via a trial-and-error forward FE method in the small-strain regime. These findings are useful in modeling and parameterization of white matter, especially under large deformation, and demonstrate the potential of the proposed asymmetric indentation technique to characterize other soft biological tissues with transversely isotropic properties.

VEGF Promotes Glycolysis in Pancreatic Cancer via HIF1α Up-Regulation

BACKGROUND

Vascular endothelial growth factor (VEGF) is highly expressed in many types of tumors, including pancreatic cancer. Tumor cellderived VEGF promotes angiogenesis and tumor progression. However, the role of VEGF in glucose metabolism remains unclear.

OBJECTIVE

We investigated the role and the underlying mechanism of VEGF in the glucose metabolism of pancreatic cancer cells.

METHOD

Pancreatic cancer cells were stimulated with VEGF165 for 1 or 2 h. The oxygen consumption rates (OCR) and extracellular acidification rates (ECAR) were measured using the Seahorse XF96 Extracellular Flux Analyzer. Glycolytic enzymes were detected by quantitative real-time PCR. Neuropilin 1 (NRP1) was silenced by shRNA in order to investigate its role in VEGF-induced glycolysis. Immunohistochemistry (IHC) was performed to identify the correlation among VEGF, NRP1 and hypoxia inducible factor 1α (HIF1α) in pancreatic cancer tissues.

RESULTS

VEGF stimulation led to a metabolic transition from mitochondrial oxidative phosphorylation to glycolysis in pancreatic cancer. HIF1α and NRP1 protein levels were both increased after VEGF stimulation. The down-regulation of NRP1 reduced glycolysis in pancreatic cancer cells. NRP1 and VEGF levels both correlated with HIF1α expression in pancreatic tumor tissues.

CONCLUSION

VEGF enhances glycolysis in pancreatic cancer via HIF1α up-regulation. NRP1 plays a key role in VEGF-induced glycolysis.

Isolation and Characterization of a Moderately Virulent Classical Swine Fever Virus Emerging in China

Classical swine fever (CSF) is a devastating infectious disease of pigs caused by classical swine fever virus (CSFV). In China, CSF has been under control owing to extensive vaccination with the lapinized attenuated vaccine (C-strain) since 1950s, despite sporadic or endemic in many regions. However, recently, CSF outbreaks occurred in a large number of swine herds in China. Here, we isolated 15 CSFV strains from diverse C-strain-vaccinated pig farms in China and characterized the genetic variations and antigenicity of the new isolates. The new strains showed unique variations in the E2 protein and were clustered to the subgenotype 2.1d of CSFV recently emerging in China in the phylogenetic tree. Cross-neutralization test showed that the neutralizing titres of porcine anti-C-strain sera against the new isolates were substantially lower than those against both the highly virulent Shimen strain and the subgenotype 2.1b strains that were isolated in China in 2006 and 2009, respectively. In addition, experimental animal infection showed that the HLJZZ2014 strain-infected pigs displayed lower mortality and less severe clinical signs and pathological changes compared with the Shimen strain-infected pigs. The HLJZZ2014 strain was defined to be moderately virulent based on a previously established assessment system for CSFV virulence evaluation, and the virus shedding and the viral load in various tissues of the CSFV HLJZZ2014 strain-infected pigs were significantly lower than those of the Shimen strain-infected pigs. Taken together, the subgenotype 2.1d isolate of CSFV is a moderately virulent strain with molecular variations and antigenic alterations.

Brain strain uncertainty due to shape variation in and simplification of head angular velocity profiles

Head angular velocity, instead of acceleration, is more predictive of brain strains. Surprisingly, no study exists that investigates how shape variation in angular velocity profiles affects brain strains, beyond characteristics such as peak magnitude and impulse duration. In this study, we evaluated brain strain uncertainty due to variation in angular velocity profiles and further compared with that resulting from simplifying the profiles into idealized shapes. To do so, we used reconstructed head impacts from American National Football League for shape extraction and simulated head uniaxial coronal rotations from onset to full stop. The velocity profiles were scaled to maintain an identical peak velocity magnitude and duration in order to isolate the shape for investigation. Element-wise peak maximum principal strains from 44 selected impacts were obtained. We found that the shape of angular velocity profile could significantly affect brain strain magnitude (e.g., percentage difference of 4.29-17.89 % in the whole brain relative to the group average, with cumulative strain damage measure (CSDM) uncertainty range of 23.9 %) but not pattern (correlation coefficient of 0.94-0.99). Strain differences resulting from simplifying angular velocity profiles into idealized shapes were largely within the range due to shape variation, in both percentage difference and CSDM (signed difference of 3.91 % on average, with a typical range of 0-6 %). These findings provide important insight into the uncertainty or confidence in the performance of kinematics-based injury metrics. More importantly, they suggest the feasibility to simplify head angular velocity profiles into idealized shapes, at least within the confinements of the profiles evaluated, to enable real-time strain estimation via pre-computation in the future.