Finite Element Head Model Research Articles

Addressing the inconsistent electric fields of tDCS by using patient-tailored configurations in chronic stroke: Implications for treatment

Transcranial direct current stimulation (tDCS) is a promising tool to improve and speed up motor rehabilitation after stroke, but inconsistent clinical effects refrain tDCS from clinical implementation. Therefore, this study aimed to assess the need for individualized tDCS configurations in stroke, considering interindividual variability in brain anatomy and motor function representation.We simulated tDCS in individualized MRI-based finite element head models of 21 chronic stroke subjects and 10 healthy age-matched controls. An anatomy-based stimulation target, i.e. the motor hand knob, was identified with MRI, whereas a motor function-based stimulation target was identified with EEG. For each subject, we simulated conventional anodal tDCS electrode configurations and optimized electrode configurations to maximize stimulation strength within the anatomical and functional target. The normal component of the electric field was extracted and compared between subjects with stroke and healthy, age-matched controls, for both targets, during conventional and optimized tDCS.Electrical field strength was significantly lower, more variable and more frequently in opposite polarity for subjects with stroke compared to healthy age-matched subjects, both for the anatomical and functional target with conventional, i.e. non-individualized, electrode configurations. Optimized, i.e. individualized, electrode configurations increased the electrical field strength in the anatomical and functional target for subjects with stroke but did not reach the same levels as in healthy subjects.Considering individual brain structure and motor function is crucial for applying tDCS in subjects with stroke. Lack of individualized tDCS configurations in subjects with stroke results in lower electric fields in stimulation targets, which may partially explain the inconsistent clinical effects of tDCS in stroke trials.

Assessment of brain injury biomechanics in soccer heading using finite element analysis

This study presents an in silico finite element (FE) model-based biomechanical analysis of brain injury metrics and associated risks of a soccer ball impact to the head for aware and unaware athletes, considering ball impact velocity and direction. The analysis presented herein implements a validated soccer ball and 50th percentile human head computational FE model for quantifying traumatic brain injury (TBI) metrics. The brain's mechanical properties are designated using a viscoelastic-viscoplastic constitutive material model for the white and gray matter within the human head FE model. FE results show a dynamic human head-soccer ball peak contact area of approximately seven times greater than those documented for helmet-to-helmet hits in American Football. Due to the deformable nature of the soccer ball, the impact dynamics are unique depending on the location and velocity of impact. TBI injury risks also depend on the location of impact and the impact velocity. Impacts to the rear (BrIC:0.48, HIC15:180.7), side (BrIC:0.52, HIC15:176.5), and front (BrIC:0.37, HIC15:129.0) are associated with the highest injury risks. Furthermore, the FE results indicate when an athlete is aware of an incoming ball, HIC15-based Abbreviated Injury Scale 1 (AIS 1) injury risks for the front, side, and rear impacts decrease from 10.5%, 18.5%, and 19.3%, respectively, to approximately 1% in front and side impacts and under 6% in a rear impact. Lastly, the unique contact area between the head and soccer ball produces pressure gradients in the ball that translate into distinguishable stress waves in the skull and the cerebral cortex. Statement of significanceMild traumatic brain injuries (mTBI) are a worrisome aspect of participation in most sports due to difficulties in their diagnosis in competitions and the potential of long-term neurological defects. These types of injuries are not well understood for athletes playing soccer, specifically pertaining to the risks of heading a soccer ball. Studies are warranted which investigate impacts in this game to improve current knowledge. Our computational study uses finite element modeling to investigate contact between a player's head and the soccer ball. The results of this study present potential injury mechanisms and risks caused by this contact interaction to contribute to the current understanding of brain injuries in soccer and the promotion of athlete safety.

A 3-D Finite-Element Minipig Model to Assess Brain Biomechanical Responses to Blast Exposure.

Despite years of research, it is still unknown whether the interaction of explosion-induced blast waves with the head causes injury to the human brain. One way to fill this gap is to use animal models to establish “scaling laws” that project observed brain injuries in animals to humans. This requires laboratory experiments and high-fidelity mathematical models of the animal head to establish correlates between experimentally observed blast-induced brain injuries and model-predicted biomechanical responses. To this end, we performed laboratory experiments on Göttingen minipigs to develop and validate a three-dimensional (3-D) high-fidelity finite-element (FE) model of the minipig head. First, we performed laboratory experiments on Göttingen minipigs to obtain the geometry of the cerebral vasculature network and to characterize brain-tissue and vasculature material properties in response to high strain rates typical of blast exposures. Next, we used the detailed cerebral vasculature information and species-specific brain tissue and vasculature material properties to develop the 3-D high-fidelity FE model of the minipig head. Then, to validate the model predictions, we performed laboratory shock-tube experiments, where we exposed Göttingen minipigs to a blast overpressure of 210 kPa in a laboratory shock tube and compared brain pressures at two locations. We observed a good agreement between the model-predicted pressures and the experimental measurements, with differences in maximum pressure of less than 6%. Finally, to evaluate the influence of the cerebral vascular network on the biomechanical predictions, we performed simulations where we compared results of FE models with and without the vasculature. As expected, incorporation of the vasculature decreased brain strain but did not affect the predictions of brain pressure. However, we observed that inclusion of the cerebral vasculature in the model changed the strain distribution by as much as 100% in regions near the interface between the vasculature and the brain tissue, suggesting that the vasculature does not merely decrease the strain but causes drastic redistributions. This work will help establish correlates between observed brain injuries and predicted biomechanical responses in minipigs and facilitate the creation of scaling laws to infer potential injuries in the human brain due to exposure to blast waves.

Toward a Comprehensive Delineation of White Matter Tract-Related Deformation.

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) recently has 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 tract. Three new tract-related strains with exact analytical solutions were proposed, measuring the normal deformation perpendicular to the fiber tracts (i.e., tract-perpendicular strain), and shear deformation along and perpendicular 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 non-concussion). The results preliminarily showed that four tract-related strain peaks exhibited superior performance than 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.

Subject-Specific Head Model Generation by Mesh Morphing: A Personalization Framework and Its Applications.

Finite element (FE) head models have become powerful tools in many fields within neuroscience, especially for studying the biomechanics of traumatic brain injury (TBI). Subject-specific head models accounting for geometric variations among subjects are needed for more reliable predictions. However, the generation of such models suitable for studying TBIs remains a significant challenge and has been a bottleneck hindering personalized simulations. This study presents a personalization framework for generating subject-specific models across the lifespan and for pathological brains with significant anatomical changes by morphing a baseline model. The framework consists of hierarchical multiple feature and multimodality imaging registrations, mesh morphing, and mesh grouping, which is shown to be efficient with a heterogeneous dataset including a newborn, 1-year-old (1Y), 2Y, adult, 92Y, and a hydrocephalus brain. The generated models of the six subjects show competitive personalization accuracy, demonstrating the capacity of the framework for generating subject-specific models with significant anatomical differences. The family of the generated head models allows studying age-dependent and groupwise brain injury mechanisms. The framework for efficient generation of subject-specific FE head models helps to facilitate personalized simulations in many fields of neuroscience.

Sex, Age and Stature Affects Neck Biomechanical Responses in Frontal and Rear Impacts Assessed Using Finite Element Head and Neck Models

The increased incidence of injury demonstrated in epidemiological data for the elderly population, and females compared to males, has not been fully understood in the context of the biomechanical response to impact. A contributing factor to these differences in injury risk could be the variation in geometry between young and aged persons and between males and females. In this study, a new methodology, coupling a CAD and a repositioning software, was developed to reposture an existing Finite element neck while retaining a high level of mesh quality. A 5th percentile female aged neck model (F0575YO) and a 50th percentile male aged neck model (M5075YO) were developed from existing young (F0526YO and M5026YO) neck models (Global Human Body Models Consortium v5.1). The aged neck models included an increased cervical lordosis and an increase in the facet joint angles, as reported in the literature. The young and the aged models were simulated in frontal (2, 8, and 15 g) and rear (3, 7, and 10 g) impacts. The responses were compared using head and relative facet joint kinematics, and nominal intervertebral disc shear strain. In general, the aged models predicted higher tissue deformations, although the head kinematics were similar for all models. In the frontal impact, only the M5075YO model predicted hard tissue failure, attributed to the combined effect of the more anteriorly located head with age, when compared to the M5026YO, and greater neck length relative to the female models. In the rear impacts, the F0575YO model predicted higher relative facet joint shear compared to the F0526YO, and higher relative facet joint rotation and nominal intervertebral disc strain compared to the M5075YO. When comparing the male models, the relative facet joint kinematics predicted by the M5026YO and M5075YO were similar. The contrast in response between the male and female models in the rear impacts was attributed to the higher lordosis and facet angle in females compared to males. Epidemiological data reported that females were more likely to sustain Whiplash Associated Disorders in rear impacts compared to males, and that injury risk increases with age, in agreement with the findings in the present study. This study demonstrated that, although the increased lordosis and facet angle did not affect the head kinematics, changes at the tissue level were considerable (e.g., 26% higher relative facet shear in the female neck compared to the male, for rear impact) and relatable to the epidemiology. Future work will investigate tissue damage and failure through the incorporation of aged material properties and muscle activation.

Establishment of a finite element model based on craniofacial soft tissue thickness measurements and stress analysis of medical goggles

In this study, an accurate finite element (FE) stress analysis of head-mounted products for Chinese users was performed. Using craniofacial computed tomography scans of 280 Chinese individuals, the total soft tissue thickness and thickness of the fat and muscle layers for 41 landmarks were measured. The data were used to construct FE head models (FEH). An FE stress test was conducted to analyse the wearing of medical goggles using two FE models based on one-layer (FEH 1) and three-layer (FEH 3) soft tissue material parameters. When compared with the experimental results, the modelling results for FEH 3 were more realistic than those for FEH 1. Wearing medical goggles led to stress concentration over five landmark areas, A: upper medial forehead, B: temporal, C: zygion, D: infraorbital fossa and E: rhinion, of which B, C and D caused the most discomfort during long-term goggle wear. Practitioner summary: A precise FE head model can reflect the complex contact pressure of a head-related product. Two FE models based on one- and three-layer soft tissue material parameters were established and tested separately with medical goggles. The model can be used to improve the comfort of head-related products. Abbreviations: FE: finite element; FEH: FE head models; FEH 1: FE models based on one-layer; FEH 3: FE models based on three-layer; VR: virtual reality; AR: augmented reality; 3D: three-dimensional; WSU: Wayne State University; WSUBIM: Wayne State University Brain Injury Model; CT: computed tomography; MRI: magnetic resonance imaging; CFSTT: craniofacial soft tissue thickness; FSR: force sensing resistor; NURBS: non-uniform rational basis spline; SPSS: statistical product and service solutions; STL: stereolithography; STP: standard for the exchange of product model data; BDF: glyph bitmap distribution format; EEG: electroencephalogram

Head Kinematics and Injury Metrics for Laboratory Hockey-Relevant Head Impact Experiments.

Investigating head responses during hockey-related blunt impacts and hence understanding how to mitigate brain injury risk from such impacts still needs more exploration. This study used the recently developed hockey helmet testing methodology, known as the Hockey Summation of Tests for the Analysis of Risk (Hockey STAR), to collect 672 laboratory helmeted impacts. Brain strains were then calculated from the according 672 simulations using the detailed Global Human Body Models Consortium (GHBMC) finite element head model. Experimentally measured head kinematics and brain strains were used to calculate head/brain injury metrics including peak linear acceleration, peak rotational acceleration, peak rotational velocity, Gadd Severity Index (GSI), Head Injury Criteria (HIC15), Generalized Acceleration Model for Brain Injury Threshold (GAMBIT), Brain Injury Criteria (BrIC), Universal Brain Injury Criterion (UBrIC), Diffuse Axonal Multi-Axis General Equation (DAMAGE), average maximum principal strain (MPS) and cumulative strain damage measure (CSDM). Correlation analysis of kinematics-based and strain-based metrics highlighted the importance of rotational velocity. Injury metrics that use rotational velocity correlated highly to average MPS and CSDM with UBrIC yielding the strongest correlation. In summary, a comprehensive analysis for kinematics-based and strain-based injury metrics was conducted through a hybrid experimental (672 impacts) and computational (672 simulations) approach. The results can provide references for adopting brain injury metrics when using the Hockey STAR approach and guide ice hockey helmet designs that help reduce brain injury risks.

Influence of Anisotropic White Matter on Electroosmotic Flow Induced by Direct Current.

Treatment of cerebral edema remains a major challenge in clinical practice and new innovative therapies are needed. This study presents a novel approach for mitigating cerebral edema by inducing bulk fluid transport utilizing the brain’s electroosmotic property using an anatomically detailed finite element head model incorporating anisotropy in the white matter (WM). Three representative anisotropic conductivity algorithms are employed for the WM and compared with isotropic WM. The key results are (1) the electroosmotic flow (EOF) is driven from the edema region to the subarachnoid space under an applied electric field with its magnitude linearly correlated to the electric field and direction following current flow pathways; (2) the extent of EOF distribution variation correlates highly with the degree of the anisotropic ratio of the WM regions; (3) the directions of the induced EOF in the anisotropic models deviate from its isotropically defined pathways and tend to move along the principal fiber direction. The results suggest WM anisotropy should be incorporated in head models for more reliable EOF evaluations for cerebral edema mitigation and demonstrate the promise of the electroosmosis based approach to be developed as a new therapy for edema treatment as evaluated with enhanced head models incorporating WM anisotropy.

Time Window of Head Impact Kinematics Measurement for Calculation of Brain Strain and Strain Rate in American Football.

Wearable devices have been shown to effectively measure the head's movement during impacts in sports like American football. When a head impact occurs, the device is triggered to collect and save the kinematic measurements during a predefined time window. Then, based on the collected kinematics, finite element (FE) head models can calculate brain strain and strain rate, which are used to evaluate the risk of mild traumatic brain injury. To find a time window that can provide a sufficient duration of kinematics for FE analysis, we investigated 118 on-field video-confirmed football head impacts collected by the Stanford Instrumented Mouthguard. The simulation results based on the kinematics truncated to a shorter time window were compared with the original to determine the minimum time window needed for football. Because the individual differences in brain geometry influence these calculations, we included six representative brain geometries and found that larger brains need a longer time window of kinematics for accurate calculation. Among the different sizes of brains, a pre-trigger time of 40ms and a post-trigger time of 70ms were found to yield calculations of brain strain and strain rate that were not significantly different from calculations using the original 200ms time window recorded by the mouthguard. Therefore, approximately 110ms is recommended for complete modeling of impacts for football.

Anisotropic finite element modelling of traumatic brain injury: A voxel-based approach

A computationally efficient 3D human head finite element model was constructed. The model includes the mesoscale geometrical details of the brain including the distinction between white and grey matter, sulci and gyri, ventricular system, foramen magnum, and the cerebrospinal fluid. We incorporate the heterogeneity and anisotropy from diffusion tensor imaging data by applying a one-to-one voxel-based correspondence between diffusion voxels and finite elements. The voxel resolution of the model was optimized to obtain a trade-off between reduced computational cost and higher geometrical details. Three sets of constitutive material properties were extracted from the literature to validate the model against intra-cranial pressure and relative motion test data within the brain. The model exhibited good agreement at pressure tests in frontal and occipital lobes with peak pressure magnitudes of only 8% and 6% higher which occurred 0.5-3 ms earlier than those of the experimental curves at coup and countercoup sites, respectively. In addition, the evaluation of the relative displacement at six locations within the brain indicated acceptable agreement with experimental data, with our model’s performance exhibiting the highest overall score compared to several previous models by using the correlation and analysis rating method.

Rapid Estimation of Entire Brain Strain Using Deep Learning Models.

Many recent studies suggest that brain deformation resulting from head impacts are linked to the corresponding clinical outcome, such as mild traumatic brain injury (mTBI). Even if several finite element (FE) head models have been developed and validated to calculate brain deformation based on impact kinematics, the clinical application of these FE head models is limited due to the time-consuming nature of FE simulations. This work aims to accelerate the brain deformation calculation and thus improve the potential for clinical applications. We propose a deep learning head model with a five-layer deep neural network and feature engineering, and trained and tested the model on 2511 head impacts from a combination of head model simulations and on-field college football and mixed martial arts impacts. The proposed deep learning head model can calculate the maximum principal strain (Green Lagrange) for every element in the entire brain in less than 0.001 s with an average root mean squared error of 0.022 and a standard deviation of 0.001 over twenty repeats with random data partition and model initialization. Trained and tested using the dataset of 2511 head impacts, this model can be applied to various sports in the calculation of brain strain with accuracy, and its applicability can even further be extended by incorporating data from other types of head impacts. In addition to the potential clinical application in real-time brain deformation monitoring, this model will help researchers estimate the brain strain from a large number of head impacts more efficiently than using FE models.

Numerical simulation and analysis of midfacial impacts and traumatic brain injuries.

BackgroundsThe maxillofacial region is the exposed part of the human body and is susceptible to injury due to the limited protective equipment. Due to anatomic proximity of the maxillofacial skeleton and cranium, the force can be transmitted directly to the brain in case of maxillofacial impact, maxillofacial injuries are often accompanied with craniocerebral trauma. Therefore, it is necessary to study the biomechanical response mechanism of trauma to improve prevention of traumatic brain injury (TBI).MethodsTo investigate the biomechanical mechanism between the two injuries, a finite element (FE) head model including skull, midfacial bones and detailed anatomical intracranial features was successfully developed based on CT/MRI data. The model was validated by comparing it with one classical cadaver experiment. During the simulations, three different load forces were used to simulate common causes of injury seen in the clinic including boxing-type impact injury and car accident-type impact injury, and four locations on the model were considered as common injury sites in the midface.ResultsTwelve common impact scenarios were reproduced by FE simulation successfully. Simulations showed that there was a linear relationship between the severity of TBI and the collision energy. The location of TBI was directly related to the location of the impact site, and a lateral impact was more injurious to the brain than an anterior-posterior impact. The relative movement between the skull and brain could cause physical damage to the brain. The study indicated that the midfacial bones acted as a structure capable of absorbing energy and protecting the brain from impact.ConclusionsThis biomechanical information may assist surgeons better understand and diagnose brain injuries accompanied by midfacial fractures.

Analysis of HIC and Hydrostatic Pressure in the Human Head during NOCSAE Tests of American Football Helmets.

Brain damage is a serious economic and social burden. Contact sports such as American football, are one of the most common sources of concussions. The biomechanical response of the head–helmet system caused by dynamic loading plays a major role. The literature has focused on measuring the resultant kinematics that act on the head and helmet during tackles. However, few studies have focused on helmet validation tests, supported by recent findings and emerging numerical approaches. The future of helmet standards could benefit from insights at the level of injury mechanisms, using numerical tools to assess the helmets. Therefore, in this work, a numerical approach is employed to investigate the influence of intracranial pressure (ICP) on brain pathophysiology during and after helmeted impacts, which are common in American football. The helmeted impacts were performed at several impact locations according to the NOCSAE standard (configurations A, AP, B, C, D, F, R, UT). In order to evaluate the ICP levels, the αHEAD finite element head and brain model was combined with a Hybrid III-neck structure and then coupled with an American football helmet to simulate the NOCSAE impacts. In addition, the ICP level was analyzed together with the resulting HIC value, since the latter is commonly used, in this application and others, as the injury criterion. The obtained results indicate that ICP values exceed the common threshold of head injury criteria and do not correlate with HIC values. Thus, this work raises concern about applying the HIC to predict brain injury in American football direct head impacts, since it does not correlate with ICP predicted with the FE head model.

White Matter Tract-Oriented Deformation Is Dependent on Real-Time Axonal Fiber Orientation.

Traumatic axonal injury (TAI) is a critical public health issue with its pathogenesis remaining largely elusive. Finite element (FE) head models are promising tools to bridge the gap between mechanical insult, localized brain response, and resultant injury. In particular, the FE-derived deformation along the direction of white matter (WM) tracts (i.e., tract-oriented strain) has been shown to be an appropriate predictor for TAI. The evolution of fiber orientation in time during the impact and its potential influence on the tract-oriented strain remains unknown, however. To address this question, the present study leveraged an embedded element approach to track real-time fiber orientation during impacts. A new scheme to calculate the tract-oriented strain was proposed by projecting the strain tensors from pre-computed simulations along the temporal fiber direction instead of its static counterpart directly obtained from diffuse tensor imaging. The results revealed that incorporating the real-time fiber orientation not only altered the direction but also amplified the magnitude of the tract-oriented strain, resulting in a generally more extended distribution and a larger volume ratio of WM exposed to high deformation along fiber tracts. These effects were exacerbated with the impact severities characterized by the acceleration magnitudes. Results of this study provide insights into how best to incorporate fiber orientation in head injury models and derive the WM tract-oriented deformation from computational simulations, which is important for furthering our understanding of the underlying mechanisms of TAI.

The importance of modeling the human cerebral vasculature in blunt trauma

BackgroundMultiple studies describing human head finite element (FE) models have established the importance of including the major cerebral vasculature to improve the accuracy of the model predictions. However, a more detailed network of cerebral vasculature, including the major veins and arteries as well as their branch vessels, can further enhance the model-predicted biomechanical responses and help identify correlates to observed blunt-induced brain injury.MethodsWe used an anatomically accurate three-dimensional geometry of a 50th percentile U.S. male head that included the skin, eyes, sinuses, spine, skull, brain, meninges, and a detailed network of cerebral vasculature to develop a high-fidelity model. We performed blunt trauma simulations and determined the intracranial pressure (ICP), the relative displacement (RD), the von Mises stress, and the maximum principal strain. We validated our detailed-vasculature model by comparing the model-predicted ICP and RD values with experimental measurements. To quantify the influence of including a more comprehensive network of brain vessels, we compared the biomechanical responses of our detailed-vasculature model with those of a reduced-vasculature model and a no-vasculature model.ResultsFor an inclined frontal impact, the predicted ICP matched well with the experimental results in the fossa, frontal, parietal, and occipital lobes, with peak-pressure differences ranging from 2.4% to 9.4%. For a normal frontal impact, the predicted ICP matched the experimental results in the frontal lobe and lateral ventricle, with peak-pressure discrepancies equivalent to 1.9% and 22.3%, respectively. For an offset parietal impact, the model-predicted RD matched well with the experimental measurements, with peak RD differences of 27% and 24% in the right and left cerebral hemispheres, respectively. Incorporating the detailed cerebral vasculature did not influence the ICP but redistributed the brain-tissue stresses and strains by as much as 30%. In addition, our detailed-vasculature model predicted strain reductions by as much as 28% when compared to current reduced-vasculature FE models that only include the major cerebral vessels.ConclusionsOur study highlights the importance of including a detailed representation of the cerebral vasculature in FE models to more accurately estimate the biomechanical responses of the human brain to blunt impact.

Subject-specific multiscale analysis of concussion: from macroscopic loads to molecular-level damage

Sports concussion is a form of mild traumatic brain injury (mTBI) caused by an impulsive force transmitted to the head. While concussion is recognized as a complex pathophysiological process affecting the brain at multiple scales, the causal link between external load and cellular, molecular level damage in mTBI remains elusive. The present study proposes a multiscale framework to analyze concussion and demonstrates its applicability with a real-life concussion case. The multiscale analysis starts from inputting mouth guard-recorded head kinematic into a detailed finite element (FE) head model tailored to the subject's head and white matter (WM) tract morphology. The resulting WM tract-oriented strains are then extracted and input to histology-informed micromechanical models of corpus callosum subregions with axonal detail to obtain axolemma strains at a subcellular level. By comparing axolemma strains against mechanoporation thresholds obtained via molecular dynamics (MD) simulations, axonal damage is inferred corresponding to a likelihood of concussion, in line with clinical observation. This novel multiscale framework bridges the organ-to-molecule length scales and accounts both inter- and intra-subject regional variability, providing a new way of non-invasively predicting axonal damage and real-life concussion analysis. The framework may contribute to a better understanding of the mechanistic causes behind concussion. Statement of SignificanceThis study reports a multiscale computational framework for concussion, for the first time revealing a picture of how a global impact to the head measured on the field transfers to the cellular level of axons and finally down to the molecular level. Demonstrated with a real-life concussion case using a detailed and subject-specific head model, the results show molecular level damage corresponds to a likelihood of concussion, in line with clinical observation. An insight into the multiscale mechanical consequences is critical for a better understanding of the complex pathophysiological process affecting the brain at impact, which today are still poorly understood. Analyzing the concussive injury mechanisms the whole way from brains to molecules may also have significant clinical relevance. We show that in a typical injury scenario, the axolemma sustains large enough strains to entail pore formation in the adjoining lipid bilayer. Proration is found to occur in bilayer regions lacking ganglioside lipids, which provides important implications for the treatment of brain injury and other related neurodegenerative diseases.

Multi-Channel-Based Differential Pathlength Factor Estimation for Continuous-Wave fNIRS

Functional near-infrared spectroscopy (fNIRS) in brain imaging needs to be robust to subject-wise variability. The use of a fixed differential pathlength factor (DPF) per wavelength for the entire brain will degrade the accuracy of hemodynamic responses. Since the tissue composition varies within the brain, correct DPF values should be used for various emitter-detector distances and brain regions. In this article, a DPF estimation method combining a state-space model of the modified Beer-Lambert law (mBLL), a parameter model for estimating the reduced scattering coefficients, and dual square-root cubature Kalman filters (SCKFs) is proposed. To validate the proposed method, known light intensities (six channels, two wavelengths) and reference DPFs are generated using NIRFAST (a Matlab toolbox) using a presumed paradigm, known tissue properties, a Balloon model, and a finite element head model consisting of 58,818 mesh elements. Then, the DPF values are estimated using a Jacobian matrix from the head model and the mBLL. The results show that the estimated concentration changes correlate well with the reference data. Also, the estimated DPFs showed relative errors less than 1.33% maximum and 0.75% on average. A one-tailed $t$ -test revealed that the estimated DPFs matched the reference DPFs with more than 99.9% confidence. The developed method can efficiently access the actual DPFs even if emitter-detector distances vary significantly and the tissue properties are not uniform. With the developed state-space models for dual SCKFs, real-time estimation of the DPFs from one experiment to another has become plausible.

A new comparative testing method for equestrian helmets

The helmet is the only head protection element for jockey in the falling situation. To date, the standard test for shock absorption capacity of the equestrian helmet consists of only linear impacts and uses outdated injury criteria. Previous studies have shown the existence of normal and tangential components of the impact velocity. The present study presents a new method for the evaluation of equestrian helmet against moderate brain injury coupling experimental tests and numerical simulations. The experimental tests consist in helmeted and instrumented hybrid III dummy head impacting a rigid anvil under linear and oblique impact configurations. These impact conditions allow to highlight the protective effect of the helmet under tangential impact by using an advanced finite element head model and make it possible to estimate the level of brain injury risk by computing the brain shear stress. The method was applied to a set of 7 equestrian helmet models leading to a total of 86 experimental impact tests and 86 numerical simulations of brain response. It was observed that the moderate risk of commotion was ranged from 20% to 90%. It is shown that the most critical impacts are the linear-lateral configuration as well as the oblique impact leading to rotation around the vertical axis, leading to around 85% and 65%, respectively, risks of moderate neurological injuries. Based on this new test method, the study allows us to compare the protective capability of a given equestrian helmet and possibly compare equestrian helmets via a dedicated rating system.

Electroosmosis Based Novel Treatment Approach for Cerebral Edema.

Cerebral edema characterized as an abnormal accumulation of interstitial fluid has not been treated effectively. We propose a novel edema treatment approach to drive edematous fluid out of the brain by direct current utilizing brain tissue's electroosmotic property. A finite element (FE) head model is developed and employed to assess the feasibility of the approach. First, the capacity of the model for electric field prediction is validated against human experiments. Second, two electrode configurations (S and D-montage) are designed to evaluate the distribution of the electric field, electroosmotic flow (EOF), current density, and temperature across the brain under an applied direct current. The S-montage is shown to induce an average EOF velocity of 7e-4 mm/s underneath the anode by a voltage of 15 V, and the D-montage induces a velocity of 9e-4 mm/s by a voltage of 5 V. Meanwhile, the brain temperature in both configurations is below 38 °C, which is within the safety range. Further, the magnitude of EOF is proportional to the electric field, and the EOF direction follows the current flow from anode to cathode. The EOF velocity in the white matter is significantly higher than that in the gray matter under the anode where the fluid is to be drawn out. The proposed electroosmosis based approach allows alleviating brain edema within the critical time window by direct current. The approach may be further developed as a new treatment solely or as a complement to existing conventional treatments of edema.