Modeling of the Brain for Injury Prevention

Biomechanical Analysis of Woodpecker Response During Pecking Using a Two-Dimensional Computational Model

Traumatic brain injury (TBI) and chronic traumatic encephalopathy (CTE) due to the impact is a critical health concern. Impact mitigation strategy is a vital design paradigm to reduce the burden of TBI and CTE. In this regard, woodpecker biomimicry continues to attract attention. However, a direct comparison between a woodpecker and human biomechanical responses is lacking. Toward this end, we investigate the biomechanical response of a woodpecker during pecking using a two-dimensional head model. We also analyze the response of concurrent human head model to facilitate direct comparison with woodpecker response. The head models of woodpecker and human were built from medical images, the material properties were adopted from the literature. Both woodpecker and human head models were subjected to head kinematics obtained during pecking and resulting biomechanical response is studied. For the pecking cycle simulated in this work, peak rotational velocity and acceleration were ∼15 rad/s and 7,057 rad/s². These peak values are commensurate with the kinematics threshold values reported in human TBI. Our results show that, for the same input acceleration, the strains and stresses in the woodpecker brain are approximately six times lower than that of the human brain. The stress reduction is mainly attributed to the smaller size of the woodpecker head. The effect of pecking frequency and multiple pecking cycles have also been studied. It is observed that the strains and stresses in the brain are increased by ∼100% as pecking frequency is doubled. During multiple pecking cycle, dwell period of ∼90 ms tend to relax the stresses in the woodpecker brain; however, the amount of relaxation depends on the value of the decay constant. The comparison of biomechanical response against the axonal injury threshold suggests that for peak rotational acceleration of 7,057 rad/s² the maximum principal strain in the brains of woodpecker and human exceed the threshold limit. Acceleration scaling relationship between a woodpecker and equivalent human response is also developed as a function of head size. We obtain a scaling factor, ahaw, of 0.11 for baseline head sizes and a scaling factor of 1.03 as the human head size approaches woodpecker head size.

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.

Prediction of brain deformations and risk of traumatic brain injury due to closed-head impact: quantitative analysis of the effects of boundary conditions and brain tissue constitutive model

In this study, we investigate the effects of modelling choices for the brain-skull interface (layers of tissues between the brain and skull that determine boundary conditions for the brain) and the constitutive model of brain parenchyma on the brain responses under violent impact as predicted using computational biomechanics model. We used the head/brain model from Total HUman Model for Safety (THUMS)-extensively validated finite element model of the human body that has been applied in numerous injury biomechanics studies. The computations were conducted using a well-established nonlinear explicit dynamics finite element code LS-DYNA. We employed four approaches for modelling the brain-skull interface and four constitutive models for the brain tissue in the numerical simulations of the experiments on post-mortem human subjects exposed to violent impacts reported in the literature. The brain-skull interface models included direct representation of the brain meninges and cerebrospinal fluid, outer brain surface rigidly attached to the skull, frictionless sliding contact between the brain and skull, and a layer of spring-type cohesive elements between the brain and skull. We considered Ogden hyperviscoelastic, Mooney-Rivlin hyperviscoelastic, neo-Hookean hyperviscoelastic and linear viscoelastic constitutive models of the brain tissue. Our study indicates that the predicted deformations within the brain and related brain injury criteria are strongly affected by both the approach of modelling the brain-skull interface and the constitutive model of the brain parenchyma tissues. The results suggest that accurate prediction of deformations within the brain and risk of brain injury due to violent impact using computational biomechanics models may require representation of the meninges and subarachnoidal space with cerebrospinal fluid in the model and application of hyperviscoelastic (preferably Ogden-type) constitutive model for the brain tissue.

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.

A Three-Dimensional Computational Human Head Model That Captures Live Human Brain Dynamics

Diffuse axonal injury (DAI) is a debilitating consequence of traumatic brain injury (TBI) attributed to abnormal stretching of axons caused by blunt head trauma or acceleration of the head. We developed an anatomically accurate, subject-specific, three-dimensional (3D) computational model of the human brain, and used it to study the dynamic deformations in the substructures of the brain when the head is subjected to rotational accelerations. The computational head models use anatomy and morphology of the white matter fibers obtained using MRI. Subject-specific full-field shearing motions in live human brains obtained through a recently developed tagged MRI imaging technique are then used to validate the models by comparing the measured and predicted heterogeneous dynamic mechanical response of the brain. These results are used to elucidate the dynamics of local shearing deformations in the brain substructures caused by rotational acceleration of the head. Our work demonstrates that the rotational dynamics of the brain has a timescale of ∼100 ms as determined by the shearing wave speeds, and thus the injuries associated with rotational accelerations likely occur over these time scales. After subject-specific validation using the live human subject data, a representative subject-specific head model is used to simulate a real life scenario that resulted in a concussive injury. Results suggest that regions of the brain, in the form of a toroid, encompassing the white matter, the cortical gray matter, and outer parts of the limbic system have a higher susceptibility to injury under axial rotations of the head.

Personalized heterogeneous deformable model for fast volumetric registration

BACKGROUND

Biomechanical deformable volumetric registration can help improve safety of surgical interventions by ensuring the operations are extremely precise. However, this technique has been limited by the accuracy and the computational efficiency of patient-specific modeling.

METHODS

This study presents a tissue-tissue coupling strategy based on penalty method to model the heterogeneous behavior of deformable body, and estimate the personalized tissue-tissue coupling parameters in a data-driven way. Moreover, considering that the computational efficiency of biomechanical model is highly dependent on the mechanical resolution, a practical coarse-to-fine scheme is proposed to increase runtime efficiency. Particularly, a detail enrichment database is established in an offline fashion to represent the mapping relationship between the deformation results of high-resolution hexahedral mesh extracted from the raw medical data and a newly constructed low-resolution hexahedral mesh. At runtime, the mechanical behavior of human organ under interactions is simulated with this low-resolution hexahedral mesh, then the microstructures are synthesized in virtue of the detail enrichment database.

RESULTS

The proposed method is validated by volumetric registration in an abdominal phantom compression experiments. Our personalized heterogeneous deformable model can well describe the coupling effects between different tissues of the phantom. Compared with high-resolution heterogeneous deformable model, the low-resolution deformable model with our detail enrichment database can achieve 9.4× faster, and the average target registration error is 3.42 mm, which demonstrates that the proposed method shows better volumetric registration performance than state-of-the-art.

CONCLUSIONS

Our framework can well balance the precision and efficiency, and has great potential to be adopted in the practical augmented reality image-guided robotic systems.

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.

A shape-based helmet fitting system for concussion protection

Helmets are widely used as protection against sports-related concussions. The degree of concussion protection offered by a helmet may be related to the fit between the helmet and head. This paper presents the design of a prototype helmet fitting recommendation system using shape-based helmet fitting. The shape-based helmet fitting system uses a Kinect sensor to scan a client's head and then compares the head shape to helmet shapes from a database of off-the-shelf helmets. A slice extraction method is used to compare a standard reference slice extracted from the head to a corresponding slice from the helmet. The degree to which the helmet fits the client's head is calculated and displayed to the user. The prototype system could potentially help a concussion expert make recommendations about helmet fit to clients, if more research about the effects of helmet fitting on concussion protection becomes available.

EFFECT OF CEREBROSPINAL FLUID MODELED WITH DIFFERENT MATERIAL PROPERTIES ON A HUMAN FINITE ELEMENT HEAD MODEL

The aim of this study was to enhance head-injury prediction, this paper investigated the behavior of cerebrospinal fluid (CSF) in finite element (FE) modeling. Nine different material properties selected according to material definitions and property values were used to represent CSF in FE head models. To evaluate the influence of CSF material parameters on brain mechanical responses, the models were validated against available cadaver experiment data. Results showed that coup pressure increased whereas contrecoup pressure decreased when the head sustained purely translational impact with increased bulk modulus when CSF was modeled as fluid. However, with increased bulk modulus, coup pressure, contrecoup pressure and relative skull-brain motions decreased under rotational impact. When CSF was assumed to be an elastic material, coup pressure increased whereas contrecoup pressure decreased with increased elastic modulus when the head was subjected to purely translational impact. However, the variation trend was not obvious during head rotation. Results also indicated that when subjected to brain strain and von Mises stress, the model was prone to underestimate brain injury when CSF was modeled as an elastic material, especially during purely translational impact to the head. The model with CSF as fluid reduced the strain rate of brain, which was more likely to be realistic than the model with CSF as a viscoelastic material. These findings suggested that significantly higher values of the bulk modulus of CSF modeled as fluid were needed to predict intracranial dynamic responses and brain injury during head impact.

A Pre-computed Brain Response Atlas for Instantaneous Strain Estimation in Contact Sports

Finite element models of the human head play an important role in investigating the mechanisms of traumatic brain injury, including sports concussion. A critical limitation, however, is that they incur a substantial computational cost to simulate even a single impact. Therefore, current simulation schemes significantly hamper brain injury studies based on model-estimated tissue-level responses. In this study, we present a pre-computed brain response atlas (pcBRA) to substantially increase the simulation efficiency in estimating brain strains using isolated rotational acceleration impulses parameterized with four independent variables (peak magnitude and duration, and rotational axis azimuth and elevation angles) with values determined from on-field measurements. Using randomly generated testing datasets, the partially established pcBRA achieved a 100% success rate in interpolation based on element-wise differences in accumulated peak strain ([Formula: see text]) according to a "double-10%" criterion or average regional [Formula: see text] in generic regions and the corpus callosum. A similar performance was maintained in extrapolation. The pcBRA performance was further successfully validated against directly simulated responses from two independently measured typical real-world rotational profiles. The computational cost to estimate element-wise whole-brain or regional [Formula: see text] was 6 s and <0.01 s, respectively, vs. ~50 min directly simulating a 40 ms impulse. These findings suggest the pcBRA could substantially increase the throughput in impact simulation without significant loss of accuracy from the estimation itself and, thus, its potential to accelerate the exploration of the mechanisms of sports concussion in general. If successful, the pcBRA may also become a diagnostic adjunct in conjunction with sensors that measure head impact kinematics on the field to objectively monitor and identify tissue-level brain trauma in real-time for "return-to-play" decision-making on the sideline.

Head impact accelerations for brain strain-related responses in contact sports: a model-based investigation

Both linear [Formula: see text] and rotational [Formula: see text] accelerations contribute to head impacts on the field in contact sports; however, they are often isolated in injury studies. It is critical to evaluate the feasibility of estimating brain responses using isolated instead of full degrees-of-freedom (DOFs) accelerations. In this study, we investigated the sensitivities of regional brain strain-related responses to resultant [Formula: see text] and [Formula: see text] as well as the relative contributions of these acceleration components to the responses via random sampling and linear regression using parameterized, triangulated head impacts with kinematic variable values based on on-field measurements. Two independently established and validated finite element models of the human head were employed to evaluate model-consistency and dependency in results: the Dartmouth Head Injury Model and Simulated Injury Monitor. For the majority of the brain, volume-weighted regional peak strain, strain rate, and von Mises stress accumulated from the simulation significantly correlated with the product of the magnitude and duration of [Formula: see text], or effectively, the rotational velocity, but not to [Formula: see text]. Responses from [Formula: see text]-only were comparable to the full-DOF counterparts especially when normalized by injury-causing thresholds (e.g., volume fractions of large differences virtually diminished (i.e., [Formula: see text]1 %) at typical difference percentage levels of 1-4 % on average). These model-consistent results support the inclusion of both rotational acceleration magnitude and duration into kinematics-based injury metrics and demonstrate the feasibility of estimating strain-related responses from isolated [Formula: see text] for analyses of strain-induced injury relevant to contact sports without significant loss of accuracy, especially for the cerebrum.