Computational Head Models Research Articles

ATPS-03A SEMI-AUTOMATED PLATFORM FOR RAPID SIMULATION OF TTFIELDS DISTRIBUTION IN THE BRAIN

Tumor Treating Fields (TTFields) are low intensity alternating electric fields in the intermediate frequency range approved for the treatment of glioblastoma. TTFields are delivered through two pairs of transducer arrays placed on the patient's scalp. Preclinical studies show that TTFields efficacy increases with higher field intensity. Therefore optimizing the array layout on the head to maximize field intensity at the tumor is widely practiced. To better understand how TTFields distribute within the brain, realistic computational head models for performing simulation studies have been developed. A bottle-neck associated with such simulations is the placement of the transducer arrays on the virtual head. When placing arrays on patients, the disks naturally align parallel to the skin, and good electrical contact between the arrays and the skin occurs as the medical gel deforms to match the scalp contour. However, virtual models are made of rigidly defined surfaces. Therefore, placing the arrays on the model requires careful alignment of the array to the defined skin contour. Previously, array placement on virtual models was performed manually. However, this procedure is time consuming, limiting the rate at which different array layouts can be simulated. Here we present an automated method for rapidly generating simulations of TTFields distributions within the brain. For this purpose, we developed an algorithm that automatically places the arrays on a computational phantom. We integrated this algorithm into a simulation pipeline that uses a commercial Finite Element simulation package and a realistic head model. Using this pipeline we were able to rapidly simulate multiple array layouts on the head. Our simulations support the findings of prior studies demonstrating that the location of the arrays on the head impacts the field distribution within the brain, and that the array layout can be optimized to deliver maximal field intensities to desired regions within the brain.

Magnetic Resonance Current Density Imaging for Customizing Transcranial Direct Current Stimulation - A Simulation Study

Transcranial direct current stimulation (tDCS) involves passing low currents through the brain and is a promising tool for inducing alteration of cortical excitability. However, tDCS presents challenges in terms of optimization of the electrode placement and stimulation parameters especially in cases of heterogeneously damaged cortical structures. The analysis of a simplified multi-shell computational head-model indicated that the neuronal fibers lying perpendicular to the electrode-scalp interface in the white matter (WM) beneath the stimulating anode are subjected to a depolarizing drive while those lying parallel to the electrode-scalp interface are exposed to a hyperpolarizing drive, but in the gray matter (GM) the opposite was observed. Moreover, parameter sensitivity analysis revealed that the neuronal fibers lying perpendicular to the electrode-scalp interface in the GM beneath the anode were subjected to a depolarizing drive while those lying parallel to the electrode-scalp interface were exposed to a hyperpolarizing drive when the WM and GM conductivity were 0.13S/m and 0.2S/m respectively. However, when the WM conductivity was increased to 1.1 S/m (GM at 0.2 S/m), the results were reversed. This highlighted the importance of accurate conductivity values in computational head-model in the cases of diseased brain tissue. Here, anisotropic conductivity and fiber trajectories can be estimated from diffusion magnetic resonance imaging (dMRI) which provides excellent anatomical details, and can be used to differentiate between healthy, ischemic, and perilesional brain tissue. Such dMRI-guided patient-specific head-models can be used to optimize electrode placement and stimulation parameters that is based on the distributions of the generalized activating function. Here, a Magnetic Resonance Current Density Imaging (MRCDI) based method is proposed for dMRI-guided subject-specific parameter estimation and model validation that is a novel approach in our opinion.

The Importance of Structural Anisotropy in Computational Models of Traumatic Brain Injury

Understanding the mechanisms of injury might prove useful in assisting the development of methods for the management and mitigation of traumatic brain injury (TBI). Computational head models can provide valuable insight into the multi-length-scale complexity associated with the primary nature of diffuse axonal injury. It involves understanding how the trauma to the head (at the centimeter length scale) translates to the white-matter tissue (at the millimeter length scale), and even further down to the axonal-length scale, where physical injury to axons (e.g., axon separation) may occur. However, to accurately represent the development of TBI, the biofidelity of these computational models is of utmost importance. There has been a focused effort to improve the biofidelity of computational models by including more sophisticated material definitions and implementing physiologically relevant measures of injury. This paper summarizes recent computational studies that have incorporated structural anisotropy in both the material definition of the white matter and the injury criterion as a means to improve the predictive capabilities of computational models for TBI. We discuss the role of structural anisotropy on both the mechanical response of the brain tissue and on the development of injury. We also outline future directions in the computational modeling of TBI.

Angular Impact Mitigation system for bicycle helmets to reduce head acceleration and risk of traumatic brain injury

Angular acceleration of the head is a known cause of traumatic brain injury (TBI), but contemporary bicycle helmets lack dedicated mechanisms to mitigate angular acceleration. A novel Angular Impact Mitigation (AIM) system for bicycle helmets has been developed that employs an elastically suspended aluminum honeycomb liner to absorb linear acceleration in normal impacts as well as angular acceleration in oblique impacts. This study tested bicycle helmets with and without AIM technology to comparatively assess impact mitigation. Normal impact tests were performed to measure linear head acceleration. Oblique impact tests were performed to measure angular head acceleration and neck loading. Furthermore, acceleration histories of oblique impacts were analyzed in a computational head model to predict the resulting risk of TBI in the form of concussion and diffuse axonal injury (DAI). Compared to standard helmets, AIM helmets resulted in a 14% reduction in peak linear acceleration (p<0.001), a 34% reduction in peak angular acceleration (p<0.001), and a 22–32% reduction in neck loading (p<0.001). Computational results predicted that AIM helmets reduced the risk of concussion and DAI by 27% and 44%, respectively. In conclusion, these results demonstrated that AIM technology could effectively improve impact mitigation compared to a contemporary expanded polystyrene-based bicycle helmet, and may enhance prevention of bicycle-related TBI. Further research is required.

Importance of Muscle Activations for Biofidelic Pediatric Neck Response in Computational Models

Objective: During dynamic injury scenarios, such as motor vehicle crashes, neck biomechanics contribute to head excursion and acceleration, influencing head injuries. One important tool in understanding head and neck dynamics is computational modeling. However, realistic and stable muscle activations for major muscles are required to realize meaningful kinematic responses. The objective was to determine cervical muscle activation states for 6-year-old, 10-year-old, and adult 50th percentile male computational head and neck models. Currently, pediatric models including muscle activations are unable to maintain the head in an equilibrium position, forcing models to begin from nonphysiologic conditions. Recent work has realized a stationary initial geometry and cervical muscle activations by first optimizing responses against gravity. Accordingly, our goal was to apply these methods to Duke University's head–neck model validated using living muscle response and pediatric cadaveric data. Methods: Activation schemes maintaining an upright, stable head for 22 muscle pairs were found using LS-OPT. Two optimization problems were investigated: a relaxed state, which minimized muscle fatigue, and a tensed activation state, which maximized total muscle force. The model's biofidelity was evaluated by the kinematic response to gravitational and frontal impact loading conditions. Model sensitivity and uncertainty analyses were performed to assess important parameters for pediatric muscle response. Sensitivity analysis was conducted using multiple activation time histories. These included constant activations and an optimal muscle activation time history, which varied the activation level of flexor and extensor groups, and activation initiation and termination times. Results: Relaxed muscle activations decreased with increasing age, maintaining upright posture primarily through extensor activation. Tensed musculature maintained upright posture through coactivation of flexors and extensors, producing up to 32 times the force of the relaxed state. Without muscle activation, the models fell into flexion due to gravitational loading. Relaxed musculature produced 28.6–35.8 N of force to the head, whereas tensed musculature produced 450–1023 N. Pediatric model stiffnesses were most sensitive to muscle physiological cross-sectional area. Conclusions: Though muscular loads were not large enough to cause vertebral compressive failure, they would provide a prestressed state that could protect the vertebrae during tensile loading but might exacerbate risk during compressive loading. For example, in the 10-year-old, a load of 602 N was produced, though estimated compressive failure tolerance is only 2.8 kN. Including muscles and time-variant activation schemes is vital for producing biofidelic models because both vary by age. The pediatric activations developed represent physiologically appropriate sets of initial conditions and are based on validated adult cadaveric data. Supplemental materials are available for this article. Go to the publisher's online edition of Traffic Injury Prevention to view the supplemental file.

MATERIAL PROPERTIES OF ADULT RAT SKULL

Development of advanced computational rat head models requires accurate material properties of the rat brain, meninges, skull, and other soft tissues. This study investigated adult rat skull material properties, which are very limited in the current literature. A total of 20 skull specimens were harvested from 10 adult rats. High resolution (16 μm) microcomputed tomography scans were performed for each specimen to observe dimensional changes within each specimen and internal porosities through the cross sections. The specimens were tested in three-point bending at loading velocities of 0.02 and 200 mm/s. The elastic modulus, energy absorbed to failure, energy density, and bending stress were calculated using classical beam theory. Results demonstrated that bending velocity (strain rate) had significant effect on elastic modulus and bending stress, but not on energy and energy density. The Young's moduli of rat skull measured in this study were comparable to those measured from the adult human skull.

A tissue-level anisotropic criterion for brain injury based on microstructural axonal deformation

Different length scales from micrometers to several decimeters play an important role in diffuse axonal injury. The kinematics at the head level result in local impairments at the cellular level. Finite element methods can be used for predicting brain injury caused by a mechanical loading of the head. Because of its oriented microstructure, the sensitivity of brain tissue to a mechanical load can be expected to be orientation dependent. However, the criteria for injury that are currently used at the tissue level in finite element head models are isotropic and therefore do not consider this orientation dependence, which might inhibit a reliable assessment of injury. In this study, an anisotropic brain injury criterion is developed that is able to describe the effects of the oriented microstructure based on micromechanical simulations. The effects of both the main axonal direction and of local deviations from this direction are accounted for. With the anisotropic criterion for brain injury, computational head models will be able to account for aspects of diffuse axonal injury at the cellular level and can therefore more reliably predict injury.

Biomechanics of Traumatic Brain Injury: Influences of the Morphologic Heterogeneities of the Cerebral Cortex

Traumatic brain injury (TBI) can be caused by accidents and often leads to permanent health issues or even death. Brain injury criteria are used for assessing the probability of TBI, if a certain mechanical load is applied. The currently used injury criteria in the automotive industry are based on global head kinematics. New methods, based on finite element modeling, use brain injury criteria at lower scale levels, e.g., tissue-based injury criteria. However, most current computational head models lack the anatomical details of the cerebrum. To investigate the influence of the morphologic heterogeneities of the cerebral cortex, a numerical model of a representative part of the cerebral cortex with a detailed geometry has been developed. Several different geometries containing gyri and sulci have been developed for this model. Also, a homogeneous geometry has been made to analyze the relative importance of the heterogeneities. The loading conditions are based on a computational head model simulation. The results of this model indicate that the heterogeneities have an influence on the equivalent stress. The maximum equivalent stress in the heterogeneous models is increased by a factor of about 1.3–1.9 with respect to the homogeneous model, whereas the mean equivalent stress is increased by at most 10%. This implies that tissue-based injury criteria may not be accurately applied to most computational head models used nowadays, which do not account for sulci and gyri.

Comparisons of Computed Mobile Phone Induced SAR in the SAM Phantom to That in Anatomically Correct Models of the Human Head.

The specific absorption rates (SAR) determined computationally in the specific anthropomorphic mannequin (SAM) and anatomically correct models of the human head when exposed to a mobile phone model are compared as part of a study organized by IEEE Standards Coordinating Committee 34, SubCommittee 2, and Working Group 2, and carried out by an international task force comprising 14 government, academic, and industrial research institutions. The detailed study protocol defined the computational head and mobile phone models. The participants used different finite-difference time-domain software and independently positioned the mobile phone and head models in accordance with the protocol. The results show that when the pinna SAR is calculated separately from the head SAR, SAM produced a higher SAR in the head than the anatomically correct head models. Also the larger (adult) head produced a statistically significant higher peak SAR for both the 1- and 10-g averages than did the smaller (child) head for all conditions of frequency and position.

Physically correlated muscle activation for a human head and neck computational model

A computational 50th percentile male head and neck complex model was correlated to physical experimental data. The computational model utilizes 15 muscle pairs represented by the Hill Muscle Model with the complete head/neck system modeled using MADYMO™. The model was used for analysis and optimization of activation and deactivation of muscle activity in flexion and extension. Sensitivity analysis performed using the model shows that, of the multiple parameters within the Hill Model, activation level and timing prove to have the greatest effect on the system kinematics. In addition, the rate by which an activation level is changed becomes an important factor in the simulation. With the use of numerical optimization techniques, a pattern was determined for the applied activation/deactivation rates and timing of flexors and extensors during flexion and extension of the head. The numerical optimization result correlated to within 9% of measured value during the initial flexion of the head. The optimized activation model reflected an activation onset 90 ms after the start of the impulse load, which agrees with published reaction times of muscles. Activation and deactivation rates for the extensors were found to be 1.7 and 0.29%, respectively. While the onset of activation of the flexor muscles occurred before rebound, it was found that muscles, at near the mid-plane, were triggered by the optimized model to abate the flexion. Rates of activation and deactivation of the flexors were found to be 0.9 and 0.3%, respectively. Both the extensors as well as the flexors were found to activate only up to 70% before deactivating. Therefore, it was evident from this study that using the Hill Muscle Model with the activation parameter modeled as binary, 0 or 100%, may lead to inaccurate simulation results.