Mechanistic Insights into Human Brain Impact Dynamics through Modal Analysis

Characterizing Natural Frequencies of the Hybrid III and NOCSAE Headforms

The vibrational characteristics of the Hybrid III and NOCSAE headforms are not well understood. It is hypothesized that they may perform differently in certain loading environments due to their structural differences; their frequency responses may differ depending on the impact characteristics. Short-duration impacts excite a wider range of headform frequencies than longer-duration (padded) impacts. While headforms generally perform similarly during padded head impacts where resonant frequencies are avoided, excitation of resonant frequencies during short-duration impacts can result in differences in kinematic measurements between headforms for the matched impacts. This study aimed to identify the natural frequencies of each headform through experimental modal analysis techniques. An impulse hammer was used to excite various locations on both the Hybrid III and NOCSAE headforms. The resulting frequency response functions were analyzed to determine the first natural frequencies. The average first natural frequency of the NOCSAE headform was 812 Hz. The Hybrid III headform did not exhibit any natural frequencies below 1000 Hz. Comparisons of our results with previous studies of the human head suggest that the NOCSAE headform's vibrational response aligns more closely with that of the human head, as it exhibits lower natural frequencies. This insight is particularly relevant for assessing head injury risk in short-duration impact scenarios, where resonant frequencies can influence the injury outcome.

Transcranial ultrasound estimation of viscoelasticity and fluidity in brain tumors aided by transcranial shear waves

Cerebral diseases, such as brain tumors, are intricately linked to the mechanical properties of brain tissues. Estimating the mechanical properties of brain tumors using transcranial ultrasound is a promising approach. However, the complexity of cranial features introduces challenges, such as ultrasound attenuation and interference from multidirectional transcranial shear waves induced by impact vibrations. To address these issues, this study proposes a transcranial ultrasound estimation method assisted by transcranial shear vibrations. Transcranial vibrations apply shear forces on the parietal bone, inducing unidirectional transcranial shear waves within brain tissue, as validated through simulations. Shear waves at different frequencies were captured via transcranial ultrasound, which were used to assess the viscoelasticity and fluidity of brain tumors. Transcranial experimental validations were conducted in 3D-printed models with tumor phantoms and ex vivo animal tumors. Vibration safety assessments were also performed. The results demonstrate that transcranial ultrasound can detect micron displacements induced by transcranial shear waves. In phantom and ex vivo animal experiments, speed distribution maps were employed to determine the size and location of one or two tumors enclosed in the skull model. The results revealed that the proposed approach could detect tumors with a minimum diameter of 0.8 cm and an inter-tumor distance of 0.8 cm. Notably, significant differences in viscoelasticity and fluidity between normal brain tissue and brain tumors were found (p<0.001). The maximum assessment errors for the elasticity, viscosity, and fluidity using transcranial ultrasound were 11.90%, 4.82%, and 0.73%, respectively, indicating that fluidity was more robust than viscoelasticity. The maximum accelerations of the skull were only 3.21 ms-2.

Characteristics of head frequency response in blunt impacts: a biomechanical modeling study

Existing evaluation criteria for head impact injuries are typically based on time-domain features, and less attention has been paid to head frequency responses for head impact injury assessment. The purpose of the current study is, therefore, to understand the characteristics of human body head frequency response in blunt impacts via finite element (FE) modeling and the wavelet packet analysis method. FE simulation results show that head frequency response in blunt impacts could be affected by the impact boundary condition. The head energy peak and its frequency increase with the increase in impact; a stiffer impact block is associated with a higher head energy peak, and a bigger impact block could result in a high proportion of the energy peak. Regression analysis indicates that only the head energy peak has a high correlation with exiting head injury criteria, which implies that the amplitude-frequency aggregation characteristic but not the frequency itself of the head acceleration response has predictability for head impact injury in blunt impacts. The findings of the current study may provide additional criteria for head impact injury evaluation and new ideas for head impact injury protection.

Neuroimaging, wearable sensors, and blood-based biomarkers reveal hyperacute changes in the brain after sub-concussive impacts

Impacts in mixed martial arts (MMA) have been studied mainly in regard to the long-term effects of concussions. However, repetitive sub-concussive head impacts at the hyperacute phase (minutes after impact), are not understood. The head experiences rapid acceleration similar to a concussion, but without clinical symptoms. We utilize portable neuroimaging technology - transcranial Doppler (TCD) ultrasound and functional near infrared spectroscopy (fNIRS) - to estimate the extent of pre- and post-differences following contact and non-contact sparring sessions in nine MMA athletes. In addition, the extent of changes in neurofilament light (NfL) protein biomarker concentrations, and neurocognitive/balance parameters were determined following impacts. Athletes were instrumented with sensor-based mouth guards to record head kinematics. TCD and fNIRS results demonstrated significantly increased blood flow velocity (p = 0.01) as well as prefrontal (p = 0.01) and motor cortex (p = 0.04) oxygenation, only following the contact sparring sessions. This increase after contact was correlated with the cumulative angular acceleration experienced during impacts (p = 0.01). In addition, the NfL biomarker demonstrated positive correlations with angular acceleration (p = 0.03), and maximum principal and fiber strain (p = 0.01). On average athletes experienced 23.9 ± 2.9 g peak linear acceleration, 10.29 ± 1.1 rad/s peak angular velocity, and 1,502.3 ± 532.3 rad/s2 angular acceleration. Balance parameters were significantly increased following contact sparring for medial-lateral (ML) center of mass (COM) sway, and ML ankle angle (p = 0.01), illustrating worsened balance. These combined results reveal significant changes in brain hemodynamics and neurophysiological parameters that occur immediately after sub-concussive impacts and suggest that the physical impact to the head plays an important role in these changes.

Hyper-acute effects of sub-concussive soccer headers on brain function and hemodynamics

Introduction

Sub-concussive head impacts in soccer are drawing increasing research attention regarding their acute and long-term effects as players may experience thousands of headers in a single season. During these impacts, the head experiences rapid acceleration similar to what occurs during a concussion, but without the clinical implications. The physical mechanism and response to repetitive impacts are not completely understood. The objective of this work was to examine the immediate functional outcomes of sub-concussive level impacts from soccer heading in a natural, non-laboratory environment.

Methods

Twenty university level soccer athletes were instrumented with sensor-mounted bite bars to record impacts from 10 consecutive soccer headers. Pre- and post-header measurements were collected to determine hyper-acute changes, i.e., within minutes after exposure. This included measuring blood flow velocity using transcranial Doppler (TCD) ultrasound, oxyhemoglobin concentration using functional near infrared spectroscopy imaging (fNIRS), and upper extremity dual-task (UEF) neurocognitive testing.

Results

On average, the athletes experienced 30.7 ± 8.9 g peak linear acceleration and 7.2 ± 3.1 rad/s peak angular velocity, respectively. Results from fNIRS measurements showed an increase in the brain oxygenation for the left prefrontal cortex (PC) (p = 0.002), and the left motor cortex (MC) (p = 0.007) following the soccer headers. Additional analysis of the fNIRS time series demonstrates increased sample entropy of the signal after the headers in the right PC (p = 0.02), right MC (p = 0.004), and left MC (p = 0.04).

Discussion

These combined results reveal some variations in brain oxygenation immediately detected after repetitive headers. Significant changes in balance and neurocognitive function were not observed in this study, indicating a mild level of head impacts. This is the first study to observe hemodynamic changes immediately after sub-concussive impacts using non-invasive portable imaging technology. In combination with head kinematic measurements, this information can give new insights and a framework for immediate monitoring of sub-concussive impacts on the head.

Measuring vibrations on a biofidelic brain using ferroelectret nanogenerator

Our knowledge of traumatic brain injury has been fast growing with the emergence of new markers pointing to various neurological changes that the brain undergoes during an impact or any other form of concussive event. In this work, we study the modality of deformations on a biofidelic brain system when subject to blunt impacts, highlighting the importance of the time-dependent behavior of the resulting waves propagating through the brain. This study is carried out using two different approaches involving optical (Particle Image Velocimetry) and mechanical (flexible sensors) in the biofidelic brain. Results show that the system has a natural mechanical frequency of [Formula: see text] 25 oscillations per second, which was confirmed by both methods, showing a positive correlation with one another. The consistency of these results with previously reported brain pathology validates the use of either technique, and establishes a new, simpler mechanism to study brain vibrations by using flexible piezoelectric patches. The visco-elastic nature of the biofidelic brain is validated by observing the the relationship between both methods at two different time intervals, by using the information of the strain and stress inside the brain from the Particle Image Velocimetry and flexible sensor, respectively. A non-linear stress-strain relationship was observed and justified to support the same.

Assessment of vibration modulated regional cerebral blood flow with MRI

Human brain experiences vibration of certain magnitude and frequency during various physical activities such as vehicle transportation and machine operation, which may cause traumatic brain injury or other brain diseases. However, the mechanisms of brain pathogenesis due to vibration are not fully elucidated due to the lack of techniques to study brain functions while applying vibration to the brain at a specific magnitude and frequency. Here, this study reported a custom-built head-worn electromagnetic actuator that applied vibration to the brain in vivo at an accurate frequency inside a magnetic resonance imaging scanner while cerebral blood flow (CBF) was acquired. Using this technique, CBF values from 45 healthy volunteers were quantitatively measured immediately following vibration at 20, 30, 40 Hz, respectively. Results showed increasingly reduced CBF with increasing frequency at multiple regions of the brain, while the size of the regions expanded. Importantly, the vibration-induced CBF reduction regions largely fell inside the brain's default mode network (DMN), with about 58 or 46% overlap at 30 or 40 Hz, respectively. These findings demonstrate that vibration as a mechanical stimulus can change strain conditions, which may induce CBF reduction in the brain with regional differences in a frequency-dependent manner. Furthermore, the overlap between vibration-induced CBF reduction regions and DMN suggested a potential relationship between external mechanical stimuli and cognitive functions.

Morphological changes in glial cells arrangement under mechanical loading: A quantitative study

The mechanical properties and microstructure of brain tissue, as its two main physical parameters, could be affected by mechanical stimuli. In previous studies, microstructural alterations due to mechanical loading have received less attention than the mechanical properties of the tissue. Therefore, the current study aimed to investigate the effect of ex-vivo mechanical forces on the micro-architecture of brain tissue including axons and glial cells. A three-step loading protocol (i.e., loading-recovery-loading) including eight strain levels from 5% to 40% was applied to bovine brain samples with axons aligned in one preferred direction (each sample experienced only one level of strain). After either the first or secondary loading step, the samples were fixed, cut in planes parallel and perpendicular to the loading direction, and stained for histology. The histological images were analyzed to measure the end-to-end length of axons and glial cell-cell distances. The results showed that after both loading steps, as the strain increased, the changes in the cell nuclei arrangement in the direction parallel to axons were more significant compared to the other two perpendicular directions. Based on this evidence, we hypothesized that the spatial pattern of glial cells is highly affected by the orientation of axonal fibers. Moreover, the results revealed that in both loading steps, the maximum cell-cell distance occurred at 15% strain, and this distance decreased for higher strains. Since 15% strain is close to the previously reported brain injury threshold, this evidence could suggest that at higher strains, the axons start to rupture, causing a reduction in the displacement of glial cells. Accordingly, it was concluded that more attention to glial cells' architecture during mechanical loading may lead to introduce a new biomarker for brain injury.

Finding the Spatial Co-Variation of Brain Deformation With Principal Component Analysis

OBJECTIVE

Strain and strain rate are effective traumatic brain injury metrics. In finite element (FE) head model, thousands of elements were used to represent the spatial distribution of these metrics. Owing that these metrics are resulted from brain inertia, their spatial distribution can be represented in more concise pattern. Since head kinematic features and brain deformation vary largely across head impact types (Zhan et al., 2021), we applied principal component analysis (PCA) to find the spatial co-variation of injury metrics (maximum principal strain (MPS), MPS rate (MPSR) and MPS × MPSR) in four impact types: simulation, football, mixed martial arts and car crashes, and used the PCA to find patterns in these metrics and improve the machine learning head model (MLHM).

METHODS

We applied PCA to decompose the injury metrics for all impacts in each impact type, and investigate the spatial co-variation using the first principal component (PC1). Furthermore, we developed a MLHM to predict PC1 and then inverse-transform to predict for all brain elements. The accuracy, the model complexity and the size of training dataset of PCA-MLHM are compared with previous MLHM (Zhan et al., 2021).

RESULTS

PC1 explained variance on the datasets. Based on PC1 coefficients, the corpus callosum and midbrain exhibit high variance on all datasets. Finally, the PCA-MLHM reduced model parameters by 74% with a similar MPS estimation accuracy.

CONCLUSION

The brain injury metric in a dataset can be decomposed into mean components and PC1 with high explained variance.

SIGNIFICANCE

The spatial co-variation analysis enables better interpretation of the patterns in brain injury metrics. It also improves the efficiency of MLHM.

Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond

Recent progress in machine learning (ML), together with advanced computational power, have provided new research opportunities in cardiovascular modeling. While classifying patient outcomes and medical image segmentation with ML have already shown significant promising results, ML for the prediction of biomechanics such as blood flow or tissue dynamics is in its infancy. This perspective article discusses some of the challenges in using ML for replacing well-established physics-based models in cardiovascular biomechanics. Specifically, we discuss the large landscape of input features in 3D patient-specific modeling as well as the high-dimensional output space of field variables that vary in space and time. We argue that the end purpose of such ML models needs to be clearly defined and the tradeoff between the loss in accuracy and the gained speedup carefully interpreted in the context of translational modeling. We also discuss several exciting venues where ML could be strategically used to augment traditional physics-based modeling in cardiovascular biomechanics. In these applications, ML is not replacing physics-based modeling, but providing opportunities to solve ill-defined problems, improve measurement data quality, enable a solution to computationally expensive problems, and interpret complex spatiotemporal data by extracting hidden patterns. In summary, we suggest a strategic integration of ML in cardiovascular biomechanics modeling where the ML model is not the end goal but rather a tool to facilitate enhanced modeling.

An Overview of the Effectiveness of Bicycle Helmet Designs in Impact Testing

Cycling accidents are the leading cause of sports-related head injuries in the US. Conventional bicycle helmets typically consist of polycarbonate shell over Expanded Polystyrene (EPS) foam and are tested with drop tests to evaluate a helmet’s ability to reduce head kinematics. Within the last decade, novel helmet technologies have been proposed to mitigate brain injuries during bicycle accidents, which necessitates the evaluation of their effectiveness in impact testing as compared to conventional helmets. In this paper, we reviewed the literature to collect and analyze the kinematic data of drop test experiments carried out on helmets with different technologies. In order to provide a fair comparison across different types of tests, we clustered the datasets with respect to their normal impact velocities, impact angular momentum, and the type of neck apparatus. When we analyzed the data based on impact velocity and angular momentum clusters, we found that the bicycle helmets that used rotation damping based technology, namely MIPS, had significantly lower peak rotational acceleration (PRA) and Generalized Acceleration Model for Brain Injury Threshold (GAMBIT) as compared to the conventional EPS liner helmets (p < 0.01). SPIN helmets had a superior performance in PRA compared to conventional helmets (p < 0.05) in the impact angular momentum clustered group, but not in the impact-velocity clustered comparisons. We also analyzed other recently developed helmets that primarily use collapsible structures in their liners, such as WaveCel and Koroyd. In both of the impact velocity and angular momentum groups, helmets based on the WaveCel technology had significantly lower peak linear acceleration (PLA), PRA, and GAMBIT at low impact velocities as compared to the conventional helmets, respectively (p < 0.05). The protective gear with the airbag technology, namely Hövding, also performed significantly better compared to the conventional helmets in the analyzed kinematic-based injury metrics (p < 0.001), possibly due to its advantage in helmet size and stiffness. We also observed that the differences in the kinematic datasets strongly depend on the type of neck apparatus. Our findings highlight the importance and benefits of developing new technologies and impact testing standards for bicycle helmet designs for better prevention of traumatic brain injury (TBI).

MR Imaging of Human Brain Mechanics In Vivo: New Measurements to Facilitate the Development of Computational Models of Brain Injury

Computational models of the brain and its biomechanical response to skull accelerations are important tools for understanding and predicting traumatic brain injuries (TBIs). However, most models have been developed using experimental data collected on animal models and cadaveric specimens, both of which differ from the living human brain. Here we describe efforts to noninvasively measure the biomechanical response of the human brain with MRI-at non-injurious strain levels-and generate data that can be used to develop, calibrate, and evaluate computational brain biomechanics models. Specifically, this paper reports on a project supported by the National Institute of Neurological Disorders and Stroke to comprehensively image brain anatomy and geometry, mechanical properties, and brain deformations that arise from impulsive and harmonic skull loadings. The outcome of this work will be a publicly available dataset ( http://www.nitrc.org/projects/bbir ) that includes measurements on both males and females across an age range from adolescence to older adulthood. This article describes the rationale and approach for this study, the data available, and how these data may be used to develop new computational models and augment existing approaches; it will serve as a reference to researchers interested in using these data.

Stochastic model for football's collective dynamics

In this paper, we study collective interaction dynamics emerging in the game of football (soccer). To do so, we surveyed a database containing body-sensor traces measured during three professional football matches, where we observed statistical patterns that we used to propose a stochastic model for the players' motion in the field. The model, which is based on linear interactions, captures to a good approximation the spatiotemporal dynamics of a football team. Our theoretical framework, therefore, can be an effective analytical tool to uncover the underlying cooperative mechanisms behind the complexity of football plays. Moreover, we showed that it can provide handy theoretical support for coaches to evaluate teams' and players' performances in both training sessions and competitive scenarios.

Vibration-based elastic parameter identification of the diploë and cortical tables in dry cranial bones

Various human skull models feature a layered cranial structure composed of homogeneous cortical tables and the inner diploë. However, there is a lack of fundamental validation work of such three-layer cranial bone models by combining high-fidelity computational modeling and rigorous experiments. Here, non-contact vibration experiments are conducted on an assortment of dry bone segments from the largest cranial bone regions (parietal, frontal, occipital, and temporal) to estimate the first handful of modal frequencies and damping ratios, as well as mode shapes, in the audio frequency regime. Numerical models that consider the cortical tables and the diploë as domains with separate isotropic material properties are constructed for each bone segment using a routine that identifies the cortical table-diploë boundaries from micro-computed tomography scan images, and reconstructs a three-dimensional geometry layer by layer. The material properties for cortical tables and diploë are obtained using a Hounsfield Unit-based mass density calculation combined with a parameter identification scheme for Young's modulus estimation. With the identified parameters, the average error between experimental and numerical modal frequencies is 1.3% and the modal assurance criterion values for most modes are above 0.90, indicating that the layered model is suitable for predicting the vibrational behavior of cranial bone. The proposed layered modeling and identified elastic parameters are also useful to support computational modeling of cranial guided waves and mode conversion in medical ultrasound. Additionally, the diploë elastic properties are rarely reported in the literature, making this work a fundamental characterization effort that can guide in the selection of material properties for human head models that consider layered cranial bone.

The relationship between brain injury criteria and brain strain across different types of head impacts can be different

Multiple brain injury criteria (BIC) are developed to quickly quantify brain injury risks after head impacts. These BIC originated from different head impact types (e.g. sports and car crashes) are widely used in risk evaluation. However, the accuracy of using the BIC on brain injury risk estimation across head impact types has not been evaluated. Physiologically, brain strain is often considered the key parameter of brain injury. To evaluate the BIC's risk estimation accuracy across five datasets comprising different head impact types, linear regression was used to model 95% maximum principal strain, 95% maximum principal strain at the corpus callosum and cumulative strain damage (15%) on 18 BIC. The results show significantly different relationships between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain across head impact types. The accuracy of brain strain regression is generally decreasing if the BIC regression models are fitted on a dataset with a different type of head impact rather than on the dataset with the same type. Given this finding, this study raises concerns for applying BIC to estimate the brain injury risks for head impacts different from the head impacts on which the BIC was developed.

Natural oscillatory modes of 3D deformation of the human brain in vivo

Natural modes and frequencies of three-dimensional (3D) deformation of the human brain were identified from in vivo tagged magnetic resonance images (MRI) acquired dynamically during transient mild acceleration of the head. Twenty 3D strain fields, estimated from tagged MRI image volumes in 19 adult subjects, were analyzed using dynamic mode decomposition (DMD). These strain fields represented dynamic, 3D brain deformations during constrained head accelerations, either involving rotation about the vertical axis of the neck or neck extension. DMD results reveal fundamental oscillatory modes of deformation at damped frequencies near 7 Hz (in neck rotation) and 11 Hz (in neck extension). Modes at these frequencies were found consistently among all subjects. These characteristic features of 3D human brain deformation are important for understanding the response of the brain in head impacts and provide valuable quantitative criteria for the evaluation and use of computer models of brain mechanics.

The Immune System's Role in the Consequences of Mild Traumatic Brain Injury (Concussion)

Mild traumatic brain injury (mild TBI), often referred to as concussion, is the most common form of TBI and affects millions of people each year. A history of mild TBI increases the risk of developing emotional and neurocognitive disorders later in life that can impact on day to day living. These include anxiety and depression, as well as neurodegenerative conditions such as chronic traumatic encephalopathy (CTE) and Alzheimer's disease (AD). Actions of brain resident or peripherally recruited immune cells are proposed to be key regulators across these diseases and mood disorders. Here, we will assess the impact of mild TBI on brain and patient health, and evaluate the recent evidence for immune cell involvement in its pathogenesis.

Brain response of a computational head model for prescribed skull kinematics and simulated football helmet impact boundary conditions

Computational human body models (HBM) present a novel approach to predict brain response in football impact scenarios, with prescribed kinematic boundary conditions for the HBM skull typically used at present. However, computational optimization of helmets requires simulation of the coupled helmet and HBM model; which is much more complex and has not been assessed in the context of brain deformation and existing simplified approaches. In the current study, two boundary conditions and the resulting brain deformations were compared using a HBM head model: (1) a prescribed skull kinematics (PK) boundary condition using measured head kinematics from experimental impacts; and (2) a novel detailed simulation of a HBM head and neck, helmet and linear impactor (HBM-S). While lateral and rear impacts exhibited similar levels of maximum principal strain (MPS) in the brain tissue using both boundary conditions, differences were noted in the frontal orientation (at 9.3 m/s, MPS was 0.39 for PK, 0.54 for HBM-S). Importantly, both PK and HBM-S boundary conditions produced a similar distribution of MPS throughout the brain for each impact orientation considered. Within the corpus callosum and thalamus, high MPS was associated with lateral impacts and lower values with frontal and rear impacts. The good correspondence of both boundary conditions is encouraging for future optimization of helmet designs. A limitation of the PK approach is the need for experimental head kinematics data, while the HBM-S can predict brain response for varying impact conditions and helmet configurations, with potential as a tool to improve helmet protection performance.

Viscoelasticity of children and adolescent brains through MR elastography

Magnetic Resonance Elastography (MRE) is an elasticity imaging technique that allows a safe, fast, and non-invasive evaluation of the mechanical properties of biological tissues in vivo. Since mechanical properties reflect a tissue's composition and arrangement, MRE is a powerful tool for the investigation of the microstructural changes that take place in the brain during childhood and adolescence. The goal of this study was to evaluate the viscoelastic properties of the brain in a population of healthy children and adolescents in order to identify potential age and sex dependencies. We hypothesize that because of myelination, age dependent changes in the mechanical properties of the brain will occur during childhood and adolescence. Our sample consisted of 26 healthy individuals (13 M, 13 F) with age that ranged from 7-17 years (mean: 11.9 years). We performed multifrequency MRE at 40, 60, and 80 Hz actuation frequencies to acquire the complex-valued shear modulus G = G' + iG″ with the fundamental MRE parameters being the storage modulus (G'), the loss modulus (G″), and the magnitude of complex-valued shear modulus (|G|). We fitted a springpot model to these frequency-dependent MRE parameters in order to obtain the parameter α, which is related to tissue's microstructure, and the elasticity parameter k, which was converted to a shear modulus parameter (μ) through viscosity (η). We observed no statistically significant variation in the parameter μ, but a significant increase of the microstructural parameter α of the white matter with increasing age (p < 0.05). Therefore, our MRE results suggest that subtle microstructural changes such as neural tissue's enhanced alignment and geometrical reorganization during childhood and adolescence could result in significant biomechanical changes. In line with previously reported MRE data for adults, we also report significantly higher shear modulus (μ) for female brains when compared to males (p < 0.05). The data presented here can serve as a clinical baseline in the analysis of the pediatric and adolescent brain's viscoelasticity over this age span, as well as extending our understanding of the biomechanics of brain development.

Amplified Flow Imaging (aFlow): A Novel MRI-Based Tool to Unravel the Coupled Dynamics Between the Human Brain and Cerebrovasculature

With each heartbeat, periodic variations in arterial blood pressure are transmitted along the vasculature, resulting in localized deformations of the arterial wall and its surrounding tissue. Quantification of such motions may help understand various cerebrovascular conditions, yet it has proven technically challenging thus far. We introduce a new image processing algorithm called amplified Flow (aFlow) which allows to study the coupled brain-blood flow motion by combining the amplification of cine and 4D flow MRI. By incorporating a modal analysis technique known as dynamic mode decomposition into the algorithm, aFlow is able to capture the characteristics of transient events present in the brain and arterial wall deformation. Validating aFlow, we tested it on phantom simulations mimicking arterial walls motion and observed that aFlow displays almost twice higher SNR than its predecessor amplified MRI (aMRI). We then applied aFlow to 4D flow and cine MRI datasets of 5 healthy subjects, finding high correlations between blood flow velocity and tissue deformation in selected brain regions, with correlation values r = 0.61 , 0.59, 0.52 for the pons, frontal and occipital lobe ( ). Finally, we explored the potential diagnostic applicability of aFlow by studying intracranial aneurysm dynamics, which seems to be indicative of rupture risk. In two patients, aFlow successfully visualized the imperceptible aneurysm wall motion, additionally quantifying the increase in the high frequency wall displacement after a one-year follow-up period (20%, 76%). These preliminary data suggest that aFlow may provide a novel imaging biomarker for the assessment of aneurysms evolution, with important potential diagnostic implications.

Association Between Proteomic Blood Biomarkers and DTI/NODDI Metrics in Adolescent Football Players: A Pilot Study

While neuroimaging and blood biomarker have been two of the most active areas of research in the neurotrauma community, these fields rarely intersect to delineate subconcussive brain injury. The aim of the study was to examine the association between diffusion MRI techniques [diffusion tensor imaging (DTI) and neurite orientation/dispersion density imaging (NODDI)] and brain-injury blood biomarker levels [tau, neurofilament-light (NfL), glial-fibrillary-acidic-protein (GFAP)] in high-school football players at their baseline, aiming to detect cumulative neuronal damage from prior seasons. Twenty-five football players were enrolled in the study. MRI measures and blood samples were obtained during preseason data collection. The whole-brain, tract-based spatial statistics was conducted for six diffusion metrics: fractional anisotropy (FA), mean diffusivity (MD), axial/radial diffusivity (AD, RD), neurite density index (NDI), and orientation dispersion index (ODI). Five players were ineligible for MRIs, and three serum samples were excluded due to hemolysis, resulting in 17 completed set of diffusion metrics and blood biomarker levels for association analysis. Our permutation-based regression model revealed that serum tau levels were significantly associated with MD and NDI in various axonal tracts; specifically, elevated serum tau levels correlated to elevated MD (p = 0.0044) and reduced NDI (p = 0.016) in the corpus callosum and surrounding white matter tracts (e.g., longitudinal fasciculus). Additionally, there was a negative association between NfL and ODI in the focal area of the longitudinal fasciculus. Our data suggest that high school football players may develop axonal microstructural abnormality in the corpus callosum and surrounding white matter tracts, such as longitudinal fasciculus. A future study is warranted to determine the longitudinal multimodal relationship in response to repetitive exposure to sports-related head impacts.

Neuro-Ophthalmologic Response to Repetitive Subconcussive Head Impacts: A Randomized Clinical Trial

Importance

Subconcussive head impacts have emerged as a complex public health concern. The oculomotor system is sensitive to brain trauma; however, neuro-ophthalmologic response to subconcussive head impacts remains unclear.

Objective

To examine whether subconcussive head impacts cause impairments in neuro-ophthalmologic function as measured by the King-Devick test (KDT) and oculomotor function as measured by the near point of convergence.

Design, Setting, and Participants

In this randomized clinical trial, adult soccer players were randomized into either a heading group or kicking (control) group. The heading group executed 10 headers with soccer balls projected at a speed of 25 mph. The kicking-control group followed the same protocol but with 10 kicks. Peak linear and rotational head accelerations were assessed with a triaxial accelerometer. The KDT speed and error and near point of convergence were assessed at baseline (preheading or prekicking) and at 0, 2, and 24 hours after heading or kicking.

Exposures

Ten soccer-ball headings or kicks.

Main Outcomes and Measures

The primary outcome was the group-by-time interaction of KDT speed at 0 hours after heading or kicking. The secondary outcomes included KDT speed at 2 hours and 24 hours after heading or kicking, KDT error, and near point of convergence.

Results

A total of 78 individuals enrolled (heading group, n = 40; kicking-control group, n = 38). Eleven individuals (heading group: 4 women; mean [SD] age, 22.5 [1.0] years; kicking-control group, 3 women and 4 men; mean [SD] age, 20.9 [1.1] years) voluntarily withdrew from the study. Data from 67 participants with a mean (SD) age of 20.6 (1.7) years were eligible for analysis (heading, n = 36; kicking-control, n = 31). Mean (SD) peak linear accelerations and peak rotational accelerations per impact for the heading group were 33.2 (6.8) g and 3.6 (1.4) krad/s2, respectively. Conversely, soccer kicking did not induce a detectable level of head acceleration. Both groups showed improvements in KDT speed (heading group: 0 hours, -1.2 [95% CI, -2.2 to -0.1] seconds; P = .03; 2 hours, -1.3 [95% CI, -2.6 to 0] seconds; P = .05; 24 hours, -3.2 [95% CI, -4.3 to -2.2] seconds; P < .001; kicking-control group: 0 hours, -3.3 [95% CI, -4.1 to -2.5] seconds; P < .001; 2 hours, -4.1 [95% CI, -5.1 to -3.1] seconds; P < .001; 24 hours, -5.2 [95% CI, -6.2 to -4.2] seconds; P < .001). Group differences occurred at all postintervention points; the kicking-control group performed KDT faster at 0 hours (-2.2 [95% CI, -0.8 to -3.5] seconds; P = .001), 2 hours (-2.8 [95% CI, -1.2 to -4.4] seconds; P < .001), and 24 hours after the intervention (-2.0 [95% CI, -0.5 to -3.4] seconds; P = .007) compared with those of the heading group.

Conclusions and Relevance

These data support the hypothesis that neuro-ophthalmologic function is affected, at least in the short term, by subconcussive head impacts that may affect some individuals in some contact sports. Further studies may help determine if these measures can be a useful clinical tool in detecting acute subconcussive injury.

Trial Registration

ClinicalTrials.gov Identifier: NCT03488381.

Low-Rank Representation of Head Impact Kinematics: A Data-Driven Emulator

Head motion induced by impacts has been deemed as one of the most important measures in brain injury prediction, given that the vast majority of brain injury metrics use head kinematics as input. Recently, researchers have focused on using fast approaches, such as machine learning, to approximate brain deformation in real time for early brain injury diagnosis. However, training such models requires large number of kinematic measurements, and therefore data augmentation is required given the limited on-field measured data available. In this study we present a principal component analysis-based method that emulates an empirical low-rank substitution for head impact kinematics, while requiring low computational cost. In characterizing our existing data set of 537 head impacts, each consisting of 6 degrees of freedom measurements, we found that only a few modes, e.g., 15 in the case of angular velocity, is sufficient for accurate reconstruction of the entire data set. Furthermore, these modes are predominantly low frequency since over 70% of the angular velocity response can be captured by modes that have frequencies under 40 Hz. We compared our proposed method against existing impact parametrization methods and showed significantly better performance in injury prediction using a range of kinematic-based metrics—such as head injury criterion (HIC), rotational injury criterion (RIC), and brain injury metric (BrIC)—and brain tissue deformation-based metrics—such as brain angle metric (BAM), maximum principal strain (MPS), and axonal fiber strains (FS). In all cases, our approach reproduced injury metrics similar to the ground truth measurements with no significant difference, whereas the existing methods obtained significantly different (p < 0.01) values as well as substantial differences in injury classification sensitivity and specificity. This emulator will enable us to provide the necessary data augmentation to build a head impact kinematic data set of any size. The emulator and corresponding examples are available on our website¹.

Validation and Comparison of Instrumented Mouthguards for Measuring Head Kinematics and Assessing Brain Deformation in Football Impacts

Because of the rigid coupling between the upper dentition and the skull, instrumented mouthguards have been shown to be a viable way of measuring head impact kinematics for assisting in understanding the underlying biomechanics of concussions. This has led various companies and institutions to further develop instrumented mouthguards. However, their use as a research tool for understanding concussive impacts makes quantification of their accuracy critical, especially given the conflicting results from various recent studies. Here we present a study that uses a pneumatic impactor to deliver impacts characteristic to football to a Hybrid III headform, in order to validate and compare five of the most commonly used instrumented mouthguards. We found that all tested mouthguards gave accurate measurements for the peak angular acceleration, the peak angular velocity, brain injury criteria values (mean average errors < 13, 8, 13%, respectively), and the mouthguards with long enough sampling time windows are suitable for a convolutional neural network-based brain model to calculate the brain strain (mean average errors < 9%). Finally, we found that the accuracy of the measurement varies with the impact locations yet is not sensitive to the impact velocity for the most part.

A machine learning approach for magnetic resonance image-based mouse brain modeling and fast computation in controlled cortical impact

Computational modeling of the brain is crucial for the study of traumatic brain injury. An anatomically accurate model with refined details could provide the most accurate computational results. However, computational models with fine mesh details could take prolonged computation time that impedes the clinical translation of the models. Therefore, a way to construct a model with low computational cost while maintaining a computational accuracy comparable with that of the high-fidelity model is desired. In this study, we constructed magnetic resonance (MR) image-based finite element (FE) models of a mouse brain for simulations of controlled cortical impact. The anatomical details were kept by mapping each image voxel to a corresponding FE mesh element. We constructed a super-resolution neural network that could produce computational results of a refined FE model with a mesh size of 70 μm from a coarse FE model with a mesh size of 280 μm. The peak signal-to-noise ratio of the reconstructed results was 33.26 dB, while the computational speed was increased by 50-fold. This proof-of-concept study showed that using machine learning techniques, MR image-based computational modeling could be applied and evaluated in a timely fashion. This paved ways for fast FE modeling and computation based on MR images. Results also support the potential clinical applications of MR image-based computational modeling of the human brain in a variety of scenarios such as brain impact and intervention.Graphical abstract MR image-based FE models with different mesh sizes were generated for CCI. The training and testing data sets were computed with 5 different impact locations and 3 different impact velocities. High-resolution strain maps were estimated using a SR neural network with greatly reduced computational cost.

MR elastography frequency-dependent and independent parameters demonstrate accelerated decrease of brain stiffness in elder subjects

OBJECTIVES

To analyze the mechanical properties in different regions of the brain in healthy adults in a wide age range: 26 to 76 years old.

METHODS

We used a multifrequency magnetic resonance elastography (MRE) protocol to analyze the effect of age on frequency-dependent (storage and loss moduli, G' and G″, respectively) and frequency-independent parameters (μ₁, μ₂, and η, as determined by a standard linear solid model) of the cerebral parenchyma, cortical gray matter (GM), white matter (WM), and subcortical GM structures of 46 healthy male and female subjects. The multifrequency behavior of the brain and frequency-independent parameters were analyzed across different age groups.

RESULTS

The annual change rate ranged from - 0.32 to - 0.36% for G' and - 0.43 to - 0.55% for G″ for the cerebral parenchyma, cortical GM, and WM. For the subcortical GM, changes in G' ranged from - 0.18 to - 0.23%, and G″ changed - 0.43%. Interestingly, males exhibited decreased elasticity, while females exhibited decreased viscosity with respect to age in some regions of subcortical GM. Significantly decreased values were also found in subjects over 60 years old.

CONCLUSION

Values of G' and G″ at 60 Hz and the frequency-independent μ₂ of the caudate, putamen, and thalamus may serve as parameters that characterize the aging effect on the brain. The decrease in brain stiffness accelerates in elderly subjects.

KEY POINTS

• We used a multifrequency MRE protocol to assess changes in the mechanical properties of the brain with age. • Frequency-dependent (storage moduli G' and loss moduli G″) and frequency-independent (μ₁, μ₂, and η) parameters can bequantitatively measured by our protocol. • The decreased value of viscoelastic properties due to aging varies in different regions of subcortical GM in males and females, and the decrease in brain stiffness is accelerated in elderly subjects over 60 years old.

Tension Strain-Softening and Compression Strain-Stiffening Behavior of Brain White Matter

Brain, the most important component of the central nervous system (CNS), is a soft tissue with a complex structure. Understanding the role of brain tissue microstructure in mechanical properties is essential to have a more profound knowledge of how brain development, disease, and injury occur. While many studies have investigated the mechanical behavior of brain tissue under various loading conditions, there has not been a clear explanation for variation reported for material properties of brain tissue. The current study compares the ex-vivo mechanical properties of brain tissue under two loading modes, namely compression and tension, and aims to explain the differences observed by closely examining the microstructure under loading. We tested bovine brain samples under uniaxial tension and compression loading conditions, and fitted hyperelastic material parameters. At 20% strain, we observed that the shear modulus of brain tissue in compression is about 6 times higher than in tension. In addition, we observed that brain tissue exhibited strain-stiffening in compression and strain-softening in tension. In order to investigate the effect of loading modes on the tissue microstructure, we fixed the samples using a novel method that enabled keeping the samples at the loaded stage during the fixation process. Based on the results of histology, we hypothesize that during compressive loading, the strain-stiffening behavior of the tissue could be attributed to glial cell bodies being pushed against surroundings, contacting each other and resisting compression, while during tension, cell connections are detached and the tissue displays softening behavior.

A Nonlinear Reduced-Order Model of the Corpus Callosum Under Planar Coronal Excitation

Traumatic brain injury (TBI) is often associated with microstructural tissue damage in the brain, which results from its complex biomechanical behavior. Recent studies have shown that the deep white matter (WM) region of the human brain is susceptible to being damaged due to strain localization in that region. Motivated by these studies, in this paper, we propose a geometrically nonlinear dynamical reduced order model (ROM) to model and study the dynamics of the deep WM region of the human brain under coronal excitation. In this model, the brain hemispheres were modeled as lumped masses connected via viscoelastic links, resembling the geometry of the corpus callosum (CC). Employing system identification techniques, we determined the unknown parameters of the ROM, and ensured the accuracy of the ROM by comparing its response against the response of an advanced finite element (FE) model. Next, utilizing modal analysis techniques, we determined the energy distribution among the governing modes of vibration of the ROM and concluded that the demonstrated nonlinear behavior of the FE model might be predominantly due to the special geometry of the brain deep WM region. Furthermore, we observed that, for sufficiently high input energies, high frequency harmonics at approximately 45 Hz, were generated in the response of the CC, which, in turn, are associated with high-frequency oscillations of the CC. Such harmonics might potentially lead to strain localization in the CC. This work is a step toward understanding the brain dynamics during traumatic injury.

A knowledge map analysis of brain biomechanics: Current evidence and future directions

Although brain, one of the most complex organs in the mammalian body, has been subjected to many studies from physiological and pathological points of view, there remain significant gaps in the available knowledge regarding its biomechanics. This article reviews the research trends in brain biomechanics with a focus on injury. We used published scientific articles indexed by Web of Science database over the past 40 years and tried to address the gaps that still exist in this field. We analyzed the data using VOSviewer, which is a software tool designed for scientometric studies. The results of this study showed that the response of brain tissue to external forces has been one of the significant research topics among biomechanicians. These studies have addressed the effects of mechanical forces on the brain and mechanisms of traumatic brain injury, as well as characterized changes in tissue behavior under trauma and other neurological diseases to provide new diagnostic and monitoring methods. In this study, some challenges in the field of brain injury biomechanics have been identified and new directions toward understanding the gaps in this field are suggested.

Biomechanics of Periventricular Injury

Periventricular injury is frequently noted as one aspect of severe traumatic brain injury (TBI) and the presence of the ventricles has been hypothesized to be a primary pathogenesis associated with the prevalence of periventricular injury in patients with TBI. Although substantial endeavors have been made to elucidate the potential mechanism, a thorough explanation for this hypothesis appears lacking. In this study, a three-dimensional (3D) finite element (FE) model of the human head with an accurate representation of the cerebral ventricles is developed accounting for the fluid properties of the intraventricular cerebrospinal fluid (CSF) as well as its interaction with the brain. An additional model is developed by replacing the intraventricular CSF with a substitute with brain material. Both models are subjected to rotational accelerations with magnitudes suspected to induce severe diffuse axonal injury. The results reveal that the presence of the ventricles leads to increased strain in the periventricular region, providing a plausible explanation for the vulnerability of the periventricular region. In addition, the strain-exacerbation effect associated with the presence of the ventricles is also noted in the paraventricular region, although less pronounced than that in the periventricular region. The current study advances the understanding of the periventricular injury mechanism as well as the detrimental effects that the ventricles exert on the periventricular and paraventricular brain tissue.

Multi-Directional Dynamic Model for Traumatic Brain Injury Detection

Given the worldwide adverse impact of traumatic brain injury (TBI) on the human population, its diagnosis and prediction are of utmost importance. Historically, many studies have focused on associating head kinematics to brain injury risk. Recently, there has been a push toward using computationally expensive finite element (FE) models of the brain to create tissue deformation metrics of brain injury. Here, we develop a new brain injury metric, the brain angle metric (BAM), based on the dynamics of a 3 degree-of-freedom lumped parameter brain model. The brain model is built based on the measured natural frequencies of an FE brain model simulated with live human impact data. We show that it can be used to rapidly estimate peak brain strains experienced during head rotational accelerations that cause mild TBI. In our data set, the simplified model correlates with peak principal FE strain (R² = 0.82). Further, coronal and axial brain model displacement correlated with fiber-oriented peak strain in the corpus callosum (R² = 0.77). Our proposed injury metric BAM uses the maximum angle predicted by our brain model and is compared against a number of existing rotational and translational kinematic injury metrics on a data set of head kinematics from 27 clinically diagnosed injuries and 887 non-injuries. We found that BAM performed comparably to peak angular acceleration, translational acceleration, and angular velocity in classifying injury and non-injury events. Metrics that separated time traces into their directional components had improved model deviance compare with those that combined components into a single time trace magnitude. Our brain model can be used in future work to rapidly approximate the peak strain resulting from mild to moderate head impacts and to quickly assess brain injury risk.

Micromechanical heterogeneity of the rat pia-arachnoid complex

To better understand the onset of damage occurring in the brain upon traumatic events, it is essential to analyze how external mechanical loads propagate through the skull and meninges and down to the brain cortex. However, despite their crucial role as structural dampers protecting the brain, the mechanical properties and dynamic behavior of the meningeal layers are still poorly understood. Here, we characterized the local mechanical heterogeneity of rat pia-arachnoid complex (PAC) at the microscale via atomic force microscopy (AFM) indentation experiments to understand how microstructural variations at the tissue level can differentially affect load propagation. By coupling AFM mechanical testing with fresh tissue immunofluorescent staining, we could directly observe the effect of specific anatomical features on the local mechanical properties of tissue. We observed a two-fold stiffening of vascularized tissue when compared to non-vascularized PAC (with instantaneous Young's modulus distribution means of 1.32  ±  0.03 kPa and 2.79  ±  0.08 kPa, respectively), and statistically significant differences between regions of low- and high-vimentin density, reflecting trabecular density (with means of 0.67  ±  0.05 kPa and 1.29  ±  0.06 kPa, respectively). No significant differences were observed between cortical and cerebellar PAC. Additionally, by performing force relaxation experiments at the AFM, we identified the characteristic time constant τ₁ of PAC tissue to be in the range of 2.7  ±  1.2 s to 3.1  ±  0.9 s for the different PAC regions analyzed. Taken together, the results presented point at the complex biomechanical nature of the meningeal tissue, and underscore the need to account for its heterogeneity when modeling its behavior into finite element simulations or other computational methods enabling the prediction of load propagation during injury events. STATEMENT OF SIGNIFICANCE: The meningeal layers are pivotal in shielding the brain during injury events, yet the mechanical properties of this complex biological interface are still under investigation. Here, we present the first anatomically-informed micromechanical characterization of mammalian pia-arachnoid complex (PAC). We developed a protocol for the isolation and fresh immunostaining of rat PAC and subjected the tissue to AFM indentation and relaxation experiments, while visualizing the local anatomy via fluorescence microscopy. We found statistically significant variations in regional PAC stiffness according to the degree of vascularization and trabecular cell density, besides identifying the tissue's characteristic relaxation constant. In essence, this study captures the relationship between anatomy and mechanical heterogeneity in a key component of the brain-skull interface for the first time.

Intracranial pressure-based validation and analysis of traumatic brain injury using a new three-dimensional finite element human head model

Traumatic brain injuries are life-threatening injuries that can lead to long-term incapacitation and death. Over the years, numerous finite element human head models have been developed to understand the injury mechanisms of traumatic brain injuries. Many of these models are erroneous and used ellipsoidal or spherical geometries to represent brain. This work is focused on the development of high-quality, comprehensive three-dimensional finite element human head model with accurate representation of cerebral sulci and gyri structures in order to study traumatic brain injury mechanisms. Present geometry, predicated on magnetic resonance imaging data consist of three rudimentary components, that is, skull, cerebrospinal fluid with the ventricular system, and the soft tissues comprising the cerebrum, cerebellum, and brain stem. The brain is modeled as a hyperviscoelastic material. Meshed model with 10 nodes modified tetrahedral type element (C3D10M) is validated against two cadaver-based impact experiments by comparing the intracranial pressures at different locations of the head. Our results indicate a better agreement with cadaver results, specifically for the case of frontal and parietal intracranial pressure values. Existing literature focuses mostly on intracranial pressure validation, while the effects of von Mises stress on brain injury are not analyzed in detail. In this work, a detailed interpretation of neurological damage resulting from impact injury is performed by analyzing von Mises stress and intracranial pressure distribution across numerous segments of the brain. A reasonably good correlation with experimental data signifies the robustness of the model for predicting injury mechanisms based on clinical predictions of injury tolerance criteria.

Molecular imaging of neuroinflammation in patients after mild traumatic brain injury: a longitudinal 123 I-CLINDE single photon emission computed tomography study

BACKGROUND AND PURPOSE

Neuroinflammation has been proposed as part of the pathogenesis of post-concussion symptoms (PCS), but the inflammatory response of the human brain to mild traumatic brain injury (mTBI) remains unknown. We hypothesized that a neuroinflammatory response is present in mTBI at 1-2 weeks post-injury and persists in patients with PCS.

METHODS

We scanned 14 patients with mTBI without signs of structural damage at 1-2 weeks and 3-4 months post-injury and 22 healthy controls once using the single photon emission computed tomography tracer ¹²³ I-CLINDE, which visualizes translocator protein (TSPO), a protein upregulated in active immune cells. PCS was defined as three or more persisting symptoms from the Rivermead Post Concussion Symptoms Questionnaire at 3 months post-injury.

RESULTS

Across brain regions, patients had significantly higher ¹²³ I-CLINDE binding to TSPO than healthy controls, both at 1-2 weeks after the injury in all patients (P = 0.011) and at 3-4 months in the seven patients with PCS (P = 0.006) and in the six patients with good recovery (P = 0.018). When the nine brain regions were tested separately and results were corrected for multiple comparisons, no individual region differed significantly, but all estimated parameters indicated increased ¹²³ I-CLINDE binding to TSPO, ranging from 2% to 19% in all patients at 1-2 weeks, 13% to 27% in patients with PCS at 3-4 months and -9% to 17% in patients with good recovery at 3-4 months.

CONCLUSIONS

Neuroinflammation was present in mTBI at 1-2 weeks post-injury and persisted at 3-4 months post-injury with a tendency to be most pronounced in patients with PCS.

Optimization of a Multifrequency Magnetic Resonance Elastography Protocol for the Human Brain

BACKGROUND AND PURPOSE

The brain's stiffness measurements from magnetic resonance elastography (MRE) strongly depend on actuation frequencies, which makes cross-study comparisons challenging. We performed a preliminary study to acquire optimal sets of actuation frequencies to accurately obtain rheological parameters for the whole brain (WB), white matter (WM), and gray matter (GM).

METHODS

Six healthy volunteers aged between 26 and 72 years old went through MRE with a modified single-shot spin-echo echo planar imaging pulse sequence embedded with motion encoding gradients on a 3T scanner. Frequency-independent brain material properties and best-fit material model were determined from the frequency-dependent brain tissue response data (20 -80 Hz), by comparing four different linear viscoelastic material models (Maxwell, Kelvin-Voigt, Springpot, and Zener). During the material fitting, spatial averaging of complex shear moduli (G*) obtained under single actuation frequency was performed, and then rheological parameters were acquired. Since clinical scan time is limited, a combination of three actuation frequencies that would provide the most accurate approximation and lowest fitting error was determined for WB, WM, and GM by optimizing for the lowest Bayesian information criterion (BIC).

RESULTS

BIC scores for the Zener and Springpot models showed these models approximate the multifrequency response of the tissue best. The best-fit frequency combinations for the reference Zener and Springpot models were identified to be 30-60-70 and 30-40-80 Hz, respectively, for the WB.

CONCLUSIONS

Optimal sets of actuation frequencies to accurately obtain rheological parameters for WB, WM, and GM were determined from shear moduli measurements obtained via 3-dimensional direct inversion. We believe that our study is a first-step in developing a region-specific multifrequency MRE protocol for the human brain.

Development of a Second-Order System for Rapid Estimation of Maximum Brain Strain

Diffuse brain injuries are assessed with deformation-based criteria that utilize metrics based on rotational head kinematics to estimate brain injury severity. Although numerous metrics have been proposed, many are based on empirically-derived models that use peak kinematics, which often limit their applicability to a narrow range of head impact conditions. However, over a broad range of impact conditions, brain deformation response to rotational head motion behaves similarly to a second-order mechanical system, which utilizes the full kinematic time history of a head impact. This study describes a new brain injury metric called Diffuse Axonal Multi-Axis General Evaluation (DAMAGE). DAMAGE is based on the equations of motion of a three-degree-of-freedom, coupled 2nd-order system, and predicts maximum brain strain using the directionally dependent angular acceleration time-histories from a head impact. Parameters for the effective mass, stiffness, and damping were determined using simplified rotational pulses which were applied multiaxially to a 50th percentile adult human male finite element model. DAMAGE was then validated with a separate database of 1747 head impacts including helmet, crash, and sled tests and human volunteer responses. Relative to existing rotational brain injury metrics that were evaluated in this study, DAMAGE was found to be the best predictor of maximum brain strain.