Physics-informed UNets for discovering hidden elasticity in heterogeneous materials

Soft biological tissues often have complex mechanical properties due to variation in structural components. In this paper, we develop a novel UNet-based neural network model for inversion in elasticity (El-UNet) to infer the spatial distributions of mechanical parameters from strain maps as input images, normal stress boundary conditions, and domain physics information. We show superior performance - both in terms of accuracy and computational cost - by El-UNet compared to fully-connected physics-informed neural networks in estimating unknown parameters and stress distributions for isotropic linear elasticity. We characterize different variations of El-UNet and propose a self-adaptive spatial loss weighting approach. To validate our inversion models, we performed various finite-element simulations of isotropic domains with heterogenous distributions of material parameters to generate synthetic data. El-UNet is faster and more accurate than the fully-connected physics-informed implementation in resolving the distribution of unknown fields. Among the tested models, the self-adaptive spatially weighted models had the most accurate reconstructions in equal computation times. The learned spatial weighting distribution visibly corresponded to regions that the unweighted models were resolving inaccurately. Our work demonstrates a computationally efficient inversion algorithm for elasticity imaging using convolutional neural networks and presents a potential fast framework for three-dimensional inverse elasticity problems that have proven unachievable through previously proposed methods.

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.

End to end stroke triage using cerebrovascular morphology and machine learning

Background

Rapid and accurate triage of acute ischemic stroke (AIS) is essential for early revascularization and improved patient outcomes. Response to acute reperfusion therapies varies significantly based on patient-specific cerebrovascular anatomy that governs cerebral blood flow. We present an end-to-end machine learning approach for automatic stroke triage.

Methods

Employing a validated convolutional neural network (CNN) segmentation model for image processing, we extract each patient's cerebrovasculature and its morphological features from baseline non-invasive angiography scans. These features are used to detect occlusion's presence and the site automatically, and for the first time, to estimate collateral circulation without manual intervention. We then use the extracted cerebrovascular features along with commonly used clinical and imaging parameters to predict the 90 days functional outcome for each patient.

Results

The CNN model achieved a segmentation accuracy of 94% based on the Dice similarity coefficient (DSC). The automatic stroke detection algorithm had a sensitivity and specificity of 92% and 94%, respectively. The models for occlusion site detection and automatic collateral grading reached 96% and 87.2% accuracy, respectively. Incorporating the automatically extracted cerebrovascular features significantly improved the 90 days outcome prediction accuracy from 0.63 to 0.83.

Conclusion

The fast, automatic, and comprehensive model presented here can improve stroke diagnosis, aid collateral assessment, and enhance prognostication for treatment decisions, using cerebrovascular morphology.

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.

Ultrasound, photoacoustic, and magnetic resonance imaging to study hyperacute pathophysiology of traumatic and vascular brain injury

BACKGROUND AND PURPOSE

Cerebrovascular dynamics and pathomechanisms that evolve in the minutes and hours following traumatic vascular injury in the brain remain largely unknown. We investigated the pathophysiology evolution in mice within the first 3 hours after closed-head traumatic brain injury (TBI) and subarachnoid hemorrhage (SAH), two significant traumatic vascular injuries.

METHODS

We took a multimodal imaging approach using photoacoustic imaging, color Doppler ultrasound, and MRI to track injury outcomes using a variety of metrics.

RESULTS

Brain oxygenation and velocity-weighted volume of blood flow (VVF) values significantly decreased from baseline to 15 minutes after both TBI and SAH. TBI resulted in 19.2% and 41.0% ipsilateral oxygenation and VVF reductions 15 minutes postinjury, while SAH resulted in 43.9% and 85.0% ipsilateral oxygenation and VVF reduction (p < .001). We found partial recovery of oxygenation from 15 minutes to 3 hours after injury for TBI but not SAH. Hemorrhage, edema, reduced perfusion, and altered diffusivity were evident from MRI scans acquired 90-150 minutes after injury in both injury models, although the spatial distribution was mostly focal for TBI and diffuse for SAH.

CONCLUSIONS

The results reveal that the cerebral oxygenation deficits immediately following injuries are reversible for TBI and irreversible for SAH. Our findings can inform future studies on mitigating these early responses to improve long-term recovery.

Sensor-Based Assessment of Variability in Daily Physical Activity and Frailty

INTRODUCTION

Frailty is a common geriatric syndrome associated with decline in physiological reserve. While several digital biomarkers of daily physical activity (DPA) have been used in frailty assessment, the association between DPA variability and frailty is still not clear. The goal of this study was to determine the association between frailty and DPA variability.

METHODS

This is an observational cross-sectional study conducted between September 2012 and November 2013. Older adults (≥65 years), without any severe mobility disorder, and the ability to walk 10 m (with or without an assistive device) were eligible for the study. DPA including sitting, standing, walking, lying, and postural transition were recorded for 48 h continuously. DPA variability was analyzed from two perspectives: (i) DPA duration variability in terms of coefficient of variation (CoV) of sitting, standing, walking, and lying down durations; and (ii) DPA performance variability in terms of CoV of sit-to-stand (SiSt) and stand-to-sit (StSi) durations and stride time (i.e., slope of power spectral density - PSD).

RESULTS

Data was analyzed from 126 participants (44 non-frail, 60 pre-frail, and 22 frail). For DPA duration variability, CoV of lying and walking duration was significantly larger among non-frail compared to pre-frail and frail groups (p < 0.03, d = 0.89 ± 0.40). For DPA performance variability, StSi CoV and PSD slope were significantly smaller for non-frail compared to pre-frail and frail groups (p < 0.05, d = 0.78 ± 0.19).

CONCLUSION

Lower DPA duration variability in pre-frail and frail groups may be attributed to the set daily routines frail older adults tend to follow, compared to variable physical activity routines of non-frail older adults. Higher DPA performance variability in the frail group may be attributed to reduced physiological capabilities toward walking for longer durations and the reduced muscle strength in the lower extremities, leading to incosistency in performing postural transitions.

Associating Frailty and Dynamic Dysregulation between Motor and Cardiac Autonomic Systems

Frailty is a geriatric syndrome associated with the lack of physiological reserve and consequent adverse outcomes (therapy complications and death) in older adults. Recent research has shown associations between heart rate (HR) dynamics (HR changes during physical activity) with frailty. The goal of the present study was to determine the effect of frailty on the interconnection between motor and cardiac systems during a localized upper-extremity function (UEF) test. Fifty-six older adults aged 65 or older were recruited and performed the UEF task of rapid elbow flexion for 20-seconds with the right arm. Frailty was assessed using the Fried phenotype. Wearable gyroscopes and electrocardiography were used to measure motor function and HR dynamics. Using convergent cross-mapping (CCM) the interconnection between motor (angular displacement) and cardiac (HR) performance was assessed. A significantly weaker interconnection was observed among pre-frail and frail participants compared to non-frail individuals (p<0.01, effect size=0.81±0.08). Using logistic models pre-frailty and frailty were identified with sensitivity and specificity of 82% to 89%, using motor, HR dynamics, and interconnection parameters. Findings suggested a strong association between cardiac-motor interconnection and frailty. Adding CCM parameters in a multimodal model may provide a promising measure of frailty.

Abstract 106: Novel Imaging-based Morphological Markers For Improved Prediction Of Stroke Outcomes: A Machine Learning Approach

Patient selection for acute reperfusion therapies of acute ischemic stroke is based on assessment of the core and tissue-at-risk and the time from stroke onset. Response to treatment and clinical outcomes, however, vary significantly since patient-specific cerebrovascular anatomy plays a vital role in governing flow and reperfusion. We developed an end-to-end automated machine learning approach to extract advanced cerebrovascular morphological features, including tortuosity and collateral index, to improve outcome prediction, and patient selection.

Methods: Using our validated automatic cerebrovascular segmentation and feature extraction algorithm, we obtained the morphological properties of the brain vasculature, including vessel tortuosity, length, diameter, and bifurcation patterns, in MR angiography scans of 100 anonymized stroke patients. Each patient’s collateral index was also automatically graded using a previously established probabilistic atlas of healthy cerebrovasculature. To predict 90-day functional outcomes (mRS), a statistical model was trained using patients' clinical features (demographics, comorbidities, and baseline NIHSS), imaging features (initial ASPECTS, perfusion mismatch volume, and collateral index), and the extracted morphologic features (length, diameter, number of branches, vessel tortuosity, fractal dimension, and total volume). The predictions were compared against ground truth mRS to assess model accuracy. We also compared the model’s performance against conventional outcome predictors with only the clinical and imaging features.

Results: The conventional model including the common clinical and imaging features excluding the collateral index gave an area under the curve (AUC) of 0.63, which is comparable to previously published results. Including the automatically scored collateral index improved the accuracy to 0.74. We found that further including the morphological features to the predictor model significantly improved AUC accuracy to 0.83.

Conclusion: Including automatically extracted cerebrovascular morphologic features in traditional clinical and imaging markers significantly improves outcome prediction and response to treatment in stroke patients.

Elasticity imaging using physics-informed neural networks: Spatial discovery of elastic modulus and Poisson's ratio

Elasticity imaging is a technique that discovers the spatial distribution of mechanical properties of tissue using deformation and force measurements under various loading conditions. Given the complexity of this discovery, most existing methods approximate only one material parameter while assuming homogeneous distributions for the others. We employ physics-informed neural networks (PINN) in linear elasticity problems to discover the space-dependent distribution of both elastic modulus (E) and Poisson's ratio (ν) simultaneously, using strain data, normal stress boundary conditions, and the governing physics. We validated our model on three examples. First, we experimentally loaded hydrogel samples with embedded stiff inclusions, representing tumorous tissue, and compared the approximations against ground truth determined through tensile tests. Next, using data from finite element simulation of a rectangular domain containing a stiff circular inclusion, the PINN model accurately localized the inclusion and estimated both E and ν. We observed that in a heterogeneous domain, assuming a homogeneous ν distribution increases estimation error for stiffness as well as the area of the stiff inclusion, which could have clinical importance when determining size and stiffness of tumorous tissue. Finally, our model accurately captured spatial distribution of mechanical properties and the tissue interfaces on data from another computational model, simulating uniaxial loading of a rectangular hydrogel sample containing a human brain slice with distinct gray matter and white matter regions and complex geometrical features. This elasticity imaging implementation has the potential to be used in clinical imaging scenarios to reliably discover the spatial distribution of mechanical parameters and identify material interfaces such as tumors. STATEMENT OF SIGNIFICANCE: Our work is the first implementation of physics-informed neural networks to reconstruct both material parameters - Young's modulus and Poisson's ratio - and stress distributions for isotropic linear elastic materials by having deformation and force measurements. We comprehensively validate our model using experimental measurements and synthetic data generated using finite element modeling. Our method can be implemented in clinical elasticity imaging scenarios to improve diagnosis of tumors and for mechanical characterization of biomaterials and biological tissues in a minimally invasive manner.

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.

Physics-Informed Neural Networks for Brain Hemodynamic Predictions Using Medical Imaging

Determining brain hemodynamics plays a critical role in the diagnosis and treatment of various cerebrovascular diseases. In this work, we put forth a physics-informed deep learning framework that augments sparse clinical measurements with one-dimensional (1D) reduced-order model (ROM) simulations to generate physically consistent brain hemodynamic parameters with high spatiotemporal resolution. Transcranial Doppler (TCD) ultrasound is one of the most common techniques in the current clinical workflow that enables noninvasive and instantaneous evaluation of blood flow velocity within the cerebral arteries. However, it is spatially limited to only a handful of locations across the cerebrovasculature due to the constrained accessibility through the skull's acoustic windows. Our deep learning framework uses in vivo real-time TCD velocity measurements at several locations in the brain combined with baseline vessel cross-sectional areas acquired from 3D angiography images and provides high-resolution maps of velocity, area, and pressure in the entire brain vasculature. We validate the predictions of our model against in vivo velocity measurements obtained via four-dimensional (4D) flow magnetic resonance imaging (MRI) scans. We then showcase the clinical significance of this technique in diagnosing cerebral vasospasm (CVS) by successfully predicting the changes in vasospastic local vessel diameters based on corresponding sparse velocity measurements. We show this capability by generating synthetic blood flow data after cerebral vasospasm at various levels of stenosis. Here, we demonstrate that the physics-based deep learning approach can estimate and quantify the subject-specific cerebral hemodynamic variables with high accuracy despite lacking knowledge of inlet and outlet boundary conditions, which is a significant limitation for the accuracy of the conventional purely physics-based computational models.

Novel imaging markers for altered cerebrovascular morphology in aging, stroke, and Alzheimer's disease

BACKGROUND AND PURPOSE

Altered brain vasculature is a key phenomenon in several neurologic disorders. This paper presents a quantitative assessment of the anatomical variations in the Circle of Willis (CoW) and vascular morphology in healthy aging, acute ischemic stroke (AIS) and Alzheimer's Disease (AD).

METHODS

We used our novel automatic method to segment and extract geometric features of the cerebral vasculature from MR angiography scans of 175 healthy subjects, which were used to create a probabilistic atlas of cerebrovasculature and to study normal aging and intersubject variations in CoW anatomy. Subsequently, we quantified and analyzed vascular alterations in 45AIS and 50 AD patients, two prominent cerebrovascular and neurodegenerative disorders.

RESULTS

In the sampled cohort, we determined that the CoW is fully formed in only 35% of healthy adults and found significantly (p < .05) increased tortuosity and fractality, with increasing age and also with disease in both AIS and AD. We also found significantly lower vessel length, volume, and number of branches in AIS patients, as expected. The AD cerebral vessels exhibited significantly smaller diameter and more complex branching patterns, compared to age-matched healthy adults. These changes were significantly heightened (p < .05) among healthy, early onset mild AD, and moderate/severe dementia groups.

CONCLUSION

Although our study does not include longitudinal data due to paucity of such datasets, the specific geometric features and quantitative comparisons demonstrate the potential for using vascular morphology as a noninvasive imaging biomarker for neurologic disorders.

4D Reconstruction and Identification of Carotid Artery Stenosis Utilizing a Novel Pulsatile Ultrasound Phantom

As a major application focus of vascular ultrasonography, the carotid artery has long been the subject of phantom design and procedure focus. It is therefore important to devise procedures that are minimally invasive and informative, initially using a physiologically accurate anthropomorphic phantom to validate the methodology. In this article, a novel phantom design protocol is presented that enables the efficient production of a pulsatile ultrasound phantom consisting of soft and vascular tissue mimics, as well as a blood surrogate fluid. These components when combined give the phantom high acoustic compatibility and lifelike mechanical properties. The phantom was developed using "at-home" purchasable components and 3D printing technology. The phantom was subsequently used to develop a 4D reconstruction algorithm of the pulsing vessel in MATLAB. In pattern with recent developments in medical imaging, the 4D reconstruction enables clinicians to view vessel wall motion in a 3D space without the need for manual intervention. The reconstruction algorithm also produces measured inner luminal areas and vessel wall thickness, providing further information relating to structural properties and stenosis, as well as elastic properties such as arterial stiffness, which could provide helpful markers for disease diagnosis. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Constructing a pulsatile ultrasound phantom model Support Protocol: Creating a vascular mimic mold Basic Protocol 2: Creating a 4D reconstruction from ultrasound frames.

The importance of axonal directions in the brainstem injury during neurosurgical interventions

Brainstem, which connects the distal part of the brain and the spinal cord, contains main motor and sensory nerves and facilitates communication between the cerebrum, cerebellum, and spinal cord. Due to the complicated anatomy and neurostructure of brainstem, surgical interventions to resect brainstem tumors are particularly challenging, and new approaches to reduce the risk of surgical brain injury are of utmost importance. Although previous studies have investigated the structural anisotropy of brain white matter, the effect of axonal fibers on the mechanical properties of white matter has not yet been fully understood. The current study aims to compare the effect of axonal orientation on changes in material properties of brainstem under large deformations and failure through a novel approach. Using diffusion tensor imaging (DTI) on ex-vivo bovine brains, we determined the orientation of axons in brainstem. We extracted brainstem samples in two orthogonal directions, parallel and perpendicular to the axons, and subjected to uniaxial tension to reach the failure at loading rates of 50 mm/min and 150 mm/min. The results showed that the tearing energy and failure strain of samples with axons parallel to the force direction were approximately 1.5 times higher than the samples with axons perpendicular to the force direction. The results also revealed that as the sample's initial length increases, its failure strain decreases. These results emphasize the importance of the axon orientation in the mechanical properties of brainstem, and suggest that considering the directional-dependent behavior for this tissue could help to propose new surgical interventions for reducing the risk of injury during tumor resection.

Dual-task performance is associated with brain MRI Morphometry in individuals with mild cognitive impairment

BACKGROUND AND PURPOSE

Cognitive impairment is a critical health problem in the elderly population. Research has shown that patients with mild cognitive impairment (MCI) may develop dementia in later years. Therefore, early identification of MCI could allow for interventions to help delay the progression of this devastating disease. Our objective in this study was to detect the early presence of MCI in elderly patients via neuroimaging and dual-task performance.

METHODS

Brain MRI scans from 21 older adult volunteers, including cognitively healthy adults (HA, n = 9, age = 68-79 years) and mild cognitively impaired (MCI, n = 12, age = 66-92 years) were analyzed using automatic segmentation techniques. Regional volume, surface area, and thickness measures were correlated with simultaneous performance of motor and cognitive tasks (dual-task) within a novel upper-extremity function (UEF) test, using multivariate analysis of variance models.

RESULTS

We found significant associations of dual-task performance with volume of five cortical brain regions (P ≤ .048) and thickness of 13 regions (P ≤ .043) within the frontal, temporal, and parietal lobes. There was a significant interaction effect of cognitive group on dual-task score for the inferior temporal gyrus volume (P ≤ .034), and the inferior parietal lobule, inferior temporal gyrus, and middle temporal gyrus average thickness (P ≤ .037).

CONCLUSIONS

This study highlighted the potential of dual-tasking and MRI morphometric changes as a simple and accurate tool for early detection of cognitive impairment among community-dwelling older adults. The strong interaction effects of cognitive group on UEF dual-task score suggest higher association between atrophy of these brain structures and compromised dual-task performance among the MCI group.

Automatic segmentation, feature extraction and comparison of healthy and stroke cerebral vasculature

Accurate segmentation of cerebral vasculature and a quantitative assessment of its morphology is critical to various diagnostic and therapeutic purposes and is pertinent to studying brain health and disease. However, this is still a challenging task due to the complexity of the vascular imaging data. We propose an automated method for cerebral vascular segmentation without the need of any manual intervention as well as a method to skeletonize the binary segmented map to extract vascular geometric features and characterize vessel structure. We combine a Hessian-based probabilistic vessel-enhancing filtering with an active-contour-based technique to segment magnetic resonance and computed tomography angiograms (MRA and CTA) and subsequently extract the vessel centerlines and diameters to calculate the geometrical properties of the vasculature. Our method was validated using a 3D phantom of the Circle-of-Willis region, demonstrating 84% mean Dice similarity coefficient (DSC) and 85% mean Pearson's correlation coefficient (PCC) with minimal modified Hausdorff distance (MHD) error (3 surface pixels at most), and showed superior performance compared to existing segmentation algorithms upon quantitative comparison using DSC, PCC and MHD. We subsequently applied our algorithm to a dataset of 40 subjects, including 1) MRA scans of healthy subjects (n = 10, age = 30 ± 9), 2) MRA scans of stroke patients (n = 10, age = 51 ± 15), 3) CTA scans of healthy subjects (n = 10, age = 62 ± 12), and 4) CTA scans of stroke patients (n = 10, age = 68 ± 11), and obtained a quantitative comparison between the stroke and normal vasculature for both imaging modalities. The vascular network in stroke patients compared to age-adjusted healthy subjects was found to have a significantly (p < 0.05) higher tortuosity (3.24 ± 0.88 rad/cm vs. 7.17 ± 1.61 rad/cm for MRA, and 4.36 ± 1.32 rad/cm vs. 7.80 ± 0.92 rad/cm for CTA), higher fractal dimension (1.36 ± 0.28 vs. 1.71 ± 0.14 for MRA, and 1.56 ± 0.05 vs. 1.69 ± 0.20 for CTA), lower total length (3.46 ± 0.99 m vs. 2.20 ± 0.67 m for CTA), lower total volume (61.80 ± 18.79 ml vs. 34.43 ± 22.9 ml for CTA), lower average diameter (2.4 ± 0.21 mm vs. 2.18 ± 0.07 mm for CTA), and lower average branch length (4.81 ± 1.97 mm vs. 8.68 ± 2.03 mm for MRA), respectively. We additionally studied the change in vascular features with respect to aging and imaging modality. While we observed differences between features as a result of aging, statistical analysis did not show any significant differences, whereas we found that the number of branches were significantly different (p < 0.05) between the two imaging modalities (201 ± 73 for MRA vs. 189 ± 69 for CTA). Our segmentation and feature extraction algorithm can be applied on any imaging modality and can be used in the future to automatically obtain the 3D segmented vasculature for diagnosis and treatment planning as well as to study morphological changes due to stroke and other cerebrovascular diseases (CVD) in the clinic.

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.

Mind the gap: A mechanobiological hypothesis for the role of gap junctions in the mechanical properties of injured brain tissue

Despite more than half a century of work on the brain biomechanics, there are still significant unknowns about this tissue. Since the brain is highly susceptible to injury, damage biomechanics has been one of the main areas of interest to the researchers in the field of brain biomechanics. In many previous studies, mechanical properties of brain tissue under sub-injury and injury level loading conditions have been addressed; however, to the best of our knowledge, the role of cell-cell interactions in the mechanical behavior of brain tissue has not been well examined yet. This note introduces the hypothesis that gap junctions as the major type of cell-cell junctions in the brain tissue play a pivotal role in the mechanical properties of the tissue and their failure during injury leads to changes in brain's material properties. According to this hypothesis, during an injury, the gap junctions are damaged, leading to a decrease in tissue stiffness, whereas following the injury, new junction proteins are expressed, leading to an increase in tissue stiffness. We suggest that considering the mechanobiological effect of gap junctions in the material properties of brain tissue may help better understand the brain injury mechanism.

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.

Structural Anisotropy vs. Mechanical Anisotropy: The Contribution of Axonal Fibers to the Material Properties of Brain White Matter

Brain's micro-structure plays a critical role in its macro-structure material properties. Since the structural anisotropy in the brain white matter has been introduced due to axonal fibers, considering the direction of axons in the continuum models has been mediated to improve the results of computational simulations. The aim of the current study was to investigate the role of fiber direction in the material properties of brain white matter and compare the mechanical behavior of the anisotropic white matter and the isotropic gray matter. Diffusion tensor imaging (DTI) was employed to detect the direction of axons in white matter samples, and tensile stress-relaxation loads up to 20% strains were applied on bovine gray and white matter samples. In order to calculate the nonlinear and time-dependent properties of white matter and gray matter, a visco-hyperelastic model was used. The results indicated that the mechanical behavior of white matter in two orthogonal directions, parallel and perpendicular to axonal fibers, are significantly different. This difference indicates that brain white matter could be assumed as an anisotropic material and axons have contribution in the mechanical properties. Also, up to 15% strain, white matter samples with axons parallel to the force direction are significantly stiffer than both the gray matter samples and white matter samples with axons perpendicular to the force direction. Moreover, the elastic moduli of white matter samples with axons both parallel and perpendicular to the loading direction and gray matter samples at 15-20% strain are not significantly different. According to these observations, it is suggested that axons have negligible roles in the material properties of white matter when it is loaded in the direction perpendicular to the axon direction. Finally, this observation showed that the anisotropy of brain tissue not only has effects on the elastic behavior, but also has effects on the viscoelastic behavior.

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.

Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment

BACKGROUND

Frailty is a highly recognized geriatric syndrome resulting in decline in reserve across multiple physiological systems. Impaired physical function is one of the major indicators of frailty. The goal of this study was to evaluate an algorithm that discriminates between frailty groups (non-frail and pre-frail/frail) based on gait performance parameters derived from unsupervised daily physical activity (DPA).

METHODS

DPA was acquired for 48 h from older adults (≥65 years) using a tri-axial accelerometer motion-sensor. Continuous bouts of walking for 20s, 30s, 40s, 50s and 60s without pauses were identified from acceleration data. These were then used to extract qualitative measures (gait variability, gait asymmetry, and gait irregularity) and quantitative measures (total continuous walking duration and maximum number of continuous steps) to characterize gait performance. Association between frailty and gait performance parameters was assessed using multinomial logistic models with frailty as the dependent variable, and gait performance parameters along with demographic parameters as independent variables.

RESULTS

One hundred twenty-six older adults (44 non-frail, 60 pre-frail, and 22 frail, based on the Fried index) were recruited. Step- and stride-times, frequency domain gait variability, and continuous walking quantitative measures were significantly different between non-frail and pre-frail/frail groups (p < 0.05). Among the five different durations (20s, 30s, 40s, 50s and 60s), gait performance parameters extracted from 60s continuous walks provided the best frailty assessment results. Using the 60s gait performance parameters in the logistic model, pre-frail/frail group (vs. non-frail) was identified with 76.8% sensitivity and 80% specificity.

DISCUSSION

Everyday walking characteristics were found to be associated with frailty. Along with quantitative measures of physical activity, qualitative measures are critical elements representing the early stages of frailty. In-home gait assessment offers an opportunity to screen for and monitor frailty.

TRIAL REGISTRATION

The clinical trial was retrospectively registered on June 18th, 2013 with ClinicalTrials.gov, identifier NCT01880229.

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.

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.

FRAILTY ASSESSMENT BASED ON THE QUALITY OF DAILY WALKING

Frailty is an increasingly recognized geriatric syndrome resulting in age-related decline in reserve across multiple physiologic systems. An impaired physical function is a prime indicator of frailty. In this study, we aim to implement a body-worn sensor to characterize the quantity and quality of everyday walking, and establish associations between gait impairment and frailty. Daily physical activity was acquired for 48 hours from 125 older adults (≥65 years; 44 non-frail, 60 pre-frail, and 21 frail based on the Fried gold standard) using a tri-axial accelerometer motion-sensor. Continuous purposeful walks (≥60s) without pauses were identified from time-domain acceleration data. Power spectral density (PSD) analysis was performed to define higher gait variability, which was identified by a shorter and wider PSD peak. Association between frailty and gait parameters was assessed using multivariable nominal logistic models with frailty as the dependent variable, and demographic parameters along with the gait parameters as the independent variables. Stride times, PSD gait variability, and total and maximum continuous purposeful walking duration were significantly different between non-frail and pre-frail/frail groups (p<0.05). Using a step-wise model with the above qualitative and quantitative gait parameters as predictors, the pre-frail/frail group (vs. non-frail) was identified with 71.4% sensitivity and 75.4% specificity. Everyday walking characteristics were found to be accurate determinants of frailty. Along with quantitative measures of physical activity, qualitative measures are critical elements representing the stages of frailty. In-home gait analysis is advantageous over clinical gait analysis as it enables cost- and space-effective continuous monitoring.

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.

The Effect of Pain Relief on Daily Physical Activity: In-Home Objective Physical Activity Assessment in Chronic Low Back Pain Patients after Paravertebral Spinal Block

This study evaluates the effect of paravertebral spinal injection (PSI), utilizing both subjective and objective assessments in chronic low back pain (LBP) associated with facet joint arthrosis over a one-month duration. Subjective questionnaires included the visual analogue scale (VAS) for pain, the Oswestry Disability Index, the Health Survey SF-12, and the short Falls Efficacy Scale International (FES-I). Objective assessments included in-clinic gait and Timed Up and Go (TUG) tests using wearable sensors, as well as 48 h daily physical activity (DPA) monitored using a chest-worn triaxial accelerometer. Subjective and objective measures were performed prior to treatment, immediately after the treatment, and one month after the treatment. Eight LBP patients were recruited for this study (mean age = 54 ± 13 years, body mass index = 31.41 ± 6.52 kg/m², 50% males). Results show significant decrease in pain (~55%, p < 0.05) and disability (Oswestry scores, ~21%, p < 0.05). In-clinic gait and TUG were also significantly improved (~16% and ~18% faster walking and shorter TUG, p < 0.05); however, DPA, including the percentage of physical activities (walking and standing) and the number of steps, showed no significant change after PSI (p > 0.25; effect size ≤ 0.44). We hypothesize that DPA may continue to be truncated to an extent by conditioned fear-avoidance, a psychological state that may prevent increase in daily physical activity to avoid pain.

Mechanistic Insights into Human Brain Impact Dynamics through Modal Analysis

Although concussion is one of the greatest health challenges today, our physical understanding of the cause of injury is limited. In this Letter, we simulated football head impacts in a finite element model and extracted the most dominant modal behavior of the brain's deformation. We showed that the brain's deformation is most sensitive in low frequency regimes close to 30 Hz, and discovered that for most subconcussive head impacts, the dynamics of brain deformation is dominated by a single global mode. In this Letter, we show the existence of localized modes and multimodal behavior in the brain as a hyperviscoelastic medium. This dynamical phenomenon leads to strain concentration patterns, particularly in deep brain regions, which is consistent with reported concussion pathology.

Detection of American Football Head Impacts Using Biomechanical Features and Support Vector Machine Classification

Accumulation of head impacts may contribute to acute and long-term brain trauma. Wearable sensors can measure impact exposure, yet current sensors do not have validated impact detection methods for accurate exposure monitoring. Here we demonstrate a head impact detection method that can be implemented on a wearable sensor for detecting field football head impacts. Our method incorporates a support vector machine classifier that uses biomechanical features from the time domain and frequency domain, as well as model predictions of head-neck motions. The classifier was trained and validated using instrumented mouthguard data from collegiate football games and practices, with ground truth data labels established from video review. We found that low frequency power spectral density and wavelet transform features (10~30 Hz) were the best performing features. From forward feature selection, fewer than ten features optimized classifier performance, achieving 87.2% sensitivity and 93.2% precision in cross-validation on the collegiate dataset (n = 387), and over 90% sensitivity and precision on an independent youth dataset (n = 32). Accurate head impact detection is essential for studying and monitoring head impact exposure on the field, and the approach in the current paper may help to improve impact detection performance on wearable sensors.

Pilot Findings of Brain Displacements and Deformations during Roller Coaster Rides

With 300,000,000 riders annually, roller coasters are a popular recreational activity. Although the number of roller coaster injuries is relatively low, the precise effect of roller coaster rides on our brains remains unknown. Here we present the quantitative characterization of brain displacements and deformations during roller coaster rides. For two healthy adult male subjects, we recorded head accelerations during three representative rides, and, for comparison, during running and soccer headers. From the recordings, we simulated brain displacements and deformations using rigid body dynamics and finite element analyses. Our findings show that despite having lower linear accelerations than sports head impacts, roller coasters may lead to brain displacements and strains comparable to mild soccer headers. The peak change in angular velocity on the rides was 9.9 rad/sec, which was higher than the 5.6 rad/sec in soccer headers with ball velocities reaching 7 m/sec. Maximum brain surface displacements of 4.0 mm and maximum principal strains of 7.6% were higher than in running and similar to soccer headers, but below the reported average concussion strain. Brain strain rates during roller coaster rides were similar to those in running, and lower than those in soccer headers. Strikingly, on the same ride and at a similar position, the two subjects experienced significantly different head kinematics and brain deformation. These results indicate that head motion and brain deformation during roller coaster rides are highly sensitive to individual subjects. Although our study suggests that roller coaster rides do not present an immediate risk of acute brain injury, their long-term effects require further longitudinal study.

Mechanical behavior of porcine thoracic aorta in physiological and supra-physiological intraluminal pressures

Understanding the mechanical behavior of aorta under supra-physiological loadings is an important aspect of modeling tissue behavior in various applications that involve large deformations. Utilizing inflation–extension experiments, the mechanical behavior of porcine descending thoracic aortic segments under physiological and supra-physiological intraluminal pressures was investigated. The pressure was changed in the range of 0–70 kPa and the deformation of the segment was determined in three dimensions using a custom-made motion capture system. An orthotropic Fung-type constitutive model was characterized by implementing a novel computationally efficient framework that ensured material stability for numerical simulations. The nonlinear rising trend of circumferential stretch ratio [Formula: see text] from outer toward inner wall was significantly increased at higher pressures. The increase in [Formula: see text] from physiological pressure (13 kPa) to 70 kPa was 13% at the outer wall and 22% at the inner wall; in this pressure range, the longitudinal stretch ratio [Formula: see text] increased 20%. A significant nonlinearity in the material behavior was observed as in the same pressure range, and the circumferential and longitudinal Cauchy stresses at the inner wall were increased 16 and 18 times, respectively. The overall constitutive model was verified in several loading paths in the [Formula: see text] space to confirm its applicability in multi-axial loading conditions.

Modeling and Optimization of Airbag Helmets for Preventing Head Injuries in Bicycling

Bicycling is the leading cause of sports-related traumatic brain injury. Most of the current bike helmets are made of expanded polystyrene (EPS) foam and ultimately designed to prevent blunt trauma, e.g., skull fracture. However, these helmets have limited effectiveness in preventing brain injuries. With the availability of high-rate micro-electrical-mechanical systems sensors and high energy density batteries, a new class of helmets, i.e., expandable helmets, can sense an impending collision and expand to protect the head. By allowing softer liner medium and larger helmet sizes, this novel approach in helmet design provides the opportunity to achieve much lower acceleration levels during collision and may reduce the risk of brain injury. In this study, we first develop theoretical frameworks to investigate impact dynamics of current EPS helmets and airbag helmets-as a form of expandable helmet design. We compared our theoretical models with anthropomorphic test dummy drop test experiments. Peak accelerations obtained from these experiments with airbag helmets achieve up to an 8-fold reduction in the risk of concussion compared to standard EPS helmets. Furthermore, we construct an optimization framework for airbag helmets to minimize concussion and severe head injury risks at different impact velocities, while avoiding excessive deformation and bottoming-out. An optimized airbag helmet with 0.12 m thickness at 72 ± 8 kPa reduces the head injury criterion (HIC) value to 190 ± 25 at 6.2 m/s head impact velocity compared to a HIC of 1300 with a standard EPS helmet. Based on a correlation with previously reported HIC values in the literature, this airbag helmet design substantially reduces the risks of severe head injury up to 9 m/s.

Energy-based constitutive modelling of local material properties of canine aortas

This study aims at determining the in vitro anisotropic mechanical behaviour of canine aortic tissue. We specifically focused on spatial variations of these properties along the axis of the vessel. We performed uniaxial stretch tests on canine aortic samples in both circumferential and longitudinal directions, as well as histological examinations to derive the tissue's fibre orientations. We subsequently characterized a constitutive model that incorporates both phenomenological and structural elements to account for macroscopic and microstructural behaviour of the tissue. We showed the two fibre families were oriented at similar angles with respect to the aorta's axis. We also found significant changes in mechanical behaviour of the tissue as a function of axial position from proximal to distal direction: the fibres become more aligned with the aortic axis from 46° to 30°. Also, the linear shear modulus of media decreased as we moved distally along the aortic axis from 139 to 64 kPa. These changes derived from the parameters in the nonlinear constitutive model agreed well with the changes in tissue structure. In addition, we showed that isotropic contribution, carried by elastic lamellae, to the total stress induced in the tissue decreases at higher stretch ratios, whereas anisotropic stress, carried by collagen fibres, increases. The constitutive models can be readily used to design computational models of tissue deformation during physiological loading cycles. The findings of this study extend the understanding of local mechanical properties that could lead to region-specific diagnostics and treatment of arterial diseases.

Resonance of human brain under head acceleration

Although safety standards have reduced fatal head trauma due to single severe head impacts, mild trauma from repeated head exposures may carry risks of long-term chronic changes in the brain's function and structure. To study the physical sensitivities of the brain to mild head impacts, we developed the first dynamic model of the skull-brain based on in vivo MRI data. We showed that the motion of the brain can be described by a rigid-body with constrained kinematics. We further demonstrated that skull-brain dynamics can be approximated by an under-damped system with a low-frequency resonance at around 15 Hz. Furthermore, from our previous field measurements, we found that head motions in a variety of activities, including contact sports, show a primary frequency of less than 20 Hz. This implies that typical head exposures may drive the brain dangerously close to its mechanical resonance and lead to amplified brain-skull relative motions. Our results suggest a possible cause for mild brain trauma, which could occur due to repetitive low-acceleration head oscillations in a variety of recreational and occupational activities.

Six Degree-of-Freedom Measurements of Human Mild Traumatic Brain Injury

This preliminary study investigated whether direct measurement of head rotation improves prediction of mild traumatic brain injury (mTBI). Although many studies have implicated rotation as a primary cause of mTBI, regulatory safety standards use 3 degree-of-freedom (3DOF) translation-only kinematic criteria to predict injury. Direct 6DOF measurements of human head rotation (3DOF) and translation (3DOF) have not been previously available to examine whether additional DOFs improve injury prediction. We measured head impacts in American football, boxing, and mixed martial arts using 6DOF instrumented mouthguards, and predicted clinician-diagnosed injury using 12 existing kinematic criteria and 6 existing brain finite element (FE) criteria. Among 513 measured impacts were the first two 6DOF measurements of clinically diagnosed mTBI. For this dataset, 6DOF criteria were the most predictive of injury, more than 3DOF translation-only and 3DOF rotation-only criteria. Peak principal strain in the corpus callosum, a 6DOF FE criteria, was the strongest predictor, followed by two criteria that included rotation measurements, peak rotational acceleration magnitude and Head Impact Power (HIP). These results suggest head rotation measurements may improve injury prediction. However, more 6DOF data is needed to confirm this evaluation of existing injury criteria, and to develop new criteria that considers directional sensitivity to injury.

Computational simulation of the mechanical response of brain tissue under blast loading

In the present study, numerical simulations of nonlinear wave propagation and shock formation in brain tissue have been presented and a new mechanism of injury for blast-induced neurotrauma (BINT) is proposed. A quasilinear viscoelastic (QLV) constitutive material model was used that encompasses the nonlinearity as well as the rate dependence of the tissue relevant to BINT modeling. A one-dimensional model was implemented using the discontinuous Galerkin finite element method and studied with displacement- and pressure-input boundary conditions. The model was validated against LS-DYNA finite element code and theoretical results for specific conditions that resulted in shock wave formation. It was shown that a continuous wave can become a shock wave as it propagates in the QLV brain tissue when the initial changes in acceleration are beyond a certain limit. The high spatial gradient of stress and strain at the shock front cause large relative motions at the cellular scale at high temporal rates even when the maximum stresses and strains are relatively low. This gradient-induced local deformation may occur away from the boundary and is proposed as a contributing factor to the diffuse nature of BINT.

Mechanical response of brain tissue under blast loading

In this study, a framework for understanding the propagation of stress waves in brain tissue under blast loading has been developed. It was shown that tissue nonlinearity and rate dependence are the key parameters in predicting the mechanical behavior under such loadings, as they determine whether traveling waves could become steeper and eventually evolve into shock discontinuities. To investigate this phenomenon, in the present study, brain tissue has been characterized as a quasi-linear viscoelastic (QLV) material and a nonlinear constitutive model has been developed for the tissue that spans from medium loading rates up to blast rates. It was shown that development of shock waves is possible inside the head in response to high rate compressive pressure waves. Finally, it was argued that injury to the nervous tissue at the microstructural level could be partly attributed to the high stress gradients with high rates generated at the shock front and this was proposed as a mechanism of injury in brain tissue.