Peak Maximum Principal Strain Research Articles

American Football On-Field Head Impact Kinematics: Influence of Acceleration Signal Characteristics on Peak Maximal Principal Strain.

Recorded head kinematics from head-impact measurement devices (HIMd) are pivotal for evaluating brain stress and strain through head finite element models (hFEM). The variability in kinematic recording windows across HIMd presents challenges as they yield inconsistent hFEM responses. Despite establishing an ideal recording window for maximum principal strain (MPS) in brain tissue, uncertainties persist about the impact characteristics influencing vulnerability when this window is shortened. This study aimed to scrutinize factors within impact kinematics affecting the reliability of different recording windows on whole-brain peak MPS using a validated hFEM. Utilizing 53 on-field head impacts recorded via an instrumented mouthguard during a Canadian varsity football game, 10 recording windows were investigated with varying pre- and post-impact-trigger durations. Tukey pair-wise comparisons revealed no statistically significant differences in MPS responses for the different recording windows. However, specific impacts showed marked variability up to 40%. It was found, through correlation analyses, that impacts with lower peak linear acceleration exhibited greater response variability across different pre-trigger durations. Signal shape, analyzed through spectral analysis, influenced the time required for MPS development, resulting in specific impacts requiring a prolonged post-trigger duration. This study adds to the existing consensus on standardizing HIMd acquisition time windows and sheds light on impact characteristics leading to peak MPS variation across different head impact kinematic recording windows. Considering impact characteristics in research assessments is crucial, as certain impacts, affected by recording duration, may lead to significant errors in peak MPS responses during cumulative longitudinal exposure assessments.

Biomechanical Study of Three Cannulated Screws Configurations for Femur Neck Fracture: A Finite Element Analysis.

To improve the performance of cannulated screws (CSs) in the treatment of femoral neck fractures (FNF), a number of new screw configurations have been proposed. However, most of the studies have only analyzed the biomechanical performance of different screw configurations under static conditions. This study aimed to investigate the biomechanical performance of three cannulated screws configurations under different loadings through finite element analysis. In this FEA study, nine numerical models of proximal femur were employed to analyze the mechanical response of various fracture types and different fixation strategies (three inverted triangular parallel cannulated screws (TCS), four non-parallel cannulated screws (FCS) and biplane double-supported screw fixation (BDSF) respectively). The maximum principal strain (MPS) on the proximal femur and the von Mises stress on the screws were compared for different models. In Pauwels I and II fractures, FCS had the lowest peak MPS on the proximal femur and the BDSF had highest peak MPS value. In Pauwels III fractures, BDSF performance in MPS is improved and better than FCS under partial loading conditions. FCS exhibits the lowest von Mises stress in all load conditions for all fracture types, demonstrating minimal risk of screws breakage. FCS is an ideal screw configuration for the treatment of FNF. And BDSF has shown potential in the treatment of Pauwels type III FNF.

Modal analysis of computational human brain dynamics during helmeted impacts

Sports-related mild traumatic brain injury (mTBI) is a growing public health concern, affecting millions in the U.S., annually. Current helmets are primarily designed to mitigate head kinematics, despite the importance of the brain substructures mechanics in mTBI mechanism. Therefore, it is crucial to consider the dynamical behavior of brain substructures, which has been shown in prior studies to be associated with strain concentration. Here, we studied the modal behavior and strain patterns of the substructures of the brain finite element (FE) model through Dynamic Mode Decomposition. We conducted side and front impact pendulum tests on a dummy headform equipped with hockey, football, ski, and bicycle helmets. After simulating the impact tests using a brain FE model, we calculated the dynamic modes of this computational model for the whole brain, corpus callosum, brainstem, and cerebellum. The main mode of oscillation in all regions for all helmet types occurred around the frequency regime of 7–15 Hz. Also, in cerebellum, a second harmonic was observed at 40–50 Hz in front impact, and 38 and 62 Hz in side impact in bicycle and ski helmets, respectively. Furthermore, we analyzed the correlation between the modal response and peak maximum principal strain (MPS). These analyses mostly showed a direct association between the computational modal behavior and MPS, where helmet tests with closely spaced modes and high-frequency modal amplitudes led to higher MPS values. This association between the computational modal behavior and strain patterns demonstrated a potential for improving helmet designs through a novel design objective.Statement of significance: Sport-related mild traumatic brain injury (mTBI), which is one of the leading cause of death, can be reduced in severity by using headgears including helmets. Despite the recent innovations and technologies in helmet design, there are important factors that still have been missed. While it’s been shown that the brain substructures mechanics play an important role in mTBI mechanism, current helmets are designed to only mitigate the head kinematics. Moreover, dynamical behavior of these substructures, and existence of multimodal behavior in the brain are factors that have not been addressed in designing helmets. This paper shows the effect of different helmet types on modal behavior of the brain substructures and how the dynamical modes of these regions can be affected by using various helmet types.

A Morphologically Individualized Deep Learning Brain Injury Model.

The brain injury modeling community has recommended improving model subject specificity and simulation efficiency. Here, we extend an instantaneous (< 1 sec) convolutional neural network (CNN) brain model based on the anisotropic Worcester Head Injury Model (WHIM) V1.0 to account for strain differences due to individual morphological variations. Linear scaling factors relative to the generic WHIM along the three anatomical axes are used as additional CNN inputs. To generate training samples, the WHIM is randomly scaled to pair with augmented head impacts randomly generated from real-world data for simulation. An estimation of voxelized peak maximum principal strain of the whole-brain is said to be successful when the linear regression slope and Pearson's correlation coefficient relative to directly simulated do not deviate from 1.0 (when identical) by more than 0.1. Despite a modest training dataset (N = 1363 vs. ∼5.7 k previously), the individualized CNN achieves a success rate of 86.2% in cross-validation for scaled model responses, and 92.1% for independent generic model testing for impacts considered as complete capture of kinematic events. Using 11 scaled subject-specific models (with scaling factors determined from pre-established regression models based on head dimensions and sex and age information, and notably, without neuroimages), the morphologically individualized CNN remains accurate for impacts that also yield successful estimations for the generic WHIM. The individualized CNN instantly estimates subject-specific and spatially detailed peak strains of the entire brain and thus, supersedes others that report a scalar peak strain value incapable of informing the location of occurrence. This tool could be especially useful for youths and females due to their anticipated greater morphological differences relative to the generic model, even without the need for individual neuroimages. It has potential for a wide range of applications for injury mitigation purposes and the design of head protective gears. The voxelized strains also allow for convenient data sharing and promote collaboration among research groups.

The Effects of True Triaxial Loading and Unloading Rates on the Damage Mechanical Properties of Sandstone

Coal is the main energy source in China. In the process of coal resource mining, the surrounding rock of roadways is often in the complex stress environment of “three heights and one disturbance”. At the same time, rocks in the stratum are often in a three-way unequal pressure state under the action of geological structure, and conventional rock mechanics tests cannot study the mechanical properties of rocks under actual stress conditions; thus, this is based on the self-developed true triaxial multifunctional fluid–structure coupling test system to study the damage mechanical Properties of Sandstone. The results are shown as follows: With an increase in loading rate, the peak damage Dcr of sandstone decreases, but the initial damage Da increases in the elastic stage, and the brittleness of sandstone weakens. With the increase in the unloading rate, Dcr increases, but Da decreases in the elastic stage, and the sandstone brittleness increases first, then decreases. In addition, the peak maximum principal strain ε1maxfirst decreases rapidly and then slowly; the peak minimum principal strain ε3max increases first, then decreases slowly, and increases slowly; the peak intermediate principal strain ε2max decreases slowly; and the peak volume strain εvmax increases rapidly first and then slowly with increases in the loading rate. With an increase in the unloading rate, ε1max increases rapidly first, then decreases slowly, then increases rapidly and finally increases slowly; ε3max first decreases slowly, then increases slowly, and finally decreases slowly; and ε2max increases slowly then decreases slowly. εvmax decreases rapidly first and then increases slowly with increasing loading rate.

American Football Helmet Effectiveness Against a Strain-Based Concussion Mechanism.

Brain strain is increasingly being used in helmet design and safety performance evaluation as it is generally considered as the primary mechanism of concussion. In this study, we investigate whether different helmet designs can meaningfully alter brain strains using two commonly used metrics, peak maximum principal strain (MPS) of the whole brain and cumulative strain damage measure (CSDM). A convolutional neural network (CNN) that instantly produces detailed brain strains is first tested for accuracy for helmeted head impacts. Based on N = 144 impacts in 12 impact conditions from three random and representative helmet models, we conclude that the CNN is sufficiently accurate for helmet testing applications, for elementwise MPS (success rate of 98.6%), whole-brain peak MPS and CSDM (coefficient of determination of 0.977 and 0.980, with root mean squared error of 0.015 and 0.029, respectively). We then apply the technique to 23 football helmet models (N = 1104 impacts) to reproduce elementwise MPS. Assuming a concussion would occur when peak MPS or CSDM exceeds a threshold, we sweep their thresholds across the value ranges to evaluate the number of predicted hypothetical concussions that different helmets sustain across the impact conditions. Relative to the 12 impact conditions tested, we find that the "best" and "worst" helmets differ by an average of 22.5% in terms of predicted concussions, ranging from 0 to 42% (the latter achieved at the threshold value of 0.28 for peak MPS and 0.4 for CSDM, respectively). Such a large variation among helmets in strain-based concussion predictions demonstrate that helmet designs can still be optimized in a clinically meaningful way. The robustness and accuracy of the CNN tool also suggest its potential for routine use for helmet design and safety performance evaluation in the future.The CNN is freely available online at https://github.com/Jilab-biomechanics/CNN-brain-strains .

Instantaneous Brain Strain Estimation for Automotive Head Impacts via Deep Learning.

Efficient brain strain estimation is critical for routine application of a head injury model. Lately, a convolutional neural network (CNN) has been successfully developed to estimate spatially detailed brain strains instantly and accurately in contact sports. Here, we extend its application to automotive head impacts, where impact profiles are typically more complex with longer durations. Head impact kinematics (N=458) from two public databases were used to generate augmented impacts (N=2694). They were simulated using the anisotropic Worcester Head Injury Model (WHIM) V1.0, which provided baseline elementwise peak maximum principal strain (MPS). For each augmented impact, rotational velocity (vrot) and the corresponding rotational acceleration (arot) profiles were concatenated as static images to serve as CNN input. Three training strategies were evaluated: 1) "baseline", using random initial weights; 2) "transfer learning", using weight transfer from a previous CNN model trained on head impacts drawn from contact sports; and 3) "combined training", combining previous training data from contact sports (N=5661) for training. The combined training achieved the best performances. For peak MPS, the CNN achieved a coefficient of determination (R2) of 0.932 and root mean squared error (RMSE) of 0.031 for the real-world testing dataset. It also achieved a success rate of 60.5% and 94.8% for elementwise MPS, where the linear regression slope, k, and correlation coefficient, r, between estimated and simulated MPS did not deviate from 1.0 (when identical) by more than 0.1 and 0.2, respectively. Cumulative strain damage measure (CSDM) from the CNN estimation was also highly accurate compared to those from direct simulation across a range of thresholds (R2 of 0.899-0.943 with RMSE of 0.054-0.069). Finally, the CNN achieved an average k and r of 0.98±0.12 and 0.90±0.07, respectively, for six reconstructed car crash impacts drawn from two other sources independent of the training dataset. Importantly, the CNN is able to efficiently estimate elementwise MPS with sufficient accuracy while conventional kinematic injury metrics cannot. Therefore, the CNN has the potential to supersede current kinematic injury metrics that can only approximate a global peak MPS or CSDM. The CNN technique developed here may offer enhanced utility in the design and development of head protective countermeasures, including in the automotive industry. This is the first study aimed at instantly estimating spatially detailed brain strains for automotive head impacts, which employs >8.8 thousand impact simulations generated from ~1.5 years of nonstop computations on a high-performance computing platform.

Effective Head Impact Kinematics to Preserve Brain Strain.

Conventional kinematics-based brain injury metrics often approximate peak maximum principal strain (MPS) of the whole brain but ignore the anatomical location of occurrence. In this study, we develop effective impact kinematics consisting of peak rotational velocity and theassociated rotational axis to preserve not only peak MPS but also spatially detailed MPS. A pre-computed brain response atlas (pcBRA) serves as a common reference. A training dataset (N = 3069) is used to develop a convolutional neural network (CNN) to automate impact simplification. When preserving peak MPS alone, the CNN-estimated effective peak rotational velocity achieves a coefficient of determination ([Formula: see text]) of ~0.96 relative to the directly identified counterpart, far outperforming nominal peak velocity from the resultant profiles ([Formula: see text] of ~0.34). Impacts from a subset of data (N = 1900) are also successfully matched with pcBRA idealized impacts based on elementwise MPS, where their regression slope and Pearson correlation coefficient do not deviate from 1.0 (when identical) by more than 0.1. The CNN-estimated effective peak rotation velocity and rotational axis are sufficiently accurate for ~73.5% of the impacts. This is not possible for the nominal peak velocity or any other conventional injury metric. The performance may be further improved by expanding the pcBRA to include deceleration and focusing on region-wise strains. This study establishes a new avenue to reduce an arbitrary head impact into an idealized but actual "impact mode" characterized by triplets of basic kinematic variables. They retain specific physical interpretations of head impact and may be an advancement over state-of-the-art kinematics-based scalar metrics for more effective impact comparison in the future.

Purposeful Heading Performed by Female Youth Soccer Players Leads to Strain Development in Deep Brain Structures.

Head impacts in soccer have been associated with both short- and long-term neurological consequences. Youth players' brains are especially vulnerable given that their brains are still developing, and females are at an increased risk of traumatic brain injury (TBI) compared to males. Approximately 90% of head impacts in soccer occur from purposeful heading. Accordingly, this study assessed the relationship between kinematic variables and brain strain during purposeful headers in female youth soccer players. A convenience sample of 36 youth female soccer players (13.4 [0.9] years of age) from three elite youth soccer teams wore wireless sensors to quantify head impact magnitudes during games. Purposeful heading events were categorized by game scenario (e.g., throw-in, goal kick) for 60 regular season games (20 games per team). A total of 434 purposeful headers were identified. Finite element model simulations were performed to calculate average peak maximum principal strain (APMPS) in the corpus callosum, thalamus, and brainstem on a subset of 110 representative head impacts. Rotational velocity was strongly associated with APMPS in these three regions of the brain (r = 0.83–0.87). Linear acceleration was weakly associated with APMPS (r = 0.13–0.31). Game scenario did not predict APMPS during soccer games (p > 0.05). Results demonstrated considerable APMPS in the corpus callosum (mean = 0.102) and thalamus (mean = 0.083). In addition, the results support the notion that rotational velocity is a better predictor of brain strain than linear acceleration and may be a potential indicator of changes to the brain.

Instantaneous Whole-Brain Strain Estimation in Dynamic Head Impact.

Head injury models are notoriously time consuming and resource demanding in simulations, which prevents routine application. Here, we extend a convolutional neural network (CNN) to instantly estimate element-wise distribution of peak maximum principal strain (MPS) of the entire brain (>36 k speedup accomplished on a low-end computing platform). To achieve this, head impact rotational velocity and acceleration temporal profiles are combined into two-dimensional images to serve as CNN input for training and prediction of MPS. Compared with the directly simulated counterparts, the CNN-estimated responses (magnitude and distribution) are sufficiently accurate for 92.1% of the cases via 10-fold cross-validation using impacts drawn from the real world (n = 5661; range of peak rotational velocity in augmented data extended to 2-40 rad/sec). The success rate further improves to 97.1% for "in-range" impacts (n = 4298). When using the same CNN architecture to train (n = 3064) and test on an independent, reconstructed National Football League (NFL) impact dataset (n = 53; 20 concussions and 33 non-injuries), 51 out of 53, or 96.2% of the cases, are sufficiently accurate. The estimated responses also achieve virtually identical concussion prediction performances relative to the directly simulated counterparts, and they often outperform peak MPS of the whole brain (e.g., accuracy of 0.83 vs. 0.77 via leave-one-out cross-validation). These findings support the use of CNN for accurate and efficient estimation of spatially detailed brain strains across the vast majority of head impacts in contact sports. Our technique may hold the potential to transform traumatic brain injury (TBI) research and the design and testing standards of head protective gears by facilitating the transition from acceleration-based approximation to strain-based design and analysis. This would have broad implications in the TBI biomechanics field to accelerate new scientific discoveries. The pre-trained CNN is freely available online at https://github.com/Jilab-biomechanics/CNN-brain-strains.

A network-based responsefeature matrix as a brain injury metric.

Conventional brain injury metrics are scalars that treat the whole head/brain as a single unit but do not characterize the distribution of brain responses. Here, we establish a network-based "response feature matrix" to characterize the magnitude and distribution of impact-induced brain strains. The network nodes and edges encode injury risks to the gray matter regions and their white matter interconnections, respectively. The utility of the metricis illustrated in injury prediction using three independent, real-world datasets: two reconstructed impact datasets from the National Football League (NFL) and Virginia Tech, respectively, and measured concussive and non-injury impacts from Stanford University. Injury predictions with leave-one-out cross-validationare conducted using the two reconstructed datasets separately, and then by combining all datasets into one. Using support vector machine, the network-based injury predictor consistently outperforms four baseline scalar metrics including peak maximum principal strain of the whole brain (MPS), peak linear/rotational acceleration, and peak rotational velocity across all five selected performance measures (e.g., maximized accuracy of 0.887 vs. 0.774 and 0.849 for MPS and rotational acceleration with corresponding positive predictive values of 0.938, 0.772, and 0.800, respectively, using the reconstructed NFL dataset). With sufficient training data, real-world injury prediction is similar to leave-one-out in-sample evaluation, suggesting the potential advantage of the network-based injury metric over conventional scalar metrics. The network-based response feature matrix significantly extends scalar metrics by sampling the brain strains more completely, which may serve as a useful framework potentially allowing for other applications such as characterizing injury patterns or facilitating targetedmulti-scale modeling in the future.

Non-uniform strain distribution in anterolateral capsule of knee: Implications for surgical repair.

The existence of a ligamentous structure within the anterolateral capsule, which can be injured in combination with the anterior cruciate ligament, has been debated. Therefore, the purpose of this study was to determine the magnitude and direction of the strain in the anterolateral capsule in response to external loads applied to the knee. The anterolateral capsule was hypothesized to not function like a traditional ligament. A 6-degree-of-freedom robotic testing system was used to apply ten external loads to human cadaveric knees (n = 7) in the intact and anterior cruciate ligament (ACL) deficient states. The position of strain markers was recorded on the midsubstance of the anterolateral capsule during the resulting joint kinematics to determine the magnitude and direction of the maximum principal strain. The peak maximum principal strain ranged from 22% to 52% depending on the loading condition. When histograms of strain magnitude values were analyzed to determine strain uniformity, the mean kurtosis was 1.296 ± 0.955, lower than a typical ligament, and the mean variance was 0.015 ± 0.008, higher than a typical ligament. The mean angles of the strain direction vectors compared to the proposed ligament ranged between 38° and 130° (p < 0.05). The magnitude of the maximum principal strain in the anterolateral capsule is much larger than a typical ligament and does not demonstrate a uniform strain distribution. The direction of strain is also not aligned with the proposed ligament. Clinical Significance: Reconstruction methods using tendons will not produce normal joint function due to replacement of a multi-axial structure with a uni-axial structure. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res.

Evaluation of Brain Response during Head Impact in Youth Athletes Using an Anatomically Accurate Finite Element Model.

During normal participation in football, players are exposed to repetitive subconcussive head impacts, or impacts that do not result in signs and symptoms of concussion. To better understand the effects of repetitive subconcussive impacts, the biomechanics of on-field head impacts and resulting brain deformation need to be well characterized. The current study evaluates local brain response to typical youth football head impacts using the atlas-based brain model (ABM), an anatomically accurate brain finite element (FE) model. Head impact kinematic data were collected from three local youth football teams using the Head Impact Telemetry (HIT) System. The azimuth and elevation angles were used to identify impacts near six locations of interest, and low, moderate, and high acceleration magnitudes (5th, 50th, and 95th percentiles, respectively) were calculated from the grouped impacts for FE simulation. Strain response in the brain was evaluated by examining the range and peak maximum principal strain (MPS) values in each element. A total of 40,538 impacts from 119 individual athletes were analyzed. Impacts to the facemask resulted in 0.18 MPS for the high magnitude impact category. This was 1.5 times greater than the oblique impact location, which resulted in the lowest strain value of 0.12 for high magnitude impacts. Overall, higher strains resulted from a 95th percentile lateral impact (41.0g, 2556 rad/sec2) with two predominant axes of rotation than from a 95th percentile frontal impact (67.6g, 2641 rad/sec2) with a single predominant axis of rotation. These findings highlight the importance of accounting for directional dependence and relative contribution of axes of rotation when evaluating head impact response.

Mesh Convergence Behavior and the Effect of Element Integration of a HumanHead Injury Model.

Numerous head injury models exist that vary in mesh density by orders of magnitude. A careful study of the mesh convergence behavior is necessary, especially in terms of strain most relevant to brain injury. To this end, as well as to investigate the effect of element integration scheme on simulated strains, we re-meshed the Worcester Head Injury Model at five mesh densities (~ 7.2-1000k high-quality hexahedral elements of the brain). Results from explicit dynamic simulations of three cadaveric impacts and an in vivo head rotation were compared. First, scalar metrics of the whole brain only considering magnitude were used, including peak maximum principal strain and population-based median strain. They were further extended to deep white matter regions and the entire brain elements, respectively, to form two "response vectors" to account for both magnitude and distribution. Using benchmark enhanced full-integration elements (C3D8I), a minimum of 202.8k brain elements werenecessary to converge for response vectors of the deep white matterregions. This model was further used to simulate with reduced integration (C3D8R). We found that hourglass energy higher than the commonrule of thumb (e.g., up to 44.38% vs. < 10% of internal energy) was necessary to maintain comparable strain relative to C3D8I. Based on these results, it is recommended that a humanhead injury model should have a minimum number of 202.8 k elements, oran average element size of no larger than 1.8mm, for the brain. C3D8R formulation with relax stiffness hourglass control using a high scaling factor is also recommended to achieve sufficient accuracy without substantial computational cost.

Comparative study of adhesive joint designs for composite trusses based on numerical models

In the context of lightweight structure design for the transportation and robotics industries, new types of composite structures are being developed, in the form of trusses made of fiber-reinforced polymer composite members of small diameter (a few millimeters thick at most). Some concepts of wound trusses can be found in the literature, but in more general cases, for which a predefined wound truss shape is not usable, individual truss members must be joined together. The axial strength of the composite members allow them to carry a high load, and the joints between those members should be strong enough to carry this load as well. With the objective of developing an efficient joint design for an application in thin composite trusses (member thickness ranging from 0.5 to 5 mm), finite element models of several adhesive joint designs were built, and their strengths were compared. The comparison was made using the same joint configuration (number of members, member cross-sectional area, joint dimensions) and loading conditions. Adhesive failure was considered in this study, and the strength of each design was determined from the value of the peak maximum principal strain in the adhesive layer, as this failure criterion is suitable for the toughened adhesive material used in the models. A trade-off between the strength, weight and manufacturability of each joint design was made in order to conclude on their overall performance. Results suggested that, among the joint designs modelled, round-based composite rods inserted in a tubular metallic piece are the most efficient in terms of strength-to-weight ratio.

Primary blast waves induced brain dynamics influenced by head orientations.

There is controversy regarding the directional dependence of head responses subjected to blast loading. The goal of this work is to characterize the role of head orientation in the mechanics of blast wave-head interactions as well as the load transmitting to the brain. A three-dimensional human head model with anatomical details was reconstructed from computed tomography images. Three different head orientations with respect to the oncoming blast wave, i.e., front-on with head facing blast, back-on with head facing away from blast, and side-on with right side exposed to blast, were considered. The reflected pressure at the blast wave-head interface positively correlated with the skull curvature. It is evidenced by the maximum reflected pressure occurring at the eye socket with the largest curvature on the skull. The reflected pressure pattern along with the local skull areas could further influence the intracranial pressure distributions within the brain. We did find out that the maximum coup pressure of 1.031MPa in the side-on case as well as the maximum contrecoup pressure of -0.124MPa in the back-on case. Moreover, the maximum principal strain (MPS) was also monitored due to its indication to diffuse brain injury. It was observed that the peak MPS located in the frontal cortex region regardless of the head orientation. However, the local peak MPS within each individual function region of the brain depended on the head orientation. The detailed interactions between blast wave and head orientations provided insights for evaluating the brain dynamics, as well as biomechanical factors leading to traumatic brain injury.

Intracranial Pressure Influences the Behavior of the Optic Nerve Head.

In this work, the biomechanical responses of the optic nerve head (ONH) to acute elevations in intracranial pressure (ICP) were systematically investigated through numerical modeling. An orthogonal experimental design was developed to quantify the influence of ten input factors that govern the anatomy and material properties of the ONH on the peak maximum principal strain (MPS) in the lamina cribrosa (LC) and postlaminar neural tissue (PLNT). Results showed that the sensitivity of ONH responses to various input factors was region-specific. In the LC, the peak MPS was most strongly dependent on the sclera thickness, LC modulus, and scleral canal size, whereas in the PLNT, the peak MPS was more sensitive to the scleral canal size, neural tissue modulus, and pia mater modulus. The enforcement of clinically relevant ICP in the retro-orbital subarachnoid space influenced the sensitivity analysis. It also induced much larger strains in the PLNT than in the LC. Moreover, acute elevation of ICP leads to dramatic strain distribution changes in the PLNT, but had minimal impact on the LC. This work could help to better understand patient-specific responses, to provide guidance on biomechanical factors resulting in optic nerve diseases, such as glaucoma, papilledema, and ischemic optic neuropathy, and to illuminate the possibilities for exploiting their potential to treat and prevent ONH diseases.

Upper Cervical Spine Loading Simulating a Dynamic Low-Speed Collision Significantly Increases the Risk of Pain Compared to Quasi-Static Loading With Equivalent Neck Kinematics.

Dynamic cervical spine loading can produce facet capsule injury. Despite a large proportion of neck pain being attributable to the C2/C3 facet capsule, potential mechanisms are not understood. This study replicated low-speed frontal and rear-end traffic collisions in occiput-C3 human cadaveric cervical spine specimens and used kinematic and full-field strain analyses to assess injury. Specimens were loaded quasi-statically in flexion and extension before and after dynamic rotation of C3 at 100 deg/s. Global kinematics in the sagittal plane were tracked at 1 kHz, and C2/C3 facet capsule full-field strains were measured. Dynamic loading did not alter the kinematics from those during quasi-static (QS) loading, but maximum principal strain (MPS) and shear strain (SS) were significantly higher (p = 0.028) in dynamic flexion than for the same quasi-static conditions. The full-field strain analysis demonstrated that capsule strain was inhomogeneous, and that the peak MPS generally occurred in the anterior aspect and along the line of the C2/C3 facet joint. The strain magnitude in dynamic flexion continued to rise after the rotation of C3 had stopped, with a peak MPS of 12.52 ± 4.59% and a maximum SS of 5.34 ± 1.60%. The peak MPS in loading representative of rear-end collisions approached magnitudes previously shown to induce pain in vivo, whereas strain analysis using linear approaches across the facet joint was lower and may underestimate injury risk compared to full-field analysis. The time at which peak MPS occurred suggests that the deceleration following a collision is critical in relation to the production of injurious strains within the facet capsule.

Conversion Equation between the Drop Height in the New York University Impactor and the Impact Force in the Infinite Horizon Impactor in the Contusion Spinal Cord Injury Model.

There are several widely used devices for controlled contusion of the spinal cord, including the Ohio State University device, the University of British Columbia multi-mechanisms injury device, the New York University (NYU) impactor, and the Infinite Horizon (IH) impactor. Although various devices and protocols have been used to generate consistent injury severities, further investigation of the relationship between the key parameters of different spinal cord injury (SCI) contusion devices (e.g., drop height in the NYU impactor and impact force in the IH impactor) will improve our understanding of SCI mechanisms. A three-dimensional finite element model of the rat spinal cord from T9 to T10 that included the white and gray matters, dura mater, and cerebrospinal fluid was developed to investigate the von-Mises stress, maximum principal strain, and maximum displacement of the spinal cord for the drop height in the NYU impactor and the impact force in the IH impactor. A quantitative relationship was established as a conversion equation between two key parameters--i.e., the drop height and the impact force--in the NYU and IH impactors from regression equations for peak von-Mises stress, peak maximum principal strain, and maximum displacement in the spinal cord with respect to drop height and impact force with very high coefficients of determination. The consistent correlation was represented as a simple equation (Force = (28.2 ± 3.2) · Height((0.83 ± 0.07))) under the experimental conditions of a 10-g rod in the NYU impactor and an impact velocity of 125 mm/sec in the IH impactor. Thus, the key biomechanical parameter for a contusion device can be converted or translated to that of another device to analyze experimental results from multiple contusion devices.

Maximum principal strain as a criterion for prediction of orthodontic mini-implants failure in subject-specific finite element models.

To investigate the most reliable stress or strain parameters in subject-specific finite element (FE) models to predict success or failure of orthodontic mini-implants (OMIs). Subject-specific FE analysis was applied to 28 OMIs used for anchorage. Each model was developed using two computed tomography data sets, the first taken before OMI placement and the second taken immediately after placement. Of the 28 OMIs, 6 failed during the first 5 months, and 22 were successful. The bone compartment was divided into four zones in the FE models, and peak stress and strain parameters were calculated for each. Logistic regression of the failure (vs success) of OMIs on the stress and strain parameters in the models was conducted to verify the ability of these parameters to predict OMI failure. Failure was significantly dependent on principal strain parameters rather than stress parameters. Peak maximum principal strain in the bone 0.5 to 1 mm from the OMI surface was the best predictor of failure (R(2) = 0.8151). We propose the use of the maximum principal strain as a criterion for predicting OMI failure in FE models.