Vestibulocollic and Cervicocollic Muscle Reflexes in a Finite Element Neck Model During Multidirectional Impacts

Integration of Muscle Pre-tension and Activation to Evaluate Neck Muscle Strain Injury Risk during Simulated Rear Impacts Using a Finite Element Neck Model

Prevention of rear-impact neck injuries remains challenging for safety designers due to a lack of understanding of the tissue-level response and injury risk. Soft tissue injuries have been inferred from clinical, cadaveric, and numerical studies; however, there is a paucity of data for neck muscle injury, commonly reported as muscle pain. The goal of this study was to investigate the effect of muscle pre-tension and activation on muscle strain and injury risk resulting from low-severity rear impacts using a detailed finite element head and neck model (HNM). The HNM was extracted from the GHBMC average stature male model and re-postured to match a volunteer study, with measured T1 kinematics applied as boundary conditions to the HNM. Three cases were simulated for three impact severities: the baseline repostured HNM, the HNM including muscle pre-tension, and the HNM with muscle pre-tension and muscle activation. The head kinematics, vertebral kinematics, muscle strains, and three neck injury criteria were calculated to assess injury risk. The kinematic response of the neck model demonstrated an S-shaped pattern, followed by extension in the rear impact cases. The maximum kinetics, kinematics, and muscle strains occurred later in the impact during the extension phase. The distribution and magnitude of muscle strain depended on muscle pre-tension and activation, and the largest predicted strains occurred at locations associated with muscle injury reported in the literature. The HNM with muscle pre-tension and muscle activation provides a tool to assess rear impact response and could inform injury mitigation strategies in the future.

Quantification of training-induced alterations in body composition via automated machine learning analysis of MRI images in the thigh region: A pilot study in young females

The maintenance of an appropriate ratio of body fat to muscle mass is essential for the preservation of health and performance, as excessive body fat is associated with an increased risk of various diseases. Accurate body composition assessment requires precise segmentation of structures. In this study we developed a novel automatic machine learning approach for volumetric segmentation and quantitative assessment of MRI volumes and investigated the efficacy of using a machine learning algorithm to assess muscle, subcutaneous adipose tissue (SAT), and bone volume of the thigh before and after a strength training. Eighteen healthy, young, female volunteers were randomly allocated to two groups: intervention group (IG) and control group (CG). The IG group followed an 8-week strength endurance training plan that was conducted two times per week. Before and after the training, the subjects of both groups underwent MRI scanning. The evaluation of the image data was performed by a machine learning system which is based on a 3D U-Net-based Convolutional Neural Network. The volumes of muscle, bone, and SAT were each examined using a 2 (GROUP [IG vs. CG]) × 2 (TIME [pre-intervention vs. post-intervention]) analysis of variance (ANOVA) with repeated measures for the factor TIME. The results of the ANOVA demonstrate significant TIME × GROUP interaction effects for the muscle volume (F1,16 = 12.80, p = 0.003, ηP 2 = 0.44) with an increase of 2.93% in the IG group and no change in the CG (-0.62%, p = 0.893). There were no significant changes in bone or SAT volume between the groups. This study supports the use of artificial intelligence systems to analyze MRI images as a reliable tool for monitoring training responses on body composition.

Human head-neck model and its application thresholds: a narrative review

There have been many studies on human head-neck biomechanical models in the last two decades, and the associated modelling techniques were constantly evolving at the same time. Computational approaches have been widely leveraged, in parallel to conventional physical tests, to investigate biomechanics and injuries of the head-neck system in fields like the automotive industry, orthopedic, sports medicine, etc. The purpose of this manuscript is to provide a global review of the existing knowledge related to the modelling approaches, structural and biomechanical characteristics, validation, and application of the present head-neck models. This endeavor aims to support further enhancements and validations in modelling practices, particularly addressing the lack of data for model validation, as well as to prospect future advances in terms of the topics. Seventy-four models subject to the proposed selection criteria are considered. Based on previously established and validated head-neck computational models, most of the studies performed in-depth investigations of included cases, which revolved around four specific subjects: physiopathology, treatment evaluation, collision condition, and sports injury. Through the review of the recent 20 years of research, the summarized modelling information indicated existing deficiencies and future research topics, as well as provided references for subsequent head-neck model development and application.

Influence of the surrounding environment on the response of seated midsize male volunteers subjected to lateral sled accelerations

Predicting vehicle occupants' posture during evasive manoeuvres is crucial for assessing their safety in the event of a collision. Volunteer experiments have been performed in the past under lateral accelerations, both within a vehicle cabin and on a seat mounted on a sled. However, discrepancies in the volunteer responses between both setups have been identified. This study hypothesizes that the response of the volunteers differs as a consequence of the proximity to the frame in the vehicle cabin, in comparison to the absence of such a structure on the sled. The present study conducted a novel sled experiment, on which five volunteers with anthropometry comparable to the 50th percentile male were subjected to 0.3 g lateral accelerations, with three different surrounding environments. In twelve pulses, an additional lateral structure was placed to the right or left side of the seated volunteers. The volunteers were asked to either brace or relax their muscles. The results show significant differences between the configurations with and without the structure placed on the right side. This effect was observed for both the lateral excursion of the upper body and the corresponding rotation when the volunteers were relaxed (p < 0.01). The average maximum lateral head rotation decreased from 27° to 14° with the structure on the right. No significant difference in head rotation was found for the braced muscle configuration. This study supports the hypothesis that the proximity to a surrounding environment influences human responses during dynamic loading. Nevertheless, there was no significant difference in maximum muscle activation between the configurations, but a faster reaction of the sternocleidomastoid muscle with the presence of the structure.

Implant Design and Cervical Spinal Biomechanics and Neurorehabilitation: A Finite Element Investigation

INTRODUCTION

The cervical spine, pivotal for mobility and overall body function, can be affected by cervical spondylosis, a major contributor to neural disorders. Prevalent in both general and military populations, especially among pilots, cervical spondylosis induces pain and limits spinal capabilities. Anterior Cervical Discectomy and Fusion (ACDF) surgery, proposed by Cloward in the 1950s, is a promising solution for restoring natural cervical curvature. The study objective was to investigate the impacts of ACDF implant design on postsurgical cervical biomechanics and neurorehabilitation outcomes by utilizing a biofield head-neck finite element (FE) platform that can facilitate scenario-specific perturbations of neck muscle activations. This study addresses the critical need to enhance computational models, specifically FE modeling, for ACDF implant design.

MATERIALS AND METHODS

We utilized a validated head-neck FE model to investigate spine-implant biomechanical interactions. An S-shaped dynamic cage incorporating titanium (Ti) and polyetheretherketone (PEEK) materials was modeled at the C4/C5 level. The loading conditions were carefully designed to mimic helmet-to-helmet impact in American football, providing a realistic and challenging scenario. The analysis included intervertebral joint motion, disk pressure, and implant von Mises stress.

RESULTS

The PEEK implant demonstrated an increased motion in flexion and lateral bending at the contiguous spinal (C4/C5) level. In flexion, the Ti implant showed a modest 5% difference under 0% activation conditions, while PEEK exhibited a more substantial 14% difference. In bending, PEEK showed a 24% difference under 0% activation conditions, contrasting with Ti's 17%. The inclusion of the head resulted in an average increase of 18% in neck angle and 14% in C4/C5 angle. Disk pressure was influenced by implant material, muscle activation level, and the presence of the head. Polyetheretherketone exhibited lower stress values at all intervertebral disc levels, with a significant effect at the C6/C7 levels. Muscle activation level significantly influenced disk stress at all levels, with higher activation yielding higher stress. Titanium implant consistently showed higher disk stress values than PEEK, with an orders-of-magnitude difference in von Mises stress. Excluding the head significantly affected disk and implant stress, emphasizing its importance in accurate implant performance simulation.

CONCLUSIONS

This study emphasized the use of a biofidelic head-neck model to assess ACDF implant designs. Our results indicated that including neck muscles and head structures improves biomechanical outcome measures. Furthermore, unlike Ti implants, our findings showed that PEEK implants maintain neck motion at the affected level and reduce disk stresses. Practitioners can use this information to enhance postsurgery outcomes and reduce the likelihood of secondary surgeries. Therefore, this study makes an important contribution to computational biomechanics and implant design domains by advancing computational modeling and theoretical knowledge on ACDF-spine interaction dynamics.

Quantifying the Importance of Active Muscle Repositioning a Finite Element Neck Model in Flexion Using Kinematic, Kinetic, and Tissue-Level Responses

PURPOSE

Non-neutral neck positions are important initial conditions in impact scenarios, associated with a higher incidence of injury. Repositioning in finite element (FE) neck models is often achieved by applying external boundary conditions (BCs) to the head while constraining the first thoracic vertebra (T1). However, in vivo, neck muscles contract to achieve a desired head and neck position generating initial loads and deformations in the tissues. In the present study, a new muscle-based repositioning method was compared to traditional applied BCs using a contemporary FE neck model for forward head flexion of 30°.

METHODS

For the BC method, an external moment (2.6 Nm) was applied to the head with T1 fixed, while for the muscle-based method, the flexors and extensors were co-contracted under gravity loading to achieve the target flexion.

RESULTS

The kinematic response from muscle contraction was within 10% of the in vivo experimental data, while the BC method differed by 18%. The intervertebral disc forces from muscle contraction were agreeable with the literature (167 N compression, 12 N shear), while the BC methodology underpredicted the disc forces owing to the lack of spine compression. Correspondingly, the strains in the annulus fibrosus increased by an average of 60% across all levels due to muscle contraction compared to BC method.

CONCLUSION

The muscle repositioning method enhanced the kinetic response and subsequently led to differences in tissue-level responses compared to the conventional BC method. The improved kinematics and kinetics quantify the importance of repositioning FE neck models using active muscles to achieve non-neutral neck positions.

Modular incorporation of deformable spine and 3D neck musculature into a simplified human body finite element model

Spinal injuries are a concern for automotive applications, requiring large parametric studies to understand spinal injury mechanisms under complex loading conditions. Finite element computational human body models (e.g. Global Human Body Models Consortium (GHBMC) models) can be used to identify spinal injury mechanisms. However, the existing GHBMC detailed models (with high computational time) or GHBMC simplified models (lacking vertebral fracture prediction capabilities) are not ideal for studying spinal injury mechanisms in large parametric studies. To overcome these limitations, a modular 50th percentile male simplified occupant model combining advantages of both the simplified and detailed models, M50-OS + DeformSpine, was developed by incorporating the deformable spine and 3D neck musculature from the detailed GHBMC model M50-O (v6.0) into the simplified GHBMC model M50-OS (v2.3). This new modular model was validated against post-mortem human subject test data in four rigid hub impactor tests and two frontal impact sled tests. The M50-OS + DeformSpine model showed good agreement with experimental test data with an average CORrelation and Analysis (CORA) score of 0.82 for the hub impact tests and 0.75 for the sled impact tests. CORA scores were statistically similar overall between the M50-OS + DeformSpine (0.79 ± 0.11), M50-OS (0.79 ± 0.11), and M50-O (0.82 ± 0.11) models (p > 0.05). This new model is computationally 6 times faster than the detailed M50-O model, with added spinal injury prediction capabilities over the simplified M50-OS model.

Development and verification of a physiologically motivated internal controller for the open-source extended Hill-type muscle model in LS-DYNA

Nowadays, active human body models are becoming essential tools for the development of integrated occupant safety systems. However, their broad application in industry and research is limited due to the complexity of incorporated muscle controllers, the long simulation runtime, and the non-regular use of physiological motor control approaches. The purpose of this study is to address the challenges in all indicated directions by implementing a muscle controller with several physiologically inspired control strategies into an open-source extended Hill-type muscle model formulated as LS-DYNA user-defined umat41 subroutine written in the Fortran programming language. This results in increased usability, runtime performance and physiological accuracy compared to the standard muscle material existing in LS-DYNA. The proposed controller code is verified with extensive experimental data that include findings for arm muscles, the cervical spine region, and the whole body. Selected verification experiments cover three different muscle activation situations: (1) passive state, (2) open-loop and closed-loop muscle activation, and (3) reflexive behaviour. Two whole body finite element models, the 50th percentile female VIVA OpenHBM and the 50th percentile male THUMS v5, are used for simulations, complemented by the simplified arm model extracted from the 50th percentile male THUMS v3. The obtained results are evaluated additionally with the CORrelation and Analysis methodology and the mean squared error method, showing good to excellent biofidelity and sufficient agreement with the experimental data. It was shown additionally how the integrated controller allows simplified mimicking of the movements for similar musculoskeletal models using the parameters transfer method. Furthermore, the Hill-type muscle model presented in this paper shows better kinematic behaviour even in the passive case compared to the existing one in LS-DYNA due to its improved damping and elastic properties. These findings provide a solid evidence base motivating the application of the enhanced muscle material with the internal controller in future studies with Active Human Body Models under different loading conditions.

Neck stabilization through sensory integration of vestibular and visual motion cues

Background

To counteract gravity, trunk motion, and other perturbations, the human head-neck system requires continuous muscular stabilization. In this study, we combine a musculoskeletal neck model with models of sensory integration (SI) to unravel the role of vestibular, visual, and muscle sensory cues in head-neck stabilization and relate SI conflicts and postural instability to motion sickness.

Method

A 3D multisegment neck model with 258 Hill-type muscle elements was extended with postural stabilization using SI of vestibular (semicircular and otolith) and visual (rotation rate, verticality, and yaw) cues using the multisensory observer model (MSOM) and the subjective vertical conflict model (SVC). Dynamic head-neck stabilization was studied using empirical datasets, including 6D trunk perturbations and a 4 m/s2 slalom drive inducing motion sickness.

Results

Recorded head translation and rotation are well matched when using all feedback loops with MSOM or SVC or assuming perfect perception. A basic version of the model, including muscle, but omitting vestibular and visual perception, shows that muscular feedback can stabilize the neck in all conditions. However, this model predicts excessive head rotations in conditions with trunk rotation and in the slalom. Adding feedback of head rotational velocity sensed by the semicircular canals effectively reduces head rotations at mid-frequencies. Realistic head rotations at low frequencies are obtained by adding vestibular and visual feedback of head rotation based on the MSOM or SVC model or assuming perfect perception. The MSOM with full vision well captures all conditions, whereas the MSOM excluding vision well captures all conditions without vision. The SVC provides two estimates of verticality, with a vestibular estimate SVCvest, which is highly effective in controlling head verticality, and an integrated vestibular/visual estimate SVCint which can complement SVCvest in conditions with vision. As expected, in the sickening drive, SI models imprecisely estimate verticality, resulting in sensory conflict and postural instability.

Conclusion

The results support the validity of SI models in postural stabilization, where both MSOM and SVC provide credible results. The results in the sickening drive show imprecise sensory integration to enlarge head motion. This uniquely links the sensory conflict theory and the postural instability theory in motion sickness causation.

Development and Validation of an Active Muscle Simplified Finite Element Human Body Model in a Standing Posture

Active muscles play an important role in postural stabilization, and muscle-induced joint stiffening can alter the kinematic response of the human body, particularly that of the lower extremities, under dynamic loading conditions. There are few full-body human body finite element models with active muscles in a standing posture. Thus, the objective of this study was to develop and validate the M50-PS+Active model, an average-male simplified human body model in a standing posture with active musculature. The M50-PS+Active model was developed by incorporating 116 skeletal muscles, as one-dimensional beam elements with a Hill-type material model and closed-loop Proportional Integral Derivative (PID) controller muscle activation strategy, into the Global Human Body Models Consortium (GHBMC) simplified pedestrian model M50-PS. The M50-PS+Active model was first validated in a gravity standing test, showing the effectiveness of the active muscles in maintaining a standing posture under gravitational loading. The knee kinematics of the model were compared against volunteer kinematics in unsuited and suited step-down tests from NASA's active response gravity offload system (ARGOS) laboratory. The M50-PS+Active model showed good biofidelity with volunteer kinematics with an overall CORA score of 0.80, as compared to 0.64 (fair) in the passive M50-PS model. The M50-PS+Active model will serve as a useful tool to study the biomechanics of the human body in vehicle-pedestrian accidents, public transportation braking, and space missions piloted in a standing posture.

Sensitivity Analysis for Multidirectional Spaceflight Loading and Muscle Deconditioning on Astronaut Response

A sensitivity analysis for loading conditions and muscle deconditioning on astronaut response for spaceflight transient accelerations was carried out using a mid-size male human body model with active musculature. The model was validated in spaceflight-relevant 2.5-15 g loading magnitudes in seven volunteer tests, showing good biofidelity (CORA: 0.69). Sensitivity analysis was carried out in simulations varying pulse magnitude (5, 10, and 15 g), rise time (32.5 and 120 ms), and direction (10 directions: frontal, rear, vertical, lateral, and their combination) along with muscle size change (± 15% change) and responsiveness (pre-braced, relaxed, vs. delayed response) changes across 600 simulations. Injury metrics were most sensitive to the loading direction (50%, partial-R²) and least sensitive to muscle size changes (0.2%). The pulse magnitude also had significant effect on the injury metrics (16%), whereas muscle responsiveness (3%) and pulse rise time (2%) had only slight effects. Frontal and upward loading directions were the worst for neck, spine, and lower extremity injury metrics, whereas rear and downward directions were the worst for head injury metrics. Higher magnitude pulses and pre-bracing also increased the injury risk.

Analysis of control strategies for VIVA OpenHBM with active reflexive neck muscles

Modeling muscle activity in the neck muscles of a finite element (FE) human body model can be based on two biological reflex systems. One approach is to approximate the Vestibulocollic reflex (VCR) function, which maintains the head orientation relative to a fixed reference in space. The second system tries to maintain the head posture relative to the torso, similar to the Cervicocolic reflex (CCR). Strategies to combine these two neck muscle controller approaches in a single head-neck FE model were tested, optimized, and compared to rear-impact volunteer data. The first approach, Combined-Control, assumed that both controllers simultaneously controlled all neck muscle activations. In the second approach, Distributed-Control, one controller was used to regulate activation of the superficial muscles while a different controller acted on deep neck muscles. The results showed that any muscle controller that combined the two approaches was less effective than only using one of VCR- or CCR-based systems on its own. A passive model had the best objective rating for cervical spine kinematics, but the addition of a single active controller provided the best response for both head and cervical spine kinematics. The present study demonstrates the difficulty in completely capturing representative head and cervical spine responses to rear-impact loading and identified a controller capturing the VCR reflex as the best candidate to investigate whiplash injury mechanisms through FE modeling.

Quantitative cervical spine injury responses in whiplash loading with a numerical method of natural neural reflex consideration

BACKGROUND AND OBJECTIVE

Neural reflex is hypothesized as a regulating step in spine stabilizing system. However, neural reflex control is still in its infancy to consider in the previous finite element analysis of head-neck system for various applications. The purpose of this study is to investigate the influences of neural reflex control on neck biomechanical responses, then provide a new way to achieve an accurate biomechanical analysis for head-neck system with a finite element model.

METHODS

A new FE head-neck model with detailed active muscles and spinal cord modeling was established and globally validated at multi-levels. Then, it was coupled with our previously developed neuromuscular head-neck model to analyze the effects of vestibular and proprioceptive reflexes on biomechanical responses of head-neck system in a typical spinal injury loading condition (whiplash). The obtained effects were further analyzed by comparing a review of epidemiologic data on cervical spine injury situations.

RESULT

The results showed that the active model (AM) with neural reflex control obviously presented both rational head-neck kinematics and tissue injury risk referring to the previous experimental and epidemiologic studies, when compared with the passive model (PM) without it. Tissue load concentration locations as well as stress/strain levels were both changed due to the muscle activation forces caused by neural reflex control during the whole loading process. For the bony structures, the AM showed a peak stress level accounting for only about 25% of the PM. For the discs, the stress concentrated location was transferred from C2-C6 in the PM to C4-C6 in the AM. For the spinal cord, the strain concentrated locations were transferred from C1 segment to around C4 segment when the effects of neural reflex control were implemented, while the gray matter and white matter peak strains were reduced to 1/3 and 1/2 of the PM, respectively. All these were well correlated with epidemiological studies on clinical cervical spine injuries.

CONCLUSION

In summary, the present work demonstrated necessity of considering neural reflex in FE analysis of a head-neck system as well as our model biofidelity. Overall results also verified the previous hypothesis and further quantitatively indicated that the muscle activation caused by neural reflex is providing a protection for the neck in impact loading by decreasing the strain level and changing the possible injury to lower spinal cord level to reduce injury severity.