Computer Methods in Biomechanics and Biomedical Engineering

AO_SCPLOWBSTRACTC_SCPLOWMuscle fibre type distribution influences both the metabolic and contractile properties of individual muscles. However, as humans tend to self-optimize their gait pattern to minimize cost of transport, these changes in muscle properties may influence gait biomechanics in manners that are difficult to isolate in in vivo experiments. The purpose of this study was to predict the influence of muscle fibre type distribution on the metabolic cost and biomechanics of simulated walking and running. We implemented a muscle model that could predict recruitment of slow and fast twitch muscle fibres in a framework for predictive musculoskeletal simulation. Subsequently, we employed the framework to investigate how metabolic cost of transport, stride length, stride frequency, and mechanical work performed by slow and fast twich muscle fibres were influenced by fibre type distribution across locomotion speeds from 1.0 to 4.5 m {middle dot} s-1. Our results predict that cost of transport increases as slow twitch area fraction decreases, while stride length and frequency was minimally affected by fibre type distribution at speeds resulting in walking. In contrast, fibre type distribution interacts with locomotion speed at speeds resulting in running. Specifically, we predict the existence of a threshold speed below which cost of transport decreases with an increasing proportion of slow twitch fibres, while cost of transport increases with increasing proportions of slow twitch fibres above it. The shift in fibre type distribution was accompanied by an increase in stride frequency and decrease in stride length. These shifts in spatiotemporal characteristics appear to allow the muscles to operate at speeds close to those that achieve peak mechanical efficiency. Taken together, the results of this study predict that muscle fibre type distribution may influence both the energetics and biomechanics of gait, and that this influence is dependent upon the locomotion speed.

The corpus callosum is the largest white matter structure connecting the two cerebral hemispheres and is anatomically divided into three major subregions along the anteroposterior axis: the genu, midbody, and splenium. The splenium is frequently affected in traumatic head impacts, yet the biomechanical basis for this selective vulnerability remains poorly understood. Clinical studies have long hypothesized that the falx cerebri contributes to the splenial susceptibility because of its close anatomical relationship with the posterior corpus callosum, although direct verification is lacking. To address this, a high-resolution finite element head model with explicit representations of the genu, midbody, and splenium was employed. Two model variants, differing only in the presence or absence of an anatomically and mechanically detailed falx, were used to simulate ten head impacts covering a range of loading directions and severities. Peak strain, strain rate, and shear stress were quantified in each corpus callosum subregion and compared using linear mixed-effects models. The results showed that inclusion of the falx altered the regional distribution of mechanical responses within the corpus callosum. Across the simulated impacts, the splenium consistently exhibited greater strain, strain rate, and shear stress than the genu and midbody when the falx was present. In contrast, these preferentially larger splenial deformation were not consistently observed when the falx was absent. Statistical analyses demonstrated significant region-dependent effects of the falx, with falx-induced increases in strain, strain rate, and shear stress being significantly greater in the splenium than in the genu and midbody (p < 0.05). These findings verified the hypothesis that the falx selectively amplified mechanical loading within the splenium, thereby contributing to its heightened vulnerability to injury. This work provides a plausible biomechanical explanation for the frequent involvement of the splenium in brain trauma patients and highlights the heterogeneous influence of the falx on mechanical responses across corpus callosum subregions.

AO_SCPLOWBSTRACTC_SCPLOWMuscle efficiency decreases with increasing size, largely due to a relative decrease in its mechanical output. Muscle mechanical output depends on its activation, strain, and strain rate and thus varies between different muscles within a limb during locomotion. Distinct muscle coordination patterns are required for efficient cycling, and so we would expect that the coordination patterns for efficient cycling or indeed locomotion would change across animal sizes. We tested whether muscle coordination would change with muscle size using data derived from human cycling: this paradigm allowed for controlled changes in both crank torque and cadence, allowing the multifactorial problem of muscle power output to be decomposed. We used kinematic and pedal data from 12 cyclists undergoing steady pedalling at cadences from 80 to 140 r.p.m. and generated musculoskeletal simulations of their movements. We introduced novel multisegment muscle models in the simulation that incorporated the internal muscle mass and thus accounted for the scaling effects of muscle tissue inertia. We solved the simulations for the muscle activity that was required to minimise the metabolic cost during cycling for each condition. The masses of the muscle models were scaled across five orders of magnitude. The predicted muscle activations were classified by Principal Component analysis to identify whether the coordination of muscle activity was modulated across models with different sized muscles. Analysis of variance revealed significant changes in coordination at the large-scale factors. This study shows how the coordination of muscle activity during locomotion will likely change across a range of body sizes due to the non-linear effects of the inertial mass within the muscle tissues.

Acute subdural hematoma (ASDH) is a prevalent injury with high mortality and morbidity, often resulting from bridging vein (BV) disruption secondary to cortical relative motion. As a thin membrane enveloping the brain surface and anchoring BVs, the pia mater is hypothesized to play a critical mechanical role in cortical response and hence ASDH pathogenesis. Finite element (FE) head models are valuable tools to predict ASDH occurrence during impacts. However, the pia mater is often represented as an elastic material in existing FE head models, despite experimental evidence reporting its nonlinear mechanical behavior. In this study, both linear (Young's modulus of 11.5 MPa) and nonlinear (the stress-strain curve derived from pial tension tests) material models of the pia mater were implemented in one FE head model. The models were subjected to three experimental impact loadings, one of which was known to cause ASDH and two of which were not. Results demonstrated that, across all simulated impacts, the model with nonlinear pia mater properties predicted larger cortical displacements and BV responses than the linear model. For the impact with known ASDH occurrence, the predicted BV strain was 0.17 for the nonlinear model and 0.094 for the linear model, with only the former approaching the reported rupture strain range of the BV-superior sagittal sinus complex (0.29 {+/-} 0.13). These findings verified the mechanical importance of the pia mater in cortical responses and hence the prediction of ASDH, suggesting that conventional linear pia modeling might over-constrain cortical motion, leading to underestimation of BV strain and ASDH risk. The current study supported the adoption of experimentally derived nonlinear pia mater properties in FE head models to improve the reliability of ASDH prediction.

Experimental studies involving mechanical loading of the human head and brain in vivo are necessarily limited, making computational modeling essential for advancing our understanding of brain biomechanics. Demographic factors such as age and gender are known to influence brain anatomical structures, material properties, and potentially vulnerability to injurious loading. To address this, we construct six group-average brain models stratified by age and gender from a total of 135 subjects, and investigate the mechanical responses of these "group-average brains" using computational simulations. We use Pearson correlations to assess to what degree the group-average models represent individuals within each demographic category, showing strong correlations. Further, our p-value hypothesis test of the first principal strain across the six groups shows significant differences. This study demonstrates that age- and gender-stratified group-average models can effectively represent biomechanical responses of the individuals within the groups, and can reveal meaningful demographic differences that may influence susceptibility to traumatic brain injury. We show that the age-dependent change in material properties plays a greater role than anatomical changes in driving differences in the deformations. We hope to see increased utilization of these group-average models in both research and clinical applications.

Functional spinal cord repair in zebrafish is governed by regeneration-favorable biochemical and mechanical cues within the lesion microenvironment. Alterations in extracellular matrix composition and stiffness are closely associated with axon regeneration. However, experimentally dissecting the interplay between mechanical signals and axonal regrowth in vivo remains technically challenging. Here, we present an agent-based modeling framework to simulate stiffness-mediated axonal growth trajectories across the lesion. We use this model to explore potential mechanisms underlying the characteristic growth patterns observed during zebrafish spinal cord regeneration. Computational predictions were qualitatively compared with confocal imaging data obtained from larval zebrafish. These phenomenological comparisons revealed a close agreement between simulated and experimentally observed axon growth, indicating that experimentally observed patterns could be governed by transient changes in the stiffness profile of the spinal cord and lesion microenvironment. Hence, our computational framework provides an in silico platform for investigating the role of mechanical cues in axon regeneration in the injured spinal cord.

Trees are routinely pruned to mitigate the risk of wind damage, but there are few studies examining changes in wind loads after pruning, especially for large conifers. In this study, ten Colorado spruces (Picea pungens) were monitored before and after a series of pruning treatments. Trees were pruned to raise or thin crowns over a range of severities between 0% and 40%. Wind-induced bending moments were measured using two calibrated displacement probes installed orthogonally on the lower stem of each tree. Using a hierarchical Bayesian model, the relationship between maximum wind speeds and bending moments was quantified, consistent with theoretical and empirical expectations, as a non-linear power law. Random intercepts for model coefficients were used to account for individual variability in aerodynamic behavior among experimental trees, and predictions were made using the median response marginalized over the observed trees. The modeled relationship between wind speeds and bending moments was physically reasonable and like existing measurements with scaling exponents below two. Despite considerable variation among experimental trees, the aerodynamic behavior of trees, as indicated by model coefficients, was not clearly altered by pruning treatments, and, correspondingly, model predictions of bending moments over the range of observed wind speeds remained similar for all pruning treatments. Ultimately, the study yielded weak evidence for a change in bending moments following conventional pruning treatments for Colorado spruce, and the practical value of pruning to mitigate risk appeared limited for the studied conditions. Highlights- Wind loads were monitored on large Colorado spruce after crown raising and thinning - A hierarchical Bayesian model quantified wind speed and bending moment power laws - Negligible change in bending moments was found for all pruning types and severities - Conventional pruning methods may not mitigate risk for Colorado spruce

This study examined how tempo, dynamics, and string influence upper-extremity physical exposure in professional violinists and how exposure variability is distributed among musical characteristics, between-subject differences, and residual variability. Twelve violinists performed seven standardized scales while bilateral upper-arm and wrist kinematics and shoulder and forearm muscle activity were recorded. Linear mixed-effects models showed that faster tempo increased right upper-arm velocity and bilateral forearm activity while reducing right upper-arm and wrist ranges of motion. Louder dynamics increased bilateral forearm and right trapezius activity and right-wrist ranges of motion. Higher-posture strings increased right upper-arm elevation and right shoulder muscle activity. Variance analysis identified exposures predominantly related to musical characteristics, jointly related to musical characteristics and between-subject differences, predominantly related to between-subject differences, or mainly unexplained. These findings support future exposure prediction from musical characteristics and targeted prevention through repertoire-based workload management, structured recovery, and individualized technique-focused strategies.

As the duration of space flights increases, so does the need to optimize off-planet microbial growth. Microbes can both be unintentionally brought into space and cause human disease or be intentionally harnessed for on-site bioengineering functions. However, optimizing microbial growth is challenging due to an insufficient understanding of how microbial communities are affected by the extraterrestrial environment. To address this gap, we have modified a previously developed model for cell growth in microgravity. By improving the functional form used for cell growth as well as the code usability, we enable further research into how microbial communities are influenced by gravity. Applying this model to isolate individual effects of gravity on cell growth indicates that a lack of gravity-driven flow decreases cell growth in microgravity, while the absence of sedimentation increases cell growth in microgravity. These opposite effects likely contribute to the system-dependent effects of microgravity observed experimentally.

The control of uterine activity during pregnancy is a complex process that involves regulating myometrial excitability across multiple scales. While numerous studies have investigated various regulatory mechanisms and established the contributions of ion channels and gap junctions, how these mechanisms interact to produce observed changes in uterine activity remains poorly understood. Pivotal to these efforts are computational models that effectively capture gestational changes in excitability across scales. In this study, we propose a multiscale computational modeling framework that can reproduce measured activity at the cellular and tissue scales at a given gestational stage. At the cellular level, we identify key ion currents underlying the observed electrophysiological properties based on a literature review of their regulation and a sensitivity analysis of the Tong 2011 uterine smooth muscle cell activation model. The conductances of these ion currents are then fit to reproduce characteristic resting membrane potentials and burst properties using Bayesian optimization. To extend to the tissue level, we employ an anisotropic monodomain model, parameterized by the resistivity of late pregnancy uterine muscle, to investigate electrical propagation in a two-dimensional section of uterine tissue. We then apply the multiscale model to study myometrial activation in late pregnancy and elucidate the contributions of ion channel and gap junction regulation in transitioning the uterus from a quiescent state to labor. Our resulting model successfully reproduces measured electrophysiological properties at the cellular level and characteristic single-spike and burst-propagation patterns at the tissue level across the three late-pregnant time points analyzed (days 16/17, 18/19, and 20/21) in a murine model. Furthermore, our results suggest that the regulation of the conductances of the voltage-dependent potassium current (IK1), L-type calcium current (ICaL), and sodium current (INa) is most important in determining preterm uterine excitability. The framework established here will promote the development of more gestationally relevant models to better understand labor progression and the factors involved in dysfunctional labor.

Muscle force sharing during locomotion is influenced by the mechanical demands of movement and the contractile properties of synergistic muscles. In cats, plantarflexor muscles exhibit distinct functional specialization, with the slow-fibred soleus maintaining relatively constant force across conditions while faster muscles such as the plantaris and gastrocnemius increase force production with increasing locomotor demand. However, it remains unclear whether similar force-sharing patterns occur in larger animals with different musculoskeletal designs. Therefore, the purpose of this study was to examine force sharing between the superficial digital flexor (SDF) and medial gastrocnemius (MG) muscles during treadmill locomotion in sheep. Tendon buckle force transducers were surgically implanted on the SDF and MG tendons of seven sheep, and in vivo muscle forces were recorded during walking and trotting across different speeds and inclines. Both muscles increased force with increasing speed and incline; however, speed had a substantially greater effect than incline. The SDF consistently produced greater absolute force than the MG across all conditions, whereas the MG exhibited slightly larger relative increases in force with increasing speed. Time to peak force decreased with increasing speed in both muscles, although the SDF reached peak force later in stance than the MG across conditions. In contrast to the distinct specialization observed in cats, neither muscle displayed a relatively condition-independent, soleus-like force contribution. These findings suggest that force sharing in sheep is more distributed across synergistic muscles and may reflect the influence of musculoskeletal design, tendon compliance, and mixed fibre-type composition on muscle function in larger species.

Pulmonary embolism (PE) is a sudden blockage of lung arteries, usually caused by a blood clot that travels from the deep veins of the legs. As the world becomes more sedentary and lifestyle diseases emerge, deaths from PE are expected to rise in the next 20 years. For instance, the United States records annual deaths of 60 per 100,000 people. The degree to which these deaths are affected by demographic, socioeconomic and environmental predisposing factors as well as how they vary across time and space remains an open science question. In this paper, we conduct a detailed statistical and spatial-temporal study PE mortality counts across US counties from 2005 to 2022. Our study shows that study shows that PE mortality is not randomly distributed in space and time but concentrated in most counties in Arkansas, Mississippi, Kansas, Missouri, Oklahoma, Louisiana, Nebraska, Tennessee, and Texas. We also established that age is a statistically significant predictor (mean coefficient of 0.52) of PE mortality especially in counties of Mississippi, Kansas, Missouri, Tennessee, Illinois, Kentucky, Texas and Virginia. Our results thus provide empirical support for prioritizing regionally targeted PE prevention policies. Furthermore, the adopted county-level analysis uncovered granular geographic patterns that are usually obscured in state or national level analysis. Our study thus provides actionable evidence to support geographically tailored strategies aimed at reducing mortality by pinpointing counties with consistently elevated PE mortality risk at different timescales.

Background and PurposeBadminton imposes considerable cardiovascular and musculoskeletal stress. Physiological profiling can identify modifiable injury risk factors and inform exercise-based prevention and rehabilitation. This study compared cardiovascular recovery, neuromuscular activation, and limb strength between elite and recreational male university badminton players to derive preliminary physiological benchmarks for injury risk stratification and exercise rehabilitation guidance. MethodsForty male athletes (20 elite: national/university representatives with [&ge;]5 years of competitive experience; 20 recreational: <3 years of experience) completed assessments of heart rate recovery (HRR), biceps brachii surface electromyography (sEMG; SENIAM protocol), handgrip strength (JAMAR dynamometry), and maximal bodyweight squat repetitions. Independent-sample t-tests with Cohens d ( = 0.05) and Pearson correlations were applied. ResultsElite players demonstrated significantly greater handgrip strength (49.00{+/-}6.12 vs. 39.00{+/-}5.45 kg, p = 0.001, d = 1.72) and lower-limb (LL) strength (60.35{+/-}11.29 vs. 41.75{+/-}6.72 repetitions, p < 0.001, d = 1.96). Normalized sEMG root mean square (RMS) was higher in elite athletes during flexion (11.56{+/-}4.16% vs. 7.26{+/-}5.15%, p = 0.004, d = 0.94) and extension (12.67{+/-}4.56% vs. 7.85{+/-}5.73%, p = 0.003, d = 0.94). HRR did not differ significantly between groups (p = 0.17, d = 0.43, observed power = 0.34). Elite players nonetheless showed a more favorable recovery distribution. sEMG -HRR correlations were weak and non-significant in both groups. ConclusionsElite badminton players exhibit a distinct physiological profile of greater strength and more efficient neuromuscular activation. These preliminary cross-sectional findings may support the design of exercise-based injury-prevention and rehabilitation in university badminton.

Total knee replacement restores mobility in patients with advanced osteoarthritis, yet many individuals still experience limited ability to perform high-flexion tasks such as squatting. Current preoperative planning relies on static imaging and cannot predict how different implant alignment choices will affect postoperative dynamic function. This study developed a predictive simulation framework that uses bi-level inverse optimal control to link preoperative implant alignment directly to expected postoperative squat kinematics. Subject-specific musculoskeletal models were constructed for six total knee replacement patients using experimental squat data. Bi-level inverse optimal control was applied to identify both individualised and group-level cost functions. The individualised setting provided subject-specific accuracy, while the group-level setting derived a single group-level cost function as an initial step toward preoperative use without requiring postoperative motion data. The individualised setting reproduced experimental trajectories with low errors across all joints (mean apex difference 1.53{degrees}, root-mean-square error 5.15{degrees}, normalised root-mean-square error 11.15%, Pearson correlation 0.96). The group-level setting yielded higher but acceptable errors (mean apex difference 5.70{degrees}, root-mean-square error 6.75{degrees}, normalised root-mean-square error 17.53%, Pearson correlation 0.95) while preserving the general pattern and phasing of the motion. Squat depth emerged naturally from the optimisation rather than being prescribed. This framework may provide a basis for future quantitative tools to evaluate how implant alignment choices influence postoperative squat performance, potentially improving functional outcomes in total knee replacement. These results suggest that the proposed IOC framework can reproduce key features of post-TKR squat kinematics, but further out-of-sample validation is required before it can be used for preoperative prediction or translated into tools aimed at improving functional outcomes in total knee replacement.

Computational Hill-type muscle models are widely used to simulate muscle force production because of their efficiency and physiological interpretability. However, their formulation relies on limiting assumptions, including debated multiscale simplifications, a simplified excitation-activation dynamics and an inability to capture slow and fast fibres. Moreover, existing Hill-type models remain insufficiently validated across physiological scales, fibre types, and contraction modes. We addressed these limitations by developing a multiscale fibre-type specific Hill-type neuromuscular actuator with mechanistic excitation-activation dynamics and systematically validated it against comprehensive experimental benchmarks. The model built upon a previously proposed motoneuron-driven actuator incorporating calcium-kinetics-based activation dynamics. The excitation-activation formulation was further refined to strengthen its physiological basis, while the contraction dynamics was extended by including an activation- and length-dependent force-velocity relationship, elastic tendon, passive elastic element, and the fibre-type-specific effects of yielding and sag. Validation was performed against four benchmark datasets spanning motor-unit and whole-muscle scales, including slow and fast fibres under both isometric and dynamic conditions. Experimental force traces were obtained from six muscles of rats and cats using a broad range of stimulation frequencies, muscle lengths, and imposed length changes, combining previous literature datasets with experiments performed ad hoc for this study. Overall, the model reproduced forces across all benchmark conditions, with mean absolute errors typically below 15% of the maximum isometric force, although larger errors were observed in specific submaximal and dynamic trials. The inclusion of physiologically based excitation-activation dynamics, together with yielding and sag, improved model performance under submaximal activation conditions. This study presents the first systematic validation of a single multiscale Hill-type neuromuscular actuator against comprehensive experimental motor unit and muscle force data, providing a benchmark framework for the development and assessment of future models. Author summarySkeletal muscles generate force through a complex sequence of events that links neural signals to muscle contraction. Because direct measurements are difficult to obtain, researchers often rely on computer models to investigate neuromuscular function and estimate muscle forces. However, most modelling approaches rely on simplifying assumptions about how force is generated across different biological scales, how muscles are activated, and how slow and fast muscle fibres behave. Moreover, they have not been validated against comprehensive experimental data. As a result, it remains unclear how accurately these models can reproduce muscle force across different physiological conditions. In this study, we established the first comprehensive set of experimental benchmarks spanning both motor-unit and whole-muscle scales, including slow and fast muscles under isometric and dynamic conditions. We used these benchmarks to validate a newly developed multiscale muscle model that explicitly represents the physiological pathway from neural stimulation to force production. The model incorporates experimentally based descriptions of calcium dynamics, activation, tendon elasticity, and fibre-type-specific contractile properties. We then compared simulated and experimental force responses across a wide range of stimulation frequencies, muscle lengths, and length-change conditions.

The osteocyte network in bone is believed to play an important role for how bone tissues sense and respond to mechanical stimulation. Yet, bone adaptation to mechanical loads is often conceptualised as a simple response to mechanical stimuli, such as Wolffs law, which is based on mechanical variables only and takes no account of the cellular basis of mechanosensation. Wolffs law presumes the existence of a reference mechanical stimulus, the mechanical setpoint, above which bone is consolidated, and under which bone is removed. In this paper, we develop a theory of bone tissue sensing and adaptation based on osteocytes to provide new understanding of the role played by osteocyte signals in mechanical adaptation. In this theory, the mechanical setpoint of Frosts mechanostat is explicitly embodied as osteocyte properties involved in mechanotransduction. The mechanical setpoint is allowed to adapt due to the replacement of osteocytes during remodelling, making the setpoint space and time dependent. We propose a mathematical model to implement this new theory of bone adapation and present numerical simulations of this model to explore how mechanobiological response curves (effective Wolffs laws) are modulated by setpoint adaptation during remodelling. By accounting for varying osteocyte populations within bone tissue, we explore bone adaptation under osteocyte disruptions, which is particularly relevant to age-related bone loss. Our model suggests that biological disruptions of remodelling balance cannot always be compensated by mechanical feedback, and that setpoint adaptation during remodelling may have significant observable consequences, such as hysteresis in bone response signatures that resemble lazy zones.

In the current clinical practice, the diagnosis of spinal disorders and their surgical planning are critically based on imaging data. To complement this data, patient-specific finite element models have been developed and showed to be powerful tools for evaluating spine mechanics. Most of them rely on Computational Tomography (CT) scans - which have a high resolution but are seldom available in routine clinical practice - while only a recent few models are on less invasive Magnetic Resonance Imaging (MRI). Yet, despite the proliferation of these computational models, encompassing detailed anatomical and functional information, the rheological assumptions they are built upon are based on tissue-sample mechanical response data, which leaves a gap in the quantitative analysis on how such assumptions influence the macroscopic response of a functional spinal unit. Aiming at addressing these shortcomings, the main purpose of this work is to introduce a quantitative computational assessment of the macroscopic impact of commonly adopted rheological models - from linear elasticity to fiber-reinforced nonlinear hyperelasticity - in several loading conditions, focusing on a lumbar unit which is considered as a typical benchmark system. We also propose a reconstruction procedure to accurately describe subject-specific anatomy from MRI data, including the intervertebral disc and its nucleus pulposus. Bones are modeled as linear elastic media, whereas for the AF, we consider three different mechanical models - namely, isotropic linear elasticity and the Holzapfel-Gasser-Ogden model with and without fiber reinforcement. Model verification on an idealized geometry demonstrates numerical consistency, while parametric orthostatic simulations highlight the need for nonlinear formulations to capture anisotropy and strain-stiffening behavior of the intervertebral disc. Then, we carry out flexion, lateral bending, and torsion tests on a subject-specific reconstructed functional unit, for which we provide parametric analysis in terms of momentum magnitude and resulting range of motion. These tests further confirm the need for a nonlinear rheology of the annulus fibrosus and provide a quantitative assessment of the differences between the constitutive laws considered. Moreover, successful comparisons with the literature, in terms of macroscopic deformation under several loading conditions, serve as partial validation for our computational model.

Purpose: Accurate assessment of the pressure gradient ({Delta}P) across aortic coarctation (CoA) is critical for determining disease severity and the need for intervention. Current non-invasive methods are unreliable, while invasive catheterization remains the clinical gold standard. This study evaluates a novel MRI acquisition strategy, 4D-FlowP, that simultaneously encodes blood velocity and acceleration to enable reliable non-invasive pressure gradient mapping in CoA. Methods: Patient-specific compliant aortic phantoms were created from clinical MRI data of two patients with CoA. Additional geometries were synthetically generated by increasing stenosis severity. Phantoms were studied in an MRI compatible flow loop under physiologically realistic flow and pressure conditions. Pressure gradients were estimated using conventional 4D-Flow MRI, 4D-FlowP, and fluid-structure interaction (FSI) simulations. Results were compared against ground-truth catheter-based measurements across multiple flow rates and stenosis severities. Results: Conventional 4D-Flow consistently underestimated {Delta}P (slope = 0.63, R2=0.75) relative to catheter measurements. In contrast, 4D-FlowP demonstrated substantially improved agreement (slope = 0.95, R2=0.75). FSI simulations showed the highest overall agreement with catheter-derived {Delta}P (slope = 1.14, R2=0.82). Scan times for 4D-FlowP were comparable to 4D-Flow (26 vs. 24 minutes). Conclusion: 4D-FlowP enables a more accurate MRI-based pressure gradient mapping in CoA than conventional 4D-Flow, when compared to ground truth catheter measurements. These findings support further in vivo evaluation of 4D-FlowP as a non-invasive alternative for functional assessment of CoA severity

Traumatic brain injury (TBI) remains a global health challenge with mechanisms that are still insufficiently understood. While neuroimaging has been used to probe microstructural alterations and their association with head kinematics, findings remain heterogeneous. Finite element (FE) head modelling offers a more robust alternative, demonstrating a superior correlation with observed microstructural changes compared to traditional impact exposure metrics. However, most existing FE models are derived from single-subject scans or generic atlases, which often fail to represent specific study cohorts and introduce significant output variability. This study presents a reproducible computational framework that generates a cohort-specific template brain from MRI scans of adolescent male rugby players to produce a representative FE head model. The model was validated against cadaveric head experiments, demonstrating strong agreement with observed nodal displacements. Furthermore, simulations comparing the template-based model to subject-specific FE models with the identical impact conditions revealed significant differences in brain response. These results underscore the critical necessity of subject-specific modelling for the personalised characterisation of brain biomechanics. Our framework utilizes open-access tools, ensuring full reproducibility for research groups seeking to develop population-, sex-, or ethnicity-specific models. By providing a more accurate representation of cohort-average and individual brain responses, this work contributes to the improved mapping of mechanical strain to clinical findings and neurological alterations. TRANSPARENCY, RIGOR, AND REPRODUCIBILITY SUMMARYThis study is part of an ongoing longitudinal study in New Zealand. All procedures conducted in this study are in accordance with the ethics approval from the New Zealand Health and Disability Ethics Committee (20/NTB/14). All participants aged 16 and older provided informed consent, while participants under 16 provided assent with parental consent. For this study, general exclusion criteria included contraindication to MRI, neurological/psychiatric conditions, and dental braces affecting imaging quality. A total of 78 male high school rugby players (aged 14-18 years old) participated in this study. Inclusion criteria required no mTBI within the past six months prior to start of study, no history of mTBI incident with loss of consciousness, no neurological disorders, no history of drug or excessive alcohol use and no diagnosis of dementia or delirium. Each scan included a multi-parametric MRI scan (i.e. structural, diffusion, functional MRI), and a cognitive and symptom assessment. More details of the parameters and tests used are reported in the manuscript. To record head acceleration exposure across the whole season, an instrumented mouthguard was provided for each rugby player. A control group (14-18 years old) composed of non-collision sport, male athletes, was recruited and scanned at a single timepoint following the same protocol as the rugby players. The same inclusion and exclusion criteria were applied for the control group, with the addition of no self-reported mTBI history or participation in collision sports within the past two years. The primary aim of this study is to establish a computational framework that enables the creation of an average brain from MRI scans of subjects and to develop an FE model. Moreso, this FE model will incorporate fibre dispersion parameter from diffusion MRI and be validated against human head cadaveric experiments reported in the literature. This study is among the few to present a complete framework from MRI to finite element modelling using open-access tools, making it reproducible.

Hearts change their wall thickness (concentric growth) and chamber size (eccentric growth) as they adapt to circulatory demands and the intrinsic function of their contractile cells. Factors associated with wall thickening include variants of sarcomeric proteins that enhance contractility, mitochondrial dysfunction, and hypertension. Chambers can dilate due to many factors including sarcomeric variants that depress contractility and aortic and / or mitral valve insufficiency. Despite intensive study, the mechanisms that regulate cardiac growth remain unclear. It is also uncertain whether inherited variants induce growth via the same mechanisms as more common clinical pathologies, such as hypertension. Here we show that computer simulations of a beating left ventricle reproduce both variant and non-variant-related growth patterns when myocytes grow concentrically to regulate intracellular ATP concentration and eccentrically to maintain titin-based intracellular stress. The simulations support the hypothesis that cardiac growth reflects homeostatic feedback through three interacting systems whereby myocytes add or remove mitochondria and sarcomeres (1) in parallel to match ATP generation to myocardial energy demand, and (2) in series to regulate passive tension, while (3) the autonomic nervous system regulates cardiac power, and thus myocardial ATPase, via baroreflex control. The new framework provides a mechanistic basis for the patterns of eccentric and concentric growth induced by a wide range of clinically-relevant conditions and could facilitate in silico testing of potential therapies for cardiac disease. Significance statementHearts grow in response to both physiological and pathological stimuli. The patterns of concentric (wall thickening / thinning) and eccentric (chamber dilation / constriction) induced by different challenges are well recognized but the underlying mechanisms remain unclear. This work presents simulations of a beating left ventricle where (1) concentric growth is regulated by myocytes attempting to stabilize the intracellular ATP concentration and (2) eccentric growth is regulated by titin-mediated stress. The calculations reproduce the growth associated with inherited variants of sarcomeric proteins, mitochondrial dysfunction, hypertension, and both mitral and aortic valve insufficiency. The new ability to predict cardiac growth and its potential modification by treatments, including myotropes, brings the field closer to in silico optimization of therapy for cardiovascular disease.