Biology subjects

Gritsenko, V.

Publications and source records attributed to Gritsenko, V..

13 recordsLinked to original sources

bioRxiv2026

A Growth-Based Framework for Scaling Infant Anthropometry in Motion Capture Studies

Biomechanical analysis of infant motor behavior is complicated by rapid growth and large inter-subject variability in body size and segment proportions. We present a growth-adjusted anthropometric scaling framework that integrates Centers for Disease Control and Prevention (CDC) growth charts with three-dimensional motion capture to estimate subject-specific infant biomechanics during prone play. Nineteen infants aged 2-6 months were recorded during spontaneous prone movement. Weight-for-age percentiles were computed using CDC LMS parameters and used to infer body length from length-for-age references, ensuring internal consistency between weight and length estimates. Body segments were scaled using published infant proportions and validated against head and shoulder widths measured from motion capture. Infant head and trunk proportions were substantially larger than adult values, whereas limb proportions were similar. Estimated and measured dimensions of head and trunk width showed good agreement, with mean root mean squared errors of approximately 3 cm. Adult-based scaling produced head and trunk length errors of up to 6 cm. This growth-adjusted framework improves anatomical fidelity and enables normalization of biomechanical measures across infants with differing growth trajectories.

bioengineering↗

bioRxiv2025

Stroke-Related Changes in Tonic and Phasic Muscle Recruitment During Reaching Reveal Pathway-Specific Motor Deficits

Upper limb motor deficits are common after stroke and often persist despite rehabilitation. While clinical assessments emphasize movement quality, they do not capture the underlying neuromuscular impairments, particularly in individuals with mild deficits. This study aimed to characterize stroke-related changes in muscle recruitment during reaching by separating tonic (gravity-compensating) and phasic (intersegmental dynamics-related) components of EMG activity. We recorded surface EMG from 12 upper limb muscles during goal-directed reaching in 8 individuals with unilateral ischemic stroke and 9 controls. Using principal component analysis, we extracted tonic and phasic components and compared their amplitude, directional tuning, and coactivation patterns across groups. Group differences were evaluated with generalized linear mixed-effects models, regression, and correlation analyses. Even individuals with mild stroke exhibited abnormal muscle recruitment. Proximal muscles were over-recruited in directions that typically require less activation, indicating altered directional tuning. Phasic activation of distal muscles was significantly reduced and worsened with time post-stroke (R2 = 0.52, p = 0.002). Tonic overactivation of proximal muscles was present across all stroke participants. Muscle coactivation patterns were hemisphere-specific: right-hemisphere stroke reduced tonic coactivation in contralateral arms, whereas left-hemisphere stroke increased it. Abnormal phasic coactivation between proximal and distal muscles correlated with impaired intersegmental dynamics compensation (R2 = 0.67, p = 0.013). Tonic and phasic impairments were often correlated, suggesting shared disruption of corticospinal and reticulospinal pathways. These findings reveal distinct yet interacting deficits in muscle recruitment following stroke, supporting the development of neuromechanically-informed tools for individualized rehabilitation.

neuroscience↗

bioRxiv2024

Evaluating Joint Angle Data for Clinical Assessment Using Multidimensional Inverse Kinematics with Average Segment Morphometry.

BackgroundQuantitative movement analysis is increasingly used to assess motor deficits, but joint angle calculations depend on assumptions about limb segment lengths. These lengths are often estimated from average anthropometric proportions rather than measured directly. The extent to which such assumptions influence joint angle accuracy and variability remains unclear. MethodsIn prior studies, we recorded reaching movements in nine healthy adults using active-marker motion capture system. In this study, we computed arm joint angles with a dynamic model scaled using either measured segment lengths (Individual method) or proportions based on body height (Average method). We compared segment proportions and the variability in joint angle trajectories arising from segment length assumptions (between-subject variability) with within-subject variability across repeated movements. ResultsSegment length proportions remained unchanged despite increases in population height. Joint angle trajectories derived from the two scaling methods were very similar. Segment length assumptions had only minor effects on joint angle amplitudes, primarily due to kinematic redundancy, and these effects were substantially smaller than the within-subject variability observed across repeated movements in most individuals. Importantly, while segment length estimates shifted absolute joint angle amplitudes, they did not alter the shape of angular trajectories. Conclusions/SignificanceMorphological variability in segment lengths contributes less to joint angle variability than the variability expressed by individuals across repeated movements. This indicates that movement variability is driven more by how the nervous system selects among redundant motor solutions than by body morphology. These findings suggest that clinical assessments of range of motion and movement quality are robust to the method of segment length estimation, supporting the reliability of motion capture-based assessments in both research and clinical settings. Author SummaryUnderstanding how people move is important for diagnosing and treating movement disfunction. Motion capture technology allows researchers and clinicians to measure how joints move, but these calculations depend on knowing the lengths of each arm segment. In practice, these lengths are often estimated from body height instead of being measured directly. We tested whether this assumption affects joint angle measurements. Using previously recorded reaching movements in healthy adults, we compared joint angles calculated from estimated versus measured arm segment lengths. We found that using estimated lengths had only a very small effect on joint angle trajectories, and these effects were much smaller than the natural variability that occurs when the same person repeats the same movement. This means that differences in body proportions do not significantly affect how arm movements are measured. Our findings show that motion capture is a reliable tool for assessing movement quality, even when exact body proportions are not measured, making it more practical for use in both research and clinical care.

bioengineering↗

bioRxiv2024

How muscle synergies fail to solve the muscle redundancy problem during human reaching

Neural control of movement has to overcome the problem of redundancy in the multidimensional musculoskeletal system. The problem can be solved by reducing the dimensionality of the control space of motor commands, i.e., through muscle synergies or motor primitives. Evidence for this solution exists, multiple studies have obtained muscle synergies using decomposition methods. These synergies vary across different workspaces and are present in both dominant and non-dominant limbs. Here we explore the dimensionality of control space by examining muscle activity patterns across reaching movements in different directions starting from different postures performed bilaterally by healthy individuals. We further explore the effect of biomechanical constraints on the dimensionality of control space. We are building on top of prior work showing that muscle activity profiles can be explained by applied moments about the limb joints that reflect the biomechanical constraints. These muscle torques derived from motion capture represent the combined actions of muscle contractions that are under the control of the nervous system. Here we test the generalizability of the relationship between muscle torques and muscle activity profiles across different starting positions and between limbs. We also test a hypothesis that the dimensionality of control space is shaped by biomechanical constraints. We used principal component analysis to evaluate the contribution of individual muscles to producing muscle torques across different workspaces and in both dominant and non-dominant limbs. Results generalize and support the hypothesis. We show that the muscle torques that support the limb against gravity are produced by more consistent combinations of muscle co-contraction than those that produce propulsion. This effect was the strongest in the non-dominant arm moving in the lateral workspace on one side of the body.

neuroscience↗

bioRxiv2023

Does joint impedance improve dynamic leg simulations with explicit and implicit solvers?

The nervous system predicts and executes complex motion of body segments actuated by the coordinated action of muscles. When a stroke or other traumatic injury disrupts neural processing, the impeded behavior has not only kinematic but also kinetic attributes that require interpretation. Biomechanical models could allow medical specialists to observe these dynamic variables and instantaneously diagnose mobility issues that may otherwise remain unnoticed. However, the real-time and subject-specific dynamic computations necessitate the optimization these simulations. In this study, we explored the effects of intrinsic viscoelasticity, choice of numerical integration method, and decrease in sampling frequency on the accuracy and stability of the simulation. The bipedal model with 17 rotational degrees of freedom (DOF)--describing hip, knee, ankle, and standing foot contact--was instrumented with viscoelastic elements with a resting length in the middle of the DOF range of motion. The accumulation of numerical errors was evaluated in dynamic simulations using swing-phase experimental kinematics. The relationship between viscoelasticity, sampling rates, and the integrator type was evaluated. The optimal selection of these three factors resulted in an accurate reconstruction of joint kinematics (err < 1%) and kinetics (err < 5%) with increased simulation time steps. Notably, joint viscoelasticity reduced the integration errors of explicit methods and had minimal to no additional benefit for implicit methods. Gained insights have the potential to improve diagnostic tools and accurize real-time feedback simulations used in the functional recovery of neuromuscular diseases and intuitive control of modern prosthetic solutions.

bioinformatics↗

bioRxiv2022

Neuromechanical Coupling is Reflected in the Spatial Organization of the Spinal Motoneuron Pools

Recent advances in neuroprosthetics have shown that activating the surviving spinal circuitry below the level of spinal damage can produce complex movements and restore function. This is because the spinal cord circuitry plays an important role in the control of complex movements by integrating descending execution-related signals with feedback sensory signals. To generate efficient movements through muscle contractions, the nervous system embeds the anatomical and dynamical properties of the controlled body parts, a concept termed neuromechanical tuning. The motoneurons innervating the skeletal muscles of the body form complex spatial structures that span multiple segments. The goal of this study is to determine if these structures can support neuromechanical tuning. We performed a comparative analysis of the spatial organization of the motoneuron pools and the functional organization of the muscles they innervate. We developed a three-dimensional model of the spatial organization of motoneuron pools innervating upper limb muscles in the macaque and quantified their relative distances. We then utilized a musculoskeletal model of the macaque upper extremity to characterize the functional relationship between the muscles innervated by the motoneuron pools. We found that the distances between motoneuron pools mimicked the agonistic or antagonistic actions of muscles they innervate. This shows that the spatial organization of the motoneuron pools embeds the functional musculoskeletal anatomy evolved to support the large repertoire of upper limb movements in primates. This spinal anatomy may play an important role in the neuromechanical tuning and simplify the sensorimotor control of the arm.

neuroscience↗

bioRxiv2020

Principles for coding associative memories in a compact neural network

A major goal in neuroscience is to elucidate the principles by which memories are stored in a neural network. Here, we have systematically studied how the four types of associative memories (short- and long-term memories, each formed using positive and negative associations) are encoded within the compact neural network of C. elegans worms. Interestingly, short-term, but not long-term, memories are evident in the sensory system. Long-term memories are relegated to inner layers of the network, allowing the sensory system to resume innate functionality. Furthermore, a small set of sensory neurons is allocated for coding short-term memories, a design that can increase memory capacity and limit non-innate behavioral responses. Notably, individual sensory neurons may code for the conditioned stimulus or the experience valence. Interneurons integrate these information to modulate animal behavior upon memory reactivation. This comprehensive study reveals basic principles by which memories are encoded within a neural network, and highlights the central roles of sensory neurons in memory formation.

neuroscience↗

bioRxiv2020

Functional and Structural Moment Arm Validation for Musculoskeletal Models: A Study of the Human Forearm and Hand

The development of realistic musculoskeletal models is a fundamental goal for the theoretical progress in sensorimotor control and its engineering applications, e.g., in the biomimetic control of artificial limbs. Yet, accurate models require extensive experimental measures to validate structural and functional parameters describing muscle state over the full physiological range of motion. However, available experimental measurements of, for example, muscle moment arms are sparse and often disparate. Validation of these models is not trivial because of the highly complex anatomy and behavior of human limbs. In this study, we developed a method to validate and scale kinematic muscle parameters using posture-dependent moment arm profiles, isometric force measurements, and a computational detection of assembly errors. We used a previously published model with 18 degrees of freedom (DOFs) and 32 musculotendon actuators with force generated from a Hill-type muscle model. The muscle path from origin to insertion with wrapping geometry was used to model the muscle lengths and moment arms. We simulated moment arm profiles across the full physiological range of motion and compared them to an assembled dataset of published and merged experimental profiles. The muscle paths were adjusted using custom metrics based on root-mean-square and correlation coefficient of the difference between simulated and experimental values. Since the available measurements were sparse and the examination of high-dimensional muscles is challenging, we developed analyses to identify common failures, i.e., moment arm functional flipping due to the sign reversal in simulated moments and the imbalance of force generation between antagonistic groups in postural extremes. The validated model was used to evaluate the expected errors in torque generation with the assumption of constant instead of the posture-dependent moment arms at the wrist flexion-extension DOF, which is the critical joint in our model with the largest number of crossing muscles. We found that there was a reduction of joint torques by about 35% in the extreme quartiles of the wrist DOF. The use of realistic musculoskeletal models is essential for the reconstruction of hand dynamics. These models may improve the understanding of muscle actions and help in the design and control of artificial limbs in future applications. New & NoteworthyRealistic models of human limbs are a development goal required for the understanding of motor control and its applications in biomedical fields. However, developing accurate models is restrained by the lack of accurate and reliable musculoskeletal measurements in humans. Here, we have overcome this challenge by using multi-stage validation of simulated structures using both experimental data and the identification of structural failures in the high-dimensional muscle paths. We demonstrate that the rigorous structural and functional validation method is essential for the understanding of force generation at the wrist.

neuroscience↗

bioRxiv2019

Approximating complex musculoskeletal biomechanics using multidimensional autogenerating polynomials

Computational models of the musculoskeletal system are scientific tools used to study human movement, quantify the effects of injury and disease, and plan surgical interventions. Additionally, these models could also be used to intuitively link biological control signals and realistic high-dimensional articulated prosthetic limbs. However, implementing fast and accurate musculoskeletal computations that can be used to control a prosthetic limb in real-time is a challenging problem. As muscles typically span multiple joints, the wrapping over complex geometrical constraints changes their moment arms and length as a function of joint angle and, thus, their ability to generate joint torques. As a result of these biomechanical complexities, calculating these muscle state variables in real-time is a difficult simulation problem. Here, we report a method to accurately and efficiently calculate these variables for the forearm muscles that actuate the hand and wrist across multiple postures. The posture dependent muscle geometry, moment arms and lengths of modeled muscles, were captured using autogenerating polynomials that expanded their optimal selection of terms using information measurements. The iterative process approximated 33 musculotendon actuators, each spanning up to 6 DOFs in an 18 DOF model of the human arm and hand, defined over the full physiological range of motion. Using these polynomials, the entire forearm anatomy could be computed in <10 {micro}s, which is far better than what is required for real-time performance, and with low errors in moment arms (below 5%) and lengths (below 0.4%). Moreover, we demonstrate that the number of elements in these autogenerating polynomials does not increase exponentially with the increase in complexity of muscles, increasing linearly instead. The similar structure and function of muscles are represented with specific invariant polynomial terms. Dimensionality reduction using the polynomial terms alone resulted in clusters comprised of muscles with similar functions, suggesting that the polynomials themselves captured biologically relevant features of muscle structure and function. We propose that this novel method of describing musculoskeletal biomechanics might further improve the applications of detailed and scalable models for the description of human movement.

physiology↗

bioRxiv2019

Distinct Proximal and Distal Corticospinal Signals Embed Limb Dynamics Through the Modulation of Stiffness

How the neural motor system recruits muscles to support the arm against gravity is a matter of active debate. It is unknown how the neural motor system compensates for the changing gravity-related joint moments either when holding a steady-state posture or during movement between postural steady states, e.g., during reaching. Here we used single-pulse transcranial magnetic stimulation to compare the roles that the human primary motor cortex plays in the muscle recruitment to compensate for gravity. We hypothesized that the motor cortex contributes to muscle recruitment to both maintain posture and to compensate for changes in gravitational passive joint moments during movement. To test this hypothesis, we used visual targets in virtual reality to instruct five postures and three movements with or against gravity. We then measured the amplitude and gain of motor evoked potentials in multiple muscles of the arm at several phases of the reaching motion and during posture maintenance. Stimulation below the resting motor threshold, calibrated to the biceps muscle, caused motor evoked potentials in all muscles during all postural and reaching tasks. The amplitude of motor evoked potentials was proportional to the motoneuronal excitability measured as muscle activity. The coefficient of proportionality was positively correlated with the postural component of muscle moment during posture and movement. Altogether our results support the hypothesis. The observed contribution of the motor cortex to the recruitment of multiple antagonistic muscles suggests a whole-limb strategy for overcoming passive gravity-related moments with both active muscle moments and muscle co-activation that modulates limb impedance. New & NoteworthyMaintaining static posture and producing motion appear to be contradictory tasks for the nervous system. In contrast to this seeming dichotomy, our results show that the motorcortical control signals play the same role in both tasks when it is framed in biomechanical terms, i.e., muscle contractions needed to compensate for gravity.

neuroscience↗

bioRxiv2019

Force-based assessment captures unseen changes in normal and abnormal arm motion

The whole repertoire of complex human motion is enabled by forces applied by our muscles and controlled by the nervous system. The of stroke on the complex multi-joint motor control is difficult to quantify in a meaningful way that informs about the underlying deficit in the active motor control and intersegmental coordination. We tested the idea that post-stroke deficit can be quantified with high sensitivity using motion capture and inverse modeling of a broad range of reaching movements. Our hypothesis is that muscle moments estimated based on active joint torques provide a more sensitive measure of post-stroke motor deficits than joint angle and angular velocity. The motion of twenty-two participants was captured while performing reaching movements in a center-out task, presented in virtual reality. We used inverse dynamics analysis to derive active joint torques that were the result of muscle contractions, termed muscle torques, that caused the recorded multi-joint motion. We then applied a novel analysis to separate the component of muscle torque related to gravity compensation from that related to intersegmental dynamics. Our results show that individual reaching movements can be characterized with higher information content using muscle torques rather than joint angles. Moreover, muscle torques allow for distinguishing between the individual motor deficits due to aging or stroke from the typical differences in reaching between healthy individuals. This novel quantitative assessment method may be used in conjunction with home-based gaming motion-capture technology for remote monitoring of motor deficits and inform the development of evidence-based robotic therapy interventions. New and NoteworthyFunctional deficits seen in task performance have biomechanical underpinnings, seen only through the analysis of forces. Our study has shown that estimating muscle moments can quantify with high sensitivity post-stroke deficits in intersegmental coordination. An assessment developed based on this method could help quantify less observable deficits in mildly affected stroke patients. It may also bridge the gap between evidence from studies of constrained or robotically manipulated movements and research with functional and unconstrained movements.

neuroscience↗

bioRxiv2017

The Nonlinear Relationship Between Sensory And Motor Primitives During Reaching Movements

The stabilizing role of sensory feedback in relation to realistic 3-dimensional movement dynamics remains poorly understood. The objective of this study was to quantify how primary afferent activity contributes to shaping muscle activity patterns during reaching movements. To achieve this objective, we designed a virtual reality task that guided healthy human subjects through a set of planar reaching movements with controlled kinematic and dynamic conditions that minimized inter-subject variability. Next, we integrated human upper-limb models of musculoskeletal dynamics and proprioception to analyze motion and major muscle activation patterns during these tasks. We recorded electromyographic and motion-capture data and used the integrated model to simulate joint kinematics, joint torques due to muscle contractions, muscle length changes, and simulated primary afferent feedback. The parameters of the primary afferent model were altered systematically to evaluate the effect of fusimotor drive. The experimental and simulated data were analyzed with hierarchical clustering. We found that the muscle activity patterns contained flexible task-dependent groups that consisted of co-activating agonistic and antagonistic muscles that changed with the dynamics of the task. The activity of muscles spanning only the shoulder generally grouped into a proximal cluster, while the muscles spanning the wrist grouped into a distal cluster. The bifunctional muscle spanning the shoulder and elbow were flexibly grouped with either proximal or distal cluster based on the dynamical requirements of the task. The composition and activation of these groups reflected the relative contribution of active and passive forces to each motion. In contrast, the simulated primary afferent feedback was most related to joint kinematics rather than dynamics, even though the primary afferent models had nonlinear dynamical components and variable fusimotor drive. Simulated physiological changes to the fusimotor drive were not sufficient to reproduce the dynamical features in muscle activity pattern. Altogether, these results suggest that sensory feedback signals are in a different domain from that of muscle activation signals. This indicates that to solve the neuromechanical problem, the central nervous system controls limb dynamics through task-dependent co-activation of muscles and non-linear modulation of monosynaptic primary afferent feedback. New & NoteworthyHere we answered the fundamental question in sensorimotor transformation of how primary afferent signals can contribute to the compensation for limb dynamics evident in muscle activity. We combined computational and experimental approaches to create a new experimental paradigm that challenges the nervous system with passive limb dynamics that either assists or resists the desired movement. We found that the active dynamical features present in muscle activity are unlikely to arise from direct feedback from primary afferents.

neuroscience↗

bioRxiv2017

Gravitational Signals Underlie Tonic Muscle Activity During Goal-Directed Reaching

Human reaching movements require complex muscle activations to produce the forces necessary to move the limb in a controlled manner. How gravity and the complex kinetic properties of the limb contribute to the generation of the muscle activation pattern by the central nervous system (CNS) is a long-standing question in neuroscience. To address this question, muscle activity is often subdivided into static and phasic components. The former is thought to be related to posture maintenance and transitions between postures. The latter represents the remainder of muscle activity and is thought to be related to active movement production and the compensation for the kinetic properties of the limb. In the present study, we directly addressed how this subdivision of muscle activity into static and phasic components is related to the corresponding components of active muscle torques. Eight healthy subjects pointed in virtual reality to visual targets arranged to create a standard center-out reaching task in three dimensions. Muscle activity and motion capture data were synchronously collected during the movements. The motion capture data were used to calculate gravitational and dynamic components of active muscle torques using a dynamic model of the arm with 5 degrees of freedom. Principal Component Analysis (PCA) was then applied to muscle activity and the torque components, separately, to reduce the dimensionality of the data. Muscle activity was also reconstructed from gravitational and dynamic torque components. Results show that the gravitational and dynamic components of muscle torque represent a significant amount of variance in muscle activity. This method could be used to identify static and phasic components of muscle activity using muscle torques. The contribution of both components to the overall muscle activity was largely equal, unlike their relative contribution to active muscle torques, which may reflect a neural control strategy.

neuroscience↗