Valeriya Gritsenko - Academia.edu (original) (raw)

Papers by Valeriya Gritsenko

Neural circuits embed limb dynamics for motor control and sensorimotor integration. The somatotop... more Neural circuits embed limb dynamics for motor control and sensorimotor integration. The somatotopic organization of motoneuron pools in the spinal cord may support these computations. Here, we tested if the spatial organization of motoneurons is related to the musculoskeletal anatomy. We created a 3D model of motoneuron locations within macaque spinal cord and compared the spatial distribution of motoneurons to the anatomical organization of the muscles they innervate. We demonstrated that the spatial distribution of motoneuron pools innervating the upper limb and the anatomical relationships between the muscles they innervate were similar between macaque and human species. Using comparative analysis, we found that the distances between motoneuron pools innervating synergistic muscles were the shortest, followed by those innervating antagonistic muscles. Such spatial organization can support the co-activation of synergistic muscles and reciprocal inhibition of antagonistic muscles. The spatial distribution of motoneurons may play an important role in embedding musculoskeletal dynamics.

Journal of Visualized Experiments, Jan 11, 2024

bioRxiv (Cold Spring Harbor Laboratory), Feb 14, 2024

PLOS ONE, Jul 3, 2023

The nervous system predicts and executes complex motion of body segments actuated by the coordina... more 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 contactwas 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.

This paper presents a novel method for solving the inverse kinematic problem of capturing human r... more This paper presents a novel method for solving the inverse kinematic problem of capturing human reaching movements using a dynamic biomechanical model. The model consists of rigid segments connected by joints and actuated by markers. The method was validated against a rotation matrix-based method using motion capture data recorded during reaching movements performed by healthy human volunteers. The results showed that the proposed method achieved low errors in joint angles. The angles were comparable to those calculated using the standard marker-based method. The proposed bioinspired method can be used in real-time medical applications, such as rehabilitation, assessment, and prosthesis/orthosis control.

bioRxiv (Cold Spring Harbor Laboratory), May 15, 2017

Human reaching movements require complex muscle activations to produce the forces necessary to mo... more 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 and controversial question in neuroscience. To tackle this issue, muscle activity is often subdivided into static and phasic components. The former corresponds to posture maintenance and transitions between postures. The latter corresponds to active movement production and the compensation for the kinetic properties of the limb. In the present study, we improved the methodology for this subdivision of muscle activity into static and phasic components by relating them to joint torques. Ten 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 postural 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 postural and dynamic components of muscle torque represent a significant amount of variance in muscle activity. This method could be used to define static and phasic components of muscle activity using muscle torques.

bioRxiv (Cold Spring Harbor Laboratory), Feb 9, 2023

The nervous system predicts and executes complex motion of body segments actuated by the coordina... more 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 complexities of the human musculoskeletal system and its interactions with the environment cr... more The complexities of the human musculoskeletal system and its interactions with the environment creates a difficult challenge for the neural control of movement. The consensus is that the nervous system solves this challenge by embedding the dynamical properties of the body and the environment. However, the modality of control signals and how they are generated appropriately for the task demands are a matter of active debate. We used transcranial magnetic stimulation over the primary motor cortex to show that the excitability of the corticospinal tract is modulated to compensate for limb dynamics during reaching tasks in humans. Surprisingly, few profiles of corticospinal modulation in some muscles and conditions reflected Newtonian parameters of movement, such as kinematics or active torques. Instead, the overall corticospinal excitability was differentially modulated in proximal and distal muscles, which corresponded to different stiffness at proximal and distal joints. This suggests that the descending corticospinal signal determines the proximal and distal impedance of the arm for independent functional control of reaching and grasping. Significance Statement The nervous system integrates both the physical properties of the human body and the environment to create a rich repertoire of actions. How these calculations are happening remains poorly understood. Neural activity is known to be correlated with different variables from the Newtonian equations of motion that describe forces acting on the body. In contrast, our data show that the overall activity of the descending neural signals is less related to the individual Newtonian variables and more related to limb impedance. We show that the physical properties of the arm are controlled by two distinct proximal and distal descending neural signals modulating components of limb stiffness. This identifies distinct neural control mechanisms for the transport and manipulation actions of reach. .

PLOS Computational Biology, Dec 16, 2020

Computational models of the musculoskeletal system are scientific tools used to study human movem... more Computational models of the musculoskeletal system are scientific tools used to study human movement, quantify the effects of injury and disease, plan surgical interventions, or control realistic high-dimensional articulated prosthetic limbs. If the models are sufficiently accurate, they may embed complex relationships within the sensorimotor system. These potential benefits are limited by the challenge of implementing fast and accurate musculoskeletal computations. A typical hand muscle spans over 3 degrees of freedom (DOF), wrapping over complex geometrical constraints that change its moment arms and lead to complex posture-dependent variation in torque generation. Here, we report a method to accurately and efficiently calculate musculotendon length and moment arms across all physiological postures of the forearm muscles that actuate the hand and wrist. Then, we use this model to test the hypothesis that the functional similarities of muscle actions are embedded in muscle structure. 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 μs, 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 increasing muscle complexity; complexity increases linearly instead. Dimensionality reduction using the polynomial terms alone resulted in clusters comprised of muscles with similar PLOS COMPUTATIONAL BIOLOGY

bioRxiv (Cold Spring Harbor Laboratory), May 17, 2017

the sensorimotor integration during unconstrained reaching movements in the presence of variable ... more the sensorimotor integration during unconstrained reaching movements in the presence of variable environmental forces remains poorly understood. the objective of this study was to quantify how much the primary afferent activity of muscle spindles can contribute to shaping muscle coactivation patterns during reaching movements with complex dynamics. to achieve this objective, we designed a virtual reality task that guided healthy human participants through a set of planar reaching movements with controlled kinematic and dynamic conditions that were accompanied by variable muscle co-contraction. Next, we approximated the Ia afferent activity using a phenomenological model of the muscle spindle and muscle lengths derived from a musculoskeletal model. the parameters of the spindle model were altered systematically to evaluate the effect of fusimotor drive on the shape of the temporal profile of afferent activity during movement. The experimental and simulated data were analyzed with hierarchical clustering. We found that the pattern of co-activation of agonistic and antagonistic muscles changed based on whether passive forces in each movement played assistive or resistive roles in limb dynamics. the reaching task with assistive limb dynamics was associated with the most muscle co-contraction. In contrast, the simulated Ia afferent profiles were not changing between tasks and they were largely reciprocal with homonymous muscle activity. Simulated physiological changes to the fusimotor drive were not sufficient to reproduce muscle co-contraction. These results largely rule out the static set and α-γ coactivation as the main types of fusimotor drive that transform the monosynaptic Ia afferent feedback into task-dependent co-contraction of antagonistic muscles. We speculate that another type of nonlinear transformation of Ia afferent signals that is independent of signals modulating the activity of α motoneurons is required for Ia afferent-based co-contraction. This transformation could either be applied through a complex nonlinear profile of fusimotor drive that is not yet experimentally observed or through presynaptic inhibition. Movement is the product of interactions between neural signals and the musculoskeletal dynamics that depends on limb anatomy 1-4. The motor control problem is then solved within a system with coupled neural and mechanical dynamical elements 5-7. Therefore, the relationship between neural signals driving muscle contraction and the resulting motion is nonlinear. Muscle contractions generate forces that sum into active moments defined by the agonistic or antagonistic relationships between the muscle's moment arms around a given axis of rotation of the joint. The components of these forces that sum to zero moment, such as forces produced by balanced

PubMed, Sep 1, 2022

The human motor system has evolved to perform efficient motor control in Earth's gravity. Altered... more The human motor system has evolved to perform efficient motor control in Earth's gravity. Altered gravity environments, such as microgravity and hypergravity, pose unique challenges for performing fine motor tasks with object manipulation. Altered gravity has been shown to reduce the speed and accuracy of complex manual tasks. This study aims to leverage electromyography (EMG) and virtual reality (VR) technologies to provide insights into the neuromuscular mechanism of object weight compensation. Seven healthy subjects were recruited to perform arm and hand movements, including a customized Box and Block Test with three different block weights, 0 (VR), 0.02, and 0.1 kg. EMG was recorded from 15 muscles of arm and hand while manipulating objects instrumented with force sensors to collect contact forces. Muscle co-contraction extracted from EMGs of antagonistic muscles was used as a measure of joint stiffness for each task. Results show that the co-contraction levels increased in the task with the heavy object and decreased in the VR task. This relationship suggests that the internal expectations of the object weight and the proprioceptive and haptic feedback from the contact with the object are driving the co-contraction of antagonistic muscles.

The whole repertoire of complex human motion is enabled by forces applied by our muscles and cont... more 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 Noteworthy Functional 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.

Journal of Neurophysiology, 2023

Animal studies suggest that the corticospinal tract and its collaterals are crucial for producing... more Animal studies suggest that the corticospinal tract and its collaterals are crucial for producing postural adjustments that accompany movement in limbs other than the moving limb. Here we provide evidence for a similar control schema for both arm posture maintenance and gravity compensation during movement of the same limb. The observed interplay between the postural and movement control signals within the corticospinal tract may help explain the underlying neural motor deficits after stroke.

bioRxiv (Cold Spring Harbor Laboratory), Sep 18, 2019

The whole repertoire of complex human motion is enabled by forces applied by our muscles and cont... more 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 Noteworthy Functional 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.

bioRxiv (Cold Spring Harbor Laboratory), May 17, 2017

the sensorimotor integration during unconstrained reaching movements in the presence of variable ... more the sensorimotor integration during unconstrained reaching movements in the presence of variable environmental forces remains poorly understood. the objective of this study was to quantify how much the primary afferent activity of muscle spindles can contribute to shaping muscle coactivation patterns during reaching movements with complex dynamics. to achieve this objective, we designed a virtual reality task that guided healthy human participants through a set of planar reaching movements with controlled kinematic and dynamic conditions that were accompanied by variable muscle co-contraction. Next, we approximated the Ia afferent activity using a phenomenological model of the muscle spindle and muscle lengths derived from a musculoskeletal model. the parameters of the spindle model were altered systematically to evaluate the effect of fusimotor drive on the shape of the temporal profile of afferent activity during movement. The experimental and simulated data were analyzed with hierarchical clustering. We found that the pattern of co-activation of agonistic and antagonistic muscles changed based on whether passive forces in each movement played assistive or resistive roles in limb dynamics. the reaching task with assistive limb dynamics was associated with the most muscle co-contraction. In contrast, the simulated Ia afferent profiles were not changing between tasks and they were largely reciprocal with homonymous muscle activity. Simulated physiological changes to the fusimotor drive were not sufficient to reproduce muscle co-contraction. These results largely rule out the static set and α-γ coactivation as the main types of fusimotor drive that transform the monosynaptic Ia afferent feedback into task-dependent co-contraction of antagonistic muscles. We speculate that another type of nonlinear transformation of Ia afferent signals that is independent of signals modulating the activity of α motoneurons is required for Ia afferent-based co-contraction. This transformation could either be applied through a complex nonlinear profile of fusimotor drive that is not yet experimentally observed or through presynaptic inhibition. Movement is the product of interactions between neural signals and the musculoskeletal dynamics that depends on limb anatomy 1-4. The motor control problem is then solved within a system with coupled neural and mechanical dynamical elements 5-7. Therefore, the relationship between neural signals driving muscle contraction and the resulting motion is nonlinear. Muscle contractions generate forces that sum into active moments defined by the agonistic or antagonistic relationships between the muscle's moment arms around a given axis of rotation of the joint. The components of these forces that sum to zero moment, such as forces produced by balanced

Goal: Motion capture is used for recording complex human movements that is increasingly applied i... more Goal: Motion capture is used for recording complex human movements that is increasingly applied in medicine. We describe a novel algorithm of combining a machine learning approach with biomechanics to enable robust analysis of motion capture data to obtain joint angles. Methods: A multilayer perceptron and a recurrent neural network were compared in their capacity to estimate the joint angles of the human arm. The networks were pre-trained using a kinematic model of the human arm. We evaluated our models on a dataset containing movements with three degrees of freedom comprising wrist flexion/extension, wrist abduction/adduction, and hand pronation/supination. Results: A recurrent neural network model with long short-term memory architecture can solve the inverse kinematics problem for three rotational degrees of freedom with the least error; it performed faster than real time. Conclusions: This shows that it is feasible to rely on pre-trained neural networks for real-time calculatio...

<p>A, Error in predicting each subject's qualitative score from regressions fitted to t... more <p>A, Error in predicting each subject's qualitative score from regressions fitted to the rest of the participants. Mean errors are expressed as % of the correct score; error bars show s.d. across 10 movements. B, Symbols show the same data as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104487#pone-0104487-g003&quot; target="_blank">Fig. 3B</a>; lines show regressions for datasets with one subject's data point removed. C, Histogram of intraclass correlation coefficients for relationships between individual human raters and the mean qualitative score. D, Colored lines show reducing errors as more raters score movements of the same participants per movement type, limb, and participant.</p

This is replication data for the following manuscript: E.V. Olesh, S. Yakovenko, and V. Gritsenko... more This is replication data for the following manuscript: E.V. Olesh, S. Yakovenko, and V. Gritsenko (2014) Automated Assessment of Upper Extremity Movement Impairment Due To Stroke. PLOS ONE. In press. The data comprises joint angles in time captured from people with stroke performing several arm movements that are part of clinical tests of arm function

Objective: To test the efficacy of functional electric stimuation (FES) assisted exercise therapy... more Objective: To test the efficacy of functional electric stimuation (FES) assisted exercise therapy (FES-ET) on a worktation in the subacute phase of recovery from a stroke. Design: Single-blind, randomly controlled comparison of ighand low-intensity treatment. Setting: Laboratory in a rehabilitation hospital. Participants: Nineteen stroke survivors (10 men, 9 women; ean age standard deviation, 60.6 5.8y), with upper-exremity hemiplegia (mean poststroke time, 48 17d). The main nclusion criteria were: stroke occurred within 3 months of nset of trial and resulted in severe upper-limb dysfunction, nd FES produced adequate hand opening. Intervention: An FES stimulator and an exercise workstaion with instrumented objects were used by 2 groups to perorm specific motor tasks with their affected upper extremity. en subjects in the high-intensity FES-ET group received ES-ET for 1 hour a day on 15 to 20 consecutive workdays. ine subjects in the low-intensity FES-ET group received 15 inutes of s...

Neural circuits embed limb dynamics for motor control and sensorimotor integration. The somatotop... more Neural circuits embed limb dynamics for motor control and sensorimotor integration. The somatotopic organization of motoneuron pools in the spinal cord may support these computations. Here, we tested if the spatial organization of motoneurons is related to the musculoskeletal anatomy. We created a 3D model of motoneuron locations within macaque spinal cord and compared the spatial distribution of motoneurons to the anatomical organization of the muscles they innervate. We demonstrated that the spatial distribution of motoneuron pools innervating the upper limb and the anatomical relationships between the muscles they innervate were similar between macaque and human species. Using comparative analysis, we found that the distances between motoneuron pools innervating synergistic muscles were the shortest, followed by those innervating antagonistic muscles. Such spatial organization can support the co-activation of synergistic muscles and reciprocal inhibition of antagonistic muscles. The spatial distribution of motoneurons may play an important role in embedding musculoskeletal dynamics.

Journal of Visualized Experiments, Jan 11, 2024

bioRxiv (Cold Spring Harbor Laboratory), Feb 14, 2024

PLOS ONE, Jul 3, 2023

The nervous system predicts and executes complex motion of body segments actuated by the coordina... more 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 contactwas 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.

This paper presents a novel method for solving the inverse kinematic problem of capturing human r... more This paper presents a novel method for solving the inverse kinematic problem of capturing human reaching movements using a dynamic biomechanical model. The model consists of rigid segments connected by joints and actuated by markers. The method was validated against a rotation matrix-based method using motion capture data recorded during reaching movements performed by healthy human volunteers. The results showed that the proposed method achieved low errors in joint angles. The angles were comparable to those calculated using the standard marker-based method. The proposed bioinspired method can be used in real-time medical applications, such as rehabilitation, assessment, and prosthesis/orthosis control.

bioRxiv (Cold Spring Harbor Laboratory), May 15, 2017

Human reaching movements require complex muscle activations to produce the forces necessary to mo... more 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 and controversial question in neuroscience. To tackle this issue, muscle activity is often subdivided into static and phasic components. The former corresponds to posture maintenance and transitions between postures. The latter corresponds to active movement production and the compensation for the kinetic properties of the limb. In the present study, we improved the methodology for this subdivision of muscle activity into static and phasic components by relating them to joint torques. Ten 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 postural 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 postural and dynamic components of muscle torque represent a significant amount of variance in muscle activity. This method could be used to define static and phasic components of muscle activity using muscle torques.

bioRxiv (Cold Spring Harbor Laboratory), Feb 9, 2023

The nervous system predicts and executes complex motion of body segments actuated by the coordina... more 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 complexities of the human musculoskeletal system and its interactions with the environment cr... more The complexities of the human musculoskeletal system and its interactions with the environment creates a difficult challenge for the neural control of movement. The consensus is that the nervous system solves this challenge by embedding the dynamical properties of the body and the environment. However, the modality of control signals and how they are generated appropriately for the task demands are a matter of active debate. We used transcranial magnetic stimulation over the primary motor cortex to show that the excitability of the corticospinal tract is modulated to compensate for limb dynamics during reaching tasks in humans. Surprisingly, few profiles of corticospinal modulation in some muscles and conditions reflected Newtonian parameters of movement, such as kinematics or active torques. Instead, the overall corticospinal excitability was differentially modulated in proximal and distal muscles, which corresponded to different stiffness at proximal and distal joints. This suggests that the descending corticospinal signal determines the proximal and distal impedance of the arm for independent functional control of reaching and grasping. Significance Statement The nervous system integrates both the physical properties of the human body and the environment to create a rich repertoire of actions. How these calculations are happening remains poorly understood. Neural activity is known to be correlated with different variables from the Newtonian equations of motion that describe forces acting on the body. In contrast, our data show that the overall activity of the descending neural signals is less related to the individual Newtonian variables and more related to limb impedance. We show that the physical properties of the arm are controlled by two distinct proximal and distal descending neural signals modulating components of limb stiffness. This identifies distinct neural control mechanisms for the transport and manipulation actions of reach. .

PLOS Computational Biology, Dec 16, 2020

Computational models of the musculoskeletal system are scientific tools used to study human movem... more Computational models of the musculoskeletal system are scientific tools used to study human movement, quantify the effects of injury and disease, plan surgical interventions, or control realistic high-dimensional articulated prosthetic limbs. If the models are sufficiently accurate, they may embed complex relationships within the sensorimotor system. These potential benefits are limited by the challenge of implementing fast and accurate musculoskeletal computations. A typical hand muscle spans over 3 degrees of freedom (DOF), wrapping over complex geometrical constraints that change its moment arms and lead to complex posture-dependent variation in torque generation. Here, we report a method to accurately and efficiently calculate musculotendon length and moment arms across all physiological postures of the forearm muscles that actuate the hand and wrist. Then, we use this model to test the hypothesis that the functional similarities of muscle actions are embedded in muscle structure. 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 μs, 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 increasing muscle complexity; complexity increases linearly instead. Dimensionality reduction using the polynomial terms alone resulted in clusters comprised of muscles with similar PLOS COMPUTATIONAL BIOLOGY

bioRxiv (Cold Spring Harbor Laboratory), May 17, 2017

the sensorimotor integration during unconstrained reaching movements in the presence of variable ... more the sensorimotor integration during unconstrained reaching movements in the presence of variable environmental forces remains poorly understood. the objective of this study was to quantify how much the primary afferent activity of muscle spindles can contribute to shaping muscle coactivation patterns during reaching movements with complex dynamics. to achieve this objective, we designed a virtual reality task that guided healthy human participants through a set of planar reaching movements with controlled kinematic and dynamic conditions that were accompanied by variable muscle co-contraction. Next, we approximated the Ia afferent activity using a phenomenological model of the muscle spindle and muscle lengths derived from a musculoskeletal model. the parameters of the spindle model were altered systematically to evaluate the effect of fusimotor drive on the shape of the temporal profile of afferent activity during movement. The experimental and simulated data were analyzed with hierarchical clustering. We found that the pattern of co-activation of agonistic and antagonistic muscles changed based on whether passive forces in each movement played assistive or resistive roles in limb dynamics. the reaching task with assistive limb dynamics was associated with the most muscle co-contraction. In contrast, the simulated Ia afferent profiles were not changing between tasks and they were largely reciprocal with homonymous muscle activity. Simulated physiological changes to the fusimotor drive were not sufficient to reproduce muscle co-contraction. These results largely rule out the static set and α-γ coactivation as the main types of fusimotor drive that transform the monosynaptic Ia afferent feedback into task-dependent co-contraction of antagonistic muscles. We speculate that another type of nonlinear transformation of Ia afferent signals that is independent of signals modulating the activity of α motoneurons is required for Ia afferent-based co-contraction. This transformation could either be applied through a complex nonlinear profile of fusimotor drive that is not yet experimentally observed or through presynaptic inhibition. Movement is the product of interactions between neural signals and the musculoskeletal dynamics that depends on limb anatomy 1-4. The motor control problem is then solved within a system with coupled neural and mechanical dynamical elements 5-7. Therefore, the relationship between neural signals driving muscle contraction and the resulting motion is nonlinear. Muscle contractions generate forces that sum into active moments defined by the agonistic or antagonistic relationships between the muscle's moment arms around a given axis of rotation of the joint. The components of these forces that sum to zero moment, such as forces produced by balanced

PubMed, Sep 1, 2022

The human motor system has evolved to perform efficient motor control in Earth's gravity. Altered... more The human motor system has evolved to perform efficient motor control in Earth's gravity. Altered gravity environments, such as microgravity and hypergravity, pose unique challenges for performing fine motor tasks with object manipulation. Altered gravity has been shown to reduce the speed and accuracy of complex manual tasks. This study aims to leverage electromyography (EMG) and virtual reality (VR) technologies to provide insights into the neuromuscular mechanism of object weight compensation. Seven healthy subjects were recruited to perform arm and hand movements, including a customized Box and Block Test with three different block weights, 0 (VR), 0.02, and 0.1 kg. EMG was recorded from 15 muscles of arm and hand while manipulating objects instrumented with force sensors to collect contact forces. Muscle co-contraction extracted from EMGs of antagonistic muscles was used as a measure of joint stiffness for each task. Results show that the co-contraction levels increased in the task with the heavy object and decreased in the VR task. This relationship suggests that the internal expectations of the object weight and the proprioceptive and haptic feedback from the contact with the object are driving the co-contraction of antagonistic muscles.

The whole repertoire of complex human motion is enabled by forces applied by our muscles and cont... more 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 Noteworthy Functional 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.

Journal of Neurophysiology, 2023

Animal studies suggest that the corticospinal tract and its collaterals are crucial for producing... more Animal studies suggest that the corticospinal tract and its collaterals are crucial for producing postural adjustments that accompany movement in limbs other than the moving limb. Here we provide evidence for a similar control schema for both arm posture maintenance and gravity compensation during movement of the same limb. The observed interplay between the postural and movement control signals within the corticospinal tract may help explain the underlying neural motor deficits after stroke.

bioRxiv (Cold Spring Harbor Laboratory), Sep 18, 2019

The whole repertoire of complex human motion is enabled by forces applied by our muscles and cont... more 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 Noteworthy Functional 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.

bioRxiv (Cold Spring Harbor Laboratory), May 17, 2017

the sensorimotor integration during unconstrained reaching movements in the presence of variable ... more the sensorimotor integration during unconstrained reaching movements in the presence of variable environmental forces remains poorly understood. the objective of this study was to quantify how much the primary afferent activity of muscle spindles can contribute to shaping muscle coactivation patterns during reaching movements with complex dynamics. to achieve this objective, we designed a virtual reality task that guided healthy human participants through a set of planar reaching movements with controlled kinematic and dynamic conditions that were accompanied by variable muscle co-contraction. Next, we approximated the Ia afferent activity using a phenomenological model of the muscle spindle and muscle lengths derived from a musculoskeletal model. the parameters of the spindle model were altered systematically to evaluate the effect of fusimotor drive on the shape of the temporal profile of afferent activity during movement. The experimental and simulated data were analyzed with hierarchical clustering. We found that the pattern of co-activation of agonistic and antagonistic muscles changed based on whether passive forces in each movement played assistive or resistive roles in limb dynamics. the reaching task with assistive limb dynamics was associated with the most muscle co-contraction. In contrast, the simulated Ia afferent profiles were not changing between tasks and they were largely reciprocal with homonymous muscle activity. Simulated physiological changes to the fusimotor drive were not sufficient to reproduce muscle co-contraction. These results largely rule out the static set and α-γ coactivation as the main types of fusimotor drive that transform the monosynaptic Ia afferent feedback into task-dependent co-contraction of antagonistic muscles. We speculate that another type of nonlinear transformation of Ia afferent signals that is independent of signals modulating the activity of α motoneurons is required for Ia afferent-based co-contraction. This transformation could either be applied through a complex nonlinear profile of fusimotor drive that is not yet experimentally observed or through presynaptic inhibition. Movement is the product of interactions between neural signals and the musculoskeletal dynamics that depends on limb anatomy 1-4. The motor control problem is then solved within a system with coupled neural and mechanical dynamical elements 5-7. Therefore, the relationship between neural signals driving muscle contraction and the resulting motion is nonlinear. Muscle contractions generate forces that sum into active moments defined by the agonistic or antagonistic relationships between the muscle's moment arms around a given axis of rotation of the joint. The components of these forces that sum to zero moment, such as forces produced by balanced

Goal: Motion capture is used for recording complex human movements that is increasingly applied i... more Goal: Motion capture is used for recording complex human movements that is increasingly applied in medicine. We describe a novel algorithm of combining a machine learning approach with biomechanics to enable robust analysis of motion capture data to obtain joint angles. Methods: A multilayer perceptron and a recurrent neural network were compared in their capacity to estimate the joint angles of the human arm. The networks were pre-trained using a kinematic model of the human arm. We evaluated our models on a dataset containing movements with three degrees of freedom comprising wrist flexion/extension, wrist abduction/adduction, and hand pronation/supination. Results: A recurrent neural network model with long short-term memory architecture can solve the inverse kinematics problem for three rotational degrees of freedom with the least error; it performed faster than real time. Conclusions: This shows that it is feasible to rely on pre-trained neural networks for real-time calculatio...

<p>A, Error in predicting each subject's qualitative score from regressions fitted to t... more <p>A, Error in predicting each subject's qualitative score from regressions fitted to the rest of the participants. Mean errors are expressed as % of the correct score; error bars show s.d. across 10 movements. B, Symbols show the same data as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104487#pone-0104487-g003&quot; target="_blank">Fig. 3B</a>; lines show regressions for datasets with one subject's data point removed. C, Histogram of intraclass correlation coefficients for relationships between individual human raters and the mean qualitative score. D, Colored lines show reducing errors as more raters score movements of the same participants per movement type, limb, and participant.</p

This is replication data for the following manuscript: E.V. Olesh, S. Yakovenko, and V. Gritsenko... more This is replication data for the following manuscript: E.V. Olesh, S. Yakovenko, and V. Gritsenko (2014) Automated Assessment of Upper Extremity Movement Impairment Due To Stroke. PLOS ONE. In press. The data comprises joint angles in time captured from people with stroke performing several arm movements that are part of clinical tests of arm function

Objective: To test the efficacy of functional electric stimuation (FES) assisted exercise therapy... more Objective: To test the efficacy of functional electric stimuation (FES) assisted exercise therapy (FES-ET) on a worktation in the subacute phase of recovery from a stroke. Design: Single-blind, randomly controlled comparison of ighand low-intensity treatment. Setting: Laboratory in a rehabilitation hospital. Participants: Nineteen stroke survivors (10 men, 9 women; ean age standard deviation, 60.6 5.8y), with upper-exremity hemiplegia (mean poststroke time, 48 17d). The main nclusion criteria were: stroke occurred within 3 months of nset of trial and resulted in severe upper-limb dysfunction, nd FES produced adequate hand opening. Intervention: An FES stimulator and an exercise workstaion with instrumented objects were used by 2 groups to perorm specific motor tasks with their affected upper extremity. en subjects in the high-intensity FES-ET group received ES-ET for 1 hour a day on 15 to 20 consecutive workdays. ine subjects in the low-intensity FES-ET group received 15 inutes of s...