11,443 research outputs found

    Modulation of Stretch Reflexes of the Finger Flexors by Sensory Feedback from the Proximal Upper Limb Poststroke

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    Neural coupling of proximal and distal upper limb segments may have functional implications in the recovery of hemiparesis after stroke. The goal of the present study was to investigate whether the stretch reflex response magnitude of spastic finger flexor muscles poststroke is influenced by sensory input from the shoulder and the elbow and whether reflex coupling of muscles throughout the upper limb is altered in spastic stroke survivors. Through imposed extension of the metacarpophalangeal (MCP) joints, stretch of the relaxed finger flexors of the four fingers was imposed in 10 relaxed stroke subjects under different conditions of proximal sensory input, namely static arm posture (3 different shoulder/elbow postures) and electrical stimulation (surface stimulation of biceps brachii or triceps brachii, or none). Fast (300°/s) imposed stretch elicited stretch reflex flexion torque at the MCP joints and reflex electromyographic (EMG) activity in flexor digitorum superficialis. Both measures were greatest in an arm posture of 90° of elbow flexion and neutral shoulder position. Biceps stimulation resulted in greater MCP stretch reflex flexion torque. Fast imposed stretch also elicited reflex EMG activity in nonstretched heteronymous upper limb muscles, both proximal and distal. These results suggest that in the spastic hemiparetic upper limb poststroke, sensorimotor coupling of proximal and distal upper limb segments is involved in both the increased stretch reflex response of the finger flexors and an increased reflex coupling of heteronymous muscles. Both phenomena may be mediated through changes poststroke in the spinal reflex circuits and/or in the descending influence of supraspinal pathways

    A methodology for assessing the effect of correlations among muscle synergy activations on task-discriminating information

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    Muscle synergies have been hypothesized to be the building blocks used by the central nervous system to generate movement. According to this hypothesis, the accomplishment of various motor tasks relies on the ability of the motor system to recruit a small set of synergies on a single-trial basis and combine them in a task-dependent manner. It is conceivable that this requires a fine tuning of the trial-to-trial relationships between the synergy activations. Here we develop an analytical methodology to address the nature and functional role of trial-to-trial correlations between synergy activations, which is designed to help to better understand how these correlations may contribute to generating appropriate motor behavior. The algorithm we propose first divides correlations between muscle synergies into types (noise correlations, quantifying the trial-to-trial covariations of synergy activations at fixed task, and signal correlations, quantifying the similarity of task tuning of the trial-averaged activation coefficients of different synergies), and then uses single-trial methods (task-decoding and information theory) to quantify their overall effect on the task-discriminating information carried by muscle synergy activations. We apply the method to both synchronous and time-varying synergies and exemplify it on electromyographic data recorded during performance of reaching movements in different directions. Our method reveals the robust presence of information-enhancing patterns of signal and noise correlations among pairs of synchronous synergies, and shows that they enhance by 9–15% (depending on the set of tasks) the task-discriminating information provided by the synergy decompositions. We suggest that the proposed methodology could be useful for assessing whether single-trial activations of one synergy depend on activations of other synergies and quantifying the effect of such dependences on the task-to-task differences in muscle activation patterns

    Effect of Sensory Feedback from the Proximal Upper Limb on Voluntary Isometric Finger Flexion and Extension in Hemiparetic Stroke Subjects

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    This study investigated the potential influence of proximal sensory feedback on voluntary distal motor activity in the paretic upper limb of hemiparetic stroke survivors and the potential effect of voluntary distal motor activity on proximal muscle activity. Ten stroke subjects and 10 neurologically intact control subjects performed maximum voluntary isometric flexion and extension, respectively, at the metacarpophalangeal (MCP) joints of the fingers in two static arm postures and under three conditions of electrical stimulation of the arm. The tasks were quantified in terms of maximum MCP torque [MCP flexion (MCPflex) or MCP extension (MCPext)] and activity of targeted (flexor digitorum superficialis or extensor digitorum communis) and nontargeted upper limb muscles. From a previous study on the MCP stretch reflex poststroke, we expected stroke subjects to exhibit a modulation of voluntary MCP torque production by arm posture and electrical stimulation and increased nontargeted muscle activity. Posture 1 (flexed elbow, neutral shoulder) led to greater MCPflex in stroke subjects than posture 2 (extended elbow, flexed shoulder). Electrical stimulation did not influence MCPflex or MCPext in either subject group. In stroke subjects, posture 1 led to greater nontargeted upper limb flexor activity during MCPflex and to greater elbow flexor and extensor activity during MCPext. Stroke subjects exhibited greater elbow flexor activity during MCPflex and greater elbow flexor and extensor activity during MCPext than control subjects. The results suggest that static arm posture can modulate voluntary distal motor activity and accompanying muscle activity in the paretic upper limb poststroke

    The VITEWRITE Model of Handwriting Production

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    This article describes the VITEWRITE model for generating handwriting movements. The model consists of a sequential controller, or motor program, that interacts with a trajectory generator to move a hand with redundant degrees of freedom. The neural trajectory generator is the Vector Integration to Endpoint (VITE) model for synchronous variable-speed control of multijoint movements. VITE properties enable a simple control strategy to generate complex handwritten script if the hand model contains redundant degrees of freedom. The controller launches transient directional commands to independent hand synergies at times when the hand begins to move, or when a velocity peak in the outflow command to a given synergy occurs. The VITE model translates these temporally disjoint synergy commands into smooth curvilinear trajectories among temporally overlapping synergetic movements. Each synergy exhibits a unimodal velocity profile during any stroke, generates letters that are invariant under speed and size rescaling, and enables effortless connection of letter shapes into words. Speed and size rescaling are achieved by scalar GO and GRO signals that express computationally simple volitional commands. Psychophysical data such as the isochrony principle, asymmetric velocity profiles, and the two-thirds power law relating movement curvature and velocity arise as emergent properties of model interactions.Office of Naval Research (N00014-92-J-1309); National Science Foundation (IRI-90-24877, IRI-87-16960); Air Force Office of Scientific Research (F49620-92-J-0225); Defense Advanced Research Projects Agency (AFOSR 90-0083

    Age-related differences in adaptation during childhood: The influences of muscular power production and segmental energy flow caused by muscles

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    Acquisition of skillfulness is not only characterized by a task-appropriate application of muscular forces but also by the ability to adapt performance to changing task demands. Previous research suggests that there is a different developmental schedule for adaptation at the kinematic compared to the neuro-muscular level. The purpose of this study was to determine how age-related differences in neuro-muscular organization affect the mechanical construction of pedaling at different levels of the task. By quantifying the flow of segmental energy caused by muscles, we determined the muscular synergies that construct the movement outcome across movement speeds. Younger children (5-7 years; n = 11), older children (8-10 years; n = 8), and adults (22-31 years; n = 8) rode a stationary ergometer at five discrete cadences (60, 75, 90, 105, and 120 rpm) at 10% of their individually predicted peak power output. Using a forward dynamics simulation, we determined the muscular contributions to crank power, as well as muscular power delivered to the crank directly and indirectly (through energy absorption and transfer) during the downstroke and the upstroke of the crank cycle. We found significant age Ă— cadence interactions for (1) peak muscular power at the hip joint [Wilks' Lambda = 0.441, F(8,42) = 2.65, p = 0.019] indicating that at high movement speeds children produced less peak power at the hip than adults, (2) muscular power delivered to the crank during the downstroke and the upstroke of the crank cycle [Wilks' Lambda = 0.399, F(8,42) = 3.07, p = 0.009] indicating that children delivered a greater proportion of the power to the crank during the upstroke when compared to adults, (3) hip power contribution to limb power [Wilks' Lambda = 0.454, F(8,42) = 2.54, p = 0.023] indicating a cadence-dependence of age-related differences in the muscular synergy between hip extensors and plantarflexors. The results demonstrate that in spite of a successful performance, children construct the task of pedaling differently when compared to adults, especially when they are pushed to their performance limits. The weaker synergy between hip extensors and plantarflexors suggests that a lack of inter-muscular coordination, rather than muscular power production per se, is a factor that limits children's performance ranges

    A Neural Network Model for Cursive Script Production

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    This article describes a neural network model, called the VITEWRITE model, for generating handwriting movements. The model consists of a sequential controller, or motor program, that interacts with a trajectory generator to move a. hand with redundant degrees of freedom. The neural trajectory generator is the Vector Integration to Endpoint (VITE) model for synchronous variable-speed control of multijoint movements. VITE properties enable a simple control strategy to generate complex handwritten script if the hand model contains redundant degrees of freedom. The proposed controller launches transient directional commands to independent hand synergies at times when the hand begins to move, or when a velocity peak in a given synergy is achieved. The VITE model translates these temporally disjoint synergy commands into smooth curvilinear trajectories among temporally overlapping synergetic movements. The separate "score" of onset times used in most prior models is hereby replaced by a self-scaling activity-released "motor program" that uses few memory resources, enables each synergy to exhibit a unimodal velocity profile during any stroke, generates letters that are invariant under speed and size rescaling, and enables effortless. connection of letter shapes into words. Speed and size rescaling are achieved by scalar GO and GRO signals that express computationally simple volitional commands. Psychophysical data concerning band movements, such as the isochrony principle, asymmetric velocity profiles, and the two-thirds power law relating movement curvature and velocity arise as emergent properties of model interactions.National Science Foundation (IRI 90-24877, IRI 87-16960); Office of Naval Research (N00014-92-J-1309); Air Force Office of Scientific Research (F49620-92-J-0499); Defense Advanced Research Projects Agency (90-0083

    In silico case studies of compliant robots: AMARSI deliverable 3.3

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    In the deliverable 3.2 we presented how the morphological computing ap- proach can significantly facilitate the control strategy in several scenarios, e.g. quadruped locomotion, bipedal locomotion and reaching. In particular, the Kitty experimental platform is an example of the use of morphological computation to allow quadruped locomotion. In this deliverable we continue with the simulation studies on the application of the different morphological computation strategies to control a robotic system

    On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

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    Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas

    A Model of Movement Coordinates in Motor Cortex: Posture-Dependent Changes in the Gain and Direction of Single Cell Tuning Curves

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    Central to the problem of elucidating the cortical mechanisms that mediate movement behavior is an investigation of the coordinate systems by which movement variables are encoded in the firing rates of individual motor cortical neurons. In the last decade, neurophysiologists have probed how the preferred direction of an individual motor cortical cell (as determined by a center-out task) will change with posture because such changes are useful for inferring underlying cordinates. However, while the importance of shifts in preferred direction is well-known and widely accepted, posture-dependent changes in the depth of modulation of a cell's tuning curve, i.e. gain changes, have not been similarly identified as a means of coordinate inference. This paper develops a vector field framework which, by viewing the preferred direction and the gain of a cell's tuning curve as dual components of a unitary response vector, can compute how each aspect of cell response covaries with posture as a function of the coordinate system in which a given cell is hypothesized to encode its movement information. This integrated approach leads to a model of motor cortical cell activity that codifies the following four observations: 1) cell activity correlates with hand movement direction, 2) cell activity correlates with hand movement speed, 3) preferred directions vary with posture, and 4) the modulation depth of tuning curves varies with posture. Finally, the model suggests general methods for testing coordinate hypotheses at the single cell level and example protocols arc simulated for three possible coordinate systems: Cartesian spatial, shoulder-centered, and joint angle.Defense Advanced Research Projects Agency (N00014-92-J-4015); Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-90-00530, IRI-97-20333); Office of Naval Research (N00014-91-J-4100, N00014-92-J-1309, N00014-94-l-0940, N00014-95-1-0657)
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