Gravitational and dynamic components of muscle torque underlie tonic and phasic muscle activity during goal-directed reaching

Olesh, Erienne V.; Pollard, Bradley S.; Gritsenko, Valeriya

doi:10.1101/138263

Cited by 6 publications

(19 citation statements)

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“…We applied a simple and widely-used decomposition method to isolate the tonic (gravity-dependent force) and the phasic (inertial-dependent force) EMG components from the full EMG signal (Buneo et al, 1994; d’Avella et al, 2006, 2008; Flanders and Herrmann, 1992; Flanders et al, 1994, 1996; Olesh et al, 2017; Prange et al, 2009b, 2012; Russo et al, 2014). The tonic component emanates from the motionless rest-periods before and after the movement (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…We found that antigravity muscles exhibited periods of negativity precisely during the acceleration phase of a downward movement and the deceleration phase of an upward movement, when gravity can assist the movement (Figure 3 & 4). Notably, many studies already observed negativity of the phasic component of muscular activations and forces during vertical movements (Buneo et al, 1994; d’Avella et al, 2006, 2008; Flanders and Herrmann, 1992; Flanders et al, 1994, 1996; Olesh et al, 2017; Russo et al, 2014). However, this phenomenon was primarily ignored and attributed to erratic errors in the separation of noisy signals.…”

Section: Discussionmentioning

confidence: 99%

“…Such a neural policy is thought to facilitate the production of accurate movements to changing directions, amplitudes, durations, and loads, by merely scaling the inertial-dependent part of the motor command. Albeit based upon old literature, this influential Compensation hypothesis still guides current research in various fields such as motor control (d’Avella et al, 2008; Guigon et al, 2007; Kadmon Harpaz et al, 2014; Olesh et al, 2017; Russo et al, 2014), movement perception (Cook et al, 2013; Edey et al, 2019) or neuro-rehabilitation (Krabben et al, 2012; Prange et al, 2009a, 2009b, 2012; Raj et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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A cross-species neural integration of gravity for motor optimisation

Gaveau

Grosprêtre

Angelaki

et al. 2019

Preprint

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15Recent kinematic results, combined with model simulations, have provided support for the 16 hypothesis that the human brain uses an internal model of gravity to shape motor patterns that 17 minimise muscle effort. Because many different muscular activation patterns can give rise to 18 the same trajectory, here we analyse muscular activation patterns during single-degree-of-19 freedom arm movements in various directions, which allow to specifically investigating 20 gravity-related movement properties. Using a well-known decomposition method of tonic and 21 phasic electromyographic activities, we demonstrate that phasic EMGs present systematic 22 negative phases. This negativity demonstrates that gravity effects are harvested to save 23 muscle effort and reveals that the brain implements an optimal motor plan using gravity to 24 accelerate downward and decelerate upward movements. Furthermore, for the first time, we 25 compare experimental findings in humans to monkeys, thereby generalising the Effort-26 optimization strategy across species. 27 the velocity and acceleration of the limb -and the gravity forces -related to the position of 51

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A cross-species neural integration of gravity for motor optimisation

Gaveau

Grosprêtre

Angelaki

et al. 2019

Preprint

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“…The dynamic mechanical model of a human upper-limb (Olesh et al, 2017) was used to compute joint torques from joint motions. The model was constructed in Simulink (MathWorks, Inc.); it comprised three segments and five DOFs, including the shoulder (flexion/extension, abduction/adduction, internal/external rotation), elbow (flexion/extension), and wrist (flexion/extension).…”

Section: Task Dynamicsmentioning

confidence: 99%

The primary afferent activity cannot capture the dynamical features of muscle activity during reaching movements

Hardesty

Boots

Yakovenko

et al. 2017

Preprint

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The stabilizing role of sensory feedback in relation to movement dynamics remains to be poorly understood in realistic three-dimensional movements of limbs. The objective of this experimental and computational study was to classify the contribution of sensory feedback from muscle spindles to the control of assistive and resistive limb dynamics during human pointing movements. We integrated a human upper-limb musculoskeletal model with a model of Ia primary afferent discharge to analyze motion and muscle activation patterns during reaching movements in virtual reality (VR). The reaching target locations in VR were selected to define movements with varying roles of gravity and interaction torques that created diverse dynamical contexts. Nine healthy human subjects performed the VR task by pointing to the reaching targets with visual feedback of their arm location. Motion capture and electromyography (EMG) were recorded, and joint torques and Ia primary afferent discharge were estimated using the integrated model. The experimental and simulated data were analyzed with hierarchal clustering (Gritsenko et al., 2016). The clustering analysis of EMG and predicted proprioceptive signals showed a divergent relationship between muscle activation and sensory feedback patterns. Even though the Ia models had nonlinear dynamical components, their output was still most related to the anatomical grouping of muscles and less so to the dynamical contexts of each movement, reflected in muscle activations. Altogether, these results suggest that sensory feedback is nonlinearly related to muscle activation profiles and that it may contribute information necessary for coupling between proximal and distal muscle groups.

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“…The component of muscle torque that is responsible for intersegmental coordination can be extracted computationally. The inverse dynamic simulations used to estimate muscle torque can be ran without gravity, simulating the active torques necessary to produce the observed motion without gravity (Olesh et al 2017). This dynamic component of muscle torque also reflects the forces that are produced during planar reaches with gravitational support.…”

Section: Introductionmentioning

confidence: 99%

Muscle torques provide more sensitive measures of post-stroke movement deficits than joint angles

Adcock

et al. 2019

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Background and Purpose:The way we move changes throughout our lifetime and often we move less as we age. Distinguishing the motor deficits caused by a stroke from changes in motion due to normal aging is important for the accurate assessment of post-stroke recovery and to determine the effectiveness of treatment. The whole repertoire of complex human motion is enabled by forces applied by our muscles and controlled by the nervous system. However, the current medical standard for assessing motor deficits is based on quantifying movement, without a comprehensive analysis of the active forces that cause this movement. The objective here is to estimate active muscle forces from the quantitative recording of motion. Methods:The motion of twenty-two people was captured when reaching to virtual targets in a center-out task. Eight of the participants had chronic hemiparesis after a stroke, and another six participants were of similar age to the stroke participants. All participants served as their own controls. We used inverse dynamic analysis to derive muscle moments, which were the result of the neural control signals to muscles and caused the recorded multijointed movements. These muscle moments were separated into forces that were related only to movement production from those only related to posture maintenance against gravity. We then compared these muscle forces between limbs to assess how stroke in one hemisphere disrupts the control signals in individuals with hemiparesis compared to the young and age-matched individuals. Results:We show that both aging and stroke causes the control signals from dominant and nondominant hemispheres to be less symmetrical in a pattern that indicates worsening neural control of intersegmental dynamics. We also show that the force-based assessment provides consistently higher quality measures of individual motor deficits due to stroke compared to traditional motion-based assessment. Using the force-based assessment, but not motion-based assessment, it was possible to distinguish the motor deficits due to stroke from age-related movement variability. Conclusions:The results of our study show that it is feasible to distinguish between age-related and stroke-related deficits in the neural control of reaching using inverse dynamic analysis of motion. This is useful for objective home-based monitoring using wearable and mobile devices of patients recovering from a stroke and elderly people at risk of disability. Our results further indicate that the disruption of the neural control of intersegmental dynamics contributes not only to motor deficits after stroke but also to the inefficiency of movement in the elderly.

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Gravitational and dynamic components of muscle torque underlie tonic and phasic muscle activity during goal-directed reaching

Cited by 6 publications

References 75 publications

A cross-species neural integration of gravity for motor optimisation

A cross-species neural integration of gravity for motor optimisation

The primary afferent activity cannot capture the dynamical features of muscle activity during reaching movements

Muscle torques provide more sensitive measures of post-stroke movement deficits than joint angles

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