Neural basis for hand muscle synergies in the primate spinal cord

Takei, Toshinobu; Confais, Joachim; Tomatsu, Saeka; Oya, Tomomichi; Seki, Kazuhiko

doi:10.1073/pnas.1704328114

Cited by 143 publications

(164 citation statements)

References 45 publications

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“…This supports the notion that the neuromuscular system has a modular organisation that simplifies the control problem [17,18]. Spinal circuitry consisting of a network of premotor interneurons and motor neurons may generate basic movement patterns by mediating the synergistic drive to multiple muscles [19,20]. Stimulation of interneuronal regions in the spinal cord shows that these microcircuits are organised into discrete modules, each generating a specific pattern of muscular forces [21,22].…”

Section: Introductionsupporting

confidence: 63%

Network structure of the human musculoskeletal system shapes neural interactions on multiple timescales

Kerkman

Daffertshofer

Gollo

et al. 2017

Preprint

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Human motor control requires the coordination of muscle activity under the anatomical constraints imposed by the musculoskeletal system. Interactions within the central nervous system are fundamental to motor coordination, but the principles governing functional integration remain poorly understood. We used network analysis to investigate the relationship between anatomical and functional connectivity amongst 36 muscles. Anatomical networks were defined by the physical connections between muscles and functional networks were based on intermuscular coherence assessed during postural tasks. We found a modular structure of functional networks that was strongly shaped by the anatomical constraints of the musculoskeletal system. Muscle networks exhibited a multilayer architecture with functional interactions at distinct timescales. Changes in postural tasks were associated with a frequency-dependent reconfiguration of the coupling between functional modules. Combined, these findings suggest these spectral modes may signify a coordinative structure for flexibly organising muscle activity during postural control. More broadly, our multi-level network approach to the motor system offers a unique window into the coordinated neural circuitry that generates synergistic input to muscles in different behaviours.not peer-reviewed)

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Section: Introductionsupporting

confidence: 63%

Network structure of the human musculoskeletal system shapes neural interactions on multiple timescales

Kerkman

Daffertshofer

Gollo

et al. 2017

Preprint

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“…Accordingly, in this study, we examined the functional roles of the modularity as the constraint of neuromuscular control. Although to clarify the relationship between the muscle synergies extracted from EMG data based on the decomposing technique and the physiologically observed neuronal structure of the modularity [22,23,26] was beyond the scope of this paper, we demonstrated that modular controllers that would be represented by muscle synergies play various important roles in the coordination in the nervous system to generate desired motor behaviours. Previous findings have shown that muscle synergies were shared across behaviours with different biomechanical demands [11][12][13][14].…”

Section: Modularity In the Neuronal Circuitmentioning

confidence: 96%

“…In the present study, we constructed a descending neural network model with three intermediate layers representing neurons in the primary motor cortex (M1), spinal modules and motoneurons controlling individual muscles. Recent studies have provided evidence of modular controllers in the neuronal circuitry [15,[20][21][22][23]. Based on these studies, we regarded the modules as interneuronal circuitry in the spinal cord [2,22,23].…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies have provided evidence of modular controllers in the neuronal circuitry [15,[20][21][22][23]. Based on these studies, we regarded the modules as interneuronal circuitry in the spinal cord [2,22,23]. The neurons of each layer have descending connections based on the empirical findings of the relationship between neurons in M1 and muscle synergies [24,25] and of the correlation between the activations of interneurons (regarded as the modules) and certain muscles [22,23,26].…”

Section: Introductionmentioning

confidence: 99%

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Modularity speeds up motor learning by overcoming mechanical bias in musculoskeletal geometry

Hagio

Kouzaki

2018

J. R. Soc. Interface.

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We can easily learn and perform a variety of movements that fundamentally require complex neuromuscular control. Many empirical findings have demonstrated that a wide range of complex muscle activation patterns could be well captured by the combination of a few functional modules, the so-called muscle synergies. Modularity represented by muscle synergies would simplify the control of a redundant neuromuscular system. However, how the reduction of neuromuscular redundancy through a modular controller contributes to sensorimotor learning remains unclear. To clarify such roles, we constructed a simple neural network model of the motor control system that included three intermediate layers representing neurons in the primary motor cortex, spinal interneurons organized into modules and motoneurons controlling upper-arm muscles. After a model learning period to generate the desired shoulder and/or elbow joint torques, we compared the adaptation to a novel rotational perturbation between modular and non-modular models. A series of simulations demonstrated that the modules reduced the effect of the bias in the distribution of muscle pulling directions, as well as in the distribution of torques associated with individual cortical neurons, which led to a more rapid adaptation to multi-directional force generation. These results suggest that modularity is crucial not only for reducing musculoskeletal redundancy but also for overcoming mechanical bias due to the musculoskeletal geometry allowing for faster adaptation to certain external environments.

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“…The results showing clear patterns of conditional transfer entropy between EMGs suggest the involvement of additional pathways that interconnect different motor neuron pools. A spinal network of interneurons interconnecting motor neuron pools within the spinal cord is involved in the coordination of muscle activity (Levine et al, 2014;Takei et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Information decomposition of multichannel EMG to map functional interactions in the distributed motor system

Boonstra

Faes

Kerkman

et al. 2019

Preprint

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The central nervous system needs to coordinate multiple muscles during postural control. Here we used multivariate information decomposition to investigate the functional interactions in multichannel EMG during these tasks. A set of information measures were estimated from an instantaneous linear regression model and a timelagged VAR model fitted to the EMG envelopes of 36 muscles. We used network analysis to quantify the structure of functional interactions between muscles and compared them across experimental conditions. Conditional mutual information and transfer entropy revealed sparse networks dominated by local connections between muscles. When comparing the muscle networks across task conditions, we observed significant effects in the muscles involved in performing those tasks: pointing behavior affected upper body muscles and postural behavior mainly affected lower body muscles. However, information decomposition revealed distinct patterns underlying both effects. Manual and bimanual pointing were associated with reduced transfer to the pectoralis major muscles, but an increase in total information compared to no pointing, while postural instability resulted in increased information transfer, storage and information in the abductor longus muscles compared to normal stability. These findings show that directed interactions between muscles are widespread and task-dependent and can be assessed from surface EMG recorded during static postural tasks. We discuss the task-related effects in terms of gain modulations of spinal reflex pathways.

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Neural basis for hand muscle synergies in the primate spinal cord

Cited by 143 publications

References 45 publications

Network structure of the human musculoskeletal system shapes neural interactions on multiple timescales

Network structure of the human musculoskeletal system shapes neural interactions on multiple timescales

Modularity speeds up motor learning by overcoming mechanical bias in musculoskeletal geometry

Information decomposition of multichannel EMG to map functional interactions in the distributed motor system

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