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Leib R, Howard IS, Millard M, Franklin DW. Behavioral Motor Performance. Compr Physiol 2023; 14:5179-5224. [PMID: 38158372 DOI: 10.1002/cphy.c220032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
The human sensorimotor control system has exceptional abilities to perform skillful actions. We easily switch between strenuous tasks that involve brute force, such as lifting a heavy sewing machine, and delicate movements such as threading a needle in the same machine. Using a structure with different control architectures, the motor system is capable of updating its ability to perform through our daily interaction with the fluctuating environment. However, there are issues that make this a difficult computational problem for the brain to solve. The brain needs to control a nonlinear, nonstationary neuromuscular system, with redundant and occasionally undesired degrees of freedom, in an uncertain environment using a body in which information transmission is subject to delays and noise. To gain insight into the mechanisms of motor control, here we survey movement laws and invariances that shape our everyday motion. We then examine the major solutions to each of these problems in the three parts of the sensorimotor control system, sensing, planning, and acting. We focus on how the sensory system, the control architectures, and the structure and operation of the muscles serve as complementary mechanisms to overcome deviations and disturbances to motor behavior and give rise to skillful motor performance. We conclude with possible future research directions based on suggested links between the operation of the sensorimotor system across the movement stages. © 2024 American Physiological Society. Compr Physiol 14:5179-5224, 2024.
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Affiliation(s)
- Raz Leib
- Neuromuscular Diagnostics, TUM School of Medicine and Health, Department of Health and Sport Sciences, Technical University of Munich, Munich, Germany
| | - Ian S Howard
- School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth, UK
| | - Matthew Millard
- Institute of Sport and Movement Science, University of Stuttgart, Stuttgart, Germany
- Institute of Engineering and Computational Mechanics, University of Stuttgart, Stuttgart, Germany
| | - David W Franklin
- Neuromuscular Diagnostics, TUM School of Medicine and Health, Department of Health and Sport Sciences, Technical University of Munich, Munich, Germany
- Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich, Munich, Germany
- Munich Data Science Institute (MDSI), Technical University of Munich, Munich, Germany
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Kalidindi HT, Crevecoeur F. Human reaching control in dynamic environments. Curr Opin Neurobiol 2023; 83:102810. [PMID: 37950956 DOI: 10.1016/j.conb.2023.102810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 10/09/2023] [Accepted: 10/19/2023] [Indexed: 11/13/2023]
Abstract
Closed-loop models of movement control have attracted growing interest in how the nervous system transforms sensory information into motor commands, and several brain structures have been identified as neural substrates for these computational operations. Recently, several studies have focused on how these models need to be updated when environmental parameters change. Current evidence suggests that when the task changes, rapid control updates enable flexible modifications of current actions and online decisions. At the same time, when movement dynamics change, humans use different strategies based on a combination of adaptation and modulation of controller sensitivity to exogenous perturbations (robust control). This review proposes a unified framework to capture these results based on online estimation of model parameters with dynamic updates in control. The reviewed studies also identify the time scales of associated behavioral mechanisms to guide future research on the neural basis of movement control.
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Affiliation(s)
- Hari T Kalidindi
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics, University of Louvain (UCLouvain), Belgium; Institute of Neuroscience, UCLouvain, Belgium
| | - Frédéric Crevecoeur
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics, University of Louvain (UCLouvain), Belgium; Institute of Neuroscience, UCLouvain, Belgium.
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Torell F, Franklin S, Franklin DW, Dimitriou M. Goal-directed modulation of stretch reflex gains is reduced in the non-dominant upper limb. Eur J Neurosci 2023; 58:3981-4001. [PMID: 37727025 DOI: 10.1111/ejn.16148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 08/08/2023] [Accepted: 08/31/2023] [Indexed: 09/21/2023]
Abstract
Most individuals experience their dominant arm as being more dexterous than the non-dominant arm, but the neural mechanisms underlying this asymmetry in motor behaviour are unclear. Using a delayed-reach task, we have recently demonstrated strong goal-directed tuning of stretch reflex gains in the dominant upper limb of human participants. Here, we used an equivalent experimental paradigm to address the neural mechanisms that underlie the preparation for reaching movements with the non-dominant upper limb. There were consistent effects of load, preparatory delay duration and target direction on the long latency stretch reflex. However, by comparing stretch reflex responses in the non-dominant arm with those previously documented in the dominant arm, we demonstrate that goal-directed tuning of short and long latency stretch reflexes is markedly weaker in the non-dominant limb. The results indicate that the motor performance asymmetries across the two upper limbs are partly due to the more sophisticated control of reflexive stiffness in the dominant limb, likely facilitated by the superior goal-directed control of muscle spindle receptors. Our findings therefore suggest that fusimotor control may play a role in determining performance of complex motor behaviours and support existing proposals that the dominant arm is better supplied than the non-dominant arm for executing more complex tasks, such as trajectory control.
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Affiliation(s)
- Frida Torell
- Physiology Section, Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
| | - Sae Franklin
- Neuromuscular Diagnostics, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany
| | - David W Franklin
- Neuromuscular Diagnostics, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany
- Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich, Munich, Germany
- Munich Data Science Institute (MDSI), Technical University of Munich, Munich, Germany
| | - Michael Dimitriou
- Physiology Section, Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
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De Comite A, Lefèvre P, Crevecoeur F. Continuous evaluation of cost-to-go for flexible reaching control and online decisions. PLoS Comput Biol 2023; 19:e1011493. [PMID: 37756355 PMCID: PMC10561875 DOI: 10.1371/journal.pcbi.1011493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 10/09/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
Humans consider the parameters linked to movement goal during reaching to adjust their control strategy online. Indeed, rapid changes in target structure or disturbances interfering with their initial plan elicit rapid changes in behavior. Here, we hypothesize that these changes could result from the continuous use of a decision variable combining motor and cognitive components. We combine an optimal feedback controller with a real-time evaluation of the expected cost-to-go, which considers target- and movement-related costs, in a common theoretical framework. This model reproduces human behaviors in presence of changes in the target structure occurring during movement and of online decisions to flexibly change target following external perturbations. It also predicts that the time taken to decide to select a novel goal after a perturbation depends on the amplitude of the disturbance and on the rewards of the different options, which is a direct result of the continuous monitoring of the cost-to-go. We show that this result was present in our previously collected dataset. Together our developments point towards a continuous evaluation of the cost-to-go during reaching to update control online and make efficient decisions about movement goal.
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Affiliation(s)
- Antoine De Comite
- Institute of Neuroscience, UCLouvain, Louvain-la-Neuve, Belgium
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics, UCLouvain, Louvain-la-Neuve, Belgium
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Philippe Lefèvre
- Institute of Neuroscience, UCLouvain, Louvain-la-Neuve, Belgium
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics, UCLouvain, Louvain-la-Neuve, Belgium
| | - Frédéric Crevecoeur
- Institute of Neuroscience, UCLouvain, Louvain-la-Neuve, Belgium
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics, UCLouvain, Louvain-la-Neuve, Belgium
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Torell F, Franklin S, Franklin DW, Dimitriou M. Assistive Loading Promotes Goal-Directed Tuning of Stretch Reflex Gains. eNeuro 2023; 10:ENEURO.0438-22.2023. [PMID: 36781230 PMCID: PMC9972504 DOI: 10.1523/eneuro.0438-22.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 01/24/2023] [Accepted: 01/30/2023] [Indexed: 02/15/2023] Open
Abstract
Voluntary movements are prepared before they are executed. Preparatory activity has been observed across the CNS and recently documented in first-order neurons of the human PNS (i.e., in muscle spindles). Changes seen in sensory organs suggest that independent modulation of stretch reflex gains may represent an important component of movement preparation. The aim of the current study was to further investigate the preparatory modulation of short-latency stretch reflex responses (SLRs) and long-latency stretch reflex responses (LLRs) of the dominant upper limb of human subjects. Specifically, we investigated how different target parameters (target distance and direction) affect the preparatory tuning of stretch reflex gains in the context of goal-directed reaching, and whether any such tuning depends on preparation duration and the direction of background loads. We found that target distance produced only small variations in reflex gains. In contrast, both SLR and LLR gains were strongly modulated as a function of target direction, in a manner that facilitated the upcoming voluntary movement. This goal-directed tuning of SLR and LLR gains was present or enhanced when the preparatory delay was sufficiently long (>250 ms) and the homonymous muscle was unloaded [i.e., when a background load was first applied in the direction of homonymous muscle action (assistive loading)]. The results extend further support for a relatively slow-evolving process in reach preparation that functions to modulate reflexive muscle stiffness, likely via the independent control of fusimotor neurons. Such control can augment voluntary goal-directed movement and is triggered or enhanced when the homonymous muscle is unloaded.
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Affiliation(s)
- Frida Torell
- Physiology Section, Department of Integrative Medical Biology, Umeå University, S-901 87 Umeå, Sweden
| | - Sae Franklin
- Neuromuscular Diagnostics, Department of Sport and Health Sciences, Technical University of Munich, D-80992 Munich, Germany
| | - David W Franklin
- Neuromuscular Diagnostics, Department of Sport and Health Sciences, Technical University of Munich, D-80992 Munich, Germany
- Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich, D-80992 Munich, Germany
- Munich Data Science Institute (MDSI), Technical University of Munich, 85748 Munich, Germany
| | - Michael Dimitriou
- Physiology Section, Department of Integrative Medical Biology, Umeå University, S-901 87 Umeå, Sweden
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