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Churchland MM, Shenoy KV. Preparatory activity and the expansive null-space. Nat Rev Neurosci 2024; 25:213-236. [PMID: 38443626 DOI: 10.1038/s41583-024-00796-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/26/2024] [Indexed: 03/07/2024]
Abstract
The study of the cortical control of movement experienced a conceptual shift over recent decades, as the basic currency of understanding shifted from single-neuron tuning towards population-level factors and their dynamics. This transition was informed by a maturing understanding of recurrent networks, where mechanism is often characterized in terms of population-level factors. By estimating factors from data, experimenters could test network-inspired hypotheses. Central to such hypotheses are 'output-null' factors that do not directly drive motor outputs yet are essential to the overall computation. In this Review, we highlight how the hypothesis of output-null factors was motivated by the venerable observation that motor-cortex neurons are active during movement preparation, well before movement begins. We discuss how output-null factors then became similarly central to understanding neural activity during movement. We discuss how this conceptual framework provided key analysis tools, making it possible for experimenters to address long-standing questions regarding motor control. We highlight an intriguing trend: as experimental and theoretical discoveries accumulate, the range of computational roles hypothesized to be subserved by output-null factors continues to expand.
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Affiliation(s)
- Mark M Churchland
- Department of Neuroscience, Columbia University, New York, NY, USA.
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA.
- Kavli Institute for Brain Science, Columbia University, New York, NY, USA.
| | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Neurobiology, Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
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2
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Arce-McShane FI, Sessle BJ, Ram Y, Ross CF, Hatsopoulos NG. Multiple regions of sensorimotor cortex encode bite force and gape. Front Syst Neurosci 2023; 17:1213279. [PMID: 37808467 PMCID: PMC10556252 DOI: 10.3389/fnsys.2023.1213279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 08/21/2023] [Indexed: 10/10/2023] Open
Abstract
The precise control of bite force and gape is vital for safe and effective breakdown and manipulation of food inside the oral cavity during feeding. Yet, the role of the orofacial sensorimotor cortex (OSMcx) in the control of bite force and gape is still largely unknown. The aim of this study was to elucidate how individual neurons and populations of neurons in multiple regions of OSMcx differentially encode bite force and static gape when subjects (Macaca mulatta) generated different levels of bite force at varying gapes. We examined neuronal activity recorded simultaneously from three microelectrode arrays implanted chronically in the primary motor (MIo), primary somatosensory (SIo), and cortical masticatory (CMA) areas of OSMcx. We used generalized linear models to evaluate encoding properties of individual neurons and utilized dimensionality reduction techniques to decompose population activity into components related to specific task parameters. Individual neurons encoded bite force more strongly than gape in all three OSMCx areas although bite force was a better predictor of spiking activity in MIo vs. SIo. Population activity differentiated between levels of bite force and gape while preserving task-independent temporal modulation across the behavioral trial. While activation patterns of neuronal populations were comparable across OSMCx areas, the total variance explained by task parameters was context-dependent and differed across areas. These findings suggest that the cortical control of static gape during biting may rely on computations at the population level whereas the strong encoding of bite force at the individual neuron level allows for the precise and rapid control of bite force.
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Affiliation(s)
- Fritzie I. Arce-McShane
- Department of Oral Health Sciences, School of Dentistry, University of Washington, Seattle, WA, United States
- Graduate Program in Neuroscience, University of Washington, Seattle, WA, United States
| | - Barry J. Sessle
- Faculty of Dentistry and Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Yasheshvini Ram
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, United States
| | - Callum F. Ross
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, United States
| | - Nicholas G. Hatsopoulos
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, United States
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3
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Mira RM, Molinari Tosatti L, Sacco M, Scano A. Detailed characterization of physiological EMG activations and directional tuning of upper-limb and trunk muscles in point-to-point reaching movements. Curr Res Physiol 2021; 4:60-72. [PMID: 34746827 PMCID: PMC8562137 DOI: 10.1016/j.crphys.2021.02.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 02/20/2021] [Accepted: 02/23/2021] [Indexed: 11/25/2022] Open
Abstract
In recent years, several studies have investigated upper-limb motion in a variety of scenarios including motor control, physiology, rehabilitation and industry. Such applications assess people’s kinematics and muscular performances, focusing on typical movements that simulate daily-life tasks. However, often only a limited interpretation of the EMG patterns is provided. In fact, rarely the assessments separate phasic (movement-related) and tonic (postural) EMG components, as well as the EMG in the acceleration and deceleration phases. With this paper, we provide a comprehensive and detailed characterization of the activity of upper-limb and trunk muscles in healthy people point-to-point upper limb movements. Our analysis includes in-depth muscle activation magnitude assessment, separation of phasic (movement-related) and tonic (postural) EMG activations, directional tuning, distinction between activations in the acceleration and deceleration phases. Results from our study highlight a predominant postural activity with respect to movement related muscular activity. The analysis based on the acceleration phase sheds light on finer motor control strategies, highlighting the role of each muscle in the acceleration and deceleration phase. The results of this study are applicable to several research fields, including physiology, rehabilitation, design of robots and assistive solutions, exoskeletons. Upper-limb motion is assessed with kinematics and EMG in many scenarios: motor control, physiology, rehabilitation, industry Separation of phasic (movement-related) and tonic (postural) EMG, and of acceleration and deceleration phases Comprehensive and detailed characterization of the EMG of upper-limb and trunk muscles in point-to-point upper limb movements EMG magnitude assessment, phasic and tonic EMG activations, directional tuning, acceleration and deceleration phases
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Affiliation(s)
- Robert Mihai Mira
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council of Italy (CNR), 23900, Lecco, Italy
| | - Lorenzo Molinari Tosatti
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council of Italy (CNR), 23900, Lecco, Italy
| | - Marco Sacco
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council of Italy (CNR), 23900, Lecco, Italy
| | - Alessandro Scano
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council of Italy (CNR), 23900, Lecco, Italy
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4
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Maintained Representations of the Ipsilateral and Contralateral Limbs during Bimanual Control in Primary Motor Cortex. J Neurosci 2020; 40:6732-6747. [PMID: 32703902 DOI: 10.1523/jneurosci.0730-20.2020] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 07/15/2020] [Accepted: 07/16/2020] [Indexed: 12/26/2022] Open
Abstract
Primary motor cortex (M1) almost exclusively controls the contralateral side of the body. However, M1 activity is also modulated during ipsilateral body movements. Previous work has shown that M1 activity related to the ipsilateral arm is independent of the M1 activity related to the contralateral arm. How do these patterns of activity interact when both arms move simultaneously? We explored this problem by training 2 monkeys (male, Macaca mulatta) in a postural perturbation task while recording from M1. Loads were applied to one arm at a time (unimanual) or both arms simultaneously (bimanual). We found 83% of neurons (n = 236) were responsive to both the unimanual and bimanual loads. We also observed a small reduction in activity magnitude during the bimanual loads for both limbs (25%). Across the unimanual and bimanual loads, neurons largely maintained their preferred load directions. However, there was a larger change in the preferred loads for the ipsilateral limb (∼25%) than the contralateral limb (∼9%). Lastly, we identified the contralateral and ipsilateral subspaces during the unimanual loads and found they captured a significant amount of the variance during the bimanual loads. However, the subspace captured more of the bimanual variance related to the contralateral limb (97%) than the ipsilateral limb (66%). Our results highlight that, even during bimanual motor actions, M1 largely retains its representations of the contralateral and ipsilateral limbs.SIGNIFICANCE STATEMENT Previous work has shown that primary motor cortex (M1) represents information related to the contralateral limb, its downstream target, but also reflects information related to the ipsilateral limb. Can M1 still represent both sources of information when performing simultaneous movements of the limbs? Here we record from M1 during a postural perturbation task. We show that activity related to the contralateral limb is maintained between unimanual and bimanual motor actions, whereas the activity related to the ipsilateral limb undergoes a small change between unimanual and bimanual motor actions. Our results indicate that two independent representations can be maintained and expressed simultaneously in M1.
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5
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Maeda RS, Gribble PL, Pruszynski JA. Learning New Feedforward Motor Commands Based on Feedback Responses. Curr Biol 2020; 30:1941-1948.e3. [PMID: 32275882 DOI: 10.1016/j.cub.2020.03.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 02/17/2020] [Accepted: 03/02/2020] [Indexed: 10/24/2022]
Abstract
Learning a new motor task modifies feedforward (i.e., voluntary) motor commands and such learning also changes the sensitivity of feedback responses (i.e., reflexes) to mechanical perturbations [1-9]. For example, after people learn to generate straight reaching movements in the presence of an external force field or learn to reduce shoulder muscle activity when generating pure elbow movements with shoulder fixation, evoked stretch reflex responses to mechanical perturbations reflect the learning expressed during self-initiated reaching. Such a transfer from feedforward motor commands to feedback responses is thought to take place because of shared neural circuits at the level of the spinal cord, brainstem, and cerebral cortex [10-13]. The presence of shared neural resources also predicts the transfer from feedback responses to feedforward motor commands. Little is known about such a transfer presumably because it is relatively hard to elicit learning in reflexes without engaging associated voluntary responses following mechanical perturbations. Here, we demonstrate such transfer by leveraging two approaches to elicit stretch reflexes while minimizing engagement of voluntary motor responses in the learning process: applying very short mechanical perturbations [14-19] and instructing participants to not respond to them [20-26]. Taken together, our work shows that transfer between feedforward and feedback control is bidirectional, furthering the notion that these processes share common neural circuits that underlie motor learning and transfer.
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Affiliation(s)
- Rodrigo S Maeda
- Brain and Mind Institute, Western University, London, ON N6A5B7, Canada; Robarts Research Institute, Western University, London, ON N6A5B7, Canada; Department of Psychology, Western University, London, ON N6A5C2, Canada
| | - Paul L Gribble
- Brain and Mind Institute, Western University, London, ON N6A5B7, Canada; Department of Psychology, Western University, London, ON N6A5C2, Canada; Department of Physiology and Pharmacology, Western University, London, ON N6A5C1, Canada
| | - J Andrew Pruszynski
- Brain and Mind Institute, Western University, London, ON N6A5B7, Canada; Robarts Research Institute, Western University, London, ON N6A5B7, Canada; Department of Psychology, Western University, London, ON N6A5C2, Canada; Department of Physiology and Pharmacology, Western University, London, ON N6A5C1, Canada.
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6
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Heming EA, Cross KP, Takei T, Cook DJ, Scott SH. Independent representations of ipsilateral and contralateral limbs in primary motor cortex. eLife 2019; 8:e48190. [PMID: 31625506 PMCID: PMC6824843 DOI: 10.7554/elife.48190] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 10/17/2019] [Indexed: 02/04/2023] Open
Abstract
Several lines of research demonstrate that primary motor cortex (M1) is principally involved in controlling the contralateral side of the body. However, M1 activity has been correlated with both contralateral and ipsilateral limb movements. Why does ipsilaterally-related activity not cause contralateral motor output? To address this question, we trained monkeys to counter mechanical loads applied to their right and left limbs. We found >50% of M1 neurons had load-related activity for both limbs. Contralateral loads evoked changes in activity ~10ms sooner than ipsilateral loads. We also found corresponding population activities were distinct, with contralateral activity residing in a subspace that was orthogonal to the ipsilateral activity. Thus, neural responses for the contralateral limb can be extracted without interference from the activity for the ipsilateral limb, and vice versa. Our results show that M1 activity unrelated to downstream motor targets can be segregated from activity related to the downstream motor output.
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Affiliation(s)
- Ethan A Heming
- Centre for Neuroscience StudiesQueen’s UniversityKingstonCanada
| | - Kevin P Cross
- Centre for Neuroscience StudiesQueen’s UniversityKingstonCanada
| | - Tomohiko Takei
- Centre for Neuroscience StudiesQueen’s UniversityKingstonCanada
- Graduate School of Medicine, The Hakubi Center for Advanced ResearchKyoto UniversityKyotoJapan
| | - Douglas J Cook
- Centre for Neuroscience StudiesQueen’s UniversityKingstonCanada
- Department of SurgeryQueen’s UniversityKingstonCanada
- Department of SurgeryDalhousie UniversityHalifaxCanada
| | - Stephen H Scott
- Centre for Neuroscience StudiesQueen’s UniversityKingstonCanada
- Department of MedicineQueen’s UniversityKingstonCanada
- Department of Biomedical and Molecular SciencesQueen’s UniversityKingstonCanada
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7
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Correlations Between Primary Motor Cortex Activity with Recent Past and Future Limb Motion During Unperturbed Reaching. J Neurosci 2018; 38:7787-7799. [PMID: 30037832 DOI: 10.1523/jneurosci.2667-17.2018] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Revised: 06/29/2018] [Accepted: 07/16/2018] [Indexed: 11/21/2022] Open
Abstract
Many studies highlight that human movements are highly successful yet display a surprising amount of variability from trial to trial. There is a consistent pattern of variability throughout movement: initial motor errors are corrected by the end of movement, suggesting the presence of a powerful online control process. Here, we analyze the trial-by-trial variability of goal-directed reaching in nonhuman primates (five male Rhesus monkeys) and demonstrate that they display a similar pattern of variability during reaching, including a strong negative correlation between initial and late hand motion. We then demonstrate that trial-to-trial neural variability of primary motor cortex (M1) is positively correlated with variability of future hand motion (τ = ∼160 ms) during reaching. Furthermore, the variability of M1 activity is also correlated with variability of past hand motion (τ = ∼90 ms), but in the opposite polarity (i.e., negative correlation). Partial correlation analysis demonstrated that M1 activity independently reflects the variability of both past and future hand motions. These findings provide support for the hypothesis that M1 activity is involved in online feedback control of motor actions.SIGNIFICANCE STATEMENT Previous studies highlight that primary motor cortex (M1) rapidly responds to either visual or mechanical disturbances, suggesting its involvement in online feedback control. However, these studies required external disturbances to the motor system and it is not clear whether a similar feedback process addresses internal noise/errors generated by the motor system itself. Here, we introduce a novel analysis that evaluates how variations in the activity of M1 neurons covary with variations in hand motion on a trial-to-trial basis. The analyses demonstrate that M1 activity is correlated with hand motion in both the near future and the recent past, but with opposite polarity. These results suggest that M1 is involved in online feedback motor control to address errors/noise within the motor system.
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8
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Russo AA, Bittner SR, Perkins SM, Seely JS, London BM, Lara AH, Miri A, Marshall NJ, Kohn A, Jessell TM, Abbott LF, Cunningham JP, Churchland MM. Motor Cortex Embeds Muscle-like Commands in an Untangled Population Response. Neuron 2018; 97:953-966.e8. [PMID: 29398358 DOI: 10.1016/j.neuron.2018.01.004] [Citation(s) in RCA: 143] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2017] [Revised: 10/24/2017] [Accepted: 12/31/2017] [Indexed: 01/02/2023]
Abstract
Primate motor cortex projects to spinal interneurons and motoneurons, suggesting that motor cortex activity may be dominated by muscle-like commands. Observations during reaching lend support to this view, but evidence remains ambiguous and much debated. To provide a different perspective, we employed a novel behavioral paradigm that facilitates comparison between time-evolving neural and muscle activity. We found that single motor cortex neurons displayed many muscle-like properties, but the structure of population activity was not muscle-like. Unlike muscle activity, neural activity was structured to avoid "tangling": moments where similar activity patterns led to dissimilar future patterns. Avoidance of tangling was present across tasks and species. Network models revealed a potential reason for this consistent feature: low tangling confers noise robustness. Finally, we were able to predict motor cortex activity from muscle activity by leveraging the hypothesis that muscle-like commands are embedded in additional structure that yields low tangling.
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Affiliation(s)
- Abigail A Russo
- Department of Neuroscience, Columbia University Medical Center, New York, NY 10032, USA; Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | - Sean R Bittner
- Department of Neuroscience, Columbia University Medical Center, New York, NY 10032, USA; Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | - Sean M Perkins
- Zuckerman Institute, Columbia University, New York, NY 10027, USA; Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Jeffrey S Seely
- Department of Neuroscience, Columbia University Medical Center, New York, NY 10032, USA; Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | | | - Antonio H Lara
- Department of Neuroscience, Columbia University Medical Center, New York, NY 10032, USA; Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | - Andrew Miri
- Department of Neuroscience, Columbia University Medical Center, New York, NY 10032, USA; Zuckerman Institute, Columbia University, New York, NY 10027, USA; Departments of Biochemistry and Molecular Biophysics, Columbia University Medical Center, New York, NY 10032, USA
| | - Najja J Marshall
- Department of Neuroscience, Columbia University Medical Center, New York, NY 10032, USA; Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | - Adam Kohn
- Department of Ophthalmology and Visual Sciences, Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Yeshiva University, Bronx, NY 10461, USA
| | - Thomas M Jessell
- Department of Neuroscience, Columbia University Medical Center, New York, NY 10032, USA; Zuckerman Institute, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science, Columbia University Medical Center, New York, NY 10032, USA; Howard Hughes Medical Institute, Columbia University, New York, NY 10032, USA; Departments of Biochemistry and Molecular Biophysics, Columbia University Medical Center, New York, NY 10032, USA
| | - Laurence F Abbott
- Department of Neuroscience, Columbia University Medical Center, New York, NY 10032, USA; Zuckerman Institute, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science, Columbia University Medical Center, New York, NY 10032, USA; Department of Physiology and Cellular Biophysics, Columbia University Medical Center, New York, NY 10032, USA; Center for Theoretical Neuroscience, Columbia University Medical Center, New York, NY 10032, USA
| | - John P Cunningham
- Zuckerman Institute, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University Medical Center, New York, NY 10032, USA; Department of Statistics, Columbia University, New York, NY 10027, USA
| | - Mark M Churchland
- Department of Neuroscience, Columbia University Medical Center, New York, NY 10032, USA; Zuckerman Institute, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science, Columbia University Medical Center, New York, NY 10032, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA.
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9
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Córdova Bulens D, Crevecoeur F, Thonnard JL, Lefèvre P. Optimal use of limb mechanics distributes control during bimanual tasks. J Neurophysiol 2017; 119:921-932. [PMID: 29118194 DOI: 10.1152/jn.00371.2017] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Abstract
Bimanual tasks involve the coordination of both arms, which often offers redundancy in the ways a task can be completed. The distribution of control across limbs is often considered from the perspective of handedness. In this context, although there are differences across dominant and nondominant arms during reaching control ( Sainburg 2002 ), previous studies have shown that the brain tends to favor the dominant arm when performing bimanual tasks ( Salimpour and Shadmehr 2014 ). However, biomechanical factors known to influence planning and control in unimanual tasks may also generate limb asymmetries in force generation, but their influence on bimanual control has remained unexplored. We investigated this issue in a series of experiments in which participants were instructed to generate a 20-N force with both arms, with or without perturbation of the target force during the trial. We modeled the task in the framework of optimal feedback control of a two-link model with six human-like muscles groups. The biomechanical model predicted a differential contribution of each arm dependent on the orientation of the target force and joint configuration that was quantitatively matched by the participants' behavior, regardless of handedness. Responses to visual perturbations were strongly influenced by the perturbation direction, such that online corrections also reflected an optimal use of limb biomechanics. These results show that the nervous system takes biomechanical constraints into account when optimizing the distribution of forces generated across limbs during both movement planning and feedback control of a bimanual task. NEW & NOTEWORTHY Here, we studied a bimanual force production task to examine the effects of biomechanical constraints on the distribution of control across limbs. Our findings show that the central nervous system optimizes the distribution of force across the two arms according to the joint configuration of the upper limbs. We further show that the underlying mechanisms influence both movement planning and online corrective responses to sudden changes in the target force.
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Affiliation(s)
- David Córdova Bulens
- Institute of Neuroscience, Université Catholique de Louvain , Brussels , Belgium.,Institute of Information and Communication Technologies, Electronics, and Applied Mathematics, Université Catholique de Louvain , Louvain-la-Neuve , Belgium
| | - Frédéric Crevecoeur
- Institute of Neuroscience, Université Catholique de Louvain , Brussels , Belgium.,Institute of Information and Communication Technologies, Electronics, and Applied Mathematics, Université Catholique de Louvain , Louvain-la-Neuve , Belgium
| | - Jean-Louis Thonnard
- Institute of Neuroscience, Université Catholique de Louvain , Brussels , Belgium.,Physical and Rehabilitation Medicine Department, Cliniques Universitaires Saint-Luc, Brussels , Belgium
| | - Philippe Lefèvre
- Institute of Neuroscience, Université Catholique de Louvain , Brussels , Belgium.,Institute of Information and Communication Technologies, Electronics, and Applied Mathematics, Université Catholique de Louvain , Louvain-la-Neuve , Belgium
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10
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Omrani M, Kaufman MT, Hatsopoulos NG, Cheney PD. Perspectives on classical controversies about the motor cortex. J Neurophysiol 2017; 118:1828-1848. [PMID: 28615340 PMCID: PMC5599665 DOI: 10.1152/jn.00795.2016] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Revised: 06/06/2017] [Accepted: 06/13/2017] [Indexed: 11/22/2022] Open
Abstract
Primary motor cortex has been studied for more than a century, yet a consensus on its functional contribution to movement control is still out of reach. In particular, there remains controversy as to the level of control produced by motor cortex ("low-level" movement dynamics vs. "high-level" movement kinematics) and the role of sensory feedback. In this review, we present different perspectives on the two following questions: What does activity in motor cortex reflect? and How do planned motor commands interact with incoming sensory feedback during movement? The four authors each present their independent views on how they think the primary motor cortex (M1) controls movement. At the end, we present a dialogue in which the authors synthesize their views and suggest possibilities for moving the field forward. While there is not yet a consensus on the role of M1 or sensory feedback in the control of upper limb movements, such dialogues are essential to take us closer to one.
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Affiliation(s)
- Mohsen Omrani
- Brain Health Institute, Rutgers University, Piscataway, New Jersey;
| | | | - Nicholas G Hatsopoulos
- Department of Organismal Biology & Anatomy, Committees on Computational Neuroscience and Neurobiology, University of Chicago, Chicago, Illinois; and
| | - Paul D Cheney
- Department of Molecular & Integrative Physiology, University of Kansas Medical Center, Kansas City, Kansas
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11
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Omrani M, Murnaghan CD, Pruszynski JA, Scott SH. Distributed task-specific processing of somatosensory feedback for voluntary motor control. eLife 2016; 5. [PMID: 27077949 PMCID: PMC4876645 DOI: 10.7554/elife.13141] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2015] [Accepted: 04/13/2016] [Indexed: 12/27/2022] Open
Abstract
Corrective responses to limb disturbances are surprisingly complex, but the neural
basis of these goal-directed responses is poorly understood. Here we show that
somatosensory feedback is transmitted to many sensory and motor cortical regions
within 25 ms of a mechanical disturbance applied to the monkey’s arm. When limb
feedback was salient to an ongoing motor action (task engagement), neurons in
parietal area 5 immediately (~25 ms) increased their response to limb disturbances,
whereas neurons in other regions did not alter their response until 15 to 40 ms
later. In contrast, initiation of a motor action elicited by a limb disturbance
(target selection) altered neural responses in primary motor cortex ~65 ms after the
limb disturbance, and then in dorsal premotor cortex, with no effect in parietal
regions until 150 ms post-perturbation. Our findings highlight broad parietofrontal
circuits that provide the neural substrate for goal-directed corrections, an
essential aspect of highly skilled motor behaviors. DOI:http://dx.doi.org/10.7554/eLife.13141.001 Humans and other animals can change a movement they are making in a split second,
such as when a basketball player has to move around an unexpected opponent to shoot a
ball through the hoop. These on-the-fly corrections rely on information about the
movement that comes in from the senses. However, it was unclear how the brain and
spinal cord process this sensory information to guide movement. Omrani et al. have now recorded electrical activity from the brains of monkeys while
the animals tried to keep their hand at a target. Each monkey wore a robotic
exoskeleton that would occasionally move the monkey’s arm. Even if the monkey was not
engaged in a motor task, a small nudge of the limb by the robot caused neural
activity to spread rapidly throughout the sensory and motor regions of the cerebral
cortex (the outer layer of the brain). In some trials, when the monkey was actively trying to keep its hand at a target, the
robot would again nudge the monkey’s arm slightly. Omrani et al. observed that within
25 milliseconds of this nudge, the activity in an area of the cortex called parietal
area 5 responded even more, suggesting that this area was using information from the
senses to actively deal with the change in arm position. This change in activity then
spread to other parts of the brain. In another set of trials, the monkey was trained to move to a second target if the
robot nudged its arm. In this case, the activity in an area called the primary motor
cortex increased even more, likely supporting the monkey’s ability to rapidly move to
this second target. Overall, the study by Omrani et al. highlights the complex way
that sensory feedback is processed in the cerebral cortex, supporting our ability to
perform highly skilled motor actions. DOI:http://dx.doi.org/10.7554/eLife.13141.002
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Affiliation(s)
- Mohsen Omrani
- Centre for Neuroscience Studies, Queen's Univertsity, Kingston, Canada.,Brain Health Institute, Rutgers Biomedical and Health Sciences, New Jersey, United States
| | | | - J Andrew Pruszynski
- Centre for Neuroscience Studies, Queen's Univertsity, Kingston, Canada.,Physiology and Pharmacology, Schulich School of Medicine and Dentistry, Robarts Research Institute, University of Western Ontario, Ontario, Canada
| | - Stephen H Scott
- Centre for Neuroscience Studies, Queen's Univertsity, Kingston, Canada.,Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Canada.,Department of Medicine, Queen's University, Kingston, Canada
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