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Morecraft RJ, Ge J, Stilwell-Morecraft KS, Lemon RN, Ganguly K, Darling WG. Terminal organization of the corticospinal projection from the arm/hand region of the rostral primary motor cortex (M1r or old M1) to the cervical enlargement (C5-T1) in rhesus monkey. J Comp Neurol 2023; 531:1996-2018. [PMID: 37938897 PMCID: PMC10842044 DOI: 10.1002/cne.25557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 06/12/2023] [Accepted: 10/13/2023] [Indexed: 11/10/2023]
Abstract
High-resolution anterograde tracers and stereology were used to study the terminal organization of the corticospinal projection (CSP) from the rostral portion of the primary motor cortex (M1r) to spinal levels C5-T1. Most of this projection (90%) terminated contralaterally within laminae V-IX, with the densest distribution in lamina VII. Moderate bouton numbers occurred in laminae VI, VIII, and IX with few in lamina V. Within lamina VII, labeling occurred over the distal-related dorsolateral subsectors and proximal-related ventromedial subsectors. Within motoneuron lamina IX, most terminations occurred in the proximal-related dorsomedial quadrant, followed by the distal-related dorsolateral quadrant. Segmentally, the contralateral lamina VII CSP gradually declined from C5-T1 but was consistently distributed at C5-C7 in lamina IX. The ipsilateral CSP ended in axial-related lamina VIII and adjacent ventromedial region of lamina VII. These findings demonstrate the M1r CSP influences distal and proximal/axial-related spinal targets. Thus, the M1r CSP represents a transitional CSP, positioned between the caudal M1 (M1c) CSP, which is 98% contralateral and optimally organized to mediate distal upper extremity movements (Morecraft et al., 2013), and dorsolateral premotor (LPMCd) CSP being 79% contralateral and optimally organized to mediate proximal/axial movements (Morecraft et al., 2019). This distal to proximal CSP gradient corresponds to the clinical deficits accompanying caudal to rostral motor cortex injury. The lamina IX CSP is considered in the light of anatomical and neurophysiological evidence which suggests M1c gives rise to the major proportion of the cortico-motoneuronal (CM) projection, while there is a limited M1r CM projection.
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Affiliation(s)
- Robert J. Morecraft
- Division of Basic Biomedical Sciences, Laboratory of Neurological Sciences, The University of South Dakota, Sanford School of Medicine, Vermillion, South Dakota, USA
| | - Jizhi Ge
- Division of Basic Biomedical Sciences, Laboratory of Neurological Sciences, The University of South Dakota, Sanford School of Medicine, Vermillion, South Dakota, USA
| | - Kimberly S. Stilwell-Morecraft
- Division of Basic Biomedical Sciences, Laboratory of Neurological Sciences, The University of South Dakota, Sanford School of Medicine, Vermillion, South Dakota, USA
| | - Roger N. Lemon
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Karunesh Ganguly
- Department of Neurology, Weill Institute for Neuroscience, University of California San Francisco, San Francisco, California, USA
- Neurology Service, SFVAHSC, San Francisco, California, USA
| | - Warren G. Darling
- Department of Health and Human Physiology, Motor Control Laboratories, The University of Iowa, Iowa City, Iowa, USA
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2
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Compartmentalized dynamics within a common multi-area mesoscale manifold represent a repertoire of human hand movements. Neuron 2022; 110:154-174.e12. [PMID: 34678147 PMCID: PMC9701546 DOI: 10.1016/j.neuron.2021.10.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 07/11/2021] [Accepted: 10/01/2021] [Indexed: 01/07/2023]
Abstract
The human hand is unique in the animal kingdom for unparalleled dexterity, ranging from complex prehension to fine finger individuation. How does the brain represent such a diverse repertoire of movements? We evaluated mesoscale neural dynamics across the human "grasp network," using electrocorticography and dimensionality reduction methods, for a repertoire of hand movements. Strikingly, we found that the grasp network represented both finger and grasping movements alike. Specifically, the manifold characterizing the multi-areal neural covariance structure was preserved during all movements across this distributed network. In contrast, latent neural dynamics within this manifold were surprisingly specific to movement type. Aligning latent activity to kinematics further uncovered distinct submanifolds despite similarities in synergistic coupling of joints between movements. We thus find that despite preserved neural covariance at the distributed network level, mesoscale dynamics are compartmentalized into movement-specific submanifolds; this mesoscale organization may allow flexible switching between a repertoire of hand movements.
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3
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Nason SR, Mender MJ, Vaskov AK, Willsey MS, Ganesh Kumar N, Kung TA, Patil PG, Chestek CA. Real-time linear prediction of simultaneous and independent movements of two finger groups using an intracortical brain-machine interface. Neuron 2021; 109:3164-3177.e8. [PMID: 34499856 DOI: 10.1016/j.neuron.2021.08.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 06/07/2021] [Accepted: 08/10/2021] [Indexed: 11/27/2022]
Abstract
Modern brain-machine interfaces can return function to people with paralysis, but current upper extremity brain-machine interfaces are unable to reproduce control of individuated finger movements. Here, for the first time, we present a real-time, high-speed, linear brain-machine interface in nonhuman primates that utilizes intracortical neural signals to bridge this gap. We created a non-prehensile task that systematically individuates two finger groups, the index finger and the middle-ring-small fingers combined. During online brain control, the ReFIT Kalman filter could predict individuated finger group movements with high performance. Next, training ridge regression decoders with individual movements was sufficient to predict untrained combined movements and vice versa. Finally, we compared the postural and movement tuning of finger-related cortical activity to find that individual cortical units simultaneously encode multiple behavioral dimensions. Our results suggest that linear decoders may be sufficient for brain-machine interfaces to execute high-dimensional tasks with the performance levels required for naturalistic neural prostheses.
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Affiliation(s)
- Samuel R Nason
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Matthew J Mender
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alex K Vaskov
- Robotics Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Matthew S Willsey
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Nishant Ganesh Kumar
- Section of Plastic Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI 48109, USA
| | - Theodore A Kung
- Section of Plastic Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI 48109, USA
| | - Parag G Patil
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Department of Neurology, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Cynthia A Chestek
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; Robotics Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA; Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA; Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA.
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4
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Zhang Y, Yang Q, Zhang L, Ran Y, Wang G, Celler BG, Su SW, Xu P, Yao D. Noise-assisted Multivariate Empirical Mode Decomposition based Causal Decomposition for brain-physiological network in bivariate and multiscale time series. J Neural Eng 2021; 18. [PMID: 33690185 DOI: 10.1088/1741-2552/abecf2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 03/09/2021] [Indexed: 11/11/2022]
Abstract
Objective.Noise-assisted Multivariate Empirical Mode Decomposition (NA-MEMD) based Causal Decomposition depicts a cause and effect relationship that is not based on the term of prediction, but rather on the phase dependence of time series. Here, we present the NA-MEMD based Causal Decomposition approach according to the covariation and power views traced to Hume and Kant: a priori cause-effect interaction is first acquired, and the presence of a candidate cause and of the effect is then computed from the sensory input somehow.Approach.Based on the definition of NA-MEMD based Causal Decomposition, we show such causal relation is a phase relation where the candidate causes are not merely followed by effects, but rather produce effects.Main results.The predominant methods used in neuroscience (Granger causality, EMD-based Causal Decomposition) are validated, showing the applicability of NA-MEMD based Causal Decomposition, particular to brain physiological processes in bivariate and multiscale time series.Significance.We point to the potential use in the causality inference analysis in a complex dynamic process.
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Affiliation(s)
- Yi Zhang
- The School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 610054, CHINA
| | - Qin Yang
- The School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, 611731, CHINA
| | - Lifu Zhang
- The School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 610054, CHINA
| | - Yu Ran
- The School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, 611731, CHINA
| | - Guan Wang
- The School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, 611731, CHINA
| | - Branko G Celler
- Biomedical Systems Laboratory, University of New South Wales, Sydney 2052, N.S.W., Sydney, New South Wales, 2052, AUSTRALIA
| | - Steven W Su
- Centre for Health Technologies, Faculty of Engineering and Information Technology, University of Technology Sydney, 15 Broadway, Ultimo, Sydney, New South Wales, 2007, AUSTRALIA
| | - Peng Xu
- Key Laboratory for Neuro Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, 611731, CHINA
| | - Dezhong Yao
- Key Laboratory for Neuro Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 610054, CHINA
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5
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Goodman JM, Tabot GA, Lee AS, Suresh AK, Rajan AT, Hatsopoulos NG, Bensmaia S. Postural Representations of the Hand in the Primate Sensorimotor Cortex. Neuron 2019; 104:1000-1009.e7. [PMID: 31668844 PMCID: PMC7172114 DOI: 10.1016/j.neuron.2019.09.004] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 06/04/2019] [Accepted: 09/05/2019] [Indexed: 01/07/2023]
Abstract
Manual dexterity requires proprioceptive feedback about the state of the hand. To date, study of the neural basis of proprioception in the cortex has focused primarily on reaching movements to the exclusion of hand-specific behaviors such as grasping. To fill this gap, we record both time-varying hand kinematics and neural activity evoked in somatosensory and motor cortices as monkeys grasp a variety of objects. We find that neurons in the somatosensory cortex, as well as in the motor cortex, preferentially track time-varying postures of multi-joint combinations spanning the entire hand. This contrasts with neural responses during reaching movements, which preferentially track time-varying movement kinematics of the arm, such as velocity and speed of the limb, rather than its time-varying postural configuration. These results suggest different representations of arm and hand movements suited to the different functional roles of these two effectors.
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Affiliation(s)
- James M Goodman
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
| | - Gregg A Tabot
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
| | - Alex S Lee
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
| | - Aneesha K Suresh
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
| | - Alexander T Rajan
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
| | - Nicholas G Hatsopoulos
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA; Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA; Grossman Institute for Neuroscience, Quantitative Biology, and Human Behavior, University of Chicago, Chicago, IL, USA
| | - Sliman Bensmaia
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA; Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA; Grossman Institute for Neuroscience, Quantitative Biology, and Human Behavior, University of Chicago, Chicago, IL, USA.
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6
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Edwards LL, King EM, Buetefisch CM, Borich MR. Putting the "Sensory" Into Sensorimotor Control: The Role of Sensorimotor Integration in Goal-Directed Hand Movements After Stroke. Front Integr Neurosci 2019; 13:16. [PMID: 31191265 PMCID: PMC6539545 DOI: 10.3389/fnint.2019.00016] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Accepted: 05/03/2019] [Indexed: 12/31/2022] Open
Abstract
Integration of sensory and motor information is one-step, among others, that underlies the successful production of goal-directed hand movements necessary for interacting with our environment. Disruption of sensorimotor integration is prevalent in many neurologic disorders, including stroke. In most stroke survivors, persistent paresis of the hand reduces function and overall quality of life. Current rehabilitative methods are based on neuroplastic principles to promote motor learning that focuses on regaining motor function lost due to paresis, but the sensory contributions to motor control and learning are often overlooked and currently understudied. There is a need to evaluate and understand the contribution of both sensory and motor function in the rehabilitation of skilled hand movements after stroke. Here, we will highlight the importance of integration of sensory and motor information to produce skilled hand movements in healthy individuals and individuals after stroke. We will then discuss how compromised sensorimotor integration influences relearning of skilled hand movements after stroke. Finally, we will propose an approach to target sensorimotor integration through manipulation of sensory input and motor output that may have therapeutic implications.
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Affiliation(s)
- Lauren L Edwards
- Neuroscience Graduate Program, Graduate Division of Biological and Biomedical Sciences, Emory University, Atlanta, GA, United States
| | - Erin M King
- Neuroscience Graduate Program, Graduate Division of Biological and Biomedical Sciences, Emory University, Atlanta, GA, United States
| | - Cathrin M Buetefisch
- Department of Rehabilitation Medicine, Laney Graduate School, Emory University, Atlanta, GA, United States.,Department of Neurology, Emory University, Atlanta, GA, United States.,Department of Radiology and Imaging Sciences, School of Medicine, Emory University, Atlanta, GA, United States
| | - Michael R Borich
- Department of Rehabilitation Medicine, Laney Graduate School, Emory University, Atlanta, GA, United States
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7
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Choi H, You KJ, Thakor NV, Schieber MH, Shin HC. Single-Finger Neural Basis Information-Based Neural Decoder for Multi-Finger Movements. IEEE Trans Neural Syst Rehabil Eng 2018; 26:2240-2248. [PMID: 30334763 DOI: 10.1109/tnsre.2018.2875731] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In this paper, we investigate the relationship between single and multi-finger movements. By exploiting the neural correlation between the temporal firing patterns between movements, we show that the Pearson's correlation coefficient for the physically related movement pairs are greater than those of others; the firing rates of the neurons that are tuned to a single-finger movements also increases when the corresponding multi-finger movements are instructed. We also use a hierarchical cluster analysis to verify not only the relationship between the single and multi-finger movements, but also the relationship between the flexion and extension movements. Furthermore, we propose a novel decoding method of modeling neural firing patterns while omitting the training process of the multi-finger movements. For the decoding, the Skellam and Gaussian probability distributions are used as mathematical models. The probabilistic distribution model of the multi-finger movements was estimated using the neural activity that was acquired during single-finger movements. As a result, the proposed neural decoding accuracy comparable with that of the supervised neural decoding accuracy when all of the neurons were used for the multi-finger movements. These results suggest that only the neural activities of single-finger movements can be exploited for the control of dexterous multi-finger neuroprosthetics.
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8
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Muscle Synergies Obtained from Comprehensive Mapping of the Cortical Forelimb Representation Using Stimulus Triggered Averaging of EMG Activity. J Neurosci 2018; 38:8759-8771. [PMID: 30150363 DOI: 10.1523/jneurosci.2519-17.2018] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Revised: 07/16/2018] [Accepted: 08/20/2018] [Indexed: 01/01/2023] Open
Abstract
Neuromuscular control of voluntary movement may be simplified using muscle synergies similar to those found using non-negative matrix factorization. We recently identified synergies in electromyography (EMG) recordings associated with both voluntary movement and movement evoked by high-frequency long-duration intracortical microstimulation applied to the forelimb representation of the primary motor cortex (M1). The goal of this study was to use stimulus-triggered averaging (StTA) of EMG activity to investigate the synergy profiles and weighting coefficients associated with poststimulus facilitation, as synergies may be hard-wired into elemental cortical output modules and revealed by StTA. We applied StTA at low (LOW, ∼15 μA) and high intensities (HIGH, ∼110 μA) to 247 cortical locations of the M1 forelimb region in two male rhesus macaques while recording the EMG of 24 forelimb muscles. Our results show that 10-11 synergies accounted for 90% of the variation in poststimulus EMG facilitation peaks from the LOW-intensity StTA dataset while only 4-5 synergies were needed for the HIGH-intensity dataset. Synergies were similar across monkeys and current intensities. Most synergy profiles strongly activated only one or two muscles; all joints were represented and most, but not all, joint directions of motion were represented. Cortical maps of the synergy weighting coefficients suggest only a weak organization. StTA of M1 resulted in highly diverse muscle activations, suggestive of the limiting condition of requiring a synergy for each muscle to account for the patterns observed.SIGNIFICANCE STATEMENT Coordination of muscle activity and the neural origin of potential muscle synergies remains a fundamental question of neuroscience. We previously demonstrated that high-frequency long-duration intracortical microstimulation-evoked synergies were unrelated to voluntary movement synergies and were not clearly organized in the cortex. Here we present stimulus-triggered averaging facilitation-related muscle synergies, suggesting that when fundamental cortical output modules are activated, synergies approach the limit of single-muscle control. Thus, we conclude that if the CNS controls movement via linear synergies, those synergies are unlikely to be called from M1. This information is critical for understanding neural control of movement and the development of brain-machine interfaces.
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9
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Zaaimi B, Dean LR, Baker SN. Different contributions of primary motor cortex, reticular formation, and spinal cord to fractionated muscle activation. J Neurophysiol 2018; 119:235-250. [PMID: 29046427 PMCID: PMC5866475 DOI: 10.1152/jn.00672.2017] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 10/12/2017] [Accepted: 10/12/2017] [Indexed: 12/12/2022] Open
Abstract
Coordinated movement requires patterned activation of muscles. In this study, we examined differences in selective activation of primate upper limb muscles by cortical and subcortical regions. Five macaque monkeys were trained to perform a reach and grasp task, and electromyogram (EMG) was recorded from 10 to 24 muscles while weak single-pulse stimuli were delivered through microelectrodes inserted in the motor cortex (M1), reticular formation (RF), or cervical spinal cord (SC). Stimulus intensity was adjusted to a level just above threshold. Stimulus-evoked effects were assessed from averages of rectified EMG. M1, RF, and SC activated 1.5 ± 0.9, 1.9 ± 0.8, and 2.5 ± 1.6 muscles per site (means ± SD); only M1 and SC differed significantly. In between recording sessions, natural muscle activity in the home cage was recorded using a miniature data logger. A novel analysis assessed how well natural activity could be reconstructed by stimulus-evoked responses. This provided two measures: normalized vector length L, reflecting how closely aligned natural and stimulus-evoked activity were, and normalized residual R, measuring the fraction of natural activity not reachable using stimulus-evoked patterns. Average values for M1, RF, and SC were L = 119.1 ± 9.6, 105.9 ± 6.2, and 109.3 ± 8.4% and R = 50.3 ± 4.9, 56.4 ± 3.5, and 51.5 ± 4.8%, respectively. RF was significantly different from M1 and SC on both measurements. RF is thus able to generate an approximation to the motor output with less activation than required by M1 and SC, but M1 and SC are more precise in reaching the exact activation pattern required. Cortical, brainstem, and spinal centers likely play distinct roles, as they cooperate to generate voluntary movements. NEW & NOTEWORTHY Brainstem reticular formation, primary motor cortex, and cervical spinal cord intermediate zone can all activate primate upper limb muscles. However, brainstem output is more efficient but less precise in producing natural patterns of motor output than motor cortex or spinal cord. We suggest that gross muscle synergies from the reticular formation are sculpted and refined by motor cortex and spinal circuits to reach the finely fractionated output characteristic of dexterous primate upper limb movements.
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Affiliation(s)
- Boubker Zaaimi
- Institute of Neuroscience, Newcastle University , Newcastle upon Tyne , United Kingdom
| | - Lauren R Dean
- Institute of Neuroscience, Newcastle University , Newcastle upon Tyne , United Kingdom
| | - Stuart N Baker
- Institute of Neuroscience, Newcastle University , Newcastle upon Tyne , United Kingdom
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10
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Leo A, Handjaras G, Bianchi M, Marino H, Gabiccini M, Guidi A, Scilingo EP, Pietrini P, Bicchi A, Santello M, Ricciardi E. A synergy-based hand control is encoded in human motor cortical areas. eLife 2016; 5. [PMID: 26880543 PMCID: PMC4786436 DOI: 10.7554/elife.13420] [Citation(s) in RCA: 79] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2015] [Accepted: 02/13/2016] [Indexed: 01/17/2023] Open
Abstract
How the human brain controls hand movements to carry out different tasks is still debated. The concept of synergy has been proposed to indicate functional modules that may simplify the control of hand postures by simultaneously recruiting sets of muscles and joints. However, whether and to what extent synergic hand postures are encoded as such at a cortical level remains unknown. Here, we combined kinematic, electromyography, and brain activity measures obtained by functional magnetic resonance imaging while subjects performed a variety of movements towards virtual objects. Hand postural information, encoded through kinematic synergies, were represented in cortical areas devoted to hand motor control and successfully discriminated individual grasping movements, significantly outperforming alternative somatotopic or muscle-based models. Importantly, hand postural synergies were predicted by neural activation patterns within primary motor cortex. These findings support a novel cortical organization for hand movement control and open potential applications for brain-computer interfaces and neuroprostheses. DOI:http://dx.doi.org/10.7554/eLife.13420.001 The human hand can perform an enormous range of movements with great dexterity. Some common everyday actions, such as grasping a coffee cup, involve the coordinated movement of all four fingers and thumb. Others, such as typing, rely on the ability of individual fingers to move relatively independently of one another. This flexibility is possible in part because of the complex anatomy of the hand, with its 27 bones and their connecting joints and muscles. But with this complexity comes a huge number of possibilities. Any movement-related task – such as picking up a cup – can be achieved via many different combinations of muscle contractions and joint positions. So how does the brain decide which muscles and joints to use? One theory is that the brain simplifies this problem by encoding particularly useful patterns of joint movements as distinct units or “synergies”. A given task can then be performed by selecting from a small number of synergies, avoiding the need to choose between huge numbers of options every time movement is required. Leo et al. now provide the first direct evidence for the encoding of synergies by the human brain. Volunteers lying inside a brain scanner reached towards virtual objects – from tennis rackets to toothpicks – while activity was recorded from the area of the brain that controls hand movements. As predicted, the scans showed specific and reproducible patterns of activity. Analysing these patterns revealed that each corresponded to a particular combination of joint positions. These activity patterns, or synergies, could even be ‘decoded’ to work out which type of movement a volunteer had just performed. Future experiments should examine how the brain combines synergies with sensory feedback to allow movements to be adjusted as they occur. Such findings could help to develop brain-computer interfaces and systems for controlling the movement of artificial limbs. DOI:http://dx.doi.org/10.7554/eLife.13420.002
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Affiliation(s)
- Andrea Leo
- Laboratory of Clinical Biochemistry and Molecular Biology, University of Pisa, Pisa, Italy.,Research Center 'E. Piaggio', University of Pisa, Pisa, Italy
| | - Giacomo Handjaras
- Laboratory of Clinical Biochemistry and Molecular Biology, University of Pisa, Pisa, Italy
| | - Matteo Bianchi
- Research Center 'E. Piaggio', University of Pisa, Pisa, Italy.,Advanced Robotics Department, Istituto Italiano di Tecnologia, Genova, Italy
| | - Hamal Marino
- Research Center 'E. Piaggio', University of Pisa, Pisa, Italy
| | - Marco Gabiccini
- Research Center 'E. Piaggio', University of Pisa, Pisa, Italy.,Advanced Robotics Department, Istituto Italiano di Tecnologia, Genova, Italy.,Department of Civil and Industrial Engineering, University of Pisa, Pisa, Italy
| | - Andrea Guidi
- Research Center 'E. Piaggio', University of Pisa, Pisa, Italy
| | - Enzo Pasquale Scilingo
- Research Center 'E. Piaggio', University of Pisa, Pisa, Italy.,Department of Information Engineering, University of Pisa, Pisa, Italy
| | - Pietro Pietrini
- Laboratory of Clinical Biochemistry and Molecular Biology, University of Pisa, Pisa, Italy.,Research Center 'E. Piaggio', University of Pisa, Pisa, Italy.,Clinical Psychology Branch, Pisa University Hospital, Pisa, Italy.,IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Antonio Bicchi
- Research Center 'E. Piaggio', University of Pisa, Pisa, Italy.,Advanced Robotics Department, Istituto Italiano di Tecnologia, Genova, Italy
| | - Marco Santello
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, United States
| | - Emiliano Ricciardi
- Laboratory of Clinical Biochemistry and Molecular Biology, University of Pisa, Pisa, Italy.,Research Center 'E. Piaggio', University of Pisa, Pisa, Italy
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11
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Santello M, Bianchi M, Gabiccini M, Ricciardi E, Salvietti G, Prattichizzo D, Ernst M, Moscatelli A, Jörntell H, Kappers AML, Kyriakopoulos K, Albu-Schäffer A, Castellini C, Bicchi A. Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands. Phys Life Rev 2016; 17:1-23. [PMID: 26923030 DOI: 10.1016/j.plrev.2016.02.001] [Citation(s) in RCA: 109] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Accepted: 02/02/2016] [Indexed: 12/30/2022]
Abstract
The term 'synergy' - from the Greek synergia - means 'working together'. The concept of multiple elements working together towards a common goal has been extensively used in neuroscience to develop theoretical frameworks, experimental approaches, and analytical techniques to understand neural control of movement, and for applications for neuro-rehabilitation. In the past decade, roboticists have successfully applied the framework of synergies to create novel design and control concepts for artificial hands, i.e., robotic hands and prostheses. At the same time, robotic research on the sensorimotor integration underlying the control and sensing of artificial hands has inspired new research approaches in neuroscience, and has provided useful instruments for novel experiments. The ambitious goal of integrating expertise and research approaches in robotics and neuroscience to study the properties and applications of the concept of synergies is generating a number of multidisciplinary cooperative projects, among which the recently finished 4-year European project "The Hand Embodied" (THE). This paper reviews the main insights provided by this framework. Specifically, we provide an overview of neuroscientific bases of hand synergies and introduce how robotics has leveraged the insights from neuroscience for innovative design in hardware and controllers for biomedical engineering applications, including myoelectric hand prostheses, devices for haptics research, and wearable sensing of human hand kinematics. The review also emphasizes how this multidisciplinary collaboration has generated new ways to conceptualize a synergy-based approach for robotics, and provides guidelines and principles for analyzing human behavior and synthesizing artificial robotic systems based on a theory of synergies.
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Affiliation(s)
- Marco Santello
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, USA.
| | - Matteo Bianchi
- Research Center 'E. Piaggio', University of Pisa, Pisa, Italy; Advanced Robotics Department, Istituto Italiano di Tecnologia (IIT), Genova, Italy
| | - Marco Gabiccini
- Research Center 'E. Piaggio', University of Pisa, Pisa, Italy; Advanced Robotics Department, Istituto Italiano di Tecnologia (IIT), Genova, Italy; Department of Civil and Industrial Engineering, University of Pisa, Pisa, Italy
| | - Emiliano Ricciardi
- Molecular Mind Laboratory, Dept. Surgical, Medical, Molecular Pathology and Critical Care, University of Pisa, Pisa, Italy; Research Center 'E. Piaggio', University of Pisa, Pisa, Italy
| | - Gionata Salvietti
- Department of Information Engineering and Mathematics, University of Siena, Siena, Italy
| | - Domenico Prattichizzo
- Department of Information Engineering and Mathematics, University of Siena, Siena, Italy; Advanced Robotics Department, Istituto Italiano di Tecnologia (IIT), Genova, Italy
| | - Marc Ernst
- Department of Cognitive Neuroscience and CITEC, Bielefeld University, Bielefeld, Germany
| | - Alessandro Moscatelli
- Department of Cognitive Neuroscience and CITEC, Bielefeld University, Bielefeld, Germany; Department of Systems Medicine and Centre of Space Bio-Medicine, Università di Roma "Tor Vergata", 00173, Rome, Italy
| | - Henrik Jörntell
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | | | - Kostas Kyriakopoulos
- School of Mechanical Engineering, National Technical University of Athens, Greece
| | - Alin Albu-Schäffer
- DLR - German Aerospace Center, Institute of Robotics and Mechatronics, Oberpfaffenhofen, Germany
| | - Claudio Castellini
- DLR - German Aerospace Center, Institute of Robotics and Mechatronics, Oberpfaffenhofen, Germany
| | - Antonio Bicchi
- Research Center 'E. Piaggio', University of Pisa, Pisa, Italy; Advanced Robotics Department, Istituto Italiano di Tecnologia (IIT), Genova, Italy.
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12
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Tagliabue M, Ciancio AL, Brochier T, Eskiizmirliler S, Maier MA. Differences between kinematic synergies and muscle synergies during two-digit grasping. Front Hum Neurosci 2015; 9:165. [PMID: 25859208 PMCID: PMC4374551 DOI: 10.3389/fnhum.2015.00165] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Accepted: 03/10/2015] [Indexed: 12/19/2022] Open
Abstract
The large number of mechanical degrees of freedom of the hand is not fully exploited during actual movements such as grasping. Usually, angular movements in various joints tend to be coupled, and EMG activities in different hand muscles tend to be correlated. The occurrence of covariation in the former was termed kinematic synergies, in the latter muscle synergies. This study addresses two questions: (i) Whether kinematic and muscle synergies can simultaneously accommodate for kinematic and kinetic constraints. (ii) If so, whether there is an interrelation between kinematic and muscle synergies. We used a reach-grasp-and-pull paradigm and recorded the hand kinematics as well as eight surface EMGs. Subjects had to either perform a precision grip or side grip and had to modify their grip force in order to displace an object against a low or high load. The analysis was subdivided into three epochs: reach, grasp-and-pull, and static hold. Principal component analysis (PCA, temporal or static) was performed separately for all three epochs, in the kinematic and in the EMG domain. PCA revealed that (i) Kinematic- and muscle-synergies can simultaneously accommodate kinematic (grip type) and kinetic task constraints (load condition). (ii) Upcoming grip and load conditions of the grasp are represented in kinematic- and muscle-synergies already during reach. Phase plane plots of the principal muscle-synergy against the principal kinematic synergy revealed (iii) that the muscle-synergy is linked (correlated, and in phase advance) to the kinematic synergy during reach and during grasp-and-pull. Furthermore (iv), pair-wise correlations of EMGs during hold suggest that muscle-synergies are (in part) implemented by coactivation of muscles through common input. Together, these results suggest that kinematic synergies have (at least in part) their origin not just in muscular activation, but in synergistic muscle activation. In short: kinematic synergies may result from muscle synergies.
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Affiliation(s)
- Michele Tagliabue
- Neuroscience Research Federation FR3636, CNRS, Université Paris Descartes Paris, France ; Centre de Neurophysique, Physiologie et Pathologie, UMR 8119, CNRS, Université Paris Descartes Sorbonne Paris Cité, Paris, France
| | - Anna Lisa Ciancio
- Laboratory of Biomedical Robotic and Biomicrosystem, Università Campus Bio-Medico di Roma Roma, Italy
| | - Thomas Brochier
- Institut de Neurosciences de la Timone, UMR 7289, CNRS, Aix-Marseille Université Marseille, France
| | - Selim Eskiizmirliler
- Neuroscience Research Federation FR3636, CNRS, Université Paris Descartes Paris, France ; Life Sciences Department, Université Paris Diderot Sorbonne Paris Cité, Paris, France
| | - Marc A Maier
- Neuroscience Research Federation FR3636, CNRS, Université Paris Descartes Paris, France ; Life Sciences Department, Université Paris Diderot Sorbonne Paris Cité, Paris, France
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13
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Morris R, Whishaw IQ. Arm and hand movement: current knowledge and future perspective. Front Neurol 2015; 6:19. [PMID: 25705202 PMCID: PMC4319398 DOI: 10.3389/fneur.2015.00019] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2014] [Accepted: 01/24/2015] [Indexed: 12/02/2022] Open
Affiliation(s)
- Renée Morris
- Department of Anatomy, Translational Neuroscience Facility, School of Medical Sciences, UNSW Australia , Sydney, NSW , Australia
| | - Ian Q Whishaw
- Department of Neuroscience, Canadian Centre for Behavioural Neuroscience, University of Lethbridge , Lethbridge, AB , Canada
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14
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Aumann TD, Prut Y. Do sensorimotor β-oscillations maintain muscle synergy representations in primary motor cortex? Trends Neurosci 2014; 38:77-85. [PMID: 25541288 DOI: 10.1016/j.tins.2014.12.002] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2014] [Revised: 11/04/2014] [Accepted: 12/01/2014] [Indexed: 11/24/2022]
Abstract
Coherent β-oscillations are a dominant feature of the sensorimotor system yet their function remains enigmatic. We propose that, in addition to cell intrinsic and/or local network interactions, they are supported by activity propagating recurrently around closed neural 'loops' between primary motor cortex (M1), muscles, and back to M1 via somatosensory pathways. Individual loops reciprocally connect individual muscle synergies ('motor primitives') with their representations in M1, and the conduction time around each loop resonates with the periodic spiking of its constituent neurons/muscles. During β-oscillations, this resonance strengthens within-loop connectivity (via long-term potentiation, LTP), whereas non-resonance between different loops weakens connectivity (via long-term depression, LTD) between M1 representations of different muscle synergies. In this way, β-oscillations help maintain accurate and discrete representations of muscle synergies in M1.
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Affiliation(s)
- Tim D Aumann
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia.
| | - Yifat Prut
- Department of Medical Neurobiology, Institute for Medical Research Israel-Canada (IMRIC) and The Edmond and Lily Safra Center for Brain Sciences (ELSC), The Hebrew University of Jerusalem, Jerusalem, Israel
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15
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Serruya MD. Bottlenecks to clinical translation of direct brain-computer interfaces. Front Syst Neurosci 2014; 8:226. [PMID: 25520632 PMCID: PMC4251316 DOI: 10.3389/fnsys.2014.00226] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2014] [Accepted: 11/10/2014] [Indexed: 12/17/2022] Open
Abstract
Despite several decades of research into novel brain-implantable devices to treat a range of diseases, only two—cochlear implants for sensorineural hearing loss and deep brain stimulation for movement disorders—have yielded any appreciable clinical benefit. Obstacles to translation include technical factors (e.g., signal loss due to gliosis or micromotion), lack of awareness of current clinical options for patients that the new therapy must outperform, traversing between federal and corporate funding needed to support clinical trials, and insufficient management expertise. This commentary reviews these obstacles preventing the translation of promising new neurotechnologies into clinical application and suggests some principles that interdisciplinary teams in academia and industry could adopt to enhance their chances of success.
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Affiliation(s)
- Mijail D Serruya
- Department of Neurology, Thomas Jefferson University Philadelphia, PA, USA
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