1
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Rodriguez AC, Perich MG, Miller L, Humphries MD. Motor cortex latent dynamics encode spatial and temporal arm movement parameters independently. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.26.542452. [PMID: 37292834 PMCID: PMC10246015 DOI: 10.1101/2023.05.26.542452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The fluid movement of an arm requires multiple spatiotemporal parameters to be set independently. Recent studies have argued that arm movements are generated by the collective dynamics of neurons in motor cortex. An untested prediction of this hypothesis is that independent parameters of movement must map to independent components of the neural dynamics. Using a task where monkeys made a sequence of reaching movements to randomly placed targets, we show that the spatial and temporal parameters of arm movements are independently encoded in the low-dimensional trajectories of population activity in motor cortex: Each movement's direction corresponds to a fixed neural trajectory through neural state space and its speed to how quickly that trajectory is traversed. Recurrent neural network models show this coding allows independent control over the spatial and temporal parameters of movement by separate network parameters. Our results support a key prediction of the dynamical systems view of motor cortex, but also argue that not all parameters of movement are defined by different trajectories of population activity.
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Affiliation(s)
| | - Matthew G. Perich
- Département de neurosciences, Faculté de médecine, Université de Montréal, Montréal, Canada
- Québec Artificial Intelligence Institute (Mila), Québec, Canada
| | - Lee Miller
- Northwestern University, Department of Biomedical Engineering, Chicago, USA
| | - Mark D. Humphries
- School of Psychology, University of Nottingham, Nottingham, United Kingdom
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2
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Braun JM, Fauth M, Berger M, Huang NS, Simeoni E, Gaeta E, Rodrigues do Carmo R, García-Betances RI, Arredondo Waldmeyer MT, Gail A, Larsen JC, Manoonpong P, Tetzlaff C, Wörgötter F. A brain machine interface framework for exploring proactive control of smart environments. Sci Rep 2024; 14:11054. [PMID: 38744976 DOI: 10.1038/s41598-024-60280-7] [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: 06/23/2023] [Accepted: 04/21/2024] [Indexed: 05/16/2024] Open
Abstract
Brain machine interfaces (BMIs) can substantially improve the quality of life of elderly or disabled people. However, performing complex action sequences with a BMI system is onerous because it requires issuing commands sequentially. Fundamentally different from this, we have designed a BMI system that reads out mental planning activity and issues commands in a proactive manner. To demonstrate this, we recorded brain activity from freely-moving monkeys performing an instructed task and decoded it with an energy-efficient, small and mobile field-programmable gate array hardware decoder triggering real-time action execution on smart devices. Core of this is an adaptive decoding algorithm that can compensate for the day-by-day neuronal signal fluctuations with minimal re-calibration effort. We show that open-loop planning-ahead control is possible using signals from primary and pre-motor areas leading to significant time-gain in the execution of action sequences. This novel approach provides, thus, a stepping stone towards improved and more humane control of different smart environments with mobile brain machine interfaces.
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Affiliation(s)
- Jan-Matthias Braun
- SDU Applied AI and Data Science, University of Southern Denmark, 5230, Odense, Denmark.
| | - Michael Fauth
- Department for Computational Neuroscience, University of Göttingen, 37077, Göttingen, Germany
| | - Michael Berger
- Sensorimotor Group, German Primate Center - Leibniz-Institute for Primate Research, 37077, Göttingen, Germany
- Cognitive Neuroscience Laboratory, German Primate Center - Leibniz-Institute for Primate Research, 37077, Göttingen, Germany
- Faculty of Biology and Psychology, University of Göttingen, 37077, Göttingen, Germany
| | - Nan-Sheng Huang
- Embodied AI and Neurorobotics Lab, University of Southern Denmark, 5230, Odense, Denmark
| | - Ezequiel Simeoni
- Life Supporting Technologies Research Group, ETSIT, Universidad Politécnica de Madrid, 28040, Madrid, Spain
| | - Eugenio Gaeta
- Life Supporting Technologies Research Group, ETSIT, Universidad Politécnica de Madrid, 28040, Madrid, Spain
| | | | - Rebeca I García-Betances
- Life Supporting Technologies Research Group, ETSIT, Universidad Politécnica de Madrid, 28040, Madrid, Spain
| | | | - Alexander Gail
- Sensorimotor Group, German Primate Center - Leibniz-Institute for Primate Research, 37077, Göttingen, Germany
- Cognitive Neuroscience Laboratory, German Primate Center - Leibniz-Institute for Primate Research, 37077, Göttingen, Germany
- Faculty of Biology and Psychology, University of Göttingen, 37077, Göttingen, Germany
| | - Jørgen C Larsen
- Embodied AI and Neurorobotics Lab, University of Southern Denmark, 5230, Odense, Denmark
| | - Poramate Manoonpong
- Embodied AI and Neurorobotics Lab, University of Southern Denmark, 5230, Odense, Denmark
- Bio-inspired Robotics and Neural Engineering Lab, Vidyasirimedhi Institute of Science and Technology, Rayong, 21210, Thailand
| | - Christian Tetzlaff
- Department for Computational Neuroscience, University of Göttingen, 37077, Göttingen, Germany
- Group of Computational Synaptic Physiology, Department for Neuro- and Sensory Physiology, University Medical Center Göttingen, 37073, Göttingen, Germany
| | - Florentin Wörgötter
- Department for Computational Neuroscience, University of Göttingen, 37077, Göttingen, Germany
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3
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Zimnik AJ, Cora Ames K, An X, Driscoll L, Lara AH, Russo AA, Susoy V, Cunningham JP, Paninski L, Churchland MM, Glaser JI. Identifying Interpretable Latent Factors with Sparse Component Analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.05.578988. [PMID: 38370650 PMCID: PMC10871230 DOI: 10.1101/2024.02.05.578988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
In many neural populations, the computationally relevant signals are posited to be a set of 'latent factors' - signals shared across many individual neurons. Understanding the relationship between neural activity and behavior requires the identification of factors that reflect distinct computational roles. Methods for identifying such factors typically require supervision, which can be suboptimal if one is unsure how (or whether) factors can be grouped into distinct, meaningful sets. Here, we introduce Sparse Component Analysis (SCA), an unsupervised method that identifies interpretable latent factors. SCA seeks factors that are sparse in time and occupy orthogonal dimensions. With these simple constraints, SCA facilitates surprisingly clear parcellations of neural activity across a range of behaviors. We applied SCA to motor cortex activity from reaching and cycling monkeys, single-trial imaging data from C. elegans, and activity from a multitask artificial network. SCA consistently identified sets of factors that were useful in describing network computations.
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Affiliation(s)
- Andrew J Zimnik
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - K Cora Ames
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
| | - Xinyue An
- Department of Neurology, Northwestern University, Chicago, IL, USA
- Interdepartmental Neuroscience Program, Northwestern University, Chicago, IL, USA
| | - Laura Driscoll
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Allen Institute for Neural Dynamics, Allen Institute, Seattle, CA, USA
| | - Antonio H Lara
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - Abigail A Russo
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
| | - Vladislav Susoy
- Department of Physics, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - John P Cunningham
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Department of Statistics, Columbia University, New York, NY, USA
| | - Liam Paninski
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University, New York, NY, USA
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
- Department of Statistics, Columbia University, New York, NY, USA
| | - Mark M Churchland
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, 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 Medical Center, New York, NY, USA
| | - Joshua I Glaser
- Department of Neurology, Northwestern University, Chicago, IL, USA
- Department of Computer Science, Northwestern University, Evanston, IL, USA
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4
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Verhein JR, Vyas S, Shenoy KV. Methylphenidate modulates motor cortical dynamics and behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.15.562405. [PMID: 37905157 PMCID: PMC10614820 DOI: 10.1101/2023.10.15.562405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Methylphenidate (MPH, brand: Ritalin) is a common stimulant used both medically and non-medically. Though typically prescribed for its cognitive effects, MPH also affects movement. While it is known that MPH noncompetitively blocks the reuptake of catecholamines through inhibition of dopamine and norepinephrine transporters, a critical step in exploring how it affects behavior is to understand how MPH directly affects neural activity. This would establish an electrophysiological mechanism of action for MPH. Since we now have biologically-grounded network-level hypotheses regarding how populations of motor cortical neurons plan and execute movements, there is a unique opportunity to make testable predictions regarding how systemic MPH administration - a pharmacological perturbation - might affect neural activity in motor cortex. To that end, we administered clinically-relevant doses of MPH to Rhesus monkeys as they performed an instructed-delay reaching task. Concomitantly, we measured neural activity from dorsal premotor and primary motor cortex. Consistent with our predictions, we found dose-dependent and significant effects on reaction time, trial-by-trial variability, and movement speed. We confirmed our hypotheses that changes in reaction time and variability were accompanied by previously established population-level changes in motor cortical preparatory activity and the condition-independent signal that precedes movements. We expected changes in speed to be a result of changes in the amplitude of motor cortical dynamics and/or a translation of those dynamics in activity space. Instead, our data are consistent with a mechanism whereby the neuromodulatory effect of MPH is to increase the gain and/or the signal-to-noise of motor cortical dynamics during reaching. Continued work in this domain to better understand the brain-wide electrophysiological mechanism of action of MPH and other psychoactive drugs could facilitate more targeted treatments for a host of cognitive-motor disorders.
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Affiliation(s)
- Jessica R Verhein
- Medical Scientist Training Program, Stanford School of Medicine, Stanford University, Stanford, CA
- Neurosciences Graduate Program, Stanford School of Medicine, Stanford University, Stanford, CA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA
- Current affiliations: Psychiatry Research Residency Training Program, University of California, San Francisco, San Francisco, CA
| | - Saurabh Vyas
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA
- Department of Bioengineering, Stanford University, Stanford, CA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY
| | - Krishna V Shenoy
- Neurosciences Graduate Program, Stanford School of Medicine, Stanford University, Stanford, CA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA
- Department of Bioengineering, Stanford University, Stanford, CA
- Department of Electrical Engineering, Stanford University, Stanford, CA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA
- Department of Neurobiology, Stanford University, Stanford, CA
- Bio-X Program, Stanford University, Stanford, CA
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5
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Smoulder AL, Marino PJ, Oby ER, Snyder SE, Miyata H, Pavlovsky NP, Bishop WE, Yu BM, Chase SM, Batista AP. A neural basis of choking under pressure. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.16.537007. [PMID: 37090659 PMCID: PMC10120738 DOI: 10.1101/2023.04.16.537007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Incentives tend to drive improvements in performance. But when incentives get too high, we can "choke under pressure" and underperform when it matters most. What neural processes might lead to choking under pressure? We studied Rhesus monkeys performing a challenging reaching task in which they underperform when an unusually large "jackpot" reward is at stake. We observed a collapse in neural information about upcoming movements for jackpot rewards: in the motor cortex, neural planning signals became less distinguishable for different reach directions when a jackpot reward was made available. We conclude that neural signals of reward and motor planning interact in the motor cortex in a manner that can explain why we choke under pressure. One-Sentence Summary In response to exceptionally large reward cues, animals can "choke under pressure", and this corresponds to a collapse in the neural information about upcoming movements.
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6
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Meirhaeghe N, Riehle A, Brochier T. Parallel movement planning is achieved via an optimal preparatory state in motor cortex. Cell Rep 2023; 42:112136. [PMID: 36807145 DOI: 10.1016/j.celrep.2023.112136] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 12/16/2022] [Accepted: 02/01/2023] [Indexed: 02/22/2023] Open
Abstract
How do patterns of neural activity in the motor cortex contribute to the planning of a movement? A recent theory developed for single movements proposes that the motor cortex acts as a dynamical system whose initial state is optimized during the preparatory phase of the movement. This theory makes important yet untested predictions about preparatory dynamics in more complex behavioral settings. Here, we analyze preparatory activity in non-human primates planning not one but two movements simultaneously. As predicted by the theory, we find that parallel planning is achieved by adjusting preparatory activity within an optimal subspace to an intermediate state reflecting a trade-off between the two movements. The theory quantitatively accounts for the relationship between this intermediate state and fluctuations in the animals' behavior down at the trial level. These results uncover a simple mechanism for planning multiple movements in parallel and further point to motor planning as a controlled dynamical process.
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Affiliation(s)
- Nicolas Meirhaeghe
- Institut de Neurosciences de la Timone (INT), UMR 7289, CNRS, Aix-Marseille Université, 13005 Marseille, France.
| | - Alexa Riehle
- Institut de Neurosciences de la Timone (INT), UMR 7289, CNRS, Aix-Marseille Université, 13005 Marseille, France; Institute of Neuroscience and Medicine (INM-6), Jülich Research Centre, 52428 Jülich, Germany
| | - Thomas Brochier
- Institut de Neurosciences de la Timone (INT), UMR 7289, CNRS, Aix-Marseille Université, 13005 Marseille, France
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7
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Hadjidimitrakis K, De Vitis M, Ghodrati M, Filippini M, Fattori P. Anterior-posterior gradient in the integrated processing of forelimb movement direction and distance in macaque parietal cortex. Cell Rep 2022; 41:111608. [DOI: 10.1016/j.celrep.2022.111608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 07/16/2022] [Accepted: 10/14/2022] [Indexed: 11/09/2022] Open
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8
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Inagaki HK, Chen S, Daie K, Finkelstein A, Fontolan L, Romani S, Svoboda K. Neural Algorithms and Circuits for Motor Planning. Annu Rev Neurosci 2022; 45:249-271. [PMID: 35316610 DOI: 10.1146/annurev-neuro-092021-121730] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The brain plans and executes volitional movements. The underlying patterns of neural population activity have been explored in the context of movements of the eyes, limbs, tongue, and head in nonhuman primates and rodents. How do networks of neurons produce the slow neural dynamics that prepare specific movements and the fast dynamics that ultimately initiate these movements? Recent work exploits rapid and calibrated perturbations of neural activity to test specific dynamical systems models that are capable of producing the observed neural activity. These joint experimental and computational studies show that cortical dynamics during motor planning reflect fixed points of neural activity (attractors). Subcortical control signals reshape and move attractors over multiple timescales, causing commitment to specific actions and rapid transitions to movement execution. Experiments in rodents are beginning to reveal how these algorithms are implemented at the level of brain-wide neural circuits.
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Affiliation(s)
| | - Susu Chen
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Kayvon Daie
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA.,Allen Institute for Neural Dynamics, Seattle, Washington, USA;
| | - Arseny Finkelstein
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA.,Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv-Yafo, Israel
| | - Lorenzo Fontolan
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Sandro Romani
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Karel Svoboda
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA.,Allen Institute for Neural Dynamics, Seattle, Washington, USA;
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9
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Inagaki HK, Chen S, Ridder MC, Sah P, Li N, Yang Z, Hasanbegovic H, Gao Z, Gerfen CR, Svoboda K. A midbrain-thalamus-cortex circuit reorganizes cortical dynamics to initiate movement. Cell 2022; 185:1065-1081.e23. [PMID: 35245431 PMCID: PMC8990337 DOI: 10.1016/j.cell.2022.02.006] [Citation(s) in RCA: 63] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 11/15/2021] [Accepted: 02/03/2022] [Indexed: 01/06/2023]
Abstract
Motor behaviors are often planned long before execution but only released after specific sensory events. Planning and execution are each associated with distinct patterns of motor cortex activity. Key questions are how these dynamic activity patterns are generated and how they relate to behavior. Here, we investigate the multi-regional neural circuits that link an auditory "Go cue" and the transition from planning to execution of directional licking. Ascending glutamatergic neurons in the midbrain reticular and pedunculopontine nuclei show short latency and phasic changes in spike rate that are selective for the Go cue. This signal is transmitted via the thalamus to the motor cortex, where it triggers a rapid reorganization of motor cortex state from planning-related activity to a motor command, which in turn drives appropriate movement. Our studies show how midbrain can control cortical dynamics via the thalamus for rapid and precise motor behavior.
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Affiliation(s)
- Hidehiko K Inagaki
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Max Planck Florida Institute for Neuroscience, Jupiter, FL 33458, USA.
| | - Susu Chen
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Department of Neuroscience, Physiology, and Pharmacology, University College London, London WC1E 6BT, UK
| | - Margreet C Ridder
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Pankaj Sah
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia; Joint Center for Neuroscience and Neural Engineering, and Department of Biology, Southern University of Science and Technology, Shenzhen, Guangdong Province 518055, China
| | - Nuo Li
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Zidan Yang
- Max Planck Florida Institute for Neuroscience, Jupiter, FL 33458, USA
| | - Hana Hasanbegovic
- Department of Neuroscience, Erasmus MC, Rotterdam, 3015GE, The Netherlands
| | - Zhenyu Gao
- Department of Neuroscience, Erasmus MC, Rotterdam, 3015GE, The Netherlands
| | | | - Karel Svoboda
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Allen Institute for Neural Dynamics, Seattle, WA 98109, USA.
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10
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Sun X, O'Shea DJ, Golub MD, Trautmann EM, Vyas S, Ryu SI, Shenoy KV. Cortical preparatory activity indexes learned motor memories. Nature 2022; 602:274-279. [PMID: 35082444 PMCID: PMC9851374 DOI: 10.1038/s41586-021-04329-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Accepted: 12/09/2021] [Indexed: 01/21/2023]
Abstract
The brain's remarkable ability to learn and execute various motor behaviours harnesses the capacity of neural populations to generate a variety of activity patterns. Here we explore systematic changes in preparatory activity in motor cortex that accompany motor learning. We trained rhesus monkeys to learn an arm-reaching task1 in a curl force field that elicited new muscle forces for some, but not all, movement directions2,3. We found that in a neural subspace predictive of hand forces, changes in preparatory activity tracked the learned behavioural modifications and reassociated4 existing activity patterns with updated movements. Along a neural population dimension orthogonal to the force-predictive subspace, we discovered that preparatory activity shifted uniformly for all movement directions, including those unaltered by learning. During a washout period when the curl field was removed, preparatory activity gradually reverted in the force-predictive subspace, but the uniform shift persisted. These persistent preparatory activity patterns may retain a motor memory of the learned field5,6 and support accelerated relearning of the same curl field. When a set of distinct curl fields was learned in sequence, we observed a corresponding set of field-specific uniform shifts which separated the associated motor memories in the neural state space7-9. The precise geometry of these uniform shifts in preparatory activity could serve to index motor memories, facilitating the acquisition, retention and retrieval of a broad motor repertoire.
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Affiliation(s)
- Xulu Sun
- Department of Biology, Stanford University, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
| | - Daniel J O'Shea
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Matthew D Golub
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Eric M Trautmann
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Saurabh Vyas
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Stephen I Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Krishna V Shenoy
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Department of Neurosurgery, Stanford University, Stanford, CA, USA.
- Department of Neurobiology, Stanford University, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
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11
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Abstract
Significant experimental, computational, and theoretical work has identified rich structure within the coordinated activity of interconnected neural populations. An emerging challenge now is to uncover the nature of the associated computations, how they are implemented, and what role they play in driving behavior. We term this computation through neural population dynamics. If successful, this framework will reveal general motifs of neural population activity and quantitatively describe how neural population dynamics implement computations necessary for driving goal-directed behavior. Here, we start with a mathematical primer on dynamical systems theory and analytical tools necessary to apply this perspective to experimental data. Next, we highlight some recent discoveries resulting from successful application of dynamical systems. We focus on studies spanning motor control, timing, decision-making, and working memory. Finally, we briefly discuss promising recent lines of investigation and future directions for the computation through neural population dynamics framework.
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Affiliation(s)
- Saurabh Vyas
- Department of Bioengineering, Stanford University, Stanford, California 94305, USA; .,Wu Tsai Neurosciences Institute, Stanford University, Stanford, California 94305, USA
| | - Matthew D Golub
- Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA.,Wu Tsai Neurosciences Institute, Stanford University, Stanford, California 94305, USA
| | - David Sussillo
- Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA.,Wu Tsai Neurosciences Institute, Stanford University, Stanford, California 94305, USA.,Google AI, Google Inc., Mountain View, California 94305, USA
| | - Krishna V Shenoy
- Department of Bioengineering, Stanford University, Stanford, California 94305, USA; .,Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA.,Wu Tsai Neurosciences Institute, Stanford University, Stanford, California 94305, USA.,Department of Neurobiology, Bio-X Institute, Neurosciences Program, and Howard Hughes Medical Institute, Stanford University, Stanford, California 94305, USA
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12
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Sutter K, Oostwoud Wijdenes L, van Beers RJ, Medendorp WP. Movement preparation time determines movement variability. J Neurophysiol 2021; 125:2375-2383. [PMID: 34038240 DOI: 10.1152/jn.00087.2020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Faster movements are typically more variable-a speed-accuracy trade-off known as Fitts' law. Are movements that are initiated faster also more variable? Neurophysiological work has associated larger neural variability during motor preparation with longer reaction time (RT) and larger movement variability, implying that movement variability decreases with increasing RT. Here, we recorded over 30,000 reaching movements in 11 human participants who moved to visually cued targets. Half of the visual cues were accompanied by a beep to evoke a wide RT range in each participant. Results show that initial reach variability decreases with increasing RT, for voluntarily produced RTs up to ∼300 ms, whereas other kinematic aspects and endpoint accuracy remained unaffected. We conclude that movement preparation time determines initial movement variability. We suggest that the chosen movement preparation time reflects a trade-off between movement initiation and precision.NEW & NOTEWORTHY Fitts' law describes the speed-accuracy trade-off in the execution of human movements. We examined whether there is also a trade-off between movement planning time and initial movement precision. We show that shorter reaction times result in higher initial movement variability. In other words, movement preparation time determines movement variability.
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Affiliation(s)
- Katrin Sutter
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Leonie Oostwoud Wijdenes
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Robert J van Beers
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.,Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - W Pieter Medendorp
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
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13
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Al Borno M, Vyas S, Shenoy KV, Delp SL. High-fidelity musculoskeletal modeling reveals that motor planning variability contributes to the speed-accuracy tradeoff. eLife 2020; 9:57021. [PMID: 33325369 PMCID: PMC7787661 DOI: 10.7554/elife.57021] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 12/15/2020] [Indexed: 11/16/2022] Open
Abstract
A long-standing challenge in motor neuroscience is to understand the relationship between movement speed and accuracy, known as the speed-accuracy tradeoff. Here, we introduce a biomechanically realistic computational model of three-dimensional upper extremity movements that reproduces well-known features of reaching movements. This model revealed that the speed-accuracy tradeoff, as described by Fitts’ law, emerges even without the presence of motor noise, which is commonly believed to underlie the speed-accuracy tradeoff. Next, we analyzed motor cortical neural activity from monkeys reaching to targets of different sizes. We found that the contribution of preparatory neural activity to movement duration (MD) variability is greater for smaller targets than larger targets, and that movements to smaller targets exhibit less variability in population-level preparatory activity, but greater MD variability. These results propose a new theory underlying the speed-accuracy tradeoff: Fitts’ law emerges from greater task demands constraining the optimization landscape in a fashion that reduces the number of ‘good’ control solutions (i.e., faster reaches). Thus, contrary to current beliefs, the speed-accuracy tradeoff could be a consequence of motor planning variability and not exclusively signal-dependent noise.
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Affiliation(s)
- Mazen Al Borno
- Department of Bioengineering, Stanford University, Stanford, United States.,Department of Computer Science and Engineering, University of Colorado Denver, Denver, United States
| | - Saurabh Vyas
- Department of Bioengineering, Stanford University, Stanford, United States
| | - Krishna V Shenoy
- Department of Bioengineering, Stanford University, Stanford, United States.,Neurosciences Program, Stanford University, Stanford, United States.,Department of Electrical Engineering, Stanford University, Stanford, United States.,Wu Tsai Neuroscience Institute, Stanford University, Stanford, United States.,Department of Neurobiology, Stanford University, Stanford, United States.,Howard Hughes Medical Institute, Stanford University, Stanford, United States
| | - Scott L Delp
- Department of Bioengineering, Stanford University, Stanford, United States.,Department of Mechanical Engineering, Stanford University, Stanford, United States
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Vyas S, O'Shea DJ, Ryu SI, Shenoy KV. Causal Role of Motor Preparation during Error-Driven Learning. Neuron 2020; 106:329-339.e4. [PMID: 32053768 PMCID: PMC7185427 DOI: 10.1016/j.neuron.2020.01.019] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Revised: 11/12/2019] [Accepted: 01/16/2020] [Indexed: 11/28/2022]
Abstract
Current theories suggest that an error-driven learning process updates trial-by-trial to facilitate motor adaptation. How this process interacts with motor cortical preparatory activity-which current models suggest plays a critical role in movement initiation-remains unknown. Here, we evaluated the role of motor preparation during visuomotor adaptation. We found that preparation time was inversely correlated to variance of errors on current trials and mean error on subsequent trials. We also found causal evidence that intracortical microstimulation during motor preparation was sufficient to disrupt learning. Surprisingly, stimulation did not affect current trials, but instead disrupted the update computation of a learning process, thereby affecting subsequent trials. This is consistent with a Bayesian estimation framework where the motor system reduces its learning rate by virtue of lowering error sensitivity when faced with uncertainty. This interaction between motor preparation and the error-driven learning system may facilitate new probes into mechanisms underlying trial-by-trial adaptation.
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Affiliation(s)
- Saurabh Vyas
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
| | - Daniel J O'Shea
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Neurosciences Graduate Program, Stanford University, Stanford, CA 94305, USA
| | - Stephen I Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Palo Alto Medical Foundation, Palo Alto, CA 94301, USA
| | - Krishna V Shenoy
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Department of Neurobiology, Stanford University, Stanford, CA 94305, USA; Bio-X Program, Stanford University, Stanford, CA 94305, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
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