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Article ; Online: Selective modulation of cortical population dynamics during neuroprosthetic skill learning.

Zippi, Ellen L / You, Albert K / Ganguly, Karunesh / Carmena, Jose M

2022 Volume 12, Issue 1, Page(s) 15948

Abstract: Brain-machine interfaces (BMIs) provide a framework for studying how cortical population dynamics evolve over learning in a task in which the mapping between neural activity and behavior is precisely defined. Learning to control a BMI is associated with ... ...

Abstract	Brain-machine interfaces (BMIs) provide a framework for studying how cortical population dynamics evolve over learning in a task in which the mapping between neural activity and behavior is precisely defined. Learning to control a BMI is associated with the emergence of coordinated neural dynamics in populations of neurons whose activity serves as direct input to the BMI decoder (direct subpopulation). While previous work shows differential modification of firing rate modulation in this population relative to a population whose activity was not directly input to the BMI decoder (indirect subpopulation), little is known about how learning-related changes in cortical population dynamics within these groups compare.To investigate this, we monitored both direct and indirect subpopulations as two macaque monkeys learned to control a BMI. We found that while the combined population increased coordinated neural dynamics, this increase in coordination was primarily driven by changes in the direct subpopulation. These findings suggest that motor cortex refines cortical dynamics by increasing neural variance throughout the entire population during learning, with a more pronounced coordination of firing activity in subpopulations that are causally linked to behavior.
MeSH term(s)	Animals ; Brain-Computer Interfaces ; Learning ; Macaca ; Motor Cortex/physiology ; Neurons/physiology ; Population Dynamics
Language	English
Publishing date	2022-09-24
Publishing country	England
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-20218-3
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum.

Zippi, Ellen L / Shvartsman, Gabrielle F / Vendrell-Llopis, Nuria / Wallis, Joni D / Carmena, Jose M

Scientific reports

2023 Volume 13, Issue 1, Page(s) 17810

Abstract: Although brain-machine interfaces (BMIs) are directly controlled by the modulation of a select local population of neurons, distributed networks consisting of cortical and subcortical areas have been implicated in learning and maintaining control. ... ...

Abstract	Although brain-machine interfaces (BMIs) are directly controlled by the modulation of a select local population of neurons, distributed networks consisting of cortical and subcortical areas have been implicated in learning and maintaining control. Previous work in rodents has demonstrated the involvement of the striatum in BMI learning. However, the prefrontal cortex has been largely ignored when studying motor BMI control despite its role in action planning, action selection, and learning abstract tasks. Here, we compare local field potentials simultaneously recorded from primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), and the caudate nucleus of the striatum (Cd) while nonhuman primates perform a two-dimensional, self-initiated, center-out task under BMI control and manual control. Our results demonstrate the presence of distinct neural representations for BMI and manual control in M1, DLPFC, and Cd. We find that neural activity from DLPFC and M1 best distinguishes control types at the go cue and target acquisition, respectively, while M1 best predicts target-direction at both task events. We also find effective connectivity from DLPFC → M1 throughout both control types and Cd → M1 during BMI control. These results suggest distributed network activity between M1, DLPFC, and Cd during BMI control that is similar yet distinct from manual control.
MeSH term(s)	Animals ; Brain-Computer Interfaces ; Motor Cortex/physiology ; Cadmium ; Prefrontal Cortex/physiology ; Learning
Chemical Substances	Cadmium (00BH33GNGH)
Language	English
Publishing date	2023-10-19
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-44405-y
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Article: Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum.

Zippi, Ellen L / Shvartsman, Gabrielle F / Vendrell-Llopis, Nuria / Wallis, Joni D / Carmena, Jose M

bioRxiv : the preprint server for biology

2023

Abstract	Although brain-machine interfaces (BMIs) are directly controlled by the modulation of a select local population of neurons, distributed networks consisting of cortical and subcortical areas have been implicated in learning and maintaining control. Previous work in rodent BMI has demonstrated the involvement of the striatum in BMI learning. However, the prefrontal cortex has been largely ignored when studying motor BMI control despite its role in action planning, action selection, and learning abstract tasks. Here, we compare local field potentials simultaneously recorded from the primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), and the caudate nucleus of the striatum (Cd) while nonhuman primates perform a two-dimensional, self-initiated, center-out task under BMI control and manual control. Our results demonstrate the presence of distinct neural representations for BMI and manual control in M1, DLPFC, and Cd. We find that neural activity from DLPFC and M1 best distinguish between control types at the go cue and target acquisition, respectively. We also found effective connectivity from DLPFC→M1 throughout trials across both control types and Cd→M1 during BMI control. These results suggest distributed network activity between M1, DLPFC, and Cd during BMI control that is similar yet distinct from manual control.
Language	English
Publishing date	2023-06-01
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.05.31.542532
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Article ; Online: Invariant neural dynamics drive commands to control different movements.

Athalye, Vivek R / Khanna, Preeya / Gowda, Suraj / Orsborn, Amy L / Costa, Rui M / Carmena, Jose M

Current biology : CB

2023 Volume 33, Issue 14, Page(s) 2962–2976.e15

Abstract: It has been proposed that the nervous system has the capacity to generate a wide variety of movements because it reuses some invariant code. Previous work has identified that dynamics of neural population activity are similar during different movements, ... ...

Abstract	It has been proposed that the nervous system has the capacity to generate a wide variety of movements because it reuses some invariant code. Previous work has identified that dynamics of neural population activity are similar during different movements, where dynamics refer to how the instantaneous spatial pattern of population activity changes in time. Here, we test whether invariant dynamics of neural populations are actually used to issue the commands that direct movement. Using a brain-machine interface (BMI) that transforms rhesus macaques' motor-cortex activity into commands for a neuroprosthetic cursor, we discovered that the same command is issued with different neural-activity patterns in different movements. However, these different patterns were predictable, as we found that the transitions between activity patterns are governed by the same dynamics across movements. These invariant dynamics are low dimensional, and critically, they align with the BMI, so that they predict the specific component of neural activity that actually issues the next command. We introduce a model of optimal feedback control (OFC) that shows that invariant dynamics can help transform movement feedback into commands, reducing the input that the neural population needs to control movement. Altogether our results demonstrate that invariant dynamics drive commands to control a variety of movements and show how feedback can be integrated with invariant dynamics to issue generalizable commands.
MeSH term(s)	Animals ; Macaca mulatta ; Movement/physiology ; Feedback ; Brain-Computer Interfaces ; Motor Cortex/physiology
Language	English
Publishing date	2023-07-03
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1071731-6
ISSN	1879-0445 ; 0960-9822
ISSN (online)	1879-0445
ISSN	0960-9822
DOI	10.1016/j.cub.2023.06.027
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Article ; Online: Skilled independent control of individual motor units via a non-invasive neuromuscular-machine interface.

Formento, Emanuele / Botros, Paul / Carmena, Jose M

Journal of neural engineering

2021 Volume 18, Issue 6

Abstract: Objective. ...

Abstract	Objective.
MeSH term(s)	Arm/physiology ; Brain-Computer Interfaces ; Humans ; Isometric Contraction/physiology ; Motor Neurons/physiology ; Muscle, Skeletal/physiology
Language	English
Publishing date	2021-11-23
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2170901-4
ISSN	1741-2552 ; 1741-2560
ISSN (online)	1741-2552
ISSN	1741-2560
DOI	10.1088/1741-2552/ac35ac
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Article ; Online: Advances in neuroprosthetic learning and control.

Carmena, Jose M

PLoS biology

2013 Volume 11, Issue 5, Page(s) e1001561

Abstract: Significant progress has occurred in the field of brain-machine interfaces (BMI) since the first demonstrations with rodents, monkeys, and humans controlling different prosthetic devices directly with neural activity. This technology holds great ... ...

Abstract	Significant progress has occurred in the field of brain-machine interfaces (BMI) since the first demonstrations with rodents, monkeys, and humans controlling different prosthetic devices directly with neural activity. This technology holds great potential to aid large numbers of people with neurological disorders. However, despite this initial enthusiasm and the plethora of available robotic technologies, existing neural interfaces cannot as yet master the control of prosthetic, paralyzed, or otherwise disabled limbs. Here I briefly discuss recent advances from our laboratory into the neural basis of BMIs that should lead to better prosthetic control and clinically viable solutions, as well as new insights into the neurobiology of action.
MeSH term(s)	Animals ; Artificial Intelligence ; Haplorhini ; Humans ; Neural Prostheses ; Robotics ; User-Computer Interface
Language	English
Publishing date	2013-05-21
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2126776-5
ISSN	1545-7885 ; 1544-9173
ISSN (online)	1545-7885
ISSN	1544-9173
DOI	10.1371/journal.pbio.1001561
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Article ; Online: Diverse operant control of different motor cortex populations during learning.

Vendrell-Llopis, Nuria / Fang, Ching / Qü, Albert J / Costa, Rui M / Carmena, Jose M

Current biology : CB

2022 Volume 32, Issue 7, Page(s) 1616–1622.e5

Abstract: During motor learning, ...

Abstract	During motor learning,
MeSH term(s)	Animals ; Brain-Computer Interfaces ; Learning/physiology ; Mice ; Motor Cortex/physiology ; Motor Neurons/physiology ; Pyramidal Tracts/physiology
Language	English
Publishing date	2022-02-25
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1071731-6
ISSN	1879-0445 ; 0960-9822
ISSN (online)	1879-0445
ISSN	0960-9822
DOI	10.1016/j.cub.2022.02.006
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Article ; Online: Beta band oscillations in motor cortex reflect neural population signals that delay movement onset.

Khanna, Preeya / Carmena, Jose M

eLife

2017 Volume 6

Abstract: Motor cortical beta oscillations have been reported for decades, yet their behavioral correlates remain unresolved. Some studies link beta oscillations to changes in underlying neural activity, but the specific behavioral manifestations of these reported ...

Abstract	Motor cortical beta oscillations have been reported for decades, yet their behavioral correlates remain unresolved. Some studies link beta oscillations to changes in underlying neural activity, but the specific behavioral manifestations of these reported changes remain elusive. To investigate how changes in population neural activity, beta oscillations, and behavior are linked, we recorded multi-scale neural activity from motor cortex while three macaques performed a novel neurofeedback task. Subjects volitionally brought their beta oscillatory power to an instructed state and subsequently executed an arm reach. Reaches preceded by a reduction in beta power exhibited significantly faster movement onset times than reaches preceded by an increase in beta power. Further, population neural activity was found to shift farther from a movement onset state during beta oscillations that were neurofeedback-induced or naturally occurring during reaching tasks. This finding establishes a population neural basis for slowed movement onset following periods of beta oscillatory activity.
Language	English
Publishing date	2017-05-03
Publishing country	England
Document type	Journal Article
ZDB-ID	2687154-3
ISSN	2050-084X ; 2050-084X
ISSN (online)	2050-084X
ISSN	2050-084X
DOI	10.7554/eLife.24573
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Article ; Online: Neural reinforcement: re-entering and refining neural dynamics leading to desirable outcomes.

Athalye, Vivek R / Carmena, Jose M / Costa, Rui M

Current opinion in neurobiology

2019 Volume 60, Page(s) 145–154

Abstract: How do organisms learn to do again, on-demand, a behavior that led to a desirable outcome? Dopamine-dependent cortico-striatal plasticity provides a framework for learning behavior's value, but it is less clear how it enables the brain to re-enter ... ...

Abstract	How do organisms learn to do again, on-demand, a behavior that led to a desirable outcome? Dopamine-dependent cortico-striatal plasticity provides a framework for learning behavior's value, but it is less clear how it enables the brain to re-enter desired behaviors and refine them over time. Reinforcing behavior is achieved by re-entering and refining the neural patterns that produce it. We review studies using brain-machine interfaces which reveal that reinforcing cortical population activity requires cortico-basal ganglia circuits. Then, we propose a formal framework for how reinforcement in cortico-basal ganglia circuits acts on the neural dynamics of cortical populations. We propose two parallel mechanisms: i) fast reinforcement which selects the inputs that permit the re-entrance of the particular cortical population dynamics which naturally produced the desired behavior, and ii) slower reinforcement which leads to refinement of cortical population dynamics and more reliable production of neural trajectories driving skillful behavior on-demand.
MeSH term(s)	Basal Ganglia ; Corpus Striatum ; Dopamine ; Neural Pathways ; Reinforcement, Psychology
Chemical Substances	Dopamine (VTD58H1Z2X)
Language	English
Publishing date	2019-12-24
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Review
ZDB-ID	1078046-4
ISSN	1873-6882 ; 0959-4388
ISSN (online)	1873-6882
ISSN	0959-4388
DOI	10.1016/j.conb.2019.11.023
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Article ; Online: Hybrid dedicated and distributed coding in PMd/M1 provides separation and interaction of bilateral arm signals.

Dixon, Tanner C / Merrick, Christina M / Wallis, Joni D / Ivry, Richard B / Carmena, Jose M

PLoS computational biology

2021 Volume 17, Issue 11, Page(s) e1009615

Abstract: Pronounced activity is observed in both hemispheres of the motor cortex during preparation and execution of unimanual movements. The organizational principles of bi-hemispheric signals and the functions they serve throughout motor planning remain unclear. ...

Abstract	Pronounced activity is observed in both hemispheres of the motor cortex during preparation and execution of unimanual movements. The organizational principles of bi-hemispheric signals and the functions they serve throughout motor planning remain unclear. Using an instructed-delay reaching task in monkeys, we identified two components in population responses spanning PMd and M1. A "dedicated" component, which segregated activity at the level of individual units, emerged in PMd during preparation. It was most prominent following movement when M1 became strongly engaged, and principally involved the contralateral hemisphere. In contrast to recent reports, these dedicated signals solely accounted for divergence of arm-specific neural subspaces. The other "distributed" component mixed signals for each arm within units, and the subspace containing it did not discriminate between arms at any stage. The statistics of the population response suggest two functional aspects of the cortical network: one that spans both hemispheres for supporting preparatory and ongoing processes, and another that is predominantly housed in the contralateral hemisphere and specifies unilateral output.
MeSH term(s)	Animals ; Arm/physiology ; Macaca mulatta/physiology ; Motor Cortex/physiology ; Psychomotor Performance/physiology
Language	English
Publishing date	2021-11-22
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1009615
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