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Zippi EL, Shvartsman GF, Vendrell-Llopis N, Wallis JD, Carmena JM. Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum. Sci Rep 2023; 13:17810. [PMID: 37857827 PMCID: PMC10587077 DOI: 10.1038/s41598-023-44405-y] [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: 05/31/2023] [Accepted: 10/07/2023] [Indexed: 10/21/2023] Open
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.
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Affiliation(s)
- Ellen L Zippi
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Gabrielle F Shvartsman
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA
| | - Nuria Vendrell-Llopis
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA
| | - Joni D Wallis
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
| | - Jose M Carmena
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA.
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA.
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Zippi EL, Shvartsman GF, Vendrell-Llopis N, Wallis JD, Carmena JM. Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.31.542532. [PMID: 37398143 PMCID: PMC10312492 DOI: 10.1101/2023.05.31.542532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
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.
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Affiliation(s)
- Ellen L. Zippi
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA
| | - Gabrielle F. Shvartsman
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA
| | - Nuria Vendrell-Llopis
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA
| | - Joni D. Wallis
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA
- Department of Psychology, University of California, Berkeley, Berkeley, CA
| | - Jose M. Carmena
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA
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Modelling and prediction of the dynamic responses of large-scale brain networks during direct electrical stimulation. Nat Biomed Eng 2021; 5:324-345. [PMID: 33526909 DOI: 10.1038/s41551-020-00666-w] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 11/24/2020] [Indexed: 01/19/2023]
Abstract
Direct electrical stimulation can modulate the activity of brain networks for the treatment of several neurological and neuropsychiatric disorders and for restoring lost function. However, precise neuromodulation in an individual requires the accurate modelling and prediction of the effects of stimulation on the activity of their large-scale brain networks. Here, we report the development of dynamic input-output models that predict multiregional dynamics of brain networks in response to temporally varying patterns of ongoing microstimulation. In experiments with two awake rhesus macaques, we show that the activities of brain networks are modulated by changes in both stimulation amplitude and frequency, that they exhibit damping and oscillatory response dynamics, and that variabilities in prediction accuracy and in estimated response strength across brain regions can be explained by an at-rest functional connectivity measure computed without stimulation. Input-output models of brain dynamics may enable precise neuromodulation for the treatment of disease and facilitate the investigation of the functional organization of large-scale brain networks.
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Zoll RS, Schindler CB, Massey TL, Drew DS, Maharbiz MM, Pister KSJ. MEMS-Actuated Carbon Fiber Microelectrode for Neural Recording. IEEE Trans Nanobioscience 2019; 18:234-239. [PMID: 30892226 DOI: 10.1109/tnb.2019.2905505] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Microwire and microelectrode arrays used for cortical neural recording typically consist of tens to hundreds of recording sites, but often only a fraction of these sites are in close enough proximity to firing neurons to record single-unit activity. Recent work has demonstrated precise, depth-controllable mechanisms for the insertion of single neural recording electrodes, but these methods are mostly only capable of inserting electrodes which elicit an adverse biological response. We present an electrostatic-based actuator capable of inserting individual carbon fiber microelectrodes which elicit minimal to no adverse biological response. The device is shown to insert a carbon fiber recording electrode into an agar brain phantom and can record an artificial neural signal in saline. This technique provides a platform generalizable to many microwire-style recording electrodes.
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Cinel C, Valeriani D, Poli R. Neurotechnologies for Human Cognitive Augmentation: Current State of the Art and Future Prospects. Front Hum Neurosci 2019; 13:13. [PMID: 30766483 PMCID: PMC6365771 DOI: 10.3389/fnhum.2019.00013] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 01/10/2019] [Indexed: 01/10/2023] Open
Abstract
Recent advances in neuroscience have paved the way to innovative applications that cognitively augment and enhance humans in a variety of contexts. This paper aims at providing a snapshot of the current state of the art and a motivated forecast of the most likely developments in the next two decades. Firstly, we survey the main neuroscience technologies for both observing and influencing brain activity, which are necessary ingredients for human cognitive augmentation. We also compare and contrast such technologies, as their individual characteristics (e.g., spatio-temporal resolution, invasiveness, portability, energy requirements, and cost) influence their current and future role in human cognitive augmentation. Secondly, we chart the state of the art on neurotechnologies for human cognitive augmentation, keeping an eye both on the applications that already exist and those that are emerging or are likely to emerge in the next two decades. Particularly, we consider applications in the areas of communication, cognitive enhancement, memory, attention monitoring/enhancement, situation awareness and complex problem solving, and we look at what fraction of the population might benefit from such technologies and at the demands they impose in terms of user training. Thirdly, we briefly review the ethical issues associated with current neuroscience technologies. These are important because they may differentially influence both present and future research on (and adoption of) neurotechnologies for human cognitive augmentation: an inferior technology with no significant ethical issues may thrive while a superior technology causing widespread ethical concerns may end up being outlawed. Finally, based on the lessons learned in our analysis, using past trends and considering other related forecasts, we attempt to forecast the most likely future developments of neuroscience technology for human cognitive augmentation and provide informed recommendations for promising future research and exploitation avenues.
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Affiliation(s)
- Caterina Cinel
- Brain Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom
| | - Davide Valeriani
- Brain Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom
- Department of Otolaryngology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, United States
| | - Riccardo Poli
- Brain Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom
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