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Ahmed AA, Alegret N, Almeida B, Alvarez-Puebla R, Andrews AM, Ballerini L, Barrios-Capuchino JJ, Becker C, Blick RH, Bonakdar S, Chakraborty I, Chen X, Cheon J, Chilla G, Coelho Conceicao AL, Delehanty J, Dulle M, Efros AL, Epple M, Fedyk M, Feliu N, Feng M, Fernández-Chacón R, Fernandez-Cuesta I, Fertig N, Förster S, Garrido JA, George M, Guse AH, Hampp N, Harberts J, Han J, Heekeren HR, Hofmann UG, Holzapfel M, Hosseinkazemi H, Huang Y, Huber P, Hyeon T, Ingebrandt S, Ienca M, Iske A, Kang Y, Kasieczka G, Kim DH, Kostarelos K, Lee JH, Lin KW, Liu S, Liu X, Liu Y, Lohr C, Mailänder V, Maffongelli L, Megahed S, Mews A, Mutas M, Nack L, Nakatsuka N, Oertner TG, Offenhäusser A, Oheim M, Otange B, Otto F, Patrono E, Peng B, Picchiotti A, Pierini F, Pötter-Nerger M, Pozzi M, Pralle A, Prato M, Qi B, Ramos-Cabrer P, Genger UR, Ritter N, Rittner M, Roy S, Santoro F, Schuck NW, Schulz F, Şeker E, Skiba M, Sosniok M, Stephan H, Wang R, Wang T, Wegner KD, Weiss PS, Xu M, Yang C, Zargarian SS, Zeng Y, Zhou Y, Zhu D, Zierold R, Parak WJ. Interfacing with the Brain: How Nanotechnology Can Contribute. ACS NANO 2025; 19:10630-10717. [PMID: 40063703 PMCID: PMC11948619 DOI: 10.1021/acsnano.4c10525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2024] [Revised: 12/19/2024] [Accepted: 12/24/2024] [Indexed: 03/26/2025]
Abstract
Interfacing artificial devices with the human brain is the central goal of neurotechnology. Yet, our imaginations are often limited by currently available paradigms and technologies. Suggestions for brain-machine interfaces have changed over time, along with the available technology. Mechanical levers and cable winches were used to move parts of the brain during the mechanical age. Sophisticated electronic wiring and remote control have arisen during the electronic age, ultimately leading to plug-and-play computer interfaces. Nonetheless, our brains are so complex that these visions, until recently, largely remained unreachable dreams. The general problem, thus far, is that most of our technology is mechanically and/or electrically engineered, whereas the brain is a living, dynamic entity. As a result, these worlds are difficult to interface with one another. Nanotechnology, which encompasses engineered solid-state objects and integrated circuits, excels at small length scales of single to a few hundred nanometers and, thus, matches the sizes of biomolecules, biomolecular assemblies, and parts of cells. Consequently, we envision nanomaterials and nanotools as opportunities to interface with the brain in alternative ways. Here, we review the existing literature on the use of nanotechnology in brain-machine interfaces and look forward in discussing perspectives and limitations based on the authors' expertise across a range of complementary disciplines─from neuroscience, engineering, physics, and chemistry to biology and medicine, computer science and mathematics, and social science and jurisprudence. We focus on nanotechnology but also include information from related fields when useful and complementary.
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Affiliation(s)
- Abdullah
A. A. Ahmed
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
- Department
of Physics, Faculty of Applied Science, Thamar University, Dhamar 87246, Yemen
| | - Nuria Alegret
- Biogipuzkoa
HRI, Paseo Dr. Begiristain
s/n, 20014 Donostia-San
Sebastián, Spain
- Basque
Foundation for Science, Ikerbasque, 48013 Bilbao, Spain
| | - Bethany Almeida
- Department
of Chemical and Biomolecular Engineering, Clarkson University, Potsdam, New York 13699, United States
| | - Ramón Alvarez-Puebla
- Universitat
Rovira i Virgili, 43007 Tarragona, Spain
- ICREA, 08010 Barcelona, Spain
| | - Anne M. Andrews
- Department
of Chemistry and Biochemistry, University
of California, Los Angeles, Los
Angeles, California 90095, United States
- Neuroscience
Interdepartmental Program, University of
California, Los Angeles, Los Angeles, California 90095, United States
- Department
of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience
& Human Behavior, and Hatos Center for Neuropharmacology, University of California, Los Angeles, Los Angeles, California 90095, United States
- California
Nanosystems Institute, University of California,
Los Angeles, Los Angeles, California 90095, United States
| | - Laura Ballerini
- Neuroscience
Area, International School for Advanced
Studies (SISSA/ISAS), Trieste 34136, Italy
| | | | - Charline Becker
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
| | - Robert H. Blick
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
| | - Shahin Bonakdar
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
- National
Cell Bank Department, Pasteur Institute
of Iran, P.O. Box 1316943551, Tehran, Iran
| | - Indranath Chakraborty
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
- School
of Nano Science and Technology, Indian Institute
of Technology Kharagpur, Kharagpur 721302, India
| | - Xiaodong Chen
- Innovative
Center for Flexible Devices (iFLEX), Max Planck − NTU Joint
Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Jinwoo Cheon
- Institute
for Basic Science Center for Nanomedicine, Seodaemun-gu, Seoul 03722, Korea
- Advanced
Science Institute, Yonsei University, Seodaemun-gu, Seoul 03722, Korea
- Department
of Chemistry, Yonsei University, Seodaemun-gu, Seoul 03722, Korea
| | - Gerwin Chilla
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
| | | | - James Delehanty
- U.S. Naval
Research Laboratory, Washington, D.C. 20375, United States
| | - Martin Dulle
- JCNS-1, Forschungszentrum
Jülich, 52428 Jülich, Germany
| | | | - Matthias Epple
- Inorganic
Chemistry and Center for Nanointegration Duisburg-Essen (CeNIDE), University of Duisburg-Essen, 45117 Essen, Germany
| | - Mark Fedyk
- Center
for Neuroengineering and Medicine, UC Davis, Sacramento, California 95817, United States
| | - Neus Feliu
- Zentrum
für Angewandte Nanotechnologie CAN, Fraunhofer-Institut für Angewandte Polymerforschung IAP, 20146 Hamburg, Germany
| | - Miao Feng
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
| | - Rafael Fernández-Chacón
- Instituto
de Biomedicina de Sevilla (IBiS), Hospital
Universitario Virgen del Rocío/Consejo Superior de Investigaciones
Científicas/Universidad de Sevilla, 41013 Seville, Spain
- Departamento
de Fisiología Médica y Biofísica, Facultad de
Medicina, Universidad de Sevilla, CIBERNED,
ISCIII, 41013 Seville, Spain
| | | | - Niels Fertig
- Nanion
Technologies GmbH, 80339 München, Germany
| | | | - Jose A. Garrido
- ICREA, 08010 Barcelona, Spain
- Catalan
Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, 08193 Bellaterra, Spain
| | | | - Andreas H. Guse
- The Calcium
Signaling Group, Department of Biochemistry and Molecular Cell Biology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Norbert Hampp
- Fachbereich
Chemie, Universität Marburg, 35032 Marburg, Germany
| | - Jann Harberts
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
- Drug Delivery,
Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia
- Melbourne
Centre for Nanofabrication, Victorian Node
of the Australian National Fabrication Facility, Clayton, Victoria 3168, Australia
| | - Jili Han
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
| | - Hauke R. Heekeren
- Executive
University Board, Universität Hamburg, 20148 Hamburg Germany
| | - Ulrich G. Hofmann
- Section
for Neuroelectronic Systems, Department for Neurosurgery, University Medical Center Freiburg, 79108 Freiburg, Germany
- Faculty
of Medicine, University of Freiburg, 79110 Freiburg, Germany
| | - Malte Holzapfel
- Zentrum
für Angewandte Nanotechnologie CAN, Fraunhofer-Institut für Angewandte Polymerforschung IAP, 20146 Hamburg, Germany
| | | | - Yalan Huang
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
| | - Patrick Huber
- Institute
for Materials and X-ray Physics, Hamburg
University of Technology, 21073 Hamburg, Germany
- Center
for X-ray and Nano Science CXNS, Deutsches
Elektronen-Synchrotron DESY, 22607 Hamburg, Germany
| | - Taeghwan Hyeon
- Center
for Nanoparticle Research, Institute for Basic Science (IBS), Seoul 08826, Republic of Korea
- School
of Chemical and Biological Engineering, and Institute of Chemical
Processes, Seoul National University, Seoul 08826, Republic of Korea
| | - Sven Ingebrandt
- Institute
of Materials in Electrical Engineering 1, RWTH Aachen University, 52074 Aachen, Germany
| | - Marcello Ienca
- Institute
for Ethics and History of Medicine, School of Medicine and Health, Technische Universität München (TUM), 81675 München, Germany
| | - Armin Iske
- Fachbereich
Mathematik, Universität Hamburg, 20146 Hamburg, Germany
| | - Yanan Kang
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
| | | | - Dae-Hyeong Kim
- Center
for Nanoparticle Research, Institute for Basic Science (IBS), Seoul 08826, Republic of Korea
- School
of Chemical and Biological Engineering, and Institute of Chemical
Processes, Seoul National University, Seoul 08826, Republic of Korea
| | - Kostas Kostarelos
- Catalan
Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, 08193 Bellaterra, Spain
- Centre
for Nanotechnology in Medicine, Faculty of Biology, Medicine &
Health and The National Graphene Institute, University of Manchester, Manchester M13 9PL, United
Kingdom
| | - Jae-Hyun Lee
- Institute
for Basic Science Center for Nanomedicine, Seodaemun-gu, Seoul 03722, Korea
- Advanced
Science Institute, Yonsei University, Seodaemun-gu, Seoul 03722, Korea
| | - Kai-Wei Lin
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
| | - Sijin Liu
- State Key
Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese
Academy of Sciences, Beijing 100085, China
- University
of the Chinese Academy of Sciences, Beijing 100049, China
| | - Xin Liu
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
| | - Yang Liu
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
| | - Christian Lohr
- Fachbereich
Biologie, Universität Hamburg, 20146 Hamburg, Germany
| | - Volker Mailänder
- Department
of Dermatology, Center for Translational Nanomedicine, Universitätsmedizin der Johannes-Gutenberg,
Universität Mainz, 55131 Mainz, Germany
- Max Planck
Institute for Polymer Research, Ackermannweg 10, 55129 Mainz, Germany
| | - Laura Maffongelli
- Institute
of Medical Psychology, University of Lübeck, 23562 Lübeck, Germany
| | - Saad Megahed
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
- Physics
Department, Faculty of Science, Al-Azhar
University, 4434104 Cairo, Egypt
| | - Alf Mews
- Fachbereich
Chemie, Universität Hamburg, 20146 Hamburg, Germany
| | - Marina Mutas
- Zentrum
für Angewandte Nanotechnologie CAN, Fraunhofer-Institut für Angewandte Polymerforschung IAP, 20146 Hamburg, Germany
| | - Leroy Nack
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
| | - Nako Nakatsuka
- Laboratory
of Chemical Nanotechnology (CHEMINA), Neuro-X
Institute, École Polytechnique Fédérale de Lausanne
(EPFL), Geneva CH-1202, Switzerland
| | - Thomas G. Oertner
- Institute
for Synaptic Neuroscience, University Medical
Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Andreas Offenhäusser
- Institute
of Biological Information Processing - Bioelectronics, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Martin Oheim
- Université
Paris Cité, CNRS, Saints Pères
Paris Institute for the Neurosciences, 75006 Paris, France
| | - Ben Otange
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
| | - Ferdinand Otto
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
| | - Enrico Patrono
- Institute
of Physiology, Czech Academy of Sciences, Prague 12000, Czech Republic
| | - Bo Peng
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
| | | | - Filippo Pierini
- Department
of Biosystems and Soft Matter, Institute
of Fundamental Technological Research, Polish Academy of Sciences, 02-106 Warsaw, Poland
| | - Monika Pötter-Nerger
- Head and
Neurocenter, Department of Neurology, University
Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Maria Pozzi
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
| | - Arnd Pralle
- University
at Buffalo, Department of Physics, Buffalo, New York 14260, United States
| | - Maurizio Prato
- CIC biomaGUNE, Basque Research and Technology
Alliance (BRTA), 20014 Donostia-San
Sebastián, Spain
- Department
of Chemical and Pharmaceutical Sciences, University of Trieste, 34127 Trieste, Italy
- Basque
Foundation for Science, Ikerbasque, 48013 Bilbao, Spain
| | - Bing Qi
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
- School
of Life Sciences, Southern University of
Science and Technology, Shenzhen, 518055, China
| | - Pedro Ramos-Cabrer
- CIC biomaGUNE, Basque Research and Technology
Alliance (BRTA), 20014 Donostia-San
Sebastián, Spain
- Basque
Foundation for Science, Ikerbasque, 48013 Bilbao, Spain
| | - Ute Resch Genger
- Division
Biophotonics, Federal Institute for Materials Research and Testing
(BAM), 12489 Berlin, Germany
| | - Norbert Ritter
- Executive
Faculty Board, Faculty for Mathematics, Informatics and Natural Sciences, Universität Hamburg, 20345 Hamburg, Germany
| | - Marten Rittner
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
| | - Sathi Roy
- Zentrum
für Angewandte Nanotechnologie CAN, Fraunhofer-Institut für Angewandte Polymerforschung IAP, 20146 Hamburg, Germany
- Department
of Mechanical Engineering, Indian Institute
of Technology Kharagpur, Kharagpur 721302, India
| | - Francesca Santoro
- Institute
of Biological Information Processing - Bioelectronics, Forschungszentrum Jülich, 52425 Jülich, Germany
- Faculty
of Electrical Engineering and Information Technology, RWTH Aachen, 52074 Aachen, Germany
| | - Nicolas W. Schuck
- Institute
of Psychology, Universität Hamburg, 20146 Hamburg, Germany
- Max Planck
Research Group NeuroCode, Max Planck Institute
for Human Development, 14195 Berlin, Germany
- Max Planck
UCL Centre for Computational Psychiatry and Ageing Research, 14195 Berlin, Germany
| | - Florian Schulz
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
| | - Erkin Şeker
- University
of California, Davis, Davis, California 95616, United States
| | - Marvin Skiba
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
| | - Martin Sosniok
- Zentrum
für Angewandte Nanotechnologie CAN, Fraunhofer-Institut für Angewandte Polymerforschung IAP, 20146 Hamburg, Germany
| | - Holger Stephan
- Helmholtz-Zentrum
Dresden-Rossendorf, Institute of Radiopharmaceutical
Cancer Research, 01328 Dresden, Germany
| | - Ruixia Wang
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
- Deutsches
Elektronen-Synchrotron DESY, 22607 Hamburg, Germany
| | - Ting Wang
- State Key
Laboratory of Organic Electronics and Information Displays & Jiangsu
Key Laboratory for Biosensors, Institute of Advanced Materials (IAM),
Jiangsu National Synergetic Innovation Center for Advanced Materials
(SICAM), Nanjing University of Posts and
Telecommunications, Nanjing 210023, China
| | - K. David Wegner
- Division
Biophotonics, Federal Institute for Materials Research and Testing
(BAM), 12489 Berlin, Germany
| | - Paul S. Weiss
- Department
of Chemistry and Biochemistry, University
of California, Los Angeles, Los
Angeles, California 90095, United States
- California
Nanosystems Institute, University of California,
Los Angeles, Los Angeles, California 90095, United States
- Department
of Bioengineering, University of California,
Los Angeles, Los Angeles, California 90095, United States
- Department
of Materials Science and Engineering, University
of California, Los Angeles, Los
Angeles, California 90095, United States
| | - Ming Xu
- State Key
Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese
Academy of Sciences, Beijing 100085, China
- University
of the Chinese Academy of Sciences, Beijing 100049, China
| | - Chenxi Yang
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
| | - Seyed Shahrooz Zargarian
- Department
of Biosystems and Soft Matter, Institute
of Fundamental Technological Research, Polish Academy of Sciences, 02-106 Warsaw, Poland
| | - Yuan Zeng
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
| | - Yaofeng Zhou
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
| | - Dingcheng Zhu
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
- College
of Material, Chemistry and Chemical Engineering, Key Laboratory of
Organosilicon Chemistry and Material Technology, Ministry of Education,
Key Laboratory of Organosilicon Material Technology, Hangzhou Normal University, Hangzhou 311121, China
| | - Robert Zierold
- Fachbereich
Physik, Universität Hamburg, 22761 Hamburg, Germany
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2
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Feulner B, Perich MG, Miller LE, Clopath C, Gallego JA. A neural implementation model of feedback-based motor learning. Nat Commun 2025; 16:1805. [PMID: 39979257 PMCID: PMC11842561 DOI: 10.1038/s41467-024-54738-5] [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: 02/21/2023] [Accepted: 11/18/2024] [Indexed: 02/22/2025] Open
Abstract
Animals use feedback to rapidly correct ongoing movements in the presence of a perturbation. Repeated exposure to a predictable perturbation leads to behavioural adaptation that compensates for its effects. Here, we tested the hypothesis that all the processes necessary for motor adaptation may emerge as properties of a controller that adaptively updates its policy. We trained a recurrent neural network to control its own output through an error-based feedback signal, which allowed it to rapidly counteract external perturbations. Implementing a biologically plausible plasticity rule based on this same feedback signal enabled the network to learn to compensate for persistent perturbations through a trial-by-trial process. The network activity changes during learning matched those from populations of neurons from monkey primary motor cortex - known to mediate both movement correction and motor adaptation - during the same task. Furthermore, our model natively reproduced several key aspects of behavioural studies in humans and monkeys. Thus, key features of trial-by-trial motor adaptation can arise from the internal properties of a recurrent neural circuit that adaptively controls its output based on ongoing feedback.
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Affiliation(s)
- Barbara Feulner
- Department of Bioengineering, Imperial College London, London, UK
| | - Matthew G Perich
- Département de neurosciences, Faculté de médecine, Université de Montréal, Montréal, QC, Canada
- Mila (Quebec Artificial Intelligence Institute), Montréal, QC, Canada
| | - Lee E Miller
- Department of Neuroscience, Northwestern University, Chicago, IL, USA
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University, and Shirley Ryan Ability Lab, Chicago, IL, USA
| | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, UK.
| | - Juan A Gallego
- Department of Bioengineering, Imperial College London, London, UK.
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3
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Magalhães SS, Lucas-Ochoa AM, Gonzalez-Cuello AM, Fernández-Villalba E, Pereira Toralles MB, Herrero MT. The mind-machine connection: adaptive information processing and new technologies promoting mental health in older adults. Neuroscientist 2025:10738584251318948. [PMID: 39969013 DOI: 10.1177/10738584251318948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2025]
Abstract
The human brain demonstrates an exceptional adaptability, which encompasses the ability to regulate emotions, exhibit cognitive flexibility, and generate behavioral responses, all supported by neuroplasticity. Brain-computer interfaces (BCIs) employ adaptive algorithms and machine learning techniques to adapt to variations in the user's brain activity, allowing for customized interactions with external devices. Older adults may experience cognitive decline, which could affect the ability to learn and adapt to new technologies such as BCIs, but both (human brain and BCI) demonstrate adaptability in their responses. The human brain is skilled at quickly switching between tasks and regulating emotions, while BCIs can modify signal-processing algorithms to accommodate changes in brain activity. Furthermore, the human brain and BCI participate in knowledge acquisition; the first one strengthens cognitive abilities through exposure to new experiences, and the second one improves performance through ongoing adjustment and improvement. Current research seeks to incorporate emotional states into BCI systems to improve the user experience, despite the exceptional emotional regulation abilities of the human brain. The implementation of BCIs for older adults could be more effective, inclusive, and beneficial in improving their quality of life. This review aims to improve the understanding of brain-machine interfaces and their implications for mental health in older adults.
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Affiliation(s)
- S S Magalhães
- Clinical and Experimental Neuroscience (NiCE-IMIB Pascual Parilla), Institute for Aging Research, School of Medicine, University of Murcia, Murcia, Spain
- Institute of Health Sciences, Postgraduate Program in Interactive Processes of Organs and Systems, Federal University of Bahia (UFBA) of Brazil, Salvador, Brazil
| | - A M Lucas-Ochoa
- Clinical and Experimental Neuroscience (NiCE-IMIB Pascual Parilla), Institute for Aging Research, School of Medicine, University of Murcia, Murcia, Spain
| | - A M Gonzalez-Cuello
- Clinical and Experimental Neuroscience (NiCE-IMIB Pascual Parilla), Institute for Aging Research, School of Medicine, University of Murcia, Murcia, Spain
| | - E Fernández-Villalba
- Clinical and Experimental Neuroscience (NiCE-IMIB Pascual Parilla), Institute for Aging Research, School of Medicine, University of Murcia, Murcia, Spain
| | - M B Pereira Toralles
- Institute of Health Sciences, Postgraduate Program in Interactive Processes of Organs and Systems, Federal University of Bahia (UFBA) of Brazil, Salvador, Brazil
| | - M T Herrero
- Clinical and Experimental Neuroscience (NiCE-IMIB Pascual Parilla), Institute for Aging Research, School of Medicine, University of Murcia, Murcia, Spain
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4
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Derosiere G, Shokur S, Vassiliadis P. Reward signals in the motor cortex: from biology to neurotechnology. Nat Commun 2025; 16:1307. [PMID: 39900901 PMCID: PMC11791067 DOI: 10.1038/s41467-024-55016-0] [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: 08/13/2024] [Accepted: 11/25/2024] [Indexed: 02/05/2025] Open
Abstract
Over the past decade, research has shown that the primary motor cortex (M1), the brain's main output for movement, also responds to rewards. These reward signals may shape motor output in its final stages, influencing movement invigoration and motor learning. In this Perspective, we highlight the functional roles of M1 reward signals and propose how they could guide advances in neurotechnologies for movement restoration, specifically brain-computer interfaces and non-invasive brain stimulation. Understanding M1 reward signals may open new avenues for enhancing motor control and rehabilitation.
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Affiliation(s)
- Gerard Derosiere
- Lyon Neuroscience Research Center, Impact team, INSERM U1028 - CNRS UMR5292, Lyon 1 University, Bron, France.
| | - Solaiman Shokur
- Translational Neural Engineering Laboratory, Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Sensorimotor Neurotechnology Lab (SNL), The BioRobotics Institute, Health Interdisciplinary Center and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
- MySpace Lab, Department of Clinical Neurosciences, University Hospital of Lausanne, University of Lausanne, Lausanne, Switzerland
- MINE Lab, Università Vita-Salute San Raffaele, Milano, Italy
| | - Pierre Vassiliadis
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland.
- Defitech Chair of Clinical Neuroengineering, INX, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland.
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5
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Jalalvandi M, Sharini H, Shafaghi L, Alam NR. Deciphering brain activation during wrist movements: comparative fMRI and fNIRS analysis of active, passive, and imagery states. Exp Brain Res 2024; 243:36. [PMID: 39739121 DOI: 10.1007/s00221-024-06977-7] [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: 08/11/2024] [Accepted: 12/06/2024] [Indexed: 01/02/2025]
Abstract
Understanding the complex activation patterns of brain regions during motor tasks is crucial. Integrated functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS) offers advanced insights into how brain activity fluctuates with motor activities. This study explores neuronal activation patterns in the cerebral cortex during active, passive, and imagined wrist movements using these functional imaging techniques. Data were collected from 10 right-handed volunteers performing a motor task using fMRI and fNIRS. fMRI utilized a 3T scanner and a 20-channel head coil, while fNIRS recorded data with a 48-channel device at 765 nm and 855 nm. Analysis focused on key motor and sensory cortices using NIRS-SPM and SPM12, applying a significance threshold of p < 0.05 and a minimum cluster size of 10 voxels for group analysis. Super-threshold voxels were identified with FWE thresholding in SPM12. For activation map extraction we focused on the primary motor cortex, primary somatosensory cortex, somatosensory association cortex, premotor cortex, and supplementary motor cortex. Both fMRI and fNIRS detected activation in the primary motor cortex (M1). The primary somatosensory cortex was found to influence movement direction coding, with smaller activation sizes for upward movements. Combining fNIRS with fMRI provided clearer differentiation of brain activation patterns for wrist movements in various directions and conditions (p < 0.05). This study highlights variations in left motor cortex activity across different movement states. fNIRS proved effective in detecting brain function and showed strong correlation with fMRI results, suggesting it as a viable alternative for those unable to undergo fMRI.
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Affiliation(s)
- Maziar Jalalvandi
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamid Sharini
- Department of Biomedical Engineering, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Lida Shafaghi
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Nader Riyahi Alam
- Medical Physics and Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran.
- Concordia University, PERFORM Preventive Medicine and Personal Health Care Center, Montreal, Quebec, Canada.
- PERFORM Center, Concordia University, 3 Rue Harbridge, Dollard Des Ormeaux (D.D.O.), Montreal, Quebec, Canada.
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6
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Qu J. A dance movement quality evaluation model using transformer encoder and convolutional neural network. Sci Rep 2024; 14:32058. [PMID: 39738658 PMCID: PMC11685562 DOI: 10.1038/s41598-024-83608-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Accepted: 12/16/2024] [Indexed: 01/02/2025] Open
Abstract
The purpose of this study is to put forward a new evaluation model of dance movement quality to deal with the subjectivity and inconsistency in traditional evaluation methods. In view of the complexity and diversity of dance art and the widespread popularity of dance videos on social media, it is particularly urgent to develop an automatic and efficient tool for evaluating the quality of dance movements. Therefore, this study puts forward the Transformer Convolutional Neural Network with Dynamic and Static Streams (TransCNN-DSSS) model, which combines the analysis of dynamic flow and static flow, and makes use of the advantages of Transformer and Convolutional Neural Network (CNN) to deeply analyze and evaluate the dance movements. The core of the model is Quality Score Decoupling (QSD), which decouples and weights different quality dimensions of dance movements through attention mechanism, such as accuracy, fluency and expressiveness. Score Prediction module (SPM) uses Transformer network to further process the fused features, and outputs the final evaluation score through the full connection layer. In the experimental part, the TransCNN-DSSS model is trained and tested on the marked dance movement dataset. The performance of the model is evaluated by accuracy, recall and F1 score. The results show that the model has achieved 90% accuracy, 89% recall and F1 score of 0.90 in the task of evaluating the quality of dance movements. These results prove the effectiveness and reliability of the model. In addition, the adaptability test of the model in different dance styles also shows good generalization ability. The research contribution of this study is to put forward a new evaluation model of dance movement quality, which provides an objective and automatic evaluation tool for dance teaching, competition scoring and fans.
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Affiliation(s)
- Jiping Qu
- School of Literature, Law and Art, East China University of Technology, Nanchang, 330013, China.
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7
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Brannigan J, McClanahan A, Hui F, Fargen KM, Pinter N, Oxley TJ. Superior cortical venous anatomy for endovascular device implantation: a systematic review. J Neurointerv Surg 2024; 16:1353-1359. [PMID: 38538056 DOI: 10.1136/jnis-2023-021434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 03/06/2024] [Indexed: 11/24/2024]
Abstract
Endovascular electrode arrays provide a minimally invasive approach to access intracranial structures for neural recording and stimulation. These arrays are currently used as brain-computer interfaces (BCIs) and are deployed within the superior sagittal sinus (SSS), although cortical vein implantation could improve the quality and quantity of recorded signals. However, the anatomy of the superior cortical veins is heterogenous and poorly characterised. MEDLINE and Embase databases were systematically searched from inception to December 15, 2023 for studies describing the anatomy of the superior cortical veins. A total of 28 studies were included: 19 cross-sectional imaging studies, six cadaveric studies, one intraoperative anatomical study and one review. There was substantial variability in cortical vein diameter, length, confluence angle, and location relative to the underlying cortex. The mean number of SSS branches ranged from 11 to 45. The vein of Trolard was most often reported as the largest superior cortical vein, with a mean diameter ranging from 2.1 mm to 3.3 mm. The mean vein of Trolard was identified posterior to the central sulcus. One study found a significant age-related variability in cortical vein diameter and another identified myoendothelial sphincters at the base of the cortical veins. Cortical vein anatomical data are limited and inconsistent. The vein of Trolard is the largest tributary vein of the SSS; however, its relation to the underlying cortex is variable. Variability in cortical vein anatomy may necessitate individualized pre-procedural planning of training and neural decoding in endovascular BCI. Future focus on the relation to the underlying cortex, sulcal vessels, and vessel wall anatomy is required.
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Affiliation(s)
- Jamie Brannigan
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Alexander McClanahan
- Department of Radiology, University of Arkansas System, Little Rock, Arkansas, USA
| | - Ferdinand Hui
- Neuroscience Institute, Queen's Medical Center Hale Pūlama Mau, Hawaii, Hawaii, USA
| | - Kyle M Fargen
- Neurological Surgery and Radiology, Wake Forest University, Winston-Salem, North Carolina, USA
| | - Nandor Pinter
- Department of Neurosurgery, University at Buffalo, Buffalo, New York, USA
| | - Thomas J Oxley
- Vascular Bionics Laboratory, Departments of Medicine, Neurology and Surgery, Melbourne Brain Centre at the Royal Melbourne Hospital, University of Melbourne, Parkville, Victoria, Australia
- Department of Neurosurgery, Mount Sinai Health System, New York, New York, USA
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8
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Vitória MA, Fernandes FG, van den Boom M, Ramsey N, Raemaekers M. Decoding Single and Paired Phonemes Using 7T Functional MRI. Brain Topogr 2024; 37:731-747. [PMID: 38261272 PMCID: PMC11393141 DOI: 10.1007/s10548-024-01034-6] [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: 07/24/2023] [Accepted: 01/12/2024] [Indexed: 01/24/2024]
Abstract
Several studies have shown that mouth movements related to the pronunciation of individual phonemes are represented in the sensorimotor cortex. This would theoretically allow for brain computer interfaces that are capable of decoding continuous speech by training classifiers based on the activity in the sensorimotor cortex related to the production of individual phonemes. To address this, we investigated the decodability of trials with individual and paired phonemes (pronounced consecutively with one second interval) using activity in the sensorimotor cortex. Fifteen participants pronounced 3 different phonemes and 3 combinations of two of the same phonemes in a 7T functional MRI experiment. We confirmed that support vector machine (SVM) classification of single and paired phonemes was possible. Importantly, by combining classifiers trained on single phonemes, we were able to classify paired phonemes with an accuracy of 53% (33% chance level), demonstrating that activity of isolated phonemes is present and distinguishable in combined phonemes. A SVM searchlight analysis showed that the phoneme representations are widely distributed in the ventral sensorimotor cortex. These findings provide insights about the neural representations of single and paired phonemes. Furthermore, it supports the notion that speech BCI may be feasible based on machine learning algorithms trained on individual phonemes using intracranial electrode grids.
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Affiliation(s)
- Maria Araújo Vitória
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Francisco Guerreiro Fernandes
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Max van den Boom
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Nick Ramsey
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Mathijs Raemaekers
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands.
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9
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Bashford L, Rosenthal IA, Kellis S, Bjånes D, Pejsa K, Brunton BW, Andersen RA. Neural subspaces of imagined movements in parietal cortex remain stable over several years in humans. J Neural Eng 2024; 21:046059. [PMID: 39134021 PMCID: PMC11350602 DOI: 10.1088/1741-2552/ad6e19] [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: 01/19/2024] [Revised: 07/15/2024] [Accepted: 08/12/2024] [Indexed: 08/21/2024]
Abstract
Objective.A crucial goal in brain-machine interfacing is the long-term stability of neural decoding performance, ideally without regular retraining. Long-term stability has only been previously demonstrated in non-human primate experiments and only in primary sensorimotor cortices. Here we extend previous methods to determine long-term stability in humans by identifying and aligning low-dimensional structures in neural data.Approach.Over a period of 1106 and 871 d respectively, two participants completed an imagined center-out reaching task. The longitudinal accuracy between all day pairs was assessed by latent subspace alignment using principal components analysis and canonical correlations analysis of multi-unit intracortical recordings in different brain regions (Brodmann Area 5, Anterior Intraparietal Area and the junction of the postcentral and intraparietal sulcus).Main results.We show the long-term stable representation of neural activity in subspaces of intracortical recordings from higher-order association areas in humans.Significance.These results can be practically applied to significantly expand the longevity and generalizability of brain-computer interfaces.Clinical TrialsNCT01849822, NCT01958086, NCT01964261.
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Affiliation(s)
- L Bashford
- Division of Biology and Biological Engineering, and T&C Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, CA, United States of America
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - I A Rosenthal
- Division of Biology and Biological Engineering, and T&C Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, CA, United States of America
| | - S Kellis
- Division of Biology and Biological Engineering, and T&C Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, CA, United States of America
| | - D Bjånes
- Division of Biology and Biological Engineering, and T&C Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, CA, United States of America
| | - K Pejsa
- Division of Biology and Biological Engineering, and T&C Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, CA, United States of America
| | - B W Brunton
- Department of Biology, University of Washington, Seattle, WA, United States of America
| | - R A Andersen
- Division of Biology and Biological Engineering, and T&C Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, CA, United States of America
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10
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Panda R, Deluisi JA, Lee TG, Davis S, Muñoz-Orozco I, Albin RL, Vesia M. Improving efficacy of repetitive transcranial magnetic stimulation for treatment of Parkinson disease gait disorders. Front Hum Neurosci 2024; 18:1445595. [PMID: 39253068 PMCID: PMC11381384 DOI: 10.3389/fnhum.2024.1445595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Accepted: 08/07/2024] [Indexed: 09/11/2024] Open
Abstract
Parkinson disease (PD) is a neurodegenerative disorder that causes motor and cognitive deficits, presenting complex challenges for therapeutic interventions. Repetitive transcranial magnetic stimulation (rTMS) is a type of neuromodulation that can produce plastic changes in neural activity. rTMS has been trialed as a therapy to treat motor and non-motor symptoms in persons with Parkinson disease (PwP), particularly treatment-refractory postural instability and gait difficulties such as Freezing of Gait (FoG), but clinical outcomes have been variable. We suggest improving rTMS neuromodulation therapy for balance and gait abnormalities in PwP by targeting brain regions in cognitive-motor control networks. rTMS studies in PwP often targeted motor targets such as the primary motor cortex (M1) or supplementary motor area (SMA), overlooking network interactions involved in posture-gait control disorders. We propose a shift in focus toward alternative stimulation targets in basal ganglia-cortex-cerebellum networks involved in posture-gait control, emphasizing the dorsolateral prefrontal cortex (dlPFC), cerebellum (CB), and posterior parietal cortex (PPC) as potential targets. rTMS might also be more effective if administered during behavioral tasks designed to activate posture-gait control networks during stimulation. Optimizing stimulation parameters such as dosage and frequency as used clinically for the treatment of depression may also be useful. A network-level perspective suggests new directions for exploring optimal rTMS targets and parameters to maximize neural plasticity to treat postural instabilities and gait difficulties in PwP.
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Affiliation(s)
- Rupsha Panda
- Department of Psychology, University of Michigan, Ann Arbor, MI, United States
| | - Joseph A. Deluisi
- School of Kinesiology, University of Michigan, Ann Arbor, MI, United States
| | - Taraz G. Lee
- Department of Psychology, University of Michigan, Ann Arbor, MI, United States
| | - Sheeba Davis
- School of Kinesiology, University of Michigan, Ann Arbor, MI, United States
| | | | - Roger L. Albin
- Department of Neurology, University of Michigan, Ann Arbor, MI, United States
- Neurology Service & GRECC, VAAAHS, Ann Arbor, MI, United States
| | - Michael Vesia
- School of Kinesiology, University of Michigan, Ann Arbor, MI, United States
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11
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Luo Y, Mu W, Wang L, Wang J, Wang P, Gan Z, Zhang L, Kang X. An EEG channel selection method for motor imagery based on Fisher score and local optimization. J Neural Eng 2024; 21:036030. [PMID: 38842111 DOI: 10.1088/1741-2552/ad504a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 05/24/2024] [Indexed: 06/07/2024]
Abstract
Objective. Multi-channel electroencephalogram (EEG) technology in brain-computer interface (BCI) research offers the advantage of enhanced spatial resolution and system performance. However, this also implies that more time is needed in the data processing stage, which is not conducive to the rapid response of BCI. Hence, it is a necessary and challenging task to reduce the number of EEG channels while maintaining decoding effectiveness.Approach. In this paper, we propose a local optimization method based on the Fisher score for within-subject EEG channel selection. Initially, we extract the common spatial pattern characteristics of EEG signals in different bands, calculate Fisher scores for each channel based on these characteristics, and rank them accordingly. Subsequently, we employ a local optimization method to finalize the channel selection.Main results. On the BCI Competition IV Dataset IIa, our method selects an average of 11 channels across four bands, achieving an average accuracy of 79.37%. This represents a 6.52% improvement compared to using the full set of 22 channels. On our self-collected dataset, our method similarly achieves a significant improvement of 24.20% with less than half of the channels, resulting in an average accuracy of 76.95%.Significance. This research explores the importance of channel combinations in channel selection tasks and reveals that appropriately combining channels can further enhance the quality of channel selection. The results indicate that the model selected a small number of channels with higher accuracy in two-class motor imagery EEG classification tasks. Additionally, it improves the portability of BCI systems through channel selection and combinations, offering the potential for the development of portable BCI systems.
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Affiliation(s)
- Yangjie Luo
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
| | - Wei Mu
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
| | - Lu Wang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
| | - Junkongshuai Wang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
| | - Pengchao Wang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
| | - Zhongxue Gan
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
- Ji Hua Laboratory, Foshan, People's Republic of China
| | - Lihua Zhang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
- Ji Hua Laboratory, Foshan, People's Republic of China
| | - Xiaoyang Kang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
- Ji Hua Laboratory, Foshan, People's Republic of China
- Yiwu Research Institute of Fudan University, Yiwu City, People's Republic of China
- Research Center for Intelligent Sensing, Zhejiang Lab, Hangzhou, People's Republic of China
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12
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Schone HR, Udeozor M, Moninghoff M, Rispoli B, Vandersea J, Lock B, Hargrove L, Makin TR, Baker CI. Biomimetic versus arbitrary motor control strategies for bionic hand skill learning. Nat Hum Behav 2024; 8:1108-1123. [PMID: 38499772 PMCID: PMC11199138 DOI: 10.1038/s41562-023-01811-6] [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: 02/22/2023] [Accepted: 12/21/2023] [Indexed: 03/20/2024]
Abstract
A long-standing engineering ambition has been to design anthropomorphic bionic limbs: devices that look like and are controlled in the same way as the biological body (biomimetic). The untested assumption is that biomimetic motor control enhances device embodiment, learning, generalization and automaticity. To test this, we compared biomimetic and non-biomimetic control strategies for non-disabled participants when learning to control a wearable myoelectric bionic hand operated by an eight-channel electromyography pattern-recognition system. We compared motor learning across days and behavioural tasks for two training groups: biomimetic (mimicking the desired bionic hand gesture with biological hand) and arbitrary control (mapping an unrelated biological hand gesture with the desired bionic gesture). For both trained groups, training improved bionic limb control, reduced cognitive reliance and increased embodiment over the bionic hand. Biomimetic users had more intuitive and faster control early in training. Arbitrary users matched biomimetic performance later in training. Furthermore, arbitrary users showed increased generalization to a new control strategy. Collectively, our findings suggest that biomimetic and arbitrary control strategies provide different benefits. The optimal strategy is probably not strictly biomimetic, but rather a flexible strategy within the biomimetic-to-arbitrary spectrum, depending on the user, available training opportunities and user requirements.
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Affiliation(s)
- Hunter R Schone
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
- Institute of Cognitive Neuroscience, University College London, London, UK.
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Malcolm Udeozor
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Mae Moninghoff
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Beth Rispoli
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - James Vandersea
- Medical Center Orthotics and Prosthetics, Silver Spring, MD, USA
| | | | - Levi Hargrove
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA
- The Regenstein Foundation Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL, USA
| | - Tamar R Makin
- Institute of Cognitive Neuroscience, University College London, London, UK.
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
| | - Chris I Baker
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
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13
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Fortunato C, Bennasar-Vázquez J, Park J, Chang JC, Miller LE, Dudman JT, Perich MG, Gallego JA. Nonlinear manifolds underlie neural population activity during behaviour. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.18.549575. [PMID: 37503015 PMCID: PMC10370078 DOI: 10.1101/2023.07.18.549575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
There is rich variety in the activity of single neurons recorded during behaviour. Yet, these diverse single neuron responses can be well described by relatively few patterns of neural co-modulation. The study of such low-dimensional structure of neural population activity has provided important insights into how the brain generates behaviour. Virtually all of these studies have used linear dimensionality reduction techniques to estimate these population-wide co-modulation patterns, constraining them to a flat "neural manifold". Here, we hypothesised that since neurons have nonlinear responses and make thousands of distributed and recurrent connections that likely amplify such nonlinearities, neural manifolds should be intrinsically nonlinear. Combining neural population recordings from monkey, mouse, and human motor cortex, and mouse striatum, we show that: 1) neural manifolds are intrinsically nonlinear; 2) their nonlinearity becomes more evident during complex tasks that require more varied activity patterns; and 3) manifold nonlinearity varies across architecturally distinct brain regions. Simulations using recurrent neural network models confirmed the proposed relationship between circuit connectivity and manifold nonlinearity, including the differences across architecturally distinct regions. Thus, neural manifolds underlying the generation of behaviour are inherently nonlinear, and properly accounting for such nonlinearities will be critical as neuroscientists move towards studying numerous brain regions involved in increasingly complex and naturalistic behaviours.
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Affiliation(s)
- Cátia Fortunato
- Department of Bioengineering, Imperial College London, London UK
| | | | - Junchol Park
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn VA, USA
| | - Joanna C. Chang
- Department of Bioengineering, Imperial College London, London UK
| | - Lee E. Miller
- Department of Neurosciences, Northwestern University, Chicago IL, USA
- Department of Biomedical Engineering, Northwestern University, Chicago IL, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago IL, USA, and Shirley Ryan Ability Lab, Chicago, IL, USA
| | - Joshua T. Dudman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn VA, USA
| | - Matthew G. Perich
- Department of Neurosciences, Faculté de médecine, Université de Montréal, Montréal, Québec, Canada
- Québec Artificial Intelligence Institute (MILA), Montréal, Québec, Canada
| | - Juan A. Gallego
- Department of Bioengineering, Imperial College London, London UK
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14
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Losanno E, Badi M, Roussinova E, Bogaard A, Delacombaz M, Shokur S, Micera S. An Investigation of Manifold-Based Direct Control for a Brain-to-Body Neural Bypass. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2024; 5:271-280. [PMID: 38766541 PMCID: PMC11100864 DOI: 10.1109/ojemb.2024.3381475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 02/06/2024] [Accepted: 03/11/2024] [Indexed: 05/22/2024] Open
Abstract
Objective: Brain-body interfaces (BBIs) have emerged as a very promising solution for restoring voluntary hand control in people with upper-limb paralysis. The BBI module decoding motor commands from brain signals should provide the user with intuitive, accurate, and stable control. Here, we present a preliminary investigation in a monkey of a brain decoding strategy based on the direct coupling between the activity of intrinsic neural ensembles and output variables, aiming at achieving ease of learning and long-term robustness. Results: We identified an intrinsic low-dimensional space (called manifold) capturing the co-variation patterns of the monkey's neural activity associated to reach-to-grasp movements. We then tested the animal's ability to directly control a computer cursor using cortical activation along the manifold axes. By daily recalibrating only scaling factors, we achieved rapid learning and stable high performance in simple, incremental 2D tasks over more than 12 weeks of experiments. Finally, we showed that this brain decoding strategy can be effectively coupled to peripheral nerve stimulation to trigger voluntary hand movements. Conclusions: These results represent a proof of concept of manifold-based direct control for BBI applications.
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Affiliation(s)
- E. Losanno
- The Biorobotics Institute and Department of Excellence in Robotics and AIScuola Superiore Sant'Anna56025PisaItaly
- Modular Implantable Neuroprostheses (MINE) LaboratoryUniversità Vita-Salute San Raffaele and Scuola Superiore Sant'AnnaMilanItaly
| | - M. Badi
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of BioengineeringÉcole Polytechnique Fédérale de Lausanne (EPFL)1015LausanneSwitzerland
| | - E. Roussinova
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of BioengineeringÉcole Polytechnique Fédérale de Lausanne (EPFL)1015LausanneSwitzerland
| | - A. Bogaard
- Department of Neuroscience and Movement Sciences, Platform of Translational Neurosciences, Section of Medicine, Faculty of Sciences and MedicineUniversity of Fribourg1700FribourgSwitzerland
| | - M. Delacombaz
- Department of Neuroscience and Movement Sciences, Platform of Translational Neurosciences, Section of Medicine, Faculty of Sciences and MedicineUniversity of Fribourg1700FribourgSwitzerland
| | - S. Shokur
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of BioengineeringÉcole Polytechnique Fédérale de Lausanne (EPFL)1015LausanneSwitzerland
| | - S. Micera
- The Biorobotics Institute and Department of Excellence in Robotics and AIScuola Superiore Sant'Anna56025PisaItaly
- Modular Implantable Neuroprostheses (MINE) LaboratoryUniversità Vita-Salute San Raffaele and Scuola Superiore Sant'AnnaMilanItaly
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of BioengineeringÉcole Polytechnique Fédérale de Lausanne (EPFL)1015LausanneSwitzerland
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15
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Yan S, Zhao G, Zhang Q, Liu L, Bai X, Jin H. Altered resting-state brain function in endurance athletes. Cereb Cortex 2024; 34:bhae076. [PMID: 38494416 DOI: 10.1093/cercor/bhae076] [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: 11/10/2023] [Revised: 02/01/2024] [Accepted: 02/02/2024] [Indexed: 03/19/2024] Open
Abstract
Previous research has confirmed significant differences in regional brain activity and functional connectivity between endurance athletes and non-athletes. However, no studies have investigated the differences in topological efficiency of the brain functional network between endurance athletes and non-athletes. Here, we compared differences in regional activities, functional connectivity, and topological properties to explore the functional basis associated with endurance training. The results showed significant correlations between Regional Homogeneity in the motor cortex, visual cortex, cerebellum, and the training intensity parameters. Alterations in functional connectivity among the motor cortex, visual cortex, cerebellum, and the inferior frontal gyrus and cingulate gyrus were significantly correlated with training intensity parameters. In addition, the graph theoretical analysis results revealed a significant reduction in global efficiency among athletes. This decline is mainly caused by decreased nodal efficiency and nodal local efficiency of the cerebellar regions. Notably, the sensorimotor regions, such as the precentral gyrus and supplementary motor areas, still exhibit increased nodal efficiency and nodal local efficiency. This study not only confirms the improvement of regional activity in brain regions related to endurance training, but also offers novel insights into the mechanisms through which endurance athletes undergo changes in the topological efficiency of the brain functional network.
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Affiliation(s)
- Shizhen Yan
- School of Health, Fujian Medical University, Fuzhou 350122, China
| | - Guang Zhao
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin 300387, China
| | - Qihan Zhang
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin 300387, China
| | - Liqing Liu
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin 300387, China
| | - Xuejun Bai
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin 300387, China
| | - Hua Jin
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin 300387, China
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16
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Cashaback JGA, Allen JL, Chou AHY, Lin DJ, Price MA, Secerovic NK, Song S, Zhang H, Miller HL. NSF DARE-transforming modeling in neurorehabilitation: a patient-in-the-loop framework. J Neuroeng Rehabil 2024; 21:23. [PMID: 38347597 PMCID: PMC10863253 DOI: 10.1186/s12984-024-01318-9] [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: 07/10/2023] [Accepted: 01/25/2024] [Indexed: 02/15/2024] Open
Abstract
In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identify where and how computational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation.
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Affiliation(s)
- Joshua G A Cashaback
- Biomedical Engineering, Mechanical Engineering, Kinesiology and Applied Physiology, Biome chanics and Movement Science Program, Interdisciplinary Neuroscience Graduate Program, University of Delaware, 540 S College Ave, Newark, DE, 19711, USA.
| | - Jessica L Allen
- Department of Mechanical Engineering, University of Florida, Gainesville, USA
| | | | - David J Lin
- Division of Neurocritical Care and Stroke Service, Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, USA
- Department of Veterans Affairs, Center for Neurorestoration and Neurotechnology, Rehabilitation Research and Development Service, Providence, USA
| | - Mark A Price
- Department of Mechanical and Industrial Engineering, Department of Kinesiology, University of Massachusetts Amherst, Amherst, USA
| | - Natalija K Secerovic
- School of Electrical Engineering, The Mihajlo Pupin Institute, University of Belgrade, Belgrade, Serbia
- Laboratory for Neuroengineering, Institute for Robotics and Intelligent Systems ETH Zürich, Zurich, Switzerland
| | - Seungmoon Song
- Mechanical and Industrial Engineering, Northeastern University, Boston, USA
| | - Haohan Zhang
- Department of Mechanical Engineering, University of Utah, Salt Lake City, USA
| | - Haylie L Miller
- School of Kinesiology, University of Michigan, 830 N University Ave, Ann Arbor, MI, 48109, USA.
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17
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Lorenz EA, Su X, Skjæret-Maroni N. A review of combined functional neuroimaging and motion capture for motor rehabilitation. J Neuroeng Rehabil 2024; 21:3. [PMID: 38172799 PMCID: PMC10765727 DOI: 10.1186/s12984-023-01294-6] [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: 06/23/2023] [Accepted: 12/11/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Technological advancements in functional neuroimaging and motion capture have led to the development of novel methods that facilitate the diagnosis and rehabilitation of motor deficits. These advancements allow for the synchronous acquisition and analysis of complex signal streams of neurophysiological data (e.g., EEG, fNIRS) and behavioral data (e.g., motion capture). The fusion of those data streams has the potential to provide new insights into cortical mechanisms during movement, guide the development of rehabilitation practices, and become a tool for assessment and therapy in neurorehabilitation. RESEARCH OBJECTIVE This paper aims to review the existing literature on the combined use of motion capture and functional neuroimaging in motor rehabilitation. The objective is to understand the diversity and maturity of technological solutions employed and explore the clinical advantages of this multimodal approach. METHODS This paper reviews literature related to the combined use of functional neuroimaging and motion capture for motor rehabilitation following the PRISMA guidelines. Besides study and participant characteristics, technological aspects of the used systems, signal processing methods, and the nature of multimodal feature synchronization and fusion were extracted. RESULTS Out of 908 publications, 19 were included in the final review. Basic or translation studies were mainly represented and based predominantly on healthy participants or stroke patients. EEG and mechanical motion capture technologies were most used for biomechanical data acquisition, and their subsequent processing is based mainly on traditional methods. The system synchronization techniques at large were underreported. The fusion of multimodal features mainly supported the identification of movement-related cortical activity, and statistical methods were occasionally employed to examine cortico-kinematic relationships. CONCLUSION The fusion of motion capture and functional neuroimaging might offer advantages for motor rehabilitation in the future. Besides facilitating the assessment of cognitive processes in real-world settings, it could also improve rehabilitative devices' usability in clinical environments. Further, by better understanding cortico-peripheral coupling, new neuro-rehabilitation methods can be developed, such as personalized proprioceptive training. However, further research is needed to advance our knowledge of cortical-peripheral coupling, evaluate the validity and reliability of multimodal parameters, and enhance user-friendly technologies for clinical adaptation.
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Affiliation(s)
- Emanuel A Lorenz
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Xiaomeng Su
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Nina Skjæret-Maroni
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
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18
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Rodríguez-Azar PI, Mejía-Muñoz JM, Cruz-Mejía O, Torres-Escobar R, López LVR. Fog Computing for Control of Cyber-Physical Systems in Industry Using BCI. SENSORS (BASEL, SWITZERLAND) 2023; 24:149. [PMID: 38203012 PMCID: PMC10781321 DOI: 10.3390/s24010149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 12/23/2023] [Accepted: 12/25/2023] [Indexed: 01/12/2024]
Abstract
Brain-computer interfaces use signals from the brain, such as EEG, to determine brain states, which in turn can be used to issue commands, for example, to control industrial machinery. While Cloud computing can aid in the creation and operation of industrial multi-user BCI systems, the vast amount of data generated from EEG signals can lead to slow response time and bandwidth problems. Fog computing reduces latency in high-demand computation networks. Hence, this paper introduces a fog computing solution for BCI processing. The solution consists in using fog nodes that incorporate machine learning algorithms to convert EEG signals into commands to control a cyber-physical system. The machine learning module uses a deep learning encoder to generate feature images from EEG signals that are subsequently classified into commands by a random forest. The classification scheme is compared using various classifiers, being the random forest the one that obtained the best performance. Additionally, a comparison was made between the fog computing approach and using only cloud computing through the use of a fog computing simulator. The results indicate that the fog computing method resulted in less latency compared to the solely cloud computing approach.
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Affiliation(s)
- Paula Ivone Rodríguez-Azar
- Departamento de Ingeniería Industrial y Manufactura, Instituto de Ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico
| | - Jose Manuel Mejía-Muñoz
- Departamento de Ingeniería Eléctrica, Instituto de Ingenieria y Tecnologia, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico;
| | - Oliverio Cruz-Mejía
- Departamento de Ingeniería Industrial, FES Aragón, Universidad Nacional Autónoma de México, Mexico 57171, Mexico;
| | | | - Lucero Verónica Ruelas López
- Departamento de Ingeniería Eléctrica, Instituto de Ingenieria y Tecnologia, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico;
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19
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Lin S, Jiang J, Huang K, Li L, He X, Du P, Wu Y, Liu J, Li X, Huang Z, Zhou Z, Yu Y, Gao J, Lei M, Wu H. Advanced Electrode Technologies for Noninvasive Brain-Computer Interfaces. ACS NANO 2023; 17:24487-24513. [PMID: 38064282 DOI: 10.1021/acsnano.3c06781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2023]
Abstract
Brain-computer interfaces (BCIs) have garnered significant attention in recent years due to their potential applications in medical, assistive, and communication technologies. Building on this, noninvasive BCIs stand out as they provide a safe and user-friendly method for interacting with the human brain. In this work, we provide a comprehensive overview of the latest developments and advancements in material, design, and application of noninvasive BCIs electrode technology. We also explore the challenges and limitations currently faced by noninvasive BCI electrode technology and sketch out the technological roadmap from three dimensions: Materials and Design; Performances; Mode and Function. We aim to unite research efforts within the field of noninvasive BCI electrode technology, focusing on the consolidation of shared goals and fostering integrated development strategies among a diverse array of multidisciplinary researchers.
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Affiliation(s)
- Sen Lin
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Jingjing Jiang
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Kai Huang
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
- State Key Laboratory of Information Photonics and Optical Communications and School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Lei Li
- National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
| | - Xian He
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
| | - Peng Du
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
| | - Yufeng Wu
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
| | - Junchen Liu
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
- State Key Laboratory of Information Photonics and Optical Communications and School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Xilin Li
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
- Advanced Institute for Brain and Intelligence, Guangxi University, Nanning 530004, China
| | - Zhibao Huang
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Zenan Zhou
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Yuanhang Yu
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Jiaxin Gao
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Ming Lei
- State Key Laboratory of Information Photonics and Optical Communications and School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Hui Wu
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
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20
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Safaie M, Chang JC, Park J, Miller LE, Dudman JT, Perich MG, Gallego JA. Preserved neural dynamics across animals performing similar behaviour. Nature 2023; 623:765-771. [PMID: 37938772 PMCID: PMC10665198 DOI: 10.1038/s41586-023-06714-0] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 10/04/2023] [Indexed: 11/09/2023]
Abstract
Animals of the same species exhibit similar behaviours that are advantageously adapted to their body and environment. These behaviours are shaped at the species level by selection pressures over evolutionary timescales. Yet, it remains unclear how these common behavioural adaptations emerge from the idiosyncratic neural circuitry of each individual. The overall organization of neural circuits is preserved across individuals1 because of their common evolutionarily specified developmental programme2-4. Such organization at the circuit level may constrain neural activity5-8, leading to low-dimensional latent dynamics across the neural population9-11. Accordingly, here we suggested that the shared circuit-level constraints within a species would lead to suitably preserved latent dynamics across individuals. We analysed recordings of neural populations from monkey and mouse motor cortex to demonstrate that neural dynamics in individuals from the same species are surprisingly preserved when they perform similar behaviour. Neural population dynamics were also preserved when animals consciously planned future movements without overt behaviour12 and enabled the decoding of planned and ongoing movement across different individuals. Furthermore, we found that preserved neural dynamics extend beyond cortical regions to the dorsal striatum, an evolutionarily older structure13,14. Finally, we used neural network models to demonstrate that behavioural similarity is necessary but not sufficient for this preservation. We posit that these emergent dynamics result from evolutionary constraints on brain development and thus reflect fundamental properties of the neural basis of behaviour.
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Affiliation(s)
- Mostafa Safaie
- Department of Bioengineering, Imperial College London, London, UK
| | - Joanna C Chang
- Department of Bioengineering, Imperial College London, London, UK
| | - Junchol Park
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, TX, USA
| | - Lee E Miller
- Departments of Physiology, Biomedical Engineering and Physical Medicine and Rehabilitation, Northwestern University and Shirley Ryan Ability Lab, Chicago, IL, USA
| | - Joshua T Dudman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, TX, USA
| | - Matthew G Perich
- Département de Neurosciences, Faculté de Médecine, Université de Montréal, Montreal, Quebec, Canada.
- Mila, Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada.
| | - Juan A Gallego
- Department of Bioengineering, Imperial College London, London, UK.
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21
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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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22
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Fadli RA, Yamanouchi Y, Jovanovic LI, Popovic MR, Marquez-Chin C, Nomura T, Milosevic M. Effectiveness of motor and prefrontal cortical areas for brain-controlled functional electrical stimulation neuromodulation. J Neural Eng 2023; 20:056022. [PMID: 37714143 DOI: 10.1088/1741-2552/acfa22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 09/15/2023] [Indexed: 09/17/2023]
Abstract
Objective. Brain-computer interface (BCI)-controlled functional electrical stimulation (FES) could excite the central nervous system to enhance upper limb motor recovery. Our current study assessed the effectiveness of motor and prefrontal cortical activity-based BCI-FES to help elucidate the underlying neuromodulation mechanisms of this neurorehabilitation approach.Approach. The primary motor cortex (M1) and prefrontal cortex (PFC) BCI-FES interventions were performed for 25 min on separate days with twelve non-disabled participants. During the interventions, a single electrode from the contralateral M1 or PFC was used to detect event-related desynchronization (ERD) in the calibrated frequency range. If the BCI system detected ERD within 15 s of motor imagery, FES activated wrist extensor muscles. Otherwise, if the BCI system did not detect ERD within 15 s, a subsequent trial was initiated without FES. To evaluate neuromodulation effects, corticospinal excitability was assessed using single-pulse transcranial magnetic stimulation, and cortical excitability was assessed by motor imagery ERD and resting-state functional connectivity before, immediately, 30 min, and 60 min after each intervention.Main results. M1 and PFC BCI-FES interventions had similar success rates of approximately 80%, while the M1 intervention was faster in detecting ERD activity. Consequently, only the M1 intervention effectively elicited corticospinal excitability changes for at least 60 min around the targeted cortical area in the M1, suggesting a degree of spatial localization. However, cortical excitability measures did not indicate changes after either M1 or PFC BCI-FES.Significance. Neural mechanisms underlying the effectiveness of BCI-FES neuromodulation may be attributed to the M1 direct corticospinal projections and/or the closer timing between ERD detection and FES, which likely enhanced Hebbian-like plasticity by synchronizing cortical activation detected by the BCI system with the sensory nerve activation and movement related reafference elicited by FES.
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Affiliation(s)
- Rizaldi A Fadli
- Graduate School of Engineering Science, Department of Mechanical Science and Bioengineering, Osaka University, 1-3 Machikaneyama, Toyonaka 560-8531, Japan
- Department of Biomedical Engineering, University of Miami College of Engineering, 1251 Memorial Drive, Coral Gables, FL 33146, United States of America
- The Miami Project to Cure Paralysis, Department of Neurological Surgery, University of Miami Miller School of Medicine, 1095 NW 14th Terrace, Miami, FL 33136, United States of America
| | - Yuki Yamanouchi
- Graduate School of Engineering Science, Department of Mechanical Science and Bioengineering, Osaka University, 1-3 Machikaneyama, Toyonaka 560-8531, Japan
| | - Lazar I Jovanovic
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, Ontario M5S 3G9, Canada
- KITE Research Institute, Toronto Rehabilitation Institute-University Health Network, 520 Sutherland Drive, Toronto, Ontario M4G 3V9, Canada
| | - Milos R Popovic
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, Ontario M5S 3G9, Canada
- KITE Research Institute, Toronto Rehabilitation Institute-University Health Network, 520 Sutherland Drive, Toronto, Ontario M4G 3V9, Canada
- CRANIA, University Health Network & University of Toronto. 550 University Avenue, Toronto, Ontario M5G 2A2, Canada
| | - Cesar Marquez-Chin
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, Ontario M5S 3G9, Canada
- KITE Research Institute, Toronto Rehabilitation Institute-University Health Network, 520 Sutherland Drive, Toronto, Ontario M4G 3V9, Canada
- CRANIA, University Health Network & University of Toronto. 550 University Avenue, Toronto, Ontario M5G 2A2, Canada
| | - Taishin Nomura
- Graduate School of Engineering Science, Department of Mechanical Science and Bioengineering, Osaka University, 1-3 Machikaneyama, Toyonaka 560-8531, Japan
| | - Matija Milosevic
- Graduate School of Engineering Science, Department of Mechanical Science and Bioengineering, Osaka University, 1-3 Machikaneyama, Toyonaka 560-8531, Japan
- Department of Biomedical Engineering, University of Miami College of Engineering, 1251 Memorial Drive, Coral Gables, FL 33146, United States of America
- The Miami Project to Cure Paralysis, Department of Neurological Surgery, University of Miami Miller School of Medicine, 1095 NW 14th Terrace, Miami, FL 33136, United States of America
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23
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Voloh B, Maisson DJN, Cervera RL, Conover I, Zambre M, Hayden B, Zimmermann J. Hierarchical action encoding in prefrontal cortex of freely moving macaques. Cell Rep 2023; 42:113091. [PMID: 37656619 PMCID: PMC10591875 DOI: 10.1016/j.celrep.2023.113091] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 06/23/2023] [Accepted: 08/18/2023] [Indexed: 09/03/2023] Open
Abstract
Our natural behavioral repertoires include coordinated actions of characteristic types. To better understand how neural activity relates to the expression of actions and action switches, we studied macaques performing a freely moving foraging task in an open environment. We developed a novel analysis pipeline that can identify meaningful units of behavior, corresponding to recognizable actions such as sitting, walking, jumping, and climbing. On the basis of transition probabilities between these actions, we found that behavior is organized in a modular and hierarchical fashion. We found that, after regressing out many potential confounders, actions are associated with specific patterns of firing in each of six prefrontal brain regions and that, overall, encoding of action category is progressively stronger in more dorsal and more caudal prefrontal regions. Together, these results establish a link between selection of units of primate behavior on one hand and neuronal activity in prefrontal regions on the other.
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Affiliation(s)
- Benjamin Voloh
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - David J-N Maisson
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | | | - Indirah Conover
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Mrunal Zambre
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Benjamin Hayden
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA; Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jan Zimmermann
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA; Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN 55455, USA.
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24
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Bashford L, Rosenthal I, Kellis S, Bjånes D, Pejsa K, Brunton BW, Andersen RA. Neural subspaces of imagined movements in parietal cortex remain stable over several years in humans. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.05.547767. [PMID: 37461446 PMCID: PMC10350015 DOI: 10.1101/2023.07.05.547767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
A crucial goal in brain-machine interfacing is long-term stability of neural decoding performance, ideally without regular retraining. Here we demonstrate stable neural decoding over several years in two human participants, achieved by latent subspace alignment of multi-unit intracortical recordings in posterior parietal cortex. These results can be practically applied to significantly expand the longevity and generalizability of future movement decoding devices.
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Affiliation(s)
- L Bashford
- Division of Biology and Biological Engineering, and T&C Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, CA, USA
| | - I Rosenthal
- Division of Biology and Biological Engineering, and T&C Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, CA, USA
| | - S Kellis
- Division of Biology and Biological Engineering, and T&C Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, CA, USA
| | - D Bjånes
- Division of Biology and Biological Engineering, and T&C Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, CA, USA
| | - K Pejsa
- Division of Biology and Biological Engineering, and T&C Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, CA, USA
| | - BW Brunton
- Department of Biology, University of Washington, Seattle, WA, USA
| | - RA Andersen
- Division of Biology and Biological Engineering, and T&C Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, CA, USA
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25
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Abstract
Brain-machine interfaces (BMIs) aim to treat sensorimotor neurological disorders by creating artificial motor and/or sensory pathways. Introducing artificial pathways creates new relationships between sensory input and motor output, which the brain must learn to gain dexterous control. This review highlights the role of learning in BMIs to restore movement and sensation, and discusses how BMI design may influence neural plasticity and performance. The close integration of plasticity in sensory and motor function influences the design of both artificial pathways and will be an essential consideration for bidirectional devices that restore both sensory and motor function.
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Affiliation(s)
- Maria C Dadarlat
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
| | - Ryan A Canfield
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | - Amy L Orsborn
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
- Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, USA
- Washington National Primate Research Center, Seattle, Washington, USA
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26
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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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Makin TR, Micera S, Miller LE. Neurocognitive and motor-control challenges for the realization of bionic augmentation. Nat Biomed Eng 2023; 7:344-348. [PMID: 36050524 PMCID: PMC9975114 DOI: 10.1038/s41551-022-00930-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Robotic fingers and arms that augment the motor abilities of non-disabled individuals are increasingly feasible yet face neurocognitive barriers and hurdles in efferent motor control.
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Affiliation(s)
- Tamar R Makin
- Institute of Cognitive Neuroscience, University College London, London, UK.
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
| | - Silvestro Micera
- Bertarelli Foundation Chair in Translational Neural Engineering, Center for Neuroprosthetics and Institute of Bioengineering, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland.
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy.
| | - Lee E Miller
- Department of Physiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
- Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA.
- Shirley Ryan AbilityLab, Chicago, IL, USA.
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28
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Schone HR, Udeozor M, Moninghoff M, Rispoli B, Vandersea J, Lock B, Hargrove L, Makin TR, Baker CI. Should bionic limb control mimic the human body? Impact of control strategy on bionic hand skill learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.07.525548. [PMID: 36945476 PMCID: PMC10028741 DOI: 10.1101/2023.02.07.525548] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
A longstanding engineering ambition has been to design anthropomorphic bionic limbs: devices that look like and are controlled in the same way as the biological body (biomimetic). The untested assumption is that biomimetic motor control enhances device embodiment, learning, generalization, and automaticity. To test this, we compared biomimetic and non-biomimetic control strategies for able-bodied participants when learning to operate a wearable myoelectric bionic hand. We compared motor learning across days and behavioural tasks for two training groups: Biomimetic (mimicking the desired bionic hand gesture with biological hand) and Arbitrary control (mapping an unrelated biological hand gesture with the desired bionic gesture). For both trained groups, training improved bionic limb control, reduced cognitive reliance, and increased embodiment over the bionic hand. Biomimetic users had more intuitive and faster control early in training. Arbitrary users matched biomimetic performance later in training. Further, arbitrary users showed increased generalization to a novel control strategy. Collectively, our findings suggest that biomimetic and arbitrary control strategies provide different benefits. The optimal strategy is likely not strictly biomimetic, but rather a flexible strategy within the biomimetic to arbitrary spectrum, depending on the user, available training opportunities and user requirements.
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Affiliation(s)
- Hunter R. Schone
- Laboratory of Brain & Cognition, National Institutes of Mental Health, National Institutes of Health, Bethesda, MD, USA
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Malcolm Udeozor
- Laboratory of Brain & Cognition, National Institutes of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Mae Moninghoff
- Laboratory of Brain & Cognition, National Institutes of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Beth Rispoli
- Laboratory of Brain & Cognition, National Institutes of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - James Vandersea
- Medical Center Orthotics & Prosthetics, Silver Spring, MD, USA
| | | | - Levi Hargrove
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA
- The Regenstein Foundation Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL, USA
| | - Tamar R Makin
- Institute of Cognitive Neuroscience, University College London, London, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Chris I. Baker
- Laboratory of Brain & Cognition, National Institutes of Mental Health, National Institutes of Health, Bethesda, MD, USA
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Priorelli M, Stoianov IP. Flexible intentions: An Active Inference theory. Front Comput Neurosci 2023; 17:1128694. [PMID: 37021085 PMCID: PMC10067605 DOI: 10.3389/fncom.2023.1128694] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 03/03/2023] [Indexed: 04/07/2023] Open
Abstract
We present a normative computational theory of how the brain may support visually-guided goal-directed actions in dynamically changing environments. It extends the Active Inference theory of cortical processing according to which the brain maintains beliefs over the environmental state, and motor control signals try to fulfill the corresponding sensory predictions. We propose that the neural circuitry in the Posterior Parietal Cortex (PPC) compute flexible intentions-or motor plans from a belief over targets-to dynamically generate goal-directed actions, and we develop a computational formalization of this process. A proof-of-concept agent embodying visual and proprioceptive sensors and an actuated upper limb was tested on target-reaching tasks. The agent behaved correctly under various conditions, including static and dynamic targets, different sensory feedbacks, sensory precisions, intention gains, and movement policies; limit conditions were individuated, too. Active Inference driven by dynamic and flexible intentions can thus support goal-directed behavior in constantly changing environments, and the PPC might putatively host its core intention mechanism. More broadly, the study provides a normative computational basis for research on goal-directed behavior in end-to-end settings and further advances mechanistic theories of active biological systems.
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30
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Peterson V, Merk T, Bush A, Nikulin V, Kühn AA, Neumann WJ, Richardson RM. Movement decoding using spatio-spectral features of cortical and subcortical local field potentials. Exp Neurol 2023; 359:114261. [PMID: 36349662 DOI: 10.1016/j.expneurol.2022.114261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 09/26/2022] [Accepted: 10/25/2022] [Indexed: 12/30/2022]
Abstract
The first commercially sensing enabled deep brain stimulation (DBS) devices for the treatment of movement disorders have recently become available. In the future, such devices could leverage machine learning based brain signal decoding strategies to individualize and adapt therapy in real-time. As multi-channel recordings become available, spatial information may provide an additional advantage for informing machine learning models. To investigate this concept, we compared decoding performances from single channels vs. spatial filtering techniques using intracerebral multitarget electrophysiology in Parkinson's disease patients undergoing DBS implantation. We investigated the feasibility of spatial filtering in invasive neurophysiology and the putative utility of combined cortical ECoG and subthalamic local field potential signals for decoding grip-force, a well-defined and continuous motor readout. We found that adding spatial information to the model can improve decoding (6% gain in decoding), but the spatial patterns and additional benefit was highly individual. Beyond decoding performance results, spatial filters and patterns can be used to obtain meaningful neurophysiological information about the brain networks involved in target behavior. Our results highlight the importance of individualized approaches for brain signal decoding, for which multielectrode recordings and spatial filtering can improve precision medicine approaches for clinical brain computer interfaces.
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Affiliation(s)
- Victoria Peterson
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, USA.
| | - Timon Merk
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Alan Bush
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Vadim Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Andrea A Kühn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Wolf-Julian Neumann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, USA
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Somatosensory ECoG-based brain-machine interface with electrical stimulation on medial forebrain bundle. Biomed Eng Lett 2022; 13:85-95. [PMID: 36711163 PMCID: PMC9873859 DOI: 10.1007/s13534-022-00256-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 11/21/2022] [Accepted: 12/02/2022] [Indexed: 12/24/2022] Open
Abstract
Brain-machine interface (BMI) provides an alternative route for controlling an external device with one's intention. For individuals with motor-related disability, the BMI technologies can be used to replace or restore motor functions. Therefore, BMIs for movement restoration generally decode the neural activity from the motor-related brain regions. In this study, however, we designed a BMI system that uses sensory-related neural signals for BMI combined with electrical stimulation for reward. Four-channel electrocorticographic (ECoG) signals were recorded from the whisker-related somatosensory cortex of rats and converted to extract the BMI signals to control the one-dimensional movement of a dot on the screen. At the same time, we used operant conditioning with electrical stimulation on medial forebrain bundle (MFB), which provides a virtual reward to motivate the rat to move the dot towards the desired center region. The BMI task training was performed for 7 days with ECoG recording and MFB stimulation. Animals successfully learned to move the dot location to the desired position using S1BF neural activity. This study successfully demonstrated that it is feasible to utilize the neural signals from the whisker somatosensory cortex for BMI system. In addition, the MFB electrical stimulation is effective for rats to learn the behavioral task for BMI.
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Feng J, Gu Y, Xu W. Hand surgery in a new "hand-brain" era: change the hand, rebuild the brain. Sci Bull (Beijing) 2022; 67:1932-1934. [PMID: 36546197 DOI: 10.1016/j.scib.2022.08.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Juntao Feng
- Hand Surgery Department, The National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China; Department of Hand and Upper Extremity Surgery, Center for the Reconstruction of Limb Function, Jing'an District Central Hospital, Fudan University, Shanghai 200040, China
| | - Yudong Gu
- Hand Surgery Department, The National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China; Department of Hand and Upper Extremity Surgery, Center for the Reconstruction of Limb Function, Jing'an District Central Hospital, Fudan University, Shanghai 200040, China; Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and Collaborative Innovation Center of Brain Science, Fudan University, Shanghai 200032, China
| | - Wendong Xu
- Hand Surgery Department, The National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China; Department of Hand and Upper Extremity Surgery, Center for the Reconstruction of Limb Function, Jing'an District Central Hospital, Fudan University, Shanghai 200040, China; Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and Collaborative Innovation Center of Brain Science, Fudan University, Shanghai 200032, China; Research Unit of Synergistic Reconstruction of Upper and Lower Limbs after Brain Injury, Chinese Academy of Medical Sciences, Beijing 100730, China.
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Jang SJ, Yang YJ, Ryun S, Kim JS, Chung CK, Jeong J. Decoding trajectories of imagined hand movement using electrocorticograms for brain-machine interface. J Neural Eng 2022; 19. [PMID: 35985293 DOI: 10.1088/1741-2552/ac8b37] [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: 04/22/2022] [Accepted: 08/19/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Reaching hand movement is an important motor skill actively examined in brain-computer interface (BCI). Among various components of movement analyzed is the hand's trajectory, which describes the hand's continuous positions in three-dimensional space. While a large body of studies have investigated the decoding of real movements and the reconstruction of real hand movement trajectories from neural signals, fewer studies have attempted to decode the trajectory of imagined hand movement. To develop BCI systems for patients with hand motor dysfunctions, the systems essentially require to achieve movement-free control of external devices, which is only possible through successful decoding of purely imagined hand movement. APPROACH To achieve this goal, this study used a machine learning technique (i.e., the variational Bayesian least square) to analyze the electrocorticogram (ECoG) of eighteen epilepsy patients obtained from when they performed movement execution (ME) and kinesthetic movement imagination (KMI) of the reach-and-grasp hand action. MAIN RESULTS The variational Bayesian decoding model was able to successfully predict the imagined trajectories of hand movement significantly above chance level. The Pearson's correlation coefficient between imagined and predicted trajectories was 0.3393 and 0.4936 for the KMI (KMI trials only) and MEKMI paradigm (alternating trials of ME and KMI) respectively. SIGNIFICANCE This study demonstrated a high accuracy of prediction for trajectories of imagined hand movement, and more importantly, higher decoding accuracy of imagined trajectories in the MEKMI paradigm than in the KMI paradigm solely.
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Affiliation(s)
- Sang Jin Jang
- Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 411 E16-1(YBS Building) Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, South Korea 34141, Daejeon, Daejeon, 34141, Korea (the Republic of)
| | - Yu Jin Yang
- Seoul National University College of Natural Sciences, 103, Daehak-ro, Jongno-gu, Seoul, Republic of Korea, Seoul, 03080, Korea (the Republic of)
| | - Seokyun Ryun
- Seoul National University College of Natural Sciences, 103, Daehak-ro, Jongno-gu, Seoul, Republic of Korea, Seoul, 03080, Korea (the Republic of)
| | - June Sic Kim
- Seoul National University College of Natural Sciences, 103, Daehak-ro, Jongno-gu, Seoul, Republic of Korea, Seoul, 03080, Korea (the Republic of)
| | - Chun Kee Chung
- Seoul National University College of Natural Sciences, 103, Daehak-ro, Jongno-gu, Seoul, Republic of Korea, Seoul, 03080, Korea (the Republic of)
| | - Jaeseung Jeong
- Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 514 E16-1(YBS Building) Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, South Korea 34141, Daejeon, 34141, Korea (the Republic of)
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34
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Huang Y, Zhang X, Shen X, Chen S, Principe J, Wang Y. Extracting synchronized neuronal activity from local field potentials based on a marked point process framework. J Neural Eng 2022; 19. [PMID: 35921802 DOI: 10.1088/1741-2552/ac86a3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 08/03/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Brain-machine interfaces (BMIs) translate neural activity into motor commands to restore motor functions for people with paralysis. Local field potentials (LFPs) are promising for long-term BMIs, since the quality of the recording lasts longer than single neuronal spikes. Inferring neuronal spike activity from population activities such as LFPs is challenging, because LFPs stem from synaptic currents flowing in the neural tissue produced by various neuronal ensembles and reflect neural synchronization. Existing studies that combine LFPs with spikes leverage the spectrogram of LFPs, which can neither detect the transient characteristics of LFP features (here, neuromodulation in a specific frequency band) with high accuracy, nor correlate them with relevant neuronal activity with a sufficient time resolution. APPROACH We propose a feature extraction and validation framework to directly extract LFP neuromodulations related to synchronized spike activity using recordings from the primary motor cortex of six Sprague Dawley (SD) rats during a lever-press task. We first select important LFP frequency bands relevant to behavior, and then implement a marked point process (MPP) methodology to extract transient LFP neuromodulations. We validate the LFP feature extraction by examining the correlation with the pairwise synchronized firing probability of important neurons, which are selected according to their contribution to behavioral decoding. The highly correlated synchronized firings identified by the LFP neuromodulations are fed into a decoder to check whether they can serve as a reliable neural data source for movement decoding. MAIN RESULTS We find that the gamma band (30-80Hz) LFP neuromodulations demonstrate significant correlation with synchronized firings. Compared with traditional spectrogram-based method, the higher-temporal resolution MPP method captures the synchronized firing patterns with fewer false alarms, and demonstrates significantly higher correlation than single neuron spikes. The decoding performance using the synchronized neuronal firings identified by the LFP neuromodulations can reach 90% compared to the full recorded neuronal ensembles. SIGNIFICANCE Our proposed framework successfully extracts the sparse LFP neuromodulations that can identify temporal synchronized neuronal spikes with high correlation. The identified neuronal spike pattern demonstrates high decoding performance, which reveals the possibility of using LFP as an effective modality for long-term BMI decoding.
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Affiliation(s)
- Yifan Huang
- Hong Kong University of Science and Technology Department of Electronic and Computer Engineering, 4218D,ECE Department, CLEAR WATER BAY ROAD, hong kong, hong kong, 00000, HONG KONG
| | - Xiang Zhang
- Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology Department of Electronic and Computer Engineering, 4218D,ECE Department, CLEAR WATER BAY ROAD, hong kong, Kowloon, 00000, HONG KONG
| | - Xiang Shen
- Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology Department of Electronic and Computer Engineering, 4218D,ECE Department, CLEAR WATER BAY ROAD, hong kong, Kowloon, 00000, HONG KONG
| | - Shuhang Chen
- Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology Department of Electronic and Computer Engineering, 4218D,ECE Department, CLEAR WATER BAY ROAD, hong kong, Kowloon, 00000, HONG KONG
| | - Jose Principe
- Department of Electrical and Computer Engineering, University of Florida, PO Box 116130, Gainesville, FL 32611-6130, USA, Florida, 00000, UNITED STATES
| | - Yiwen Wang
- Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology Department of Electronic and Computer Engineering, Clear Water Bay, Kowloon, HONG KONG
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Tsay JS, Kim HE, Saxena A, Parvin DE, Verstynen T, Ivry RB. Dissociable use-dependent processes for volitional goal-directed reaching. Proc Biol Sci 2022; 289:20220415. [PMID: 35473382 PMCID: PMC9043705 DOI: 10.1098/rspb.2022.0415] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 03/23/2022] [Indexed: 01/14/2023] Open
Abstract
Repetition of specific movement biases subsequent actions towards the practiced movement, a phenomenon known as use-dependent learning (UDL). Recent experiments that impose strict constraints on planning time have revealed two sources of use-dependent biases, one arising from dynamic changes occurring during motor planning and another reflecting a stable shift in motor execution. Here, we used a distributional analysis to examine the contribution of these biases in reaching. To create the conditions for UDL, the target appeared at a designated 'frequent' location on most trials, and at one of six 'rare' locations on other trials. Strikingly, the heading angles were bimodally distributed, with peaks at both frequent and rare target locations. Despite having no constraints on planning time, participants exhibited a robust bias towards the frequent target when movements were self-initiated quickly, the signature of a planning bias; notably, the peak near the rare target was shifted in the frequently practiced direction, the signature of an execution bias. Furthermore, these execution biases were not only replicated in a delayed-response task but were also insensitive to reward. Taken together, these results extend our understanding of how volitional movements are influenced by recent experience.
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Affiliation(s)
- Jonathan S. Tsay
- Department of Psychology, University of California, Berkeley, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, USA
| | - Hyosub E. Kim
- Department of Physical Therapy, University of Delaware, Newark, DE, USA
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA
| | - Arohi Saxena
- Department of Psychology, University of California, Berkeley, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, USA
| | - Darius E. Parvin
- Department of Psychology, University of California, Berkeley, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, USA
| | - Timothy Verstynen
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Richard B. Ivry
- Department of Psychology, University of California, Berkeley, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, USA
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Liu Y, Caracoglia J, Sen S, Freud E, Striem-Amit E. Are reaching and grasping effector-independent? Similarities and differences in reaching and grasping kinematics between the hand and foot. Exp Brain Res 2022; 240:1833-1848. [PMID: 35426511 PMCID: PMC9142431 DOI: 10.1007/s00221-022-06359-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 03/24/2022] [Indexed: 11/30/2022]
Abstract
While reaching and grasping are highly prevalent manual actions, neuroimaging studies provide evidence that their neural representations may be shared between different body parts, i.e., effectors. If these actions are guided by effector-independent mechanisms, similar kinematics should be observed when the action is performed by the hand or by a cortically remote and less experienced effector, such as the foot. We tested this hypothesis with two characteristic components of action: the initial ballistic stage of reaching, and the preshaping of the digits during grasping based on object size. We examined if these kinematic features reflect effector-independent mechanisms by asking participants to reach toward and to grasp objects of different widths with their hand and foot. First, during both reaching and grasping, the velocity profile up to peak velocity matched between the hand and the foot, indicating a shared ballistic acceleration phase. Second, maximum grip aperture and time of maximum grip aperture of grasping increased with object size for both effectors, indicating encoding of object size during transport. Differences between the hand and foot were found in the deceleration phase and time of maximum grip aperture, likely due to biomechanical differences and the participants’ inexperience with foot actions. These findings provide evidence for effector-independent visuomotor mechanisms of reaching and grasping that generalize across body parts.
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Affiliation(s)
- Yuqi Liu
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC, 20057, USA.
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Sciences and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
| | - James Caracoglia
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC, 20057, USA
- Division of Graduate Medical Sciences, Boston University Medical Center, Boston, MA, 02215, USA
| | - Sriparna Sen
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC, 20057, USA
| | - Erez Freud
- Department of Psychology, York University, Toronto, ON, M3J 1P3, Canada
- Centre for Vision Research, York University, Toronto, ON, M3J 1P3, Canada
| | - Ella Striem-Amit
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC, 20057, USA.
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