1
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Greene JJ, Gorelik P, Mazor O, Guarin DL, Malk R, Hadlock T. Freeing the Animal Model: A Modular, Wirelessly Powered, Implantable Electronic Platform. Plast Reconstr Surg 2024; 153:568e-572e. [PMID: 37184506 DOI: 10.1097/prs.0000000000010676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
SUMMARY Fully implantable electronic devices in freely roaming animal models are useful in biomedical research, but their development is prohibitively resource intensive for many laboratories. The advent of miniaturized microcontrollers with onboard wireless data exchange capabilities has enabled cost-efficient development of myriad do-it-yourself electronic devices that are easily customizable with open-source software ( https://www.arduino.cc/ ). Likewise, the global proliferation of mobile devices has led to the development of low-cost miniaturized wireless power technology. The authors present a low-cost, rechargeable, and fully implantable electronic device comprising a commercially available, open-source, wirelessly powered microcontroller that is readily customizable with myriad readily available miniature sensors and actuators. The authors demonstrate the utility of this platform for chronic nerve stimulation in the freely roaming rat with intermittent wireless charging over 4 weeks. Device assembly was achieved within 2 hours and necessitated only basic soldering equipment. Component costs totaled $115 per device. Wireless data transfer and wireless recharging of device batteries was achieved within 30 minutes, and no harmful heat generation occurred during charging or discharging cycles, as measured by external thermography and internal device temperature monitoring. Wireless communication enabled triggered cathodic pulse stimulation of the facial nerve at various user-selected programmed frequencies (1, 5, and 10 Hz) for periods of 4 weeks or longer. This implantable electronic platform could be further miniaturized and expanded to study a vast array of biomedical research questions in live animal models. CLINICAL RELEVANCE STATEMENT The clinical relevance of electrical stimulation in neural recovery remains controversial, and long-term neural stimulation in small animal models is challenging. We have developed a low-cost, fully implantable, wirelessly powered nerve stimulation device to facilitate further research in nerve stimulation in animal models.
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Affiliation(s)
| | - Pavel Gorelik
- Research Instrumentation Core Facility, Department of Neurobiology, Harvard Medical School
| | - Ofer Mazor
- Research Instrumentation Core Facility, Department of Neurobiology, Harvard Medical School
| | | | - Ronit Malk
- From the Massachusetts Eye & Ear Infirmary
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2
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Franch M, Yellapantula S, Parajuli A, Kharas N, Wright A, Aazhang B, Dragoi V. Visuo-frontal interactions during social learning in freely moving macaques. Nature 2024; 627:174-181. [PMID: 38355804 PMCID: PMC10959748 DOI: 10.1038/s41586-024-07084-x] [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: 03/21/2023] [Accepted: 01/16/2024] [Indexed: 02/16/2024]
Abstract
Social interactions represent a ubiquitous aspect of our everyday life that we acquire by interpreting and responding to visual cues from conspecifics1. However, despite the general acceptance of this view, how visual information is used to guide the decision to cooperate is unknown. Here, we wirelessly recorded the spiking activity of populations of neurons in the visual and prefrontal cortex in conjunction with wireless recordings of oculomotor events while freely moving macaques engaged in social cooperation. As animals learned to cooperate, visual and executive areas refined the representation of social variables, such as the conspecific or reward, by distributing socially relevant information among neurons in each area. Decoding population activity showed that viewing social cues influences the decision to cooperate. Learning social events increased coordinated spiking between visual and prefrontal cortical neurons, which was associated with improved accuracy of neural populations to encode social cues and the decision to cooperate. These results indicate that the visual-frontal cortical network prioritizes relevant sensory information to facilitate learning social interactions while freely moving macaques interact in a naturalistic environment.
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Affiliation(s)
- Melissa Franch
- Deparment of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, TX, USA
| | - Sudha Yellapantula
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Arun Parajuli
- Deparment of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, TX, USA
| | - Natasha Kharas
- Deparment of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, TX, USA
| | - Anthony Wright
- Deparment of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, TX, USA
| | - Behnaam Aazhang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Valentin Dragoi
- Deparment of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, TX, USA.
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA.
- Neuroengineering Initiative, Rice University, Houston, TX, USA.
- Houston Methodist Research Institute, Houston, TX, USA.
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3
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Jia Q, Liu Y, Lv S, Wang Y, Jiao P, Xu W, Xu Z, Wang M, Cai X. Wireless closed-loop deep brain stimulation using microelectrode array probes. J Zhejiang Univ Sci B 2024; 25:803-823. [PMID: 39420519 PMCID: PMC11494161 DOI: 10.1631/jzus.b2300400] [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: 06/05/2023] [Accepted: 08/25/2023] [Indexed: 03/02/2024]
Abstract
Deep brain stimulation (DBS), including optical stimulation and electrical stimulation, has been demonstrated considerable value in exploring pathological brain activity and developing treatments for neural disorders. Advances in DBS microsystems based on implantable microelectrode array (MEA) probes have opened up new opportunities for closed-loop DBS (CL-DBS) in situ. This technology can be used to detect damaged brain circuits and test the therapeutic potential for modulating the output of these circuits in a variety of diseases simultaneously. Despite the success and rapid utilization of MEA probe-based CL-DBS microsystems, key challenges, including excessive wired communication, need to be urgently resolved. In this review, we considered recent advances in MEA probe-based wireless CL-DBS microsystems and outlined the major issues and promising prospects in this field. This technology has the potential to offer novel therapeutic options for psychiatric disorders in the future.
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Affiliation(s)
- Qianli Jia
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yaoyao Liu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shiya Lv
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiding Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Peiyao Jiao
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Xu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhaojie Xu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mixia Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China.
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Xinxia Cai
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China. ,
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China. ,
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4
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Alreja A, Ward MJ, Ma Q, Russ BE, Bickel S, Van Wouwe NC, González-Martínez JA, Neimat JS, Abel TJ, Bagić A, Parker LS, Richardson RM, Schroeder CE, Morency LP, Ghuman AS. A new paradigm for investigating real-world social behavior and its neural underpinnings. Behav Res Methods 2023; 55:2333-2352. [PMID: 35877024 PMCID: PMC10841340 DOI: 10.3758/s13428-022-01882-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/15/2022] [Indexed: 11/08/2022]
Abstract
Eye tracking and other behavioral measurements collected from patient-participants in their hospital rooms afford a unique opportunity to study natural behavior for basic and clinical translational research. We describe an immersive social and behavioral paradigm implemented in patients undergoing evaluation for surgical treatment of epilepsy, with electrodes implanted in the brain to determine the source of their seizures. Our studies entail collecting eye tracking with other behavioral and psychophysiological measurements from patient-participants during unscripted behavior, including social interactions with clinical staff, friends, and family in the hospital room. This approach affords a unique opportunity to study the neurobiology of natural social behavior, though it requires carefully addressing distinct logistical, technical, and ethical challenges. Collecting neurophysiological data synchronized to behavioral and psychophysiological measures helps us to study the relationship between behavior and physiology. Combining across these rich data sources while participants eat, read, converse with friends and family, etc., enables clinical-translational research aimed at understanding the participants' disorders and clinician-patient interactions, as well as basic research into natural, real-world behavior. We discuss data acquisition, quality control, annotation, and analysis pipelines that are required for our studies. We also discuss the clinical, logistical, and ethical and privacy considerations critical to working in the hospital setting.
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Affiliation(s)
- Arish Alreja
- Center for the Neural Basis of Cognition, Carnegie Mellon University and University of Pittsburgh, Pittsburgh, USA.
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, USA.
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA.
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, USA.
| | - Michael J Ward
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, USA
- David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
| | - Qianli Ma
- Language Technologies Institute, Carnegie Mellon University, Pittsburgh, USA
| | - Brian E Russ
- Nathan Kline Institute for Psychiatric Research, Orangeburg, USA
| | - Stephan Bickel
- Department of Neurosurgery and Neurology, Northwell Health, The Feinstein Institutes for Medical Research, Manhasset, USA
| | - Nelleke C Van Wouwe
- Department of Neurological Surgery, University of Louisville, Louisville, USA
| | | | - Joseph S Neimat
- Department of Neurological Surgery, University of Louisville, Louisville, USA
| | - Taylor J Abel
- Center for the Neural Basis of Cognition, Carnegie Mellon University and University of Pittsburgh, Pittsburgh, USA
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, USA
- Brain Institute, University of Pittsburgh, Pittsburgh, USA
| | - Anto Bagić
- Department of Neurology, University of Pittsburgh, Pittsburgh, USA
| | - Lisa S Parker
- School of Public Health, University of Pittsburgh, Pittsburgh, USA
| | - R Mark Richardson
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, USA
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, USA
| | - Charles E Schroeder
- Nathan Kline Institute for Psychiatric Research, Orangeburg, USA
- Departments of Neurosurgery and Psychiatry, Columbia University, New York, USA
| | | | - Avniel Singh Ghuman
- Center for the Neural Basis of Cognition, Carnegie Mellon University and University of Pittsburgh, Pittsburgh, USA
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, USA
- Brain Institute, University of Pittsburgh, Pittsburgh, USA
- Departments of Psychology, Neurobiology, and Psychiatry, University of Pittsburgh, Pittsburgh, USA
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5
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Hansmeyer L, Yurt P, Agha N, Trunk A, Berger M, Calapai A, Treue S, Gail A. Home-Enclosure-Based Behavioral and Wireless Neural Recording Setup for Unrestrained Rhesus Macaques. eNeuro 2023; 10:ENEURO.0285-22.2022. [PMID: 36564215 PMCID: PMC9836026 DOI: 10.1523/eneuro.0285-22.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 12/05/2022] [Accepted: 12/06/2022] [Indexed: 12/24/2022] Open
Abstract
Electrophysiological studies with behaving nonhuman primates often require the separation of animals from their social group as well as partial movement restraint to perform well-controlled experiments. When the research goal per se does not mandate constraining the animals' movements, there are often still experimental needs imposed by tethered data acquisition. Recent technological advances meanwhile allow wireless neurophysiological recordings at high band-width in limited-size enclosures. Here, we demonstrate wireless neural recordings at single unit resolution from unrestrained rhesus macaques while they performed self-paced, structured visuomotor tasks on our custom-built, stand-alone touchscreen system [eXperimental Behavioral Instrument (XBI)] in their home environment. We were able to successfully characterize neural tuning to task parameters, such as visuo-spatial selectivity during movement planning and execution, as expected from existing findings obtained via setup-based neurophysiology recordings. We conclude that when movement restraint and/or a highly controlled, insulated environment are not necessary for scientific reasons, cage-based wireless neural recordings are a viable option. We propose an approach that allows the animals to engage in a self-paced manner with our XBI device, both for fully automatized training and cognitive testing, as well as neural data acquisition in their familiar environment, maintaining auditory and sometimes visual contact with their conspecifics.
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Affiliation(s)
- Laura Hansmeyer
- Cognitive Neuroscience Laboratory, German Primate Center, 37077 Göttingen, Germany
- Göttingen Graduate Center for Neurosciences, Biophysics, and Molecular Biosciences, University of Göttingen, 37073 Göttingen, Germany
| | - Pinar Yurt
- Cognitive Neuroscience Laboratory, German Primate Center, 37077 Göttingen, Germany
- Georg-August University School of Science, 37073 Göttingen, Germany
| | - Naubahar Agha
- Cognitive Neuroscience Laboratory, German Primate Center, 37077 Göttingen, Germany
| | - Attila Trunk
- Cognitive Neuroscience Laboratory, German Primate Center, 37077 Göttingen, Germany
| | - Michael Berger
- Cognitive Neuroscience Laboratory, German Primate Center, 37077 Göttingen, Germany
- Laboratory of Neural Systems, The Rockefeller University, New York, NY 10065
| | - Antonino Calapai
- Cognitive Neuroscience Laboratory, German Primate Center, 37077 Göttingen, Germany
- Leibniz ScienceCampus Primate Cognition, 37077 Göttingen, Germany
| | - Stefan Treue
- Cognitive Neuroscience Laboratory, German Primate Center, 37077 Göttingen, Germany
- Bernstein Center for Computational Neuroscience, University of Göttingen, 37073 Göttingen, Germany
- Leibniz ScienceCampus Primate Cognition, 37077 Göttingen, Germany
- Faculty for Biology and Psychology, University of Göttingen, 37073 Göttingen, Germany
| | - Alexander Gail
- Cognitive Neuroscience Laboratory, German Primate Center, 37077 Göttingen, Germany
- Bernstein Center for Computational Neuroscience, University of Göttingen, 37073 Göttingen, Germany
- Leibniz ScienceCampus Primate Cognition, 37077 Göttingen, Germany
- Faculty for Biology and Psychology, University of Göttingen, 37073 Göttingen, Germany
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6
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Simeral JD, Hosman T, Saab J, Flesher SN, Vilela M, Franco B, Kelemen J, Brandman DM, Ciancibello JG, Rezaii PG, Eskandar EN, Rosler DM, Shenoy KV, Henderson JM, Nurmikko AV, Hochberg LR. Home Use of a Percutaneous Wireless Intracortical Brain-Computer Interface by Individuals With Tetraplegia. IEEE Trans Biomed Eng 2021; 68:2313-2325. [PMID: 33784612 PMCID: PMC8218873 DOI: 10.1109/tbme.2021.3069119] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
OBJECTIVE Individuals with neurological disease or injury such as amyotrophic lateral sclerosis, spinal cord injury or stroke may become tetraplegic, unable to speak or even locked-in. For people with these conditions, current assistive technologies are often ineffective. Brain-computer interfaces are being developed to enhance independence and restore communication in the absence of physical movement. Over the past decade, individuals with tetraplegia have achieved rapid on-screen typing and point-and-click control of tablet apps using intracortical brain-computer interfaces (iBCIs) that decode intended arm and hand movements from neural signals recorded by implanted microelectrode arrays. However, cables used to convey neural signals from the brain tether participants to amplifiers and decoding computers and require expert oversight, severely limiting when and where iBCIs could be available for use. Here, we demonstrate the first human use of a wireless broadband iBCI. METHODS Based on a prototype system previously used in pre-clinical research, we replaced the external cables of a 192-electrode iBCI with wireless transmitters and achieved high-resolution recording and decoding of broadband field potentials and spiking activity from people with paralysis. Two participants in an ongoing pilot clinical trial completed on-screen item selection tasks to assess iBCI-enabled cursor control. RESULTS Communication bitrates were equivalent between cabled and wireless configurations. Participants also used the wireless iBCI to control a standard commercial tablet computer to browse the web and use several mobile applications. Within-day comparison of cabled and wireless interfaces evaluated bit error rate, packet loss, and the recovery of spike rates and spike waveforms from the recorded neural signals. In a representative use case, the wireless system recorded intracortical signals from two arrays in one participant continuously through a 24-hour period at home. SIGNIFICANCE Wireless multi-electrode recording of broadband neural signals over extended periods introduces a valuable tool for human neuroscience research and is an important step toward practical deployment of iBCI technology for independent use by individuals with paralysis. On-demand access to high-performance iBCI technology in the home promises to enhance independence and restore communication and mobility for individuals with severe motor impairment.
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Affiliation(s)
| | - Thomas Hosman
- CfNN and the School of Engineering, Brown University
| | - Jad Saab
- CfNN and the School of Engineering, Brown University. He is now with Insight Data Science, New York City, NY
| | - Sharlene N. Flesher
- Department of Electrical Engineering, Department of Neurosurgery, and Howard Hughes Medical Institute, Stanford University. She is now with Apple Inc., Cupertino, CA
| | - Marco Vilela
- School of Engineering, Brown University. He is now with Takeda, Cambridge, MA
| | - Brian Franco
- Department of Neurology, Massachusetts General Hospital, Boston, MA. He is now with NeuroPace Inc., Mountain View, CA
| | - Jessica Kelemen
- Department of Neurology, Massachusetts General Hospital, Boston
| | - David M. Brandman
- School of Engineering, Brown University. He is now with the Department of Neurosurgery, Emory University, Atlanta, GA
| | - John G. Ciancibello
- School of Engineering, Brown University. He is now with the Center for Bioelectronic Medicine at the Feinstein Institute for Medical Research, Manhasset, NY
| | - Paymon G. Rezaii
- Department of Neurosurgery, Stanford University. He is now with the School of Medicine, Tulane University
| | - Emad N. Eskandar
- Department of Neurosurgery, Massachusetts General Hospital. He is now with the Department of Neurosurgery, Albert Einstein College of Medicine, Montefiore Medical Center, NY
| | - David M. Rosler
- CfNN and the School of Engineering and the Robert J. & Nancy D. Carney Institute for Brain Science, Brown University, and also with the Department of Neurology, Massachusetts General Hospital
| | - Krishna V. Shenoy
- Departments of Electrical Engineering, Bioengineering and Neurobiology, Wu Tsai Neurosciences Institute, and the Bio-X Institute, Stanford, and also with the Howard Hughes Medical Institute, Stanford University
| | - Jaimie M. Henderson
- Department of Neurosurgery and Wu Tsai Neurosciences Institute and the Bio-X Institute, Stanford University
| | - Arto V. Nurmikko
- School of Engineering, Department of Physics, and the Robert J. & Nancy D. Carney Institute for Brain Science, Brown University
| | - Leigh R. Hochberg
- CfNN, and the School of Engineering and the Robert J. & Nancy D. Carney Institute for Brain Science, Brown University, and the Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School
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7
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Griggs DJ, Bloch J, Chavan S, Coubrough KM, Conley W, Morrisroe K, Yazdan-Shahmorad A. Autonomous cage-side system for remote training of non-human primates. J Neurosci Methods 2021; 348:108969. [PMID: 33039414 PMCID: PMC8384435 DOI: 10.1016/j.jneumeth.2020.108969] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Revised: 10/06/2020] [Accepted: 10/06/2020] [Indexed: 01/05/2023]
Abstract
BACKGROUND Training non-human primates (NHPs) for translational medical experimentation is an essential yet time consuming process. To increase training efficiency, some training systems have been designed for NHPs to use at their home cages. Several autonomous cage-side tablet-based systems have been proposed, but none of these systems allow for remote monitoring and task modification while also being wireless, low-cost, light weight, and portable. NEW METHOD Here we present ACTS: an Autonomous Cage-side Training System which meets all these criteria. ACTS consists of 1) a touchscreen tablet and a speaker attached to the subject's home cage, 2) an inexpensive reward system made from a slightly modified fish feeder, and 3), a laptop operating the system wirelessly and remotely via a router. RESULTS We were able to test the system and wirelessly train two macaques in their home cages. Remote access enabled us to control ACTS from up to 90 m, through up to 3 walls, and through a floor of a building. The device is compatible with different reward pellet sizes and could run about two hours with a ∼4 mm pellet size. The animals were able to generalize the task when transferred to a traditional experimental rig. COMPARISON WITH EXISTING METHODS The low cost and modest skill required to build and implement ACTS lowers the barrier for NHP researchers and caregivers to deploy autonomous, remotely controlled tablet-based cage-side systems. CONCLUSION ACTS can be used for low-cost, wireless cage-side training of NHPs being prepared for translational medical experimentation.
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Affiliation(s)
- Devon J Griggs
- Dept. Electrical and Computer Engineering, University of Washington, Seattle, WA, United States; Washington National Primate Research Center, Seattle, WA, United States
| | - Julien Bloch
- Washington National Primate Research Center, Seattle, WA, United States; Dept. of Bioengineering, University of Washington, Seattle, WA, United States
| | - Shivalika Chavan
- Dept. of Bioengineering, University of Washington, Seattle, WA, United States
| | - Kali M Coubrough
- Dept. of Bioengineering, University of Washington, Seattle, WA, United States
| | - William Conley
- South Kitsap High School, Port Orchard, WA, United States
| | - Kelly Morrisroe
- Washington National Primate Research Center, Seattle, WA, United States
| | - Azadeh Yazdan-Shahmorad
- Dept. Electrical and Computer Engineering, University of Washington, Seattle, WA, United States; Washington National Primate Research Center, Seattle, WA, United States; Dept. of Bioengineering, University of Washington, Seattle, WA, United States.
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8
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Berger M, Agha NS, Gail A. Wireless recording from unrestrained monkeys reveals motor goal encoding beyond immediate reach in frontoparietal cortex. eLife 2020; 9:e51322. [PMID: 32364495 PMCID: PMC7228770 DOI: 10.7554/elife.51322] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 05/02/2020] [Indexed: 11/25/2022] Open
Abstract
System neuroscience of motor cognition regarding the space beyond immediate reach mandates free, yet experimentally controlled movements. We present an experimental environment (Reach Cage) and a versatile visuo-haptic interaction system (MaCaQuE) for investigating goal-directed whole-body movements of unrestrained monkeys. Two rhesus monkeys conducted instructed walk-and-reach movements towards targets flexibly positioned in the cage. We tracked 3D multi-joint arm and head movements using markerless motion capture. Movements show small trial-to-trial variability despite being unrestrained. We wirelessly recorded 192 broad-band neural signals from three cortical sensorimotor areas simultaneously. Single unit activity is selective for different reach and walk-and-reach movements. Walk-and-reach targets could be decoded from premotor and parietal but not motor cortical activity during movement planning. The Reach Cage allows systems-level sensorimotor neuroscience studies with full-body movements in a configurable 3D spatial setting with unrestrained monkeys. We conclude that the primate frontoparietal network encodes reach goals beyond immediate reach during movement planning.
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Affiliation(s)
- Michael Berger
- Cognitive Neuroscience Laboratory, German Primate Center – Leibniz-Institute for Primate ResearchGoettingenGermany
- Faculty of Biology and Psychology, University of GoettingenGoettingenGermany
| | - Naubahar Shahryar Agha
- Cognitive Neuroscience Laboratory, German Primate Center – Leibniz-Institute for Primate ResearchGoettingenGermany
| | - Alexander Gail
- Cognitive Neuroscience Laboratory, German Primate Center – Leibniz-Institute for Primate ResearchGoettingenGermany
- Faculty of Biology and Psychology, University of GoettingenGoettingenGermany
- Leibniz-ScienceCampus Primate CognitionGoettingenGermany
- Bernstein Center for Computational NeuroscienceGoettingenGermany
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9
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Milton R, Shahidi N, Dragoi V. Dynamic states of population activity in prefrontal cortical networks of freely-moving macaque. Nat Commun 2020; 11:1948. [PMID: 32327660 PMCID: PMC7181779 DOI: 10.1038/s41467-020-15803-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Accepted: 03/23/2020] [Indexed: 12/22/2022] Open
Abstract
Neural responses in the cerebral cortex change dramatically between the 'synchronized' state during sleep and 'desynchronized' state during wakefulness. Our understanding of cortical state emerges largely from experiments performed in sensory areas of head-fixed or tethered rodents due to technical limitations of recording from larger freely-moving animals for several hours. Here, we report a system integrating wireless electrophysiology, wireless eye tracking, and real-time video analysis to examine the dynamics of population activity in a high-level, executive area - dorsolateral prefrontal cortex (dlPFC) of unrestrained monkey. This technology allows us to identify cortical substates during quiet and active wakefulness, and transitions in population activity during rest. We further show that narrow-spiking neurons exhibit stronger synchronized fluctuations in population activity than broad-spiking neurons regardless of state. Our results show that cortical state is controlled by behavioral demands and arousal by asymmetrically modulating the slow response fluctuations of local excitatory and inhibitory cell populations.
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Affiliation(s)
- Russell Milton
- Department of Neurobiology & Anatomy, McGovern Medical School, University of Texas, Houston, TX, 77030, USA
| | - Neda Shahidi
- Department of Neurobiology & Anatomy, McGovern Medical School, University of Texas, Houston, TX, 77030, USA
| | - Valentin Dragoi
- Department of Neurobiology & Anatomy, McGovern Medical School, University of Texas, Houston, TX, 77030, USA.
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, 77005, USA.
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10
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Vizvari Z, Toth A, Sari Z, Klincsik M, Kuljic B, Szakall T, Odry A, Mathe K, Szabo I, Karadi Z, Odry P. Measurement system with real time data converter for conversion of I 2 S data stream to UDP protocol data. Heliyon 2020; 6:e03760. [PMID: 32346631 PMCID: PMC7182730 DOI: 10.1016/j.heliyon.2020.e03760] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 01/01/2020] [Accepted: 04/06/2020] [Indexed: 01/24/2023] Open
Abstract
A central goal of systems neuroscience is to simultaneously measure the activities of all achievable neurons in the brain at millisecond resolution in freely moving animals. This paper describes a protocol converter which is part of a measurement acquisition system for multichannel real time recording of brain signals. In practice, in such techniques, a primary consideration of reliability leads to great necessity towards increasing the sampling rate of these signals while simultaneously increasing the resolution of A/D conversion to 24 bits or even to the unprecedented 32 bits per sample. In fact, this was the guiding principle for our team in the present study. By increasing the temporal and amplitude resolution, it is supposed that we get enabled to discover or recognize and identify new signal components which have previously been masked at a "low" temporal and amplitude resolution, and these new signal components, in the future, are likely to contribute to a deeper understanding of the workings of the brain.
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Affiliation(s)
- Zoltan Vizvari
- Department of Environmental Engineering, Faculty of Engineering and Information Technology, University of Pécs, Boszorkány str. 2, H-7624 Pécs, Hungary
| | - Attila Toth
- Institute of Physiology, Medical School, University of Pécs, Szigeti str 12, H-7624 Pécs, Hungary
| | - Zoltan Sari
- Department of Information Technology, Faculty of Engineering and Information Technology, University of Pécs, Boszorkány str. 2, H-7624 Pécs, Hungary
| | - Mihaly Klincsik
- Department of Mathematics, Faculty of Engineering and Information Technology, University of Pécs, Boszorkány str. 2, H-7624 Pécs, Hungary
| | - Bojan Kuljic
- College of Applied Sciences, Subotica Tech, Marka Oreškoviċa 16, 24000 Subotica, Serbia
| | - Tibor Szakall
- College of Applied Sciences, Subotica Tech, Marka Oreškoviċa 16, 24000 Subotica, Serbia
| | - Akos Odry
- Institute of Information Technology, University of Dunaujvaros, Táncsics M. str. 1/A, H-2401 Dunaújváros, Hungary
| | - Kalman Mathe
- Faculty of Engineering and Information Technology, University of Pécs, Boszorkány str. 2, H-7624 Pécs, Hungary
| | - Imre Szabo
- Department of Behavioural Sciences, Medical School, University of Pécs, Szigeti str 12, H-7624 Pécs, Hungary
| | - Zoltan Karadi
- Institute of Physiology, Medical School, University of Pécs, Szigeti str 12, H-7624 Pécs, Hungary
| | - Peter Odry
- Institute of Information Technology, University of Dunaujvaros, Táncsics M. str. 1/A, H-2401 Dunaújváros, Hungary
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11
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Abstract
Training nonhuman primates (NHPs) to perform cognitive tasks is essential for many neuroscientific investigations, yet laboratory training is a time-consuming process with inherent limitations. Habituating NHPs to the laboratory staff and experimental equipment can take months before NHPs are ready to proceed to the primary tasks. Laboratory training also necessarily separates NHPs from their home-room social group and typically involves some form of restraint or limited mobility, and data collection is often limited to a few hours per day so that multiple NHPs can be trained on the same equipment. Consequently, it can often take a year to train NHPs on complex cognitive tasks. To overcome these issues, we developed a low-cost, open-source, wireless touchscreen training system that can be installed in the home-room environment. The automated device can run continuously all day, including over weekends, without experimenter intervention. The system utilizes real-time facial recognition to initiate subject-specific tasks and provide accurate data logging, without the need for implanted microchips or separation of the NHPs. The system allows NHPs to select their preferred reward on each trial and to work when and for as long as they desire, and it can analyze task performance in real time and adapt the task parameters in order to expedite training. We demonstrate that NHPs consistently use this system on a daily basis to quickly learn complex behavioral tasks. The system therefore addresses many of the welfare and experimental limitations of laboratory-based training of NHPs and provides a platform for wireless electrophysiological investigations in more naturalistic, freely moving environments.
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12
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Rosenthal J, Sharma A, Kampianakis E, Reynolds MS. A 25 Mbps, 12.4 pJ/b DQPSK Backscatter Data Uplink for the NeuroDisc Brain-Computer Interface. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2019; 13:858-867. [PMID: 31478872 DOI: 10.1109/tbcas.2019.2938511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Wireless brain-computer interfaces (BCIs) are used to study neural activity in freely moving non-human primates (NHPs). However, the high energy consumption of conventional active radios is proving to be an obstacle as research drives for wireless BCIs that can provide continuous high-rate data uplinks for longer durations (i.e. multiple days). We present a differential quadrature phase shift keying (DQPSK) backscatter uplink for the NeuroDisc BCI as an alternative to active radios. The uplink achieves a 25 Mbps throughput while operating in the 915 MHz industrial, scientific, and medical (ISM) band. The DQPSK backscatter modulator was measured to have an error-vector magnitude (EVM) of 9.7% and a measured power consumption of 309 μW during continuous, full-rate transmissions, yielding an analog communication efficiency of 12.4 pJ/bit. The NeuroDisc is capable of recording 16 channels of neural data with 16-bit resolution at up to 20 kSps per channel with a measured input-referred noise of 2.35 μV. In previous work, we demonstrated the DQPSK backscatter uplink, but bandwidth constraints in the signal chain limited the uplink rate to 6.25 Mbps and the neural sampling rate to 5 kSps. This work provides new innovations to increase the bandwidth of the system, including an ultra-high frequency (UHF) antenna design with a -10 dB return loss bandwidth of 12.5 MHz and a full-duplex receiver with an average self-jammer cancellation of 89 dB. We present end-to-end characterization of the NeuroDisc and validate the backscatter uplink using pre-recorded neural data as well as in vivo recordings from a pigtail macaque.
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13
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Umeda T, Koizumi M, Katakai Y, Saito R, Seki K. Decoding of muscle activity from the sensorimotor cortex in freely behaving monkeys. Neuroimage 2019; 197:512-526. [PMID: 31015029 DOI: 10.1016/j.neuroimage.2019.04.045] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2018] [Revised: 04/12/2019] [Accepted: 04/16/2019] [Indexed: 01/06/2023] Open
Abstract
Remarkable advances have recently been made in the development of Brain-Machine Interface (BMI) technologies for restoring or enhancing motor function. However, the application of these technologies may be limited to patients in static conditions, as these developments have been largely based on studies of animals (e.g., non-human primates) in constrained movement conditions. The ultimate goal of BMI technology is to enable individuals to move their bodies naturally or control external devices without physical constraints. Here, we demonstrate accurate decoding of muscle activity from electrocorticogram (ECoG) signals in unrestrained, freely behaving monkeys. We recorded ECoG signals from the sensorimotor cortex as well as electromyogram signals from multiple muscles in the upper arm while monkeys performed two types of movements with no physical restraints, as follows: forced forelimb movement (lever-pull task) and natural whole-body movement (free movement within the cage). As in previous reports using restrained monkeys, we confirmed that muscle activity during forced forelimb movement was accurately predicted from simultaneously recorded ECoG data. More importantly, we demonstrated that accurate prediction of muscle activity from ECoG data was possible in monkeys performing natural whole-body movement. We found that high-gamma activity in the primary motor cortex primarily contributed to the prediction of muscle activity during natural whole-body movement as well as forced forelimb movement. In contrast, the contribution of high-gamma activity in the premotor and primary somatosensory cortices was significantly larger during natural whole-body movement. Thus, activity in a larger area of the sensorimotor cortex was needed to predict muscle activity during natural whole-body movement. Furthermore, decoding models obtained from forced forelimb movement could not be generalized to natural whole-body movement, which suggests that decoders should be built individually and according to different behavior types. These results contribute to the future application of BMI systems in unrestrained individuals.
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Affiliation(s)
- Tatsuya Umeda
- Department of Neurophysiology, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo, 1878502, Japan.
| | - Masashi Koizumi
- Department of Neurophysiology, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo, 1878502, Japan
| | - Yuko Katakai
- Administrative Section of Primate Research Facility, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo, 1878502, Japan; The Corporation for Production and Research of Laboratory Primates, Tsukuba, Ibaraki, 3050003, Japan
| | - Ryoichi Saito
- Administrative Section of Primate Research Facility, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo, 1878502, Japan
| | - Kazuhiko Seki
- Department of Neurophysiology, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo, 1878502, Japan.
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14
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Jennings CG, Landman R, Zhou Y, Sharma J, Hyman J, Movshon JA, Qiu Z, Roberts AC, Roe AW, Wang X, Zhou H, Wang L, Zhang F, Desimone R, Feng G. Opportunities and challenges in modeling human brain disorders in transgenic primates. Nat Neurosci 2017; 19:1123-30. [PMID: 27571191 DOI: 10.1038/nn.4362] [Citation(s) in RCA: 88] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Accepted: 07/19/2016] [Indexed: 12/15/2022]
Abstract
Molecular genetic tools have had a profound impact on neuroscience, but until recently their application has largely been confined to a few model species, most notably mouse, zebrafish, Drosophila melanogaster and Caenorhabditis elegans. With the development of new genome engineering technologies such as CRISPR, it is becoming increasingly feasible to apply these molecular tools in a wider range of species, including nonhuman primates. This will lead to many opportunities for brain research, but it will also pose challenges. Here we identify some of these opportunities and challenges in light of recent and foreseeable technological advances and offer some suggestions. Our main focus is on the creation of new primate disease models for understanding the pathological mechanisms of brain disorders and for developing new approaches to effective treatment. However, we also emphasize that primate genetic models have great potential to address many fundamental questions about brain function, providing an essential foundation for future progress in disease research.
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Affiliation(s)
- Charles G Jennings
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Rogier Landman
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Yang Zhou
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Jitendra Sharma
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Julia Hyman
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - J Anthony Movshon
- Center for Neural Science, New York University, New York, New York, USA
| | - Zilong Qiu
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Angela C Roberts
- Department of Physiology, Development and Neuroscience, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Anna Wang Roe
- Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University, Hangzhou, China
| | - Xiaoqin Wang
- Laboratory of Auditory Neurophysiology, Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Huihui Zhou
- The Brain Cognition and Brain Disease Institute (BCBDI) for Collaboration Research of SIAT at CAS and McGovern Institute at MIT, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Science, Shenzhen, China
| | - Liping Wang
- Shenzhen Key Lab of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, CAS Center for Excellence in Brain Science and Intelligence Technology, The Brain Cognition and Brain Disease Institute (BCBDI) for Collaboration Research of SIAT at CAS and McGovern Institute at MIT, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Science, Shenzhen, China
| | - Feng Zhang
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Robert Desimone
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Guoping Feng
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
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15
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Libey T, Fetz EE. Open-Source, Low Cost, Free-Behavior Monitoring, and Reward System for Neuroscience Research in Non-human Primates. Front Neurosci 2017; 11:265. [PMID: 28559792 PMCID: PMC5432619 DOI: 10.3389/fnins.2017.00265] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Accepted: 04/24/2017] [Indexed: 11/16/2022] Open
Abstract
We describe a low-cost system designed to document bodily movement and neural activity and deliver rewards to monkeys behaving freely in their home cage. An important application is to studying brain-machine interface (BMI) systems during free behavior, since brain signals associated with natural movement can differ significantly from those associated with more commonly used constrained conditions. Our approach allows for short-latency (<500 ms) reward delivery and behavior monitoring using low-cost off-the-shelf components. This system interfaces existing untethered recording equipment with a custom hub that controls a cage-mounted feeder. The behavior monitoring system uses a depth camera to provide real-time, easy-to-analyze, gross movement data streams. In a proof-of-concept experiment we demonstrate robust learning of neural activity using the system over 14 behavioral sessions.
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Affiliation(s)
- Tyler Libey
- Department of Bioengineering, University of WashingtonSeattle, WA, United States
| | - Eberhard E Fetz
- Department of Bioengineering, University of WashingtonSeattle, WA, United States.,Department of Physiology and Biophysics, University of WashingtonSeattle, WA, United States.,National Science Foundation ERC Center for Sensorimotor Neural EngineeringSeattle, WA, United States
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