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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 2024:1-21. [PMID: 38423536 DOI: 10.1631/jzus.b2300400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [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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Yi D, Yao Y, Wang Y, Chen L. Manufacturing Processes of Implantable Microelectrode Array for In Vivo Neural Electrophysiological Recordings and Stimulation: A State-Of-the-Art Review. JOURNAL OF MICRO- AND NANO-MANUFACTURING 2022; 10:041001. [PMID: 37860671 PMCID: PMC10583290 DOI: 10.1115/1.4063179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 08/08/2023] [Indexed: 10/21/2023]
Abstract
Electrophysiological recording and stimulation of neuron activities are important for us to understand the function and dysfunction of the nervous system. To record/stimulate neuron activities as voltage fluctuation extracellularly, microelectrode array (MEA) implants are a promising tool to provide high temporal and spatial resolution for neuroscience studies and medical treatments. The design configuration and recording capabilities of the MEAs have evolved dramatically since their invention and manufacturing process development has been a key driving force for such advancement. Over the past decade, since the White House Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative launched in 2013, advanced manufacturing processes have enabled advanced MEAs with increased channel count and density, access to more brain areas, more reliable chronic performance, as well as minimal invasiveness and tissue reaction. In this state-of-the-art review paper, three major types of electrophysiological recording MEAs widely used nowadays, namely, microwire-based, silicon-based, and flexible MEAs are introduced and discussed. Conventional design and manufacturing processes and materials used for each type are elaborated, followed by a review of further development and recent advances in manufacturing technologies and the enabling new designs and capabilities. The review concludes with a discussion on potential future directions of manufacturing process development to enable the long-term goal of large-scale high-density brain-wide chronic recordings in freely moving animals.
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Affiliation(s)
- Dongyang Yi
- Department of Mechanical and Industrial Engineering, University of Massachusetts Lowell, 1 University Avenue, Lowell, MA 01854
| | - Yao Yao
- Department of Industrial and Systems Engineering, University of Missouri, 416 South 6th Street, Columbia, MO 65211
| | - Yi Wang
- Department of Industrial and Systems Engineering, University of Missouri, E3437C Thomas & Nell Lafferre Hall, 416 South 6th Street, Columbia, MO 65211
| | - Lei Chen
- Department of Mechanical and Industrial Engineering, University of Massachusetts Lowell, 1 University Avenue, Lowell, MA 01854
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Erofeev A, Antifeev I, Bolshakova A, Bezprozvanny I, Vlasova O. In Vivo Penetrating Microelectrodes for Brain Electrophysiology. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22239085. [PMID: 36501805 PMCID: PMC9735502 DOI: 10.3390/s22239085] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/14/2022] [Accepted: 11/22/2022] [Indexed: 05/13/2023]
Abstract
In recent decades, microelectrodes have been widely used in neuroscience to understand the mechanisms behind brain functions, as well as the relationship between neural activity and behavior, perception and cognition. However, the recording of neuronal activity over a long period of time is limited for various reasons. In this review, we briefly consider the types of penetrating chronic microelectrodes, as well as the conductive and insulating materials for microelectrode manufacturing. Additionally, we consider the effects of penetrating microelectrode implantation on brain tissue. In conclusion, we review recent advances in the field of in vivo microelectrodes.
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Affiliation(s)
- Alexander Erofeev
- Laboratory of Molecular Neurodegeneration, Graduate School of Biomedical Systems and Technologies, Institute of Biomedical Systems and Biotechnology, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia
- Correspondence: (A.E.); (O.V.)
| | - Ivan Antifeev
- Laboratory of Methods and Instruments for Genetic and Immunoassay Analysis, Institute for Analytical Instrumentation of the Russian Academy of Sciences, 198095 Saint Petersburg, Russia
| | - Anastasia Bolshakova
- Laboratory of Molecular Neurodegeneration, Graduate School of Biomedical Systems and Technologies, Institute of Biomedical Systems and Biotechnology, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia
| | - Ilya Bezprozvanny
- Laboratory of Molecular Neurodegeneration, Graduate School of Biomedical Systems and Technologies, Institute of Biomedical Systems and Biotechnology, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia
- Department of Physiology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390, USA
| | - Olga Vlasova
- Laboratory of Molecular Neurodegeneration, Graduate School of Biomedical Systems and Technologies, Institute of Biomedical Systems and Biotechnology, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia
- Correspondence: (A.E.); (O.V.)
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Shen J, Xu Y, Xiao Z, Liu Y, Liu H, Wang F, Yan C, Wang L, Chen C, Wu Z, Liu Y, Mak PU, Vai MI, Pun SH, Lei TC, Zhang B. Double-Sided Sapphire Optrodes with Conductive Shielding Layers to Reduce Optogenetic Stimulation Artifacts. MICROMACHINES 2022; 13:1836. [PMID: 36363857 PMCID: PMC9695949 DOI: 10.3390/mi13111836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 10/15/2022] [Accepted: 10/18/2022] [Indexed: 06/16/2023]
Abstract
Optrodes, which are single shaft neural probes integrated with microelectrodes and optical light sources, offer a remarkable opportunity to simultaneously record and modulate neural activities using light within an animal's brain; however, a common problem with optrodes is that stimulation artifacts can be observed in the neural recordings of microelectrodes when the light source on the optrode is activated. These stimulation artifacts are undesirable contaminants, and they cause interpretation complexity when analyzing the recorded neural activities. In this paper, we tried to mitigate the effects of the stimulation artifacts by developing a low-noise, double-sided optrode integrated with multiple Electromagnetic Shielding (EMS) layers. The LED and microelectrodes were constructed separately on the top epitaxial and bottom substrate layers, and EMS layers were used to separate the microelectrodes and LED to reduce signal cross-talks. Compared with conventional single-sided designs, in which the LED and microelectrodes are constructed on the same side, our results indicate that double-sided optrodes can significantly reduce the presence of stimulation artifacts. In addition, the presence of stimulation artifacts can further be reduced by decreasing the voltage difference and increasing the rise/fall time of the driving LED pulsed voltage. With all these strategies, the presence of stimulation artifacts was significantly reduced by ~76%. As well as stimulation suppression, the sapphire substrate also provided strong mechanical stiffness and support to the optrodes, as well as improved electronic stability, thus making the double-sided sapphire optrodes highly suitable for optogenetic neuroscience research on animal models.
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Affiliation(s)
- Junyu Shen
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Yanyan Xu
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Zhengwen Xiao
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Yuebo Liu
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Honghui Liu
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Fengge Wang
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Chaokun Yan
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Liyang Wang
- State Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau 999078, China
| | - Changhao Chen
- State Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau 999078, China
| | - Zhisheng Wu
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
- State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou 510275, China
| | - Yang Liu
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
- State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou 510275, China
| | - Peng Un Mak
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau 999078, China
| | - Mang I. Vai
- State Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau 999078, China
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau 999078, China
| | - Sio Hang Pun
- State Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau 999078, China
| | - Tim C. Lei
- Department of Electrical Engineering, University of Colorado, Denver, CO 80204, USA
| | - Baijun Zhang
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
- State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou 510275, China
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McGlynn E, Walton F, Das R, Heidari H. Neural microprobe modelling and microfabrication for improved implantation and mechanical failure mitigation. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210007. [PMID: 35658676 PMCID: PMC9168446 DOI: 10.1098/rsta.2021.0007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Careful design and material selection are the most beneficial strategies to ensure successful implantation and mitigate the failure of a neural probe in the long term. In order to realize a fully flexible implantable system, the probe should be easily manipulated by neuroscientists, with the potential to bend up to 90°. This paper investigates the impact of material choice, probe geometry, and crucially, implantation angle on implantation success through finite-element method simulations in COMSOL Multiphysics followed by cleanroom microfabrication. The designs introduced in this paper were fabricated using two polyimides: (i) PI-2545 as a release layer and (ii) photodefinable HD-4110 as the probe substrate. Four different designs were microfabricated, and the implantation tests were compared between an agarose brain phantom and lamb brain samples. The probes were scanned in a 7 T PharmaScan MRI coil to investigate potential artefacts. From the simulation, a triangular base and 50 µm polymer thickness were identified as the optimum design, which produced a probe 57.7 µm thick when fabricated. The probes exhibit excellent flexibility, exemplified in three-point bending tests performed with a DAGE 4000Plus. Successful implantation is possible for a range of angles between 30° and 90°. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.
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Affiliation(s)
- Eve McGlynn
- Microelectronics Lab (meLAB), James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
| | - Finlay Walton
- Microelectronics Lab (meLAB), James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
| | - Rupam Das
- Microelectronics Lab (meLAB), James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
| | - Hadi Heidari
- Microelectronics Lab (meLAB), James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
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A Miniaturized Closed-Loop Optogenetic Brain Stimulation Device. ELECTRONICS 2022. [DOI: 10.3390/electronics11101591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
This paper presents a tetherless and miniaturized closed-loop optogenetic brain stimulation device, designed as a back mountable device for laboratory mice. The device has the ability to sense the biomarkers corresponding to major depressive disorder (MDD) from local field potential (LFP), and produces a feedback signal to control the closed-loop operation after on-device processing of the sensed signals. MDD is a chronic neurological disorder and there are still many unanswered questions about the underlying neurological mechanisms behind its occurrence. Along with other brain stimulation paradigms, optogenetics has recently proved effective in the study of MDD. Most of these experiments have used tethered and connected devices. However, the use of tethered devices in optogenetic brain stimulation experiments has the drawback of hindering the free movement of the laboratory animal subjects undergoing stimulation. To address this issue, the proposed device is small, light-weight, untethered, and back-mountable. The device consists of: (i) an optrode which houses an electrode for collecting neural signals, an optical source for delivering light stimulations, and a temperature sensor for monitoring the temperature increase at the stimulation site, (ii) a neural sensor for acquisition and pre-processing of the neural signals to obtain LFP signals in the frequency range of 4 to 200 Hz, as electrophysiological biomarkers of MDD (iii) a classifier for classification of the signal into four classes: normal, abnormal alpha, abnormal theta, and abnormal gamma oscillations, (iv) a control algorithm to select stimulation parameters based on the input class, and (v) a stimulator for generating light stimulations. The design, implementation, and evaluation of the device are presented, and the results are discussed. The neural sensor and the stimulator are circular in shape with a radius of 8 mm. Pre-recorded neural signals from the mouse hippocampus are used for the evaluation of the device.
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Sinha M, Narayanan R. Active Dendrites and Local Field Potentials: Biophysical Mechanisms and Computational Explorations. Neuroscience 2021; 489:111-142. [PMID: 34506834 PMCID: PMC7612676 DOI: 10.1016/j.neuroscience.2021.08.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 08/30/2021] [Accepted: 08/31/2021] [Indexed: 10/27/2022]
Abstract
Neurons and glial cells are endowed with membranes that express a rich repertoire of ion channels, transporters, and receptors. The constant flux of ions across the neuronal and glial membranes results in voltage fluctuations that can be recorded from the extracellular matrix. The high frequency components of this voltage signal contain information about the spiking activity, reflecting the output from the neurons surrounding the recording location. The low frequency components of the signal, referred to as the local field potential (LFP), have been traditionally thought to provide information about the synaptic inputs that impinge on the large dendritic trees of various neurons. In this review, we discuss recent computational and experimental studies pointing to a critical role of several active dendritic mechanisms that can influence the genesis and the location-dependent spectro-temporal dynamics of LFPs, spanning different brain regions. We strongly emphasize the need to account for the several fast and slow dendritic events and associated active mechanisms - including gradients in their expression profiles, inter- and intra-cellular spatio-temporal interactions spanning neurons and glia, heterogeneities and degeneracy across scales, neuromodulatory influences, and activitydependent plasticity - towards gaining important insights about the origins of LFP under different behavioral states in health and disease. We provide simple but essential guidelines on how to model LFPs taking into account these dendritic mechanisms, with detailed methodology on how to account for various heterogeneities and electrophysiological properties of neurons and synapses while studying LFPs.
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Affiliation(s)
- Manisha Sinha
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka 560012, India
| | - Rishikesh Narayanan
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka 560012, India.
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Shen J, Xu Y, Xiao Z, Liu Y, Liu H, Wang F, Yao W, Yan Z, Zhang M, Wu Z, Liu Y, Pun SH, Lei TC, Vai MI, Mak PU, Chen C, Zhang B. Influence of the Surface Material and Illumination upon the Performance of a Microelectrode/Electrolyte Interface in Optogenetics. MICROMACHINES 2021; 12:1061. [PMID: 34577704 PMCID: PMC8471589 DOI: 10.3390/mi12091061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 08/23/2021] [Accepted: 08/27/2021] [Indexed: 11/17/2022]
Abstract
Integrated optrodes for optogenetics have been becoming a significant tool in neuroscience through the combination of offering accurate stimulation to target cells and recording biological signals simultaneously. This makes it not just be widely used in neuroscience researches, but also have a great potential to be employed in future treatments in clinical neurological diseases. To optimize the integrated optrodes, this paper aimed to investigate the influence of surface material and illumination upon the performance of the microelectrode/electrolyte interface and build a corresponding evaluation system. In this work, an integrated planar optrode with a blue LED and microelectrodes was designed and fabricated. The charge transfer mechanism on the interface was theoretically modeled and experimentally verified. An evaluation system for assessing microelectrodes was also built up. Using this system, the proposed model of various biocompatible surface materials on microelectrodes was further investigated under different illumination conditions. The influence of illumination on the microelectrode/electrolyte interface was the cause of optical artifacts, which interfere the biological signal recording. It was found that surface materials had a great effect on the charge transfer capacity, electrical stability and recoverability, photostability, and especially optical artifacts. The metal with better charge transfer capacity and electrical stability is highly possible to have a better performance on the optical artifacts, regardless of its electrical recoverability and photostability under the illumination conditions of optogenetics. Among the five metals used in our investigation, iridium served as the best surface material for the proposed integrated optrodes. Thus, optimizing the surface material for optrodes could reduce optical interference, enhance the quality of the neural signal recording for optogenetics, and thus help to advance the research in neuroscience.
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Grants
- 62061160368 & 0022/2020/AFJ This research was funded by the joint funding of the Nature Science Foundation of China (NSFC) & the Macao Science and Technology Development Fund (FDCT) of China
- 2019B010132003, 2019B010132001 Science & Technology Plan of Guangdong Province, China
- 2016YFB0400105, 2017YFB0403001 the National Key Research and Development Program
- 20167612042080001 the Zhuhai Key Technology Laboratory of Wide Bandgap Semiconductor Power Electronics, Sun Yat-sen University, China
- 088/2016/A2, 0144/2019/A3, 0022/2020/AFJ, SKL-AMSV (FDCT-funded), SKL-AMSV-ADDITIONAL FUND, SKL-AMSV(UM)-2020-2022 the Science and Technology Development Fund, Macau SAR
- MYRG2018-00146-AMSV, MYRG2019-00056-AMSV the University of Macau
- 2020YFB1313502 the National Key R&D Program of China
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Affiliation(s)
- Junyu Shen
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
| | - Yanyan Xu
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
| | - Zhengwen Xiao
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
| | - Yuebo Liu
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
| | - Honghui Liu
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
| | - Fengge Wang
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
| | - Wanqing Yao
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
| | - Zhaokun Yan
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
| | - Minjie Zhang
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
| | - Zhisheng Wu
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
- State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou 510275, China
| | - Yang Liu
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
- State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou 510275, China
| | - Sio Hang Pun
- State Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau 999078, China; (S.H.P.); (M.I.V.); (C.C.)
| | - Tim C. Lei
- Department of Electrical Engineering, University of Colorado Denver, Denver, CO 80204, USA;
| | - Mang I Vai
- State Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau 999078, China; (S.H.P.); (M.I.V.); (C.C.)
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau 999078, China;
| | - Peng Un Mak
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau 999078, China;
| | - Changhao Chen
- State Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau 999078, China; (S.H.P.); (M.I.V.); (C.C.)
| | - Baijun Zhang
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
- State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou 510275, China
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Restoring upper extremity function with brain-machine interfaces. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2021; 159:153-186. [PMID: 34446245 DOI: 10.1016/bs.irn.2021.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
One of the most exciting advances to emerge in neural interface technologies has been the development of real-time brain-machine interface (BMI) neuroprosthetic devices to restore upper extremity function. BMI neuroprostheses, made possible by synergistic advances in neural recording technologies, high-speed computation and signal processing, and neuroscience, have permitted the restoration of volitional movement to patients suffering the loss of upper-extremity function. In this chapter, we review the scientific and technological advances underlying these remarkable devices. After presenting an introduction to the current state of the field, we provide an accessible technical discussion of the two fundamental requirements of a successful neuroprosthesis: signal extraction from the brain and signal decoding that results in robust prosthetic control. We close with a presentation of emerging technologies that are likely to substantially advance the field.
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Tarnavsky Eitan A, Someck S, Zajac M, Socher E, Stark E. Outan: An On-Head System for Driving µLED Arrays Implanted in Freely Moving Mice. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021; 15:303-313. [PMID: 33760740 DOI: 10.1109/tbcas.2021.3068556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
In the intact brain, neural activity can be recorded using sensing electrodes and manipulated using light stimulation. Silicon probes with integrated electrodes and µLEDs enable the detection and control of neural activity using a single implanted device. Miniaturized solutions for recordings from small freely moving animals are commercially available, but stimulation is driven by large, stationary current sources. We designed and fabricated a current source chip and integrated it into a headstage PCB that weighs 1.37 g. The proposed system provides 10-bit resolution current control for 32 channels, driving µLEDs with up to 4.6 V and sourcing up to 0.9 mA at a refresh rate of 5 kHz per channel. When calibrated against a µLED probe, the system allows linear control of light output power, up to 10 µW per µLED. To demonstrate the capabilities of the system, synthetic sequences of neural spiking activity were produced by driving multiple µLEDs implanted in the hippocampal CA1 area of a freely moving mouse. The high spatial, temporal, and amplitude resolution of the system provides a rich variety of stimulation patterns. Combined with commercially available sampling headstages, the system provides an easy to use back-end, fully utilizing the bi-directional potential of integrated opto-electronic arrays.
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Reddy JW, Lassiter M, Chamanzar M. Parylene photonics: a flexible, broadband optical waveguide platform with integrated micromirrors for biointerfaces. MICROSYSTEMS & NANOENGINEERING 2020; 6:85. [PMID: 34567695 PMCID: PMC8433189 DOI: 10.1038/s41378-020-00186-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 06/01/2020] [Accepted: 06/03/2020] [Indexed: 05/24/2023]
Abstract
Targeted light delivery into biological tissue is needed in applications such as optogenetic stimulation of the brain and in vivo functional or structural imaging of tissue. These applications require very compact, soft, and flexible implants that minimize damage to the tissue. Here, we demonstrate a novel implantable photonic platform based on a high-density, flexible array of ultracompact (30 μm × 5 μm), low-loss (3.2 dB/cm at λ = 680 nm, 4.1 dB/cm at λ = 633 nm, 4.9 dB/cm at λ = 532 nm, 6.1 dB/cm at λ = 450 nm) optical waveguides composed of biocompatible polymers Parylene C and polydimethylsiloxane (PDMS). This photonic platform features unique embedded input/output micromirrors that redirect light from the waveguides perpendicularly to the surface of the array for localized, patterned illumination in tissue. This architecture enables the design of a fully flexible, compact integrated photonic system for applications such as in vivo chronic optogenetic stimulation of brain activity.
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Affiliation(s)
- Jay W. Reddy
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA USA
| | - Maya Lassiter
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA USA
| | - Maysamreza Chamanzar
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA USA
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Das R, Moradi F, Heidari H. Biointegrated and Wirelessly Powered Implantable Brain Devices: A Review. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2020; 14:343-358. [PMID: 31944987 DOI: 10.1109/tbcas.2020.2966920] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
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13
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Kim C, Jeong J, Kim SJ. Recent Progress on Non-Conventional Microfabricated Probes for the Chronic Recording of Cortical Neural Activity. SENSORS (BASEL, SWITZERLAND) 2019; 19:E1069. [PMID: 30832357 PMCID: PMC6427797 DOI: 10.3390/s19051069] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 02/25/2019] [Accepted: 02/26/2019] [Indexed: 02/06/2023]
Abstract
Microfabrication technology for cortical interfaces has advanced rapidly over the past few decades for electrophysiological studies and neuroprosthetic devices offering the precise recording and stimulation of neural activity in the cortex. While various cortical microelectrode arrays have been extensively and successfully demonstrated in animal and clinical studies, there remains room for further improvement of the probe structure, materials, and fabrication technology, particularly for high-fidelity recording in chronic implantation. A variety of non-conventional probes featuring unique characteristics in their designs, materials and fabrication methods have been proposed to address the limitations of the conventional standard shank-type ("Utah-" or "Michigan-" type) devices. Such non-conventional probes include multi-sided arrays to avoid shielding and increase recording volumes, mesh- or thread-like arrays for minimized glial scarring and immune response, tube-type or cylindrical probes for three-dimensional (3D) recording and multi-modality, folded arrays for high conformability and 3D recording, self-softening or self-deployable probes for minimized tissue damage and extensions of the recording sites beyond gliosis, nanostructured probes to reduce the immune response, and cone-shaped electrodes for promoting tissue ingrowth and long-term recording stability. Herein, the recent progress with reference to the many different types of non-conventional arrays is reviewed while highlighting the challenges to be addressed and the microfabrication techniques necessary to implement such features.
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Affiliation(s)
- Chaebin Kim
- Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea.
| | - Joonsoo Jeong
- Department of Biomedical Engineering, School of Medicine, Pusan National University, Yangsan 50612, Korea.
| | - Sung June Kim
- Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea.
- Institute on Aging, College of Medicine, Seoul National University, Seoul 08826, Korea.
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Choi JR, Kim SM, Ryu RH, Kim SP, Sohn JW. Implantable Neural Probes for Brain-Machine Interfaces - Current Developments and Future Prospects. Exp Neurobiol 2018; 27:453-471. [PMID: 30636899 PMCID: PMC6318554 DOI: 10.5607/en.2018.27.6.453] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 11/15/2018] [Accepted: 11/15/2018] [Indexed: 12/14/2022] Open
Abstract
A Brain-Machine interface (BMI) allows for direct communication between the brain and machines. Neural probes for recording neural signals are among the essential components of a BMI system. In this report, we review research regarding implantable neural probes and their applications to BMIs. We first discuss conventional neural probes such as the tetrode, Utah array, Michigan probe, and electroencephalography (ECoG), following which we cover advancements in next-generation neural probes. These next-generation probes are associated with improvements in electrical properties, mechanical durability, biocompatibility, and offer a high degree of freedom in practical settings. Specifically, we focus on three key topics: (1) novel implantable neural probes that decrease the level of invasiveness without sacrificing performance, (2) multi-modal neural probes that measure both electrical and optical signals, (3) and neural probes developed using advanced materials. Because safety and precision are critical for practical applications of BMI systems, future studies should aim to enhance these properties when developing next-generation neural probes.
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Affiliation(s)
- Jong-Ryul Choi
- Medical Device Development Center, Daegu-Gyeongbuk Medical Innovation Foundation (DGMIF), Daegu 41061, Korea
| | - Seong-Min Kim
- Department of Medical Science, College of Medicine, Catholic Kwandong University, Gangneung 25601, Korea.,Biomedical Research Institute, Catholic Kwandong University International St. Mary's Hospital, Incheon 21711, Korea
| | - Rae-Hyung Ryu
- Laboratory Animal Center, Daegu-Gyeongbuk Medical Innovation Foundation (DGMIF), Daegu 41061, Korea
| | - Sung-Phil Kim
- Department of Human Factors Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea
| | - Jeong-Woo Sohn
- Department of Medical Science, College of Medicine, Catholic Kwandong University, Gangneung 25601, Korea.,Biomedical Research Institute, Catholic Kwandong University International St. Mary's Hospital, Incheon 21711, Korea
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Kozai TDY. The History and Horizons of Microscale Neural Interfaces. MICROMACHINES 2018; 9:E445. [PMID: 30424378 PMCID: PMC6187275 DOI: 10.3390/mi9090445] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Revised: 08/27/2018] [Accepted: 09/03/2018] [Indexed: 12/29/2022]
Abstract
Microscale neural technologies interface with the nervous system to record and stimulate brain tissue with high spatial and temporal resolution. These devices are being developed to understand the mechanisms that govern brain function, plasticity and cognitive learning, treat neurological diseases, or monitor and restore functions over the lifetime of the patient. Despite decades of use in basic research over days to months, and the growing prevalence of neuromodulation therapies, in many cases the lack of knowledge regarding the fundamental mechanisms driving activation has dramatically limited our ability to interpret data or fine-tune design parameters to improve long-term performance. While advances in materials, microfabrication techniques, packaging, and understanding of the nervous system has enabled tremendous innovation in the field of neural engineering, many challenges and opportunities remain at the frontiers of the neural interface in terms of both neurobiology and engineering. In this short-communication, we explore critical needs in the neural engineering field to overcome these challenges. Disentangling the complexities involved in the chronic neural interface problem requires simultaneous proficiency in multiple scientific and engineering disciplines. The critical component of advancing neural interface knowledge is to prepare the next wave of investigators who have simultaneous multi-disciplinary proficiencies with a diverse set of perspectives necessary to solve the chronic neural interface challenge.
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Affiliation(s)
- Takashi D Y Kozai
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15261, USA.
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA 15213, USA.
- Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA 15261, USA.
- McGowan Institute of Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15212, USA.
- NeuroTech Center, University of Pittsburgh Brain Institute, Pittsburgh, PA 15260, USA.
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