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Park D, Jeong H, Choi J, Han J, Piao H, Kim J, Park S, Song M, Kim D, Sung J, Cheong E, Choi H. Enhancing Flexible Neural Probe Performance via Platinum Deposition: Impedance Stability under Various Conditions and In Vivo Neural Signal Monitoring. MICROMACHINES 2024; 15:1058. [PMID: 39203708 PMCID: PMC11356038 DOI: 10.3390/mi15081058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2024] [Revised: 08/19/2024] [Accepted: 08/20/2024] [Indexed: 09/03/2024]
Abstract
Monitoring neural activity in the central nervous system often utilizes silicon-based microelectromechanical system (MEMS) probes. Despite their effectiveness in monitoring, these probes have a fragility issue, limiting their application across various fields. This study introduces flexible printed circuit board (FPCB) neural probes characterized by robust mechanical and electrical properties. The probes demonstrate low impedance after platinum coating, making them suitable for multiunit recordings in awake animals. This capability allows for the simultaneous monitoring of a large population of neurons in the brain, including cluster data. Additionally, these probes exhibit no fractures, mechanical failures, or electrical issues during repeated-bending tests, both during handling and monitoring. Despite the possibility of using this neural probe for signal measurement in awake animals, simply applying a platinum coating may encounter difficulties in chronic tests and other applications. Furthermore, this suggests that FPCB probes can be advanced by any method and serve as an appropriate type of tailorable neural probes for monitoring neural systems in awake animals.
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Affiliation(s)
- Daerl Park
- Department of Materials Science and Engineering, Yonsei University, Seoul 03722, Republic of Korea; (D.P.); (J.C.); (J.H.); (H.P.); (J.K.); (S.P.); (M.S.); (D.K.)
| | - Hyeonyeong Jeong
- Department of Biotechnology, Yonsei University, Seoul 03722, Republic of Korea;
| | - Jungsik Choi
- Department of Materials Science and Engineering, Yonsei University, Seoul 03722, Republic of Korea; (D.P.); (J.C.); (J.H.); (H.P.); (J.K.); (S.P.); (M.S.); (D.K.)
| | - Juyeon Han
- Department of Materials Science and Engineering, Yonsei University, Seoul 03722, Republic of Korea; (D.P.); (J.C.); (J.H.); (H.P.); (J.K.); (S.P.); (M.S.); (D.K.)
| | - Honglin Piao
- Department of Materials Science and Engineering, Yonsei University, Seoul 03722, Republic of Korea; (D.P.); (J.C.); (J.H.); (H.P.); (J.K.); (S.P.); (M.S.); (D.K.)
| | - Jaehyun Kim
- Department of Materials Science and Engineering, Yonsei University, Seoul 03722, Republic of Korea; (D.P.); (J.C.); (J.H.); (H.P.); (J.K.); (S.P.); (M.S.); (D.K.)
| | - Seonghoon Park
- Department of Materials Science and Engineering, Yonsei University, Seoul 03722, Republic of Korea; (D.P.); (J.C.); (J.H.); (H.P.); (J.K.); (S.P.); (M.S.); (D.K.)
| | - Mingu Song
- Department of Materials Science and Engineering, Yonsei University, Seoul 03722, Republic of Korea; (D.P.); (J.C.); (J.H.); (H.P.); (J.K.); (S.P.); (M.S.); (D.K.)
| | - Dowoo Kim
- Department of Materials Science and Engineering, Yonsei University, Seoul 03722, Republic of Korea; (D.P.); (J.C.); (J.H.); (H.P.); (J.K.); (S.P.); (M.S.); (D.K.)
| | - Jaesuk Sung
- Nformare Inc., Seodamun-gu, Seoul 03722, Republic of Korea;
| | - Eunji Cheong
- Department of Biotechnology, Yonsei University, Seoul 03722, Republic of Korea;
| | - Heonjin Choi
- Department of Materials Science and Engineering, Yonsei University, Seoul 03722, Republic of Korea; (D.P.); (J.C.); (J.H.); (H.P.); (J.K.); (S.P.); (M.S.); (D.K.)
- Nformare Inc., Seodamun-gu, Seoul 03722, Republic of Korea;
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2
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Lee CH, Park YK, Lee K. Recent strategies for neural dynamics observation at a larger scale and wider scope. Biosens Bioelectron 2023; 240:115638. [PMID: 37647685 DOI: 10.1016/j.bios.2023.115638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 08/15/2023] [Accepted: 08/24/2023] [Indexed: 09/01/2023]
Abstract
The tremendous technical progress in neuroscience offers opportunities to observe a more minor or/and broader dynamic picture of the brain. Moreover, the large-scale neural activity of individual neurons enables the dissection of detailed mechanistic links between neural populations and behaviors. To measure neural activity in-vivo, multi-neuron recording, and neuroimaging techniques are employed and developed to acquire more neurons. The tools introduced concurrently recorded dozens to hundreds of neurons in the coordinated brain regions and elucidated the neuronal ensembles from a massive population perspective of diverse neurons at cellular resolution. In particular, the increasing spatiotemporal resolution of neuronal monitoring across the whole brain dramatically facilitates our understanding of additional nervous system functions in health and disease. Here, we will introduce state-of-the-art neuroscience tools involving large-scale neural population recording and the long-range connections spanning multiple brain regions. Their synergic effects provide to clarify the controversial circuitry underlying neuroscience. These challenging neural tools present a promising outlook for the fundamental dynamic interplay across levels of synaptic cellular, circuit organization, and brain-wide. Hence, more observations of neural dynamics will provide more clues to elucidate brain functions and push forward innovative technology at the intersection of neural engineering disciplines. We hope this review will provide insight into the use or development of recent neural techniques considering spatiotemporal scales of brain observation.
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Affiliation(s)
- Chang Hak Lee
- Department of Brain Sciences, Daegu Gyeongbuk Institute of Science & Technology (DGIST), Daegu, South Korea
| | - Young Kwon Park
- Department of Brain Sciences, Daegu Gyeongbuk Institute of Science & Technology (DGIST), Daegu, South Korea
| | - Kwang Lee
- Department of Brain Sciences, Daegu Gyeongbuk Institute of Science & Technology (DGIST), Daegu, South Korea.
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3
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Perna A, Angotzi GN, Berdondini L, Ribeiro JF. Advancing the interfacing performances of chronically implantable neural probes in the era of CMOS neuroelectronics. Front Neurosci 2023; 17:1275908. [PMID: 38027514 PMCID: PMC10644322 DOI: 10.3389/fnins.2023.1275908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 10/10/2023] [Indexed: 12/01/2023] Open
Abstract
Tissue penetrating microelectrode neural probes can record electrophysiological brain signals at resolutions down to single neurons, making them invaluable tools for neuroscience research and Brain-Computer-Interfaces (BCIs). The known gradual decrease of their electrical interfacing performances in chronic settings, however, remains a major challenge. A key factor leading to such decay is Foreign Body Reaction (FBR), which is the cascade of biological responses that occurs in the brain in the presence of a tissue damaging artificial device. Interestingly, the recent adoption of Complementary Metal Oxide Semiconductor (CMOS) technology to realize implantable neural probes capable of monitoring hundreds to thousands of neurons simultaneously, may open new opportunities to face the FBR challenge. Indeed, this shift from passive Micro Electro-Mechanical Systems (MEMS) to active CMOS neural probe technologies creates important, yet unexplored, opportunities to tune probe features such as the mechanical properties of the probe, its layout, size, and surface physicochemical properties, to minimize tissue damage and consequently FBR. Here, we will first review relevant literature on FBR to provide a better understanding of the processes and sources underlying this tissue response. Methods to assess FBR will be described, including conventional approaches based on the imaging of biomarkers, and more recent transcriptomics technologies. Then, we will consider emerging opportunities offered by the features of CMOS probes. Finally, we will describe a prototypical neural probe that may meet the needs for advancing clinical BCIs, and we propose axial insertion force as a potential metric to assess the influence of probe features on acute tissue damage and to control the implantation procedure to minimize iatrogenic injury and subsequent FBR.
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Affiliation(s)
- Alberto Perna
- Microtechnology for Neuroelectronics Lab, Fondazione Istituto Italiano di Tecnologia, Neuroscience and Brain Technologies, Genova, Italy
- The Open University Affiliated Research Centre at Istituto Italiano di Tecnologia (ARC@IIT), Istituto Italiano di Tecnologia, Genova, Italy
| | - Gian Nicola Angotzi
- Microtechnology for Neuroelectronics Lab, Fondazione Istituto Italiano di Tecnologia, Neuroscience and Brain Technologies, Genova, Italy
| | - Luca Berdondini
- Microtechnology for Neuroelectronics Lab, Fondazione Istituto Italiano di Tecnologia, Neuroscience and Brain Technologies, Genova, Italy
| | - João Filipe Ribeiro
- Microtechnology for Neuroelectronics Lab, Fondazione Istituto Italiano di Tecnologia, Neuroscience and Brain Technologies, Genova, Italy
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4
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Windolf C, Yu H, Paulk AC, Meszéna D, Muñoz W, Boussard J, Hardstone R, Caprara I, Jamali M, Kfir Y, Xu D, Chung JE, Sellers KK, Ye Z, Shaker J, Lebedeva A, Raghavan M, Trautmann E, Melin M, Couto J, Garcia S, Coughlin B, Horváth C, Fiáth R, Ulbert I, Movshon JA, Shadlen MN, Churchland MM, Churchland AK, Steinmetz NA, Chang EF, Schweitzer JS, Williams ZM, Cash SS, Paninski L, Varol E. DREDge: robust motion correction for high-density extracellular recordings across species. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.24.563768. [PMID: 37961359 PMCID: PMC10634799 DOI: 10.1101/2023.10.24.563768] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
High-density microelectrode arrays (MEAs) have opened new possibilities for systems neuroscience in human and non-human animals, but brain tissue motion relative to the array poses a challenge for downstream analyses, particularly in human recordings. We introduce DREDge (Decentralized Registration of Electrophysiology Data), a robust algorithm which is well suited for the registration of noisy, nonstationary extracellular electrophysiology recordings. In addition to estimating motion from spikes in the action potential (AP) frequency band, DREDge enables automated tracking of motion at high temporal resolution in the local field potential (LFP) frequency band. In human intraoperative recordings, which often feature fast (period <1s) motion, DREDge correction in the LFP band enabled reliable recovery of evoked potentials, and significantly reduced single-unit spike shape variability and spike sorting error. Applying DREDge to recordings made during deep probe insertions in nonhuman primates demonstrated the possibility of tracking probe motion of centimeters across several brain regions while simultaneously mapping single unit electrophysiological features. DREDge reliably delivered improved motion correction in acute mouse recordings, especially in those made with an recent ultra-high density probe. We also implemented a procedure for applying DREDge to recordings made across tens of days in chronic implantations in mice, reliably yielding stable motion tracking despite changes in neural activity across experimental sessions. Together, these advances enable automated, scalable registration of electrophysiological data across multiple species, probe types, and drift cases, providing a stable foundation for downstream scientific analyses of these rich datasets.
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Affiliation(s)
- Charlie Windolf
- Department of Statistics, Columbia University
- Zuckerman Institute, Columbia University
| | - Han Yu
- Zuckerman Institute, Columbia University
- Department of Electrical Engineering, Columbia University
| | - Angelique C Paulk
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School
| | - Domokos Meszéna
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
| | - William Muñoz
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School
| | - Julien Boussard
- Department of Statistics, Columbia University
- Zuckerman Institute, Columbia University
| | - Richard Hardstone
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School
| | - Irene Caprara
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School
| | - Mohsen Jamali
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School
| | - Yoav Kfir
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School
| | - Duo Xu
- Weill Institute for Neurosciences, University of California San Francisco
- Department of Neurological Surgery, University of California San Francisco
| | - Jason E Chung
- Department of Neurological Surgery, University of California San Francisco
| | - Kristin K Sellers
- Weill Institute for Neurosciences, University of California San Francisco
- Department of Neurological Surgery, University of California San Francisco
| | - Zhiwen Ye
- Department of Biological Structure, University of Washington
| | - Jordan Shaker
- Department of Biological Structure, University of Washington
| | | | | | - Eric Trautmann
- Department of Neuroscience, Columbia University Medical Center
- Zuckerman Institute, Columbia University
- Grossman Center for the Statistics of Mind, Columbia University
| | - Max Melin
- David Geffen School of Medicine, University of California Los Angeles
| | - João Couto
- David Geffen School of Medicine, University of California Los Angeles
| | - Samuel Garcia
- Centre National de la Recherche Scientifique, Centre de Recherche en Neurosciences de Lyon
| | - Brian Coughlin
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School
| | - Csaba Horváth
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
| | - Richárd Fiáth
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
| | - István Ulbert
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
| | | | - Michael N Shadlen
- Zuckerman Institute, Columbia University
- Howard Hughes Medical Institute
| | | | - Anne K Churchland
- David Geffen School of Medicine, University of California Los Angeles
| | | | - Edward F Chang
- Weill Institute for Neurosciences, University of California San Francisco
- Department of Neurological Surgery, University of California San Francisco
| | - Jeffrey S Schweitzer
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School
| | - Ziv M Williams
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School
| | - Sydney S Cash
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School
| | - Liam Paninski
- Department of Statistics, Columbia University
- Zuckerman Institute, Columbia University
- Department of Neuroscience, Columbia University Medical Center
- Grossman Center for the Statistics of Mind, Columbia University
| | - Erdem Varol
- Department of Statistics, Columbia University
- Zuckerman Institute, Columbia University
- Department of Computer Science & Engineering, New York University
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5
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Király B, Domonkos A, Jelitai M, Lopes-Dos-Santos V, Martínez-Bellver S, Kocsis B, Schlingloff D, Joshi A, Salib M, Fiáth R, Barthó P, Ulbert I, Freund TF, Viney TJ, Dupret D, Varga V, Hangya B. The medial septum controls hippocampal supra-theta oscillations. Nat Commun 2023; 14:6159. [PMID: 37816713 PMCID: PMC10564782 DOI: 10.1038/s41467-023-41746-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 09/15/2023] [Indexed: 10/12/2023] Open
Abstract
Hippocampal theta oscillations orchestrate faster beta-to-gamma oscillations facilitating the segmentation of neural representations during navigation and episodic memory. Supra-theta rhythms of hippocampal CA1 are coordinated by local interactions as well as inputs from the entorhinal cortex (EC) and CA3 inputs. However, theta-nested gamma-band activity in the medial septum (MS) suggests that the MS may control supra-theta CA1 oscillations. To address this, we performed multi-electrode recordings of MS and CA1 activity in rodents and found that MS neuron firing showed strong phase-coupling to theta-nested supra-theta episodes and predicted changes in CA1 beta-to-gamma oscillations on a cycle-by-cycle basis. Unique coupling patterns of anatomically defined MS cell types suggested that indirect MS-to-CA1 pathways via the EC and CA3 mediate distinct CA1 gamma-band oscillations. Optogenetic activation of MS parvalbumin-expressing neurons elicited theta-nested beta-to-gamma oscillations in CA1. Thus, the MS orchestrates hippocampal network activity at multiple temporal scales to mediate memory encoding and retrieval.
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Affiliation(s)
- Bálint Király
- Lendület Laboratory of Systems Neuroscience, Institute of Experimental Medicine, Budapest, Hungary
- Department of Biological Physics, Institute of Physics, Eötvös Loránd University, Budapest, Hungary
| | - Andor Domonkos
- Subcortical Modulation Research Group, Institute of Experimental Medicine, Budapest, Hungary
| | - Márta Jelitai
- Subcortical Modulation Research Group, Institute of Experimental Medicine, Budapest, Hungary
| | - Vítor Lopes-Dos-Santos
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Sergio Martínez-Bellver
- Lendület Laboratory of Systems Neuroscience, Institute of Experimental Medicine, Budapest, Hungary
- Department of Anatomy and Human Embryology, Faculty of Medicine and Odontology, University of Valencia, Valencia, Spain
| | - Barnabás Kocsis
- Lendület Laboratory of Systems Neuroscience, Institute of Experimental Medicine, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Dániel Schlingloff
- Lendület Laboratory of Systems Neuroscience, Institute of Experimental Medicine, Budapest, Hungary
| | - Abhilasha Joshi
- Department of Pharmacology, University of Oxford, Oxford, UK
| | - Minas Salib
- Department of Pharmacology, University of Oxford, Oxford, UK
| | - Richárd Fiáth
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
| | - Péter Barthó
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
| | - István Ulbert
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
| | - Tamás F Freund
- Laboratory of Cerebral Cortex Research, Institute of Experimental Medicine, Budapest, Hungary
| | - Tim J Viney
- Department of Pharmacology, University of Oxford, Oxford, UK
| | - David Dupret
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Viktor Varga
- Subcortical Modulation Research Group, Institute of Experimental Medicine, Budapest, Hungary
| | - Balázs Hangya
- Lendület Laboratory of Systems Neuroscience, Institute of Experimental Medicine, Budapest, Hungary.
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6
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Edge computing on TPU for brain implant signal analysis. Neural Netw 2023; 162:212-224. [PMID: 36921432 DOI: 10.1016/j.neunet.2023.02.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 01/18/2023] [Accepted: 02/23/2023] [Indexed: 03/04/2023]
Abstract
The ever-increasing number of recording sites of silicon-based probes imposes a great challenge for detecting and evaluating single-unit activities in an accurate and efficient manner. Currently separate solutions are available for high precision offline evaluation and separate solutions for embedded systems where computational resources are more limited. We propose a deep learning-based spike sorting system, that utilizes both unsupervised and supervised paradigms to learn a general feature embedding space and detect neural activity in raw data as well as predict the feature vectors for sorting. The unsupervised component uses contrastive learning to extract features from individual waveforms, while the supervised component is based on the MobileNetV2 architecture. One of the key advantages of our system is that it can be trained on multiple, diverse datasets simultaneously, resulting in greater generalizability than previous deep learning-based models. We demonstrate that the proposed model does not only reaches the accuracy of current state-of-art offline spike sorting methods but has the unique potential to run on edge Tensor Processing Units (TPUs), specialized chips designed for artificial intelligence and edge computing. We compare our model performance with state of art solutions on paired datasets as well as on hybrid recordings as well. The herein demonstrated system paves the way to the integration of deep learning-based spike sorting algorithms into wearable electronic devices, which will be a crucial element of high-end brain-computer interfaces.
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Ahnood A, Chambers A, Gelmi A, Yong KT, Kavehei O. Semiconducting electrodes for neural interfacing: a review. Chem Soc Rev 2023; 52:1491-1518. [PMID: 36734845 DOI: 10.1039/d2cs00830k] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
In the past 50 years, the advent of electronic technology to directly interface with neural tissue has transformed the fields of medicine and biology. Devices that restore or even replace impaired bodily functions, such as deep brain stimulators and cochlear implants, have ushered in a new treatment era for previously intractable conditions. Meanwhile, electrodes for recording and stimulating neural activity have allowed researchers to unravel the vast complexities of the human nervous system. Recent advances in semiconducting materials have allowed effective interfaces between electrodes and neuronal tissue through novel devices and structures. Often these are unattainable using conventional metallic electrodes. These have translated into advances in research and treatment. The development of semiconducting materials opens new avenues in neural interfacing. This review considers this emerging class of electrodes and how it can facilitate electrical, optical, and chemical sensing and modulation with high spatial and temporal precision. Semiconducting electrodes have advanced electrically based neural interfacing technologies owing to their unique electrochemical and photo-electrochemical attributes. Key operation modalities, namely sensing and stimulation in electrical, biochemical, and optical domains, are discussed, highlighting their contrast to metallic electrodes from the application and characterization perspective.
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Affiliation(s)
- Arman Ahnood
- School of Engineering, RMIT University, VIC 3000, Australia
| | - Andre Chambers
- School of Physics, University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Amy Gelmi
- School of Science, RMIT University, VIC 3000, Australia
| | - Ken-Tye Yong
- School of Biomedical Engineering, University of Sydney, Sydney, NSW 2006, Australia.,The University of Sydney Nano Institute, Sydney, NSW 2006, Australia.
| | - Omid Kavehei
- School of Biomedical Engineering, University of Sydney, Sydney, NSW 2006, Australia.,The University of Sydney Nano Institute, Sydney, NSW 2006, Australia.
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8
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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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9
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Chiappalone M, Cota VR, Carè M, Di Florio M, Beaubois R, Buccelli S, Barban F, Brofiga M, Averna A, Bonacini F, Guggenmos DJ, Bornat Y, Massobrio P, Bonifazi P, Levi T. Neuromorphic-Based Neuroprostheses for Brain Rewiring: State-of-the-Art and Perspectives in Neuroengineering. Brain Sci 2022; 12:1578. [PMID: 36421904 PMCID: PMC9688667 DOI: 10.3390/brainsci12111578] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/09/2022] [Accepted: 11/17/2022] [Indexed: 08/27/2023] Open
Abstract
Neuroprostheses are neuroengineering devices that have an interface with the nervous system and supplement or substitute functionality in people with disabilities. In the collective imagination, neuroprostheses are mostly used to restore sensory or motor capabilities, but in recent years, new devices directly acting at the brain level have been proposed. In order to design the next-generation of neuroprosthetic devices for brain repair, we foresee the increasing exploitation of closed-loop systems enabled with neuromorphic elements due to their intrinsic energy efficiency, their capability to perform real-time data processing, and of mimicking neurobiological computation for an improved synergy between the technological and biological counterparts. In this manuscript, after providing definitions of key concepts, we reviewed the first exploitation of a real-time hardware neuromorphic prosthesis to restore the bidirectional communication between two neuronal populations in vitro. Starting from that 'case-study', we provide perspectives on the technological improvements for real-time interfacing and processing of neural signals and their potential usage for novel in vitro and in vivo experimental designs. The development of innovative neuroprosthetics for translational purposes is also presented and discussed. In our understanding, the pursuit of neuromorphic-based closed-loop neuroprostheses may spur the development of novel powerful technologies, such as 'brain-prostheses', capable of rewiring and/or substituting the injured nervous system.
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Affiliation(s)
- Michela Chiappalone
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Vinicius R. Cota
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Marta Carè
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Mattia Di Florio
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
| | - Romain Beaubois
- IMS Laboratory, CNRS UMR 5218, University of Bordeaux, 33405 Talence, France
| | - Stefano Buccelli
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Federico Barban
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Martina Brofiga
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
| | - Alberto Averna
- Department of Neurology, Bern University Hospital, University of Bern, 3012 Bern, Switzerland
| | - Francesco Bonacini
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
| | - David J. Guggenmos
- Department of Rehabilitation Medicine, University of Kansas Medical Center, Kansas City, KS 66103, USA
- Landon Center on Aging, University of Kansas Medical Center, Kansas City, KS 66103, USA
| | - Yannick Bornat
- IMS Laboratory, CNRS UMR 5218, University of Bordeaux, 33405 Talence, France
| | - Paolo Massobrio
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
- National Institute for Nuclear Physics (INFN), 16146 Genova, Italy
| | - Paolo Bonifazi
- IKERBASQUE, The Basque Fundation, 48009 Bilbao, Spain
- Biocruces Health Research Institute, 48903 Barakaldo, Spain
| | - Timothée Levi
- IMS Laboratory, CNRS UMR 5218, University of Bordeaux, 33405 Talence, France
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10
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Frenzel J, Kupferer A, Zink M, Mayr SG. Laminin Adsorption and Adhesion of Neurons and Glial Cells on Carbon Implanted Titania Nanotube Scaffolds for Neural Implant Applications. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:3858. [PMID: 36364633 PMCID: PMC9656521 DOI: 10.3390/nano12213858] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 10/17/2022] [Accepted: 10/28/2022] [Indexed: 06/16/2023]
Abstract
Interfacing neurons persistently to conductive matter constitutes one of the key challenges when designing brain-machine interfaces such as neuroelectrodes or retinal implants. Novel materials approaches that prevent occurrence of loss of long-term adhesion, rejection reactions, and glial scarring are highly desirable. Ion doped titania nanotube scaffolds are a promising material to fulfill all these requirements while revealing sufficient electrical conductivity, and are scrutinized in the present study regarding their neuron-material interface. Adsorption of laminin, an essential extracellular matrix protein of the brain, is comprehensively analyzed. The implantation-dependent decline in laminin adsorption is revealed by employing surface characteristics such as nanotube diameter, ζ-potential, and surface free energy. Moreover, the viability of U87-MG glial cells and SH-SY5Y neurons after one and four days are investigated, as well as the material's cytotoxicity. The higher conductivity related to carbon implantation does not affect the viability of neurons, although it impedes glial cell proliferation. This gives rise to novel titania nanotube based implant materials with long-term stability, and could reduce undesirable glial scarring.
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Affiliation(s)
- Jan Frenzel
- Leibniz Institute of Surface Engineering (IOM), 04318 Leipzig, Germany
- Division of Surface Physics, Faculty of Physics and Earth Sciences, Leipzig University, 04103 Leipzig, Germany
- Research Group Biotechnology and Biomedicine, Faculty of Physics and Earth Sciences, Leipzig University, 04103 Leipzig, Germany
| | - Astrid Kupferer
- Leibniz Institute of Surface Engineering (IOM), 04318 Leipzig, Germany
- Division of Surface Physics, Faculty of Physics and Earth Sciences, Leipzig University, 04103 Leipzig, Germany
| | - Mareike Zink
- Research Group Biotechnology and Biomedicine, Faculty of Physics and Earth Sciences, Leipzig University, 04103 Leipzig, Germany
| | - Stefan G. Mayr
- Leibniz Institute of Surface Engineering (IOM), 04318 Leipzig, Germany
- Division of Surface Physics, Faculty of Physics and Earth Sciences, Leipzig University, 04103 Leipzig, Germany
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11
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Huygens synchronization of medial septal pacemaker neurons generates hippocampal theta oscillation. Cell Rep 2022; 40:111149. [PMID: 35926456 DOI: 10.1016/j.celrep.2022.111149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 05/06/2022] [Accepted: 07/07/2022] [Indexed: 11/21/2022] Open
Abstract
Episodic learning and memory retrieval are dependent on hippocampal theta oscillation, thought to rely on the GABAergic network of the medial septum (MS). To test how this network achieves theta synchrony, we recorded MS neurons and hippocampal local field potential simultaneously in anesthetized and awake mice and rats. We show that MS pacemakers synchronize their individual rhythmicity frequencies, akin to coupled pendulum clocks as observed by Huygens. We optogenetically identified them as parvalbumin-expressing GABAergic neurons, while MS glutamatergic neurons provide tonic excitation sufficient to induce theta. In accordance, waxing and waning tonic excitation is sufficient to toggle between theta and non-theta states in a network model of single-compartment inhibitory pacemaker neurons. These results provide experimental and theoretical support to a frequency-synchronization mechanism for pacing hippocampal theta, which may serve as an inspirational prototype for synchronization processes in the central nervous system from Nematoda to Arthropoda to Chordate and Vertebrate phyla.
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12
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Magisetty R, Park SM. New Era of Electroceuticals: Clinically Driven Smart Implantable Electronic Devices Moving towards Precision Therapy. MICROMACHINES 2022; 13:161. [PMID: 35208286 PMCID: PMC8876842 DOI: 10.3390/mi13020161] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 01/14/2022] [Accepted: 01/18/2022] [Indexed: 12/15/2022]
Abstract
In the name of electroceuticals, bioelectronic devices have transformed and become essential for dealing with all physiological responses. This significant advancement is attributable to its interdisciplinary nature from engineering and sciences and also the progress in micro and nanotechnologies. Undoubtedly, in the future, bioelectronics would lead in such a way that diagnosing and treating patients' diseases is more efficient. In this context, we have reviewed the current advancement of implantable medical electronics (electroceuticals) with their immense potential advantages. Specifically, the article discusses pacemakers, neural stimulation, artificial retinae, and vagus nerve stimulation, their micro/nanoscale features, and material aspects as value addition. Over the past years, most researchers have only focused on the electroceuticals metamorphically transforming from a concept to a device stage to positively impact the therapeutic outcomes. Herein, the article discusses the smart implants' development challenges and opportunities, electromagnetic field effects, and their potential consequences, which will be useful for developing a reliable and qualified smart electroceutical implant for targeted clinical use. Finally, this review article highlights the importance of wirelessly supplying the necessary power and wirelessly triggering functional electronic circuits with ultra-low power consumption and multi-functional advantages such as monitoring and treating the disease in real-time.
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Affiliation(s)
- RaviPrakash Magisetty
- Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea;
| | - Sung-Min Park
- Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea;
- Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea
- Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea
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13
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Accurate Localization of Linear Probe Electrode Arrays across Multiple Brains. eNeuro 2021; 8:ENEURO.0241-21.2021. [PMID: 34697075 PMCID: PMC8597948 DOI: 10.1523/eneuro.0241-21.2021] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 07/02/2021] [Accepted: 10/14/2021] [Indexed: 11/21/2022] Open
Abstract
Recently developed probes for extracellular electrophysiological recordings have large numbers of electrodes on long linear shanks. Linear electrode arrays, such as Neuropixels probes, have hundreds of recording electrodes distributed over linear shanks that span several millimeters. Because of the length of the probes, linear probe recordings in rodents usually cover multiple brain areas. Typical studies collate recordings across several recording sessions and animals. Neurons recorded in different sessions and animals thus have to be aligned to each other and to a standardized brain coordinate system. Here, we evaluate two typical workflows for localization of individual electrodes in standardized coordinates. These workflows rely on imaging brains with fluorescent probe tracks and warping 3D image stacks to standardized brain atlases. One workflow is based on tissue clearing and selective plane illumination microscopy (SPIM), whereas the other workflow is based on serial block-face two-photon (SBF2P) microscopy. In both cases electrophysiological features are then used to anchor particular electrodes along the reconstructed tracks to specific locations in the brain atlas and therefore to specific brain structures. We performed groundtruth experiments, in which motor cortex outputs are labeled with ChR2 and a fluorescence protein. Light-evoked electrical activity and fluorescence can be independently localized. Recordings from brain regions targeted by the motor cortex reveal better than 0.1-mm accuracy for electrode localization, independent of workflow used.
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14
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Horváth C, Tóth LF, Ulbert I, Fiáth R. Dataset of cortical activity recorded with high spatial resolution from anesthetized rats. Sci Data 2021; 8:180. [PMID: 34267214 PMCID: PMC8282648 DOI: 10.1038/s41597-021-00970-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 06/08/2021] [Indexed: 11/09/2022] Open
Abstract
Publicly available neural recordings obtained with high spatial resolution are scarce. Here, we present an electrophysiological dataset recorded from the neocortex of twenty rats anesthetized with ketamine/xylazine. The wideband, spontaneous recordings were acquired with a single-shank silicon-based probe having 128 densely-packed recording sites arranged in a 32 × 4 array. The dataset contains the activity of a total of 7126 sorted single units extracted from all layers of the cortex. Here, we share raw neural recordings, as well as spike times, extracellular spike waveforms and several properties of units packaged in a standardized electrophysiological data format. For technical validation of our dataset, we provide the distributions of derived single unit properties along with various spike sorting quality metrics. This large collection of in vivo data enables the investigation of the high-resolution electrical footprint of cortical neurons which in turn may aid their electrophysiology-based classification. Furthermore, the dataset might be used to study the laminar-specific neuronal activity during slow oscillation, a brain rhythm strongly involved in neural mechanisms underlying memory consolidation and sleep.
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Affiliation(s)
- Csaba Horváth
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Eötvös Loránd Research Network, Budapest, Hungary
- School of Ph.D. Studies, Semmelweis University, Budapest, Hungary
| | - Lili Fanni Tóth
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - István Ulbert
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Eötvös Loránd Research Network, Budapest, Hungary.
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary.
| | - Richárd Fiáth
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Eötvös Loránd Research Network, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
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15
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Chae U, Shin H, Choi N, Ji MJ, Park HM, Lee SH, Woo J, Cho Y, Kim K, Yang S, Nam MH, Yu HY, Cho IJ. Bimodal neural probe for highly co-localized chemical and electrical monitoring of neural activities in vivo. Biosens Bioelectron 2021; 191:113473. [PMID: 34237704 DOI: 10.1016/j.bios.2021.113473] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 05/31/2021] [Accepted: 06/28/2021] [Indexed: 10/21/2022]
Abstract
Investigation of the chemical and electrical signals of cells in vivo is critical for studying functional connectivity and brain diseases. Most previous studies have observed either the electrical signals or the chemical signals of cells because recording electrical signals and neurochemicals are done by fundamentally different methods. Herein, we present a bimodal MEMS neural probe that is monolithically integrated with an array of microelectrodes for recording electrical activity, microfluidic channels for sampling extracellular fluid, and a microfluidic interface chip for multiple drug delivery and sample isolation from the localized region at the cellular level. In this work, we successfully demonstrated the functionality of our probe by monitoring and modulating bimodal (electrical and chemical) neural activities through the delivery of chemicals in a co-localized brain region in vivo. We expect our bimodal probe to provide opportunities for a variety of in-depth studies of brain functions as well as for the investigation of neural circuits related to brain diseases.
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Affiliation(s)
- Uikyu Chae
- Center for BioMicrosystems, Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea; School of Electrical Engineering, Korea University, Seoul, Republic of Korea
| | - Hyogeun Shin
- Center for BioMicrosystems, Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea
| | - Nakwon Choi
- Center for BioMicrosystems, Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea; Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology (UST), Daejeon, Republic of Korea
| | - Mi-Jung Ji
- Advanced Analysis Center, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea
| | - Hyun-Mee Park
- Advanced Analysis Center, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea
| | - Soo Hyun Lee
- Department of Medical Records and Health Information Management College of Nursing and Health, Kongju National University, Gongju-si, Chungcheongnam-do, Republic of Korea
| | - Jiwan Woo
- Research Animal Resource Center, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea
| | - Yakdol Cho
- Research Animal Resource Center, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea
| | - Kanghwan Kim
- Center for BioMicrosystems, Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea
| | - Seulkee Yang
- Convergence Research Center for Brain Science, Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea
| | - Min-Ho Nam
- Convergence Research Center for Brain Science, Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea
| | - Hyun-Yong Yu
- School of Electrical Engineering, Korea University, Seoul, Republic of Korea
| | - Il-Joo Cho
- Center for BioMicrosystems, Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea; School of Electrical and Electronics Engineering, Yonsei University, Seoul, Republic of Korea; Yonsei-KIST Convergence Research Institute, Yonsei University, Seoul, Republic of Korea.
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16
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Liew YJ, Pala A, Whitmire CJ, Stoy WA, Forest CR, Stanley GB. Inferring thalamocortical monosynaptic connectivity in vivo. J Neurophysiol 2021; 125:2408-2431. [PMID: 33978507 PMCID: PMC8285656 DOI: 10.1152/jn.00591.2020] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 04/12/2021] [Accepted: 04/29/2021] [Indexed: 11/22/2022] Open
Abstract
As the tools to simultaneously record electrophysiological signals from large numbers of neurons within and across brain regions become increasingly available, this opens up for the first time the possibility of establishing the details of causal relationships between monosynaptically connected neurons and the patterns of neural activation that underlie perception and behavior. Although recorded activity across synaptically connected neurons has served as the cornerstone for much of what we know about synaptic transmission and plasticity, this has largely been relegated to ex vivo preparations that enable precise targeting under relatively well-controlled conditions. Analogous studies in vivo, where image-guided targeting is often not yet possible, rely on indirect, data-driven measures, and as a result such studies have been sparse and the dependence upon important experimental parameters has not been well studied. Here, using in vivo extracellular single-unit recordings in the topographically aligned rodent thalamocortical pathway, we sought to establish a general experimental and computational framework for inferring synaptic connectivity. Specifically, attacking this problem within a statistical signal detection framework utilizing experimentally recorded data in the ventral-posterior medial (VPm) region of the thalamus and the homologous region in layer 4 of primary somatosensory cortex (S1) revealed a trade-off between network activity levels needed for the data-driven inference and synchronization of nearby neurons within the population that results in masking of synaptic relationships. Here, we provide a framework for establishing connectivity in multisite, multielectrode recordings based on statistical inference, setting the stage for large-scale assessment of synaptic connectivity within and across brain structures.NEW & NOTEWORTHY Despite the fact that all brain function relies on the long-range transfer of information across different regions, the tools enabling us to measure connectivity across brain structures are lacking. Here, we provide a statistical framework for identifying and assessing potential monosynaptic connectivity across neuronal circuits from population spiking activity that generalizes to large-scale recording technologies that will help us to better understand the signaling within networks that underlies perception and behavior.
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Affiliation(s)
- Yi Juin Liew
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia
- Joint PhD Program in Biomedical Engineering, Georgia Institute of Technology-Emory University-Peking University, Atlanta, Georgia
| | - Aurélie Pala
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia
| | - Clarissa J Whitmire
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia
| | - William A Stoy
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia
| | - Craig R Forest
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia
| | - Garrett B Stanley
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia
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17
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McGlynn E, Nabaei V, Ren E, Galeote‐Checa G, Das R, Curia G, Heidari H. The Future of Neuroscience: Flexible and Wireless Implantable Neural Electronics. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2021; 8:2002693. [PMID: 34026431 PMCID: PMC8132070 DOI: 10.1002/advs.202002693] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 01/15/2021] [Indexed: 05/04/2023]
Abstract
Neurological diseases are a prevalent cause of global mortality and are of growing concern when considering an ageing global population. Traditional treatments are accompanied by serious side effects including repeated treatment sessions, invasive surgeries, or infections. For example, in the case of deep brain stimulation, large, stiff, and battery powered neural probes recruit thousands of neurons with each pulse, and can invoke a vigorous immune response. This paper presents challenges in engineering and neuroscience in developing miniaturized and biointegrated alternatives, in the form of microelectrode probes. Progress in design and topology of neural implants has shifted the goal post toward highly specific recording and stimulation, targeting small groups of neurons and reducing the foreign body response with biomimetic design principles. Implantable device design recommendations, fabrication techniques, and clinical evaluation of the impact flexible, integrated probes will have on the treatment of neurological disorders are provided in this report. The choice of biocompatible material dictates fabrication techniques as novel methods reduce the complexity of manufacture. Wireless power, the final hurdle to truly implantable neural interfaces, is discussed. These aspects are the driving force behind continued research: significant breakthroughs in any one of these areas will revolutionize the treatment of neurological disorders.
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Affiliation(s)
- Eve McGlynn
- Microelectronics LabJames Watt School of EngineeringUniversity of GlasgowGlasgowG12 8QQUnited Kingdom
| | - Vahid Nabaei
- Microelectronics LabJames Watt School of EngineeringUniversity of GlasgowGlasgowG12 8QQUnited Kingdom
| | - Elisa Ren
- Laboratory of Experimental Electroencephalography and NeurophysiologyDepartment of BiomedicalMetabolic and Neural SciencesUniversity of Modena and Reggio EmiliaModena41125Italy
| | - Gabriel Galeote‐Checa
- Microelectronics LabJames Watt School of EngineeringUniversity of GlasgowGlasgowG12 8QQUnited Kingdom
| | - Rupam Das
- Microelectronics LabJames Watt School of EngineeringUniversity of GlasgowGlasgowG12 8QQUnited Kingdom
| | - Giulia Curia
- Laboratory of Experimental Electroencephalography and NeurophysiologyDepartment of BiomedicalMetabolic and Neural SciencesUniversity of Modena and Reggio EmiliaModena41125Italy
| | - Hadi Heidari
- Microelectronics LabJames Watt School of EngineeringUniversity of GlasgowGlasgowG12 8QQUnited Kingdom
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18
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Rokai J, Rácz M, Fiáth R, Ulbert I, Márton G. ELVISort: encoding latent variables for instant sorting, an artificial intelligence-based end-to-end solution. J Neural Eng 2021; 18. [PMID: 33823497 DOI: 10.1088/1741-2552/abf521] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 04/06/2021] [Indexed: 11/12/2022]
Abstract
Objective.The growing number of recording sites of silicon-based probes means that an increasing amount of neural cell activities can be recorded simultaneously, facilitating the investigation of underlying complex neural dynamics. In order to overcome the challenges generated by the increasing number of channels, highly automated signal processing tools are needed. Our goal was to build a spike sorting model that can perform as well as offline solutions while maintaining high efficiency, enabling high-performance online sorting.Approach.In this paper we present ELVISort, a deep learning method that combines the detection and clustering of different action potentials in an end-to-end fashion.Main results.The performance of ELVISort is comparable with other spike sorting methods that use manual or semi-manual techniques, while exceeding the methods which use an automatic approach: ELVISort has been tested on three independent datasets and yielded average F1scores of 0.96, 0.82 and 0.81, which comparable with the results of state-of-the-art algorithms on the same data. We show that despite the good performance, ELVISort is capable to process data in real-time: the time it needs to execute the necessary computations for a sample of given length is only 1/15.71 of its actual duration (i.e. the sampling time multiplied by the number of the sampling points).Significance.ELVISort, because of its end-to-end nature, can exploit the massively parallel processing capabilities of GPUs via deep learning frameworks by processing multiple batches in parallel, with the potential to be used on other cutting-edge AI-specific hardware such as TPUs, enabling the development of integrated, portable and real-time spike sorting systems with similar performance to offline sorters.
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Affiliation(s)
- János Rokai
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest H-1117, Hungary.,School of PhD Studies, Semmelweis University, Üllői út 26, H-1085 Budapest, Hungary
| | - Melinda Rácz
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest H-1117, Hungary.,School of PhD Studies, Semmelweis University, Üllői út 26, H-1085 Budapest, Hungary
| | - Richárd Fiáth
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest H-1117, Hungary.,Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/a, H-1083 Budapest, Hungary
| | - István Ulbert
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest H-1117, Hungary.,Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/a, H-1083 Budapest, Hungary
| | - Gergely Márton
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest H-1117, Hungary.,Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/a, H-1083 Budapest, Hungary
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19
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Fiáth R, Meszéna D, Somogyvári Z, Boda M, Barthó P, Ruther P, Ulbert I. Recording site placement on planar silicon-based probes affects signal quality in acute neuronal recordings. Sci Rep 2021; 11:2028. [PMID: 33479289 PMCID: PMC7819990 DOI: 10.1038/s41598-021-81127-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 12/28/2020] [Indexed: 12/17/2022] Open
Abstract
Multisite, silicon-based probes are widely used tools to record the electrical activity of neuronal populations. Several physical features of these devices are designed to improve their recording performance. Here, our goal was to investigate whether the position of recording sites on the silicon shank might affect the quality of the recorded neural signal in acute experiments. Neural recordings obtained with five different types of high-density, single-shank, planar silicon probes from anesthetized rats were analyzed. Wideband data were filtered to extract spiking activity, then the amplitude distribution of samples and quantitative properties of the recorded brain activity (single unit yield, spike amplitude and isolation distance) were compared between sites located at different positions of the silicon shank, focusing particularly on edge and center sites. Edge sites outperformed center sites: for all five probe types there was a significant difference in the signal power computed from the amplitude distributions, and edge sites recorded significantly more large amplitude samples both in the positive and negative range. Although the single unit yield was similar between site positions, the difference in spike amplitudes was noticeable in the range corresponding to high-amplitude spikes. Furthermore, the advantage of edge sites slightly decreased with decreasing shank width. Our results might aid the design of novel neural implants in enhancing their recording performance by identifying more efficient recording site placements.
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Affiliation(s)
- Richárd Fiáth
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary. .,Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary.
| | - Domokos Meszéna
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary.,Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Zoltán Somogyvári
- Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, Hungary
| | - Mihály Boda
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Péter Barthó
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
| | - Patrick Ruther
- Department of Microsystems Engineering (IMTEK), University of Freiburg, Freiburg, Germany.,Cluster of Excellence, BrainLinks-BrainTools, University of Freiburg, Freiburg, Germany
| | - István Ulbert
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary.,Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
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20
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Fiáth R, Meszéna D, Somogyvári Z, Boda M, Barthó P, Ruther P, Ulbert I. Recording site placement on planar silicon-based probes affects signal quality in acute neuronal recordings. Sci Rep 2021; 11:2028. [PMID: 33479289 DOI: 10.1101/2020.06.01.127308] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 12/28/2020] [Indexed: 05/27/2023] Open
Abstract
Multisite, silicon-based probes are widely used tools to record the electrical activity of neuronal populations. Several physical features of these devices are designed to improve their recording performance. Here, our goal was to investigate whether the position of recording sites on the silicon shank might affect the quality of the recorded neural signal in acute experiments. Neural recordings obtained with five different types of high-density, single-shank, planar silicon probes from anesthetized rats were analyzed. Wideband data were filtered to extract spiking activity, then the amplitude distribution of samples and quantitative properties of the recorded brain activity (single unit yield, spike amplitude and isolation distance) were compared between sites located at different positions of the silicon shank, focusing particularly on edge and center sites. Edge sites outperformed center sites: for all five probe types there was a significant difference in the signal power computed from the amplitude distributions, and edge sites recorded significantly more large amplitude samples both in the positive and negative range. Although the single unit yield was similar between site positions, the difference in spike amplitudes was noticeable in the range corresponding to high-amplitude spikes. Furthermore, the advantage of edge sites slightly decreased with decreasing shank width. Our results might aid the design of novel neural implants in enhancing their recording performance by identifying more efficient recording site placements.
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Affiliation(s)
- Richárd Fiáth
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary.
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary.
| | - Domokos Meszéna
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Zoltán Somogyvári
- Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, Hungary
| | - Mihály Boda
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Péter Barthó
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
| | - Patrick Ruther
- Department of Microsystems Engineering (IMTEK), University of Freiburg, Freiburg, Germany
- Cluster of Excellence, BrainLinks-BrainTools, University of Freiburg, Freiburg, Germany
| | - István Ulbert
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
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Dunlap CF, Colachis SC, Meyers EC, Bockbrader MA, Friedenberg DA. Classifying Intracortical Brain-Machine Interface Signal Disruptions Based on System Performance and Applicable Compensatory Strategies: A Review. Front Neurorobot 2020; 14:558987. [PMID: 33162885 PMCID: PMC7581895 DOI: 10.3389/fnbot.2020.558987] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 09/09/2020] [Indexed: 12/18/2022] Open
Abstract
Brain-machine interfaces (BMIs) record and translate neural activity into a control signal for assistive or other devices. Intracortical microelectrode arrays (MEAs) enable high degree-of-freedom BMI control for complex tasks by providing fine-resolution neural recording. However, chronically implanted MEAs are subject to a dynamic in vivo environment where transient or systematic disruptions can interfere with neural recording and degrade BMI performance. Typically, neural implant failure modes have been categorized as biological, material, or mechanical. While this categorization provides insight into a disruption's causal etiology, it is less helpful for understanding degree of impact on BMI function or possible strategies for compensation. Therefore, we propose a complementary classification framework for intracortical recording disruptions that is based on duration of impact on BMI performance and requirement for and responsiveness to interventions: (1) Transient disruptions interfere with recordings on the time scale of minutes to hours and can resolve spontaneously; (2) Reversible disruptions cause persistent interference in recordings but the root cause can be remedied by an appropriate intervention; (3) Irreversible compensable disruptions cause persistent or progressive decline in signal quality, but their effects on BMI performance can be mitigated algorithmically; and (4) Irreversible non-compensable disruptions cause permanent signal loss that is not amenable to remediation or compensation. This conceptualization of intracortical BMI disruption types is useful for highlighting specific areas for potential hardware improvements and also identifying opportunities for algorithmic interventions. We review recording disruptions that have been reported for MEAs and demonstrate how biological, material, and mechanical mechanisms of disruption can be further categorized according to their impact on signal characteristics. Then we discuss potential compensatory protocols for each of the proposed disruption classes. Specifically, transient disruptions may be minimized by using robust neural decoder features, data augmentation methods, adaptive machine learning models, and specialized signal referencing techniques. Statistical Process Control methods can identify reparable disruptions for rapid intervention. In-vivo diagnostics such as impedance spectroscopy can inform neural feature selection and decoding models to compensate for irreversible disruptions. Additional compensatory strategies for irreversible disruptions include information salvage techniques, data augmentation during decoder training, and adaptive decoding methods to down-weight damaged channels.
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Affiliation(s)
- Collin F. Dunlap
- Department of Biomedical Engineering, The Ohio State University, Columbus, OH, United States
- Medical Devices and Neuromodulation, Battelle Memorial Institute, Columbus, OH, United States
| | - Samuel C. Colachis
- Medical Devices and Neuromodulation, Battelle Memorial Institute, Columbus, OH, United States
| | - Eric C. Meyers
- Medical Devices and Neuromodulation, Battelle Memorial Institute, Columbus, OH, United States
| | - Marcia A. Bockbrader
- Department of Physical Medicine and Rehabilitation, The Ohio State University, Columbus, OH, United States
| | - David A. Friedenberg
- Advanced Analytics and Health Research, Battelle Memorial Institute, Columbus, OH, United States
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Even-Chen N, Muratore DG, Stavisky SD, Hochberg LR, Henderson JM, Murmann B, Shenoy KV. Power-saving design opportunities for wireless intracortical brain-computer interfaces. Nat Biomed Eng 2020; 4:984-996. [PMID: 32747834 PMCID: PMC8286886 DOI: 10.1038/s41551-020-0595-9] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 06/30/2020] [Indexed: 12/17/2022]
Abstract
The efficacy of wireless intracortical brain-computer interfaces (iBCIs) is limited in part by the number of recording channels, which is constrained by the power budget of the implantable system. Designing wireless iBCIs that provide the high-quality recordings of today's wired neural interfaces may lead to inadvertent over-design at the expense of power consumption and scalability. Here, we report analyses of neural signals collected from experimental iBCI measurements in rhesus macaques and from a clinical-trial participant with implanted 96-channel Utah multielectrode arrays to understand the trade-offs between signal quality and decoder performance. Moreover, we propose an efficient hardware design for clinically viable iBCIs, and suggest that the circuit design parameters of current recording iBCIs can be relaxed considerably without loss of performance. The proposed design may allow for an order-of-magnitude power savings and lead to clinically viable iBCIs with a higher channel count.
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Affiliation(s)
- Nir Even-Chen
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
| | - Dante G Muratore
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Sergey D Stavisky
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Leigh R Hochberg
- Department of Veterans Affairs Medical Center, Center for Neurorestoration and Neurotechnology, Providence, RI, USA
- School of Engineering and Carney Institute for Brain Science, Brown University, Providence, RI, USA
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jaimie M Henderson
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Boris Murmann
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Neurobiology, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- The Bio-X Institute, Stanford University, Stanford, CA, USA
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Ayub S, David F, Klein E, Borel M, Paul O, Gentet LJ, Ruther P. Compact Optical Neural Probes With Up to 20 Integrated Thin-Film $\mu$LEDs Applied in Acute Optogenetic Studies. IEEE Trans Biomed Eng 2020; 67:2603-2615. [DOI: 10.1109/tbme.2020.2966293] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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24
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Kollo M, Racz R, Hanna ME, Obaid A, Angle MR, Wray W, Kong Y, Müller J, Hierlemann A, Melosh NA, Schaefer AT. CHIME: CMOS-Hosted in vivo Microelectrodes for Massively Scalable Neuronal Recordings. Front Neurosci 2020; 14:834. [PMID: 32848584 PMCID: PMC7432274 DOI: 10.3389/fnins.2020.00834] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Accepted: 07/16/2020] [Indexed: 01/20/2023] Open
Abstract
Mammalian brains consist of 10s of millions to 100s of billions of neurons operating at millisecond time scales, of which current recording techniques only capture a tiny fraction. Recording techniques capable of sampling neural activity at high spatiotemporal resolution have been difficult to scale. The most intensively studied mammalian neuronal networks, such as the neocortex, show a layered architecture, where the optimal recording technology samples densely over large areas. However, the need for application-specific designs as well as the mismatch between the three-dimensional architecture of the brain and largely two-dimensional microfabrication techniques profoundly limits both neurophysiological research and neural prosthetics. Here, we discuss a novel strategy for scalable neuronal recording by combining bundles of glass-ensheathed microwires with large-scale amplifier arrays derived from high-density CMOS in vitro MEA systems or high-speed infrared cameras. High signal-to-noise ratio (<25 μV RMS noise floor, SNR up to 25) is achieved due to the high conductivity of core metals in glass-ensheathed microwires allowing for ultrathin metal cores (down to <1 μm) and negligible stray capacitance. Multi-step electrochemical modification of the tip enables ultra-low access impedance with minimal geometric area, which is largely independent of the core diameter. We show that the microwire size can be reduced to virtually eliminate damage to the blood-brain-barrier upon insertion and we demonstrate that microwire arrays can stably record single-unit activity. Combining microwire bundles and CMOS arrays allows for a highly scalable neuronal recording approach, linking the progress in electrical neuronal recordings to the rapid progress in silicon microfabrication. The modular design of the system allows for custom arrangement of recording sites. Our approach of employing bundles of minimally invasive, highly insulated and functionalized microwires to extend a two-dimensional CMOS architecture into the 3rd dimension can be translated to other CMOS arrays, such as electrical stimulation devices.
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Affiliation(s)
- Mihaly Kollo
- Neurophysiology of Behaviour Laboratory, Francis Crick Institute, London, United Kingdom
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom
| | - Romeo Racz
- Neurophysiology of Behaviour Laboratory, Francis Crick Institute, London, United Kingdom
| | - Mina-Elraheb Hanna
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, United States
- Paradromics, Inc., Austin, TX, United States
| | - Abdulmalik Obaid
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, United States
| | | | - William Wray
- Neurophysiology of Behaviour Laboratory, Francis Crick Institute, London, United Kingdom
| | - Yifan Kong
- Paradromics, Inc., Austin, TX, United States
| | - Jan Müller
- ETH Zürich, Department of Biosystems Science and Engineering, Basel, Switzerland
- MaxWell Biosystems AG, Zurich, Switzerland
| | - Andreas Hierlemann
- ETH Zürich, Department of Biosystems Science and Engineering, Basel, Switzerland
| | - Nicholas A. Melosh
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, United States
| | - Andreas T. Schaefer
- Neurophysiology of Behaviour Laboratory, Francis Crick Institute, London, United Kingdom
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom
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25
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Schaffer L, Nagy Z, Kincses Z, Fiath R, Ulbert I. Spatial Information Based OSort for Real-Time Spike Sorting Using FPGA. IEEE Trans Biomed Eng 2020; 68:99-108. [PMID: 32746008 DOI: 10.1109/tbme.2020.2996281] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE Spiking activity of individual neurons can be separated from the acquired multi-unit activity with spike sorting methods. Processing the recorded high-dimensional neural data can take a large amount of time when performed on general-purpose computers. METHODS In this paper, an FPGA-based real-time spike sorting system is presented which takes into account the spatial correlation between the electrical signals recorded with closely-packed recording sites to cluster multi-channel neural data. The system uses a spatial window-based version of the Online Sorting algorithm, which uses unsupervised template-matching for clustering. RESULTS The test results show that the proposed system can reach an average accuracy of 86% using simulated data (16-32 neurons, 4-10 dB Signal-to-Noise Ratio), while the single-channel clustering version achieves only 74% average accuracy in the same cases on a 128-channel electrode array. The developed system was also tested on in vivo cortical recordings obtained from an anesthetized rat. CONCLUSION The proposed FPGA-based spike sorting system can process more than 11000 spikes/second, so it can be used during in vivo experiments providing real-time feedback on the location and electrophysiological properties of well-separable single units. SIGNIFICANCE The proposed spike sorting system could be used to reduce the positioning error of the closely-packed recording site during a neural measurement.
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26
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Klein L, Pothof F, Raducanu BC, Klon-Lipok J, Shapcott KA, Musa S, Andrei A, Aarts AA, Paul O, Singer W, Ruther P. High-density electrophysiological recordings in macaque using a chronically implanted 128-channel passive silicon probe. J Neural Eng 2020; 17:026036. [PMID: 32217819 DOI: 10.1088/1741-2552/ab8436] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
OBJECTIVE The analysis of interactions among local populations of neurons in the cerebral cortex (e.g. within cortical microcolumns) requires high resolution and high channel count recordings from chronically implanted laminar microelectrode arrays. The request for high-density recordings of a large number of recording sites can presently only be accomplished by probes realized using complementary metal-oxide-semiconductor (CMOS) technology. In preparation for their use in non-human primates, we aimed for neural probe validation in a head-fixed approach analyzing the long-term recording capability. APPROACH We examined chronically implanted silicon-based laminar probes, realized using a CMOS technology in combination with micromachining, to record from the primary visual cortex (V1) of a monkey. We used a passive CMOS probe that had 128 electrodes arranged at a pitch of 22.5 µm in four columns and 32 rows on a slender shank. In order to validate the performance of a dedicated microdrive, the overall dimensions of probe and interface boards were chosen to be compatible with the final active CMOS probe comprising integrated circuitry. MAIN RESULTS Using the passive probe, we recorded simultaneously local field potentials (LFP) and spiking multiunit activity (MUA) in V1 of an awake behaving macaque monkey. We found that an insertion through the dura and subsequent readjustments of the chronically implanted neural probe was possible and allowed us to record stable LFPs for more than five months. The quality of MUA degraded within the first month but remained sufficiently high to permit mapping of receptive fields during the full recording period. SIGNIFICANCE We conclude that the passive silicon probe enables semi-chronic recordings of high quality of LFP and MUA for a time span exceeding five months. The new microdrive compatible with a commercial recording chamber successfully demonstrated the readjustment of the probe position while the implemented plug structure effectively reduced brain tissue movement relative to the probe.
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Affiliation(s)
- Liane Klein
- Ernst Strüngmann Institute (ESI) gGmbH for Neuroscience in Cooperation with Max Planck Society, Deutschordenstraße 46, D-60528, Frankfurt am Main, Germany. Max Planck Institute for Brain Research, Max-von-Laue-Str. 4, D-60438, Frankfurt am Main, Germany. Institute of Cell Biology and Neuroscience, Goethe-University Frankfurt, Max von Laue Str. 13, D-60438, Frankfurt am Main, Germany. These authors have contributed equally to this work
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27
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Márton G, Tóth EZ, Wittner L, Fiáth R, Pinke D, Orbán G, Meszéna D, Pál I, Győri EL, Bereczki Z, Kandrács Á, Hofer KT, Pongrácz A, Ulbert I, Tóth K. The neural tissue around SU-8 implants: A quantitative in vivo biocompatibility study. MATERIALS SCIENCE & ENGINEERING. C, MATERIALS FOR BIOLOGICAL APPLICATIONS 2020; 112:110870. [PMID: 32409039 DOI: 10.1016/j.msec.2020.110870] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 02/26/2020] [Accepted: 03/19/2020] [Indexed: 12/30/2022]
Abstract
The use of SU-8 material in the production of neural sensors has grown recently. Despite its widespread application, a detailed systematic quantitative analysis concerning its biocompatibility in the central nervous system is lacking. In this immunohistochemical study, we quantified the neuronal preservation and the severity of astrogliosis around SU-8 devices implanted in the neocortex of rats, after a 2 months survival. We found that the density of neurons significantly decreased up to a distance of 20 μm from the implant, with an averaged density decrease to 24 ± 28% of the control. At 20 to 40 μm distance from the implant, the majority of the neurons was preserved (74 ± 39% of the control) and starting from 40 μm distance from the implant, the neuron density was control-like. The density of synaptic contacts - examined at the electron microscopic level - decreased in the close vicinity of the implant, but it recovered to the control level as close as 24 μm from the implant track. The intensity of the astroglial staining significantly increased compared to the control region, up to 560 μm and 480 μm distance from the track in the superficial and deep layers of the neocortex, respectively. Electron microscopic examination revealed that the thickness of the glial scar was around 5-10 μm thin, and the ratio of glial processes in the neuropil was not more than 16% up to a distance of 12 μm from the implant. Our data suggest that neuronal survival is affected only in a very small area around the implant. The glial scar surrounding the implant is thin, and the presence of glial elements is low in the neuropil, although the signs of astrogliosis could be observed up to about 500 μm from the track. Subsequently, the biocompatibility of the SU-8 material is high. Due to its low cost fabrication and more flexible nature, SU-8 based devices may offer a promising approach to experimental and clinical applications in the future.
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Affiliation(s)
- Gergely Márton
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest 1117, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/A, Budapest 1083, Hungary; Doctoral School on Materials Sciences and Technologies, Óbuda University, Bécsi út 96/b, Budapest 1034, Hungary.
| | - Estilla Zsófia Tóth
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest 1117, Hungary; János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Üllői út 26, Budapest 1085, Hungary.
| | - Lucia Wittner
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest 1117, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/A, Budapest 1083, Hungary; National Institute of Clinical Neuroscience, Amerikai út 57, Budapest, Hungary, 1145.
| | - Richárd Fiáth
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest 1117, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/A, Budapest 1083, Hungary.
| | - Domonkos Pinke
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/A, Budapest 1083, Hungary.
| | - Gábor Orbán
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest 1117, Hungary; Doctoral School on Materials Sciences and Technologies, Óbuda University, Bécsi út 96/b, Budapest 1034, Hungary.
| | - Domokos Meszéna
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest 1117, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/A, Budapest 1083, Hungary.
| | - Ildikó Pál
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest 1117, Hungary.
| | - Edit Lelle Győri
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest 1117, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/A, Budapest 1083, Hungary; National Institute of Clinical Neuroscience, Amerikai út 57, Budapest, Hungary, 1145
| | - Zsófia Bereczki
- Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Magyar tudósok körútja 2, Budapest 1117, Hungary
| | - Ágnes Kandrács
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest 1117, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/A, Budapest 1083, Hungary.
| | - Katharina T Hofer
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest 1117, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/A, Budapest 1083, Hungary.
| | - Anita Pongrácz
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/A, Budapest 1083, Hungary; Institute of Technical Physics and Materials Science, Centre for Energy Research, Konkoly Thege Miklós út 29-33, Budapest 1121, Hungary.
| | - István Ulbert
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest 1117, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/A, Budapest 1083, Hungary; National Institute of Clinical Neuroscience, Amerikai út 57, Budapest, Hungary, 1145.
| | - Kinga Tóth
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest 1117, Hungary.
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Waveguiding and focusing in a bio-medium with an optofluidic cell chain. Acta Biomater 2020; 103:165-171. [PMID: 31812842 DOI: 10.1016/j.actbio.2019.12.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 11/28/2019] [Accepted: 12/03/2019] [Indexed: 11/23/2022]
Abstract
Long-distance waveguiding and submicron focusing of light in a bio-medium are crucial for biomedical sensing and imaging. Disordered bio-mediums usually exhibit high scattering and absorption, which limits effective waveguiding and focusing. Here, we demonstrate an optofluidic cell chain, assembled via an optical trapping force from an optical fiber probe, to achieve long-distance waveguiding and submicron light focusing in a disordered bio-medium. By applying a trapping light at 980 nm to generate an optical force, stable binding of E. faecalis cells was achieved in a fluid to assemble cell chains of different lengths. The length could reach up to 360 µm and the incident light (at 675, 532 and 473 nm) could be focused into a beam with a waist radius of 400 nm. As a potential practical application, backscattered signals from human red blood cells were detected using the cell chains, which is expected to benefit biomedical sensing and single cell analysis. STATEMENT OF SIGNIFICANCE: With the assistance of optofluidic techniques, we assembled an E. faecalis cell chain with a length up to 360 µm to achieve long-distance waveguiding and submicron focusing at a propagation loss of 0.03 dB/µm in the bio-medium. Visible lights were launched into the cell chain and the incident lights can converge into a beam with a waist radius of 400 nm. The cell chain was further used to detect the backscattering signals from human red blood cells (RBCs), and the results indicate that the cell chain can be applied as a fully biocompatible extension of the probe for the real-time detection of RBCs in healthy and pathological states.
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29
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Rácz M, Liber C, Németh E, Fiáth R, Rokai J, Harmati I, Ulbert I, Márton G. Spike detection and sorting with deep learning. J Neural Eng 2020; 17:016038. [DOI: 10.1088/1741-2552/ab4896] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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30
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Sung C, Jeon W, Nam KS, Kim Y, Butt H, Park S. Multimaterial and multifunctional neural interfaces: from surface-type and implantable electrodes to fiber-based devices. J Mater Chem B 2020; 8:6624-6666. [DOI: 10.1039/d0tb00872a] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Development of neural interfaces from surface electrodes to fibers with various type, functionality, and materials.
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Affiliation(s)
- Changhoon Sung
- Department of Bio and Brain Engineering
- Korea Advanced Institute of Science and Technology (KAIST)
- Daejeon 34141
- Republic of Korea
| | - Woojin Jeon
- Department of Bio and Brain Engineering
- Korea Advanced Institute of Science and Technology (KAIST)
- Daejeon 34141
- Republic of Korea
| | - Kum Seok Nam
- School of Electrical Engineering
- Korea Advanced Institute of Science and Technology (KAIST)
- Daejeon 34141
- Republic of Korea
| | - Yeji Kim
- Department of Bio and Brain Engineering
- Korea Advanced Institute of Science and Technology (KAIST)
- Daejeon 34141
- Republic of Korea
| | - Haider Butt
- Department of Mechanical Engineering
- Khalifa University
- Abu Dhabi 127788
- United Arab Emirates
| | - Seongjun Park
- Department of Bio and Brain Engineering
- Korea Advanced Institute of Science and Technology (KAIST)
- Daejeon 34141
- Republic of Korea
- KAIST Institute for Health Science and Technology (KIHST)
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31
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Method for spike detection from microelectrode array recordings contaminated by artifacts of simultaneous two-photon imaging. PLoS One 2019; 14:e0221510. [PMID: 31430357 PMCID: PMC6701834 DOI: 10.1371/journal.pone.0221510] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 08/09/2019] [Indexed: 11/19/2022] Open
Abstract
The simultaneous utilization of electrophysiological recordings and two-photon imaging allows the observation of neural activity in a high temporal and spatial resolution at the same time. The three dimensional monitoring of morphological features near the microelectrode array makes the observation more precise and complex. In vitro experiments were performed on mice neocortical slices expressing the GCaMP6 genetically encoded calcium indicator for monitoring the neural activity with two-photon microscopy around the implanted microelectrodes. A special filtering algorithm was used for data analysis to eliminate the artefacts caused by the imaging laser. Utilization of a special filtering algorithm allowed us to detect and sort single unit activities from simultaneous two-photon imaging and electrophysiological measurement.
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32
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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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33
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Slow insertion of silicon probes improves the quality of acute neuronal recordings. Sci Rep 2019; 9:111. [PMID: 30643182 PMCID: PMC6331571 DOI: 10.1038/s41598-018-36816-z] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 11/10/2018] [Indexed: 01/02/2023] Open
Abstract
Neural probes designed for extracellular recording of brain electrical activity are traditionally implanted with an insertion speed between 1 µm/s and 1 mm/s into the brain tissue. Although the physical effects of insertion speed on the tissue are well studied, there is a lack of research investigating how the quality of the acquired electrophysiological signal depends on the speed of probe insertion. In this study, we used four different insertion speeds (0.002 mm/s, 0.02 mm/s, 0.1 mm/s, 1 mm/s) to implant high-density silicon probes into deep layers of the somatosensory cortex of ketamine/xylazine anesthetized rats. After implantation, various qualitative and quantitative properties of the recorded cortical activity were compared across different speeds in an acute manner. Our results demonstrate that after the slowest insertion both the signal-to-noise ratio and the number of separable single units were significantly higher compared with those measured after inserting probes at faster speeds. Furthermore, the amplitude of recorded spikes as well as the quality of single unit clusters showed similar speed-dependent differences. Post hoc quantification of the neuronal density around the probe track showed a significantly higher number of NeuN-labelled cells after the slowest insertion compared with the fastest insertion. Our findings suggest that advancing rigid probes slowly (~1 µm/s) into the brain tissue might result in less tissue damage, and thus in neuronal recordings of improved quality compared with measurements obtained after inserting probes with higher speeds.
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Angotzi GN, Boi F, Lecomte A, Miele E, Malerba M, Zucca S, Casile A, Berdondini L. SiNAPS: An implantable active pixel sensor CMOS-probe for simultaneous large-scale neural recordings. Biosens Bioelectron 2018; 126:355-364. [PMID: 30466053 DOI: 10.1016/j.bios.2018.10.032] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 09/25/2018] [Accepted: 10/09/2018] [Indexed: 11/18/2022]
Abstract
Large-scale neural recordings with high spatial and temporal accuracy are instrumental to understand how the brain works. To this end, it is of key importance to develop probes that can be conveniently scaled up to a high number of recording channels. Despite recent achievements in complementary metal-oxide semiconductor (CMOS) multi-electrode arrays probes, in current circuit architectures an increase in the number of simultaneously recording channels would significantly increase the total chip area. A promising approach for overcoming this scaling issue consists in the use of the modular Active Pixel Sensor (APS) concept, in which a small front-end circuit is located beneath each electrode. However, this approach imposes challenging constraints on the area of the in-pixel circuit, power consumption and noise. Here, we present an APS CMOS-probe technology for Simultaneous Neural recording that successfully addresses all these issues for whole-array read-outs at 25 kHz/channel from up to 1024 electrode-pixels. To assess the circuit performances, we realized in a 0.18 μm CMOS technology an implantable single-shaft probe with a regular array of 512 electrode-pixels with a pitch of 28 μm. Extensive bench tests showed an in-pixel gain of 45.4 ± 0.4 dB (low pass, F-3 dB = 4 kHz), an input referred noise of 7.5 ± 0.67 μVRMS (300 Hz to 7.5 kHz) and a power consumption <6 μW/pixel. In vivo acute recordings demonstrate that our SiNAPS CMOS-probe can sample full-band bioelectrical signals from each electrode, with the ability to resolve and discriminate activity from several packed neurons both at the spatial and temporal scale. These results pave the way to new generations of compact and scalable active single/multi-shaft brain recording systems.
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Affiliation(s)
| | - Fabio Boi
- Fondazione Istituto Italiano di Tecnologia (IIT), NetS3 Lab, Genova, Italy
| | - Aziliz Lecomte
- Fondazione Istituto Italiano di Tecnologia (IIT), NetS3 Lab, Genova, Italy
| | - Ermanno Miele
- Fondazione Istituto Italiano di Tecnologia (IIT), NetS3 Lab, Genova, Italy
| | - Mario Malerba
- Fondazione Istituto Italiano di Tecnologia (IIT), NetS3 Lab, Genova, Italy
| | - Stefano Zucca
- Fondazione Istituto Italiano di Tecnologia (IIT), Optical Approaches to Brain Function, Lab, Genova, Italy
| | - Antonino Casile
- Fondazione Istituto Italiano di Tecnologia (IIT), CTNSC-UniFe, Ferrara, Italy
| | - Luca Berdondini
- Fondazione Istituto Italiano di Tecnologia (IIT), NetS3 Lab, Genova, Italy
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Fine-scale mapping of cortical laminar activity during sleep slow oscillations using high-density linear silicon probes. J Neurosci Methods 2018; 316:58-70. [PMID: 30144495 DOI: 10.1016/j.jneumeth.2018.08.020] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 08/16/2018] [Accepted: 08/17/2018] [Indexed: 12/14/2022]
Abstract
BACKGROUND The cortical slow (∼1 Hz) oscillation (SO), which is thought to play an active role in the consolidation of memories, is a brain rhythm characteristic of slow-wave sleep, with alternating periods of neuronal activity and silence. Although the laminar distribution of cortical activity during SO is well-studied by using linear neural probes, traditional devices have a relatively low (20-100 μm) spatial resolution along cortical layers. NEW METHOD In this work, we demonstrate a high-density linear silicon probe fabricated to record the SO with very high spatial resolution (∼6 μm), simultaneously from multiple cortical layers. Ketamine/xylazine-induced SO was acquired acutely from the neocortex of rats, followed by the examination of the high-resolution laminar structure of cortical activity. RESULTS The probe provided high-quality extracellular recordings, and the obtained cortical laminar profiles of the SO were in good agreement with the literature data. Furthermore, we could record the simultaneous activity of 30-50 cortical single units. Spiking activity of these neurons showed layer-specific differences. COMPARISON WITH EXISTING METHODS The developed silicon probe measures neuronal activity with at least a three-fold higher spatial resolution compared with traditional linear probes. By exploiting this feature, we could determine the site of up-state initiation with a higher precision than before. Additionally, increased spatial resolution may provide more reliable spike sorting results, as well as a higher single unit yield. CONCLUSIONS The high spatial resolution provided by the electrodes allows to examine the fine structure of local population activity during sleep SO in greater detail.
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