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Shokri M, Gogliettino AR, Hottowy P, Sher A, Litke AM, Chichilnisky EJ, Pequito S, Muratore D. Spike sorting in the presence of stimulation artifacts: a dynamical control systems approach. J Neural Eng 2024; 21:016022. [PMID: 38271715 PMCID: PMC10853761 DOI: 10.1088/1741-2552/ad228f] [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: 07/05/2023] [Revised: 11/08/2023] [Accepted: 01/25/2024] [Indexed: 01/27/2024]
Abstract
Objective. Bi-directional electronic neural interfaces, capable of both electrical recording and stimulation, communicate with the nervous system to permit precise calibration of electrical inputs by capturing the evoked neural responses. However, one significant challenge is that stimulation artifacts often mask the actual neural signals. To address this issue, we introduce a novel approach that employs dynamical control systems to detect and decipher electrically evoked neural activity despite the presence of electrical artifacts.Approach. Our proposed method leverages the unique spatiotemporal patterns of neural activity and electrical artifacts to distinguish and identify individual neural spikes. We designed distinctive dynamical models for both the stimulation artifact and each neuron observed during spontaneous neural activity. We can estimate which neurons were active by analyzing the recorded voltage responses across multiple electrodes post-stimulation. This technique also allows us to exclude signals from electrodes heavily affected by stimulation artifacts, such as the stimulating electrode itself, yet still accurately differentiate between evoked spikes and electrical artifacts.Main results. We applied our method to high-density multi-electrode recordings from the primate retina in anex vivosetup, using a grid of 512 electrodes. Through repeated electrical stimulations at varying amplitudes, we were able to construct activation curves for each neuron. The curves obtained with our method closely resembled those derived from manual spike sorting. Additionally, the stimulation thresholds we estimated strongly agreed with those determined through manual analysis, demonstrating high reliability (R2=0.951for human 1 andR2=0.944for human 2).Significance. Our method can effectively separate evoked neural spikes from stimulation artifacts by exploiting the distinct spatiotemporal propagation patterns captured by a dense, large-scale multi-electrode array. This technique holds promise for future applications in real-time closed-loop stimulation systems and for managing multi-channel stimulation strategies.
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Affiliation(s)
- Mohammad Shokri
- Delft Center for Systems and Control, Delft University of Technology, Delft 2628 CN, The Netherlands
| | - Alex R Gogliettino
- Neurosciences PhD Program, Stanford University, Stanford, CA 94305, United States of America
- Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA 94305, United States of America
| | - Paweł Hottowy
- Faculty of Physics and Applied Computer Science, AGH University of Krakow, Krakow, Poland
| | - Alexander Sher
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA, United States of America
| | - Alan M Litke
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA, United States of America
| | - E J Chichilnisky
- Departments of Neurosurgery and Ophthalmology, Stanford University, Stanford, CA 94305, United States of America
| | - Sérgio Pequito
- Division of Systems and Control, Department of Information Technology, Uppsala University, 751 05 Uppsala, Sweden
| | - Dante Muratore
- Microelectronics Department, Delft University of Technology, Delft 2628 CN, The Netherlands
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2
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Kagan BJ, Gyngell C, Lysaght T, Cole VM, Sawai T, Savulescu J. The technology, opportunities, and challenges of Synthetic Biological Intelligence. Biotechnol Adv 2023; 68:108233. [PMID: 37558186 PMCID: PMC7615149 DOI: 10.1016/j.biotechadv.2023.108233] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/15/2023] [Accepted: 08/05/2023] [Indexed: 08/11/2023]
Abstract
Integrating neural cultures developed through synthetic biology methods with digital computing has enabled the early development of Synthetic Biological Intelligence (SBI). Recently, key studies have emphasized the advantages of biological neural systems in some information processing tasks. However, neither the technology behind this early development, nor the potential ethical opportunities or challenges, have been explored in detail yet. Here, we review the key aspects that facilitate the development of SBI and explore potential applications. Considering these foreseeable use cases, various ethical implications are proposed. Ultimately, this work aims to provide a robust framework to structure ethical considerations to ensure that SBI technology can be both researched and applied responsibly.
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Affiliation(s)
| | - Christopher Gyngell
- Murdoch Children's Research Institute, Melbourne, VIC, Australia; University of Melbourne, Melbourne, VIC, Australia
| | - Tamra Lysaght
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Victor M Cole
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Tsutomu Sawai
- Graduate School of Humanities and Social Sciences, Hiroshima University, Hiroshima, Japan; Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto, Japan
| | - Julian Savulescu
- Murdoch Children's Research Institute, Melbourne, VIC, Australia; University of Melbourne, Melbourne, VIC, Australia; Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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Newman JP, Zhang J, Cuevas-López A, Miller NJ, Honda T, van der Goes MSH, Leighton AH, Carvalho F, Lopes G, Lakunina A, Siegle JH, Harnett MT, Wilson MA, Voigts J. A unified open-source platform for multimodal neural recording and perturbation during naturalistic behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.30.554672. [PMID: 37693443 PMCID: PMC10491150 DOI: 10.1101/2023.08.30.554672] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Behavioral neuroscience faces two conflicting demands: long-duration recordings from large neural populations and unimpeded animal behavior. To meet this challenge, we developed ONIX, an open-source data acquisition system with high data throughput (2GB/sec) and low closed-loop latencies (<1ms) that uses a novel 0.3 mm thin tether to minimize behavioral impact. Head position and rotation are tracked in 3D and used to drive active commutation without torque measurements. ONIX can acquire from combinations of passive electrodes, Neuropixels probes, head-mounted microscopes, cameras, 3D-trackers, and other data sources. We used ONIX to perform uninterrupted, long (~7 hours) neural recordings in mice as they traversed complex 3-dimensional terrain. ONIX allowed exploration with similar mobility as non-implanted animals, in contrast to conventional tethered systems which restricted movement. By combining long recordings with full mobility, our technology will enable new progress on questions that require high-quality neural recordings during ethologically grounded behaviors.
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Affiliation(s)
- Jonathan P Newman
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Open Ephys Inc. Atlanta, GA, USA
| | - Jie Zhang
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
| | - Aarón Cuevas-López
- Open Ephys Inc. Atlanta, GA, USA
- Dept. of Electrical Engineering, Polytechnic University of Valencia, Valencia, Spain
- Open Ephys Production Site, Lisbon, Portugal
| | - Nicholas J Miller
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | - Takato Honda
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
| | - Marie-Sophie H van der Goes
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | | | | | | | - Anna Lakunina
- Allen Institute for Neural Dynamics, Seattle, Washington, USA
| | - Joshua H Siegle
- Allen Institute for Neural Dynamics, Seattle, Washington, USA
| | - Mark T Harnett
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | - Matthew A Wilson
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
| | - Jakob Voigts
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Open Ephys Inc. Atlanta, GA, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- HHMI Janelia Research Campus, Ashburn, VA, USA
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4
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Suzuki I, Matsuda N, Han X, Noji S, Shibata M, Nagafuku N, Ishibashi Y. Large-Area Field Potential Imaging Having Single Neuron Resolution Using 236 880 Electrodes CMOS-MEA Technology. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023:e2207732. [PMID: 37088859 PMCID: PMC10369302 DOI: 10.1002/advs.202207732] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/21/2023] [Indexed: 05/03/2023]
Abstract
The electrophysiological technology having a high spatiotemporal resolution at the single-cell level and noninvasive measurements of large areas provide insights on underlying neuronal function. Here, a complementary metal-oxide semiconductor (CMOS)-microelectrode array (MEA) is used that uses 236 880 electrodes each with an electrode size of 11.22 × 11.22 µm and 236 880 covering a wide area of 5.5 × 5.9 mm in presenting a detailed and single-cell-level neural activity analysis platform for brain slices, human iPS cell-derived cortical networks, peripheral neurons, and human brain organoids. Propagation pattern characteristics between brain regions changes the synaptic propagation into compounds based on single-cell time-series patterns, classification based on single DRG neuron firing patterns and compound responses, axonal conduction characteristics and changes to anticancer drugs, and network activities and transition to compounds in brain organoids are extracted. This detailed analysis of neural activity at the single-cell level using the CMOS-MEA provides a new understanding of the basic mechanisms of brain circuits in vitro and ex vivo, on human neurological diseases for drug discovery, and compound toxicity assessment.
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Affiliation(s)
- Ikuro Suzuki
- Department of Electronics, Graduate School of Engineering, Tohoku Institute of Technology, 35-1 Yagiyama Kasumicho, Taihaku-ku, Sendai, Miyagi, 982-8577, Japan
| | - Naoki Matsuda
- Department of Electronics, Graduate School of Engineering, Tohoku Institute of Technology, 35-1 Yagiyama Kasumicho, Taihaku-ku, Sendai, Miyagi, 982-8577, Japan
| | - Xiaobo Han
- Department of Electronics, Graduate School of Engineering, Tohoku Institute of Technology, 35-1 Yagiyama Kasumicho, Taihaku-ku, Sendai, Miyagi, 982-8577, Japan
| | - Shuhei Noji
- Department of Electronics, Graduate School of Engineering, Tohoku Institute of Technology, 35-1 Yagiyama Kasumicho, Taihaku-ku, Sendai, Miyagi, 982-8577, Japan
| | - Mikako Shibata
- Department of Electronics, Graduate School of Engineering, Tohoku Institute of Technology, 35-1 Yagiyama Kasumicho, Taihaku-ku, Sendai, Miyagi, 982-8577, Japan
| | - Nami Nagafuku
- Department of Electronics, Graduate School of Engineering, Tohoku Institute of Technology, 35-1 Yagiyama Kasumicho, Taihaku-ku, Sendai, Miyagi, 982-8577, Japan
| | - Yuto Ishibashi
- Department of Electronics, Graduate School of Engineering, Tohoku Institute of Technology, 35-1 Yagiyama Kasumicho, Taihaku-ku, Sendai, Miyagi, 982-8577, Japan
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5
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Hu YY, Yang G, Liang XS, Ding XS, Xu DE, Li Z, Ma QH, Chen R, Sun YY. Transcranial low-intensity ultrasound stimulation for treating central nervous system disorders: A promising therapeutic application. Front Neurol 2023; 14:1117188. [PMID: 36970512 PMCID: PMC10030814 DOI: 10.3389/fneur.2023.1117188] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 02/10/2023] [Indexed: 03/29/2023] Open
Abstract
Transcranial ultrasound stimulation is a neurostimulation technique that has gradually attracted the attention of researchers, especially as a potential therapy for neurological disorders, because of its high spatial resolution, its good penetration depth, and its non-invasiveness. Ultrasound can be categorized as high-intensity and low-intensity based on the intensity of its acoustic wave. High-intensity ultrasound can be used for thermal ablation by taking advantage of its high-energy characteristics. Low-intensity ultrasound, which produces low energy, can be used as a means to regulate the nervous system. The present review describes the current status of research on low-intensity transcranial ultrasound stimulation (LITUS) in the treatment of neurological disorders, such as epilepsy, essential tremor, depression, Parkinson's disease (PD), and Alzheimer's disease (AD). This review summarizes preclinical and clinical studies using LITUS to treat the aforementioned neurological disorders and discusses their underlying mechanisms.
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Affiliation(s)
- Yun-Yun Hu
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
- Jiangsu Key Laboratory of Neuropsychiatric Diseases, Institute of Neuroscience, Soochow University, Suzhou, Jiangsu, China
| | - Gang Yang
- Lab Center, Medical College of Soochow University, Suzhou, China
| | - Xue-Song Liang
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
- Jiangsu Key Laboratory of Neuropsychiatric Diseases, Institute of Neuroscience, Soochow University, Suzhou, Jiangsu, China
- Second Clinical College, Dalian Medical University, Dalian, Liaoning, China
| | - Xuan-Si Ding
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
- Jiangsu Key Laboratory of Neuropsychiatric Diseases, Institute of Neuroscience, Soochow University, Suzhou, Jiangsu, China
| | - De-En Xu
- Wuxi No. 2 People's Hospital, Wuxi, Jiangsu, China
| | - Zhe Li
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
- Sleep Medicine Center, Suzhou Guangji Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Quan-Hong Ma
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
- Jiangsu Key Laboratory of Neuropsychiatric Diseases, Institute of Neuroscience, Soochow University, Suzhou, Jiangsu, China
- Quan-Hong Ma
| | - Rui Chen
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
- *Correspondence: Rui Chen
| | - Yan-Yun Sun
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
- Jiangsu Key Laboratory of Neuropsychiatric Diseases, Institute of Neuroscience, Soochow University, Suzhou, Jiangsu, China
- Yan-Yun Sun
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6
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Chen ZS, Pesaran B. Improving scalability in systems neuroscience. Neuron 2021; 109:1776-1790. [PMID: 33831347 PMCID: PMC8178195 DOI: 10.1016/j.neuron.2021.03.025] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 03/11/2021] [Accepted: 03/16/2021] [Indexed: 12/30/2022]
Abstract
Emerging technologies to acquire data at increasingly greater scales promise to transform discovery in systems neuroscience. However, current exponential growth in the scale of data acquisition is a double-edged sword. Scaling up data acquisition can speed up the cycle of discovery but can also misinterpret the results or possibly slow down the cycle because of challenges presented by the curse of high-dimensional data. Active, adaptive, closed-loop experimental paradigms use hardware and algorithms optimized to enable time-critical computation to provide feedback that interprets the observations and tests hypotheses to actively update the stimulus or stimulation parameters. In this perspective, we review important concepts of active and adaptive experiments and discuss how selectively constraining the dimensionality and optimizing strategies at different stages of discovery loop can help mitigate the curse of high-dimensional data. Active and adaptive closed-loop experimental paradigms can speed up discovery despite an exponentially increasing data scale, offering a road map to timely and iterative hypothesis revision and discovery in an era of exponential growth in neuroscience.
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Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY 10016, USA; Neuroscience Institute, NYU School of Medicine, New York, NY 10016, USA.
| | - Bijan Pesaran
- Neuroscience Institute, NYU School of Medicine, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA; Department of Neurology, New York University School of Medicine, New York, NY 10016, USA.
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7
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Yang H, Yuan Y, Wang X, Li X. Closed-Loop Transcranial Ultrasound Stimulation for Real-Time Non-invasive Neuromodulation in vivo. Front Neurosci 2020; 14:445. [PMID: 32477055 PMCID: PMC7235408 DOI: 10.3389/fnins.2020.00445] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 04/09/2020] [Indexed: 12/18/2022] Open
Abstract
The closed-loop brain stimulation technique plays a key role in neural network information processing and therapies of neurological diseases. Transcranial ultrasound stimulation (TUS) is an established neuromodulation method for the neural oscillation in animals or human. All available TUS systems provide brain stimulation in an open-loop pattern. In this study, we developed a closed-loop transcranial ultrasound stimulation (CLTUS) system for real-time non-invasive neuromodulation in vivo. We used the CLTUS system to modulate the neural activities of the hippocampus of a wild-type mouse based on the phase of the theta rhythm recorded at the ultrasound-targeted location. In addition, we modulated the hippocampus of a temporal lobe epilepsy (TLE) mouse. The ultrasound stimulation increased the absolute power and reduced the relative power of the theta rhythm, which were independent of the specific phase of the theta rhythm. Compared with those of a sham stimulation, the latency of epileptic seizures was significantly increased, while the epileptic seizure duration was significantly decreased under the CLTUS. The above results indicate that the CLTUS can be used to not only modulate the neural oscillation through the theta-phase-specific manipulation of the hippocampus but also effectively inhibit the seizure of a TLE mouse in time. CLTUS has large application potentials for the understanding of the causal relationship of neural circuits as well as for timely, effective, and non-invasive therapies of neurological diseases such as epilepsy and Parkinson’s disease.
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Affiliation(s)
- Huifang Yang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China
| | - Yi Yuan
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China
| | - Xingran Wang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China
| | - Xin Li
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China
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Obien MEJ, Frey U. Large-Scale, High-Resolution Microelectrode Arrays for Interrogation of Neurons and Networks. ADVANCES IN NEUROBIOLOGY 2019; 22:83-123. [PMID: 31073933 DOI: 10.1007/978-3-030-11135-9_4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
High-density microelectrode arrays (HD-MEAs) are increasingly being used for the observation and manipulation of neurons and networks in vitro. Large-scale electrode arrays allow for long-term extracellular recording of the electrical activity from thousands of neurons simultaneously. Beyond population activity, it has also become possible to extract information of single neurons at subcellular level (e.g., the propagation of action potentials along axons). In effect, HD-MEAs have become an electrical imaging platform for label-free extraction of the structure and activation of cells in cultures and tissues. The quality of HD-MEA data depends on the resolution of the electrode array and the signal-to-noise ratio. In this chapter, we begin with an introduction to HD-MEA signals. We provide an overview of the developments on complementary metal-oxide-semiconductor or CMOS-based HD-MEA technology. We also discuss the factors affecting the performance of HD-MEAs and the trending application requirements that drive the efforts for future devices. We conclude with an outlook on the potential of HD-MEAs for advancing basic neuroscience and drug discovery.
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Affiliation(s)
- Marie Engelene J Obien
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
- MaxWell Biosystems, Basel, Switzerland.
| | - Urs Frey
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- MaxWell Biosystems, Basel, Switzerland
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Telemetry-controlled simultaneous stimulation-and-recording device (SRD) to study interhemispheric cortical circuits in rat primary somatosensory (SI) cortex. BMC Biomed Eng 2019; 1:19. [PMID: 32903340 PMCID: PMC7422589 DOI: 10.1186/s42490-019-0019-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 07/02/2019] [Indexed: 01/03/2023] Open
Abstract
Background A growing need exists for neuroscience platforms that can perform simultaneous chronic recording and stimulation of neural tissue in animal models in a telemetry-controlled fashion with signal processing for analysis of the chronic recording data and external triggering capability. We describe the system design, testing, evaluation, and implementation of a wireless simultaneous stimulation-and-recording device (SRD) for modulating cortical circuits in physiologically identified sites in primary somatosensory (SI) cortex in awake-behaving and freely-moving rats. The SRD was developed using low-cost electronic components and open-source software. The function of the SRD was assessed by bench and in-vivo testing. Results The SRD recorded spontaneous spiking and bursting neuronal activity, evoked responses to programmed intracortical microstimulation (ICMS) delivered internally by the SRD, and evoked responses to external peripheral forelimb stimulation. Conclusions The SRD is capable of wireless stimulation and recording on a predetermined schedule or can be wirelessly synchronized with external input as would be required in behavioral testing prior to, during, and following ICMS.
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Amaducci R, Reyes-Sanchez M, Elices I, Rodriguez FB, Varona P. RTHybrid: A Standardized and Open-Source Real-Time Software Model Library for Experimental Neuroscience. Front Neuroinform 2019; 13:11. [PMID: 30914940 PMCID: PMC6423167 DOI: 10.3389/fninf.2019.00011] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 02/14/2019] [Indexed: 12/05/2022] Open
Abstract
Closed-loop technologies provide novel ways of online observation, control and bidirectional interaction with the nervous system, which help to study complex non-linear and partially observable neural dynamics. These protocols are often difficult to implement due to the temporal precision required when interacting with biological components, which in many cases can only be achieved using real-time technology. In this paper we introduce RTHybrid (www.github.com/GNB-UAM/RTHybrid), a free and open-source software that includes a neuron and synapse model library to build hybrid circuits with living neurons in a wide variety of experimental contexts. In an effort to encourage the standardization of real-time software technology in neuroscience research, we compared different open-source real-time operating system patches, RTAI, Xenomai 3 and Preempt-RT, according to their performance and usability. RTHybrid has been developed to run over Linux operating systems supporting both Xenomai 3 and Preempt-RT real-time patches, and thus allowing an easy implementation in any laboratory. We report a set of validation tests and latency benchmarks for the construction of hybrid circuits using this library. With this work we want to promote the dissemination of standardized, user-friendly and open-source software tools developed for open- and closed-loop experimental neuroscience.
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Affiliation(s)
- Rodrigo Amaducci
- Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid, Spain
| | | | | | | | - Pablo Varona
- Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid, Spain
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11
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Closed-Loop Systems and In Vitro Neuronal Cultures: Overview and Applications. ADVANCES IN NEUROBIOLOGY 2019; 22:351-387. [DOI: 10.1007/978-3-030-11135-9_15] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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12
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Seu GP, Angotzi GN, Boi F, Raffo L, Berdondini L, Meloni P. Exploiting All Programmable SoCs in Neural Signal Analysis: A Closed-Loop Control for Large-Scale CMOS Multielectrode Arrays. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2018; 12:839-850. [PMID: 29993584 DOI: 10.1109/tbcas.2018.2830659] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Microelectrode array (MEA) systems with up to several thousands of recording electrodes and electrical or optical stimulation capabilities are commercially available or described in the literature. By exploiting their submillisecond and micrometric temporal and spatial resolutions to record bioelectrical signals, such emerging MEA systems are increasingly used in neuroscience to study the complex dynamics of neuronal networks and brain circuits. However, they typically lack the capability of implementing real-time feedback between the detection of neuronal spiking events and stimulation, thus restricting large-scale neural interfacing to open-loop conditions. In order to exploit the potential of such large-scale recording systems and stimulation, we designed and validated a fully reconfigurable FPGA-based processing system for closed-loop multichannel control. By adopting a Xilinx Zynq-all-programmable system on chip that integrates reconfigurable logic and a dual-core ARM-based processor on the same device, the proposed platform permits low-latency preprocessing (filtering and detection) of spikes acquired simultaneously from several thousands of electrode sites. To demonstrate the proposed platform, we tested its performances through ex vivo experiments on the mice retina using a state-of-the-art planar high-density MEA that samples 4096 electrodes at 18 kHz and record light-evoked spikes from several thousands of retinal ganglion cells simultaneously. Results demonstrate that the platform is able to provide a total latency from whole-array data acquisition to stimulus generation below 2 ms. This opens the opportunity to design closed-loop experiments on neural systems and biomedical applications using emerging generations of planar or implantable large-scale MEA systems.
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Pirog A, Bornat Y, Perrier R, Raoux M, Jaffredo M, Quotb A, Lang J, Lewis N, Renaud S. Multimed: An Integrated, Multi-Application Platform for the Real-Time Recording and Sub-Millisecond Processing of Biosignals. SENSORS (BASEL, SWITZERLAND) 2018; 18:E2099. [PMID: 29966339 PMCID: PMC6069272 DOI: 10.3390/s18072099] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 06/23/2018] [Accepted: 06/27/2018] [Indexed: 12/30/2022]
Abstract
Enhanced understanding and control of electrophysiology mechanisms are increasingly being hailed as key knowledge in the fields of modern biology and medicine. As more and more excitable cell mechanics are being investigated and exploited, the need for flexible electrophysiology setups becomes apparent. With that aim, we designed Multimed, which is a versatile hardware platform for the real-time recording and processing of biosignals. Digital processing in Multimed is an arrangement of generic processing units from a custom library. These can freely be rearranged to match the needs of the application. Embedded onto a Field Programmable Gate Array (FPGA), these modules utilize full-hardware signal processing to lower processing latency. It achieves constant latency, and sub-millisecond processing and decision-making on 64 channels. The FPGA core processing unit makes Multimed suitable as either a reconfigurable electrophysiology system or a prototyping platform for VLSI implantable medical devices. It is specifically designed for open- and closed-loop experiments and provides consistent feedback rules, well within biological microseconds timeframes. This paper presents the specifications and architecture of the Multimed system, then details the biosignal processing algorithms and their digital implementation. Finally, three applications utilizing Multimed in neuroscience and diabetes research are described. They demonstrate the system’s configurability, its multi-channel, real-time processing, and its feedback control capabilities.
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Affiliation(s)
- Antoine Pirog
- Laboratoire de l'Intégration du Matériau au Système (IMS), University of Bordeaux, Bordeaux INP, CNRS UMR 5218, F-33400 Talence, France.
| | - Yannick Bornat
- Laboratoire de l'Intégration du Matériau au Système (IMS), University of Bordeaux, Bordeaux INP, CNRS UMR 5218, F-33400 Talence, France.
| | - Romain Perrier
- Signalisation et physiopathologie cardiovasculaire, INSERM S-1180, University of Paris Sud, F-92296 Châtenay-Malabry, France.
| | - Matthieu Raoux
- Institut de Chimie et Biologie des Membranes et des Nano-objets (CBMN), University of Bordeaux, CNRS UMR 5248, F-33600 Pessac, France.
| | - Manon Jaffredo
- Institut de Chimie et Biologie des Membranes et des Nano-objets (CBMN), University of Bordeaux, CNRS UMR 5248, F-33600 Pessac, France.
| | - Adam Quotb
- Laboratoire d'Analyse et d'Architecture des Systèmes (LAAS), Federal University of Toulouse Midi-Pyrénées, CNRS UMR 8001, F-31031 Toulouse, France.
| | - Jochen Lang
- Institut de Chimie et Biologie des Membranes et des Nano-objets (CBMN), University of Bordeaux, CNRS UMR 5248, F-33600 Pessac, France.
| | - Noëlle Lewis
- Laboratoire de l'Intégration du Matériau au Système (IMS), University of Bordeaux, Bordeaux INP, CNRS UMR 5218, F-33400 Talence, France.
| | - Sylvie Renaud
- Laboratoire de l'Intégration du Matériau au Système (IMS), University of Bordeaux, Bordeaux INP, CNRS UMR 5218, F-33400 Talence, France.
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14
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O’Shea DJ, Shenoy KV. ERAASR: an algorithm for removing electrical stimulation artifacts from multielectrode array recordings. J Neural Eng 2018; 15:026020. [PMID: 29265009 PMCID: PMC5833982 DOI: 10.1088/1741-2552/aaa365] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
OBJECTIVE Electrical stimulation is a widely used and effective tool in systems neuroscience, neural prosthetics, and clinical neurostimulation. However, electrical artifacts evoked by stimulation prevent the detection of spiking activity on nearby recording electrodes, which obscures the neural population response evoked by stimulation. We sought to develop a method to clean artifact-corrupted electrode signals recorded on multielectrode arrays in order to recover the underlying neural spiking activity. APPROACH We created an algorithm, which performs estimation and removal of array artifacts via sequential principal components regression (ERAASR). This approach leverages the similar structure of artifact transients, but not spiking activity, across simultaneously recorded channels on the array, across pulses within a train, and across trials. The ERAASR algorithm requires no special hardware, imposes no requirements on the shape of the artifact or the multielectrode array geometry, and comprises sequential application of straightforward linear methods with intuitive parameters. The approach should be readily applicable to most datasets where stimulation does not saturate the recording amplifier. MAIN RESULTS The effectiveness of the algorithm is demonstrated in macaque dorsal premotor cortex using acute linear multielectrode array recordings and single electrode stimulation. Large electrical artifacts appeared on all channels during stimulation. After application of ERAASR, the cleaned signals were quiescent on channels with no spontaneous spiking activity, whereas spontaneously active channels exhibited evoked spikes which closely resembled spontaneously occurring spiking waveforms. SIGNIFICANCE We hope that enabling simultaneous electrical stimulation and multielectrode array recording will help elucidate the causal links between neural activity and cognition and facilitate naturalistic sensory protheses.
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Affiliation(s)
- Daniel J. O’Shea
- Neurosciences Program, Stanford University, Stanford, CA, U.S.A
- Departments of Electrical Engineering, Bioengineering, and Neurobiology, Stanford University, Stanford, CA, U.S.A
| | - Krishna V. Shenoy
- Neurosciences Program, Stanford University, Stanford, CA, U.S.A
- Departments of Electrical Engineering, Bioengineering, and Neurobiology, Stanford University, Stanford, CA, U.S.A
- Bio-X Program, Stanford Neurosciences Institute, Stanford University, Stanford, CA, U.S.A
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, U.S.A
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15
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Schubert R, Trenholm S, Balint K, Kosche G, Cowan CS, Mohr MA, Munz M, Martinez-Martin D, Fläschner G, Newton R, Krol J, Scherf BG, Yonehara K, Wertz A, Ponti A, Ghanem A, Hillier D, Conzelmann KK, Müller DJ, Roska B. Virus stamping for targeted single-cell infection in vitro and in vivo. Nat Biotechnol 2017; 36:81-88. [DOI: 10.1038/nbt.4034] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Accepted: 11/13/2017] [Indexed: 11/09/2022]
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16
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Mena GE, Grosberg LE, Madugula S, Hottowy P, Litke A, Cunningham J, Chichilnisky EJ, Paninski L. Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays. PLoS Comput Biol 2017; 13:e1005842. [PMID: 29131818 PMCID: PMC5703587 DOI: 10.1371/journal.pcbi.1005842] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Revised: 11/27/2017] [Accepted: 10/20/2017] [Indexed: 11/18/2022] Open
Abstract
Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis. This technology may be helpful in the design of future high-resolution sensory prostheses based on tailored stimulation (e.g., retinal prostheses), and for closed-loop neural stimulation at a much larger scale than currently possible.
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Affiliation(s)
- Gonzalo E. Mena
- Statistics Department, Columbia University, New York, New York, United States of America
| | - Lauren E. Grosberg
- Department of Neurosurgery and Hansen Experimental Physics Laboratory, Stanford University, Stanford, California, United States of America
| | - Sasidhar Madugula
- Department of Neurosurgery and Hansen Experimental Physics Laboratory, Stanford University, Stanford, California, United States of America
| | - Paweł Hottowy
- Physics and Applied Computer Science, AGH University of Science and Technology, Krakow, Poland
| | - Alan Litke
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, Santa Cruz, California, United States of America
| | - John Cunningham
- Statistics Department, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
| | - E. J. Chichilnisky
- Department of Neurosurgery and Hansen Experimental Physics Laboratory, Stanford University, Stanford, California, United States of America
| | - Liam Paninski
- Statistics Department, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
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17
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Hazan H, Ziv NE. Closed Loop Experiment Manager (CLEM)-An Open and Inexpensive Solution for Multichannel Electrophysiological Recordings and Closed Loop Experiments. Front Neurosci 2017; 11:579. [PMID: 29093659 PMCID: PMC5651259 DOI: 10.3389/fnins.2017.00579] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2017] [Accepted: 10/03/2017] [Indexed: 11/13/2022] Open
Abstract
There is growing need for multichannel electrophysiological systems that record from and interact with neuronal systems in near real-time. Such systems are needed, for example, for closed loop, multichannel electrophysiological/optogenetic experimentation in vivo and in a variety of other neuronal preparations, or for developing and testing neuro-prosthetic devices, to name a few. Furthermore, there is a need for such systems to be inexpensive, reliable, user friendly, easy to set-up, open and expandable, and possess long life cycles in face of rapidly changing computing environments. Finally, they should provide powerful, yet reasonably easy to implement facilities for developing closed-loop protocols for interacting with neuronal systems. Here, we survey commercial and open source systems that address these needs to varying degrees. We then present our own solution, which we refer to as Closed Loop Experiments Manager (CLEM). CLEM is an open source, soft real-time, Microsoft Windows desktop application that is based on a single generic personal computer (PC) and an inexpensive, general-purpose data acquisition board. CLEM provides a fully functional, user-friendly graphical interface, possesses facilities for recording, presenting and logging electrophysiological data from up to 64 analog channels, and facilities for controlling external devices, such as stimulators, through digital and analog interfaces. Importantly, it includes facilities for running closed-loop protocols written in any programming language that can generate dynamic link libraries (DLLs). We describe the application, its architecture and facilities. We then demonstrate, using networks of cortical neurons growing on multielectrode arrays (MEA) that despite its reliance on generic hardware, its performance is appropriate for flexible, closed-loop experimentation at the neuronal network level.
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Affiliation(s)
- Hananel Hazan
- Faculty of Medicine, Technion, Haifa, Israel.,Network Biology Research Laboratories, Lorry Lokey Center for Life Sciences and Engineering, Technion, Haifa, Israel
| | - Noam E Ziv
- Faculty of Medicine, Technion, Haifa, Israel.,Network Biology Research Laboratories, Lorry Lokey Center for Life Sciences and Engineering, Technion, Haifa, Israel
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18
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Targeted Stimulation Using Differences in Activation Probability across the Strength–Duration Space. Processes (Basel) 2017. [DOI: 10.3390/pr5020014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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19
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Hierlemann A. Direct Interfacing of Neurons to Highly Integrated Microsystems. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON MICRO ELECTRO MECHANICAL SYSTEMS 2017; 2017:199-204. [PMID: 28677939 PMCID: PMC5448667 DOI: 10.1109/memsys.2017.7863375] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The use of large high-density transducer arrays enables fundamentally new neuroscientific insights through enabling high-throughput monitoring of action potentials of larger neuronal networks (> 1000 neurons) over extended time to see effects of disturbances or developmental effects, and through facilitating detailed investigations of neuronal signaling characteristics at subcellular level, for example, the study of axonal signal propagation that has been largely inaccessible to established methods. Applications include research in neural diseases and pharmacology.
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Affiliation(s)
- Andreas Hierlemann
- ETH Zurich, Department of Biosystems Science and Engineering, CH-4058, Basel, Switzerland
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20
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Obien MEJ, Gong W, Frey U, Bakkum DJ. CMOS-Based High-Density Microelectrode Arrays: Technology and Applications. SERIES IN BIOENGINEERING 2017. [DOI: 10.1007/978-981-10-3957-7_1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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21
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Yada Y, Mita T, Sanada A, Yano R, Kanzaki R, Bakkum DJ, Hierlemann A, Takahashi H. Development of neural population activity toward self-organized criticality. Neuroscience 2016; 343:55-65. [PMID: 27915209 DOI: 10.1016/j.neuroscience.2016.11.031] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2016] [Revised: 11/21/2016] [Accepted: 11/21/2016] [Indexed: 12/13/2022]
Abstract
Self-organized criticality (SoC), a spontaneous dynamic state established and maintained in networks of moderate complexity, is a universal characteristic of neural systems. Such systems produce cascades of spontaneous activity that are typically characterized by power-law distributions and rich, stable spatiotemporal patterns (i.e., neuronal avalanches). Since the dynamics of the critical state confer advantages in information processing within neuronal networks, it is of great interest to determine how criticality emerges during development. One possible mechanism is developmental, and includes axonal elongation during synaptogenesis and subsequent synaptic pruning in combination with the maturation of GABAergic inhibition (i.e., the integration then fragmentation process). Because experimental evidence for this mechanism remains inconclusive, we studied the developmental variation of neuronal avalanches in dissociated cortical neurons using high-density complementary metal-oxide semiconductor (CMOS) microelectrode arrays (MEAs). The spontaneous activities of nine cultures were monitored using CMOS MEAs from 4 to 30days in vitro (DIV) at single-cell spatial resolution. While cells were immature, cultures demonstrated random-like patterns of activity and an exponential avalanche size distribution; this distribution was followed by a bimodal distribution, and finally a power-law-like distribution. The bimodal distribution was associated with a large-scale avalanche with a homogeneous spatiotemporal pattern, while the subsequent power-law distribution was associated with diverse patterns. These results suggest that the SoC emerges through a two-step process: the integration process accompanying the characteristic large-scale avalanche and the fragmentation process associated with diverse middle-size avalanches.
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Affiliation(s)
- Yuichiro Yada
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8904, Japan; Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8654, Japan; Japan Society for the Promotion of Science (JSPS) Research Fellow, 5-3-1, Koji-machi, Chiyoda-ku, Tokyo 102-0083, Japan
| | - Takeshi Mita
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8904, Japan; Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
| | - Akihiro Sanada
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8904, Japan; Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
| | - Ryuichi Yano
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8904, Japan; Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
| | - Ryohei Kanzaki
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8904, Japan; Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
| | - Douglas J Bakkum
- Department of Biosystems Science and Engineering, ETH, Mattenstrasse 26, 4058 Basel, Switzerland
| | - Andreas Hierlemann
- Department of Biosystems Science and Engineering, ETH, Mattenstrasse 26, 4058 Basel, Switzerland
| | - Hirokazu Takahashi
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8904, Japan; Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8654, Japan.
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22
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Gong W, Senčar J, Bakkum DJ, Jäckel D, Obien MEJ, Radivojevic M, Hierlemann AR. Multiple Single-Unit Long-Term Tracking on Organotypic Hippocampal Slices Using High-Density Microelectrode Arrays. Front Neurosci 2016; 10:537. [PMID: 27920665 PMCID: PMC5118563 DOI: 10.3389/fnins.2016.00537] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Accepted: 11/03/2016] [Indexed: 12/11/2022] Open
Abstract
A novel system to cultivate and record from organotypic brain slices directly on high-density microelectrode arrays (HD-MEA) was developed. This system allows for continuous recording of electrical activity of specific individual neurons at high spatial resolution while monitoring at the same time, neuronal network activity. For the first time, the electrical activity patterns of single neurons and the corresponding neuronal network in an organotypic hippocampal slice culture were studied during several consecutive weeks at daily intervals. An unsupervised iterative spike-sorting algorithm, based on PCA and k-means clustering, was developed to assign the activities to the single units. Spike-triggered average extracellular waveforms of an action potential recorded across neighboring electrodes, termed “footprints” of single-units were generated and tracked over weeks. The developed system offers the potential to study chronic impacts of drugs or genetic modifications on individual neurons in slice preparations over extended times.
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Affiliation(s)
- Wei Gong
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering ETH Zürich, Basel, Switzerland
| | - Jure Senčar
- Faculty of Electrical Engineering, University of Ljubljana Ljubljana, Slovenia
| | - Douglas J Bakkum
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering ETH Zürich, Basel, Switzerland
| | - David Jäckel
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering ETH Zürich, Basel, Switzerland
| | - Marie Engelene J Obien
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering ETH Zürich, Basel, Switzerland
| | - Milos Radivojevic
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering ETH Zürich, Basel, Switzerland
| | - Andreas R Hierlemann
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering ETH Zürich, Basel, Switzerland
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23
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Resendez SL, Jennings JH, Ung RL, Namboodiri VMK, Zhou ZC, Otis JM, Nomura H, McHenry JA, Kosyk O, Stuber GD. Visualization of cortical, subcortical and deep brain neural circuit dynamics during naturalistic mammalian behavior with head-mounted microscopes and chronically implanted lenses. Nat Protoc 2016; 11:566-97. [PMID: 26914316 PMCID: PMC5239057 DOI: 10.1038/nprot.2016.021] [Citation(s) in RCA: 174] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Genetically encoded calcium indicators for visualizing dynamic cellular activity have greatly expanded our understanding of the brain. However, owing to the light-scattering properties of the brain, as well as the size and rigidity of traditional imaging technology, in vivo calcium imaging has been limited to superficial brain structures during head-fixed behavioral tasks. These limitations can now be circumvented by using miniature, integrated microscopes in conjunction with an implantable microendoscopic lens to guide light into and out of the brain, thus permitting optical access to deep brain (or superficial) neural ensembles during naturalistic behaviors. Here we describe steps to conduct such imaging studies using mice. However, we anticipate that the protocol can be easily adapted for use in other small vertebrates. Successful completion of this protocol will permit cellular imaging of neuronal activity and the generation of data sets with sufficient statistical power to correlate neural activity with stimulus presentation, physiological state and other aspects of complex behavioral tasks. This protocol takes 6-11 weeks to complete.
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Affiliation(s)
- Shanna L. Resendez
- Departments of Psychiatry and Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC
| | | | - Randall L. Ung
- Departments of Psychiatry and Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC
| | - Vijay Mohan K. Namboodiri
- Departments of Psychiatry and Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC
| | - Zhe Charles Zhou
- Departments of Psychiatry and Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC
| | - James M. Otis
- Departments of Psychiatry and Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC
| | - Hiroshi Nomura
- Departments of Psychiatry and Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC
| | - Jenna A. McHenry
- Departments of Psychiatry and Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC
| | - Oksana Kosyk
- Departments of Psychiatry and Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC
| | - Garret D. Stuber
- Departments of Psychiatry and Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC
- Curriculum in Neurobiology, University of North Carolina, Chapel Hill, NC
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24
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Müller J, Ballini M, Livi P, Chen Y, Radivojevic M, Shadmani A, Viswam V, Jones IL, Fiscella M, Diggelmann R, Stettler A, Frey U, Bakkum DJ, Hierlemann A. High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels. LAB ON A CHIP 2015; 15:2767-80. [PMID: 25973786 PMCID: PMC5421573 DOI: 10.1039/c5lc00133a] [Citation(s) in RCA: 145] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Studies on information processing and learning properties of neuronal networks would benefit from simultaneous and parallel access to the activity of a large fraction of all neurons in such networks. Here, we present a CMOS-based device, capable of simultaneously recording the electrical activity of over a thousand cells in in vitro neuronal networks. The device provides sufficiently high spatiotemporal resolution to enable, at the same time, access to neuronal preparations on subcellular, cellular, and network level. The key feature is a rapidly reconfigurable array of 26 400 microelectrodes arranged at low pitch (17.5 μm) within a large overall sensing area (3.85 × 2.10 mm(2)). An arbitrary subset of the electrodes can be simultaneously connected to 1024 low-noise readout channels as well as 32 stimulation units. Each electrode or electrode subset can be used to electrically stimulate or record the signals of virtually any neuron on the array. We demonstrate the applicability and potential of this device for various different experimental paradigms: large-scale recordings from whole networks of neurons as well as investigations of axonal properties of individual neurons.
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Affiliation(s)
- Jan Müller
- ETH Zurich, Bio Engineering Laboratory, Department of Biosystems Science and Engineering, Mattenstrasse 26, CH-4058 Basel, Switzerland.
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25
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Gong W, Sencar J, Jäckel D, Müller J, Fiscella M, Radivojevic M, Bakkum D, Hierlemann A. Long-Term, High-Spatiotemporal Resolution Recording From Cultured Organotypic Slices with High-Density Microelectrode Arrays. INTERNATIONAL SOLID-STATE SENSORS, ACTUATORS AND MICROSYSTEMS CONFERENCE : [PROCEEDINGS]. INTERNATIONAL CONFERENCE ON SOLID-STATE SENSORS, ACTUATORS, AND MICROSYSTEMS 2015; 18:1037-1040. [PMID: 33868793 DOI: 10.1109/transducers.2015.7181103] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
A novel system to cultivate and record brain slices directly on high-density microelectrode arrays (HD-MEA) was developed. This system allows to continuously record electrical activity of selected individual neurons at high spatial resolution, while monitoring neuronal network activity at the same time. For the first time, properties of single neurons and the corresponding neuronal network in an organotypic hippocampal slice culture were studied over four consecutive weeks at daily intervals.
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Affiliation(s)
- W Gong
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
| | - J Sencar
- Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia
| | - D Jäckel
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
| | - J Müller
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
| | - M Fiscella
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
| | - M Radivojevic
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
| | - D Bakkum
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
| | - A Hierlemann
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
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26
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Buzsáki G, Stark E, Berényi A, Khodagholy D, Kipke DR, Yoon E, Wise KD. Tools for probing local circuits: high-density silicon probes combined with optogenetics. Neuron 2015; 86:92-105. [PMID: 25856489 PMCID: PMC4392339 DOI: 10.1016/j.neuron.2015.01.028] [Citation(s) in RCA: 167] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
To understand how function arises from the interactions between neurons, it is necessary to use methods that allow the monitoring of brain activity at the single-neuron, single-spike level and the targeted manipulation of the diverse neuron types selectively in a closed-loop manner. Large-scale recordings of neuronal spiking combined with optogenetic perturbation of identified individual neurons has emerged as a suitable method for such tasks in behaving animals. To fully exploit the potential power of these methods, multiple steps of technical innovation are needed. We highlight the current state of the art in electrophysiological recording methods, combined with optogenetics, and discuss directions for progress. In addition, we point to areas where rapid development is in progress and discuss topics where near-term improvements are possible and needed.
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Affiliation(s)
- György Buzsáki
- The Neuroscience Institute, New York University, School of Medicine, New York, NY 10016, USA; Center for Neural Science, New York University, School of Medicine, New York, NY 10016, USA.
| | - Eran Stark
- The Neuroscience Institute, New York University, School of Medicine, New York, NY 10016, USA
| | - Antal Berényi
- The Neuroscience Institute, New York University, School of Medicine, New York, NY 10016, USA; MTA-SZTE "Lendület" Oscillatory Neural Networks Research Group, University of Szeged, Department of Physiology, Szeged H-6720, Hungary
| | - Dion Khodagholy
- The Neuroscience Institute, New York University, School of Medicine, New York, NY 10016, USA
| | - Daryl R Kipke
- NeuroNexus Technologies, Inc., Ann Arbor, MI 48108, USA
| | - Euisik Yoon
- Center for Wireless Integrated Microsensing and Systems, The University of Michigan, Ann Arbor, MI 48109-2122, USA
| | - Kensall D Wise
- Center for Wireless Integrated Microsensing and Systems, The University of Michigan, Ann Arbor, MI 48109-2122, USA
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27
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Kameneva T, Abramian M, Zarelli D, Nĕsić D, Burkitt AN, Meffin H, Grayden DB. Spike history neural response model. J Comput Neurosci 2015; 38:463-81. [PMID: 25862472 DOI: 10.1007/s10827-015-0549-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2014] [Revised: 02/08/2015] [Accepted: 02/13/2015] [Indexed: 11/25/2022]
Abstract
There is a potential for improved efficacy of neural stimulation if stimulation levels can be modified dynamically based on the responses of neural tissue in real time. A neural model is developed that describes the response of neurons to electrical stimulation and that is suitable for feedback control neuroprosthetic stimulation. Experimental data from NZ white rabbit retinae is used with a data-driven technique to model neural dynamics. The linear-nonlinear approach is adapted to incorporate spike history and to predict the neural response of ganglion cells to electrical stimulation. To validate the fitness of the model, the penalty term is calculated based on the time difference between each simulated spike and the closest spike in time in the experimentally recorded train. The proposed model is able to robustly predict experimentally observed spike trains.
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Affiliation(s)
- Tatiana Kameneva
- Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Australia,
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Recording large extracellular spikes in microchannels along many axonal sites from individual neurons. PLoS One 2015; 10:e0118514. [PMID: 25734567 PMCID: PMC4348166 DOI: 10.1371/journal.pone.0118514] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2014] [Accepted: 01/19/2015] [Indexed: 12/29/2022] Open
Abstract
The numerous connections between neuronal cell bodies, made by their dendrites and axons, are vital for information processing in the brain. While dendrites and synapses have been extensively studied, axons have remained elusive to a large extent. We present a novel platform to study axonal physiology and information processing based on combining an 11,011-electrode high-density complementary metal-oxide semiconductor microelectrode array with a poly(dimethylsiloxane) channel device, which isolates axons from somas and, importantly, significantly amplifies recorded axonal signals. The combination of the microelectrode array with recording and stimulation capability with the microfluidic isolation channels permitted us to study axonal signal behavior at great detail. The device, featuring two culture chambers with over 30 channels spanning in between, enabled long-term recording of single spikes from isolated axons with signal amplitudes of 100 μV up to 2 mV. Propagating signals along axons could be recorded with 10 to 50 electrodes per channel. We (i) describe the performance and capabilities of our device for axonal electrophysiology, and (ii) present novel data on axonal signals facilitated by the device. Spontaneous action potentials with characteristic shapes propagated from somas along axons between the two compartments, and these unique shapes could be used to identify individual axons within channels that contained many axonal branches. Stimulation through the electrode array facilitated the identification of somas and their respective axons, enabling interfacing with different compartments of a single cell. Complex spike shapes observed in channels were traced back to single cells, and we show that more complicated spike shapes originate from a linear superposition of multiple axonal signals rather than signal distortion by the channels.
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Obien MEJ, Deligkaris K, Bullmann T, Bakkum DJ, Frey U. Revealing neuronal function through microelectrode array recordings. Front Neurosci 2015; 8:423. [PMID: 25610364 PMCID: PMC4285113 DOI: 10.3389/fnins.2014.00423] [Citation(s) in RCA: 296] [Impact Index Per Article: 32.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2014] [Accepted: 12/03/2014] [Indexed: 12/26/2022] Open
Abstract
Microelectrode arrays and microprobes have been widely utilized to measure neuronal activity, both in vitro and in vivo. The key advantage is the capability to record and stimulate neurons at multiple sites simultaneously. However, unlike the single-cell or single-channel resolution of intracellular recording, microelectrodes detect signals from all possible sources around every sensor. Here, we review the current understanding of microelectrode signals and the techniques for analyzing them. We introduce the ongoing advancements in microelectrode technology, with focus on achieving higher resolution and quality of recordings by means of monolithic integration with on-chip circuitry. We show how recent advanced microelectrode array measurement methods facilitate the understanding of single neurons as well as network function.
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Affiliation(s)
| | - Kosmas Deligkaris
- RIKEN Quantitative Biology Center, RIKEN Kobe, Japan ; Graduate School of Frontier Biosciences, Osaka University Osaka, Japan
| | | | - Douglas J Bakkum
- Department of Biosystems Science and Engineering, ETH Zurich Basel, Switzerland
| | - Urs Frey
- RIKEN Quantitative Biology Center, RIKEN Kobe, Japan ; Graduate School of Frontier Biosciences, Osaka University Osaka, Japan ; Department of Biosystems Science and Engineering, ETH Zurich Basel, Switzerland
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30
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Hierlemann A, Müller J, Bakkum D, Franke F. Highly integrated CMOS microsystems to interface with neurons at subcellular resolution. TECHNICAL DIGEST. INTERNATIONAL ELECTRON DEVICES MEETING 2015; 2015:13.2.1-13.2.4. [PMID: 33897071 DOI: 10.1109/iedm.2015.7409688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
CMOS high-density transducer arrays enable fundamentally new neuroscientific insights through, e.g., facilitating investigation of axonal signaling characteristics, with the "axonal" side of neuronal activity being largely inaccessible to established methods. They also enable high-throughput monitoring of potentially all action potentials in a larger neuronal network (> 1000 neurons) over extended time to see developmental effects or effects of disturbances. Applications include research in neural diseases and pharmacology.
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Affiliation(s)
- Andreas Hierlemann
- ETH Zurich, Department of Biosystems Science and Engineering CH-4058, Basel, Switzerland
| | - Jan Müller
- ETH Zurich, Department of Biosystems Science and Engineering CH-4058, Basel, Switzerland
| | - Douglas Bakkum
- ETH Zurich, Department of Biosystems Science and Engineering CH-4058, Basel, Switzerland
| | - Felix Franke
- ETH Zurich, Department of Biosystems Science and Engineering CH-4058, Basel, Switzerland
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31
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Neural ensemble communities: open-source approaches to hardware for large-scale electrophysiology. Curr Opin Neurobiol 2014; 32:53-9. [PMID: 25528614 DOI: 10.1016/j.conb.2014.11.004] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2014] [Revised: 11/04/2014] [Accepted: 11/08/2014] [Indexed: 01/19/2023]
Abstract
One often-overlooked factor when selecting a platform for large-scale electrophysiology is whether or not a particular data acquisition system is 'open' or 'closed': that is, whether or not the system's schematics and source code are available to end users. Open systems have a reputation for being difficult to acquire, poorly documented, and hard to maintain. With the arrival of more powerful and compact integrated circuits, rapid prototyping services, and web-based tools for collaborative development, these stereotypes must be reconsidered. We discuss some of the reasons why multichannel extracellular electrophysiology could benefit from open-source approaches and describe examples of successful community-driven tool development within this field. In order to promote the adoption of open-source hardware and to reduce the need for redundant development efforts, we advocate a move toward standardized interfaces that connect each element of the data processing pipeline. This will give researchers the flexibility to modify their tools when necessary, while allowing them to continue to benefit from the high-quality products and expertise provided by commercial vendors.
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32
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Kameneva T, Zarelli D, Nešić D, Grayden DB, Burkitt AN, Meffin H. A comparison of open-loop and closed-loop stimulation strategies to control excitation of retinal ganglion cells. Biomed Signal Process Control 2014. [DOI: 10.1016/j.bspc.2014.07.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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33
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Potter SM, El Hady A, Fetz EE. Closed-loop neuroscience and neuroengineering. Front Neural Circuits 2014; 8:115. [PMID: 25294988 PMCID: PMC4171982 DOI: 10.3389/fncir.2014.00115] [Citation(s) in RCA: 54] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2013] [Accepted: 09/01/2014] [Indexed: 01/18/2023] Open
Affiliation(s)
- Steve M Potter
- Laboratory for Neuroengineering, Coulter Department of Biomedical Engineering, Georgia Institute of Technology Atlanta, GA, USA
| | - Ahmed El Hady
- Department of Non Linear Dynamics, Max Planck Institute for Dynamics and Self Organization Goettingen, Germany
| | - Eberhard E Fetz
- Departments of Physiology and Biophysics and Bioengineering, Washington National Primate Research Center, University of Washington Seattle, WA, USA
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34
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Navajas J, Barsakcioglu DY, Eftekhar A, Jackson A, Constandinou TG, Quian Quiroga R. Minimum requirements for accurate and efficient real-time on-chip spike sorting. J Neurosci Methods 2014; 230:51-64. [PMID: 24769170 PMCID: PMC4151286 DOI: 10.1016/j.jneumeth.2014.04.018] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2014] [Revised: 04/11/2014] [Accepted: 04/14/2014] [Indexed: 11/30/2022]
Abstract
BACKGROUND Extracellular recordings are performed by inserting electrodes in the brain, relaying the signals to external power-demanding devices, where spikes are detected and sorted in order to identify the firing activity of different putative neurons. A main caveat of these recordings is the necessity of wires passing through the scalp and skin in order to connect intracortical electrodes to external amplifiers. The aim of this paper is to evaluate the feasibility of an implantable platform (i.e., a chip) with the capability to wirelessly transmit the neural signals and perform real-time on-site spike sorting. NEW METHOD We computationally modelled a two-stage implementation for online, robust, and efficient spike sorting. In the first stage, spikes are detected on-chip and streamed to an external computer where mean templates are created and sent back to the chip. In the second stage, spikes are sorted in real-time through template matching. RESULTS We evaluated this procedure using realistic simulations of extracellular recordings and describe a set of specifications that optimise performance while keeping to a minimum the signal requirements and the complexity of the calculations. COMPARISON WITH EXISTING METHODS A key bottleneck for the development of long-term BMIs is to find an inexpensive method for real-time spike sorting. Here, we simulated a solution to this problem that uses both offline and online processing of the data. CONCLUSIONS Hardware implementations of this method therefore enable low-power long-term wireless transmission of multiple site extracellular recordings, with application to wireless BMIs or closed-loop stimulation designs.
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Affiliation(s)
- Joaquin Navajas
- Centre for Systems Neuroscience, University of Leicester, 9 Salisbury Road, LE1 7QR, United Kingdom.
| | - Deren Y Barsakcioglu
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, SW7 2AZ, United Kingdom
| | - Amir Eftekhar
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, SW7 2AZ, United Kingdom
| | - Andrew Jackson
- Institute of Neuroscience, Newcastle University, Newcastle-upon-Tyne NE2 4HH, United Kingdom
| | - Timothy G Constandinou
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, SW7 2AZ, United Kingdom
| | - Rodrigo Quian Quiroga
- Centre for Systems Neuroscience, University of Leicester, 9 Salisbury Road, LE1 7QR, United Kingdom
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35
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Bakkum DJ, Radivojevic M, Frey U, Franke F, Hierlemann A, Takahashi H. Parameters for burst detection. Front Comput Neurosci 2014; 7:193. [PMID: 24567714 PMCID: PMC3915237 DOI: 10.3389/fncom.2013.00193] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2013] [Accepted: 12/23/2013] [Indexed: 11/23/2022] Open
Abstract
Bursts of action potentials within neurons and throughout networks are believed to serve roles in how neurons handle and store information, both in vivo and in vitro. Accurate detection of burst occurrences and durations are therefore crucial for many studies. A number of algorithms have been proposed to do so, but a standard method has not been adopted. This is due, in part, to many algorithms requiring the adjustment of multiple ad-hoc parameters and further post-hoc criteria in order to produce satisfactory results. Here, we broadly catalog existing approaches and present a new approach requiring the selection of only a single parameter: the number of spikes N comprising the smallest burst to consider. A burst was identified if N spikes occurred in less than T ms, where the threshold T was automatically determined from observing a probability distribution of inter-spike-intervals. Performance was compared vs. different classes of detectors on data gathered from in vitro neuronal networks grown over microelectrode arrays. Our approach offered a number of useful features including: a simple implementation, no need for ad-hoc or post-hoc criteria, and precise assignment of burst boundary time points. Unlike existing approaches, detection was not biased toward larger bursts, allowing identification and analysis of a greater range of neuronal and network dynamics.
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Affiliation(s)
- Douglas J Bakkum
- Department of Biosystems Science and Engineering, ETH Zurich Basel, Switzerland ; Research Center for Advanced Science and Technology, The University of Tokyo Tokyo, Japan
| | - Milos Radivojevic
- Department of Biosystems Science and Engineering, ETH Zurich Basel, Switzerland
| | - Urs Frey
- RIKEN Quantitative Biology Center Kobe, Japan
| | - Felix Franke
- Department of Biosystems Science and Engineering, ETH Zurich Basel, Switzerland
| | - Andreas Hierlemann
- Department of Biosystems Science and Engineering, ETH Zurich Basel, Switzerland
| | - Hirokazu Takahashi
- Research Center for Advanced Science and Technology, The University of Tokyo Tokyo, Japan ; Japan Science and Technology Agency, Precursory Research for Embryonic Science and Technology Saitama, Japan
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