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Haynes VR, Zhou Y, Crook SM. Discovering optimal features for neuron-type identification from extracellular recordings. Front Neuroinform 2024; 18:1303993. [PMID: 38371496 PMCID: PMC10869512 DOI: 10.3389/fninf.2024.1303993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 01/15/2024] [Indexed: 02/20/2024] Open
Abstract
Advancements in multichannel recordings of single-unit activity (SUA) in vivo present an opportunity to discover novel features of spatially-varying extracellularly-recorded action potentials (EAPs) that are useful for identifying neuron-types. Traditional approaches to classifying neuron-types often rely on computing EAP waveform features based on conventions of single-channel recordings and thus inherit their limitations. However, spatiotemporal EAP waveforms are the product of signals from underlying current sources being mixed within the extracellular space. We introduce a machine learning approach to demix the underlying sources of spatiotemporal EAP waveforms. Using biophysically realistic computational models, we simulate EAP waveforms and characterize them by the relative prevalence of these sources, which we use as features for identifying the neuron-types corresponding to recorded single units. These EAP sources have distinct spatial and multi-resolution temporal patterns that are robust to various sampling biases. EAP sources also are shared across many neuron-types, are predictive of gross morphological features, and expose underlying morphological domains. We then organize known neuron-types into a hierarchy of latent morpho-electrophysiological types based on differences in the source prevalences, which provides a multi-level classification scheme. We validate the robustness, accuracy, and interpretations of our demixing approach by analyzing simulated EAPs from morphologically detailed models with classification and clustering methods. This simulation-based approach provides a machine learning strategy for neuron-type identification.
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Affiliation(s)
- Vergil R. Haynes
- Laboratory for Auditory Computation and Neurophysiology, College of Health Solutions, Arizona State University, Tempe, AZ, United States
- Laboratory for Informatics and Computation in Open Neuroscience, School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, United States
| | - Yi Zhou
- Laboratory for Auditory Computation and Neurophysiology, College of Health Solutions, Arizona State University, Tempe, AZ, United States
| | - Sharon M. Crook
- Laboratory for Informatics and Computation in Open Neuroscience, School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, United States
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2
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Donoghue K, Toreyin H. A Proof-of-Concept Numerical Ising Machine for Neural Spike Localization. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38083117 DOI: 10.1109/embc40787.2023.10340471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Identifying the physical locations of neurons based on the spike waveforms captured by multiple recording channels, namely spike localization, can potentially enhance spike sorting accuracy. This study proposes a new method for spike localization, where the problem is first described as a nonconvex optimization problem and then the optimization is attempted heuristically via a numerical Ising solver. The paper first presents a quadratic unconstrained binary optimization (QUBO) formulation of spike localization. Then, a MATLAB solver simulating an Ising machine is written to solve the QUBO. The proposed method is evaluated on a 2D toy problem consisting of two electrodes and a single spike event, where the neuron location search is conducted in three different regions placed at increasing distances from the electrodes. The results indicate that the neuron can be accurately identified when in one of the nearest nodes to the electrodes, whereas the accuracy decreases to 87.5% and 75% as the search region distance increases. The study for the first time formulates the spike localization problem as a QUBO and demonstrates the feasibility of solving the resultant non-convex optimization problem heuristically using an Ising machine.Clinical Relevance- High channel-count implantable neural monitoring systems allow tracking large brain regions at the cost of increased data volumes to transmit and power dissipation. The new spike localization approach presented can potentially decrease the data volume and power consumption by enabling high accuracy spike localization at the implantable system.
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Abstract
The biological taste system has the unique ability to detect taste substances. Biomaterials originating from a biological taste system have been recognized as ideal candidates to serve as sensitive elements in the development of taste-based biosensors. In this study, we developed a taste bud organoid-based biosensor for the research of taste sensation. Taste bud organoids prepared from newborn mice were cultured and loaded onto the surface of a 64-channel microelectrode array (MEA) chip to explore the electrophysiological changes upon taste; an MEA chip was used to simultaneously record multiple-neuron firing activities from taste bud organoids under different taste stimuli, which helped to reveal the role of taste buds in taste sensing. The obtained results show that taste cells separated from the taste epithelium grew well into spherical structures under 3D culture conditions. These structures were composed of multiple cells with obvious budding structures. Moreover, the multicellular spheres were seeded on a 64-channel microelectrode array and processed with different taste stimuli. It was indicated that the MEA chip could efficiently monitor the electrophysiological signals from taste bud organoids in response to various taste stimuli. This biosensor provides a new method for the study of taste sensations and taste bud functions.
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4
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Computing Extracellular Electric Potentials from Neuronal Simulations. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1359:179-199. [DOI: 10.1007/978-3-030-89439-9_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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5
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Schulte S, Gries M, Christmann A, Schäfer KH. Using multielectrode arrays to investigate neurodegenerative effects of the amyloid-beta peptide. Bioelectron Med 2021; 7:15. [PMID: 34711287 PMCID: PMC8554832 DOI: 10.1186/s42234-021-00078-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 10/05/2021] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND Multielectrode arrays are widely used to analyze the effects of potentially toxic compounds, as well as to evaluate neuroprotective agents upon the activity of neural networks in short- and long-term cultures. Multielectrode arrays provide a way of non-destructive analysis of spontaneous and evoked neuronal activity, allowing to model neurodegenerative diseases in vitro. Here, we provide an overview on how these devices are currently used in research on the amyloid-β peptide and its role in Alzheimer's disease, the most common neurodegenerative disorder. MAIN BODY Most of the studies analysed here indicate fast responses of neuronal cultures towards aggregated forms of amyloid-β, leading to increases of spike frequency and impairments of long-term potentiation. This in turn suggests that this peptide might play a crucial role in causing the typical neuronal dysfunction observed in patients with Alzheimer's disease. CONCLUSIONS Although the number of studies using multielectrode arrays to examine the effect of the amyloid-β peptide onto neural cultures or whole compartments is currently limited, they still show how this technique can be used to not only investigate the interneuronal communication in neural networks, but also making it possible to examine the effects onto synaptic currents. This makes multielectrode arrays a powerful tool in future research on neurodegenerative diseases.
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Affiliation(s)
- Steven Schulte
- Department of Informatics and Microsystems and Technology, University of Applied Science Kaiserslautern, 66482 Zweibrücken, Germany
| | - Manuela Gries
- Department of Informatics and Microsystems and Technology, University of Applied Science Kaiserslautern, 66482 Zweibrücken, Germany
| | - Anne Christmann
- Department of Informatics and Microsystems and Technology, University of Applied Science Kaiserslautern, 66482 Zweibrücken, Germany
| | - Karl-Herbert Schäfer
- Department of Informatics and Microsystems and Technology, University of Applied Science Kaiserslautern, 66482 Zweibrücken, Germany
- Department of Pediatric Surgery, Medical Faculty Mannheim, University of Heidelberg, 68167 Mannheim, Germany
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6
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Ghirga S, Chiodo L, Marrocchio R, Orlandi JG, Loppini A. Inferring Excitatory and Inhibitory Connections in Neuronal Networks. ENTROPY 2021; 23:e23091185. [PMID: 34573810 PMCID: PMC8465838 DOI: 10.3390/e23091185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/01/2021] [Accepted: 09/02/2021] [Indexed: 11/16/2022]
Abstract
The comprehension of neuronal network functioning, from most basic mechanisms of signal transmission to complex patterns of memory and decision making, is at the basis of the modern research in experimental and computational neurophysiology. While mechanistic knowledge of neurons and synapses structure increased, the study of functional and effective networks is more complex, involving emergent phenomena, nonlinear responses, collective waves, correlation and causal interactions. Refined data analysis may help in inferring functional/effective interactions and connectivity from neuronal activity. The Transfer Entropy (TE) technique is, among other things, well suited to predict structural interactions between neurons, and to infer both effective and structural connectivity in small- and large-scale networks. To efficiently disentangle the excitatory and inhibitory neural activities, in the article we present a revised version of TE, split in two contributions and characterized by a suited delay time. The method is tested on in silico small neuronal networks, built to simulate the calcium activity as measured via calcium imaging in two-dimensional neuronal cultures. The inhibitory connections are well characterized, still preserving a high accuracy for excitatory connections prediction. The method could be applied to study effective and structural interactions in systems of excitable cells, both in physiological and in pathological conditions.
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Affiliation(s)
- Silvia Ghirga
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia (IIT), Viale Regina Elena 291, 00161 Roma, Italy;
| | - Letizia Chiodo
- Engineering Department, Campus Bio-Medico University of Rome, Via Álvaro del Portillo 21, 00154 Roma, Italy;
| | - Riccardo Marrocchio
- Institute of Sound and Vibration Research, Highfield Campus, University of Southampton, Southampton SO17 1BJ, UK;
| | | | - Alessandro Loppini
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia (IIT), Viale Regina Elena 291, 00161 Roma, Italy;
- Engineering Department, Campus Bio-Medico University of Rome, Via Álvaro del Portillo 21, 00154 Roma, Italy;
- Correspondence:
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7
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Tóth R, Miklós Barth A, Domonkos A, Varga V, Somogyvári Z. Do not waste your electrodes-principles of optimal electrode geometry for spike sorting. J Neural Eng 2021; 18. [PMID: 34181590 DOI: 10.1088/1741-2552/ac0f49] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 06/28/2021] [Indexed: 11/11/2022]
Abstract
Objective. This study examines how the geometrical arrangement of electrodes influences spike sorting efficiency, and attempts to formalise principles for the design of electrode systems enabling optimal spike sorting performance.Approach. The clustering performance of KlustaKwik, a popular toolbox, was evaluated using semi-artificial multi-channel data, generated from a library of real spike waveforms recorded in the CA1 region of mouse Hippocampusin vivo.Main results. Based on spike sorting results under various channel configurations and signal levels, a simple model was established to describe the efficiency of different electrode geometries. Model parameters can be inferred from existing spike waveform recordings, which allowed quantifying both the cooperative effect between channels and the noise dependence of clustering performance.Significance. Based on the model, analytical and numerical results can be derived for the optimal spacing and arrangement of electrodes for one- and two-dimensional electrode systems, targeting specific brain areas.
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Affiliation(s)
- Róbert Tóth
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.,Theoretical Neuroscience and Complex Systems Research Group, Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, Hungary
| | - Albert Miklós Barth
- Department of Cellular and Network Neurobiology, Institute of Experimental Medicine, Budapest, Hungary
| | - Andor Domonkos
- Department of Cellular and Network Neurobiology, Institute of Experimental Medicine, Budapest, Hungary
| | - Viktor Varga
- Department of Cellular and Network Neurobiology, Institute of Experimental Medicine, Budapest, Hungary
| | - Zoltán Somogyvári
- Theoretical Neuroscience and Complex Systems Research Group, Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, Hungary.,Neuromicrosystems Ltd, Budapest, Hungary
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8
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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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9
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Fair SR, Julian D, Hartlaub AM, Pusuluri ST, Malik G, Summerfied TL, Zhao G, Hester AB, Ackerman WE, Hollingsworth EW, Ali M, McElroy CA, Buhimschi IA, Imitola J, Maitre NL, Bedrosian TA, Hester ME. Electrophysiological Maturation of Cerebral Organoids Correlates with Dynamic Morphological and Cellular Development. Stem Cell Reports 2020; 15:855-868. [PMID: 32976764 PMCID: PMC7562943 DOI: 10.1016/j.stemcr.2020.08.017] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 08/28/2020] [Accepted: 08/28/2020] [Indexed: 12/22/2022] Open
Abstract
Cerebral organoids (COs) are rapidly accelerating the rate of translational neuroscience based on their potential to model complex features of the developing human brain. Several studies have examined the electrophysiological and neural network features of COs; however, no study has comprehensively investigated the developmental trajectory of electrophysiological properties in whole-brain COs and correlated these properties with developmentally linked morphological and cellular features. Here, we profiled the neuroelectrical activities of COs over the span of 5 months with a multi-electrode array platform and observed the emergence and maturation of several electrophysiologic properties, including rapid firing rates and network bursting events. To complement these analyses, we characterized the complex molecular and cellular development that gives rise to these mature neuroelectrical properties with immunohistochemical and single-cell transcriptomic analyses. This integrated approach highlights the value of COs as an emerging model system of human brain development and neurological disease. CO electrophysiology can be quantified with a multi-electrode array method CO electrophysiological trajectories correlate with molecular and cellular development The neurotrophin/TRK signaling pathway is active in COs by 5 months in culture
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Affiliation(s)
- Summer R Fair
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Abigail Wexner Research Institute at Nationwide Children's Hospital, 575 Children's Crossroad, Columbus, OH 43205-2716, USA
| | - Dominic Julian
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Abigail Wexner Research Institute at Nationwide Children's Hospital, 575 Children's Crossroad, Columbus, OH 43205-2716, USA
| | - Annalisa M Hartlaub
- Center for Perinatal Research, Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
| | - Sai Teja Pusuluri
- Center for Perinatal Research, Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
| | - Girik Malik
- Center for Perinatal Research, Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA; Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA
| | - Taryn L Summerfied
- Center for Perinatal Research, Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
| | - Guomao Zhao
- Department of Obstetrics and Gynecology, University of Illinois at Chicago College of Medicine, Chicago, IL 60612, USA
| | - Arelis B Hester
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Abigail Wexner Research Institute at Nationwide Children's Hospital, 575 Children's Crossroad, Columbus, OH 43205-2716, USA
| | - William E Ackerman
- Department of Obstetrics and Gynecology, University of Illinois at Chicago College of Medicine, Chicago, IL 60612, USA
| | - Ethan W Hollingsworth
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Abigail Wexner Research Institute at Nationwide Children's Hospital, 575 Children's Crossroad, Columbus, OH 43205-2716, USA
| | - Mehboob Ali
- Center for Perinatal Research, Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
| | - Craig A McElroy
- College of Pharmacy, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Irina A Buhimschi
- Department of Obstetrics and Gynecology, University of Illinois at Chicago College of Medicine, Chicago, IL 60612, USA
| | - Jaime Imitola
- Department of Neurology, Laboratory for Neural Stem Cells and Functional Neurogenetics, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Nathalie L Maitre
- Center for Perinatal Research, Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
| | - Tracy A Bedrosian
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Abigail Wexner Research Institute at Nationwide Children's Hospital, 575 Children's Crossroad, Columbus, OH 43205-2716, USA
| | - Mark E Hester
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Abigail Wexner Research Institute at Nationwide Children's Hospital, 575 Children's Crossroad, Columbus, OH 43205-2716, USA; Department of Pediatrics, The Ohio State University Wexner Medical Center, Columbus, OH, USA; Department of Neuroscience, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
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10
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Buccino AP, Ness TV, Einevoll GT, Cauwenberghs G, Hafliger PD. A Deep Learning Approach for the Classification of Neuronal Cell Types. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:999-1002. [PMID: 30440559 DOI: 10.1109/embc.2018.8512498] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Classification of neurons from extracellular recordings is mainly limited to putatively excitatory or inhibitory units based on the spike shape and firing patterns. Narrow waveforms are considered to be fast spiking inhibitory neurons and broad waveforms excitatory neurons. The aim of this work is twofold. First, we intend to use the rich spatial information from high-density Multi-Electrode Arrays (MEAs) to make classification more robust; second, we hope to be able to classify sub-types of excitatory and inhibitory neurons. We first built, in simulation, a large dataset of action potentials from detailed neural models. Then, we extracted spike features from the simulated recordings on a high-density Multi-Electrode Array model. Finally, we used a Convolutional Neural Networks (CNN), to classify the different cell types. Compared with the ground truth data from the simulated dataset, the results show that this forward modelling/machine learning approach is very robust in recognizing excitatory and inhibitory spikes (accuracy $\ge 92.15$%). Additionally, the approach can, to a certain extent, correctly classify different cell sub-types. As the detail and fidelity of neural models increase and high-density recordings become available, this approach could become a viable and robust alternative for classification of neural cell types from in-vivo extracellular recordings.
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11
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Cervantes EP, Comin CH, Junior RMC, Costa LDF. Morphological Neuron Classification Based on Dendritic Tree Hierarchy. Neuroinformatics 2019; 17:147-161. [PMID: 30008070 DOI: 10.1007/s12021-018-9388-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
The shape of a neuron can reveal interesting properties about its function. Therefore, morphological neuron characterization can contribute to a better understanding of how the brain works. However, one of the great challenges of neuroanatomy is the definition of morphological properties that can be used for categorizing neurons. This paper proposes a new methodology for neuron morphological analysis by considering different hierarchies of the dendritic tree for characterizing and categorizing neuronal cells. The methodology consists in using different strategies for decomposing the dendritic tree along its hierarchies, allowing the identification of relevant parts (possibly related to specific neuronal functions) for classification tasks. A set of more than 5000 neurons corresponding to 10 classes were examined with supervised classification algorithms based on this strategy. It was found that classification accuracies similar to those obtained by using whole neurons can be achieved by considering only parts of the neurons. Branches close to the soma were found to be particularly relevant for classification.
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Affiliation(s)
| | - Cesar Henrique Comin
- Department of Computer Science, Federal University of São Carlos, São Carlos, Brazil
| | | | - Luciano da Fontoura Costa
- São Carlos Institute of Physics, University of São Paulo, PO Box 369, 13560-970, São Carlos, SP, Brazil
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12
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Miccoli B, Lopez CM, Goikoetxea E, Putzeys J, Sekeri M, Krylychkina O, Chang SW, Firrincieli A, Andrei A, Reumers V, Braeken D. High-Density Electrical Recording and Impedance Imaging With a Multi-Modal CMOS Multi-Electrode Array Chip. Front Neurosci 2019; 13:641. [PMID: 31293372 PMCID: PMC6603149 DOI: 10.3389/fnins.2019.00641] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 06/04/2019] [Indexed: 01/11/2023] Open
Abstract
Multi-electrode arrays, both active or passive, emerged as ideal technologies to unveil intricated electrophysiological dynamics of cells and tissues. Active MEAs, designed using complementary metal oxide semiconductor technology (CMOS), stand over passive devices thanks to the possibility of achieving single-cell resolution, the reduced electrode size, the reduced crosstalk and the higher functionality and portability. Nevertheless, most of the reported CMOS MEA systems mainly rely on a single operational modality, which strongly hampers the applicability range of a single device. This can be a limiting factor considering that most biological and electrophysiological dynamics are often based on the synergy of multiple and complex mechanisms acting from different angles on the same phenomena. Here, we designed a CMOS MEA chip with 16,384 titanium nitride electrodes, 6 independent operational modalities and 1,024 parallel recording channels for neuro-electrophysiological studies. Sixteen independent active areas are patterned on the chip surface forming a 4 × 4 matrix, each one including 1,024 electrodes. Electrodes of four different sizes are present on the chip surface, ranging from 2.5 × 3.5 μm2 up to 11 × 11.0 μm2, with 15 μm pitch. In this paper, we exploited the impedance monitoring and voltage recording modalities not only to monitor the growth and development of primary rat hippocampal neurons, but also to assess their electrophysiological activity over time showing a mean spike amplitude of 144.8 ± 84.6 μV. Fixed frequency (1 kHz) and high sampling rate (30 kHz) impedance measurements were used to evaluate the cellular adhesion and growth on the chip surface. Thanks to the high-density configuration of the electrodes, as well as their dimension and pitch, the chip can appreciate the evolutions of the cell culture morphology starting from the moment of the seeding up to mature culture conditions. The measurements were confirmed by fluorescent staining. The effect of the different electrode sizes on the spike amplitudes and noise were also discussed. The multi-modality of the presented CMOS MEA allows for the simultaneous assessment of different physiological properties of the cultured neurons. Therefore, it can pave the way both to answer complex fundamental neuroscience questions as well as to aid the current drug-development paradigm.
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13
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Viswam V, Obien MEJ, Franke F, Frey U, Hierlemann A. Optimal Electrode Size for Multi-Scale Extracellular-Potential Recording From Neuronal Assemblies. Front Neurosci 2019; 13:385. [PMID: 31105515 PMCID: PMC6498989 DOI: 10.3389/fnins.2019.00385] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2019] [Accepted: 04/03/2019] [Indexed: 01/24/2023] Open
Abstract
Advances in microfabrication technology have enabled the production of devices containing arrays of thousands of closely spaced recording electrodes, which afford subcellular resolution of electrical signals in neurons and neuronal networks. Rationalizing the electrode size and configuration in such arrays demands consideration of application-specific requirements and inherent features of the electrodes. Tradeoffs among size, spatial density, sensitivity, noise, attenuation, and other factors are inevitable. Although recording extracellular signals from neurons with planar metal electrodes is fairly well established, the effects of the electrode characteristics on the quality and utility of recorded signals, especially for small, densely packed electrodes, have yet to be fully characterized. Here, we present a combined experimental and computational approach to elucidating how electrode size, and size-dependent parameters, such as impedance, baseline noise, and transmission characteristics, influence recorded neuronal signals. Using arrays containing platinum electrodes of different sizes, we experimentally evaluated the electrode performance in the recording of local field potentials (LFPs) and extracellular action potentials (EAPs) from the following cell preparations: acute brain slices, dissociated cell cultures, and organotypic slice cultures. Moreover, we simulated the potential spatial decay of point-current sources to investigate signal averaging using known signal sources. We demonstrated that the noise and signal attenuation depend more on the electrode impedance than on electrode size, per se, especially for electrodes <10 μm in width or diameter to achieve high-spatial-resolution readout. By minimizing electrode impedance of small electrodes (<10 μm) via surface modification, we could maximize the signal-to-noise ratio to electrically visualize the propagation of axonal EAPs and to isolate single-unit spikes. Due to the large amplitude of LFP signals, recording quality was high and nearly independent of electrode size. These findings should be of value in configuring in vitro and in vivo microelectrode arrays for extracellular recordings with high spatial resolution in various applications.
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Affiliation(s)
- Vijay Viswam
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- MaxWell Biosystems AG, Basel, Switzerland
| | - Marie Engelene J. Obien
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- MaxWell Biosystems AG, Basel, Switzerland
| | - Felix Franke
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Urs Frey
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- MaxWell Biosystems AG, Basel, Switzerland
| | - Andreas Hierlemann
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
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14
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Jia X, Siegle JH, Bennett C, Gale SD, Denman DJ, Koch C, Olsen SR. High-density extracellular probes reveal dendritic backpropagation and facilitate neuron classification. J Neurophysiol 2019; 121:1831-1847. [PMID: 30840526 DOI: 10.1152/jn.00680.2018] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Different neuron types serve distinct roles in neural processing. Extracellular electrical recordings are extensively used to study brain function but are typically blind to cell identity. Morphoelectrical properties of neurons measured on spatially dense electrode arrays have the potential to distinguish neuron types. We used high-density silicon probes to record from cortical and subcortical regions of the mouse brain. Extracellular waveforms of each neuron were detected across many channels and showed distinct spatiotemporal profiles among brain regions. Classification of neurons by brain region was improved with multichannel compared with single-channel waveforms. In visual cortex, unsupervised clustering identified the canonical regular-spiking (RS) and fast-spiking (FS) classes but also indicated a subclass of RS units with unidirectional backpropagating action potentials (BAPs). Moreover, BAPs were observed in many hippocampal RS cells. Overall, waveform analysis of spikes from high-density probes aids neuron identification and can reveal dendritic backpropagation. NEW & NOTEWORTHY It is challenging to identify neuron types with extracellular electrophysiology in vivo. We show that spatiotemporal action potentials measured on high-density electrode arrays can capture cell type-specific morphoelectrical properties, allowing classification of neurons across brain structures and within the cortex. Moreover, backpropagating action potentials are reliably detected in vivo from subpopulations of cortical and hippocampal neurons. Together, these results enhance the utility of dense extracellular electrophysiology for cell-type interrogation of brain network function.
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Affiliation(s)
- Xiaoxuan Jia
- Allen Institute for Brain Science , Seattle, Washington
| | | | | | - Samuel D Gale
- Allen Institute for Brain Science , Seattle, Washington
| | | | - Christof Koch
- Allen Institute for Brain Science , Seattle, Washington
| | - Shawn R Olsen
- Allen Institute for Brain Science , Seattle, Washington
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15
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Buccino AP, Kuchta M, Jæger KH, Ness TV, Berthet P, Mardal KA, Cauwenberghs G, Tveito A. How does the presence of neural probes affect extracellular potentials? J Neural Eng 2019; 16:026030. [PMID: 30703758 DOI: 10.1088/1741-2552/ab03a1] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Mechanistic modeling of neurons is an essential component of computational neuroscience that enables scientists to simulate, explain, and explore neural activity. The conventional approach to simulation of extracellular neural recordings first computes transmembrane currents using the cable equation and then sums their contribution to model the extracellular potential. This two-step approach relies on the assumption that the extracellular space is an infinite and homogeneous conductive medium, while measurements are performed using neural probes. The main purpose of this paper is to assess to what extent the presence of the neural probes of varying shape and size impacts the extracellular field and how to correct for them. APPROACH We apply a detailed modeling framework allowing explicit representation of the neuron and the probe to study the effect of the probes and thereby estimate the effect of ignoring it. We use meshes with simplified neurons and different types of probe and compare the extracellular action potentials with and without the probe in the extracellular space. We then compare various solutions to account for the probes' presence and introduce an efficient probe correction method to include the probe effect in modeling of extracellular potentials. MAIN RESULTS Our computations show that microwires hardly influence the extracellular electric field and their effect can therefore be ignored. In contrast, multi-electrode arrays (MEAs) significantly affect the extracellular field by magnifying the recorded potential. While MEAs behave similarly to infinite insulated planes, we find that their effect strongly depends on the neuron-probe alignment and probe orientation. SIGNIFICANCE Ignoring the probe effect might be deleterious in some applications, such as neural localization and parameterization of neural models from extracellular recordings. Moreover, the presence of the probe can improve the interpretation of extracellular recordings, by providing a more accurate estimation of the extracellular potential generated by neuronal models.
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Affiliation(s)
- Alessio Paolo Buccino
- Center for Integrative Neuroplasticity (CINPLA), Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway. Department of Bioengineering, University of California San Diego, San Diego, CA, United States of America
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16
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Obien MEJ, Hierlemann A, Frey U. Accurate signal-source localization in brain slices by means of high-density microelectrode arrays. Sci Rep 2019; 9:788. [PMID: 30692552 PMCID: PMC6349853 DOI: 10.1038/s41598-018-36895-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Accepted: 11/28/2018] [Indexed: 12/12/2022] Open
Abstract
Extracellular recordings by means of high-density microelectrode arrays (HD-MEAs) have become a powerful tool to resolve subcellular details of single neurons in active networks grown from dissociated cells. To extend the application of this technology to slice preparations, we developed models describing how extracellular signals, produced by neuronal cells in slices, are detected by microelectrode arrays. The models help to analyze and understand the electrical-potential landscape in an in vitro HD-MEA-recording scenario based on point-current sources. We employed two modeling schemes, (i) a simple analytical approach, based on the method of images (MoI), and (ii) an approach, based on finite-element methods (FEM). We compared and validated the models with large-scale, high-spatiotemporal-resolution recordings of slice preparations by means of HD-MEAs. We then developed a model-based localization algorithm and compared the performance of MoI and FEM models. Both models provided accurate localization results and a comparable and negligible systematic error, when the point source was in saline, a condition similar to cell-culture experiments. Moreover, the relative random error in the x-y-z-localization amounted only up to 4.3% for z-distances up to 200 μm from the HD-MEA surface. In tissue, the systematic errors of both, MoI and FEM models were significantly higher, and a pre-calibration was required. Nevertheless, the FEM values proved to be closer to the tissue experimental results, yielding 5.2 μm systematic mean error, compared to 22.0 μm obtained with MoI. These results suggest that the medium volume or "saline height", the brain slice thickness and anisotropy, and the location of the reference electrode, which were included in the FEM model, considerably affect the extracellular signal and localization performance, when the signal source is at larger distance to the array. After pre-calibration, the relative random error of the z-localization in tissue was only 3% for z-distances up to 200 μm. We then applied the model and related detailed understanding of extracellular recordings to achieve an electrically-guided navigation of a stimulating micropipette, solely based on the measured HD-MEA signals, and managed to target spontaneously active neurons in an acute brain slice for electroporation.
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Affiliation(s)
- Marie Engelene J Obien
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
- RIKEN Quantitative Biology Center, Kobe, Japan.
- MaxWell Biosystems AG, Basel, Switzerland.
| | - Andreas Hierlemann
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Urs Frey
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- RIKEN Quantitative Biology Center, Kobe, Japan
- MaxWell Biosystems AG, Basel, Switzerland
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17
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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: 17] [Impact Index Per Article: 2.8] [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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18
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Buccino AP, Ness TV, Einevoll GT, Cauwenberghs G, Hafliger PD. Localizing neuronal somata from Multi-Electrode Array in-vivo recordings using deep learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2017:974-977. [PMID: 29060036 DOI: 10.1109/embc.2017.8036988] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
With the latest development in the design and fabrication of high-density Multi-Electrode Arrays (MEA) for in-vivo neural recordings, the spatiotemporal information in the recorded signals allows for refined estimation of a neuron's location around the probe. In parallel, advances in computational models for neural activity enables simulation of recordings from neurons with detailed morphology. Our approach uses deep learning algorithms on a large set of such simulation data to extract the 3D position of the neuronal somata. Multi-compartment models from 13 different neural morphologies in layer 5 (L5) of the rat's neocortex are placed at random locations and with different alignments with respect to the MEA. The sodium trough and repolarisation peak images on the MEA serve as input features for a Convolutional Neural Network (CNN), which predicts the neural location robustly and with low error rates. The forward modeling/machine learning approach yields very accurate results for the different morphologies and is able to cope with different neuron alignments.
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19
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Buccino AP, Kordovan M, Ness TV, Merkt B, Häfliger PD, Fyhn M, Cauwenberghs G, Rotter S, Einevoll GT. Combining biophysical modeling and deep learning for multielectrode array neuron localization and classification. J Neurophysiol 2018; 120:1212-1232. [PMID: 29847231 DOI: 10.1152/jn.00210.2018] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Neural circuits typically consist of many different types of neurons, and one faces a challenge in disentangling their individual contributions in measured neural activity. Classification of cells into inhibitory and excitatory neurons and localization of neurons on the basis of extracellular recordings are frequently employed procedures. Current approaches, however, need a lot of human intervention, which makes them slow, biased, and unreliable. In light of recent advances in deep learning techniques and exploiting the availability of neuron models with quasi-realistic three-dimensional morphology and physiological properties, we present a framework for automatized and objective classification and localization of cells based on the spatiotemporal profiles of the extracellular action potentials recorded by multielectrode arrays. We train convolutional neural networks on simulated signals from a large set of cell models and show that our framework can predict the position of neurons with high accuracy, more precisely than current state-of-the-art methods. Our method is also able to classify whether a neuron is excitatory or inhibitory with very high accuracy, substantially improving on commonly used clustering techniques. Furthermore, our new method seems to have the potential to separate certain subtypes of excitatory and inhibitory neurons. The possibility of automatically localizing and classifying all neurons recorded with large high-density extracellular electrodes contributes to a more accurate and more reliable mapping of neural circuits. NEW & NOTEWORTHY We propose a novel approach to localize and classify neurons from their extracellularly recorded action potentials with a combination of biophysically detailed neuron models and deep learning techniques. Applied to simulated data, this new combination of forward modeling and machine learning yields higher performance compared with state-of-the-art localization and classification methods.
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Affiliation(s)
- Alessio P Buccino
- Center for Integrative Neuroplasticity (CINPLA), Faculty of Mathematics and Natural Sciences, University of Oslo , Oslo , Norway.,Department of Bioengineering, University of California , San Diego, California
| | - Michael Kordovan
- Bernstein Center Freiburg , Freiburg , Germany.,Faculty of Biology, University of Freiburg , Freiburg , Germany
| | - Torbjørn V Ness
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Benjamin Merkt
- Bernstein Center Freiburg , Freiburg , Germany.,Faculty of Biology, University of Freiburg , Freiburg , Germany
| | - Philipp D Häfliger
- Center for Integrative Neuroplasticity (CINPLA), Faculty of Mathematics and Natural Sciences, University of Oslo , Oslo , Norway
| | - Marianne Fyhn
- Center for Integrative Neuroplasticity (CINPLA), Faculty of Mathematics and Natural Sciences, University of Oslo , Oslo , Norway
| | - Gert Cauwenberghs
- Department of Bioengineering, University of California , San Diego, California
| | - Stefan Rotter
- Bernstein Center Freiburg , Freiburg , Germany.,Faculty of Biology, University of Freiburg , Freiburg , Germany
| | - Gaute T Einevoll
- Center for Integrative Neuroplasticity (CINPLA), Faculty of Mathematics and Natural Sciences, University of Oslo , Oslo , Norway.,Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
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20
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Karadas M, Wojciechowski AM, Huck A, Dalby NO, Andersen UL, Thielscher A. Feasibility and resolution limits of opto-magnetic imaging of neural network activity in brain slices using color centers in diamond. Sci Rep 2018. [PMID: 29540789 PMCID: PMC5852147 DOI: 10.1038/s41598-018-22793-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
We suggest a novel approach for wide-field imaging of the neural network dynamics of brain slices that uses highly sensitivity magnetometry based on nitrogen-vacancy (NV) centers in diamond. In-vitro recordings in brain slices is a proven method for the characterization of electrical neural activity and has strongly contributed to our understanding of the mechanisms that govern neural information processing. However, this traditional approach only acquires signals from a few positions, which severely limits its ability to characterize the dynamics of the underlying neural networks. We suggest to extend its scope using NV magnetometry-based imaging of the neural magnetic fields across the slice. Employing comprehensive computational simulations and theoretical analyses, we determine the spatiotemporal characteristics of the neural fields and the required key performance parameters of an NV magnetometry-based imaging setup. We investigate how the technical parameters determine the achievable spatial resolution for an optimal 2D reconstruction of neural currents from the measured field distributions. Finally, we compare the imaging of neural slice activity with that of a single planar pyramidal cell. Our results suggest that imaging of slice activity will be possible with the upcoming generation of NV magnetic field sensors, while single-shot imaging of planar cell activity remains challenging.
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Affiliation(s)
- Mürsel Karadas
- Department of Electrical Engineering, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Adam M Wojciechowski
- Department of Physics, Technical University of Denmark, 2800, Kongens Lyngby, Denmark.,Institute of Physics, Jagiellonian University, 30-348, Kraków, Poland
| | - Alexander Huck
- Department of Physics, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Nils Ole Dalby
- Department of Physics, Technical University of Denmark, 2800, Kongens Lyngby, Denmark.,Department of Drug Design and Pharmacology, Copenhagen University, 2100, Copenhagen, Denmark
| | - Ulrik Lund Andersen
- Department of Physics, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Axel Thielscher
- Department of Electrical Engineering, Technical University of Denmark, 2800, Kongens Lyngby, Denmark. .,Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, 2650, Hvidovre, Denmark.
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21
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Fiáth R, Raducanu BC, Musa S, Andrei A, Lopez CM, van Hoof C, Ruther P, Aarts A, Horváth D, Ulbert I. A silicon-based neural probe with densely-packed low-impedance titanium nitride microelectrodes for ultrahigh-resolution in vivo recordings. Biosens Bioelectron 2018; 106:86-92. [PMID: 29414094 DOI: 10.1016/j.bios.2018.01.060] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 01/10/2018] [Accepted: 01/26/2018] [Indexed: 12/26/2022]
Abstract
In this study, we developed and validated a single-shank silicon-based neural probe with 128 closely-packed microelectrodes suitable for high-resolution extracellular recordings. The 8-mm-long, 100-µm-wide and 50-µm-thick implantable shank of the probe fabricated using a 0.13-µm complementary metal-oxide-semiconductor (CMOS) metallization technology contains square-shaped (20 × 20 µm2), low-impedance (~ 50 kΩ at 1 kHz) recording sites made of rough and porous titanium nitride which are arranged in a 32 × 4 dense array with an inter-electrode pitch of 22.5 µm. The electrophysiological performance of the probe was tested in in vivo experiments by implanting it acutely into neocortical areas of anesthetized animals (rats, mice and cats). We recorded local field potentials, single- and multi-unit activity with superior quality from all layers of the neocortex of the three animal models, even after reusing the probe in multiple (> 10) experiments. The low-impedance electrodes monitored spiking activity with high signal-to-noise ratio; the peak-to-peak amplitude of extracellularly recorded action potentials of well-separable neurons ranged from 0.1 mV up to 1.1 mV. The high spatial sampling of neuronal activity made it possible to detect action potentials of the same neuron on multiple, adjacent recording sites, allowing a more reliable single unit isolation and the investigation of the spatiotemporal dynamics of extracellular action potential waveforms in greater detail. Moreover, the probe was developed with the specific goal to use it as a tool for the validation of electrophysiological data recorded with high-channel-count, high-density neural probes comprising integrated CMOS circuitry.
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Affiliation(s)
- Richárd Fiáth
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok körútja 2, H-1117 Budapest, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/A, H-1083 Budapest, Hungary.
| | - Bogdan Cristian Raducanu
- Interuniversity Microelectronics Center (IMEC), Kapeldreef 75, B-3001 Heverlee, Belgium; Electrical Engineering Department (ESAT), KU Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium
| | - Silke Musa
- Interuniversity Microelectronics Center (IMEC), Kapeldreef 75, B-3001 Heverlee, Belgium
| | - Alexandru Andrei
- Interuniversity Microelectronics Center (IMEC), Kapeldreef 75, B-3001 Heverlee, Belgium
| | - Carolina Mora Lopez
- Interuniversity Microelectronics Center (IMEC), Kapeldreef 75, B-3001 Heverlee, Belgium
| | - Chris van Hoof
- Interuniversity Microelectronics Center (IMEC), Kapeldreef 75, B-3001 Heverlee, Belgium; Electrical Engineering Department (ESAT), KU Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium
| | - Patrick Ruther
- Microsystem Materials Laboratory, Department of Microsystems Engineering (IMTEK), University of Freiburg, Georges-Koehler-Allee 103, D-79110 Freiburg, Germany; BrainLinks-BrainTools Cluster of Excellence at the University of Freiburg, Georges-Koehler-Allee 80, D-79110 Freiburg, Germany
| | - Arno Aarts
- ATLAS Neuroengineering, Kapeldreef 75, B-3000 Leuven, Belgium
| | - Domonkos Horváth
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok körútja 2, H-1117 Budapest, 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, Hungarian Academy of Sciences, Magyar tudósok körútja 2, H-1117 Budapest, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/A, H-1083 Budapest, Hungary
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22
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Chung J, Sharif F, Jung D, Kim S, Royer S. Micro-drive and headgear for chronic implant and recovery of optoelectronic probes. Sci Rep 2017; 7:2773. [PMID: 28584246 PMCID: PMC5459843 DOI: 10.1038/s41598-017-03340-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2017] [Accepted: 04/26/2017] [Indexed: 01/01/2023] Open
Abstract
Silicon probes are multisite electrodes used for the electrophysiological recording of large neuronal ensembles. Optoelectronic probes (OEPs) are recent upgrades that allow, in parallel, the delivery of local optical stimuli. The procedures to use these delicate electrodes for chronic experiments in mice are still underdeveloped and typically assume one-time uses. Here, we developed a micro-drive, a support for OEPs optical fibers, and a hat enclosure, which fabrications consist in fitting and fastening together plastic parts made with 3D printers. Excluding two parts, all components and electrodes are relatively simple to recover after the experiments, via the loosening of screws. To prevent the plugging of OEPs laser sources from altering the stability of recordings, the OEPs fibers can be transiently anchored to the hat via the tightening of screws. We test the stability of recordings in the mouse hippocampus under three different conditions: acute head-fixed, chronic head-fixed, and chronic freely moving. Drift in spike waveforms is significantly smaller in chronic compared to acute conditions, with the plugging/unplugging of head-stage and fiber connectors not affecting much the recording stability. Overall, these tools generate stable recordings of place cell in chronic conditions, and make the recovery and reuse of electrode packages relatively simple.
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Affiliation(s)
- Jinho Chung
- Center for Functional Connectomics, Korea Institute of Science and Technology, Seoul, 136-791, Republic of Korea
| | - Farnaz Sharif
- Center for Functional Connectomics, Korea Institute of Science and Technology, Seoul, 136-791, Republic of Korea.,University of Science & Technology, Daejeon, Republic of Korea
| | - Dajung Jung
- Center for Functional Connectomics, Korea Institute of Science and Technology, Seoul, 136-791, Republic of Korea.,Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
| | - Soyoun Kim
- Center for Functional Connectomics, Korea Institute of Science and Technology, Seoul, 136-791, Republic of Korea
| | - Sebastien Royer
- Center for Functional Connectomics, Korea Institute of Science and Technology, Seoul, 136-791, Republic of Korea. .,University of Science & Technology, Daejeon, Republic of Korea.
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23
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Schultz SR, Copeland CS, Foust AJ, Quicke P, Schuck R. Advances in two photon scanning and scanless microscopy technologies for functional neural circuit imaging. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2017; 105:139-157. [PMID: 28757657 PMCID: PMC5526632 DOI: 10.1109/jproc.2016.2577380] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Recent years have seen substantial developments in technology for imaging neural circuits, raising the prospect of large scale imaging studies of neural populations involved in information processing, with the potential to lead to step changes in our understanding of brain function and dysfunction. In this article we will review some key recent advances: improved fluorophores for single cell resolution functional neuroimaging using a two photon microscope; improved approaches to the problem of scanning active circuits; and the prospect of scanless microscopes which overcome some of the bandwidth limitations of current imaging techniques. These advances in technology for experimental neuroscience have in themselves led to technical challenges, such as the need for the development of novel signal processing and data analysis tools in order to make the most of the new experimental tools. We review recent work in some active topics, such as region of interest segmentation algorithms capable of demixing overlapping signals, and new highly accurate algorithms for calcium transient detection. These advances motivate the development of new data analysis tools capable of dealing with spatial or spatiotemporal patterns of neural activity, that scale well with pattern size.
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Affiliation(s)
- Simon R Schultz
- Center for Neurotechnology and Department of Bioengineering Imperial College London, South Kensington, LondonSW7 2AZ, UK
| | - Caroline S Copeland
- Center for Neurotechnology and Department of Bioengineering Imperial College London, South Kensington, LondonSW7 2AZ, UK
| | - Amanda J Foust
- Center for Neurotechnology and Department of Bioengineering Imperial College London, South Kensington, LondonSW7 2AZ, UK
| | - Peter Quicke
- Center for Neurotechnology and Department of Bioengineering Imperial College London, South Kensington, LondonSW7 2AZ, UK
| | - Renaud Schuck
- Center for Neurotechnology and Department of Bioengineering Imperial College London, South Kensington, LondonSW7 2AZ, UK
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24
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Fiáth R, Beregszászi P, Horváth D, Wittner L, Aarts AAA, Ruther P, Neves HP, Bokor H, Acsády L, Ulbert I. Large-scale recording of thalamocortical circuits: in vivo electrophysiology with the two-dimensional electronic depth control silicon probe. J Neurophysiol 2016; 116:2312-2330. [PMID: 27535370 DOI: 10.1152/jn.00318.2016] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 08/13/2016] [Indexed: 12/12/2022] Open
Abstract
Recording simultaneous activity of a large number of neurons in distributed neuronal networks is crucial to understand higher order brain functions. We demonstrate the in vivo performance of a recently developed electrophysiological recording system comprising a two-dimensional, multi-shank, high-density silicon probe with integrated complementary metal-oxide semiconductor electronics. The system implements the concept of electronic depth control (EDC), which enables the electronic selection of a limited number of recording sites on each of the probe shafts. This innovative feature of the system permits simultaneous recording of local field potentials (LFP) and single- and multiple-unit activity (SUA and MUA, respectively) from multiple brain sites with high quality and without the actual physical movement of the probe. To evaluate the in vivo recording capabilities of the EDC probe, we recorded LFP, MUA, and SUA in acute experiments from cortical and thalamic brain areas of anesthetized rats and mice. The advantages of large-scale recording with the EDC probe are illustrated by investigating the spatiotemporal dynamics of pharmacologically induced thalamocortical slow-wave activity in rats and by the two-dimensional tonotopic mapping of the auditory thalamus. In mice, spatial distribution of thalamic responses to optogenetic stimulation of the neocortex was examined. Utilizing the benefits of the EDC system may result in a higher yield of useful data from a single experiment compared with traditional passive multielectrode arrays, and thus in the reduction of animals needed for a research study.
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Affiliation(s)
- Richárd Fiáth
- Group of Comparative Psychophysiology, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary.,Faculty of Information Technology and Bionics, Pázmány Péter, Catholic University, Budapest, Hungary.,School of Ph.D. Studies, Semmelweis University, Budapest, Hungary
| | - Patrícia Beregszászi
- Faculty of Information Technology and Bionics, Pázmány Péter, Catholic University, Budapest, Hungary
| | - Domonkos Horváth
- Group of Comparative Psychophysiology, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary.,Faculty of Information Technology and Bionics, Pázmány Péter, Catholic University, Budapest, Hungary.,School of Ph.D. Studies, Semmelweis University, Budapest, Hungary
| | - Lucia Wittner
- Group of Comparative Psychophysiology, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary
| | | | - Patrick Ruther
- Department of Microsystems Engineering (IMTEK), University of Freiburg, Freiburg, Germany.,BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, Germany
| | - Hercules P Neves
- Unitec Semicondutores, Ribeirão das Neves, Brazil.,Solid State Electronics, Department of Engineering Sciences, Uppsala University, Uppsala, Sweden; and
| | - Hajnalka Bokor
- Laboratory of Thalamus Research, Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary
| | - László Acsády
- Laboratory of Thalamus Research, Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary
| | - István Ulbert
- Group of Comparative Psychophysiology, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary; .,Faculty of Information Technology and Bionics, Pázmány Péter, Catholic University, Budapest, Hungary
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25
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Neto JP, Lopes G, Frazão J, Nogueira J, Lacerda P, Baião P, Aarts A, Andrei A, Musa S, Fortunato E, Barquinha P, Kampff AR. Validating silicon polytrodes with paired juxtacellular recordings: method and dataset. J Neurophysiol 2016; 116:892-903. [PMID: 27306671 PMCID: PMC5002440 DOI: 10.1152/jn.00103.2016] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2016] [Accepted: 05/19/2016] [Indexed: 12/22/2022] Open
Abstract
Cross-validating new methods for recording neural activity is necessary to accurately interpret and compare the signals they measure. Here we describe a procedure for precisely aligning two probes for in vivo "paired-recordings" such that the spiking activity of a single neuron is monitored with both a dense extracellular silicon polytrode and a juxtacellular micropipette. Our new method allows for efficient, reliable, and automated guidance of both probes to the same neural structure with micrometer resolution. We also describe a new dataset of paired-recordings, which is available online. We propose that our novel targeting system, and ever expanding cross-validation dataset, will be vital to the development of new algorithms for automatically detecting/sorting single-units, characterizing new electrode materials/designs, and resolving nagging questions regarding the origin and nature of extracellular neural signals.
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Affiliation(s)
- Joana P Neto
- Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal; Departamento de Ciência dos Materiais, CENIMAT/I3N and CEMOP/Uninova, Caparica, Portugal; Sainsbury Wellcome Centre, University College London, London, United Kingdom
| | - Gonçalo Lopes
- Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal; Sainsbury Wellcome Centre, University College London, London, United Kingdom
| | - João Frazão
- Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Joana Nogueira
- Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal; Sainsbury Wellcome Centre, University College London, London, United Kingdom
| | - Pedro Lacerda
- Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Pedro Baião
- Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | | | | | | | - Elvira Fortunato
- Departamento de Ciência dos Materiais, CENIMAT/I3N and CEMOP/Uninova, Caparica, Portugal
| | - Pedro Barquinha
- Departamento de Ciência dos Materiais, CENIMAT/I3N and CEMOP/Uninova, Caparica, Portugal
| | - Adam R Kampff
- Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal; Sainsbury Wellcome Centre, University College London, London, United Kingdom
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26
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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: 151] [Impact Index Per Article: 16.8] [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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27
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Weir K, Blanquie O, Kilb W, Luhmann HJ, Sinning A. Comparison of spike parameters from optically identified GABAergic and glutamatergic neurons in sparse cortical cultures. Front Cell Neurosci 2015; 8:460. [PMID: 25642167 PMCID: PMC4294161 DOI: 10.3389/fncel.2014.00460] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2014] [Accepted: 12/18/2014] [Indexed: 11/16/2022] Open
Abstract
Primary neuronal cultures share many typical features with the in vivo situation, including similarities in distinct electrical activity patterns and synaptic network interactions. Here, we use multi-electrode array (MEA) recordings from spontaneously active cultures of wildtype and glutamic acid decarboxylase 67 (GAD67)-green fluorescent protein (GFP) transgenic mice to evaluate which spike parameters differ between GABAergic interneurons and principal, putatively glutamatergic neurons. To analyze this question we combine MEA recordings with optical imaging in sparse cortical cultures to assign individual spikes to visually-identified single neurons. In our culture system, excitatory and inhibitory neurons are present at a similar ratio as described in vivo, and spike waveform characteristics and firing patterns are fully developed after 2 weeks in vitro. Spike amplitude, but not other spike waveform parameters, correlated with the distance between the recording electrode and the location of the assigned neuron’s soma. Cluster analysis of spike waveform properties revealed no particular cell population that may be assigned to putative inhibitory or excitatory neurons. Moreover, experiments in primary cultures from transgenic GAD67-GFP mice, which allow optical identification of GABAergic interneurons and thus unambiguous assignment of extracellular signals, did not reveal any significant difference in spike timing and spike waveform parameters between inhibitory and excitatory neurons. Despite of our detailed characterization of spike waveform and temporal spiking properties we could not identify an unequivocal electrical parameter to discriminate between individual excitatory and inhibitory neurons in vitro. Our data suggest that under in vitro conditions cellular classifications of single neurons on the basis of their extracellular firing properties should be treated with caution.
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Affiliation(s)
- Keiko Weir
- Institute of Physiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Oriane Blanquie
- Institute of Physiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Werner Kilb
- Institute of Physiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Heiko J Luhmann
- Institute of Physiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Anne Sinning
- Institute of Physiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany
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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: 305] [Impact Index Per Article: 33.9] [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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