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Bod RB, Rokai J, Meszéna D, Fiáth R, Ulbert I, Márton G. From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings. Front Neuroinform 2022; 16:851024. [PMID: 35769832 PMCID: PMC9236662 DOI: 10.3389/fninf.2022.851024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 05/06/2022] [Indexed: 11/15/2022] Open
Abstract
The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript.
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Affiliation(s)
- Réka Barbara Bod
- Laboratory of Experimental Neurophysiology, Department of Physiology, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Târgu Mureş, Târgu Mureş, Romania
| | - János Rokai
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- School of PhD Studies, Semmelweis University, Budapest, Hungary
| | - Domokos Meszéna
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Richárd Fiáth
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - István Ulbert
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Gergely Márton
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
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Ylä-Outinen L, Tanskanen JMA, Kapucu FE, Hyysalo A, Hyttinen JAK, Narkilahti S. Advances in Human Stem Cell-Derived Neuronal Cell Culturing and Analysis. ADVANCES IN NEUROBIOLOGY 2019; 22:299-329. [DOI: 10.1007/978-3-030-11135-9_13] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Caro-Martín CR, Delgado-García JM, Gruart A, Sánchez-Campusano R. Spike sorting based on shape, phase, and distribution features, and K-TOPS clustering with validity and error indices. Sci Rep 2018; 8:17796. [PMID: 30542106 PMCID: PMC6290782 DOI: 10.1038/s41598-018-35491-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Accepted: 11/05/2018] [Indexed: 12/13/2022] Open
Abstract
Spike sorting is one of the most important data analysis problems in neurophysiology. The precision in all steps of the spike-sorting procedure critically affects the accuracy of all subsequent analyses. After data preprocessing and spike detection have been carried out properly, both feature extraction and spike clustering are the most critical subsequent steps of the spike-sorting procedure. The proposed spike sorting approach comprised a new feature extraction method based on shape, phase, and distribution features of each spike (hereinafter SS-SPDF method), which reveal significant information of the neural events under study. In addition, we applied an efficient clustering algorithm based on K-means and template optimization in phase space (hereinafter K-TOPS) that included two integrative clustering measures (validity and error indices) to verify the cohesion-dispersion among spike events during classification and the misclassification of clustering, respectively. The proposed method/algorithm was tested on both simulated data and real neural recordings. The results obtained for these datasets suggest that our spike sorting approach provides an efficient way for sorting both single-unit spikes and overlapping waveforms. By analyzing raw extracellular recordings collected from the rostral-medial prefrontal cortex (rmPFC) of behaving rabbits during classical eyeblink conditioning, we have demonstrated that the present method/algorithm performs better at classifying spikes and neurons and at assessing their modulating properties than other methods currently used in neurophysiology.
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Affiliation(s)
| | | | - Agnès Gruart
- Division of Neurosciences, Pablo de Olavide University, Seville, 41013, Spain
| | - R Sánchez-Campusano
- Division of Neurosciences, Pablo de Olavide University, Seville, 41013, Spain.
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Cvetkovic C, Basu N, Krencik R. Synaptic Microcircuit Modeling with 3D Cocultures of Astrocytes and Neurons from Human Pluripotent Stem Cells. J Vis Exp 2018. [PMID: 30176009 DOI: 10.3791/58034] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
A barrier to our understanding of how various cell types and signals contribute to synaptic circuit function is the lack of relevant models for studying the human brain. One emerging technology to address this issue is the use of three dimensional (3D) neural cell cultures, termed 'organoids' or 'spheroids', for long term preservation of intercellular interactions including extracellular adhesion molecules. However, these culture systems are time consuming and not systematically generated. Here, we detail a method to rapidly and consistently produce 3D cocultures of neurons and astrocytes from human pluripotent stem cells. First, pre-differentiated astrocytes and neuronal progenitors are dissociated and counted. Next, cells are combined in sphere-forming dishes with a Rho-Kinase inhibitor and at specific ratios to produce spheres of reproducible size. After several weeks of culture as floating spheres, cocultures ('asteroids') are finally sectioned for immunostaining or plated upon multielectrode arrays to measure synaptic density and strength. In general, it is expected that this protocol will yield 3D neural spheres that display mature cell-type restricted markers, form functional synapses, and exhibit spontaneous synaptic network burst activity. Together, this system permits drug screening and investigations into mechanisms of disease in a more suitable model compared to monolayer cultures.
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Affiliation(s)
- Caroline Cvetkovic
- Center for Neuroregeneration, Department of Neurosurgery, Houston Methodist Research Institute
| | - Nupur Basu
- Center for Neuroregeneration, Department of Neurosurgery, Houston Methodist Research Institute
| | - Robert Krencik
- Center for Neuroregeneration, Department of Neurosurgery, Houston Methodist Research Institute;
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Zeldenrust F, Wadman WJ, Englitz B. Neural Coding With Bursts-Current State and Future Perspectives. Front Comput Neurosci 2018; 12:48. [PMID: 30034330 PMCID: PMC6043860 DOI: 10.3389/fncom.2018.00048] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Accepted: 06/06/2018] [Indexed: 12/11/2022] Open
Abstract
Neuronal action potentials or spikes provide a long-range, noise-resistant means of communication between neurons. As point processes single spikes contain little information in themselves, i.e., outside the context of spikes from other neurons. Moreover, they may fail to cross a synapse. A burst, which consists of a short, high frequency train of spikes, will more reliably cross a synapse, increasing the likelihood of eliciting a postsynaptic spike, depending on the specific short-term plasticity at that synapse. Both the number and the temporal pattern of spikes in a burst provide a coding space that lies within the temporal integration realm of single neurons. Bursts have been observed in many species, including the non-mammalian, and in brain regions that range from subcortical to cortical. Despite their widespread presence and potential relevance, the uncertainties of how to classify bursts seems to have limited the research into the coding possibilities for bursts. The present series of research articles provides new insights into the relevance and interpretation of bursts across different neural circuits, and new methods for their analysis. Here, we provide a succinct introduction to the history of burst coding and an overview of recent work on this topic.
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Affiliation(s)
- Fleur Zeldenrust
- Department of Neurophysiology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Wytse J Wadman
- Cellular and Systems Neurobiology Lab, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Bernhard Englitz
- Department of Neurophysiology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, Netherlands
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Ojima A, Miyamoto N. Method for MEA Data Analysis of Drug-treated Rat Primary Neurons and Human iPSC-derived Neurons to Evaluate the Risk of Drug-induced Seizures. YAKUGAKU ZASSHI 2018; 138:823-828. [DOI: 10.1248/yakushi.17-00213-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Välkki IA, Lenk K, Mikkonen JE, Kapucu FE, Hyttinen JAK. Network-Wide Adaptive Burst Detection Depicts Neuronal Activity with Improved Accuracy. Front Comput Neurosci 2017; 11:40. [PMID: 28620291 PMCID: PMC5450420 DOI: 10.3389/fncom.2017.00040] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2016] [Accepted: 05/08/2017] [Indexed: 11/17/2022] Open
Abstract
Neuronal networks are often characterized by their spiking and bursting statistics. Previously, we introduced an adaptive burst analysis method which enhances the analysis power for neuronal networks with highly varying firing dynamics. The adaptation is based on single channels analyzing each element of a network separately. Such kind of analysis was adequate for the assessment of local behavior, where the analysis focuses on the neuronal activity in the vicinity of a single electrode. However, the assessment of the whole network may be hampered, if parts of the network are analyzed using different rules. Here, we test how using multiple channels and measurement time points affect adaptive burst detection. The main emphasis is, if network-wide adaptive burst detection can provide new insights into the assessment of network activity. Therefore, we propose a modification to the previously introduced inter-spike interval (ISI) histogram based cumulative moving average (CMA) algorithm to analyze multiple spike trains simultaneously. The network size can be freely defined, e.g., to include all the electrodes in a microelectrode array (MEA) recording. Additionally, the method can be applied on a series of measurements on the same network to pool the data for statistical analysis. Firstly, we apply both the original CMA-algorithm and our proposed network-wide CMA-algorithm on artificial spike trains to investigate how the modification changes the burst detection. Thereafter, we use the algorithms on MEA data of spontaneously active chemically manipulated in vitro rat cortical networks. Moreover, we compare the synchrony of the detected bursts introducing a new burst synchrony measure. Finally, we demonstrate how the bursting statistics can be used to classify networks by applying k-means clustering to the bursting statistics. The results show that the proposed network wide adaptive burst detection provides a method to unify the burst definition in the whole network and thus improves the assessment and classification of the neuronal activity, e.g., the effects of different pharmaceuticals. The results indicate that the novel method is adaptive enough to be usable on networks with different dynamics, and it is especially feasible when comparing the behavior of differently spiking networks, for example in developing networks.
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Affiliation(s)
- Inkeri A Välkki
- BioMediTech Institute and Faculty of Biomedical Sciences and Engineering, Tampere University of TechnologyTampere, Finland
| | - Kerstin Lenk
- BioMediTech Institute and Faculty of Biomedical Sciences and Engineering, Tampere University of TechnologyTampere, Finland
| | - Jarno E Mikkonen
- Department of Computer Science and Information Systems, University of JyväskyläJyväskylä, Finland
| | - Fikret E Kapucu
- BioMediTech Institute and Faculty of Biomedical Sciences and Engineering, Tampere University of TechnologyTampere, Finland.,Pervasive Computing, Faculty of Computing and Electrical Engineering, Tampere University of TechnologyTampere, Finland
| | - Jari A K Hyttinen
- BioMediTech Institute and Faculty of Biomedical Sciences and Engineering, Tampere University of TechnologyTampere, Finland
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Kapucu FE, Välkki I, Mikkonen JE, Leone C, Lenk K, Tanskanen JMA, Hyttinen JAK. Spectral Entropy Based Neuronal Network Synchronization Analysis Based on Microelectrode Array Measurements. Front Comput Neurosci 2016; 10:112. [PMID: 27803660 PMCID: PMC5068339 DOI: 10.3389/fncom.2016.00112] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Accepted: 10/05/2016] [Indexed: 12/03/2022] Open
Abstract
Synchrony and asynchrony are essential aspects of the functioning of interconnected neuronal cells and networks. New information on neuronal synchronization can be expected to aid in understanding these systems. Synchronization provides insight in the functional connectivity and the spatial distribution of the information processing in the networks. Synchronization is generally studied with time domain analysis of neuronal events, or using direct frequency spectrum analysis, e.g., in specific frequency bands. However, these methods have their pitfalls. Thus, we have previously proposed a method to analyze temporal changes in the complexity of the frequency of signals originating from different network regions. The method is based on the correlation of time varying spectral entropies (SEs). SE assesses the regularity, or complexity, of a time series by quantifying the uniformity of the frequency spectrum distribution. It has been previously employed, e.g., in electroencephalogram analysis. Here, we revisit our correlated spectral entropy method (CorSE), providing evidence of its justification, usability, and benefits. Here, CorSE is assessed with simulations and in vitro microelectrode array (MEA) data. CorSE is first demonstrated with a specifically tailored toy simulation to illustrate how it can identify synchronized populations. To provide a form of validation, the method was tested with simulated data from integrate-and-fire model based computational neuronal networks. To demonstrate the analysis of real data, CorSE was applied on in vitro MEA data measured from rat cortical cell cultures, and the results were compared with three known event based synchronization measures. Finally, we show the usability by tracking the development of networks in dissociated mouse cortical cell cultures. The results show that temporal correlations in frequency spectrum distributions reflect the network relations of neuronal populations. In the simulated data, CorSE unraveled the synchronizations. With the real in vitro MEA data, CorSE produced biologically plausible results. Since CorSE analyses continuous data, it is not affected by possibly poor spike or other event detection quality. We conclude that CorSE can reveal neuronal network synchronization based on in vitro MEA field potential measurements. CorSE is expected to be equally applicable also in the analysis of corresponding in vivo and ex vivo data analysis.
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Affiliation(s)
- Fikret E Kapucu
- Department of Pervasive Computing, Tampere University of TechnologyTampere, Finland; Computational Biophysics and Imaging Group, Department of Electronics and Communication Engineering, BioMediTech, Tampere University of TechnologyTampere, Finland
| | - Inkeri Välkki
- Computational Biophysics and Imaging Group, Department of Electronics and Communication Engineering, BioMediTech, Tampere University of Technology Tampere, Finland
| | - Jarno E Mikkonen
- Department of Psychology, Center for Interdisciplinary Brain Research, University of Jyväskylä Jyväskylä, Finland
| | - Chiara Leone
- Department of Management and Production Engineering, Politecnico di Torino Torino, Italy
| | - Kerstin Lenk
- Computational Biophysics and Imaging Group, Department of Electronics and Communication Engineering, BioMediTech, Tampere University of Technology Tampere, Finland
| | - Jarno M A Tanskanen
- Computational Biophysics and Imaging Group, Department of Electronics and Communication Engineering, BioMediTech, Tampere University of Technology Tampere, Finland
| | - Jari A K Hyttinen
- Computational Biophysics and Imaging Group, Department of Electronics and Communication Engineering, BioMediTech, Tampere University of Technology Tampere, Finland
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