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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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Passaro AP, Stice SL. Electrophysiological Analysis of Brain Organoids: Current Approaches and Advancements. Front Neurosci 2021; 14:622137. [PMID: 33510616 PMCID: PMC7835643 DOI: 10.3389/fnins.2020.622137] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 12/11/2020] [Indexed: 12/23/2022] Open
Abstract
Brain organoids, or cerebral organoids, have become widely used to study the human brain in vitro. As pluripotent stem cell-derived structures capable of self-organization and recapitulation of physiological cell types and architecture, brain organoids bridge the gap between relatively simple two-dimensional human cell cultures and non-human animal models. This allows for high complexity and physiological relevance in a controlled in vitro setting, opening the door for a variety of applications including development and disease modeling and high-throughput screening. While technologies such as single cell sequencing have led to significant advances in brain organoid characterization and understanding, improved functional analysis (especially electrophysiology) is needed to realize the full potential of brain organoids. In this review, we highlight key technologies for brain organoid development and characterization, then discuss current electrophysiological methods for brain organoid analysis. While electrophysiological approaches have improved rapidly for two-dimensional cultures, only in the past several years have advances been made to overcome limitations posed by the three-dimensionality of brain organoids. Here, we review major advances in electrophysiological technologies and analytical methods with a focus on advances with applicability for brain organoid analysis.
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Affiliation(s)
- Austin P. Passaro
- Regenerative Bioscience Center, University of Georgia, Athens, GA, United States
- Division of Neuroscience, Biomedical & Health Sciences Institute, University of Georgia, Athens, GA, United States
| | - Steven L. Stice
- Regenerative Bioscience Center, University of Georgia, Athens, GA, United States
- Division of Neuroscience, Biomedical & Health Sciences Institute, University of Georgia, Athens, GA, United States
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, United States
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Buccino AP, Hurwitz CL, Garcia S, Magland J, Siegle JH, Hurwitz R, Hennig MH. SpikeInterface, a unified framework for spike sorting. eLife 2020; 9:e61834. [PMID: 33170122 PMCID: PMC7704107 DOI: 10.7554/elife.61834] [Citation(s) in RCA: 100] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 11/09/2020] [Indexed: 12/21/2022] Open
Abstract
Much development has been directed toward improving the performance and automation of spike sorting. This continuous development, while essential, has contributed to an over-saturation of new, incompatible tools that hinders rigorous benchmarking and complicates reproducible analysis. To address these limitations, we developed SpikeInterface, a Python framework designed to unify preexisting spike sorting technologies into a single codebase and to facilitate straightforward comparison and adoption of different approaches. With a few lines of code, researchers can reproducibly run, compare, and benchmark most modern spike sorting algorithms; pre-process, post-process, and visualize extracellular datasets; validate, curate, and export sorting outputs; and more. In this paper, we provide an overview of SpikeInterface and, with applications to real and simulated datasets, demonstrate how it can be utilized to reduce the burden of manual curation and to more comprehensively benchmark automated spike sorters.
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Affiliation(s)
- Alessio P Buccino
- Department of Biosystems Science and Engineering, ETH ZurichZürichSwitzerland
- Centre for Integrative Neuroplasticity (CINPLA), University of OsloOsloNorway
| | - Cole L Hurwitz
- School of Informatics, University of EdinburghEdinburghUnited Kingdom
| | - Samuel Garcia
- Centre de Recherche en Neuroscience de Lyon, CNRSLyonFrance
| | | | | | | | - Matthias H Hennig
- School of Informatics, University of EdinburghEdinburghUnited Kingdom
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Cheyne JE, Montgomery JM. The cellular and molecular basis of in vivo synaptic plasticity in rodents. Am J Physiol Cell Physiol 2020; 318:C1264-C1283. [PMID: 32320288 DOI: 10.1152/ajpcell.00416.2019] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Plasticity within the neuronal networks of the brain underlies the ability to learn and retain new information. The initial discovery of synaptic plasticity occurred by measuring synaptic strength in vivo, applying external stimulation and observing an increase in synaptic strength termed long-term potentiation (LTP). Many of the molecular pathways involved in LTP and other forms of synaptic plasticity were subsequently uncovered in vitro. Over the last few decades, technological advances in recording and imaging in live animals have seen many of these molecular mechanisms confirmed in vivo, including structural changes both pre- and postsynaptically, changes in synaptic strength, and changes in neuronal excitability. A well-studied aspect of neuronal plasticity is the capacity of the brain to adapt to its environment, gained by comparing the brains of deprived and experienced animals in vivo, and in direct response to sensory stimuli. Multiple in vivo studies have also strongly linked plastic changes to memory by interfering with the expression of plasticity and by manipulating memory engrams. Plasticity in vivo also occurs in the absence of any form of external stimulation, i.e., during spontaneous network activity occurring with brain development. However, there is still much to learn about how plasticity is induced during natural learning and how this is altered in neurological disorders.
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Affiliation(s)
- Juliette E Cheyne
- Department of Physiology and Centre for Brain Research, University of Auckland, Auckland, New Zealand
| | - Johanna M Montgomery
- Department of Physiology and Centre for Brain Research, University of Auckland, Auckland, New Zealand
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A Template-Based Sequential Algorithm for Online Clustering of Spikes in Extracellular Recordings. Cognit Comput 2020. [DOI: 10.1007/s12559-020-09711-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Swain S, Gupta RK, Ratnayake K, Priyanka PD, Singh R, Jana S, Mitra K, Karunarathne A, Giri L. Confocal Imaging and k-Means Clustering of GABA B and mGluR Mediated Modulation of Ca 2+ Spiking in Hippocampal Neurons. ACS Chem Neurosci 2018; 9:3094-3107. [PMID: 30044088 DOI: 10.1021/acschemneuro.8b00297] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Imaging cytosolic calcium in neurons is emerging as a new tool in neurological disease diagnosis, drug screening, and toxicity testing. Ca2+ oscillation signatures show a significant variation depending on GPCR targeting agonists. Quantification of Ca2+ spike trains in ligand induced Ca2+ oscillations remains challenging due to their inherent heterogeneity in primary culture. Moreover, there is no framework available for identification of optimal number of clusters and distance metric to cluster Ca2+ spike trains. Using quantitative confocal imaging and clustering analysis, we show the characterization of Ca2+ spiking in GPCR targeting drug-treated primary culture of hippocampal neurons. A systematic framework for selection of the clustering method instead of an intuition-based method was used to optimize the cluster number and distance metric. The results discern neurons with diverse Ca2+ response patterns, including higher amplitude fast spiking and lower spiking responses, and their relative percentage in a neuron population in absence and presence of GPCR-targeted drugs. The proposed framework was employed to show that the clustering pattern of Ca2+ spiking can be controlled using GABAB and mGluR targeting drugs. This approach can be used for unbiased measurement of neural activity and identification of spiking population with varying amplitude and frequencies, providing a platform for high-content drug screening.
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Affiliation(s)
- Sarpras Swain
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad 502285, India
| | - Rishikesh Kumar Gupta
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad 502285, India
| | - Kasun Ratnayake
- Department of Chemistry and Biochemistry, University of Toledo, Toledo, Ohio 43606, United States
| | - Pantula Devi Priyanka
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad 502285, India
| | - Ranjana Singh
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad 502285, India
| | - Soumya Jana
- Department of Electrical Engineering, Indian Institute of Technology, Hyderabad 502285, India
| | - Kishalay Mitra
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad 502285, India
| | - Ajith Karunarathne
- Department of Chemistry and Biochemistry, University of Toledo, Toledo, Ohio 43606, United States
| | - Lopamudra Giri
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad 502285, India
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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: 29] [Impact Index Per Article: 4.8] [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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Hong KS, Aziz N, Ghafoor U. Motor-commands decoding using peripheral nerve signals: a review. J Neural Eng 2018; 15:031004. [PMID: 29498358 DOI: 10.1088/1741-2552/aab383] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
During the last few decades, substantial scientific and technological efforts have been focused on the development of neuroprostheses. The major emphasis has been on techniques for connecting the human nervous system with a robotic prosthesis via natural-feeling interfaces. The peripheral nerves provide access to highly processed and segregated neural command signals from the brain that can in principle be used to determine user intent and control muscles. If these signals could be used, they might allow near-natural and intuitive control of prosthetic limbs with multiple degrees of freedom. This review summarizes the history of neuroprosthetic interfaces and their ability to record from and stimulate peripheral nerves. We also discuss the types of interfaces available and their applications, the kinds of peripheral nerve signals that are used, and the algorithms used to decode them. Finally, we explore the prospects for future development in this area.
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Petrantonakis PC, Poirazi P. A Novel and Simple Spike Sorting Implementation. IEEE Trans Neural Syst Rehabil Eng 2017; 25:323-333. [PMID: 28113325 DOI: 10.1109/tnsre.2016.2640858] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Monitoring the activity of multiple, individual neurons that fire spikes in the vicinity of an electrode, namely perform a Spike Sorting (SS) procedure, comprises one of the most important tools for contemporary neuroscience in order to reverse-engineer the brain. As recording electrodes' technology rabidly evolves by integrating thousands of electrodes in a confined spatial setting, the algorithms that are used to monitor individual neurons from recorded signals have to become even more reliable and computationally efficient. In this work, we propose a novel framework of the SS approach in which a single-step processing of the raw (unfiltered) extracellular signal is sufficient for both the detection and sorting of the activity of individual neurons. Despite its simplicity, the proposed approach exhibits comparable performance with state-of-the-art approaches, especially for spike detection in noisy signals, and paves the way for a new family of SS algorithms with the potential for multi-recording, fast, on-chip implementations.
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