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      What we can and what we cannot see with extracellular multielectrodes

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          Abstract

          Extracellular recording is an accessible technique used in animals and humans to study the brain physiology and pathology. As the number of recording channels and their density grows it is natural to ask how much improvement the additional channels bring in and how we can optimally use the new capabilities for monitoring the brain. Here we show that for any given distribution of electrodes we can establish exactly what information about current sources in the brain can be recovered and what information is strictly unobservable. We demonstrate this in the general setting of previously proposed kernel Current Source Density method and illustrate it with simplified examples as well as using evoked potentials from the barrel cortex obtained with a Neuropixels probe and with compatible model data. We show that with conceptual separation of the estimation space from experimental setup one can recover sources not accessible to standard methods.

          Author summary

          Every set of measurements is a window into reality rendering its incomplete or distorted picture. It is often difficult to relate the obtained representation of the world to underlying ground truth. Here we show, for brain electrophysiology, for arbitrary experimental setup (distribution of electrodes), and arbitrary analytical setup (function space of current source densities), that one can identify distributions of current sources which can be recovered precisely, and those which are invisible in the system. This shows what is and what is not observable in the studied system for a given setup, allows to improve the analysis results by modifying analytical setup, and facilitates interpretation of the measured sets of LFP, ECoG and EEG recordings. In particular we show that with conceptual separation of the estimation space from experimental setup one can recover source distributions not accessible to standard methods.

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          Most cited references33

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          The origin of extracellular fields and currents--EEG, ECoG, LFP and spikes.

          Neuronal activity in the brain gives rise to transmembrane currents that can be measured in the extracellular medium. Although the major contributor of the extracellular signal is the synaptic transmembrane current, other sources--including Na(+) and Ca(2+) spikes, ionic fluxes through voltage- and ligand-gated channels, and intrinsic membrane oscillations--can substantially shape the extracellular field. High-density recordings of field activity in animals and subdural grid recordings in humans, combined with recently developed data processing tools and computational modelling, can provide insight into the cooperative behaviour of neurons, their average synaptic input and their spiking output, and can increase our understanding of how these processes contribute to the extracellular signal.
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            Theory of reproducing kernels

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              Fully integrated silicon probes for high-density recording of neural activity

              Sensory, motor and cognitive operations involve the coordinated action of large neuronal populations across multiple brain regions in both superficial and deep structures. Existing extracellular probes record neural activity with excellent spatial and temporal (sub-millisecond) resolution, but from only a few dozen neurons per shank. Optical Ca2+ imaging offers more coverage but lacks the temporal resolution needed to distinguish individual spikes reliably and does not measure local field potentials. Until now, no technology compatible with use in unrestrained animals has combined high spatiotemporal resolution with large volume coverage. Here we design, fabricate and test a new silicon probe known as Neuropixels to meet this need. Each probe has 384 recording channels that can programmably address 960 complementary metal–oxide–semiconductor (CMOS) processing-compatible low-impedance TiN sites that tile a single 10-mm long, 70 × 20-μm cross-section shank. The 6 × 9-mm probe base is fabricated with the shank on a single chip. Voltage signals are filtered, amplified, multiplexed and digitized on the base, allowing the direct transmission of noise-free digital data from the probe. The combination of dense recording sites and high channel count yielded well-isolated spiking activity from hundreds of neurons per probe implanted in mice and rats. Using two probes, more than 700 well-isolated single neurons were recorded simultaneously from five brain structures in an awake mouse. The fully integrated functionality and small size of Neuropixels probes allowed large populations of neurons from several brain structures to be recorded in freely moving animals. This combination of high-performance electrode technology and scalable chip fabrication methods opens a path towards recording of brain-wide neural activity during behaviour.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: MethodologyRole: Project administrationRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ResourcesRole: Software
                Role: InvestigationRole: ResourcesRole: SoftwareRole: Writing – original draft
                Role: InvestigationRole: ResourcesRole: Software
                Role: InvestigationRole: MethodologyRole: ResourcesRole: Writing – original draft
                Role: InvestigationRole: MethodologyRole: ResourcesRole: Writing – original draft
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                May 2021
                14 May 2021
                : 17
                : 5
                : e1008615
                Affiliations
                [1 ] Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland
                [2 ] Centre for Neural Circuits and Behaviour, Department of Physiology Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
                [3 ] University of Warsaw, Faculty of Biology, Warsaw, Poland
                [4 ] Laboratory of Emotions Neurobiology, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland
                University College London, UNITED KINGDOM
                Author notes

                The authors have declared that no competing interests exist.

                ‡ joint first authors.

                Author information
                https://orcid.org/0000-0003-4252-1608
                https://orcid.org/0000-0002-4005-8464
                https://orcid.org/0000-0002-3152-4785
                https://orcid.org/0000-0002-5569-4252
                https://orcid.org/0000-0003-3745-1000
                https://orcid.org/0000-0003-2600-1418
                https://orcid.org/0000-0001-6806-9707
                https://orcid.org/0000-0003-0812-9872
                Article
                PCOMPBIOL-D-20-02203
                10.1371/journal.pcbi.1008615
                8153483
                33989280
                491b3311-cae7-4cd0-a6d2-0587fd7fa1e5
                © 2021 Chintaluri et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 9 December 2020
                : 28 April 2021
                Page count
                Figures: 6, Tables: 0, Pages: 20
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100004281, Narodowe Centrum Nauki;
                Award ID: 2013/08/W/NZ4/00691
                Funded by: funder-id http://dx.doi.org/10.13039/501100004281, Narodowe Centrum Nauki;
                Award ID: 2015/17/B/ST7/04123
                Funded by: funder-id http://dx.doi.org/10.13039/100010663, H2020 European Research Council;
                Award ID: Laboratory of Emotions Neurobiology
                Award Recipient :
                The study received funding from the Polish National Science Centre’s grants (2013/08/W/NZ4/00691) and (2015/17/B/ST7/04123). K.K. is funded from European Research Council Starting Grant (H 415148; principal investigator: Ewelina Knapska). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Physical Sciences
                Chemistry
                Electrochemistry
                Electrode Potentials
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms
                Kernel Methods
                Research and Analysis Methods
                Simulation and Modeling
                Algorithms
                Kernel Methods
                Biology and Life Sciences
                Physiology
                Electrophysiology
                Membrane Potential
                Physical Sciences
                Mathematics
                Operator Theory
                Kernel Functions
                Engineering and Technology
                Electronics Engineering
                Electronics
                Electrodes
                Physical Sciences
                Mathematics
                Algebra
                Linear Algebra
                Eigenvalues
                Biology and Life Sciences
                Anatomy
                Musculoskeletal System
                Skeleton
                Skull
                Medicine and Health Sciences
                Anatomy
                Musculoskeletal System
                Skeleton
                Skull
                Biology and Life Sciences
                Physiology
                Electrophysiology
                Membrane Potential
                Evoked Potentials
                Biology and Life Sciences
                Physiology
                Electrophysiology
                Neurophysiology
                Evoked Potentials
                Biology and Life Sciences
                Neuroscience
                Neurophysiology
                Evoked Potentials
                Custom metadata
                vor-update-to-uncorrected-proof
                2021-05-26
                All the figures can be automatically generated using code from https://github.com/Neuroinflab/kCSD-python/tree/master/figures/ The model figures require model data we generated a few years ago, which are available at REPOD, http://dx.doi.org/10.18150/repod.6394793, details are in the text. The experimental dataset we used is available at https://github.com/Neuroinflab/kCSD-python/tree/master/figures/npx Previously Published Datasets: Thalamocortical network simulated data.: H. Głąbska, C. Chintaluri, D.K. Wójcik, 2016, http://dx.doi.org/10.18150/repod.6394793, repod 6394793.

                Quantitative & Systems biology
                Quantitative & Systems biology

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