1
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McHugh SB, Lopes-Dos-Santos V, Castelli M, Gava GP, Thompson SE, Tam SKE, Hartwich K, Perry B, Toth R, Denison T, Sharott A, Dupret D. Offline hippocampal reactivation during dentate spikes supports flexible memory. Neuron 2024:S0896-6273(24)00646-9. [PMID: 39321790 DOI: 10.1016/j.neuron.2024.08.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 06/21/2024] [Accepted: 08/29/2024] [Indexed: 09/27/2024]
Abstract
Stabilizing new memories requires coordinated neuronal spiking activity during sleep. Hippocampal sharp-wave ripples (SWRs) in the cornu ammonis (CA) region and dentate spikes (DSs) in the dentate gyrus (DG) are prime candidate network events for supporting this offline process. SWRs have been studied extensively, but the contribution of DSs remains unclear. By combining triple-ensemble (DG-CA3-CA1) recordings and closed-loop optogenetics in mice, we show that, like SWRs, DSs synchronize spiking across DG and CA principal cells to reactivate population-level patterns of neuronal coactivity expressed during prior waking experience. Notably, the population coactivity structure in DSs is more diverse and higher dimensional than that seen during SWRs. Importantly, suppressing DG granule cell spiking selectively during DSs impairs subsequent flexible memory performance during multi-object recognition tasks and associated hippocampal patterns of neuronal coactivity. We conclude that DSs constitute a second offline network event central to hippocampal population dynamics serving memory-guided behavior.
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Affiliation(s)
- Stephen B McHugh
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK.
| | - Vítor Lopes-Dos-Santos
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - Manfredi Castelli
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - Giuseppe P Gava
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - Sophie E Thompson
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - Shu K E Tam
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - Katja Hartwich
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - Brook Perry
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - Robert Toth
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - Timothy Denison
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - Andrew Sharott
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - David Dupret
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK.
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2
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Gava GP, Lefèvre L, Broadbelt T, McHugh SB, Lopes-Dos-Santos V, Brizee D, Hartwich K, Sjoberg H, Perestenko PV, Toth R, Sharott A, Dupret D. Organizing the coactivity structure of the hippocampus from robust to flexible memory. Science 2024; 385:1120-1127. [PMID: 39236189 PMCID: PMC7616439 DOI: 10.1126/science.adk9611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 07/01/2024] [Accepted: 08/05/2024] [Indexed: 09/07/2024]
Abstract
New memories are integrated into prior knowledge of the world. But what if consecutive memories exert opposing demands on the host brain network? We report that acquiring a robust (food-context) memory constrains the mouse hippocampus within a population activity space of highly correlated spike trains that prevents subsequent computation of a flexible (object-location) memory. This densely correlated firing structure developed over repeated mnemonic experience, gradually coupling neurons in the superficial sublayer of the CA1 stratum pyramidale to whole-population activity. Applying hippocampal theta-driven closed-loop optogenetic suppression to mitigate this neuronal recruitment during (food-context) memory formation relaxed the topological constraint on hippocampal coactivity and restored subsequent flexible (object-location) memory. These findings uncover an organizational principle for the peer-to-peer coactivity structure of the hippocampal cell population to meet memory demands.
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Affiliation(s)
- Giuseppe P Gava
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Laura Lefèvre
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Tabitha Broadbelt
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Stephen B McHugh
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Vítor Lopes-Dos-Santos
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Demi Brizee
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Katja Hartwich
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Hanna Sjoberg
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Pavel V Perestenko
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Robert Toth
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Andrew Sharott
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - David Dupret
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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3
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Mohammadi Z, Denman DJ, Klug A, Lei TC. A fully automatic multichannel neural spike sorting algorithm with spike reduction and positional feature. J Neural Eng 2024; 21:046039. [PMID: 39019065 PMCID: PMC11298775 DOI: 10.1088/1741-2552/ad647d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 05/31/2024] [Accepted: 07/17/2024] [Indexed: 07/19/2024]
Abstract
Objective: The sorting of neural spike data recorded by multichannel and high channel neural probes such as Neuropixels, especially in real-time, remains a significant technical challenge. Most neural spike sorting algorithms focus on sorting neural spikes post-hoc for high sorting accuracy-but reducing the processing delay for fast sorting, potentially even live sorting, is generally not possible with these algorithms.Approach: Here we report our Graph nEtwork Multichannel sorting (GEMsort) algorithm, which is largely based on graph network, to allow rapid neural spike sorting for multiple neural recording channels. This was accomplished by two innovations: In GEMsort, duplicated neural spikes recorded from multiple channels were eliminated from duplicate channels by only selecting the highest amplitude neural spike in any channel for subsequent processing. In addition, the channel from which the representative neural spike was recorded was used as an additional feature to differentiate between neural spikes recorded from different neurons having similar temporal features.Main results: Synthetic and experimentally recorded multichannel neural recordings were used to evaluate the sorting performance of GEMsort. The sorting results of GEMsort were also compared with two other state-of-the-art sorting algorithms (Kilosort and Mountainsort) in sorting time and sorting agreements.Significance: GEMsort allows rapidly sort neural spikes and is highly suitable to be implemented with digital circuitry for high processing speed and channel scalability.
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Affiliation(s)
- Zeinab Mohammadi
- Department of Electrical Engineering, University of Colorado Denver, Denver, CO, United States of America
| | - Daniel J Denman
- Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Achim Klug
- Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Tim C Lei
- Department of Electrical Engineering, University of Colorado Denver, Denver, CO, United States of America
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4
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Vincent JP, Economo MN. Assessing Cross-Contamination in Spike-Sorted Electrophysiology Data. eNeuro 2024; 11:ENEURO.0554-23.2024. [PMID: 39095090 PMCID: PMC11368414 DOI: 10.1523/eneuro.0554-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 07/06/2024] [Accepted: 07/20/2024] [Indexed: 08/04/2024] Open
Abstract
Recent advances in extracellular electrophysiology now facilitate the recording of spikes from hundreds or thousands of neurons simultaneously. This has necessitated both the development of new computational methods for spike sorting and better methods to determine spike-sorting accuracy. One long-standing method of assessing the false discovery rate (FDR) of spike sorting-the rate at which spikes are assigned to the wrong cluster-has been the rate of interspike interval (ISI) violations. Despite their near ubiquitous usage in spike sorting, our understanding of how exactly ISI violations relate to FDR, as well as best practices for using ISI violations as a quality metric, remains limited. Here, we describe an analytical solution that can be used to predict FDR from the ISI violation rate (ISIv). We test this model in silico through Monte Carlo simulation and apply it to publicly available spike-sorted electrophysiology datasets. We find that the relationship between ISIv and FDR is highly nonlinear, with additional dependencies on firing frequency, the correlation in activity between neurons, and contaminant neuron count. Predicted median FDRs in public datasets recorded in mice were found to range from 3.1 to 50.0%. We found that stochasticity in the occurrence of ISI violations as well as uncertainty in cluster-specific parameters make it difficult to predict FDR for single clusters with high confidence but that FDR can be estimated accurately across a population of clusters. Our findings will help the growing community of researchers using extracellular electrophysiology assess spike-sorting accuracy in a principled manner.
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Affiliation(s)
- Jack P Vincent
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215
- Center for Neurophotonics, Boston University, Boston, Massachusetts 02215
| | - Michael N Economo
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215
- Center for Neurophotonics, Boston University, Boston, Massachusetts 02215
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts 02215
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5
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Pachitariu M, Sridhar S, Pennington J, Stringer C. Spike sorting with Kilosort4. Nat Methods 2024; 21:914-921. [PMID: 38589517 PMCID: PMC11093732 DOI: 10.1038/s41592-024-02232-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 03/01/2024] [Indexed: 04/10/2024]
Abstract
Spike sorting is the computational process of extracting the firing times of single neurons from recordings of local electrical fields. This is an important but hard problem in neuroscience, made complicated by the nonstationarity of the recordings and the dense overlap in electrical fields between nearby neurons. To address the spike-sorting problem, we have been openly developing the Kilosort framework. Here we describe the various algorithmic steps introduced in different versions of Kilosort. We also report the development of Kilosort4, a version with substantially improved performance due to clustering algorithms inspired by graph-based approaches. To test the performance of Kilosort, we developed a realistic simulation framework that uses densely sampled electrical fields from real experiments to generate nonstationary spike waveforms and realistic noise. We found that nearly all versions of Kilosort outperformed other algorithms on a variety of simulated conditions and that Kilosort4 performed best in all cases, correctly identifying even neurons with low amplitudes and small spatial extents in high drift conditions.
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Affiliation(s)
| | - Shashwat Sridhar
- HHMI, Ashburn, VA, USA
- Department of Ophthalmology, University Medical Center Göttingen, Göttingen, Germany
| | - Jacob Pennington
- HHMI, Ashburn, VA, USA
- Department of Mathematics, Washington State University, Vancouver, WA, USA
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6
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Lee KH, Denovellis EL, Ly R, Magland J, Soules J, Comrie AE, Gramling DP, Guidera JA, Nevers R, Adenekan P, Brozdowski C, Bray SR, Monroe E, Bak JH, Coulter ME, Sun X, Broyles E, Shin D, Chiang S, Holobetz C, Tritt A, Rübel O, Nguyen T, Yatsenko D, Chu J, Kemere C, Garcia S, Buccino A, Frank LM. Spyglass: a framework for reproducible and shareable neuroscience research. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.25.577295. [PMID: 38328074 PMCID: PMC10849637 DOI: 10.1101/2024.01.25.577295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Scientific progress depends on reliable and reproducible results. Progress can also be accelerated when data are shared and re-analyzed to address new questions. Current approaches to storing and analyzing neural data typically involve bespoke formats and software that make replication, as well as the subsequent reuse of data, difficult if not impossible. To address these challenges, we created Spyglass, an open-source software framework that enables reproducible analyses and sharing of data and both intermediate and final results within and across labs. Spyglass uses the Neurodata Without Borders (NWB) standard and includes pipelines for several core analyses in neuroscience, including spectral filtering, spike sorting, pose tracking, and neural decoding. It can be easily extended to apply both existing and newly developed pipelines to datasets from multiple sources. We demonstrate these features in the context of a cross-laboratory replication by applying advanced state space decoding algorithms to publicly available data. New users can try out Spyglass on a Jupyter Hub hosted by HHMI and 2i2c: https://spyglass.hhmi.2i2c.cloud/.
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Affiliation(s)
- Kyu Hyun Lee
- Department of Physiology, University of California, San Francisco
- Howard Hughes Medical Institute, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
| | - Eric L. Denovellis
- Department of Physiology, University of California, San Francisco
- Howard Hughes Medical Institute, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
| | - Ryan Ly
- Scientific Data Division, Lawrence Berkeley National Laboratory
| | - Jeremy Magland
- Center for Computational Mathematics, Flatiron Institute
| | - Jeff Soules
- Center for Computational Mathematics, Flatiron Institute
| | - Alison E. Comrie
- Department of Physiology, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
| | - Daniel P. Gramling
- Graudate Program in Neural and Behavioral Sciences, University of Tübingen
| | - Jennifer A. Guidera
- Department of Physiology, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
- UCSF-UC Berkeley Graduate Program in Bioengineering, University of California, San Francisco
- Medical Scientist Training Program, University of California, San Francisco
| | - Rhino Nevers
- Department of Physiology, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
| | - Philip Adenekan
- Department of Physiology, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
| | - Chris Brozdowski
- Department of Physiology, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
| | - Samuel R. Bray
- Department of Physiology, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
| | - Emily Monroe
- Department of Physiology, University of California, San Francisco
| | - Ji Hyun Bak
- Department of Physiology, University of California, San Francisco
| | - Michael E. Coulter
- Department of Physiology, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
| | - Xulu Sun
- Department of Physiology, University of California, San Francisco
- Howard Hughes Medical Institute, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
| | - Emrey Broyles
- Department of Physiology, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
| | - Donghoon Shin
- Department of Physiology, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
- UCSF-UC Berkeley Graduate Program in Bioengineering, University of California, San Francisco
| | - Sharon Chiang
- Department of Neurology, University of California, San Francisco
| | | | - Andrew Tritt
- Scientific Data Division, Lawrence Berkeley National Laboratory
| | - Oliver Rübel
- Scientific Data Division, Lawrence Berkeley National Laboratory
| | | | | | - Joshua Chu
- Department of Electrical and Computer Engineering, Rice University
| | - Caleb Kemere
- Department of Electrical and Computer Engineering, Rice University
| | | | | | - Loren M. Frank
- Department of Physiology, University of California, San Francisco
- Howard Hughes Medical Institute, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
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7
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Garcia S, Windolf C, Boussard J, Dichter B, Buccino AP, Yger P. A Modular Implementation to Handle and Benchmark Drift Correction for High-Density Extracellular Recordings. eNeuro 2024; 11:ENEURO.0229-23.2023. [PMID: 38238082 PMCID: PMC10897502 DOI: 10.1523/eneuro.0229-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 12/20/2023] [Accepted: 12/21/2023] [Indexed: 02/28/2024] Open
Abstract
High-density neural devices are now offering the possibility to record from neuronal populations in vivo at unprecedented scale. However, the mechanical drifts often observed in these recordings are currently a major issue for "spike sorting," an essential analysis step to identify the activity of single neurons from extracellular signals. Although several strategies have been proposed to compensate for such drifts, the lack of proper benchmarks makes it hard to assess the quality and effectiveness of motion correction. In this paper, we present a benchmark study to precisely and quantitatively evaluate the performance of several state-of-the-art motion correction algorithms introduced in the literature. Using simulated recordings with induced drifts, we dissect the origins of the errors performed while applying a motion correction algorithm as a preprocessing step in the spike sorting pipeline. We show how important it is to properly estimate the positions of the neurons from extracellular traces in order to correctly estimate the probe motion, compare several interpolation procedures, and highlight what are the current limits for motion correction approaches.
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Affiliation(s)
- Samuel Garcia
- Centre de Recherche en Neuroscience de Lyon, CNRS, Lyon 69675, France
| | | | | | | | - Alessio P Buccino
- CatalystNeuro, Benicia, California 94510
- Allen Institute for Neural Dynamics, Seattle, Washington 98109
| | - Pierre Yger
- Institut de la Vision, Sorbonne Université, INSERM, Paris 75012, France
- Lille Neurosciences & Cognition (lilNCog)-U1172 (INSERM, Lille), Univ Lille, Centre Hospitalier Universitaire de Lille, Lille 59800, France
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8
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Clarke-Williams CJ, Lopes-Dos-Santos V, Lefèvre L, Brizee D, Causse AA, Rothaermel R, Hartwich K, Perestenko PV, Toth R, McNamara CG, Sharott A, Dupret D. Coordinating brain-distributed network activities in memory resistant to extinction. Cell 2024; 187:409-427.e19. [PMID: 38242086 PMCID: PMC7615560 DOI: 10.1016/j.cell.2023.12.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 07/13/2023] [Accepted: 12/13/2023] [Indexed: 01/21/2024]
Abstract
Certain memories resist extinction to continue invigorating maladaptive actions. The robustness of these memories could depend on their widely distributed implementation across populations of neurons in multiple brain regions. However, how dispersed neuronal activities are collectively organized to underpin a persistent memory-guided behavior remains unknown. To investigate this, we simultaneously monitored the prefrontal cortex, nucleus accumbens, amygdala, hippocampus, and ventral tegmental area (VTA) of the mouse brain from initial recall to post-extinction renewal of a memory involving cocaine experience. We uncover a higher-order pattern of short-lived beta-frequency (15-25 Hz) activities that are transiently coordinated across these networks during memory retrieval. The output of a divergent pathway from upstream VTA glutamatergic neurons, paced by a slower (4-Hz) oscillation, actuates this multi-network beta-band coactivation; its closed-loop phase-informed suppression prevents renewal of cocaine-biased behavior. Binding brain-distributed neural activities in this temporally structured manner may constitute an organizational principle of robust memory expression.
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Affiliation(s)
- Charlie J Clarke-Williams
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK.
| | - Vítor Lopes-Dos-Santos
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - Laura Lefèvre
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - Demi Brizee
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - Adrien A Causse
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - Roman Rothaermel
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - Katja Hartwich
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - Pavel V Perestenko
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - Robert Toth
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - Colin G McNamara
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - Andrew Sharott
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - David Dupret
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK.
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9
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Vincent JP, Economo MN. Assessing cross-contamination in spike-sorted electrophysiology data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.21.572882. [PMID: 38187738 PMCID: PMC10769346 DOI: 10.1101/2023.12.21.572882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Recent advances in extracellular electrophysiology now facilitate the recording of spikes from hundreds or thousands of neurons simultaneously. This has necessitated both the development of new computational methods for spike sorting and better methods to determine spike sorting accuracy. One longstanding method of assessing the false discovery rate (FDR) of spike sorting - the rate at which spikes are misassigned to the wrong cluster - has been the rate of inter-spike-interval (ISI) violations. Despite their near ubiquitous usage in spike sorting, our understanding of how exactly ISI violations relate to FDR, as well as best practices for using ISI violations as a quality metric, remain limited. Here, we describe an analytical solution that can be used to predict FDR from ISI violation rate. We test this model in silico through Monte Carlo simulation, and apply it to publicly available spike-sorted electrophysiology datasets. We find that the relationship between ISI violation rate and FDR is highly nonlinear, with additional dependencies on firing rate, the correlation in activity between neurons, and contaminant neuron count. Predicted median FDRs in public datasets were found to range from 3.1% to 50.0%. We find that stochasticity in the occurrence of ISI violations as well as uncertainty in cluster-specific parameters make it difficult to predict FDR for single clusters with high confidence, but that FDR can be estimated accurately across a population of clusters. Our findings will help the growing community of researchers using extracellular electrophysiology assess spike sorting accuracy in a principled manner.
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Affiliation(s)
- Jack P. Vincent
- Department of Biomedical Engineering, Boston University, Boston, MA
- Center for Neurophotonics, Boston University, Boston, MA
| | - Michael N. Economo
- Department of Biomedical Engineering, Boston University, Boston, MA
- Center for Neurophotonics, Boston University, Boston, MA
- Center for Systems Neuroscience, Boston University, Boston, MA
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10
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Yu H, Qi Y, Pan G. NeuSort: an automatic adaptive spike sorting approach with neuromorphic models. J Neural Eng 2023; 20:056006. [PMID: 37659393 DOI: 10.1088/1741-2552/acf61d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 09/01/2023] [Indexed: 09/04/2023]
Abstract
Objective.Spike sorting, a critical step in neural data processing, aims to classify spiking events from single electrode recordings based on different waveforms. This study aims to develop a novel online spike sorter, NeuSort, using neuromorphic models, with the ability to adaptively adjust to changes in neural signals, including waveform deformations and the appearance of new neurons.Approach.NeuSort leverages a neuromorphic model to emulate template-matching processes. This model incorporates plasticity learning mechanisms inspired by biological neural systems, facilitating real-time adjustments to online parameters.Results.Experimental findings demonstrate NeuSort's ability to track neuron activities amidst waveform deformations and identify new neurons in real-time. NeuSort excels in handling non-stationary neural signals, significantly enhancing its applicability for long-term spike sorting tasks. Moreover, its implementation on neuromorphic chips guarantees ultra-low energy consumption during computation.Significance.NeuSort caters to the demand for real-time spike sorting in brain-machine interfaces through a neuromorphic approach. Its unsupervised, automated spike sorting process makes it a plug-and-play solution for online spike sorting.
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Affiliation(s)
- Hang Yu
- State Key Lab of Brain-Machine Intelligence, Hangzhou, People's Republic of China
- College of Computer Science and Technology, Zhejiang University, Hangzhou, People's Republic of China
| | - Yu Qi
- State Key Lab of Brain-Machine Intelligence, Hangzhou, People's Republic of China
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Hangzhou, People's Republic of China
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Gang Pan
- State Key Lab of Brain-Machine Intelligence, Hangzhou, People's Republic of China
- College of Computer Science and Technology, Zhejiang University, Hangzhou, People's Republic of China
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11
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Li S, Tang Z, Yang L, Li M, Shang Z. Application of deep reinforcement learning for spike sorting under multi-class imbalance. Comput Biol Med 2023; 164:107253. [PMID: 37536094 DOI: 10.1016/j.compbiomed.2023.107253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 06/27/2023] [Accepted: 07/07/2023] [Indexed: 08/05/2023]
Abstract
Spike sorting is the basis for analyzing spike firing patterns encoded in high-dimensional information spaces. With the fact that high-density microelectrode arrays record multiple neurons simultaneously, the data collected often suffers from two problems: a few overlapping spikes and different neuronal firing rates, which both belong to the multi-class imbalance problem. Since deep reinforcement learning (DRL) assign targeted attention to categories through reward functions, we propose ImbSorter to implement spike sorting under multi-class imbalance. We describe spike sorting as a Markov sequence decision and construct a dynamic reward function (DRF) to improve the sensitivity of the agent to minor classes based on the inter-class imbalance ratios. The agent is eventually guided by the optimal strategy to classify spikes. We consider the Wave_Clus dataset, which contains overlapping spikes and diverse noise levels, and the macaque dataset, which has a multi-scale imbalance. ImbSorter is compared with classical DRL architectures, traditional machine learning algorithms, and advanced overlapping spike sorting techniques on these two above datasets. ImbSorter obtained improved results on the Macro_F1. The results show ImbSorter has a promising ability to resist overlapping and noise interference. It has high stability and promising performance in processing spikes with different degrees of skewed distribution.
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Affiliation(s)
- Suchen Li
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, 450001, China
| | - Zhuo Tang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, 450001, China
| | - Lifang Yang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, 450001, China
| | - Mengmeng Li
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, 450001, China.
| | - Zhigang Shang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, China; Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, 450001, China.
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12
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Seifert M, Roberts PA, Kafetzis G, Osorio D, Baden T. Birds multiplex spectral and temporal visual information via retinal On- and Off-channels. Nat Commun 2023; 14:5308. [PMID: 37652912 PMCID: PMC10471707 DOI: 10.1038/s41467-023-41032-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 08/18/2023] [Indexed: 09/02/2023] Open
Abstract
In vertebrate vision, early retinal circuits divide incoming visual information into functionally opposite elementary signals: On and Off, transient and sustained, chromatic and achromatic. Together these signals can yield an efficient representation of the scene for transmission to the brain via the optic nerve. However, this long-standing interpretation of retinal function is based on mammals, and it is unclear whether this functional arrangement is common to all vertebrates. Here we show that male poultry chicks use a fundamentally different strategy to communicate information from the eye to the brain. Rather than using functionally opposite pairs of retinal output channels, chicks encode the polarity, timing, and spectral composition of visual stimuli in a highly correlated manner: fast achromatic information is encoded by Off-circuits, and slow chromatic information overwhelmingly by On-circuits. Moreover, most retinal output channels combine On- and Off-circuits to simultaneously encode, or multiplex, both achromatic and chromatic information. Our results from birds conform to evidence from fish, amphibians, and reptiles which retain the full ancestral complement of four spectral types of cone photoreceptors.
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Affiliation(s)
- Marvin Seifert
- School of Life Sciences, University of Sussex, Brighton, UK.
| | - Paul A Roberts
- School of Life Sciences, University of Sussex, Brighton, UK
| | | | - Daniel Osorio
- School of Life Sciences, University of Sussex, Brighton, UK.
| | - Tom Baden
- School of Life Sciences, University of Sussex, Brighton, UK.
- Institute of Ophthalmic Research, University of Tübingen, Tübingen, Germany.
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13
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Letner JG, Patel PR, Hsieh JC, Smith Flores IM, della Valle E, Walker LA, Weiland JD, Chestek CA, Cai D. Post-explant profiling of subcellular-scale carbon fiber intracortical electrodes and surrounding neurons enables modeling of recorded electrophysiology. J Neural Eng 2023; 20:026019. [PMID: 36848679 PMCID: PMC10022369 DOI: 10.1088/1741-2552/acbf78] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 01/12/2023] [Accepted: 02/27/2023] [Indexed: 03/01/2023]
Abstract
Objective.Characterizing the relationship between neuron spiking and the signals that electrodes record is vital to defining the neural circuits driving brain function and informing clinical brain-machine interface design. However, high electrode biocompatibility and precisely localizing neurons around the electrodes are critical to defining this relationship.Approach.Here, we demonstrate consistent localization of the recording site tips of subcellular-scale (6.8µm diameter) carbon fiber electrodes and the positions of surrounding neurons. We implanted male rats with carbon fiber electrode arrays for 6 or 12+ weeks targeting layer V motor cortex. After explanting the arrays, we immunostained the implant site and localized putative recording site tips with subcellular-cellular resolution. We then 3D segmented neuron somata within a 50µm radius from implanted tips to measure neuron positions and health and compare to healthy cortex with symmetric stereotaxic coordinates.Main results.Immunostaining of astrocyte, microglia, and neuron markers confirmed that overall tissue health was indicative of high biocompatibility near the tips. While neurons near implanted carbon fibers were stretched, their number and distribution were similar to hypothetical fibers placed in healthy contralateral brain. Such similar neuron distributions suggest that these minimally invasive electrodes demonstrate the potential to sample naturalistic neural populations. This motivated the prediction of spikes produced by nearby neurons using a simple point source model fit using recorded electrophysiology and the mean positions of the nearest neurons observed in histology. Comparing spike amplitudes suggests that the radius at which single units can be distinguished from others is near the fourth closest neuron (30.7 ± 4.6µm,X-± S) in layer V motor cortex.Significance.Collectively, these data and simulations provide the first direct evidence that neuron placement in the immediate vicinity of the recording site influences how many spike clusters can be reliably identified by spike sorting.
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Affiliation(s)
- Joseph G Letner
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, United States of America
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, United States of America
| | - Paras R Patel
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, United States of America
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, United States of America
| | - Jung-Chien Hsieh
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, United States of America
| | - Israel M Smith Flores
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, United States of America
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, United States of America
| | - Elena della Valle
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, United States of America
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, United States of America
| | - Logan A Walker
- Biophysics Program, University of Michigan, Ann Arbor, MI 48109, United States of America
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, Ann Arbor, MI 48109, United States of America
| | - James D Weiland
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, United States of America
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, United States of America
- Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, Ann Arbor, MI 48105, United States of America
| | - Cynthia A Chestek
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, United States of America
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, United States of America
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, United States of America
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, United States of America
- Robotics Department, University of Michigan, Ann Arbor, MI 48109, United States of America
| | - Dawen Cai
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, United States of America
- Biophysics Program, University of Michigan, Ann Arbor, MI 48109, United States of America
- Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI 48109, United States of America
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14
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Adult-born dentate granule cells promote hippocampal population sparsity. Nat Neurosci 2022; 25:1481-1491. [PMID: 36216999 PMCID: PMC9630129 DOI: 10.1038/s41593-022-01176-5] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 09/01/2022] [Indexed: 01/13/2023]
Abstract
The dentate gyrus (DG) gates neocortical information flow to the hippocampus. Intriguingly, the DG also produces adult-born dentate granule cells (abDGCs) throughout the lifespan, but their contribution to downstream firing dynamics remains unclear. Here, we show that abDGCs promote sparser hippocampal population spiking during mnemonic processing of novel stimuli. By combining triple-(DG-CA3-CA1) ensemble recordings and optogenetic interventions in behaving mice, we show that abDGCs constitute a subset of high-firing-rate neurons with enhanced activity responses to novelty and strong modulation by theta oscillations. Selectively activating abDGCs in their 4-7-week post-birth period increases sparsity of hippocampal population patterns, whereas suppressing abDGCs reduces this sparsity, increases principal cell firing rates and impairs novel object recognition with reduced dimensionality of the network firing structure, without affecting single-neuron spatial representations. We propose that adult-born granule cells transiently support sparser hippocampal population activity structure for higher-dimensional responses relevant to effective mnemonic information processing.
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15
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Machado TA, Kauvar IV, Deisseroth K. Multiregion neuronal activity: the forest and the trees. Nat Rev Neurosci 2022; 23:683-704. [PMID: 36192596 PMCID: PMC10327445 DOI: 10.1038/s41583-022-00634-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/25/2022] [Indexed: 12/12/2022]
Abstract
The past decade has witnessed remarkable advances in the simultaneous measurement of neuronal activity across many brain regions, enabling fundamentally new explorations of the brain-spanning cellular dynamics that underlie sensation, cognition and action. These recently developed multiregion recording techniques have provided many experimental opportunities, but thoughtful consideration of methodological trade-offs is necessary, especially regarding field of view, temporal acquisition rate and ability to guarantee cellular resolution. When applied in concert with modern optogenetic and computational tools, multiregion recording has already made possible fundamental biological discoveries - in part via the unprecedented ability to perform unbiased neural activity screens for principles of brain function, spanning dozens of brain areas and from local to global scales.
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Affiliation(s)
- Timothy A Machado
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Isaac V Kauvar
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Karl Deisseroth
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
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16
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Benisty H, Song A, Mishne G, Charles AS. Review of data processing of functional optical microscopy for neuroscience. NEUROPHOTONICS 2022; 9:041402. [PMID: 35937186 PMCID: PMC9351186 DOI: 10.1117/1.nph.9.4.041402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 07/15/2022] [Indexed: 05/04/2023]
Abstract
Functional optical imaging in neuroscience is rapidly growing with the development of optical systems and fluorescence indicators. To realize the potential of these massive spatiotemporal datasets for relating neuronal activity to behavior and stimuli and uncovering local circuits in the brain, accurate automated processing is increasingly essential. We cover recent computational developments in the full data processing pipeline of functional optical microscopy for neuroscience data and discuss ongoing and emerging challenges.
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Affiliation(s)
- Hadas Benisty
- Yale Neuroscience, New Haven, Connecticut, United States
| | - Alexander Song
- Max Planck Institute for Intelligent Systems, Stuttgart, Germany
| | - Gal Mishne
- UC San Diego, Halıcığlu Data Science Institute, Department of Electrical and Computer Engineering and the Neurosciences Graduate Program, La Jolla, California, United States
| | - Adam S. Charles
- Johns Hopkins University, Kavli Neuroscience Discovery Institute, Center for Imaging Science, Department of Biomedical Engineering, Department of Neuroscience, and Mathematical Institute for Data Science, Baltimore, Maryland, United States
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17
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How Do Spike Collisions Affect Spike Sorting Performance? eNeuro 2022; 9:ENEURO.0105-22.2022. [PMID: 36171060 PMCID: PMC9532018 DOI: 10.1523/eneuro.0105-22.2022] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/15/2022] [Accepted: 06/23/2022] [Indexed: 12/15/2022] Open
Abstract
Recently, a new generation of devices have been developed to record neural activity simultaneously from hundreds of electrodes with a very high spatial density, both for in vitro and in vivo applications. While these advances enable to record from many more cells, they also challenge the already complicated process of spike sorting (i.e., extracting isolated single-neuron activity from extracellular signals). In this work, we used synthetic ground-truth recordings with controlled levels of correlations among neurons to quantitatively benchmark the performance of state-of-the-art spike sorters focusing specifically on spike collisions. Our results show that while modern template-matching-based algorithms are more accurate than density-based approaches, all methods, to some extent, failed to detect synchronous spike events of neurons with similar extracellular signals. Interestingly, the performance of the sorters is not largely affected by the spiking activity in the recordings, with respect to average firing rates and spike-train correlation levels. Since the performances of all modern spike sorting algorithms can be affected as function of the activity of the recorded neurons, scientific claims on correlations and synchrony should be carefully assessed based on the analysis provided in this paper.
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18
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A robust spike sorting method based on the joint optimization of linear discrimination analysis and density peaks. Sci Rep 2022; 12:15504. [PMID: 36109581 PMCID: PMC9477889 DOI: 10.1038/s41598-022-19771-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 09/05/2022] [Indexed: 11/08/2022] Open
Abstract
Spike sorting is a fundamental step in extracting single-unit activity from neural ensemble recordings, which play an important role in basic neuroscience and neurotechnologies. A few algorithms have been applied in spike sorting. However, when noise level or waveform similarity becomes relatively high, their robustness still faces a big challenge. In this study, we propose a spike sorting method combining Linear Discriminant Analysis (LDA) and Density Peaks (DP) for feature extraction and clustering. Relying on the joint optimization of LDA and DP: DP provides more accurate classification labels for LDA, LDA extracts more discriminative features to cluster for DP, and the algorithm achieves high performance after iteration. We first compared the proposed LDA-DP algorithm with several algorithms on one publicly available simulated dataset and one real rodent neural dataset with different noise levels. We further demonstrated the performance of the LDA-DP method on a real neural dataset from non-human primates with more complex distribution characteristics. The results show that our LDA-DP algorithm extracts a more discriminative feature subspace and achieves better cluster quality than previously established methods in both simulated and real data. Especially in the neural recordings with high noise levels or waveform similarity, the LDA-DP still yields a robust performance with automatic detection of the number of clusters. The proposed LDA-DP algorithm achieved high sorting accuracy and robustness to noise, which offers a promising tool for spike sorting and facilitates the following analysis of neural population activity.
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19
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Abe T, Kinsella I, Saxena S, Buchanan EK, Couto J, Briggs J, Kitt SL, Glassman R, Zhou J, Paninski L, Cunningham JP. Neuroscience Cloud Analysis As a Service: An open-source platform for scalable, reproducible data analysis. Neuron 2022; 110:2771-2789.e7. [PMID: 35870448 PMCID: PMC9464703 DOI: 10.1016/j.neuron.2022.06.018] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 05/06/2022] [Accepted: 06/22/2022] [Indexed: 10/17/2022]
Abstract
A key aspect of neuroscience research is the development of powerful, general-purpose data analyses that process large datasets. Unfortunately, modern data analyses have a hidden dependence upon complex computing infrastructure (e.g., software and hardware), which acts as an unaddressed deterrent to analysis users. Although existing analyses are increasingly shared as open-source software, the infrastructure and knowledge needed to deploy these analyses efficiently still pose significant barriers to use. In this work, we develop Neuroscience Cloud Analysis As a Service (NeuroCAAS): a fully automated open-source analysis platform offering automatic infrastructure reproducibility for any data analysis. We show how NeuroCAAS supports the design of simpler, more powerful data analyses and that many popular data analysis tools offered through NeuroCAAS outperform counterparts on typical infrastructure. Pairing rigorous infrastructure management with cloud resources, NeuroCAAS dramatically accelerates the dissemination and use of new data analyses for neuroscientific discovery.
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Affiliation(s)
- Taiga Abe
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University Medical Center, Columbia University, New York, NY 10027, USA
| | - Ian Kinsella
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Department of Statistics, Columbia University, New York, NY 10027, USA
| | - Shreya Saxena
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Department of Statistics, Columbia University, New York, NY 10027, USA; Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32607, USA
| | - E Kelly Buchanan
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University Medical Center, Columbia University, New York, NY 10027, USA
| | - Joao Couto
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - John Briggs
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA
| | - Sian Lee Kitt
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Ryan Glassman
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - John Zhou
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Liam Paninski
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University Medical Center, Columbia University, New York, NY 10027, USA; Department of Statistics, Columbia University, New York, NY 10027, USA
| | - John P Cunningham
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Department of Statistics, Columbia University, New York, NY 10027, USA.
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20
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Naughton JR, Varela JA, Connolly TJ, Shepard S, Dodge TE, Kempa K, Burns MJ, Christianson JP, Naughton MJ. Suppression of crosstalk in multielectrode arrays with local shielding. FRONTIERS IN NANOTECHNOLOGY 2022; 4:948337. [PMID: 39323942 PMCID: PMC11423955 DOI: 10.3389/fnano.2022.948337] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2024] Open
Abstract
Electrical crosstalk can constrain the performance of multielectrode arrays in electro- and neurophysiology, in terms of both stimulation and recording. This is especially so at high electrode density, desirable for spatiotemporal mapping of bioelectrical signals from multiple cells. Channel interference due to crosstalk is currently only partially addressed, via continuous interleaved sampling or post-data acquisition spike sorting. Here, we show that a locally-shielded electrode architecture significantly suppresses crosstalk, and enables multi-site recording at high electrode density without the need for spike sorting. Arrays of shielded electrodes, prepared by micro- and nanofabrication techniques in a vertically-oriented coaxial geometry, demonstrate at least a 400 times improvement in spatial density over the unshielded case.
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Affiliation(s)
- J. R. Naughton
- Department of Physics, Boston College, Chestnut Hill, MA, United States
| | - J. A. Varela
- Department of Psychology and Neuroscience, Boston College, Chestnut Hill, MA, United States
| | - T. J. Connolly
- Department of Biology, Boston College, Chestnut Hill, MA, United States
| | - S. Shepard
- Integrated Sciences Nanofabrication Clean Room Facility, Boston College, Chestnut Hill, MA, United States
| | - T. E. Dodge
- Department of Physics, Boston College, Chestnut Hill, MA, United States
| | - K. Kempa
- Department of Physics, Boston College, Chestnut Hill, MA, United States
| | - M. J. Burns
- Department of Physics, Boston College, Chestnut Hill, MA, United States
| | - J. P. Christianson
- Department of Psychology and Neuroscience, Boston College, Chestnut Hill, MA, United States
| | - M. J. Naughton
- Department of Physics, Boston College, Chestnut Hill, MA, United States
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21
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Radmanesh M, Rezaei AA, Jalili M, Hashemi A, Goudarzi MM. Online spike sorting via deep contractive autoencoder. Neural Netw 2022; 155:39-49. [DOI: 10.1016/j.neunet.2022.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 07/03/2022] [Accepted: 08/01/2022] [Indexed: 11/16/2022]
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22
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Király B, Hangya B. Navigating the Statistical Minefield of Model Selection and Clustering in Neuroscience. eNeuro 2022; 9:ENEURO.0066-22.2022. [PMID: 35835556 PMCID: PMC9282170 DOI: 10.1523/eneuro.0066-22.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 06/16/2022] [Accepted: 06/22/2022] [Indexed: 11/21/2022] Open
Abstract
Model selection is often implicit: when performing an ANOVA, one assumes that the normal distribution is a good model of the data; fitting a tuning curve implies that an additive and a multiplicative scaler describes the behavior of the neuron; even calculating an average implicitly assumes that the data were sampled from a distribution that has a finite first statistical moment: the mean. Model selection may be explicit, when the aim is to test whether one model provides a better description of the data than a competing one. As a special case, clustering algorithms identify groups with similar properties within the data. They are widely used from spike sorting to cell type identification to gene expression analysis. We discuss model selection and clustering techniques from a statistician's point of view, revealing the assumptions behind, and the logic that governs the various approaches. We also showcase important neuroscience applications and provide suggestions how neuroscientists could put model selection algorithms to best use as well as what mistakes should be avoided.
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Affiliation(s)
- Bálint Király
- Lendület Laboratory of Systems Neuroscience, Institute of Experimental Medicine, H-1083, Budapest, Hungary
- Department of Biological Physics, Eötvös Loránd University, H-1083, Budapest, Hungary
| | - Balázs Hangya
- Lendület Laboratory of Systems Neuroscience, Institute of Experimental Medicine, H-1083, Budapest, Hungary
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23
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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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24
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Chung JE, Sellers KK, Leonard MK, Gwilliams L, Xu D, Dougherty ME, Kharazia V, Metzger SL, Welkenhuysen M, Dutta B, Chang EF. High-density single-unit human cortical recordings using the Neuropixels probe. Neuron 2022; 110:2409-2421.e3. [PMID: 35679860 DOI: 10.1016/j.neuron.2022.05.007] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 04/10/2022] [Accepted: 05/10/2022] [Indexed: 10/18/2022]
Abstract
The action potential is a fundamental unit of neural computation. Even though significant advances have been made in recording large numbers of individual neurons in animal models, translation of these methodologies to humans has been limited because of clinical constraints and electrode reliability. Here, we present a reliable method for intraoperative recording of dozens of neurons in humans using the Neuropixels probe, yielding up to ∼100 simultaneously recorded single units. Most single units were active within 1 min of reaching target depth. The motion of the electrode array had a strong inverse correlation with yield, identifying a major challenge and opportunity to further increase the probe utility. Cell pairs active close in time were spatially closer in most recordings, demonstrating the power to resolve complex cortical dynamics. Altogether, this approach provides access to population single-unit activity across the depth of human neocortex at scales previously only accessible in animal models.
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Affiliation(s)
- Jason E Chung
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Kristin K Sellers
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Matthew K Leonard
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Laura Gwilliams
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Duo Xu
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Maximilian E Dougherty
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Viktor Kharazia
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Sean L Metzger
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA; University of California Berkeley, University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA 94720, USA
| | | | | | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA.
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25
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Gurel NZ, Sudarshan KB, Tam S, Ly D, Armour JA, Kember G, Ajijola OA. Studying Cardiac Neural Network Dynamics: Challenges and Opportunities for Scientific Computing. Front Physiol 2022; 13:835761. [PMID: 35574437 PMCID: PMC9099376 DOI: 10.3389/fphys.2022.835761] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 04/06/2022] [Indexed: 11/13/2022] Open
Abstract
Neural control of the heart involves continuous modulation of cardiac mechanical and electrical activity to meet the organism's demand for blood flow. The closed-loop control scheme consists of interconnected neural networks with central and peripheral components working cooperatively with each other. These components have evolved to cooperate control of various aspects of cardiac function, which produce measurable "functional" outputs such as heart rate and blood pressure. In this review, we will outline fundamental studies probing the cardiac neural control hierarchy. We will discuss how computational methods can guide improved experimental design and be used to probe how information is processed while closed-loop control is operational. These experimental designs generate large cardio-neural datasets that require sophisticated strategies for signal processing and time series analysis, while presenting the usual large-scale computational challenges surrounding data sharing and reproducibility. These challenges provide unique opportunities for the development and validation of novel techniques to enhance understanding of mechanisms of cardiac pathologies required for clinical implementation.
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Affiliation(s)
- Nil Z. Gurel
- UCLA Neurocardiology Research Program of Excellence, Los Angeles, CA, United States
- UCLA Cardiac Arrhythmia Center, UCLA Health System, Los Angeles, CA, United States
| | - Koustubh B. Sudarshan
- Department of Engineering Mathematics and Internetworking, Dalhousie University, Halifax, NS, Canada
| | - Sharon Tam
- UCLA Department of Bioengineering, Los Angeles, CA, United States
| | - Diana Ly
- UCLA Department of Bioengineering, Los Angeles, CA, United States
| | - J. Andrew Armour
- UCLA Neurocardiology Research Program of Excellence, Los Angeles, CA, United States
- UCLA Cardiac Arrhythmia Center, UCLA Health System, Los Angeles, CA, United States
| | - Guy Kember
- Department of Engineering Mathematics and Internetworking, Dalhousie University, Halifax, NS, Canada
| | - Olujimi A. Ajijola
- UCLA Neurocardiology Research Program of Excellence, Los Angeles, CA, United States
- UCLA Cardiac Arrhythmia Center, UCLA Health System, Los Angeles, CA, United States
- Molecular, Cellular and Integrative Physiology Program, UCLA, Los Angeles, CA, United States
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26
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Guardamagna M, Eichler R, Pedrosa R, Aarts AAA, Meyer AF, Battaglia F. The Hybrid Drive: a chronic implant device combining tetrode arrays with silicon probes for layer-resolved ensemble electrophysiology in freely moving mice. J Neural Eng 2022; 19. [PMID: 35421850 DOI: 10.1088/1741-2552/ac6771] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 04/10/2022] [Indexed: 10/18/2022]
Abstract
Objective. Understanding the function of brain cortices requires simultaneous investigation at multiple spatial and temporal scales and to link neural activity to an animal's behavior. A major challenge is to measure within- and across-layer information in actively behaving animals, in particular in mice that have become a major species in neuroscience due to an extensive genetic toolkit. Here we describe the Hybrid Drive, a new chronic implant for mice that combines tetrode arrays to record within-layer information with silicon probes to simultaneously measure across-layer information.Approach. The design of our device combines up to 14 tetrodes and 2 silicon probes, that can be arranged in custom arrays to generate unique areas-specific (and multi-area) layouts.Main Results. We show that large numbers of neurons and layer-resolved local field potentials can be recorded from the same brain region across weeks without loss in electrophysiological signal quality. The drive's lightweight structure (~3.5 g) leaves animal behavior largely unchanged, compared to other tetrode drives, during a variety of experimental paradigms. We demonstrate how the data collected with the Hybrid Drive allow state-of-the-art analysis in a series of experiments linking the spiking activity of CA1 pyramidal layer neurons to the oscillatory activity across hippocampal layers.Significance. Our new device fits a gap in the existing technology and increases the range and precision of questions that can be addressed about neural computations in freely behaving mice.
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Affiliation(s)
| | - Ronny Eichler
- Radboud University, Heyendaalseweg 135, Nijmegen, 6500 HC, NETHERLANDS
| | - Rafael Pedrosa
- Radboud University, Heyendaalseweg 135, Nijmegen, 6500 HC, NETHERLANDS
| | - Arno A A Aarts
- ATLAS Neuroengineering, Kapeldreef 75, Leuven, B-3000, BELGIUM
| | - Arne F Meyer
- Radboud University, Heyendaalseweg 135, Nijmegen, 6500 HC, NETHERLANDS
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McCready FP, Gordillo-Sampedro S, Pradeepan K, Martinez-Trujillo J, Ellis J. Multielectrode Arrays for Functional Phenotyping of Neurons from Induced Pluripotent Stem Cell Models of Neurodevelopmental Disorders. BIOLOGY 2022; 11:316. [PMID: 35205182 PMCID: PMC8868577 DOI: 10.3390/biology11020316] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 02/11/2022] [Accepted: 02/14/2022] [Indexed: 12/11/2022]
Abstract
In vitro multielectrode array (MEA) systems are increasingly used as higher-throughput platforms for functional phenotyping studies of neurons in induced pluripotent stem cell (iPSC) disease models. While MEA systems generate large amounts of spatiotemporal activity data from networks of iPSC-derived neurons, the downstream analysis and interpretation of such high-dimensional data often pose a significant challenge to researchers. In this review, we examine how MEA technology is currently deployed in iPSC modeling studies of neurodevelopmental disorders. We first highlight the strengths of in vitro MEA technology by reviewing the history of its development and the original scientific questions MEAs were intended to answer. Methods of generating patient iPSC-derived neurons and astrocytes for MEA co-cultures are summarized. We then discuss challenges associated with MEA data analysis in a disease modeling context, and present novel computational methods used to better interpret network phenotyping data. We end by suggesting best practices for presenting MEA data in research publications, and propose that the creation of a public MEA data repository to enable collaborative data sharing would be of great benefit to the iPSC disease modeling community.
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Affiliation(s)
- Fraser P. McCready
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; (F.P.M.); (S.G.-S.)
- Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Sara Gordillo-Sampedro
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; (F.P.M.); (S.G.-S.)
- Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Kartik Pradeepan
- Department of Physiology and Pharmacology, Department of Psychiatry, Robarts Research and Brain and Mind Institutes, Schulich School of Medicine and Dentistry, Western University, London, ON N6A 5B7, Canada; (K.P.); (J.M.-T.)
| | - Julio Martinez-Trujillo
- Department of Physiology and Pharmacology, Department of Psychiatry, Robarts Research and Brain and Mind Institutes, Schulich School of Medicine and Dentistry, Western University, London, ON N6A 5B7, Canada; (K.P.); (J.M.-T.)
| | - James Ellis
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; (F.P.M.); (S.G.-S.)
- Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
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28
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Urai AE, Doiron B, Leifer AM, Churchland AK. Large-scale neural recordings call for new insights to link brain and behavior. Nat Neurosci 2022; 25:11-19. [PMID: 34980926 DOI: 10.1038/s41593-021-00980-9] [Citation(s) in RCA: 87] [Impact Index Per Article: 43.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 11/08/2021] [Indexed: 12/17/2022]
Abstract
Neuroscientists today can measure activity from more neurons than ever before, and are facing the challenge of connecting these brain-wide neural recordings to computation and behavior. In the present review, we first describe emerging tools and technologies being used to probe large-scale brain activity and new approaches to characterize behavior in the context of such measurements. We next highlight insights obtained from large-scale neural recordings in diverse model systems, and argue that some of these pose a challenge to traditional theoretical frameworks. Finally, we elaborate on existing modeling frameworks to interpret these data, and argue that the interpretation of brain-wide neural recordings calls for new theoretical approaches that may depend on the desired level of understanding. These advances in both neural recordings and theory development will pave the way for critical advances in our understanding of the brain.
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Affiliation(s)
- Anne E Urai
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.,Cognitive Psychology Unit, Leiden University, Leiden, The Netherlands
| | | | | | - Anne K Churchland
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA. .,University of California Los Angeles, Los Angeles, CA, USA.
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29
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Hall NJ, Herzfeld DJ, Lisberger SG. Evaluation and resolution of many challenges of neural spike sorting: a new sorter. J Neurophysiol 2021; 126:2065-2090. [PMID: 34788137 PMCID: PMC8715051 DOI: 10.1152/jn.00047.2021] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 11/16/2021] [Accepted: 11/16/2021] [Indexed: 11/22/2022] Open
Abstract
We evaluate existing spike sorters and present a new one that resolves many sorting challenges. The new sorter, called "full binary pursuit" or FBP, comprises multiple steps. First, it thresholds and clusters to identify the waveforms of all unique neurons in the recording. Second, it uses greedy binary pursuit to optimally assign all the spike events in the original voltages to separable neurons. Third, it resolves spike events that are described more accurately as the superposition of spikes from two other neurons. Fourth, it resolves situations where the recorded neurons drift in amplitude or across electrode contacts during a long recording session. Comparison with other sorters on ground-truth data sets reveals many of the failure modes of spike sorting. We examine overall spike sorter performance in ground-truth data sets and suggest postsorting analyses that can improve the veracity of neural analyses by minimizing the intrusion of failure modes into analysis and interpretation of neural data. Our analysis reveals the tradeoff between the number of channels a sorter can process, speed of sorting, and some of the failure modes of spike sorting. FBP works best on data from 32 channels or fewer. It trades speed and number of channels for avoidance of specific failure modes that would be challenges for some use cases. We conclude that all spike sorting algorithms studied have advantages and shortcomings, and the appropriate use of a spike sorter requires a detailed assessment of the data being sorted and the experimental goals for analyses.NEW & NOTEWORTHY Electrophysiological recordings from multiple neurons across multiple channels pose great difficulty for spike sorting of single neurons. We propose methods that improve the ability to determine the number of individual neurons present in a recording and resolve near-simultaneous spike events from single neurons. We use ground-truth data sets to demonstrate the pros and cons of several current sorting algorithms and suggest strategies for determining the accuracy of spike sorting when ground-truth data are not available.
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Affiliation(s)
- Nathan J Hall
- Department of Neurobiology, Duke University School of Medicine, Durham, North Carolina
| | - David J Herzfeld
- Department of Neurobiology, Duke University School of Medicine, Durham, North Carolina
| | - Stephen G Lisberger
- Department of Neurobiology, Duke University School of Medicine, Durham, North Carolina
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30
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Strohl JJ, Gallagher JT, Gómez PN, Glynn JM, Huerta PT. Framework for automated sorting of neural spikes from Neuralynx-acquired tetrode recordings in freely-moving mice. Bioelectron Med 2021; 7:17. [PMID: 34809706 PMCID: PMC8609830 DOI: 10.1186/s42234-021-00079-3] [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: 08/23/2021] [Accepted: 10/21/2021] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Extracellular recording represents a crucial electrophysiological technique in neuroscience for studying the activity of single neurons and neuronal populations. The electrodes capture voltage traces that, with the help of analytical tools, reveal action potentials ('spikes') as well as local field potentials. The process of spike sorting is used for the extraction of action potentials generated by individual neurons. Until recently, spike sorting was performed with manual techniques, which are laborious and unreliable due to inherent operator bias. As neuroscientists add multiple electrodes to their probes, the high-density devices can record hundreds to thousands of neurons simultaneously, making the manual spike sorting process increasingly difficult. The advent of automated spike sorting software has offered a compelling solution to this issue and, in this study, we present a simple-to-execute framework for running an automated spike sorter. METHODS Tetrode recordings of freely-moving mice are obtained from the CA1 region of the hippocampus as they navigate a linear track. Tetrode recordings are also acquired from the prelimbic cortex, a region of the medial prefrontal cortex, while the mice are tested in a T maze. All animals are implanted with custom-designed, 3D-printed microdrives that carry 16 electrodes, which are bundled in a 4-tetrode geometry. RESULTS We provide an overview of a framework for analyzing single-unit data in which we have concatenated the acquisition system (Cheetah, Neuralynx) with analytical software (MATLAB) and an automated spike sorting pipeline (MountainSort). We give precise instructions on how to implement the different steps of the framework, as well as explanations of our design logic. We validate this framework by comparing manually-sorted spikes against automatically-sorted spikes, using neural recordings of the hippocampus and prelimbic cortex in freely-moving mice. CONCLUSIONS We have efficiently integrated the MountainSort spike sorter with Neuralynx-acquired neural recordings. Our framework is easy to implement and provides a high-throughput solution. We predict that within the broad field of bioelectronic medicine, those teams that incorporate high-density neural recording devices to their armamentarium might find our framework quite valuable as they expand their analytical footprint.
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Affiliation(s)
- Joshua J. Strohl
- Laboratory of Immune & Neural Networks, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York, USA, 350 Community Drive, Manhasset, NY 11030 USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, 500 Hofstra Blvd, Hempstead, NY 11549 USA
| | - Joseph T. Gallagher
- Laboratory of Immune & Neural Networks, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York, USA, 350 Community Drive, Manhasset, NY 11030 USA
| | - Pedro N. Gómez
- Laboratory of Immune & Neural Networks, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York, USA, 350 Community Drive, Manhasset, NY 11030 USA
| | - Joshua M. Glynn
- Laboratory of Immune & Neural Networks, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York, USA, 350 Community Drive, Manhasset, NY 11030 USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, 500 Hofstra Blvd, Hempstead, NY 11549 USA
| | - Patricio T. Huerta
- Laboratory of Immune & Neural Networks, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York, USA, 350 Community Drive, Manhasset, NY 11030 USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, 500 Hofstra Blvd, Hempstead, NY 11549 USA
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31
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Hermiz J, Joseph E, Hyun Lee K, Baldacci IA, Chung JE, Frank LM, Bouchard KE, Denes P. The impact of reducing signal acquisition specifications on neuronal spike sorting. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5914-5918. [PMID: 34892465 DOI: 10.1109/embc46164.2021.9630669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Measuring electrical potentials in the extracellular space of the brain is a popular technique because it can detect action potentials from putative individual neurons. Electrophysiology is undergoing a transformation where the number of recording channels, and thus number of neurons detected, is growing at a dramatic rate. This rapid scaling is paving the way for both new discoveries and commercial applications; however, as the number of channels increases there will be an increasing need to make these systems more power efficient. One area ripe for optimization are the signal acquisition specifications needed to detect and sort action potentials (i.e., "spikes") to putative single neuron sources. In this work, we take existing recordings collected using Intan hardware and modify them in a way that corresponds to reduced recording performance. The accuracy of these degraded recordings to spike sort using MountainSort4 is evaluated by comparing against expert labels. We show that despite reducing signal specifications by a factor of 2 or more, spike sorting accuracy does not change substantially. Specifically, reducing both sample rate and bit depth from 30 kHz and 16 bits to 12 kHz and 12 bits resulted in a 3% drop in spike sorting accuracy. Our results suggest that current neural acquisition systems are over-specified. These results may inform the design of next generation neural acquisition systems enabling higher channel count systems.
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Lecoq J, Oliver M, Siegle JH, Orlova N, Ledochowitsch P, Koch C. Removing independent noise in systems neuroscience data using DeepInterpolation. Nat Methods 2021; 18:1401-1408. [PMID: 34650233 PMCID: PMC8833814 DOI: 10.1038/s41592-021-01285-2] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 08/30/2021] [Indexed: 11/09/2022]
Abstract
Progress in many scientific disciplines is hindered by the presence of independent noise. Technologies for measuring neural activity (calcium imaging, extracellular electrophysiology and functional magnetic resonance imaging (fMRI)) operate in domains in which independent noise (shot noise and/or thermal noise) can overwhelm physiological signals. Here, we introduce DeepInterpolation, a general-purpose denoising algorithm that trains a spatiotemporal nonlinear interpolation model using only raw noisy samples. Applying DeepInterpolation to two-photon calcium imaging data yielded up to six times more neuronal segments than those computed from raw data with a 15-fold increase in the single-pixel signal-to-noise ratio (SNR), uncovering single-trial network dynamics that were previously obscured by noise. Extracellular electrophysiology recordings processed with DeepInterpolation yielded 25% more high-quality spiking units than those computed from raw data, while DeepInterpolation produced a 1.6-fold increase in the SNR of individual voxels in fMRI datasets. Denoising was attained without sacrificing spatial or temporal resolution and without access to ground truth training data. We anticipate that DeepInterpolation will provide similar benefits in other domains in which independent noise contaminates spatiotemporally structured datasets.
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Affiliation(s)
- Jérôme Lecoq
- MindScope Program, Allen Institute, Seattle, WA, USA.
| | | | | | | | | | - Christof Koch
- MindScope Program, Allen Institute, Seattle, WA, USA
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Lee KH, Ni YL, Colonell J, Karsh B, Putzeys J, Pachitariu M, Harris TD, Meister M. Electrode pooling can boost the yield of extracellular recordings with switchable silicon probes. Nat Commun 2021; 12:5245. [PMID: 34475396 PMCID: PMC8413349 DOI: 10.1038/s41467-021-25443-4] [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: 12/21/2020] [Accepted: 07/28/2021] [Indexed: 11/09/2022] Open
Abstract
State-of-the-art silicon probes for electrical recording from neurons have thousands of recording sites. However, due to volume limitations there are typically many fewer wires carrying signals off the probe, which restricts the number of channels that can be recorded simultaneously. To overcome this fundamental constraint, we propose a method called electrode pooling that uses a single wire to serve many recording sites through a set of controllable switches. Here we present the framework behind this method and an experimental strategy to support it. We then demonstrate its feasibility by implementing electrode pooling on the Neuropixels 1.0 electrode array and characterizing its effect on signal and noise. Finally we use simulations to explore the conditions under which electrode pooling saves wires without compromising the content of the recordings. We make recommendations on the design of future devices to take advantage of this strategy.
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Affiliation(s)
- Kyu Hyun Lee
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA, USA
| | - Yu-Li Ni
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA, USA
| | | | - Bill Karsh
- HHMI Janelia Research Campus, Ashburn, VA, USA
| | | | | | | | - Markus Meister
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA, USA.
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Hurwitz C, Kudryashova N, Onken A, Hennig MH. Building population models for large-scale neural recordings: Opportunities and pitfalls. Curr Opin Neurobiol 2021; 70:64-73. [PMID: 34411907 DOI: 10.1016/j.conb.2021.07.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 06/11/2021] [Accepted: 07/14/2021] [Indexed: 11/15/2022]
Abstract
Modern recording technologies now enable simultaneous recording from large numbers of neurons. This has driven the development of new statistical models for analyzing and interpreting neural population activity. Here, we provide a broad overview of recent developments in this area. We compare and contrast different approaches, highlight strengths and limitations, and discuss biological and mechanistic insights that these methods provide.
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Affiliation(s)
- Cole Hurwitz
- University of Edinburgh, Institute for Adaptive and Neural Computation Edinburgh, EH8 9AB, United Kingdom
| | - Nina Kudryashova
- University of Edinburgh, Institute for Adaptive and Neural Computation Edinburgh, EH8 9AB, United Kingdom
| | - Arno Onken
- University of Edinburgh, Institute for Adaptive and Neural Computation Edinburgh, EH8 9AB, United Kingdom
| | - Matthias H Hennig
- University of Edinburgh, Institute for Adaptive and Neural Computation Edinburgh, EH8 9AB, United Kingdom.
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35
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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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36
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Chen K, Jiang Y, Wu Z, Zheng N, Wang H, Hong H. HTsort: Enabling Fast and Accurate Spike Sorting on Multi-Electrode Arrays. Front Comput Neurosci 2021; 15:657151. [PMID: 34234663 PMCID: PMC8255361 DOI: 10.3389/fncom.2021.657151] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 05/28/2021] [Indexed: 11/13/2022] Open
Abstract
Spike sorting is used to classify the spikes (action potentials acquired by physiological electrodes), aiming to identify their respective firing units. Now it has been developed to classify the spikes recorded by multi-electrode arrays (MEAs), with the improvement of micro-electrode technology. However, how to improve classification accuracy and maintain low time complexity simultaneously becomes a difficulty. A fast and accurate spike sorting approach named HTsort is proposed for high-density multi-electrode arrays in this paper. Several improvements have been introduced to the traditional pipeline that is composed of threshold detection and clustering method. First, the divide-and-conquer method is employed to utilize electrode spatial information to achieve pre-clustering. Second, the clustering method HDBSCAN (hierarchical density-based spatial clustering of applications with noise) is used to classify spikes and detect overlapping events (multiple spikes firing simultaneously). Third, the template merging method is used to merge redundant exported templates according to the template similarity and the spatial distribution of electrodes. Finally, the template matching method is used to resolve overlapping events. Our approach is validated on simulation data constructed by ourselves and publicly available data and compared to other state-of-the-art spike sorters. We found that the proposed HTsort has a more favorable trade-off between accuracy and time consumption. Compared with MountainSort and SpykingCircus, the time consumption is reduced by at least 40% when the number of electrodes is 64 and below. Compared with HerdingSpikes, the classification accuracy can typically improve by more than 10%. Meanwhile, HTsort exhibits stronger robustness against background noise than other sorters. Our more sophisticated spike sorter would facilitate neurophysiologists to complete spike sorting more quickly and accurately.
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Affiliation(s)
- Keming Chen
- Key Laboratory of Radio Frequency Circuit and System, Hangzhou Dianzi University, Hangzhou, China
| | - Yangtao Jiang
- Key Laboratory of Radio Frequency Circuit and System, Hangzhou Dianzi University, Hangzhou, China
| | - Zhanxiong Wu
- Key Laboratory of Radio Frequency Circuit and System, Hangzhou Dianzi University, Hangzhou, China
| | - Nenggan Zheng
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
| | - Haochuan Wang
- Key Laboratory of Radio Frequency Circuit and System, Hangzhou Dianzi University, Hangzhou, China
| | - Hui Hong
- Key Laboratory of Radio Frequency Circuit and System, Hangzhou Dianzi University, Hangzhou, China
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Rokai J, Rácz M, Fiáth R, Ulbert I, Márton G. ELVISort: encoding latent variables for instant sorting, an artificial intelligence-based end-to-end solution. J Neural Eng 2021; 18. [PMID: 33823497 DOI: 10.1088/1741-2552/abf521] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 04/06/2021] [Indexed: 11/12/2022]
Abstract
Objective.The growing number of recording sites of silicon-based probes means that an increasing amount of neural cell activities can be recorded simultaneously, facilitating the investigation of underlying complex neural dynamics. In order to overcome the challenges generated by the increasing number of channels, highly automated signal processing tools are needed. Our goal was to build a spike sorting model that can perform as well as offline solutions while maintaining high efficiency, enabling high-performance online sorting.Approach.In this paper we present ELVISort, a deep learning method that combines the detection and clustering of different action potentials in an end-to-end fashion.Main results.The performance of ELVISort is comparable with other spike sorting methods that use manual or semi-manual techniques, while exceeding the methods which use an automatic approach: ELVISort has been tested on three independent datasets and yielded average F1scores of 0.96, 0.82 and 0.81, which comparable with the results of state-of-the-art algorithms on the same data. We show that despite the good performance, ELVISort is capable to process data in real-time: the time it needs to execute the necessary computations for a sample of given length is only 1/15.71 of its actual duration (i.e. the sampling time multiplied by the number of the sampling points).Significance.ELVISort, because of its end-to-end nature, can exploit the massively parallel processing capabilities of GPUs via deep learning frameworks by processing multiple batches in parallel, with the potential to be used on other cutting-edge AI-specific hardware such as TPUs, enabling the development of integrated, portable and real-time spike sorting systems with similar performance to offline sorters.
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Affiliation(s)
- János Rokai
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest H-1117, Hungary.,School of PhD Studies, Semmelweis University, Üllői út 26, H-1085 Budapest, Hungary
| | - Melinda Rácz
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest H-1117, Hungary.,School of PhD Studies, Semmelweis University, Üllői út 26, H-1085 Budapest, Hungary
| | - Richárd Fiáth
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest H-1117, 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, Magyar tudósok körútja 2, Budapest H-1117, Hungary.,Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/a, H-1083 Budapest, Hungary
| | - Gergely Márton
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest H-1117, Hungary.,Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/a, H-1083 Budapest, Hungary
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38
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Buccino AP, Einevoll GT. MEArec: A Fast and Customizable Testbench Simulator for Ground-truth Extracellular Spiking Activity. Neuroinformatics 2021; 19:185-204. [PMID: 32648042 PMCID: PMC7782412 DOI: 10.1007/s12021-020-09467-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
When recording neural activity from extracellular electrodes, both in vivo and in vitro, spike sorting is a required and very important processing step that allows for identification of single neurons’ activity. Spike sorting is a complex algorithmic procedure, and in recent years many groups have attempted to tackle this problem, resulting in numerous methods and software packages. However, validation of spike sorting techniques is complicated. It is an inherently unsupervised problem and it is hard to find universal metrics to evaluate performance. Simultaneous recordings that combine extracellular and patch-clamp or juxtacellular techniques can provide ground-truth data to evaluate spike sorting methods. However, their utility is limited by the fact that only a few cells can be measured at the same time. Simulated ground-truth recordings can provide a powerful alternative mean to rank the performance of spike sorters. We present here MEArec, a Python-based software which permits flexible and fast simulation of extracellular recordings. MEArec allows users to generate extracellular signals on various customizable electrode designs and can replicate various problematic aspects for spike sorting, such as bursting, spatio-temporal overlapping events, and drifts. We expect MEArec will provide a common testbench for spike sorting development and evaluation, in which spike sorting developers can rapidly generate and evaluate the performance of their algorithms.
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Affiliation(s)
- Alessio Paolo Buccino
- Centre for Integrative Neuroplasticity (CINPLA), University of Oslo, Oslo, Norway. .,Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zürich, Zürich, Switzerland.
| | - Gaute Tomas Einevoll
- Centre for Integrative Neuroplasticity (CINPLA), University of Oslo, Oslo, Norway.,Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
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Abstract
Neural correlates of external variables provide potential internal codes that guide an animal’s behaviour. Notably, first-order features of neural activity, such as single-neuron firing rates, have been implicated in encoding information. However, the extent to which higher-order features, such as multi-neuron coactivity, play primary roles in encoding information or secondary roles in supporting single-neuron codes remains unclear. Here we show that millisecond-timescale coactivity amongst hippocampal CA1 neurons discriminates distinct millisecond-lived behavioural contingencies. This contingency discrimination was unrelated to the tuning of individual neurons but instead an emergent property of their coactivity. Contingency discriminating patterns were reactivated offline after learning and their reinstatement predicted trial-by-trial memory performance. Moreover, optogenetic suppression of inputs from the upstream CA3 region selectively during learning impaired coactivity-based contingency information in CA1 and subsequent dynamic memory retrieval. These findings identify coactivity as a primary feature of neural firing that discriminates distinct behaviourally-relevant variables and supports memory retrieval.
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Sperry ZJ, Na K, Jun J, Madden LR, Socha A, Yoon E, Seymour JP, Bruns TM. High-density neural recordings from feline sacral dorsal root ganglia with thin-film array. J Neural Eng 2021; 18. [PMID: 33545709 DOI: 10.1088/1741-2552/abe398] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 02/05/2021] [Indexed: 12/26/2022]
Abstract
Objective. Dorsal root ganglia (DRG) are promising sites for recording sensory activity. Current technologies for DRG recording are stiff and typically do not have sufficient site density for high-fidelity neural data techniques.Approach. In acute experiments, we demonstrate single-unit neural recordings in sacral DRG of anesthetized felines using a 4.5µm thick, high-density flexible polyimide microelectrode array with 60 sites and 30-40µm site spacing. We delivered arrays into DRG with ultrananocrystalline diamond shuttles designed for high stiffness affording a smaller footprint. We recorded neural activity during sensory activation, including cutaneous brushing and bladder filling, as well as during electrical stimulation of the pudendal nerve and anal sphincter. We used specialized neural signal analysis software to sort densely packed neural signals.Main results. We successfully delivered arrays in five of six experiments and recorded single-unit sensory activity in four experiments. The median neural signal amplitude was 55μV peak-to-peak and the maximum unique units recorded at one array position was 260, with 157 driven by sensory or electrical stimulation. In one experiment, we used the neural analysis software to track eight sorted single units as the array was retracted ∼500μm.Significance. This study is the first demonstration of ultrathin, flexible, high-density electronics delivered into DRG, with capabilities for recording and tracking sensory information that are a significant improvement over conventional DRG interfaces.
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Affiliation(s)
- Zachariah J Sperry
- Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America.,Biointerfaces Institute, University of Michigan, Ann Arbor, MI, United States of America
| | - Kyounghwan Na
- Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States of America
| | - James Jun
- Flatiron Institute, Simons Foundation, New York City, NY, United States of America
| | - Lauren R Madden
- Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America.,Biointerfaces Institute, University of Michigan, Ann Arbor, MI, United States of America
| | - Alec Socha
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, United States of America.,Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States of America
| | - Eusik Yoon
- Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America.,Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States of America.,Center for Nanomedicine, Institute for Basic Science (IBS) and Graduate Program of Nano Biomedical Engineering (NanoBME), Advanced Science Institute, Yonsei University, Seoul, Republic of Korea
| | - John P Seymour
- Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America.,Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States of America.,University of Texas Health Science Center, Department of Neurosurgery, Houston, TX, United States of America.,Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States of America
| | - Tim M Bruns
- Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America.,Biointerfaces Institute, University of Michigan, Ann Arbor, MI, United States of America
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41
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Gava GP, McHugh SB, Lefèvre L, Lopes-dos-Santos V, Trouche S, El-Gaby M, Schultz SR, Dupret D. Integrating new memories into the hippocampal network activity space. Nat Neurosci 2021; 24:326-330. [PMID: 33603228 PMCID: PMC7116855 DOI: 10.1038/s41593-021-00804-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 01/19/2021] [Indexed: 01/30/2023]
Abstract
By investigating the topology of neuronal co-activity, we found that mnemonic information spans multiple operational axes in the mouse hippocampus network. High-activity principal cells form the core of each memory along a first axis, segregating spatial contexts and novelty. Low-activity cells join co-activity motifs across behavioral events and enable their crosstalk along two other axes. This reveals an organizational principle for continuous integration and interaction of hippocampal memories.
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Affiliation(s)
- Giuseppe P. Gava
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom,Department of Bioengineering and Centre for Neurotechnology, Imperial College London, London, United Kingdom
| | - Stephen B. McHugh
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Laura Lefèvre
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Vítor Lopes-dos-Santos
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Stéphanie Trouche
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Mohamady El-Gaby
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Simon R. Schultz
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom,Department of Bioengineering and Centre for Neurotechnology, Imperial College London, London, United Kingdom,Correspondence: and
| | - David Dupret
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom,Correspondence: and
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42
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Barron HC, Mars RB, Dupret D, Lerch JP, Sampaio-Baptista C. Cross-species neuroscience: closing the explanatory gap. Philos Trans R Soc Lond B Biol Sci 2021; 376:20190633. [PMID: 33190601 PMCID: PMC7116399 DOI: 10.1098/rstb.2019.0633] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/20/2020] [Indexed: 12/17/2022] Open
Abstract
Neuroscience has seen substantial development in non-invasive methods available for investigating the living human brain. However, these tools are limited to coarse macroscopic measures of neural activity that aggregate the diverse responses of thousands of cells. To access neural activity at the cellular and circuit level, researchers instead rely on invasive recordings in animals. Recent advances in invasive methods now permit large-scale recording and circuit-level manipulations with exquisite spatio-temporal precision. Yet, there has been limited progress in relating these microcircuit measures to complex cognition and behaviour observed in humans. Contemporary neuroscience thus faces an explanatory gap between macroscopic descriptions of the human brain and microscopic descriptions in animal models. To close the explanatory gap, we propose adopting a cross-species approach. Despite dramatic differences in the size of mammalian brains, this approach is broadly justified by preserved homology. Here, we outline a three-armed approach for effective cross-species investigation that highlights the need to translate different measures of neural activity into a common space. We discuss how a cross-species approach has the potential to transform basic neuroscience while also benefiting neuropsychiatric drug development where clinical translation has, to date, seen minimal success. This article is part of the theme issue 'Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.
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Affiliation(s)
- Helen C. Barron
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Rogier B. Mars
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford OX3 9DU, UK
- Donders Institute for Brain, Cognition and Behavior, Radboud University, 6525 AJ Nijmegen, The Netherlands
| | - David Dupret
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK
| | - Jason P. Lerch
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford OX3 9DU, UK
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, CanadaM5G 1L7
| | - Cassandra Sampaio-Baptista
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford OX3 9DU, UK
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow G12 8QB, UK
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43
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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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44
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Barron HC, Reeve HM, Koolschijn RS, Perestenko PV, Shpektor A, Nili H, Rothaermel R, Campo-Urriza N, O'Reilly JX, Bannerman DM, Behrens TEJ, Dupret D. Neuronal Computation Underlying Inferential Reasoning in Humans and Mice. Cell 2020; 183:228-243.e21. [PMID: 32946810 PMCID: PMC7116148 DOI: 10.1016/j.cell.2020.08.035] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 05/10/2020] [Accepted: 08/18/2020] [Indexed: 12/17/2022]
Abstract
Every day we make decisions critical for adaptation and survival. We repeat actions with known consequences. But we also draw on loosely related events to infer and imagine the outcome of entirely novel choices. These inferential decisions are thought to engage a number of brain regions; however, the underlying neuronal computation remains unknown. Here, we use a multi-day cross-species approach in humans and mice to report the functional anatomy and neuronal computation underlying inferential decisions. We show that during successful inference, the mammalian brain uses a hippocampal prospective code to forecast temporally structured learned associations. Moreover, during resting behavior, coactivation of hippocampal cells in sharp-wave/ripples represent inferred relationships that include reward, thereby "joining-the-dots" between events that have not been observed together but lead to profitable outcomes. Computing mnemonic links in this manner may provide an important mechanism to build a cognitive map that stretches beyond direct experience, thus supporting flexible behavior.
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Affiliation(s)
- Helen C Barron
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford OX3 9DU, UK.
| | - Hayley M Reeve
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK
| | - Renée S Koolschijn
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Pavel V Perestenko
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK
| | - Anna Shpektor
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Hamed Nili
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Roman Rothaermel
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK
| | - Natalia Campo-Urriza
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK
| | - Jill X O'Reilly
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford OX3 9DU, UK; Department of Experimental Psychology, University of Oxford, 15 Parks Road, Oxford OX1 3AQ, UK
| | - David M Bannerman
- Department of Experimental Psychology, University of Oxford, 15 Parks Road, Oxford OX1 3AQ, UK
| | - Timothy E J Behrens
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford OX3 9DU, UK; The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK
| | - David Dupret
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK.
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