1
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Shin JD, Jadhav SP. Prefrontal cortical ripples mediate top-down suppression of hippocampal reactivation during sleep memory consolidation. Curr Biol 2024:S0960-9822(24)00616-X. [PMID: 38834064 DOI: 10.1016/j.cub.2024.05.018] [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: 02/13/2024] [Revised: 04/17/2024] [Accepted: 05/09/2024] [Indexed: 06/06/2024]
Abstract
Consolidation of initially encoded hippocampal representations in the neocortex through reactivation is crucial for long-term memory formation and is facilitated by the coordination of hippocampal sharp-wave ripples (SWRs) with cortical slow and spindle oscillations during non-REM sleep. Recent evidence suggests that high-frequency cortical ripples can also coordinate with hippocampal SWRs in support of consolidation; however, the contribution of cortical ripples to reactivation remains unclear. We used high-density, continuous recordings in the hippocampus (area CA1) and prefrontal cortex (PFC) over the course of spatial learning and show that independent PFC ripples dissociated from SWRs are prevalent in NREM sleep and predominantly suppress hippocampal activity. PFC ripples paradoxically mediate top-down suppression of hippocampal reactivation rather than coordination, and this suppression is stronger for assemblies that are reactivated during coordinated CA1-PFC ripples for consolidation of recent experiences. Further, we show non-canonical, serial coordination of independent cortical ripples with slow and spindle oscillations, which are known signatures of memory consolidation. These results establish a role for prefrontal cortical ripples in top-down regulation of behaviorally relevant hippocampal representations during consolidation.
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Affiliation(s)
- Justin D Shin
- Neuroscience Program, Department of Psychology, and Volen National Center for Complex Systems, Brandeis University, 415 South Street, Waltham, MA 02453, USA
| | - Shantanu P Jadhav
- Neuroscience Program, Department of Psychology, and Volen National Center for Complex Systems, Brandeis University, 415 South Street, Waltham, MA 02453, USA.
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2
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Sprague DY, Rusch K, Dunn RL, Borchardt JM, Bubnis G, Chiu GC, Wen C, Suzuki R, Chaudhary S, Dichter B, Ly R, Onami S, Lu H, Kimura K, Yemini EI, Kato S. Unifying community-wide whole-brain imaging datasets enables robust automated neuron identification and reveals determinants of neuron positioning in C. elegans. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.28.591397. [PMID: 38746302 PMCID: PMC11092512 DOI: 10.1101/2024.04.28.591397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
We develop a data harmonization approach for C. elegans volumetric microscopy data, still or video, consisting of a standardized format, data pre-processing techniques, and a set of human-in-the-loop machine learning based analysis software tools. We unify a diverse collection of 118 whole-brain neural activity imaging datasets from 5 labs, storing these and accompanying tools in an online repository called WormID ( wormid.org ). We use this repository to generate a statistical atlas that, for the first time, enables accurate automated cellular identification that generalizes across labs, approaching human performance in some cases. We mine this repository to identify factors that influence the developmental positioning of neurons. To facilitate communal use of this repository, we created open-source software, code, web-based tools, and tutorials to explore and curate datasets for contribution to the scientific community. This repository provides a growing resource for experimentalists, theorists, and toolmakers to investigate neuroanatomical organization and neural activity across diverse experimental paradigms, develop and benchmark algorithms for automated neuron detection, segmentation, cell identification, tracking, and activity extraction, and inform models of neurobiological development and function.
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3
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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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4
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Guarina L, Le JT, Griffith TN, Santana LF, Cudmore RH. SanPy: Software for the analysis and visualization of whole-cell current-clamp recordings. Biophys J 2024; 123:759-769. [PMID: 38419330 PMCID: PMC10995421 DOI: 10.1016/j.bpj.2024.02.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/16/2024] [Accepted: 02/26/2024] [Indexed: 03/02/2024] Open
Abstract
The analysis of action potentials and other membrane voltage fluctuations provides a powerful approach for interrogating the function of excitable cells. However, a major bottleneck in the interpretation of this critical data is the lack of intuitive, agreed-upon software tools for its analysis. Here, we present SanPy, an open-source and freely available software package for the analysis and exploration of whole-cell current-clamp recordings written in Python. SanPy provides a robust computational engine with an application programming interface. Using this, we have developed a cross-platform desktop application with a graphical user interface that does not require programming. SanPy is designed to extract common parameters from action potentials, including threshold time and voltage, peak, half-width, and interval statistics. In addition, several cardiac parameters are measured, including the early diastolic duration and rate. SanPy is built to be fully extensible by providing a plugin architecture for the addition of new file loaders, analysis, and visualizations. A key feature of SanPy is its focus on quality control and data exploration. In the desktop interface, all plots of the data and analysis are linked, allowing simultaneous data visualization from different dimensions with the goal of obtaining ground-truth analysis. We provide documentation for all aspects of SanPy, including several use cases and examples. To test SanPy, we performed analysis on current-clamp recordings from heart and brain cells. Taken together, SanPy is a powerful tool for whole-cell current-clamp analysis and lays the foundation for future extension by the scientific community.
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Affiliation(s)
- Laura Guarina
- Department of Physiology & Membrane Biology, University of California-Davis School of Medicine, Davis, California
| | - Johnson Tran Le
- Department of Physiology & Membrane Biology, University of California-Davis School of Medicine, Davis, California
| | - Theanne N Griffith
- Department of Physiology & Membrane Biology, University of California-Davis School of Medicine, Davis, California
| | - Luis Fernando Santana
- Department of Physiology & Membrane Biology, University of California-Davis School of Medicine, Davis, California
| | - Robert H Cudmore
- Department of Physiology & Membrane Biology, University of California-Davis School of Medicine, Davis, California.
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5
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Kim LJ, Shin D, Leite WC, O’Neill H, Ruebel O, Tritt A, Hura GL. Simple Scattering: Lipid nanoparticle structural data repository. Front Mol Biosci 2024; 11:1321364. [PMID: 38584701 PMCID: PMC10998447 DOI: 10.3389/fmolb.2024.1321364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 02/19/2024] [Indexed: 04/09/2024] Open
Abstract
Lipid nanoparticles (LNPs) are being intensively researched and developed to leverage their ability to safely and effectively deliver therapeutics. To achieve optimal therapeutic delivery, a comprehensive understanding of the relationship between formulation, structure, and efficacy is critical. However, the vast chemical space involved in the production of LNPs and the resulting structural complexity make the structure to function relationship challenging to assess and predict. New components and formulation procedures, which provide new opportunities for the use of LNPs, would be best identified and optimized using high-throughput characterization methods. Recently, a high-throughput workflow, consisting of automated mixing, small-angle X-ray scattering (SAXS), and cellular assays, demonstrated a link between formulation, internal structure, and efficacy for a library of LNPs. As SAXS data can be rapidly collected, the stage is set for the collection of thousands of SAXS profiles from a myriad of LNP formulations. In addition, correlated LNP small-angle neutron scattering (SANS) datasets, where components are systematically deuterated for additional contrast inside, provide complementary structural information. The centralization of SAXS and SANS datasets from LNPs, with appropriate, standardized metadata describing formulation parameters, into a data repository will provide valuable guidance for the formulation of LNPs with desired properties. To this end, we introduce Simple Scattering, an easy-to-use, open data repository for storing and sharing groups of correlated scattering profiles obtained from LNP screening experiments. Here, we discuss the current state of the repository, including limitations and upcoming changes, and our vision towards future usage in developing our collective knowledge base of LNPs.
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Affiliation(s)
- Lee Joon Kim
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - David Shin
- David Shin Consulting, Berkeley, CA, United States
| | - Wellington C. Leite
- Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Hugh O’Neill
- Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Oliver Ruebel
- Scientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Andrew Tritt
- Applied Mathematics and Computational Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Greg L. Hura
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA, United States
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6
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Keles U, Dubois J, Le KJM, Tyszka JM, Kahn DA, Reed CM, Chung JM, Mamelak AN, Adolphs R, Rutishauser U. Multimodal single-neuron, intracranial EEG, and fMRI brain responses during movie watching in human patients. Sci Data 2024; 11:214. [PMID: 38365977 PMCID: PMC10873379 DOI: 10.1038/s41597-024-03029-1] [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: 10/27/2023] [Accepted: 01/31/2024] [Indexed: 02/18/2024] Open
Abstract
We present a multimodal dataset of intracranial recordings, fMRI, and eye tracking in 20 participants during movie watching. Recordings consist of single neurons, local field potential, and intracranial EEG activity acquired from depth electrodes targeting the amygdala, hippocampus, and medial frontal cortex implanted for monitoring of epileptic seizures. Participants watched an 8-min long excerpt from the video "Bang! You're Dead" and performed a recognition memory test for movie content. 3 T fMRI activity was recorded prior to surgery in 11 of these participants while performing the same task. This NWB- and BIDS-formatted dataset includes spike times, field potential activity, behavior, eye tracking, electrode locations, demographics, and functional and structural MRI scans. For technical validation, we provide signal quality metrics, assess eye tracking quality, behavior, the tuning of cells and high-frequency broadband power field potentials to familiarity and event boundaries, and show brain-wide inter-subject correlations for fMRI. This dataset will facilitate the investigation of brain activity during movie watching, recognition memory, and the neural basis of the fMRI-BOLD signal.
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Affiliation(s)
- Umit Keles
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Julien Dubois
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Kevin J M Le
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - J Michael Tyszka
- Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA
| | - David A Kahn
- Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Chrystal M Reed
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jeffrey M Chung
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Adam N Mamelak
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ralph Adolphs
- Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA.
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
| | - Ueli Rutishauser
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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7
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Pierré A, Pham T, Pearl J, Datta SR, Ritt JT, Fleischmann A. A perspective on neuroscience data standardization with Neurodata Without Borders. ARXIV 2024:arXiv:2310.04317v2. [PMID: 37873012 PMCID: PMC10593085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Neuroscience research has evolved to generate increasingly large and complex experimental data sets, and advanced data science tools are taking on central roles in neuroscience research. Neurodata Without Borders (NWB), a standard language for neurophysiology data, has recently emerged as a powerful solution for data management, analysis, and sharing. We here discuss our labs' efforts to implement NWB data science pipelines. We describe general principles and specific use cases that illustrate successes, challenges, and non-trivial decisions in software engineering. We hope that our experience can provide guidance for the neuroscience community and help bridge the gap between experimental neuroscience and data science.
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Affiliation(s)
- Andrea Pierré
- Department of Neuroscience, Division of Biology and Medicine, Brown University, Providence, USA
- The Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, USA
| | - Tuan Pham
- Department of Neuroscience, Division of Biology and Medicine, Brown University, Providence, USA
- The Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, USA
| | - Jonah Pearl
- Department of Neurobiology, Harvard Medical School, Boston, USA
| | | | - Jason T. Ritt
- The Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, USA
| | - Alexander Fleischmann
- Department of Neuroscience, Division of Biology and Medicine, Brown University, Providence, USA
- The Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, USA
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8
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Kyzar M, Kamiński J, Brzezicka A, Reed CM, Chung JM, Mamelak AN, Rutishauser U. Dataset of human-single neuron activity during a Sternberg working memory task. Sci Data 2024; 11:89. [PMID: 38238342 PMCID: PMC10796636 DOI: 10.1038/s41597-024-02943-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 01/10/2024] [Indexed: 01/22/2024] Open
Abstract
We present a dataset of 1809 single neurons recorded from the human medial temporal lobe (amygdala and hippocampus) and medial frontal lobe (anterior cingulate cortex, pre-supplementary motor area, ventral medial prefrontal cortex) across 41 sessions from 21 patients that underwent seizure monitoring with depth electrodes. Subjects performed a screening task (907 neurons) to identify images for which highly selective cells were present. Subjects then performed a working memory task (902 neurons), in which they were sequentially presented with 1-3 images for which highly selective cells were present and, following a maintenance period, were asked if the probe was identical to one of the maintained images. This Neurodata Without Borders formatted dataset includes spike times, extracellular spike waveforms, stimuli presented, behavior, electrode locations, and subject demographics. As validation, we replicate previous findings on the selectivity of concept cells and their persistent activity during working memory maintenance. This large dataset of rare human single-neuron recordings and behavior enables the investigation of the neural mechanisms of working memory in humans.
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Affiliation(s)
- Michael Kyzar
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jan Kamiński
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Center of Excellence for Neural Plasticity and Brain Disorders: BRAINCITY, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Aneta Brzezicka
- Institute of Psychology, SWPS University of Social Sciences and Humanities, Warsaw, Poland
| | - Chrystal M Reed
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jeffrey M Chung
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Adam N Mamelak
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ueli Rutishauser
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
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9
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Ly R, Avaylon M, Wulf M, Kepecs A, Rübel O. Structured behavioral data format: An NWB extension standard for task-based behavioral neuroscience experiments. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.08.574597. [PMID: 38260593 PMCID: PMC10802442 DOI: 10.1101/2024.01.08.574597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Understanding brain function necessitates linking neural activity with corresponding behavior. Structured behavioral experiments are crucial for probing the neural computations and dynamics underlying behavior; however, adequately representing their complex data is a significant challenge. Currently, a comprehensive data standard that fully encapsulates task-based experiments, integrating neural activity with the richness of behavioral context, is lacking. We designed a data model, as an extension to the NWB neurophysiology data standard, to represent structured behavioral neuroscience experiments, spanning stimulus delivery, timestamped events and responses, and simultaneous neural recordings. This data format is validated through its application to a variety of experimental designs, showcasing its potential to advance integrative analyses of neural circuits and complex behaviors. This work introduces a comprehensive data standard designed to capture and store a spectrum of behavioral data, encapsulating the multifaceted nature of modern neuroscience experiments.
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10
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Martone ME. The past, present and future of neuroscience data sharing: a perspective on the state of practices and infrastructure for FAIR. Front Neuroinform 2024; 17:1276407. [PMID: 38250019 PMCID: PMC10796549 DOI: 10.3389/fninf.2023.1276407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 10/31/2023] [Indexed: 01/23/2024] Open
Abstract
Neuroscience has made significant strides over the past decade in moving from a largely closed science characterized by anemic data sharing, to a largely open science where the amount of publicly available neuroscience data has increased dramatically. While this increase is driven in significant part by large prospective data sharing studies, we are starting to see increased sharing in the long tail of neuroscience data, driven no doubt by journal requirements and funder mandates. Concomitant with this shift to open is the increasing support of the FAIR data principles by neuroscience practices and infrastructure. FAIR is particularly critical for neuroscience with its multiplicity of data types, scales and model systems and the infrastructure that serves them. As envisioned from the early days of neuroinformatics, neuroscience is currently served by a globally distributed ecosystem of neuroscience-centric data repositories, largely specialized around data types. To make neuroscience data findable, accessible, interoperable, and reusable requires the coordination across different stakeholders, including the researchers who produce the data, data repositories who make it available, the aggregators and indexers who field search engines across the data, and community organizations who help to coordinate efforts and develop the community standards critical to FAIR. The International Neuroinformatics Coordinating Facility has led efforts to move neuroscience toward FAIR, fielding several resources to help researchers and repositories achieve FAIR. In this perspective, I provide an overview of the components and practices required to achieve FAIR in neuroscience and provide thoughts on the past, present and future of FAIR infrastructure for neuroscience, from the laboratory to the search engine.
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Affiliation(s)
- Maryann E. Martone
- Department of Neurosciences, University of California, San Diego, CA, United States
- San Francisco Veterans Administration Hospital, San Francisco, CA, United States
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11
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Ratsifandrihamanana MR, Dard RF, Denis J, Cossart R, Picardo MA. Protocol to image and analyze hippocampal network dynamics in non-anesthetized mouse pups. STAR Protoc 2023; 4:102760. [PMID: 38041819 PMCID: PMC10701450 DOI: 10.1016/j.xpro.2023.102760] [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: 09/08/2023] [Revised: 10/03/2023] [Accepted: 11/17/2023] [Indexed: 12/04/2023] Open
Abstract
Two-photon calcium imaging is a powerful technique that has revolutionized our understanding of how neural circuit dynamics supports different behaviors and cognitive processes. However, performing imaging during development remains challenging. Here, we provide a protocol to image CA1 neurons in mouse pups as well as a pipeline of analysis to analyze and share the data. We describe steps for intracerebroventricular injection, cranial window surgery, two-photon calcium imaging, and analysis of imaging data. For complete details on the use and execution of this protocol, please refer to Dard et al.1 and Denis et al.2.
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Affiliation(s)
| | - Robin F Dard
- Turing Centre for Living Systems, Aix-Marseille University, INSERM, INMED U1249, France
| | - Julien Denis
- Turing Centre for Living Systems, Aix-Marseille University, INSERM, INMED U1249, France
| | - Rosa Cossart
- Turing Centre for Living Systems, Aix-Marseille University, INSERM, INMED U1249, France.
| | - Michel A Picardo
- Turing Centre for Living Systems, Aix-Marseille University, INSERM, INMED U1249, France.
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12
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Rahimzadeh V, Jones KM, Majumder MA, Kahana MJ, Rutishauser U, Williams ZM, Cash SS, Paulk AC, Zheng J, Beauchamp MS, Collinger JL, Pouratian N, McGuire AL, Sheth SA. Benefits of sharing neurophysiology data from the BRAIN Initiative Research Opportunities in Humans Consortium. Neuron 2023; 111:3710-3715. [PMID: 37944519 PMCID: PMC10995938 DOI: 10.1016/j.neuron.2023.09.029] [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: 09/08/2023] [Revised: 09/21/2023] [Accepted: 09/21/2023] [Indexed: 11/12/2023]
Abstract
Sharing human brain data can yield scientific benefits, but because of various disincentives, only a fraction of these data is currently shared. We profile three successful data-sharing experiences from the NIH BRAIN Initiative Research Opportunities in Humans (ROH) Consortium and demonstrate benefits to data producers and to users.
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Affiliation(s)
- Vasiliki Rahimzadeh
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX 77030, USA
| | - Kathryn Maxson Jones
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX 77030, USA; Department of History, Purdue University, West Lafayette, IN 47907, USA
| | - Mary A Majumder
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX 77030, USA
| | - Michael J Kahana
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ueli Rutishauser
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Ziv M Williams
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Jie Zheng
- Department of Ophthalmology, Boston Children's Hospital, Boston, MA 02115, USA
| | - Michael S Beauchamp
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jennifer L Collinger
- Rehab Neural Engineering Labs, Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15219, USA
| | - Nader Pouratian
- Department of Neurological Surgery, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Amy L McGuire
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX 77030, USA
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA.
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13
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Amra LN, Mächler P, Fomin-Thunemann N, Kılıç K, Saisan P, Devor A, Thunemann M. Tissue Oxygen Depth Explorer: an interactive database for microscopic oxygen imaging data. Front Neuroinform 2023; 17:1278787. [PMID: 38088985 PMCID: PMC10711099 DOI: 10.3389/fninf.2023.1278787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 11/07/2023] [Indexed: 02/01/2024] Open
Affiliation(s)
- Layth N. Amra
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
| | - Philipp Mächler
- Department of Physics, University of California, San Diego, La Jolla, CA, United States
| | | | - Kıvılcım Kılıç
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
| | - Payam Saisan
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Anna Devor
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
- Harvard Medical School, Martinos Center for Biomedical Imaging, MGH, Charlestown, MA, United States
| | - Martin Thunemann
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
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14
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Olson RH, Cohen Kalafut N, Wang D. MANGEM: A web app for multimodal analysis of neuronal gene expression, electrophysiology, and morphology. PATTERNS (NEW YORK, N.Y.) 2023; 4:100847. [PMID: 38035195 PMCID: PMC10682747 DOI: 10.1016/j.patter.2023.100847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 08/07/2023] [Accepted: 09/01/2023] [Indexed: 12/02/2023]
Abstract
Single-cell techniques like Patch-seq have enabled the acquisition of multimodal data from individual neuronal cells, offering systematic insights into neuronal functions. However, these data can be heterogeneous and noisy. To address this, machine learning methods have been used to align cells from different modalities onto a low-dimensional latent space, revealing multimodal cell clusters. The use of those methods can be challenging without computational expertise or suitable computing infrastructure for computationally expensive methods. To address this, we developed a cloud-based web application, MANGEM (multimodal analysis of neuronal gene expression, electrophysiology, and morphology). MANGEM provides a step-by-step accessible and user-friendly interface to machine learning alignment methods of neuronal multimodal data. It can run asynchronously for large-scale data alignment, provide users with various downstream analyses of aligned cells, and visualize the analytic results. We demonstrated the usage of MANGEM by aligning multimodal data of neuronal cells in the mouse visual cortex.
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Affiliation(s)
| | - Noah Cohen Kalafut
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Daifeng Wang
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
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15
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Subash P, Gray A, Boswell M, Cohen SL, Garner R, Salehi S, Fisher C, Hobel S, Ghosh S, Halchenko Y, Dichter B, Poldrack RA, Markiewicz C, Hermes D, Delorme A, Makeig S, Behan B, Sparks A, Arnott SR, Wang Z, Magnotti J, Beauchamp MS, Pouratian N, Toga AW, Duncan D. A comparison of neuroelectrophysiology databases. Sci Data 2023; 10:719. [PMID: 37857685 PMCID: PMC10587056 DOI: 10.1038/s41597-023-02614-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 10/02/2023] [Indexed: 10/21/2023] Open
Abstract
As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration. This paper compares four freely available intracranial neuroelectrophysiology data repositories: Data Archive for the BRAIN Initiative (DABI), Distributed Archives for Neurophysiology Data Integration (DANDI), OpenNeuro, and Brain-CODE. The aim of this review is to describe archives that provide researchers with tools to store, share, and reanalyze both human and non-human neurophysiology data based on criteria that are of interest to the neuroscientific community. The Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB) are utilized by these archives to make data more accessible to researchers by implementing a common standard. As the necessity for integrating large-scale analysis into data repository platforms continues to grow within the neuroscientific community, this article will highlight the various analytical and customizable tools developed within the chosen archives that may advance the field of neuroinformatics.
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Affiliation(s)
- Priyanka Subash
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, 90033, USA
| | - Alex Gray
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, 90033, USA
| | - Misque Boswell
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, 90033, USA
| | - Samantha L Cohen
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, 90033, USA
| | - Rachael Garner
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, 90033, USA
| | - Sana Salehi
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, 90033, USA
| | - Calvary Fisher
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, 90033, USA
| | - Samuel Hobel
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, 90033, USA
| | - Satrajit Ghosh
- McGovern Institute for Brain Research, MIT Brain and Cognitive Sciences, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA
| | - Yaroslav Halchenko
- Department of Psychological & Brain Sciences, Center for Cognitive Neuroscience, Dartmouth Brain Imaging Center, Dartmouth College, 6207 Moore Hall, Hanover, NH, 03755, USA
| | | | - Russell A Poldrack
- Department of Psychology, Stanford University, 450 Jane Stanford Way, Stanford, CA, 94305, USA
| | - Chris Markiewicz
- Department of Psychology, Stanford University, 450 Jane Stanford Way, Stanford, CA, 94305, USA
| | - Dora Hermes
- Mayo Clinic, Department of Physiology & Biomedical Engineering, 200 1st Street SW, Rochester, MN, 55905, USA
| | - Arnaud Delorme
- Swartz Center of Computational Neuroscience, INC, University of California San Diego, La Jolla, CA, 92093, USA
| | - Scott Makeig
- Swartz Center of Computational Neuroscience, INC, University of California San Diego, La Jolla, CA, 92093, USA
| | - Brendan Behan
- Ontario Brain Institute, 1 Richmond Street West, Toronto, ON, M5H 3W4, Canada
| | | | | | - Zhengjia Wang
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | - John Magnotti
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | - Michael S Beauchamp
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | - Nader Pouratian
- Department of Neurological Surgery, University of Texas Southwestern Medical Center, 5303 Harry Hines Blvd, Dallas, TX, 75390, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, 90033, USA
| | - Dominique Duncan
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, 90033, USA.
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16
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Viejo G, Levenstein D, Skromne Carrasco S, Mehrotra D, Mahallati S, Vite GR, Denny H, Sjulson L, Battaglia FP, Peyrache A. Pynapple, a toolbox for data analysis in neuroscience. eLife 2023; 12:RP85786. [PMID: 37843985 PMCID: PMC10578930 DOI: 10.7554/elife.85786] [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] [Indexed: 10/18/2023] Open
Abstract
Datasets collected in neuroscientific studies are of ever-growing complexity, often combining high-dimensional time series data from multiple data acquisition modalities. Handling and manipulating these various data streams in an adequate programming environment is crucial to ensure reliable analysis, and to facilitate sharing of reproducible analysis pipelines. Here, we present Pynapple, the PYthon Neural Analysis Package, a lightweight python package designed to process a broad range of time-resolved data in systems neuroscience. The core feature of this package is a small number of versatile objects that support the manipulation of any data streams and task parameters. The package includes a set of methods to read common data formats and allows users to easily write their own. The resulting code is easy to read and write, avoids low-level data processing and other error-prone steps, and is open source. Libraries for higher-level analyses are developed within the Pynapple framework but are contained within a collaborative repository of specialized and continuously updated analysis routines. This provides flexibility while ensuring long-term stability of the core package. In conclusion, Pynapple provides a common framework for data analysis in neuroscience.
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Affiliation(s)
- Guillaume Viejo
- Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
- Flatiron Institute, Center for Computational NeuroscienceNew YorkUnited States
| | - Daniel Levenstein
- Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
- MILA – Quebec IA InstituteMontrealCanada
| | | | - Dhruv Mehrotra
- Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Sara Mahallati
- Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Gilberto R Vite
- Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Henry Denny
- Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Lucas Sjulson
- Departments of Psychiatry and Neuroscience, Albert Einstein College of MedicineBronxUnited States
| | - Francesco P Battaglia
- Donders Institute for Brain, Cognition and Behaviour, Radboud UniversityNijmegenNetherlands
| | - Adrien Peyrache
- Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
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17
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Légaré A, Lemieux M, Desrosiers P, De Koninck P. Zebrafish brain atlases: a collective effort for a tiny vertebrate brain. NEUROPHOTONICS 2023; 10:044409. [PMID: 37786400 PMCID: PMC10541682 DOI: 10.1117/1.nph.10.4.044409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 09/03/2023] [Accepted: 09/13/2023] [Indexed: 10/04/2023]
Abstract
In the past two decades, digital brain atlases have emerged as essential tools for sharing and integrating complex neuroscience datasets. Concurrently, the larval zebrafish has become a prominent vertebrate model offering a strategic compromise for brain size, complexity, transparency, optogenetic access, and behavior. We provide a brief overview of digital atlases recently developed for the larval zebrafish brain, intersecting neuroanatomical information, gene expression patterns, and connectivity. These atlases are becoming pivotal by centralizing large datasets while supporting the generation of circuit hypotheses as functional measurements can be registered into an atlas' standard coordinate system to interrogate its structural database. As challenges persist in mapping neural circuits and incorporating functional measurements into zebrafish atlases, we emphasize the importance of collaborative efforts and standardized protocols to expand these resources to crack the complex codes of neuronal activity guiding behavior in this tiny vertebrate brain.
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Affiliation(s)
| | - Mado Lemieux
- CERVO Brain Research Center, Québec, Québec, Canada
| | - Patrick Desrosiers
- CERVO Brain Research Center, Québec, Québec, Canada
- Université Laval, Department of Physics, Engineering Physics and Optics, Québec, Québec, Canada
| | - Paul De Koninck
- CERVO Brain Research Center, Québec, Québec, Canada
- Université Laval, Department of Biochemistry, Microbiology and Bio-informatics, Québec, Québec, Canada
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18
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Buccino AP, Winter O, Bryant D, Feng D, Svoboda K, Siegle JH. Compression strategies for large-scale electrophysiology data. J Neural Eng 2023; 20:056009. [PMID: 37651998 DOI: 10.1088/1741-2552/acf5a4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 08/31/2023] [Indexed: 09/02/2023]
Abstract
Objective.With the rapid adoption of high-density electrode arrays for recording neural activity, electrophysiology data volumes within labs and across the field are growing at unprecedented rates. For example, a one-hour recording with a 384-channel Neuropixels probe generates over 80 GB of raw data. These large data volumes carry a high cost, especially if researchers plan to store and analyze their data in the cloud. Thus, there is a pressing need for strategies that can reduce the data footprint of each experiment.Approach.Here, we establish a set of benchmarks for comparing the performance of various compression algorithms on experimental and simulated recordings from Neuropixels 1.0 (NP1) and 2.0 (NP2) probes.Main results.For lossless compression, audio codecs (FLACandWavPack) achieve compression ratios (CRs) 6% higher for NP1 and 10% higher for NP2 than the best general-purpose codecs, at the expense of decompression speed. For lossy compression, theWavPackalgorithm in 'hybrid mode' increases the CR from 3.59 to 7.08 for NP1 and from 2.27 to 7.04 for NP2 (compressed file size of ∼14% for both types of probes), without adverse effects on spike sorting accuracy or spike waveforms.Significance.Along with the tools we have developed to make compression easier to deploy, these results should encourage all electrophysiologists to apply compression as part of their standard analysis workflows.
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Affiliation(s)
- Alessio P Buccino
- Allen Institute for Neural Dynamics, Seattle, WA, United States of America
| | | | - David Bryant
- Independent Researcher, San Francisco, CA, United States of America
| | - David Feng
- Allen Institute for Neural Dynamics, Seattle, WA, United States of America
| | - Karel Svoboda
- Allen Institute for Neural Dynamics, Seattle, WA, United States of America
| | - Joshua H Siegle
- Allen Institute for Neural Dynamics, Seattle, WA, United States of America
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19
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Guarina L, Le JT, Griffith TN, Santana LF, Cudmore RH. SanPy: A whole-cell electrophysiology analysis pipeline. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.06.539660. [PMID: 37214972 PMCID: PMC10197560 DOI: 10.1101/2023.05.06.539660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
The analysis of action potentials and other membrane voltage fluctuations provide a powerful approach for interrogating the function of excitable cells. Yet, a major bottleneck in the interpretation of this critical data is the lack of intuitive, agreed upon software tools for its analysis. Here, we present SanPy, a Python-based open-source and freely available software pipeline for the analysis and exploration of whole-cell current-clamp recordings. SanPy provides a robust computational engine with an application programming interface. Using this, we have developed a cross-platform graphical user interface that does not require programming. SanPy is designed to extract common parameters from action potentials including threshold time and voltage, peak, half-width, and interval statistics. In addition, several cardiac parameters are measured including the early diastolic duration and rate. SanPy is built to be fully extensible by providing a plugin architecture for the addition of new file loaders, analysis, and visualizations. A key feature of SanPy is its focus on quality control and data exploration. In the desktop interface, all plots of the data and analysis are linked allowing simultaneous data visualization from different dimensions with the goal of obtaining ground truth analysis. We provide documentation for all aspects of SanPy including several use cases and examples. To test SanPy, we have performed analysis on current-clamp recordings from heart and brain cells. Taken together, SanPy is a powerful tool for whole-cell current-clamp analysis and lays the foundation for future extension by the scientific community.
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Affiliation(s)
- Laura Guarina
- Department of Physiology & Membrane Biology, University of California-Davis School of Medicine, Davis, California, 95616, USA
| | - Johnson Tran Le
- Department of Physiology & Membrane Biology, University of California-Davis School of Medicine, Davis, California, 95616, USA
| | - Theanne N Griffith
- Department of Physiology & Membrane Biology, University of California-Davis School of Medicine, Davis, California, 95616, USA
| | - Luis Fernando Santana
- Department of Physiology & Membrane Biology, University of California-Davis School of Medicine, Davis, California, 95616, USA
| | - Robert H Cudmore
- Department of Physiology & Membrane Biology, University of California-Davis School of Medicine, Davis, California, 95616, USA
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20
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Shin JD, Tang W, Jadhav SP. Protocol for geometric transformation of cognitive maps for generalization across hippocampal-prefrontal circuits. STAR Protoc 2023; 4:102513. [PMID: 37572325 PMCID: PMC10448425 DOI: 10.1016/j.xpro.2023.102513] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/06/2023] [Accepted: 07/25/2023] [Indexed: 08/14/2023] Open
Abstract
Memory generalization is the ability to abstract knowledge from prior experiences and is critical for flexible behavior in novel situations. Here, we describe a protocol for simultaneous recording of hippocampal (area CA1)-prefrontal cortical neural ensembles in Long-Evans rats during task generalization across two distinct environments. We describe steps for building and assembling experimental apparatuses, animal preparation and surgery, and performing experiments. We then detail procedures for histology, data processing, and assessing population geometry using Uniform Manifold Approximation and Projection. For complete details on the use and execution of this protocol, please refer to Tang et al. (2023).1.
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Affiliation(s)
- Justin D Shin
- Neuroscience Program, Department of Psychology, Volen National Center for Complex Systems, Brandeis University, Waltham, MA 02453, USA.
| | - Wenbo Tang
- Neuroscience Program, Department of Psychology, Volen National Center for Complex Systems, Brandeis University, Waltham, MA 02453, USA
| | - Shantanu P Jadhav
- Neuroscience Program, Department of Psychology, Volen National Center for Complex Systems, Brandeis University, Waltham, MA 02453, USA.
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21
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Konradi P, Troglio A, Pérez Garriga A, Pérez Martín A, Röhrig R, Namer B, Kutafina E. PyDapsys: an open-source library for accessing electrophysiology data recorded with DAPSYS. Front Neuroinform 2023; 17:1250260. [PMID: 37780458 PMCID: PMC10539619 DOI: 10.3389/fninf.2023.1250260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 08/28/2023] [Indexed: 10/03/2023] Open
Abstract
In the field of neuroscience, a considerable number of commercial data acquisition and processing solutions rely on proprietary formats for data storage. This often leads to data being locked up in formats that are only accessible by using the original software, which may lead to interoperability problems. In fact, even the loss of data access is possible if the software becomes unsupported, changed, or otherwise unavailable. To ensure FAIR data management, strategies should be established to enable long-term, independent, and unified access to data in proprietary formats. In this work, we demonstrate PyDapsys, a solution to gain open access to data that was acquired using the proprietary recording system DAPSYS. PyDapsys enables us to open the recorded files directly in Python and saves them as NIX files, commonly used for open research in the electrophysiology domain. Thus, PyDapsys secures efficient and open access to existing and prospective data. The manuscript demonstrates the complete process of reverse engineering a proprietary electrophysiological format on the example of microneurography data collected for studies on pain and itch signaling in peripheral neural fibers.
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Affiliation(s)
- Peter Konradi
- Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Alina Troglio
- Research Group Neuroscience, IZKF, RWTH Aachen, Aachen, Germany
| | - Ariadna Pérez Garriga
- Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Aarón Pérez Martín
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Rainer Röhrig
- Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Barbara Namer
- Research Group Neuroscience, IZKF, RWTH Aachen, Aachen, Germany
- Department for Neurophysiology, University Hospital RWTH Aachen, Aachen, Germany
- Institute of Physiology and Pathophysiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Ekaterina Kutafina
- Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
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22
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Versteeg C, Sedler AR, McCart JD, Pandarinath C. Expressive dynamics models with nonlinear injective readouts enable reliable recovery of latent features from neural activity. ARXIV 2023:arXiv:2309.06402v1. [PMID: 37744459 PMCID: PMC10516113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The advent of large-scale neural recordings has enabled new approaches that aim to discover the computational mechanisms of neural circuits by understanding the rules that govern how their state evolves over time. While these neural dynamics cannot be directly measured, they can typically be approximated by low-dimensional models in a latent space. How these models represent the mapping from latent space to neural space can affect the interpretability of the latent representation. We show that typical choices for this mapping (e.g., linear or MLP) often lack the property of injectivity, meaning that changes in latent state are not obligated to affect activity in the neural space. During training, non-injective readouts incentivize the invention of dynamics that misrepresent the underlying system and the computation it performs. Combining our injective Flow readout with prior work on interpretable latent dynamics models, we created the Ordinary Differential equations autoencoder with Injective Nonlinear readout (ODIN), which learns to capture latent dynamical systems that are nonlinearly embedded into observed neural activity via an approximately injective nonlinear mapping. We show that ODIN can recover nonlinearly embedded systems from simulated neural activity, even when the nature of the system and embedding are unknown. Additionally, we show that ODIN enables the unsupervised recovery of underlying dynamical features (e.g., fixed points) and embedding geometry. When applied to biological neural recordings, ODIN can reconstruct neural activity with comparable accuracy to previous state-of-the-art methods while using substantially fewer latent dimensions. Overall, ODIN's accuracy in recovering ground-truth latent features and ability to accurately reconstruct neural activity with low dimensionality make it a promising method for distilling interpretable dynamics that can help explain neural computation.
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Affiliation(s)
- Christopher Versteeg
- Wallace H. Coulter Department of Biomedical Engineering Emory University and Georgia Institute of Technology Atlanta, GA, USA
| | - Andrew R Sedler
- Wallace H. Coulter Department of Biomedical Engineering Emory University and Georgia Institute of Technology Atlanta, GA, USA
- Center for Machine Learning Georgia Institute of Technology Atlanta, GA, USA
| | - Jonathan D McCart
- Wallace H. Coulter Department of Biomedical Engineering Emory University and Georgia Institute of Technology Atlanta, GA, USA
- Center for Machine Learning Georgia Institute of Technology Atlanta, GA, USA
| | - Chethan Pandarinath
- Wallace H. Coulter Department of Biomedical Engineering Emory University and Georgia Institute of Technology Atlanta, GA, USA
- Center for Machine Learning Georgia Institute of Technology Atlanta, GA, USA
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23
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Zaki Y, Pennington ZT, Morales-Rodriguez D, Francisco TR, LaBanca AR, Dong Z, Lamsifer S, Segura SC, Chen HT, Wick ZC, Silva AJ, van der Meer M, Shuman T, Fenton A, Rajan K, Cai DJ. Aversive experience drives offline ensemble reactivation to link memories across days. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.13.532469. [PMID: 36993254 PMCID: PMC10054942 DOI: 10.1101/2023.03.13.532469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Memories are encoded in neural ensembles during learning and stabilized by post-learning reactivation. Integrating recent experiences into existing memories ensures that memories contain the most recently available information, but how the brain accomplishes this critical process remains unknown. Here we show that in mice, a strong aversive experience drives the offline ensemble reactivation of not only the recent aversive memory but also a neutral memory formed two days prior, linking the fear from the recent aversive memory to the previous neutral memory. We find that fear specifically links retrospectively, but not prospectively, to neutral memories across days. Consistent with prior studies, we find reactivation of the recent aversive memory ensemble during the offline period following learning. However, a strong aversive experience also increases co-reactivation of the aversive and neutral memory ensembles during the offline period. Finally, the expression of fear in the neutral context is associated with reactivation of the shared ensemble between the aversive and neutral memories. Taken together, these results demonstrate that strong aversive experience can drive retrospective memory-linking through the offline co-reactivation of recent memory ensembles with memory ensembles formed days prior, providing a neural mechanism by which memories can be integrated across days.
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Affiliation(s)
- Yosif Zaki
- Nash Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029
| | - Zachary T. Pennington
- Nash Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029
| | | | - Taylor R. Francisco
- Nash Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029
| | - Alexa R. LaBanca
- Nash Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029
| | - Zhe Dong
- Nash Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029
| | - Sophia Lamsifer
- Nash Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029
| | - Simón Carrillo Segura
- Graduate Program in Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, 11201
| | - Hung-Tu Chen
- Department of Psychological & Brain Sciences, Dartmouth College, Hanover, NH, 03755
| | - Zoé Christenson Wick
- Nash Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029
| | - Alcino J. Silva
- Department of Neurobiology, Psychiatry & Biobehavioral Sciences, and Psychology, Integrative Center for Learning and Memory, Brain Research Institute, UCLA, Los Angeles, CA 90095
| | | | - Tristan Shuman
- Nash Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029
| | - André Fenton
- Center for Neural Science, New York University, New York, NY, 10003
- Neuroscience Institute at the NYU Langone Medical Center, New York, NY, 10016
| | - Kanaka Rajan
- Nash Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029
| | - Denise J. Cai
- Nash Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029
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24
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Easthope E, Shamei A, Liu Y, Gick B, Fels S. Cortical control of posture in fine motor skills: evidence from inter-utterance rest position. Front Hum Neurosci 2023; 17:1139569. [PMID: 37662639 PMCID: PMC10469778 DOI: 10.3389/fnhum.2023.1139569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 06/12/2023] [Indexed: 09/05/2023] Open
Abstract
The vocal tract continuously employs tonic muscle activity in the maintenance of postural configurations. Gamma-band activity in the sensorimotor cortex underlies transient movements during speech production, yet little is known about the neural control of postural states in the vocal tract. Simultaneously, there is evidence that sensorimotor beta-band activations contribute to a system of inhibition and state maintenance that is integral to postural control in the body. Here we use electrocorticography to assess the contribution of sensorimotor beta-band activity during speech articulation and postural maintenance, and demonstrate that beta-band activity corresponds to the inhibition of discrete speech movements and the maintenance of tonic postural states in the vocal tract. Our findings identify consistencies between the neural control of posture in speech and what is previously reported in gross motor contexts, providing support for a unified theory of postural control across gross and fine motor skills.
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Affiliation(s)
- Eric Easthope
- Human Communication Technologies Lab, Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Arian Shamei
- Integrated Speech Research Lab, Department of Linguistics, University of British Columbia, Vancouver, BC, Canada
| | - Yadong Liu
- Integrated Speech Research Lab, Department of Linguistics, University of British Columbia, Vancouver, BC, Canada
| | - Bryan Gick
- Integrated Speech Research Lab, Department of Linguistics, University of British Columbia, Vancouver, BC, Canada
- Haskins Laboratories, New Haven, CT, United States
| | - Sidney Fels
- Human Communication Technologies Lab, Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
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25
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de Vries SEJ, Siegle JH, Koch C. Sharing neurophysiology data from the Allen Brain Observatory. eLife 2023; 12:e85550. [PMID: 37432073 PMCID: PMC10335829 DOI: 10.7554/elife.85550] [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: 12/13/2022] [Accepted: 06/27/2023] [Indexed: 07/12/2023] Open
Abstract
Nullius in verba ('trust no one'), chosen as the motto of the Royal Society in 1660, implies that independently verifiable observations-rather than authoritative claims-are a defining feature of empirical science. As the complexity of modern scientific instrumentation has made exact replications prohibitive, sharing data is now essential for ensuring the trustworthiness of one's findings. While embraced in spirit by many, in practice open data sharing remains the exception in contemporary systems neuroscience. Here, we take stock of the Allen Brain Observatory, an effort to share data and metadata associated with surveys of neuronal activity in the visual system of laboratory mice. Data from these surveys have been used to produce new discoveries, to validate computational algorithms, and as a benchmark for comparison with other data, resulting in over 100 publications and preprints to date. We distill some of the lessons learned about open surveys and data reuse, including remaining barriers to data sharing and what might be done to address these.
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26
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Claar LD, Rembado I, Kuyat JR, Russo S, Marks LC, Olsen SR, Koch C. Cortico-thalamo-cortical interactions modulate electrically evoked EEG responses in mice. eLife 2023; 12:RP84630. [PMID: 37358562 DOI: 10.7554/elife.84630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/27/2023] Open
Abstract
Perturbational complexity analysis predicts the presence of consciousness in volunteers and patients by stimulating the brain with brief pulses, recording EEG responses, and computing their spatiotemporal complexity. We examined the underlying neural circuits in mice by directly stimulating cortex while recording with EEG and Neuropixels probes during wakefulness and isoflurane anesthesia. When mice are awake, stimulation of deep cortical layers reliably evokes locally a brief pulse of excitation, followed by a biphasic sequence of 120 ms profound off period and a rebound excitation. A similar pattern, partially attributed to burst spiking, is seen in thalamic nuclei and is associated with a pronounced late component in the evoked EEG. We infer that cortico-thalamo-cortical interactions drive the long-lasting evoked EEG signals elicited by deep cortical stimulation during the awake state. The cortical and thalamic off period and rebound excitation, and the late component in the EEG, are reduced during running and absent during anesthesia.
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Affiliation(s)
- Leslie D Claar
- MindScope Program, Allen Institute, Seattle, United States
| | - Irene Rembado
- MindScope Program, Allen Institute, Seattle, United States
| | | | - Simone Russo
- MindScope Program, Allen Institute, Seattle, United States
- Department of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy
| | - Lydia C Marks
- MindScope Program, Allen Institute, Seattle, United States
| | - Shawn R Olsen
- MindScope Program, Allen Institute, Seattle, United States
| | - Christof Koch
- MindScope Program, Allen Institute, Seattle, United States
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27
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Gillon CJ, Lecoq JA, Pina JE, Ahmed R, Billeh YN, Caldejon S, Groblewski P, Henley TM, Kato I, Lee E, Luviano J, Mace K, Nayan C, Nguyen TV, North K, Perkins J, Seid S, Valley MT, Williford A, Bengio Y, Lillicrap TP, Zylberberg J, Richards BA. Responses of pyramidal cell somata and apical dendrites in mouse visual cortex over multiple days. Sci Data 2023; 10:287. [PMID: 37198203 DOI: 10.1038/s41597-023-02214-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 05/05/2023] [Indexed: 05/19/2023] Open
Abstract
The apical dendrites of pyramidal neurons in sensory cortex receive primarily top-down signals from associative and motor regions, while cell bodies and nearby dendrites are heavily targeted by locally recurrent or bottom-up inputs from the sensory periphery. Based on these differences, a number of theories in computational neuroscience postulate a unique role for apical dendrites in learning. However, due to technical challenges in data collection, little data is available for comparing the responses of apical dendrites to cell bodies over multiple days. Here we present a dataset collected through the Allen Institute Mindscope's OpenScope program that addresses this need. This dataset comprises high-quality two-photon calcium imaging from the apical dendrites and the cell bodies of visual cortical pyramidal neurons, acquired over multiple days in awake, behaving mice that were presented with visual stimuli. Many of the cell bodies and dendrite segments were tracked over days, enabling analyses of how their responses change over time. This dataset allows neuroscientists to explore the differences between apical and somatic processing and plasticity.
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Affiliation(s)
- Colleen J Gillon
- Department of Cell & Systems Biology, University of Toronto, Toronto, Ontario, Canada
- Department of Biological Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada
- Mila, Montréal, Québec, Canada
| | | | - Jason E Pina
- Department of Physics and Astronomy, York University, Toronto, Ontario, Canada
- Centre for Vision Research, York University, Toronto, Ontario, Canada
| | - Ruweida Ahmed
- Allen Institute, MindScope Program, Seattle, WA, USA
| | | | | | | | - Timothy M Henley
- Department of Physics and Astronomy, York University, Toronto, Ontario, Canada
- Centre for Vision Research, York University, Toronto, Ontario, Canada
| | - India Kato
- Allen Institute, MindScope Program, Seattle, WA, USA
| | - Eric Lee
- Allen Institute, MindScope Program, Seattle, WA, USA
| | | | - Kyla Mace
- Allen Institute, MindScope Program, Seattle, WA, USA
| | - Chelsea Nayan
- Allen Institute, MindScope Program, Seattle, WA, USA
| | | | - Kat North
- Allen Institute, MindScope Program, Seattle, WA, USA
| | - Jed Perkins
- Allen Institute, MindScope Program, Seattle, WA, USA
| | - Sam Seid
- Allen Institute, MindScope Program, Seattle, WA, USA
| | | | - Ali Williford
- Allen Institute, MindScope Program, Seattle, WA, USA
| | - Yoshua Bengio
- Mila, Montréal, Québec, Canada
- Département d'informatique et de recherche opérationnelle, Université de Montréal, Montréal, Québec, Canada
- Learning in Machines and Brains Program, Canadian Institute for Advanced Research, Toronto, Ontario, Canada
| | - Timothy P Lillicrap
- DeepMind, Inc, London, UK
- Centre for Computation, Mathematics and Physics in the Life Sciences and Experimental Biology, University College London, London, UK
| | - Joel Zylberberg
- Department of Physics and Astronomy, York University, Toronto, Ontario, Canada.
- Centre for Vision Research, York University, Toronto, Ontario, Canada.
- Learning in Machines and Brains Program, Canadian Institute for Advanced Research, Toronto, Ontario, Canada.
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada.
| | - Blake A Richards
- Mila, Montréal, Québec, Canada.
- Learning in Machines and Brains Program, Canadian Institute for Advanced Research, Toronto, Ontario, Canada.
- School of Computer Science, McGill University, Montréal, Québec, Canada.
- Department of Neurology & Neurosurgery, McGill University, Montréal, Québec, Canada.
- Montreal Neurological Institute, McGill University, Montréal, Québec, Canada.
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28
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Donoghue T, Cao R, Han CZ, Holman CM, Brandmeir NJ, Wang S, Jacobs J. Single neurons in the human medial temporal lobe flexibly shift representations across spatial and memory tasks. Hippocampus 2023; 33:600-615. [PMID: 37060325 PMCID: PMC10231142 DOI: 10.1002/hipo.23539] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 03/17/2023] [Accepted: 03/21/2023] [Indexed: 04/16/2023]
Abstract
Investigations into how individual neurons encode behavioral variables of interest have revealed specific representations in single neurons, such as place and object cells, as well as a wide range of cells with conjunctive encodings or mixed selectivity. However, as most experiments examine neural activity within individual tasks, it is currently unclear if and how neural representations change across different task contexts. Within this discussion, the medial temporal lobe is particularly salient, as it is known to be important for multiple behaviors including spatial navigation and memory, however the relationship between these functions is currently unclear. Here, to investigate how representations in single neurons vary across different task contexts in the medial temporal lobe, we collected and analyzed single-neuron activity from human participants as they completed a paired-task session consisting of a passive-viewing visual working memory and a spatial navigation and memory task. Five patients contributed 22 paired-task sessions, which were spike sorted together to allow for the same putative single neurons to be compared between the different tasks. Within each task, we replicated concept-related activations in the working memory task, as well as target-location and serial-position responsive cells in the navigation task. When comparing neuronal activity between tasks, we first established that a significant number of neurons maintained the same kind of representation, responding to stimuli presentations across tasks. Further, we found cells that changed the nature of their representation across tasks, including a significant number of cells that were stimulus responsive in the working memory task that responded to serial position in the spatial task. Overall, our results support a flexible encoding of multiple, distinct aspects of different tasks by single neurons in the human medial temporal lobe, whereby some individual neurons change the nature of their feature coding between task contexts.
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Affiliation(s)
| | - Runnan Cao
- Lane Department of Computer Science and Electrical Engineering, West Virginia University
| | - Claire Z. Han
- Department of Biomedical Engineering, Columbia University
| | | | | | - Shuo Wang
- Department of Radiology, Washington University in St. Louis
| | - Joshua Jacobs
- Department of Biomedical Engineering, Columbia University
- Department of Neurological Surgery, Columbia University
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29
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Olson RH, Kalafut NC, Wang D. MANGEM: a web app for Multimodal Analysis of Neuronal Gene expression, Electrophysiology and Morphology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.03.535322. [PMID: 37066386 PMCID: PMC10104012 DOI: 10.1101/2023.04.03.535322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Single-cell techniques have enabled the acquisition of multi-modal data, particularly for neurons, to characterize cellular functions. Patch-seq, for example, combines patch-clamp recording, cell imaging, and single-cell RNA-seq to obtain electrophysiology, morphology, and gene expression data from a single neuron. While these multi-modal data offer potential insights into neuronal functions, they can be heterogeneous and noisy. To address this, machine-learning methods have been used to align cells from different modalities onto a low-dimensional latent space, revealing multi-modal cell clusters. However, the use of those methods can be challenging for biologists and neuroscientists without computational expertise and also requires suitable computing infrastructure for computationally expensive methods. To address these issues, we developed a cloud-based web application, MANGEM (Multimodal Analysis of Neuronal Gene expression, Electrophysiology, and Morphology) at https://ctc.waisman.wisc.edu/mangem. MANGEM provides a step-by-step accessible and user-friendly interface to machine-learning alignment methods of neuronal multi-modal data while enabling real-time visualization of characteristics of raw and aligned cells. It can be run asynchronously for large-scale data alignment, provides users with various downstream analyses of aligned cells and visualizes the analytic results such as identifying multi-modal cell clusters of cells and detecting correlated genes with electrophysiological and morphological features. We demonstrated the usage of MANGEM by aligning Patch-seq multimodal data of neuronal cells in the mouse visual cortex.
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Affiliation(s)
| | - Noah Cohen Kalafut
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705 USA
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, 53706 USA
| | - Daifeng Wang
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705 USA
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, 53706 USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706 USA
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30
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Leprince E, Dard RF, Mortet S, Filippi C, Giorgi-Kurz M, Bourboulou R, Lenck-Santini PP, Picardo MA, Bocchio M, Baude A, Cossart R. Extrinsic control of the early postnatal CA1 hippocampal circuits. Neuron 2023; 111:888-902.e8. [PMID: 36608692 DOI: 10.1016/j.neuron.2022.12.013] [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: 06/03/2022] [Revised: 10/18/2022] [Accepted: 12/08/2022] [Indexed: 01/07/2023]
Abstract
The adult CA1 region of the hippocampus produces coordinated neuronal dynamics with minimal reliance on its extrinsic inputs. By contrast, neonatal CA1 is tightly linked to externally generated sensorimotor activity, but the circuit mechanisms underlying early synchronous activity in CA1 remain unclear. Here, using a combination of in vivo and ex vivo circuit mapping, calcium imaging, and electrophysiological recordings in mouse pups, we show that early dynamics in the ventro-intermediate CA1 are under the mixed influence of entorhinal (EC) and thalamic (VMT) inputs. Both VMT and EC can drive internally generated synchronous events ex vivo. However, movement-related population bursts detected in vivo are exclusively driven by the EC. These differential effects on synchrony reflect the different intrahippocampal targets of these inputs. Hence, cortical and subcortical pathways act differently on the neonatal CA1, implying distinct contributions to the development of the hippocampal microcircuit and related cognitive maps.
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Affiliation(s)
- Erwan Leprince
- Aix Marseille University, INSERM, INMED (UMR1249), Turing Centre for Living systems, Marseille, France
| | - Robin F Dard
- Aix Marseille University, INSERM, INMED (UMR1249), Turing Centre for Living systems, Marseille, France
| | - Salomé Mortet
- Aix Marseille University, INSERM, INMED (UMR1249), Turing Centre for Living systems, Marseille, France
| | - Caroline Filippi
- Aix Marseille University, INSERM, INMED (UMR1249), Turing Centre for Living systems, Marseille, France
| | - Marie Giorgi-Kurz
- Aix Marseille University, INSERM, INMED (UMR1249), Turing Centre for Living systems, Marseille, France
| | - Romain Bourboulou
- Department of Cell and Developmental Biology, University College London, London, UK
| | | | - Michel A Picardo
- Aix Marseille University, INSERM, INMED (UMR1249), Turing Centre for Living systems, Marseille, France
| | - Marco Bocchio
- Aix Marseille University, INSERM, INMED (UMR1249), Turing Centre for Living systems, Marseille, France; Department of Psychology, Durham University, Durham, UK
| | - Agnès Baude
- Aix Marseille University, INSERM, INMED (UMR1249), Turing Centre for Living systems, Marseille, France
| | - Rosa Cossart
- Aix Marseille University, INSERM, INMED (UMR1249), Turing Centre for Living systems, Marseille, France.
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31
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Bonacchi N, Chapuis GA, Churchland AK, DeWitt EEJ, Faulkner M, Harris KD, Huntenburg JM, Hunter M, Laranjeira IC, Rossant C, Sasaki M, Schartner MM, Shen S, Steinmetz NA, Walker EY, West SJ, Winter O, Wells MJ. A modular architecture for organizing, processing and sharing neurophysiology data. Nat Methods 2023; 20:403-407. [PMID: 36864199 PMCID: PMC7614641 DOI: 10.1038/s41592-022-01742-6] [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: 01/27/2020] [Accepted: 11/21/2022] [Indexed: 03/04/2023]
Abstract
We describe an architecture for organizing, integrating and sharing neurophysiology data within a single laboratory or across a group of collaborators. It comprises a database linking data files to metadata and electronic laboratory notes; a module collecting data from multiple laboratories into one location; a protocol for searching and sharing data and a module for automatic analyses that populates a website. These modules can be used together or individually, by single laboratories or worldwide collaborations.
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Affiliation(s)
| | - Gaelle A Chapuis
- Institute of Neurology, University College London, London, UK
- Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
| | - Anne K Churchland
- Department of Neurobiology, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Mayo Faulkner
- Institute of Neurology, University College London, London, UK
| | | | | | - Max Hunter
- Institute of Neurology, University College London, London, UK
| | | | - Cyrille Rossant
- Institute of Neurology, University College London, London, UK
| | | | | | | | | | | | - Steven J West
- Sainsbury-Wellcome Centre, University College London, London, UK
| | | | - Miles J Wells
- Institute of Neurology, University College London, London, UK
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32
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Donoghue T, Cao R, Han CZ, Holman CM, Brandmeir NJ, Wang S, Jacobs J. Single neurons in the human medial temporal lobe flexibly shift representations across spatial and memory tasks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.22.529437. [PMID: 36865334 PMCID: PMC9980106 DOI: 10.1101/2023.02.22.529437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Investigations into how individual neurons encode behavioral variables of interest have revealed specific representations in single neurons, such as place and object cells, as well as a wide range of cells with conjunctive encodings or mixed selectivity. However, as most experiments examine neural activity within individual tasks, it is currently unclear if and how neural representations change across different task contexts. Within this discussion, the medial temporal lobe is particularly salient, as it is known to be important for multiple behaviors including spatial navigation and memory, however the relationship between these functions is currently unclear. Here, to investigate how representations in single neurons vary across different task contexts in the MTL, we collected and analyzed single-neuron activity from human participants as they completed a paired-task session consisting of a passive-viewing visual working memory and a spatial navigation and memory task. Five patients contributed 22 paired-task sessions, which were spike sorted together to allow for the same putative single neurons to be compared between the different tasks. Within each task, we replicated concept-related activations in the working memory task, as well as target-location and serial-position responsive cells in the navigation task. When comparing neuronal activity between tasks, we first established that a significant number of neurons maintained the same kind of representation, responding to stimuli presentations across tasks. Further, we found cells that changed the nature of their representation across tasks, including a significant number of cells that were stimulus responsive in the working memory task that responded to serial position in the spatial task. Overall, our results support a flexible encoding of multiple, distinct aspects of different tasks by single neurons in the human MTL, whereby some individual neurons change the nature of their feature coding between task contexts.
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Affiliation(s)
| | - Runnan Cao
- Lane Department of Computer Science and Electrical Engineering, West Virginia University
| | - Claire Z Han
- Department of Biomedical Engineering, Columbia University
| | | | | | - Shuo Wang
- Department of Radiology, Washington University in St. Louis
| | - Joshua Jacobs
- Department of Biomedical Engineering, Columbia University
- Department of Neurological Surgery, Columbia University
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33
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Mittal D, Mease R, Kuner T, Flor H, Kuner R, Andoh J. Data management strategy for a collaborative research center. Gigascience 2022; 12:giad049. [PMID: 37401720 PMCID: PMC10318494 DOI: 10.1093/gigascience/giad049] [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: 09/23/2022] [Revised: 02/20/2023] [Accepted: 06/11/2023] [Indexed: 07/05/2023] Open
Abstract
The importance of effective research data management (RDM) strategies to support the generation of Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience data grows with each advance in data acquisition techniques and research methods. To maximize the impact of diverse research strategies, multidisciplinary, large-scale neuroscience research consortia face a number of unsolved challenges in RDM. While open science principles are largely accepted, it is practically difficult for researchers to prioritize RDM over other pressing demands. The implementation of a coherent, executable RDM plan for consortia spanning animal, human, and clinical studies is becoming increasingly challenging. Here, we present an RDM strategy implemented for the Heidelberg Collaborative Research Consortium. Our consortium combines basic and clinical research in diverse populations (animals and humans) and produces highly heterogeneous and multimodal research data (e.g., neurophysiology, neuroimaging, genetics, behavior). We present a concrete strategy for initiating early-stage RDM and FAIR data generation for large-scale collaborative research consortia, with a focus on sustainable solutions that incentivize incremental RDM while respecting research-specific requirements.
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Affiliation(s)
- Deepti Mittal
- Institute of Pharmacology, Heidelberg University, 69120 Heidelberg, Germany
| | - Rebecca Mease
- Institute of Physiology and Pathophysiology, Heidelberg University, 69120 Heidelberg, Germany
| | - Thomas Kuner
- Institute for Anatomy and Cell Biology, Heidelberg University, 69120 Mannheim, Germany
| | - Herta Flor
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
| | - Rohini Kuner
- Institute of Pharmacology, Heidelberg University, 69120 Heidelberg, Germany
| | - Jamila Andoh
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
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