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Vohra SK, Harth P, Isoe Y, Bahl A, Fotowat H, Engert F, Hege HC, Baum D. A Visual Interface for Exploring Hypotheses About Neural Circuits. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024; 30:3945-3958. [PMID: 37022819 PMCID: PMC11252567 DOI: 10.1109/tvcg.2023.3243668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
One of the fundamental problems in neurobiological research is to understand how neural circuits generate behaviors in response to sensory stimuli. Elucidating such neural circuits requires anatomical and functional information about the neurons that are active during the processing of the sensory information and generation of the respective response, as well as an identification of the connections between these neurons. With modern imaging techniques, both morphological properties of individual neurons as well as functional information related to sensory processing, information integration and behavior can be obtained. Given the resulting information, neurobiologists are faced with the task of identifying the anatomical structures down to individual neurons that are linked to the studied behavior and the processing of the respective sensory stimuli. Here, we present a novel interactive tool that assists neurobiologists in the aforementioned tasks by allowing them to extract hypothetical neural circuits constrained by anatomical and functional data. Our approach is based on two types of structural data: brain regions that are anatomically or functionally defined, and morphologies of individual neurons. Both types of structural data are interlinked and augmented with additional information. The presented tool allows the expert user to identify neurons using Boolean queries. The interactive formulation of these queries is supported by linked views, using, among other things, two novel 2D abstractions of neural circuits. The approach was validated in two case studies investigating the neural basis of vision-based behavioral responses in zebrafish larvae. Despite this particular application, we believe that the presented tool will be of general interest for exploring hypotheses about neural circuits in other species, genera and taxa.
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2
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Ramaswamy S. Data-driven multiscale computational models of cortical and subcortical regions. Curr Opin Neurobiol 2024; 85:102842. [PMID: 38320453 DOI: 10.1016/j.conb.2024.102842] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 01/04/2024] [Accepted: 01/05/2024] [Indexed: 02/08/2024]
Abstract
Data-driven computational models of neurons, synapses, microcircuits, and mesocircuits have become essential tools in modern brain research. The goal of these multiscale models is to integrate and synthesize information from different levels of brain organization, from cellular properties, dendritic excitability, and synaptic dynamics to microcircuits, mesocircuits, and ultimately behavior. This article surveys recent advances in the genesis of data-driven computational models of mammalian neural networks in cortical and subcortical areas. I discuss the challenges and opportunities in developing data-driven multiscale models, including the need for interdisciplinary collaborations, the importance of model validation and comparison, and the potential impact on basic and translational neuroscience research. Finally, I highlight future directions and emerging technologies that will enable more comprehensive and predictive data-driven models of brain function and dysfunction.
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Affiliation(s)
- Srikanth Ramaswamy
- Neural Circuits Laboratory, Biosciences Institute, Newcastle University, Newcastle Upon Tyne, NE2 4HH, United Kingdom.
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Anneser L, Satou C, Hotz HR, Friedrich RW. Molecular organization of neuronal cell types and neuromodulatory systems in the zebrafish telencephalon. Curr Biol 2024; 34:298-312.e4. [PMID: 38157860 PMCID: PMC10808507 DOI: 10.1016/j.cub.2023.12.003] [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: 10/03/2023] [Revised: 11/30/2023] [Accepted: 12/04/2023] [Indexed: 01/03/2024]
Abstract
The function of neuronal networks is determined not only by synaptic connectivity but also by neuromodulatory systems that broadcast information via distributed connections and volume transmission. To understand the molecular constraints that organize neuromodulatory signaling in the telencephalon of adult zebrafish, we used transcriptomics and additional approaches to delineate cell types, to determine their phylogenetic conservation, and to map the expression of marker genes at high granularity. The combinatorial expression of GPCRs and cell-type markers indicates that all neuronal cell types are subject to modulation by multiple monoaminergic systems and distinct combinations of neuropeptides. Individual cell types were associated with multiple (typically >30) neuromodulatory signaling networks but expressed only a few diagnostic GPCRs at high levels, suggesting that different neuromodulatory systems act in combination, albeit with unequal weights. These results provide a detailed map of cell types and brain areas in the zebrafish telencephalon, identify core components of neuromodulatory networks, highlight the cell-type specificity of neuropeptides and GPCRs, and begin to decipher the logic of combinatorial neuromodulation.
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Affiliation(s)
- Lukas Anneser
- Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland
| | - Chie Satou
- Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland
| | - Hans-Rudolf Hotz
- Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland
| | - Rainer W Friedrich
- Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland; University of Basel, 4003 Basel, Switzerland.
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Markert SM. Studying zebrafish nervous system structure and function in health and disease with electron microscopy. Dev Growth Differ 2023; 65:502-516. [PMID: 37740826 DOI: 10.1111/dgd.12890] [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: 05/31/2023] [Revised: 09/15/2023] [Accepted: 09/19/2023] [Indexed: 09/25/2023]
Abstract
Zebrafish (Danio rerio) is a well-established model for studying the nervous system. Findings in zebrafish often inform studies on human diseases of the nervous system and provide crucial insight into disease mechanisms. The functions of the nervous system often rely on communication between neurons. Signal transduction is achieved via release of signaling molecules in the form of neuropeptides or neurotransmitters at synapses. Snapshots of membrane dynamics of these processes are imaged by electron microscopy. Electron microscopy can reveal ultrastructure and thus synaptic processes. This is crucial both for mapping synaptic connections and for investigating synaptic functions. In addition, via volumetric electron microscopy, the overall architecture of the nervous system becomes accessible, where structure can inform function. Electron microscopy is thus of particular value for studying the nervous system. However, today a plethora of electron microscopy techniques and protocols exist. Which technique is most suitable highly depends on the research question and scope as well as on the type of tissue that is examined. This review gives an overview of the electron microcopy techniques used on the zebrafish nervous system. It aims to give researchers a guide on which techniques are suitable for their specific questions and capabilities as well as an overview of the capabilities of electron microscopy in neurobiological research in the zebrafish model.
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Ji P, Wang Y, Peron T, Li C, Nagler J, Du J. Structure and function in artificial, zebrafish and human neural networks. Phys Life Rev 2023; 45:74-111. [PMID: 37182376 DOI: 10.1016/j.plrev.2023.04.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 04/20/2023] [Indexed: 05/16/2023]
Abstract
Network science provides a set of tools for the characterization of the structure and functional behavior of complex systems. Yet a major problem is to quantify how the structural domain is related to the dynamical one. In other words, how the diversity of dynamical states of a system can be predicted from the static network structure? Or the reverse problem: starting from a set of signals derived from experimental recordings, how can one discover the network connections or the causal relations behind the observed dynamics? Despite the advances achieved over the last two decades, many challenges remain concerning the study of the structure-dynamics interplay of complex systems. In neuroscience, progress is typically constrained by the low spatio-temporal resolution of experiments and by the lack of a universal inferring framework for empirical systems. To address these issues, applications of network science and artificial intelligence to neural data have been rapidly growing. In this article, we review important recent applications of methods from those fields to the study of the interplay between structure and functional dynamics of human and zebrafish brain. We cover the selection of topological features for the characterization of brain networks, inference of functional connections, dynamical modeling, and close with applications to both the human and zebrafish brain. This review is intended to neuroscientists who want to become acquainted with techniques from network science, as well as to researchers from the latter field who are interested in exploring novel application scenarios in neuroscience.
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Affiliation(s)
- Peng Ji
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Yufan Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai 200031, China
| | - Thomas Peron
- Institute of Mathematics and Computer Science, University of São Paulo, São Carlos 13566-590, São Paulo, Brazil.
| | - Chunhe Li
- Shanghai Center for Mathematical Sciences and School of Mathematical Sciences, Fudan University, Shanghai 200433, China; Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China.
| | - Jan Nagler
- Deep Dynamics, Frankfurt School of Finance & Management, Frankfurt, Germany; Centre for Human and Machine Intelligence, Frankfurt School of Finance & Management, Frankfurt, Germany
| | - Jiulin Du
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai 200031, China.
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Du T, Zhou X, Zhang RDY, Du XF. Roles of Ca2+ activity in injury-induced migration of microglia in zebrafish in vivo. Biochem Biophys Rep 2022; 32:101340. [PMID: 36120493 PMCID: PMC9471311 DOI: 10.1016/j.bbrep.2022.101340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 08/21/2022] [Accepted: 08/30/2022] [Indexed: 10/28/2022] Open
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Svara F, Förster D, Kubo F, Januszewski M, Dal Maschio M, Schubert PJ, Kornfeld J, Wanner AA, Laurell E, Denk W, Baier H. Automated synapse-level reconstruction of neural circuits in the larval zebrafish brain. Nat Methods 2022; 19:1357-1366. [PMID: 36280717 PMCID: PMC9636024 DOI: 10.1038/s41592-022-01621-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 08/22/2022] [Indexed: 12/29/2022]
Abstract
Dense reconstruction of synaptic connectivity requires high-resolution electron microscopy images of entire brains and tools to efficiently trace neuronal wires across the volume. To generate such a resource, we sectioned and imaged a larval zebrafish brain by serial block-face electron microscopy at a voxel size of 14 × 14 × 25 nm3. We segmented the resulting dataset with the flood-filling network algorithm, automated the detection of chemical synapses and validated the results by comparisons to transmission electron microscopic images and light-microscopic reconstructions. Neurons and their connections are stored in the form of a queryable and expandable digital address book. We reconstructed a network of 208 neurons involved in visual motion processing, most of them located in the pretectum, which had been functionally characterized in the same specimen by two-photon calcium imaging. Moreover, we mapped all 407 presynaptic and postsynaptic partners of two superficial interneurons in the tectum. The resource developed here serves as a foundation for synaptic-resolution circuit analyses in the zebrafish nervous system.
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Affiliation(s)
- Fabian Svara
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
- Max Planck Institute for Neurobiology of Behavior - caesar, Bonn, Germany
- ariadne.ai ag, Buchrain, Switzerland
| | - Dominique Förster
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Fumi Kubo
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
- Center for Frontier Research, National Institute of Genetics, Mishima, Japan
| | | | - Marco Dal Maschio
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | | | - Jörgen Kornfeld
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
| | - Adrian A Wanner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Paul Scherrer Institute (PSI), Villigen, Switzerland
| | - Eva Laurell
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
| | - Winfried Denk
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
| | - Herwig Baier
- Max Planck Institute for Biological Intelligence, Martinsried, Germany.
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Computational methods for ultrastructural analysis of synaptic complexes. Curr Opin Neurobiol 2022; 76:102611. [PMID: 35952541 DOI: 10.1016/j.conb.2022.102611] [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: 02/28/2022] [Revised: 06/27/2022] [Accepted: 06/28/2022] [Indexed: 11/21/2022]
Abstract
Electron microscopy (EM) provided fundamental insights about the ultrastructure of neuronal synapses. The large amount of information present in the contemporary EM datasets precludes a thorough assessment by visual inspection alone, thus requiring computational methods for the analysis of the data. Here, I review image processing software methods ranging from membrane tracing in large volume datasets to high resolution structures of synaptic complexes. Particular attention is payed to molecular level analysis provided by recent cryo-electron microscopy and tomography methods.
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Liu TX, Davoudian PA, Lizbinski KM, Jeanne JM. Connectomic features underlying diverse synaptic connection strengths and subcellular computation. Curr Biol 2022; 32:559-569.e5. [PMID: 34914905 PMCID: PMC8825683 DOI: 10.1016/j.cub.2021.11.056] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 11/02/2021] [Accepted: 11/23/2021] [Indexed: 11/28/2022]
Abstract
Connectomes generated from electron microscopy images of neural tissue unveil the complex morphology of every neuron and the locations of every synapse interconnecting them. These wiring diagrams may also enable inference of synaptic and neuronal biophysics, such as the functional weights of synaptic connections, but this requires integration with physiological data to properly parameterize. Working with a stereotyped olfactory network in the Drosophila brain, we make direct comparisons of the anatomy and physiology of diverse neurons and synapses with subcellular and subthreshold resolution. We find that synapse density and location jointly predict the amplitude of the somatic postsynaptic potential evoked by a single presynaptic spike. Biophysical models fit to data predict that electrical compartmentalization allows axon and dendrite arbors to balance independent and interacting computations. These findings begin to fill the gap between connectivity maps and activity maps, which should enable new hypotheses about how network structure constrains network function.
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Affiliation(s)
- Tony X. Liu
- Department of Neuroscience, Yale University. 333 Cedar Street, New Haven, CT 06510,These authors contributed equally
| | - Pasha A. Davoudian
- MD/PhD Program, Yale School of Medicine. 333 Cedar Street, New Haven, CT 06510,These authors contributed equally
| | - Kristyn M. Lizbinski
- Department of Neuroscience, Yale University. 333 Cedar Street, New Haven, CT 06510,These authors contributed equally
| | - James M. Jeanne
- Department of Neuroscience, Yale University. 333 Cedar Street, New Haven, CT 06510,Lead contact,Correspondence: , Twitter: @neurojeanne
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