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Chen W, Ge X, Zhang Q, Natan RG, Fan JL, Scanziani M, Ji N. High-throughput volumetric mapping of synaptic transmission. Nat Methods 2024:10.1038/s41592-024-02309-3. [PMID: 38898094 DOI: 10.1038/s41592-024-02309-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 05/15/2024] [Indexed: 06/21/2024]
Abstract
Volumetric imaging of synaptic transmission in vivo requires high spatial and high temporal resolution. Shaping the wavefront of two-photon fluorescence excitation light, we developed Bessel-droplet foci for high-contrast and high-resolution volumetric imaging of synapses. Applying our method to imaging glutamate release, we demonstrated high-throughput mapping of excitatory inputs at >1,000 synapses per volume and >500 dendritic spines per neuron in vivo and unveiled previously unseen features of functional synaptic organization in the mouse primary visual cortex.
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Affiliation(s)
- Wei Chen
- Department of Physics, University of California, Berkeley, CA, USA
- School of Mechanical Science and Engineering - Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan, China
| | - Xinxin Ge
- Department of Physiology, University of California, San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Qinrong Zhang
- Department of Physics, University of California, Berkeley, CA, USA
| | - Ryan G Natan
- Department of Physics, University of California, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
| | - Jiang Lan Fan
- Joint Bioengineering Graduate Program, University of California, Berkeley, CA, USA
- Joint Bioengineering Graduate Program, University of California, San Francisco, CA, USA
| | - Massimo Scanziani
- Department of Physiology, University of California, San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Na Ji
- Department of Physics, University of California, Berkeley, CA, USA.
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA.
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA.
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
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2
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Bast A, Fruengel R, de Kock CPJ, Oberlaender M. Network-neuron interactions underlying sensory responses of layer 5 pyramidal tract neurons in barrel cortex. PLoS Comput Biol 2024; 20:e1011468. [PMID: 38626210 PMCID: PMC11051592 DOI: 10.1371/journal.pcbi.1011468] [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: 08/25/2023] [Revised: 04/26/2024] [Accepted: 03/14/2024] [Indexed: 04/18/2024] Open
Abstract
Neurons in the cerebral cortex receive thousands of synaptic inputs per second from thousands of presynaptic neurons. How the dendritic location of inputs, their timing, strength, and presynaptic origin, in conjunction with complex dendritic physiology, impact the transformation of synaptic input into action potential (AP) output remains generally unknown for in vivo conditions. Here, we introduce a computational approach to reveal which properties of the input causally underlie AP output, and how this neuronal input-output computation is influenced by the morphology and biophysical properties of the dendrites. We demonstrate that this approach allows dissecting of how different input populations drive in vivo observed APs. For this purpose, we focus on fast and broadly tuned responses that pyramidal tract neurons in layer 5 (L5PTs) of the rat barrel cortex elicit upon passive single whisker deflections. By reducing a multi-scale model that we reported previously, we show that three features are sufficient to predict with high accuracy the sensory responses and receptive fields of L5PTs under these specific in vivo conditions: the count of active excitatory versus inhibitory synapses preceding the response, their spatial distribution on the dendrites, and the AP history. Based on these three features, we derive an analytically tractable description of the input-output computation of L5PTs, which enabled us to dissect how synaptic input from thalamus and different cell types in barrel cortex contribute to these responses. We show that the input-output computation is preserved across L5PTs despite morphological and biophysical diversity of their dendrites. We found that trial-to-trial variability in L5PT responses, and cell-to-cell variability in their receptive fields, are sufficiently explained by variability in synaptic input from the network, whereas variability in biophysical and morphological properties have minor contributions. Our approach to derive analytically tractable models of input-output computations in L5PTs provides a roadmap to dissect network-neuron interactions underlying L5PT responses across different in vivo conditions and for other cell types.
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Affiliation(s)
- Arco Bast
- In Silico Brain Sciences Group, Max Planck Institute for Neurobiology of Behavior ˗ caesar, Bonn, Germany
- International Max Planck Research School (IMPRS) for Brain and Behavior, Bonn, Germany
| | - Rieke Fruengel
- In Silico Brain Sciences Group, Max Planck Institute for Neurobiology of Behavior ˗ caesar, Bonn, Germany
- International Max Planck Research School (IMPRS) for Brain and Behavior, Bonn, Germany
| | - Christiaan P. J. de Kock
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Marcel Oberlaender
- In Silico Brain Sciences Group, Max Planck Institute for Neurobiology of Behavior ˗ caesar, Bonn, Germany
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
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3
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Velasco I, Garcia-Cantero JJ, Brito JP, Bayona S, Pastor L, Mata S. NeuroEditor: a tool to edit and visualize neuronal morphologies. Front Neuroanat 2024; 18:1342762. [PMID: 38425804 PMCID: PMC10902916 DOI: 10.3389/fnana.2024.1342762] [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: 11/22/2023] [Accepted: 01/22/2024] [Indexed: 03/02/2024] Open
Abstract
The digital extraction of detailed neuronal morphologies from microscopy data is an essential step in the study of neurons. Ever since Cajal's work, the acquisition and analysis of neuron anatomy has yielded invaluable insight into the nervous system, which has led to our present understanding of many structural and functional aspects of the brain and the nervous system, well beyond the anatomical perspective. Obtaining detailed anatomical data, though, is not a simple task. Despite recent progress, acquiring neuron details still involves using labor-intensive, error prone methods that facilitate the introduction of inaccuracies and mistakes. In consequence, getting reliable morphological tracings usually needs the completion of post-processing steps that require user intervention to ensure the extracted data accuracy. Within this framework, this paper presents NeuroEditor, a new software tool for visualization, editing and correction of previously reconstructed neuronal tracings. This tool has been developed specifically for alleviating the burden associated with the acquisition of detailed morphologies. NeuroEditor offers a set of algorithms that can automatically detect the presence of potential errors in tracings. The tool facilitates users to explore an error with a simple mouse click so that it can be corrected manually or, where applicable, automatically. In some cases, this tool can also propose a set of actions to automatically correct a particular type of error. Additionally, this tool allows users to visualize and compare the original and modified tracings, also providing a 3D mesh that approximates the neuronal membrane. The approximation of this mesh is computed and recomputed on-the-fly, reflecting any instantaneous changes during the tracing process. Moreover, NeuroEditor can be easily extended by users, who can program their own algorithms in Python and run them within the tool. Last, this paper includes an example showing how users can easily define a customized workflow by applying a sequence of editing operations. The edited morphology can then be stored, together with the corresponding 3D mesh that approximates the neuronal membrane.
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Affiliation(s)
- Ivan Velasco
- Department of Computer Science, Universidad Rey Juan Carlos (URJC), Tulipan, Madrid, Spain
| | - Juan J. Garcia-Cantero
- Department of Computer Science, Universidad Rey Juan Carlos (URJC), Tulipan, Madrid, Spain
- Center for Computational Simulation, Universidad Politecnica de Madrid, Madrid, Spain
| | - Juan P. Brito
- Center for Computational Simulation, Universidad Politecnica de Madrid, Madrid, Spain
- DLSIIS, ETSIINF, Universidad Politecnica de Madrid, Madrid, Spain
| | - Sofia Bayona
- Department of Computer Science, Universidad Rey Juan Carlos (URJC), Tulipan, Madrid, Spain
- Center for Computational Simulation, Universidad Politecnica de Madrid, Madrid, Spain
| | - Luis Pastor
- Department of Computer Science, Universidad Rey Juan Carlos (URJC), Tulipan, Madrid, Spain
- Center for Computational Simulation, Universidad Politecnica de Madrid, Madrid, Spain
| | - Susana Mata
- Department of Computer Science, Universidad Rey Juan Carlos (URJC), Tulipan, Madrid, Spain
- Center for Computational Simulation, Universidad Politecnica de Madrid, Madrid, Spain
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4
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Mehta K, Ljungquist B, Ogden J, Nanda S, Ascoli RG, Ng L, Ascoli GA. Online conversion of reconstructed neural morphologies into standardized SWC format. Nat Commun 2023; 14:7429. [PMID: 37973857 PMCID: PMC10654402 DOI: 10.1038/s41467-023-42931-x] [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: 03/23/2023] [Accepted: 10/26/2023] [Indexed: 11/19/2023] Open
Abstract
Digital reconstructions provide an accurate and reliable way to store, share, model, quantify, and analyze neural morphology. Continuous advances in cellular labeling, tissue processing, microscopic imaging, and automated tracing catalyzed a proliferation of software applications to reconstruct neural morphology. These computer programs typically encode the data in custom file formats. The resulting format heterogeneity severely hampers the interoperability and reusability of these valuable data. Among these many alternatives, the SWC file format has emerged as a popular community choice, coalescing a rich ecosystem of related neuroinformatics resources for tracing, visualization, analysis, and simulation. This report presents a standardized specification of the SWC file format. In addition, we introduce xyz2swc, a free online service that converts all 26 reconstruction formats (and 72 variations) described in the scientific literature into the SWC standard. The xyz2swc service is available open source through a user-friendly browser interface ( https://neuromorpho.org/xyz2swc/ui/ ) and an Application Programming Interface (API).
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Affiliation(s)
- Ketan Mehta
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - Bengt Ljungquist
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - James Ogden
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - Sumit Nanda
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - Ruben G Ascoli
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA.
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5
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Ghahremani P, Boorboor S, Mirhosseini P, Gudisagar C, Ananth M, Talmage D, Role LW, Kaufman AE. NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain Images. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2022; 28:4951-4965. [PMID: 34478372 DOI: 10.1109/tvcg.2021.3109460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
We introduce NeuroConstruct, a novel end-to-end application for the segmentation, registration, and visualization of brain volumes imaged using wide-field microscopy. NeuroConstruct offers a Segmentation Toolbox with various annotation helper functions that aid experts to effectively and precisely annotate micrometer resolution neurites. It also offers an automatic neurites segmentation using convolutional neuronal networks (CNN) trained by the Toolbox annotations and somas segmentation using thresholding. To visualize neurites in a given volume, NeuroConstruct offers a hybrid rendering by combining iso-surface rendering of high-confidence classified neurites, along with real-time rendering of raw volume using a 2D transfer function for voxel classification score versus voxel intensity value. For a complete reconstruction of the 3D neurites, we introduce a Registration Toolbox that provides automatic coarse-to-fine alignment of serially sectioned samples. The quantitative and qualitative analysis show that NeuroConstruct outperforms the state-of-the-art in all design aspects. NeuroConstruct was developed as a collaboration between computer scientists and neuroscientists, with an application to the study of cholinergic neurons, which are severely affected in Alzheimer's disease.
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6
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Lindow N, Brünig FN, Dercksen VJ, Fabig G, Kiewisz R, Redemann S, Müller-Reichert T, Prohaska S, Baum D. Semi-automatic stitching of filamentous structures in image stacks from serial-section electron tomography. J Microsc 2021; 284:25-44. [PMID: 34110027 DOI: 10.1111/jmi.13039] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 05/11/2021] [Accepted: 06/02/2021] [Indexed: 11/30/2022]
Abstract
We present a software-assisted workflow for the alignment and matching of filamentous structures across a three-dimensional (3D) stack of serial images. This is achieved by combining automatic methods, visual validation, and interactive correction. After the computation of an initial automatic matching, the user can continuously improve the result by interactively correcting landmarks or matches of filaments. Supported by a visual quality assessment of regions that have been already inspected, this allows a trade-off between quality and manual labour. The software tool was developed in an interdisciplinary collaboration between computer scientists and cell biologists to investigate cell division by quantitative 3D analysis of microtubules (MTs) in both mitotic and meiotic spindles. For this, each spindle is cut into a series of semi-thick physical sections, of which electron tomograms are acquired. The serial tomograms are then stitched and non-rigidly aligned to allow tracing and connecting of MTs across tomogram boundaries. In practice, automatic stitching alone provides only an incomplete solution, because large physical distortions and a low signal-to-noise ratio often cause experimental difficulties. To derive 3D models of spindles despite dealing with imperfect data related to sample preparation and subsequent data collection, semi-automatic validation and correction is required to remove stitching mistakes. However, due to the large number of MTs in spindles (up to 30k) and their resulting dense spatial arrangement, a naive inspection of each MT is too time-consuming. Furthermore, an interactive visualisation of the full image stack is hampered by the size of the data (up to 100 GB). Here, we present a specialised, interactive, semi-automatic solution that considers all requirements for large-scale stitching of filamentous structures in serial-section image stacks. To the best of our knowledge, it is the only currently available tool which is able to process data of the type and size presented here. The key to our solution is a careful design of the visualisation and interaction tools for each processing step to guarantee real-time response, and an optimised workflow that efficiently guides the user through datasets. The final solution presented here is the result of an iterative process with tight feedback loops between the involved computer scientists and cell biologists. LAY DESCRIPTION: Electron tomography of biological samples is used for a three-dimensional (3D) reconstruction of filamentous structures, such as microtubules (MTs) in mitotic and meiotic spindles. Large-scale electron tomography can be applied to increase the reconstructed volume for the visualisation of full spindles. For this, each spindle is cut into a series of semi-thick physical sections, from which electron tomograms are acquired. The serial tomograms are then stitched and non-rigidly aligned to allow tracing and connecting of MTs across tomogram boundaries. Previously, we presented fully automatic approaches for this 3D reconstruction pipeline. However, large volumes often suffer from imperfections (ie physical distortions) caused by the image acquisition process, making it difficult to apply fully automatic approaches for matching and stitching of numerous tomograms. Therefore, we developed an interactive, semi-automatic solution that considers all requirements for large-scale stitching of microtubules in image stacks of consecutive sections. We achieved this by combining automatic methods, visual validation and interactive error correction, thus allowing the user to continuously improve the result by interactively correcting landmarks or matches of filaments. We present large-scale reconstructions of spindles in which the automatic workflow failed and where different steps of manual corrections were needed. Our approach is also applicable to other biological samples showing 3D distributions of MTs in a number of different cellular contexts.
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Affiliation(s)
- Norbert Lindow
- Department of Visual and Data-Centric Computing, Zuse Institute Berlin, Berlin, Germany
| | - Florian N Brünig
- Department of Visual and Data-Centric Computing, Zuse Institute Berlin, Berlin, Germany
| | - Vincent J Dercksen
- Department of Visual and Data-Centric Computing, Zuse Institute Berlin, Berlin, Germany
| | - Gunar Fabig
- Experimental Center, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Robert Kiewisz
- Experimental Center, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Stefanie Redemann
- School of Medicine, Center for Membrane and Cell Physiology, University of Virginia, Charlottesville, Virginia.,School of Medicine, Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia.,School of Medicine, Department of Cell Biology, University of Virginia, Charlottesville, Virginia
| | - Thomas Müller-Reichert
- Experimental Center, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Steffen Prohaska
- Department of Visual and Data-Centric Computing, Zuse Institute Berlin, Berlin, Germany
| | - Daniel Baum
- Department of Visual and Data-Centric Computing, Zuse Institute Berlin, Berlin, Germany
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7
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Velasco I, Toharia P, Benavides-Piccione R, Fernaud-Espinosa I, Brito JP, Mata S, DeFelipe J, Pastor L, Bayona S. Neuronize v2: Bridging the Gap Between Existing Proprietary Tools to Optimize Neuroscientific Workflows. Front Neuroanat 2020; 14:585793. [PMID: 33192345 PMCID: PMC7646287 DOI: 10.3389/fnana.2020.585793] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 09/07/2020] [Indexed: 12/15/2022] Open
Abstract
Knowledge about neuron morphology is key to understanding brain structure and function. There are a variety of software tools that are used to segment and trace the neuron morphology. However, these tools usually utilize proprietary formats. This causes interoperability problems since the information extracted with one tool cannot be used in other tools. This article aims to improve neuronal reconstruction workflows by facilitating the interoperability between two of the most commonly used software tools—Neurolucida (NL) and Imaris (Filament Tracer). The new functionality has been included in an existing tool—Neuronize—giving rise to its second version. Neuronize v2 makes it possible to automatically use the data extracted with Imaris Filament Tracer to generate a tracing with dendritic spine information that can be read directly by NL. It also includes some other new features, such as the ability to unify and/or correct inaccurately-formed meshes (i.e., dendritic spines) and to calculate new metrics. This tool greatly facilitates the process of neuronal reconstruction, bridging the gap between existing proprietary tools to optimize neuroscientific workflows.
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Affiliation(s)
- Ivan Velasco
- Department of Computer Science, Universidad Rey Juan Carlos, Madrid, Spain
| | - Pablo Toharia
- DATSI, ETSIINF, Universidad Politécnica de Madrid, Spain.,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid, Spain
| | - Ruth Benavides-Piccione
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid, Spain.,Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain.,Instituto Cajal (CSIC), Madrid, Spain
| | - Isabel Fernaud-Espinosa
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid, Spain.,Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain.,Instituto Cajal (CSIC), Madrid, Spain
| | - Juan P Brito
- DLSIIS, ETSIINF, Universidad Politécnica de Madrid, Madrid, Spain.,Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain
| | - Susana Mata
- Department of Computer Science, Universidad Rey Juan Carlos, Madrid, Spain.,Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain
| | - Javier DeFelipe
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Madrid, Spain.,Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain.,Instituto Cajal (CSIC), Madrid, Spain
| | - Luis Pastor
- Department of Computer Science, Universidad Rey Juan Carlos, Madrid, Spain.,Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain
| | - Sofia Bayona
- Department of Computer Science, Universidad Rey Juan Carlos, Madrid, Spain.,Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain
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8
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Huang Q, Chen Y, Liu S, Xu C, Cao T, Xu Y, Wang X, Rao G, Li A, Zeng S, Quan T. Weakly Supervised Learning of 3D Deep Network for Neuron Reconstruction. Front Neuroanat 2020; 14:38. [PMID: 32848636 PMCID: PMC7399060 DOI: 10.3389/fnana.2020.00038] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 06/05/2020] [Indexed: 11/13/2022] Open
Abstract
Digital reconstruction or tracing of 3D tree-like neuronal structures from optical microscopy images is essential for understanding the functionality of neurons and reveal the connectivity of neuronal networks. Despite the existence of numerous tracing methods, reconstructing a neuron from highly noisy images remains challenging, particularly for neurites with low and inhomogeneous intensities. Conducting deep convolutional neural network (CNN)-based segmentation prior to neuron tracing facilitates an approach to solving this problem via separation of weak neurites from a noisy background. However, large manual annotations are needed in deep learning-based methods, which is labor-intensive and limits the algorithm's generalization for different datasets. In this study, we present a weakly supervised learning method of a deep CNN for neuron reconstruction without manual annotations. Specifically, we apply a 3D residual CNN as the architecture for discriminative neuronal feature extraction. We construct the initial pseudo-labels (without manual segmentation) of the neuronal images on the basis of an existing automatic tracing method. A weakly supervised learning framework is proposed via iterative training of the CNN model for improved prediction and refining of the pseudo-labels to update training samples. The pseudo-label was iteratively modified via mining and addition of weak neurites from the CNN predicted probability map on the basis of their tubularity and continuity. The proposed method was evaluated on several challenging images from the public BigNeuron and Diadem datasets, to fMOST datasets. Owing to the adaption of 3D deep CNNs and weakly supervised learning, the presented method demonstrates effective detection of weak neurites from noisy images and achieves results similar to those of the CNN model with manual annotations. The tracing performance was significantly improved by the proposed method on both small and large datasets (>100 GB). Moreover, the proposed method proved to be superior to several novel tracing methods on original images. The results obtained on various large-scale datasets demonstrated the generalization and high precision achieved by the proposed method for neuron reconstruction.
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Affiliation(s)
- Qing Huang
- Wuhan National Laboratory for Optoelectronics-Huazhong, Britton Chance Center for Biomedical Photonics, University of Science and Technology, Wuhan, China
- Ministry of Education (MoE) Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Yijun Chen
- Wuhan National Laboratory for Optoelectronics-Huazhong, Britton Chance Center for Biomedical Photonics, University of Science and Technology, Wuhan, China
- Ministry of Education (MoE) Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Shijie Liu
- School of Mathematics and Physics, China University of Geosciences, Wuhan, China
| | - Cheng Xu
- Wuhan National Laboratory for Optoelectronics-Huazhong, Britton Chance Center for Biomedical Photonics, University of Science and Technology, Wuhan, China
- Ministry of Education (MoE) Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Tingting Cao
- Wuhan National Laboratory for Optoelectronics-Huazhong, Britton Chance Center for Biomedical Photonics, University of Science and Technology, Wuhan, China
- Ministry of Education (MoE) Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Yongchao Xu
- School of Electronics Information and Communications, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaojun Wang
- Wuhan National Laboratory for Optoelectronics-Huazhong, Britton Chance Center for Biomedical Photonics, University of Science and Technology, Wuhan, China
- Ministry of Education (MoE) Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Gong Rao
- Wuhan National Laboratory for Optoelectronics-Huazhong, Britton Chance Center for Biomedical Photonics, University of Science and Technology, Wuhan, China
- Ministry of Education (MoE) Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Anan Li
- Wuhan National Laboratory for Optoelectronics-Huazhong, Britton Chance Center for Biomedical Photonics, University of Science and Technology, Wuhan, China
- Ministry of Education (MoE) Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Shaoqun Zeng
- Wuhan National Laboratory for Optoelectronics-Huazhong, Britton Chance Center for Biomedical Photonics, University of Science and Technology, Wuhan, China
- Ministry of Education (MoE) Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Tingwei Quan
- Wuhan National Laboratory for Optoelectronics-Huazhong, Britton Chance Center for Biomedical Photonics, University of Science and Technology, Wuhan, China
- Ministry of Education (MoE) Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
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9
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Grein S, Qi G, Queisser G. Density Visualization Pipeline: A Tool for Cellular and Network Density Visualization and Analysis. Front Comput Neurosci 2020; 14:42. [PMID: 32676020 PMCID: PMC7333680 DOI: 10.3389/fncom.2020.00042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 04/17/2020] [Indexed: 12/02/2022] Open
Abstract
Neuron classification is an important component in analyzing network structure and quantifying the effect of neuron topology on signal processing. Current quantification and classification approaches rely on morphology projection onto lower-dimensional spaces. In this paper a 3D visualization and quantification tool is presented. The Density Visualization Pipeline (DVP) computes, visualizes and quantifies the density distribution, i.e., the "mass" of interneurons. We use the DVP to characterize and classify a set of GABAergic interneurons. Classification of GABAergic interneurons is of crucial importance to understand on the one hand their various functions and on the other hand their ubiquitous appearance in the neocortex. 3D density map visualization and projection to the one-dimensional x, y, z subspaces show a clear distinction between the studied cells, based on these metrics. The DVP can be coupled to computational studies of the behavior of neurons and networks, in which network topology information is derived from DVP information. The DVP reads common neuromorphological file formats, e.g., Neurolucida XML files, NeuroMorpho.org SWC files and plain ASCII files. Full 3D visualization and projections of the density to 1D and 2D manifolds are supported by the DVP. All routines are embedded within the visual programming IDE VRL-Studio for Java which allows the definition and rapid modification of analysis workflows.
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Affiliation(s)
- Stephan Grein
- Department of Mathematics, Temple University, Philadelphia, PA, United States
| | - Guanxiao Qi
- Institute of Neuroscience and Medicine (INM-10), Research Centre Jülich, Jülich, Germany
| | - Gillian Queisser
- Department of Mathematics, Temple University, Philadelphia, PA, United States
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10
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Radojević M, Meijering E. Automated Neuron Reconstruction from 3D Fluorescence Microscopy Images Using Sequential Monte Carlo Estimation. Neuroinformatics 2020; 17:423-442. [PMID: 30542954 PMCID: PMC6594993 DOI: 10.1007/s12021-018-9407-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Microscopic images of neuronal cells provide essential structural information about the key constituents of the brain and form the basis of many neuroscientific studies. Computational analyses of the morphological properties of the captured neurons require first converting the structural information into digital tree-like reconstructions. Many dedicated computational methods and corresponding software tools have been and are continuously being developed with the aim to automate this step while achieving human-comparable reconstruction accuracy. This pursuit is hampered by the immense diversity and intricacy of neuronal morphologies as well as the often low quality and ambiguity of the images. Here we present a novel method we developed in an effort to improve the robustness of digital reconstruction against these complicating factors. The method is based on probabilistic filtering by sequential Monte Carlo estimation and uses prediction and update models designed specifically for tracing neuronal branches in microscopic image stacks. Moreover, it uses multiple probabilistic traces to arrive at a more robust, ensemble reconstruction. The proposed method was evaluated on fluorescence microscopy image stacks of single neurons and dense neuronal networks with expert manual annotations serving as the gold standard, as well as on synthetic images with known ground truth. The results indicate that our method performs well under varying experimental conditions and compares favorably to state-of-the-art alternative methods.
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Affiliation(s)
- Miroslav Radojević
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus University Medical Center, Rotterdam, The Netherlands.
| | - Erik Meijering
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus University Medical Center, Rotterdam, The Netherlands
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11
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Autophagy-dependent filopodial kinetics restrict synaptic partner choice during Drosophila brain wiring. Nat Commun 2020; 11:1325. [PMID: 32165611 PMCID: PMC7067798 DOI: 10.1038/s41467-020-14781-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Accepted: 01/31/2020] [Indexed: 12/26/2022] Open
Abstract
Brain wiring is remarkably precise, yet most neurons readily form synapses with incorrect partners when given the opportunity. Dynamic axon-dendritic positioning can restrict synaptogenic encounters, but the spatiotemporal interaction kinetics and their regulation remain essentially unknown inside developing brains. Here we show that the kinetics of axonal filopodia restrict synapse formation and partner choice for neurons that are not otherwise prevented from making incorrect synapses. Using 4D imaging in developing Drosophila brains, we show that filopodial kinetics are regulated by autophagy, a prevalent degradation mechanism whose role in brain development remains poorly understood. With surprising specificity, autophagosomes form in synaptogenic filopodia, followed by filopodial collapse. Altered autophagic degradation of synaptic building material quantitatively regulates synapse formation as shown by computational modeling and genetic experiments. Increased filopodial stability enables incorrect synaptic partnerships. Hence, filopodial autophagy restricts inappropriate partner choice through a process of kinetic exclusion that critically contributes to wiring specificity.
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12
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Egger R, Narayanan RT, Guest JM, Bast A, Udvary D, Messore LF, Das S, de Kock CPJ, Oberlaender M. Cortical Output Is Gated by Horizontally Projecting Neurons in the Deep Layers. Neuron 2019; 105:122-137.e8. [PMID: 31784285 PMCID: PMC6953434 DOI: 10.1016/j.neuron.2019.10.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 09/01/2019] [Accepted: 10/02/2019] [Indexed: 12/13/2022]
Abstract
Pyramidal tract neurons (PTs) represent the major output cell type of the mammalian neocortex. Here, we report the origins of the PTs’ ability to respond to a broad range of stimuli with onset latencies that rival or even precede those of their intracortical input neurons. We find that neurons with extensive horizontally projecting axons cluster around the deep-layer terminal fields of primary thalamocortical axons. The strategic location of these corticocortical neurons results in high convergence of thalamocortical inputs, which drive reliable sensory-evoked responses that precede those in other excitatory cell types. The resultant fast and horizontal stream of excitation provides PTs throughout the cortical area with input that acts to amplify additional inputs from thalamocortical and other intracortical populations. The fast onsets and broadly tuned characteristics of PT responses hence reflect a gating mechanism in the deep layers, which assures that sensory-evoked input can be reliably transformed into cortical output. Simulations predict in vivo responses for major output cell type of the neocortex Simulations reveal strategy how to test the origins of cortical output empirically Manipulations confirm that deep-layer corticocortical neurons gate cortical output Gating of cortical output originates from deep-layer thalamocortical input stratum
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Affiliation(s)
- Robert Egger
- Max Planck Research Group In Silico Brain Sciences, Center of Advanced European Studies and Research (caesar), Ludwig-Erhard-Allee 2, 53175 Bonn, Germany
| | - Rajeevan T Narayanan
- Max Planck Research Group In Silico Brain Sciences, Center of Advanced European Studies and Research (caesar), Ludwig-Erhard-Allee 2, 53175 Bonn, Germany
| | - Jason M Guest
- Max Planck Research Group In Silico Brain Sciences, Center of Advanced European Studies and Research (caesar), Ludwig-Erhard-Allee 2, 53175 Bonn, Germany
| | - Arco Bast
- Max Planck Research Group In Silico Brain Sciences, Center of Advanced European Studies and Research (caesar), Ludwig-Erhard-Allee 2, 53175 Bonn, Germany
| | - Daniel Udvary
- Max Planck Research Group In Silico Brain Sciences, Center of Advanced European Studies and Research (caesar), Ludwig-Erhard-Allee 2, 53175 Bonn, Germany
| | - Luis F Messore
- Max Planck Research Group In Silico Brain Sciences, Center of Advanced European Studies and Research (caesar), Ludwig-Erhard-Allee 2, 53175 Bonn, Germany
| | - Suman Das
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, VU Amsterdam, De Boelelaan 1085, 1081 Amsterdam, the Netherlands
| | - Christiaan P J de Kock
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, VU Amsterdam, De Boelelaan 1085, 1081 Amsterdam, the Netherlands
| | - Marcel Oberlaender
- Max Planck Research Group In Silico Brain Sciences, Center of Advanced European Studies and Research (caesar), Ludwig-Erhard-Allee 2, 53175 Bonn, Germany.
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13
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Özel MN, Kulkarni A, Hasan A, Brummer J, Moldenhauer M, Daumann IM, Wolfenberg H, Dercksen VJ, Kiral FR, Weiser M, Prohaska S, von Kleist M, Hiesinger PR. Serial Synapse Formation through Filopodial Competition for Synaptic Seeding Factors. Dev Cell 2019; 50:447-461.e8. [PMID: 31353313 DOI: 10.1016/j.devcel.2019.06.014] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 05/15/2019] [Accepted: 06/21/2019] [Indexed: 11/15/2022]
Abstract
Following axon pathfinding, growth cones transition from stochastic filopodial exploration to the formation of a limited number of synapses. How the interplay of filopodia and synapse assembly ensures robust connectivity in the brain has remained a challenging problem. Here, we developed a new 4D analysis method for filopodial dynamics and a data-driven computational model of synapse formation for R7 photoreceptor axons in developing Drosophila brains. Our live data support a "serial synapse formation" model, where at any time point only 1-2 "synaptogenic" filopodia suppress the synaptic competence of other filopodia through competition for synaptic seeding factors. Loss of the synaptic seeding factors Syd-1 and Liprin-α leads to a loss of this suppression, filopodial destabilization, and reduced synapse formation. The failure to form synapses can cause the destabilization and secondary retraction of axon terminals. Our model provides a filopodial "winner-takes-all" mechanism that ensures the formation of an appropriate number of synapses.
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Affiliation(s)
- M Neset Özel
- Division of Neurobiology, Institute for Biology, Freie Universität Berlin, 14195 Berlin, Germany; Neuroscience Graduate Program, UT Southwestern Medical Center Dallas, Dallas, TX 75390, USA
| | - Abhishek Kulkarni
- Division of Neurobiology, Institute for Biology, Freie Universität Berlin, 14195 Berlin, Germany
| | - Amr Hasan
- Division of Neurobiology, Institute for Biology, Freie Universität Berlin, 14195 Berlin, Germany
| | - Josephine Brummer
- Department of Visual Data Analysis, Zuse Institute Berlin, 14195 Berlin, Germany
| | - Marian Moldenhauer
- Computational Medicine and Numerical Mathematics, Zuse Institute Berlin, 14195 Berlin, Germany; Department of Mathematics and Informatics, Freie Universität Berlin, 14195 Berlin, Germany
| | - Ilsa-Maria Daumann
- Division of Neurobiology, Institute for Biology, Freie Universität Berlin, 14195 Berlin, Germany
| | - Heike Wolfenberg
- Division of Neurobiology, Institute for Biology, Freie Universität Berlin, 14195 Berlin, Germany
| | - Vincent J Dercksen
- Department of Visual Data Analysis, Zuse Institute Berlin, 14195 Berlin, Germany
| | - F Ridvan Kiral
- Division of Neurobiology, Institute for Biology, Freie Universität Berlin, 14195 Berlin, Germany
| | - Martin Weiser
- Computational Medicine and Numerical Mathematics, Zuse Institute Berlin, 14195 Berlin, Germany
| | - Steffen Prohaska
- Department of Visual Data Analysis, Zuse Institute Berlin, 14195 Berlin, Germany
| | - Max von Kleist
- Department of Mathematics and Informatics, Freie Universität Berlin, 14195 Berlin, Germany.
| | - P Robin Hiesinger
- Division of Neurobiology, Institute for Biology, Freie Universität Berlin, 14195 Berlin, Germany.
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14
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Agudo Jácome L, Pöthkow K, Paetsch O, Hege HC. Three-dimensional reconstruction and quantification of dislocation substructures from transmission electron microscopy stereo pairs. Ultramicroscopy 2018; 195:157-170. [PMID: 30292862 DOI: 10.1016/j.ultramic.2018.08.015] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Revised: 07/24/2018] [Accepted: 08/24/2018] [Indexed: 11/29/2022]
Abstract
A great amount of material properties is strongly influenced by dislocations, the carriers of plastic deformation. It is therefore paramount to have appropriate tools to quantify dislocation substructures with regard to their features, e.g., dislocation density, Burgers vectors or line direction. While the transmission electron microscope (TEM) has been the most widely-used equipment implemented to investigate dislocations, it usually is limited to the two-dimensional (2D) observation of three-dimensional (3D) structures. We reconstruct, visualize and quantify 3D dislocation substructure models from only two TEM images (stereo pairs) and assess the results. The reconstruction is based on the manual interactive tracing of filiform objects on both images of the stereo pair. The reconstruction and quantification method are demonstrated on dark field (DF) scanning (S)TEM micrographs of dislocation substructures imaged under diffraction contrast conditions. For this purpose, thick regions (>300 nm) of TEM foils are analyzed, which are extracted from a Ni-base superalloy single crystal after high temperature creep deformation. It is shown how the method allows 3D quantification from stereo pairs in a wide range of tilt conditions, achieving line length and orientation uncertainties of 3% and 7°, respectively. Parameters that affect the quality of such reconstructions are discussed.
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Affiliation(s)
- Leonardo Agudo Jácome
- Bundesanstalt für Materialforschung und -prüfung (BAM), Department for Materials Technology, Unter den Eichen 87, 12205 Berlin, Germany.
| | - Kai Pöthkow
- Zuse Institute Berlin, Department for Visual Data Analysis, Takustraße 7, 14195 Berlin, Germany
| | - Olaf Paetsch
- Zuse Institute Berlin, Department for Visual Data Analysis, Takustraße 7, 14195 Berlin, Germany
| | - Hans-Christian Hege
- Zuse Institute Berlin, Department for Visual Data Analysis, Takustraße 7, 14195 Berlin, Germany
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15
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Benezra SE, Narayanan RT, Egger R, Oberlaender M, Long MA. Morphological characterization of HVC projection neurons in the zebra finch (Taeniopygia guttata). J Comp Neurol 2018; 526:1673-1689. [PMID: 29577283 DOI: 10.1002/cne.24437] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Revised: 02/17/2018] [Accepted: 02/26/2018] [Indexed: 02/03/2023]
Abstract
Singing behavior in the adult male zebra finch is dependent upon the activity of a cortical region known as HVC (proper name). The vast majority of HVC projection neurons send primary axons to either the downstream premotor nucleus RA (robust nucleus of the arcopallium, or primary motor cortex) or Area X (basal ganglia), which play important roles in song production or song learning, respectively. In addition to these long-range outputs, HVC neurons also send local axon collaterals throughout that nucleus. Despite their implications for a range of circuit models, these local processes have never been completely reconstructed. Here, we use in vivo single-neuron Neurobiotin fills to examine 40 projection neurons across 31 birds with somatic positions distributed across HVC. We show that HVC(RA) and HVC(X) neurons have categorically distinct dendritic fields. Additionally, these cell classes send axon collaterals that are either restricted to a small portion of HVC ("local neurons") or broadly distributed throughout the entire nucleus ("broadcast neurons"). Overall, these processes within HVC offer a structural basis for significant local processing underlying behaviorally relevant population activity.
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Affiliation(s)
- Sam E Benezra
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York City, New York
- Center for Neural Science, New York University, New York City, New York
| | - Rajeevan T Narayanan
- Max Planck Group: In Silico Brain Sciences, Center of Advanced European Studies and Research, Bonn, Germany
- Computational Neuroanatomy Group, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Robert Egger
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York City, New York
- Center for Neural Science, New York University, New York City, New York
- Computational Neuroanatomy Group, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Marcel Oberlaender
- Max Planck Group: In Silico Brain Sciences, Center of Advanced European Studies and Research, Bonn, Germany
- Computational Neuroanatomy Group, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Michael A Long
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York City, New York
- Center for Neural Science, New York University, New York City, New York
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16
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Rojas-Piloni G, Guest JM, Egger R, Johnson AS, Sakmann B, Oberlaender M. Relationships between structure, in vivo function and long-range axonal target of cortical pyramidal tract neurons. Nat Commun 2017; 8:870. [PMID: 29021587 PMCID: PMC5636900 DOI: 10.1038/s41467-017-00971-0] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2017] [Accepted: 08/09/2017] [Indexed: 11/09/2022] Open
Abstract
Pyramidal tract neurons (PTs) represent the major output cell type of the neocortex. To investigate principles of how the results of cortical processing are broadcasted to different downstream targets thus requires experimental approaches, which provide access to the in vivo electrophysiology of PTs, whose subcortical target regions are identified. On the example of rat barrel cortex (vS1), we illustrate that retrograde tracer injections into multiple subcortical structures allow identifying the long-range axonal targets of individual in vivo recorded PTs. Here we report that soma depth and dendritic path lengths within each cortical layer of vS1, as well as spiking patterns during both periods of ongoing activity and during sensory stimulation, reflect the respective subcortical target regions of PTs. We show that these cellular properties result in a structure-function parameter space that allows predicting a PT's subcortical target region, without the need to inject multiple retrograde tracers.The major output cell type of the neocortex - pyramidal tract neurons (PTs) - send axonal projections to various subcortical areas. Here the authors combined in vivo recordings, retrograde tracings, and reconstructions of PTs in rat somatosensory cortex to show that PT structure and activity can predict specific subcortical targets.
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Affiliation(s)
- Gerardo Rojas-Piloni
- Digital Neuroanatomy, Max Planck Florida Institute of Neuroscience, 1 Max-Planck-Way, Jupiter, FL, 33458, USA.,Departamento de Neurobiología del Desarrollo y Neurofisiología, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Boulevard Juriquilla 3001, Campus UNAM-Juriquilla, Querétaro, 76230, Mexico
| | - Jason M Guest
- Digital Neuroanatomy, Max Planck Florida Institute of Neuroscience, 1 Max-Planck-Way, Jupiter, FL, 33458, USA.,Max Planck Group: In Silico Brain Sciences, Center of Advanced European Studies and Research, Ludwig-Erhard-Allee 2, Bonn, 53175, Germany.,Bernstein Group: Computational Neuroanatomy, Max Planck Institute for Biological Cybernetics, Spemannstr. 38-44, Tübingen, 72076, Germany
| | - Robert Egger
- Bernstein Group: Computational Neuroanatomy, Max Planck Institute for Biological Cybernetics, Spemannstr. 38-44, Tübingen, 72076, Germany
| | - Andrew S Johnson
- Digital Neuroanatomy, Max Planck Florida Institute of Neuroscience, 1 Max-Planck-Way, Jupiter, FL, 33458, USA
| | - Bert Sakmann
- Digital Neuroanatomy, Max Planck Florida Institute of Neuroscience, 1 Max-Planck-Way, Jupiter, FL, 33458, USA
| | - Marcel Oberlaender
- Digital Neuroanatomy, Max Planck Florida Institute of Neuroscience, 1 Max-Planck-Way, Jupiter, FL, 33458, USA. .,Max Planck Group: In Silico Brain Sciences, Center of Advanced European Studies and Research, Ludwig-Erhard-Allee 2, Bonn, 53175, Germany. .,Bernstein Group: Computational Neuroanatomy, Max Planck Institute for Biological Cybernetics, Spemannstr. 38-44, Tübingen, 72076, Germany.
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17
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3D reconstruction and standardization of the rat facial nucleus for precise mapping of vibrissal motor networks. Neuroscience 2017; 368:171-186. [PMID: 28958919 PMCID: PMC5798596 DOI: 10.1016/j.neuroscience.2017.09.031] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Revised: 08/21/2017] [Accepted: 09/17/2017] [Indexed: 12/16/2022]
Abstract
The rodent facial nucleus (FN) comprises motoneurons (MNs) that control the facial musculature. In the lateral part of the FN, populations of vibrissal motoneurons (vMNs) innervate two groups of muscles that generate movements of the whiskers. Vibrissal MNs thus represent the terminal point of the neuronal networks that generate rhythmic whisking during exploratory behaviors and that modify whisker movements based on sensory-motor feedback during tactile-based perception. Here, we combined retrograde tracer injections into whisker-specific muscles, with large-scale immunohistochemistry and digital reconstructions to generate an average model of the rat FN. The model incorporates measurements of the FN geometry, its cellular organization and a whisker row-specific map formed by vMNs. Furthermore, the model provides a digital 3D reference frame that allows registering structural data - obtained across scales and animals - into a common coordinate system with a precision of ∼60 µm. We illustrate the registration method by injecting replication competent rabies virus into the muscle of a single whisker. Retrograde transport of the virus to vMNs enabled reconstruction of their dendrites. Subsequent trans-synaptic transport enabled mapping the presynaptic neurons of the reconstructed vMNs. Registration of these data to the FN reference frame provides a first account of the morphological and synaptic input variability within a population of vMNs that innervate the same muscle.
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18
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Ribi W, Zeil J. Three-dimensional visualization of ocellar interneurons of the orchid beeEuglossa imperialisusing micro X-ray computed tomography. J Comp Neurol 2017; 525:3581-3595. [PMID: 28608425 DOI: 10.1002/cne.24260] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Revised: 05/07/2017] [Accepted: 05/09/2017] [Indexed: 11/10/2022]
Affiliation(s)
- Willi Ribi
- Research School of Biology, The Australian National University; Canberra Australian Capital Territory Australia
| | - Jochen Zeil
- Research School of Biology, The Australian National University; Canberra Australian Capital Territory Australia
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19
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Fast assembling of neuron fragments in serial 3D sections. Brain Inform 2017; 4:183-186. [PMID: 28365869 PMCID: PMC5563299 DOI: 10.1007/s40708-017-0063-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Accepted: 03/16/2017] [Indexed: 12/03/2022] Open
Abstract
Reconstructing neurons from 3D image-stacks of serial sections of thick brain tissue is very time-consuming and often becomes a bottleneck in high-throughput brain mapping projects. We developed NeuronStitcher, a software suite for stitching non-overlapping neuron fragments reconstructed in serial 3D image sections. With its efficient algorithm and user-friendly interface, NeuronStitcher has been used successfully to reconstruct very large and complex human and mouse neurons.
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20
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Kornfeld J, Benezra SE, Narayanan RT, Svara F, Egger R, Oberlaender M, Denk W, Long MA. EM connectomics reveals axonal target variation in a sequence-generating network. eLife 2017; 6:e24364. [PMID: 28346140 PMCID: PMC5400503 DOI: 10.7554/elife.24364] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2016] [Accepted: 03/23/2017] [Indexed: 01/15/2023] Open
Abstract
The sequential activation of neurons has been observed in various areas of the brain, but in no case is the underlying network structure well understood. Here we examined the circuit anatomy of zebra finch HVC, a cortical region that generates sequences underlying the temporal progression of the song. We combined serial block-face electron microscopy with light microscopy to determine the cell types targeted by HVC(RA) neurons, which control song timing. Close to their soma, axons almost exclusively targeted inhibitory interneurons, consistent with what had been found with electrical recordings from pairs of cells. Conversely, far from the soma the targets were mostly other excitatory neurons, about half of these being other HVC(RA) cells. Both observations are consistent with the notion that the neural sequences that pace the song are generated by global synaptic chains in HVC embedded within local inhibitory networks.
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Affiliation(s)
| | - Sam E Benezra
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, United States
- Center for Neural Science, New York University, New York, United States
| | - Rajeevan T Narayanan
- Computational Neuroanatomy Group, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Bernstein Center for Computational Neuroscience, Tübingen, Germany
- Center of Advanced European Studies and Research, Bonn, Germany
| | - Fabian Svara
- Max Planck Institute of Neurobiology, Martinsried, Germany
| | - Robert Egger
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, United States
- Center for Neural Science, New York University, New York, United States
| | - Marcel Oberlaender
- Computational Neuroanatomy Group, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Bernstein Center for Computational Neuroscience, Tübingen, Germany
- Center of Advanced European Studies and Research, Bonn, Germany
| | - Winfried Denk
- Max Planck Institute of Neurobiology, Martinsried, Germany
| | - Michael A Long
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, United States
- Center for Neural Science, New York University, New York, United States
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21
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Haberl MG, Ginger M, Frick A. Dual Anterograde and Retrograde Viral Tracing of Reciprocal Connectivity. Methods Mol Biol 2017; 1538:321-340. [PMID: 27943199 DOI: 10.1007/978-1-4939-6688-2_21] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Current large-scale approaches in neuroscience aim to unravel the complete connectivity map of specific neuronal circuits, or even the entire brain. This emerging research discipline has been termed connectomics. Recombinant glycoprotein-deleted rabies virus (RABV ∆G) has become an important tool for the investigation of neuronal connectivity in the brains of a variety of species. Neuronal infection with even a single RABV ∆G particle results in high-level transgene expression, revealing the fine-detailed morphology of all neuronal features-including dendritic spines, axonal processes, and boutons-on a brain-wide scale. This labeling is eminently suitable for subsequent post-hoc morphological analysis, such as semiautomated reconstruction in 3D. Here we describe the use of a recently developed anterograde RABV ∆G variant together with a retrograde RABV ∆G for the investigation of projections both to, and from, a particular brain region. In addition to the automated reconstruction of a dendritic tree, we also give as an example the volume measurements of axonal boutons following RABV ∆G-mediated fluorescent marker expression. In conclusion RABV ∆G variants expressing a combination of markers and/or tools for stimulating/monitoring neuronal activity, used together with genetic or behavioral animal models, promise important insights in the structure-function relationship of neural circuits.
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Affiliation(s)
- Matthias G Haberl
- Neurocentre Magendie, Physiopathologie de la plasticité neuronale, INSERM, U862, Bordeaux, France
- Neurocentre Magendie, Physiopathologie de la plasticité neuronale, The Neuroscience Institute at Bordeaux, University of Bordeaux, U862, 146 rue Léo Saignat, 33077, Bordeaux, France
| | - Melanie Ginger
- Neurocentre Magendie, Physiopathologie de la plasticité neuronale, INSERM, U862, Bordeaux, France
- Neurocentre Magendie, Physiopathologie de la plasticité neuronale, The Neuroscience Institute at Bordeaux, University of Bordeaux, U862, 146 rue Léo Saignat, 33077, Bordeaux, France
| | - Andreas Frick
- Neurocentre Magendie, Physiopathologie de la plasticité neuronale, INSERM, U862, Bordeaux, France.
- Neurocentre Magendie, Physiopathologie de la plasticité neuronale, The Neuroscience Institute at Bordeaux, University of Bordeaux, U862, 146 rue Léo Saignat, 33077, Bordeaux, France.
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22
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Fuzzy-Logic Based Detection and Characterization of Junctions and Terminations in Fluorescence Microscopy Images of Neurons. Neuroinformatics 2016; 14:201-19. [PMID: 26701809 PMCID: PMC4823367 DOI: 10.1007/s12021-015-9287-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Digital reconstruction of neuronal cell morphology is an important step toward understanding the functionality of neuronal networks. Neurons are tree-like structures whose description depends critically on the junctions and terminations, collectively called critical points, making the correct localization and identification of these points a crucial task in the reconstruction process. Here we present a fully automatic method for the integrated detection and characterization of both types of critical points in fluorescence microscopy images of neurons. In view of the majority of our current studies, which are based on cultured neurons, we describe and evaluate the method for application to two-dimensional (2D) images. The method relies on directional filtering and angular profile analysis to extract essential features about the main streamlines at any location in an image, and employs fuzzy logic with carefully designed rules to reason about the feature values in order to make well-informed decisions about the presence of a critical point and its type. Experiments on simulated as well as real images of neurons demonstrate the detection performance of our method. A comparison with the output of two existing neuron reconstruction methods reveals that our method achieves substantially higher detection rates and could provide beneficial information to the reconstruction process.
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23
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Treweek JB, Chan KY, Flytzanis NC, Yang B, Deverman BE, Greenbaum A, Lignell A, Xiao C, Cai L, Ladinsky MS, Bjorkman PJ, Fowlkes CC, Gradinaru V. Whole-body tissue stabilization and selective extractions via tissue-hydrogel hybrids for high-resolution intact circuit mapping and phenotyping. Nat Protoc 2015; 10:1860-1896. [PMID: 26492141 PMCID: PMC4917295 DOI: 10.1038/nprot.2015.122] [Citation(s) in RCA: 190] [Impact Index Per Article: 21.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
To facilitate fine-scale phenotyping of whole specimens, we describe here a set of tissue fixation-embedding, detergent-clearing and staining protocols that can be used to transform excised organs and whole organisms into optically transparent samples within 1-2 weeks without compromising their cellular architecture or endogenous fluorescence. PACT (passive CLARITY technique) and PARS (perfusion-assisted agent release in situ) use tissue-hydrogel hybrids to stabilize tissue biomolecules during selective lipid extraction, resulting in enhanced clearing efficiency and sample integrity. Furthermore, the macromolecule permeability of PACT- and PARS-processed tissue hybrids supports the diffusion of immunolabels throughout intact tissue, whereas RIMS (refractive index matching solution) grants high-resolution imaging at depth by further reducing light scattering in cleared and uncleared samples alike. These methods are adaptable to difficult-to-image tissues, such as bone (PACT-deCAL), and to magnified single-cell visualization (ePACT). Together, these protocols and solutions enable phenotyping of subcellular components and tracing cellular connectivity in intact biological networks.
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Affiliation(s)
- Jennifer B Treweek
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
| | - Ken Y Chan
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
| | - Nicholas C Flytzanis
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
| | - Bin Yang
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
| | - Benjamin E Deverman
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
| | - Alon Greenbaum
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
| | - Antti Lignell
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California, USA
| | - Cheng Xiao
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
| | - Long Cai
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California, USA
| | - Mark S Ladinsky
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
| | - Pamela J Bjorkman
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
| | - Charless C Fowlkes
- Department of Computer Science, University of California, Irvine, California, USA
| | - Viviana Gradinaru
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
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24
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Narayanan RT, Egger R, Johnson AS, Mansvelder HD, Sakmann B, de Kock CPJ, Oberlaender M. Beyond Columnar Organization: Cell Type- and Target Layer-Specific Principles of Horizontal Axon Projection Patterns in Rat Vibrissal Cortex. Cereb Cortex 2015; 25:4450-68. [PMID: 25838038 PMCID: PMC4816792 DOI: 10.1093/cercor/bhv053] [Citation(s) in RCA: 79] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Vertical thalamocortical afferents give rise to the elementary functional units of sensory cortex, cortical columns. Principles that underlie communication between columns remain however unknown. Here we unravel these by reconstructing in vivo-labeled neurons from all excitatory cell types in the vibrissal part of rat primary somatosensory cortex (vS1). Integrating the morphologies into an exact 3D model of vS1 revealed that the majority of intracortical (IC) axons project far beyond the borders of the principal column. We defined the corresponding innervation volume as the IC-unit. Deconstructing this structural cortical unit into its cell type-specific components, we found asymmetric projections that innervate columns of either the same whisker row or arc, and which subdivide vS1 into 2 orthogonal [supra-]granular and infragranular strata. We show that such organization could be most effective for encoding multi whisker inputs. Communication between columns is thus organized by multiple highly specific horizontal projection patterns, rendering IC-units as the primary structural entities for processing complex sensory stimuli.
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Affiliation(s)
- Rajeevan T Narayanan
- Computational Neuroanatomy Group, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
| | - Robert Egger
- Computational Neuroanatomy Group, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany Graduate School of Neural Information Processing, University of Tuebingen, Tuebingen, Germany
| | - Andrew S Johnson
- Digital Neuroanatomy, Max Planck Florida Institute for Neuroscience, Jupiter , FL 33458, USA
| | - Huibert D Mansvelder
- Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University Amsterdam, The Netherlands
| | - Bert Sakmann
- Digital Neuroanatomy, Max Planck Florida Institute for Neuroscience, Jupiter , FL 33458, USA
| | - Christiaan P J de Kock
- Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University Amsterdam, The Netherlands
| | - Marcel Oberlaender
- Computational Neuroanatomy Group, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany Digital Neuroanatomy, Max Planck Florida Institute for Neuroscience, Jupiter , FL 33458, USA Bernstein Center for Computational Neuroscience, Tuebingen, Germany
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25
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Polavaram S, Gillette TA, Parekh R, Ascoli GA. Statistical analysis and data mining of digital reconstructions of dendritic morphologies. Front Neuroanat 2014; 8:138. [PMID: 25538569 PMCID: PMC4255610 DOI: 10.3389/fnana.2014.00138] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2014] [Accepted: 11/06/2014] [Indexed: 11/21/2022] Open
Abstract
Neuronal morphology is diverse among animal species, developmental stages, brain regions, and cell types. The geometry of individual neurons also varies substantially even within the same cell class. Moreover, specific histological, imaging, and reconstruction methodologies can differentially affect morphometric measures. The quantitative characterization of neuronal arbors is necessary for in-depth understanding of the structure-function relationship in nervous systems. The large collection of community-contributed digitally reconstructed neurons available at NeuroMorpho.Org constitutes a “big data” research opportunity for neuroscience discovery beyond the approaches typically pursued in single laboratories. To illustrate these potential and related challenges, we present a database-wide statistical analysis of dendritic arbors enabling the quantification of major morphological similarities and differences across broadly adopted metadata categories. Furthermore, we adopt a complementary unsupervised approach based on clustering and dimensionality reduction to identify the main morphological parameters leading to the most statistically informative structural classification. We find that specific combinations of measures related to branching density, overall size, tortuosity, bifurcation angles, arbor flatness, and topological asymmetry can capture anatomically and functionally relevant features of dendritic trees. The reported results only represent a small fraction of the relationships available for data exploration and hypothesis testing enabled by sharing of digital morphological reconstructions.
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Affiliation(s)
- Sridevi Polavaram
- Department of Molecular Neuroscience, Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University Fairfax, VA, USA
| | - Todd A Gillette
- Department of Molecular Neuroscience, Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University Fairfax, VA, USA
| | - Ruchi Parekh
- Department of Molecular Neuroscience, Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University Fairfax, VA, USA
| | - Giorgio A Ascoli
- Department of Molecular Neuroscience, Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University Fairfax, VA, USA
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26
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Egger R, Dercksen VJ, Udvary D, Hege HC, Oberlaender M. Generation of dense statistical connectomes from sparse morphological data. Front Neuroanat 2014; 8:129. [PMID: 25426033 PMCID: PMC4226167 DOI: 10.3389/fnana.2014.00129] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2014] [Accepted: 10/22/2014] [Indexed: 11/13/2022] Open
Abstract
Sensory-evoked signal flow, at cellular and network levels, is primarily determined by the synaptic wiring of the underlying neuronal circuitry. Measurements of synaptic innervation, connection probabilities and subcellular organization of synaptic inputs are thus among the most active fields of research in contemporary neuroscience. Methods to measure these quantities range from electrophysiological recordings over reconstructions of dendrite-axon overlap at light-microscopic levels to dense circuit reconstructions of small volumes at electron-microscopic resolution. However, quantitative and complete measurements at subcellular resolution and mesoscopic scales to obtain all local and long-range synaptic in/outputs for any neuron within an entire brain region are beyond present methodological limits. Here, we present a novel concept, implemented within an interactive software environment called NeuroNet, which allows (i) integration of sparsely sampled (sub)cellular morphological data into an accurate anatomical reference frame of the brain region(s) of interest, (ii) up-scaling to generate an average dense model of the neuronal circuitry within the respective brain region(s) and (iii) statistical measurements of synaptic innervation between all neurons within the model. We illustrate our approach by generating a dense average model of the entire rat vibrissal cortex, providing the required anatomical data, and illustrate how to measure synaptic innervation statistically. Comparing our results with data from paired recordings in vitro and in vivo, as well as with reconstructions of synaptic contact sites at light- and electron-microscopic levels, we find that our in silico measurements are in line with previous results.
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Affiliation(s)
- Robert Egger
- Computational Neuroanatomy Group, Max Planck Institute for Biological Cybernetics Tuebingen, Germany ; Graduate School of Neural Information Processing, University of Tuebingen Tuebingen, Germany ; Bernstein Center for Computational Neuroscience Tuebingen, Germany
| | - Vincent J Dercksen
- Department of Visual Data Analysis, Zuse Institute Berlin Berlin, Germany
| | - Daniel Udvary
- Computational Neuroanatomy Group, Max Planck Institute for Biological Cybernetics Tuebingen, Germany ; Graduate School of Neural Information Processing, University of Tuebingen Tuebingen, Germany ; Bernstein Center for Computational Neuroscience Tuebingen, Germany
| | | | - Marcel Oberlaender
- Computational Neuroanatomy Group, Max Planck Institute for Biological Cybernetics Tuebingen, Germany ; Bernstein Center for Computational Neuroscience Tuebingen, Germany ; Digital Neuroanatomy Group, Max Planck Florida Institute for Neuroscience Jupiter, FL, USA
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