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Yeh LH, Ivanov IE, Chandler T, Byrum JR, Chhun BB, Guo SM, Foltz C, Hashemi E, Perez-Bermejo JA, Wang H, Yu Y, Kazansky PG, Conklin BR, Han MH, Mehta SB. Permittivity tensor imaging: modular label-free imaging of 3D dry mass and 3D orientation at high resolution. Nat Methods 2024:10.1038/s41592-024-02291-w. [PMID: 38890427 DOI: 10.1038/s41592-024-02291-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Accepted: 04/24/2024] [Indexed: 06/20/2024]
Abstract
The dry mass and the orientation of biomolecules can be imaged without a label by measuring their permittivity tensor (PT), which describes how biomolecules affect the phase and polarization of light. Three-dimensional (3D) imaging of PT has been challenging. We present a label-free computational microscopy technique, PT imaging (PTI), for the 3D measurement of PT. PTI encodes the invisible PT into images using oblique illumination, polarization-sensitive detection and volumetric sampling. PT is decoded from the data with a vectorial imaging model and a multi-channel inverse algorithm, assuming uniaxial symmetry in each voxel. We demonstrate high-resolution imaging of PT of isotropic beads, anisotropic glass targets, mouse brain tissue, infected cells and histology slides. PTI outperforms previous label-free imaging techniques such as vector tomography, ptychography and light-field imaging in resolving the 3D orientation and symmetry of organelles, cells and tissue. We provide open-source software and modular hardware to enable the adoption of the method.
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Affiliation(s)
- Li-Hao Yeh
- Chan Zuckerberg Biohub, San Francisco, CA, USA
- ASML, San Jose, CA, USA
| | | | | | - Janie R Byrum
- Chan Zuckerberg Biohub, San Francisco, CA, USA
- California's Stem Cell Agency, South San Francisco, CA, USA
| | - Bryant B Chhun
- Chan Zuckerberg Biohub, San Francisco, CA, USA
- Eikon Therapeutics, Hayward, CA, USA
| | - Syuan-Ming Guo
- Chan Zuckerberg Biohub, San Francisco, CA, USA
- Insitro, South San Francisco, CA, USA
| | - Cameron Foltz
- Chan Zuckerberg Biohub, San Francisco, CA, USA
- Quantinuum, Broomfield, CO, USA
| | | | - Juan A Perez-Bermejo
- Gladstone Institutes, San Francisco, CA, USA
- Genentech, South San Francisco, CA, USA
| | | | - Yanhao Yu
- University of Southampton, Southampton, UK
| | | | - Bruce R Conklin
- Gladstone Institutes, San Francisco, CA, USA
- University of California San Francisco, San Francisco, CA, USA
| | - May H Han
- Stanford University, Palo Alto, CA, USA
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2
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Hindley N, Sanchez Avila A, Henstridge C. Bringing synapses into focus: Recent advances in synaptic imaging and mass-spectrometry for studying synaptopathy. Front Synaptic Neurosci 2023; 15:1130198. [PMID: 37008679 PMCID: PMC10050382 DOI: 10.3389/fnsyn.2023.1130198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 02/28/2023] [Indexed: 03/17/2023] Open
Abstract
Synapses are integral for healthy brain function and are becoming increasingly recognized as key structures in the early stages of brain disease. Understanding the pathological processes driving synaptic dysfunction will unlock new therapeutic opportunities for some of the most devastating diseases of our time. To achieve this we need a solid repertoire of imaging and molecular tools to interrogate synaptic biology at greater resolution. Synapses have historically been examined in small numbers, using highly technical imaging modalities, or in bulk, using crude molecular approaches. However, recent advances in imaging techniques are allowing us to analyze large numbers of synapses, at single-synapse resolution. Furthermore, multiplexing is now achievable with some of these approaches, meaning we can examine multiple proteins at individual synapses in intact tissue. New molecular techniques now allow accurate quantification of proteins from isolated synapses. The development of increasingly sensitive mass-spectrometry equipment means we can now scan the synaptic molecular landscape almost in totality and see how this changes in disease. As we embrace these new technical developments, synapses will be viewed with clearer focus, and the field of synaptopathy will become richer with insightful and high-quality data. Here, we will discuss some of the ways in which synaptic interrogation is being facilitated by methodological advances, focusing on imaging, and mass spectrometry.
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Affiliation(s)
- Nicole Hindley
- Division of Cellular and Systems Medicine, University of Dundee, Dundee, United Kingdom
- *Correspondence: Nicole Hindley,
| | - Anna Sanchez Avila
- Division of Cellular and Systems Medicine, University of Dundee, Dundee, United Kingdom
- Euan Macdonald Centre for Motor Neuron Disease, University of Edinburgh, Edinburgh, United Kingdom
| | - Christopher Henstridge
- Division of Cellular and Systems Medicine, University of Dundee, Dundee, United Kingdom
- Euan Macdonald Centre for Motor Neuron Disease, University of Edinburgh, Edinburgh, United Kingdom
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3
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ARRAY TOMOGRAPHY: 15 YEARS OF SYNAPTIC ANALYSIS. Neuronal Signal 2022; 6:NS20220013. [PMID: 36187224 PMCID: PMC9512143 DOI: 10.1042/ns20220013] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 08/31/2022] [Accepted: 09/06/2022] [Indexed: 11/25/2022] Open
Abstract
Synapses are minuscule, intricate structures crucial for the correct communication between neurons. In the 125 years since the term synapse was first coined, we have advanced a long way when it comes to our understanding of how they work and what they do. Most of the fundamental discoveries have been invariably linked to advances in technology. However, due to their size, delicate structural integrity and their sheer number, our knowledge of synaptic biology has remained somewhat elusive and their role in neurodegenerative diseases still remains largely unknown. Here, we briefly discuss some of the imaging technologies used to study synapses and focus on the utility of the high-resolution imaging technique array tomography (AT). We introduce the AT technique and highlight some of the ways it is utilised with a particular focus on its power for analysing synaptic composition and pathology in human post-mortem tissue. We also discuss some of the benefits and drawbacks of techniques for imaging synapses and highlight some recent advances in the study of form and function by combining physiology and high-resolution synaptic imaging.
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4
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Smith SJ, von Zastrow M. A Molecular Landscape of Mouse Hippocampal Neuromodulation. Front Neural Circuits 2022; 16:836930. [PMID: 35601530 PMCID: PMC9120848 DOI: 10.3389/fncir.2022.836930] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 03/30/2022] [Indexed: 12/23/2022] Open
Abstract
Adaptive neuronal circuit function requires a continual adjustment of synaptic network parameters known as “neuromodulation.” This process is now understood to be based primarily on the binding of myriad secreted “modulatory” ligands such as dopamine, serotonin and the neuropeptides to G protein-coupled receptors (GPCRs) that, in turn, regulate the function of the ion channels that establish synaptic weights and membrane excitability. Many of the basic molecular mechanisms of neuromodulation are now known, but the organization of neuromodulation at a network level is still an enigma. New single-cell RNA sequencing data and transcriptomic neurotaxonomies now offer bright new lights to shine on this critical “dark matter” of neuroscience. Here we leverage these advances to explore the cell-type-specific expression of genes encoding GPCRs, modulatory ligands, ion channels and intervening signal transduction molecules in mouse hippocampus area CA1, with the goal of revealing broad outlines of this well-studied brain structure’s neuromodulatory network architecture.
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Affiliation(s)
- Stephen J Smith
- Allen Institute for Brain Science, Seattle, WA, United States
- *Correspondence: Stephen J Smith,
| | - Mark von Zastrow
- Departments of Psychiatry and Pharmacology, University of California, San Francisco, San Francisco, CA, United States
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5
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Wichmann C, Kuner T. Heterogeneity of glutamatergic synapses: cellular mechanisms and network consequences. Physiol Rev 2022; 102:269-318. [PMID: 34727002 DOI: 10.1152/physrev.00039.2020] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Chemical synapses are commonly known as a structurally and functionally highly diverse class of cell-cell contacts specialized to mediate communication between neurons. They represent the smallest "computational" unit of the brain and are typically divided into excitatory and inhibitory as well as modulatory categories. These categories are subdivided into diverse types, each representing a different structure-function repertoire that in turn are thought to endow neuronal networks with distinct computational properties. The diversity of structure and function found among a given category of synapses is referred to as heterogeneity. The main building blocks for this heterogeneity are synaptic vesicles, the active zone, the synaptic cleft, the postsynaptic density, and glial processes associated with the synapse. Each of these five structural modules entails a distinct repertoire of functions, and their combination specifies the range of functional heterogeneity at mammalian excitatory synapses, which are the focus of this review. We describe synapse heterogeneity that is manifested on different levels of complexity ranging from the cellular morphology of the pre- and postsynaptic cells toward the expression of different protein isoforms at individual release sites. We attempt to define the range of structural building blocks that are used to vary the basic functional repertoire of excitatory synaptic contacts and discuss sources and general mechanisms of synapse heterogeneity. Finally, we explore the possible impact of synapse heterogeneity on neuronal network function.
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Affiliation(s)
- Carolin Wichmann
- Molecular Architecture of Synapses Group, Institute for Auditory Neuroscience, InnerEarLab and Institute for Biostructural Imaging of Neurodegeneration, University Medical Center Göttingen, Göttingen, Germany
| | - Thomas Kuner
- Department of Functional Neuroanatomy, Institute for Anatomy and Cell Biology, Heidelberg, Germany
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6
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Xu F, Shen Y, Ding L, Yang CY, Tan H, Wang H, Zhu Q, Xu R, Wu F, Xiao Y, Xu C, Li Q, Su P, Zhang LI, Dong HW, Desimone R, Xu F, Hu X, Lau PM, Bi GQ. High-throughput mapping of a whole rhesus monkey brain at micrometer resolution. Nat Biotechnol 2021; 39:1521-1528. [PMID: 34312500 DOI: 10.1038/s41587-021-00986-5] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 06/14/2021] [Indexed: 02/06/2023]
Abstract
Whole-brain mesoscale mapping in primates has been hindered by large brain sizes and the relatively low throughput of available microscopy methods. Here, we present an approach that combines primate-optimized tissue sectioning and clearing with ultrahigh-speed fluorescence microscopy implementing improved volumetric imaging with synchronized on-the-fly-scan and readout technique, and is capable of completing whole-brain imaging of a rhesus monkey at 1 × 1 × 2.5 µm3 voxel resolution within 100 h. We also developed a highly efficient method for long-range tracing of sparse axonal fibers in datasets numbering hundreds of terabytes. This pipeline, which we call serial sectioning and clearing, three-dimensional microscopy with semiautomated reconstruction and tracing (SMART), enables effective connectome-scale mapping of large primate brains. With SMART, we were able to construct a cortical projection map of the mediodorsal nucleus of the thalamus and identify distinct turning and routing patterns of individual axons in the cortical folds while approaching their arborization destinations.
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Affiliation(s)
- Fang Xu
- Center for Integrative Imaging, Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei, China.,CAS Key Laboratory of Brain Connectome and Manipulation, Interdisciplinary Center for Brain Information, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Yan Shen
- CAS Key Laboratory of Brain Function and Disease, and School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Lufeng Ding
- CAS Key Laboratory of Brain Function and Disease, and School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Chao-Yu Yang
- CAS Key Laboratory of Brain Function and Disease, and School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Heng Tan
- Key Laboratory of Animal Models and Human Disease Mechanism, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,Department of Pathology and Pathophysiology, Kunming Medical University, Kunming, China
| | - Hao Wang
- Center for Integrative Imaging, Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei, China.,Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
| | - Qingyuan Zhu
- Center for Integrative Imaging, Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Rui Xu
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Fengyi Wu
- CAS Key Laboratory of Brain Connectome and Manipulation, Interdisciplinary Center for Brain Information, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Yanyang Xiao
- CAS Key Laboratory of Brain Connectome and Manipulation, Interdisciplinary Center for Brain Information, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Cheng Xu
- Center for Integrative Imaging, Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Qianwei Li
- Center for Integrative Imaging, Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Peng Su
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Key Laboratory of Magnetic Resonance in Biological Systems, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, China
| | - Li I Zhang
- Zilkha Neurogenetic Institute, Center for Neural Circuits & Sensory Processing Disorders, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hong-Wei Dong
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Robert Desimone
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Fuqiang Xu
- CAS Key Laboratory of Brain Connectome and Manipulation, Interdisciplinary Center for Brain Information, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China.,State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Key Laboratory of Magnetic Resonance in Biological Systems, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China
| | - Xintian Hu
- Key Laboratory of Animal Models and Human Disease Mechanism, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China
| | - Pak-Ming Lau
- Center for Integrative Imaging, Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei, China. .,CAS Key Laboratory of Brain Connectome and Manipulation, Interdisciplinary Center for Brain Information, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China. .,CAS Key Laboratory of Brain Function and Disease, and School of Life Sciences, University of Science and Technology of China, Hefei, China. .,Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China.
| | - Guo-Qiang Bi
- Center for Integrative Imaging, Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei, China. .,CAS Key Laboratory of Brain Connectome and Manipulation, Interdisciplinary Center for Brain Information, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China. .,Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China. .,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China.
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7
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Yao Y, Taub AB, LeSauter J, Silver R. Identification of the suprachiasmatic nucleus venous portal system in the mammalian brain. Nat Commun 2021; 12:5643. [PMID: 34561434 PMCID: PMC8463669 DOI: 10.1038/s41467-021-25793-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 08/27/2021] [Indexed: 02/01/2023] Open
Abstract
There is only one known portal system in the mammalian brain - that of the pituitary gland, first identified in 1933 by Popa and Fielding. Here we describe a second portal pathway in the mouse linking the capillary vessels of the brain's clock suprachiasmatic nucleus (SCN) to those of the organum vasculosum of the lamina terminalis (OVLT), a circumventricular organ. The localized blood vessels of portal pathways enable small amounts of important secretions to reach their specialized targets in high concentrations without dilution in the general circulatory system. These brain clock portal vessels point to an entirely new route and targets for secreted SCN signals, and potentially restructures our understanding of brain communication pathways.
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Affiliation(s)
- Yifan Yao
- Columbia University Department of Psychology, 1190 Amsterdam Avenue, New York City, NY, 10027, USA
| | - Alana B'nai Taub
- Columbia University Department of Psychology, 1190 Amsterdam Avenue, New York City, NY, 10027, USA
| | - Joseph LeSauter
- Department of Neuroscience, Barnard College, 3009 Broadway, New York City, NY, 10027, USA
| | - Rae Silver
- Columbia University Department of Psychology, 1190 Amsterdam Avenue, New York City, NY, 10027, USA.
- Department of Neuroscience, Barnard College, 3009 Broadway, New York City, NY, 10027, USA.
- Department of Pathology and Cell Biology, Graduate School, Columbia University Medical School, New York City, NY, 10032, USA.
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8
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Cano-Astorga N, DeFelipe J, Alonso-Nanclares L. Three-Dimensional Synaptic Organization of Layer III of the Human Temporal Neocortex. Cereb Cortex 2021; 31:4742-4764. [PMID: 33999122 PMCID: PMC8408440 DOI: 10.1093/cercor/bhab120] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
In the present study, we have used focused ion beam/scanning electron microscopy (FIB/SEM) to perform a study of the synaptic organization of layer III of Brodmann's area 21 in human tissue samples obtained from autopsies and biopsies. We analyzed the synaptic density, 3D spatial distribution, and type (asymmetric/symmetric), as well as the size and shape of each synaptic junction of 4945 synapses that were fully reconstructed in 3D. Significant differences in the mean synaptic density between autopsy and biopsy samples were found (0.49 and 0.66 synapses/μm3, respectively). However, in both types of samples (autopsy and biopsy), the asymmetric:symmetric ratio was similar (93:7) and most asymmetric synapses were established on dendritic spines (75%), while most symmetric synapses were established on dendritic shafts (85%). We also compared several electron microscopy methods and analysis tools to estimate the synaptic density in the same brain tissue. We have shown that FIB/SEM is much more reliable and robust than the majority of the other commonly used EM techniques. The present work constitutes a detailed description of the synaptic organization of cortical layer III. Further studies on the rest of the cortical layers are necessary to better understand the functional organization of this temporal cortical region.
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Affiliation(s)
- Nicolás Cano-Astorga
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid 28223, Spain
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Madrid 28002, Spain
| | - Javier DeFelipe
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid 28223, Spain
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Madrid 28002, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), ISCIII, Madrid 28031, Spain
| | - Lidia Alonso-Nanclares
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid 28223, Spain
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Madrid 28002, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), ISCIII, Madrid 28031, Spain
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9
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Conte D, Borisyuk R, Hull M, Roberts A. A simple method defines 3D morphology and axon projections of filled neurons in a small CNS volume: Steps toward understanding functional network circuitry. J Neurosci Methods 2020; 351:109062. [PMID: 33383055 DOI: 10.1016/j.jneumeth.2020.109062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 12/11/2020] [Accepted: 12/22/2020] [Indexed: 10/22/2022]
Abstract
BACKGROUND Fundamental to understanding neuronal network function is defining neuron morphology, location, properties, and synaptic connectivity in the nervous system. A significant challenge is to reconstruct individual neuron morphology and connections at a whole CNS scale and bring together functional and anatomical data to understand the whole network. NEW METHOD We used a PC controlled micropositioner to hold a fixed whole mount of Xenopus tadpole CNS and replace the stage on a standard microscope. This allowed direct recording in 3D coordinates of features and axon projections of one or two neurons dye-filled during whole-cell recording to study synaptic connections. Neuron reconstructions were normalised relative to the ventral longitudinal axis of the nervous system. Coordinate data were stored as simple text files. RESULTS Reconstructions were at 1 μm resolution, capturing axon lengths in mm. The output files were converted to SWC format and visualised in 3D reconstruction software NeuRomantic. Coordinate data are tractable, allowing correction for histological artefacts. Through normalisation across multiple specimens we could infer features of network connectivity of mapped neurons of different types. COMPARISON WITH EXISTING METHODS Unlike other methods using fluorescent markers and utilising large-scale imaging, our method allows direct acquisition of 3D data on neurons whose properties and synaptic connections have been studied using whole-cell recording. CONCLUSIONS This method can be used to reconstruct neuron 3D morphology and follow axon projections in the CNS. After normalisation to a common CNS framework, inferences on network connectivity at a whole nervous system scale contribute to network modelling to understand CNS function.
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Affiliation(s)
- Deborah Conte
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, United Kingdom.
| | - Roman Borisyuk
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Harrison Building, North Park Road, Exeter, EX4 4QF, United Kingdom; Institute of Mathematical Problems of Biology, the Branch of Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, Pushchino, 142290, Russia; School of Computing, Engineering and Mathematics, University of Plymouth, PL4 8AA, United Kingdom.
| | - Mike Hull
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, United Kingdom; Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, EH8 9AB, United Kingdom.
| | - Alan Roberts
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, United Kingdom.
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10
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Guo SM, Yeh LH, Folkesson J, Ivanov IE, Krishnan AP, Keefe MG, Hashemi E, Shin D, Chhun BB, Cho NH, Leonetti MD, Han MH, Nowakowski TJ, Mehta SB. Revealing architectural order with quantitative label-free imaging and deep learning. eLife 2020; 9:e55502. [PMID: 32716843 PMCID: PMC7431134 DOI: 10.7554/elife.55502] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Accepted: 07/24/2020] [Indexed: 01/21/2023] Open
Abstract
We report quantitative label-free imaging with phase and polarization (QLIPP) for simultaneous measurement of density, anisotropy, and orientation of structures in unlabeled live cells and tissue slices. We combine QLIPP with deep neural networks to predict fluorescence images of diverse cell and tissue structures. QLIPP images reveal anatomical regions and axon tract orientation in prenatal human brain tissue sections that are not visible using brightfield imaging. We report a variant of U-Net architecture, multi-channel 2.5D U-Net, for computationally efficient prediction of fluorescence images in three dimensions and over large fields of view. Further, we develop data normalization methods for accurate prediction of myelin distribution over large brain regions. We show that experimental defects in labeling the human tissue can be rescued with quantitative label-free imaging and neural network model. We anticipate that the proposed method will enable new studies of architectural order at spatial scales ranging from organelles to tissue.
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Affiliation(s)
| | - Li-Hao Yeh
- Chan Zuckerberg BiohubSan FranciscoUnited States
| | | | | | | | - Matthew G Keefe
- Department of Anatomy, University of California, San FranciscoSan FranciscoUnited States
| | - Ezzat Hashemi
- Department of Neurology, Stanford UniversityStanfordUnited States
| | - David Shin
- Department of Anatomy, University of California, San FranciscoSan FranciscoUnited States
| | | | - Nathan H Cho
- Chan Zuckerberg BiohubSan FranciscoUnited States
| | | | - May H Han
- Department of Neurology, Stanford UniversityStanfordUnited States
| | - Tomasz J Nowakowski
- Department of Anatomy, University of California, San FranciscoSan FranciscoUnited States
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11
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Whole-Neuron Synaptic Mapping Reveals Spatially Precise Excitatory/Inhibitory Balance Limiting Dendritic and Somatic Spiking. Neuron 2020; 106:566-578.e8. [PMID: 32169170 DOI: 10.1016/j.neuron.2020.02.015] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Revised: 04/19/2019] [Accepted: 02/11/2020] [Indexed: 02/02/2023]
Abstract
The balance between excitatory and inhibitory (E and I) synapses is thought to be critical for information processing in neural circuits. However, little is known about the spatial principles of E and I synaptic organization across the entire dendritic tree of mammalian neurons. We developed a new open-source reconstruction platform for mapping the size and spatial distribution of E and I synapses received by individual genetically-labeled layer 2/3 (L2/3) cortical pyramidal neurons (PNs) in vivo. We mapped over 90,000 E and I synapses across twelve L2/3 PNs and uncovered structured organization of E and I synapses across dendritic domains as well as within individual dendritic segments. Despite significant domain-specific variation in the absolute density of E and I synapses, their ratio is strikingly balanced locally across dendritic segments. Computational modeling indicates that this spatially precise E/I balance dampens dendritic voltage fluctuations and strongly impacts neuronal firing output.
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12
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Kirst C, Skriabine S, Vieites-Prado A, Topilko T, Bertin P, Gerschenfeld G, Verny F, Topilko P, Michalski N, Tessier-Lavigne M, Renier N. Mapping the Fine-Scale Organization and Plasticity of the Brain Vasculature. Cell 2020; 180:780-795.e25. [PMID: 32059781 DOI: 10.1016/j.cell.2020.01.028] [Citation(s) in RCA: 153] [Impact Index Per Article: 38.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 11/20/2019] [Accepted: 01/21/2020] [Indexed: 02/06/2023]
Abstract
The cerebral vasculature is a dense network of arteries, capillaries, and veins. Quantifying variations of the vascular organization across individuals, brain regions, or disease models is challenging. We used immunolabeling and tissue clearing to image the vascular network of adult mouse brains and developed a pipeline to segment terabyte-sized multichannel images from light sheet microscopy, enabling the construction, analysis, and visualization of vascular graphs composed of over 100 million vessel segments. We generated datasets from over 20 mouse brains, with labeled arteries, veins, and capillaries according to their anatomical regions. We characterized the organization of the vascular network across brain regions, highlighting local adaptations and functional correlates. We propose a classification of cortical regions based on the vascular topology. Finally, we analysed brain-wide rearrangements of the vasculature in animal models of congenital deafness and ischemic stroke, revealing that vascular plasticity and remodeling adopt diverging rules in different models.
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Affiliation(s)
- Christoph Kirst
- Laboratoire de Plasticité Structurale, Sorbonne Université, ICM Institut du Cerveau et de la Moelle Epinière, INSERM U1127, CNRS UMR7225, 75013 Paris, France; Center for Physics and Biology and Kavli Neural Systems Insittute, The Rockefeller University, 10065 New York, NY, USA; Kavli Institute for Fundamental Neuroscience and Anatomy Department, Sandler Neuroscience Building, Suite 514G, 675 Nelson Rising Lane, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - Sophie Skriabine
- Laboratoire de Plasticité Structurale, Sorbonne Université, ICM Institut du Cerveau et de la Moelle Epinière, INSERM U1127, CNRS UMR7225, 75013 Paris, France
| | - Alba Vieites-Prado
- Laboratoire de Plasticité Structurale, Sorbonne Université, ICM Institut du Cerveau et de la Moelle Epinière, INSERM U1127, CNRS UMR7225, 75013 Paris, France
| | - Thomas Topilko
- Laboratoire de Plasticité Structurale, Sorbonne Université, ICM Institut du Cerveau et de la Moelle Epinière, INSERM U1127, CNRS UMR7225, 75013 Paris, France
| | - Paul Bertin
- Laboratoire de Plasticité Structurale, Sorbonne Université, ICM Institut du Cerveau et de la Moelle Epinière, INSERM U1127, CNRS UMR7225, 75013 Paris, France
| | | | - Florine Verny
- Laboratoire de Plasticité Structurale, Sorbonne Université, ICM Institut du Cerveau et de la Moelle Epinière, INSERM U1127, CNRS UMR7225, 75013 Paris, France
| | - Piotr Topilko
- Institut Mondor de Recherche Biomédicale, INSERM U955-Team 9, Créteil, France
| | - Nicolas Michalski
- Unité de Génétique et Physiologie de l'Audition, UMRS 1120, Institut Pasteur, INSERM, 75015 Paris, France
| | | | - Nicolas Renier
- Laboratoire de Plasticité Structurale, Sorbonne Université, ICM Institut du Cerveau et de la Moelle Epinière, INSERM U1127, CNRS UMR7225, 75013 Paris, France.
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13
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Zhang GH, Nelson DR. Eigenvalue repulsion and eigenvector localization in sparse non-Hermitian random matrices. Phys Rev E 2019; 100:052315. [PMID: 31870007 DOI: 10.1103/physreve.100.052315] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Indexed: 11/07/2022]
Abstract
Complex networks with directed, local interactions are ubiquitous in nature and often occur with probabilistic connections due to both intrinsic stochasticity and disordered environments. Sparse non-Hermitian random matrices arise naturally in this context and are key to describing statistical properties of the nonequilibrium dynamics that emerges from interactions within the network structure. Here we study one-dimensional (1D) spatial structures and focus on sparse non-Hermitian random matrices in the spirit of tight-binding models in solid state physics. We first investigate two-point eigenvalue correlations in the complex plane for sparse non-Hermitian random matrices using methods developed for the statistical mechanics of inhomogeneous two-dimensional interacting particles. We find that eigenvalue repulsion in the complex plane directly correlates with eigenvector delocalization. In addition, for 1D chains and rings with both disordered nearest-neighbor connections and self-interactions, the self-interaction disorder tends to decorrelate eigenvalues and localize eigenvectors more than simple hopping disorder. However, remarkable resistance to eigenvector localization by disorder is provided by large cycles, such as those embodied in 1D periodic boundary conditions under strong directional bias. The directional bias also spatially separates the left and right eigenvectors, leading to interesting dynamics in excitation and response. These phenomena have important implications for asymmetric random networks and highlight a need for mathematical tools to describe and understand them analytically.
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Affiliation(s)
- Grace H Zhang
- Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA
| | - David R Nelson
- Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA
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14
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Collins LT. The case for emulating insect brains using anatomical "wiring diagrams" equipped with biophysical models of neuronal activity. BIOLOGICAL CYBERNETICS 2019; 113:465-474. [PMID: 31696303 DOI: 10.1007/s00422-019-00810-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 10/29/2019] [Indexed: 06/10/2023]
Abstract
Developing whole-brain emulation (WBE) technology would provide immense benefits across neuroscience, biomedicine, artificial intelligence, and robotics. At this time, constructing a simulated human brain lacks feasibility due to limited experimental data and limited computational resources. However, I suggest that progress toward this goal might be accelerated by working toward an intermediate objective, namely insect brain emulation (IBE). More specifically, this would entail creating biologically realistic simulations of entire insect nervous systems along with more approximate simulations of non-neuronal insect physiology to make "virtual insects." I argue that this could be realistically achievable within the next 20 years. I propose that developing emulations of insect brains will galvanize the global community of scientists, businesspeople, and policymakers toward pursuing the loftier goal of emulating the human brain. By demonstrating that WBE is possible via IBE, simulating mammalian brains and eventually the human brain may no longer be viewed as too radically ambitious to deserve substantial funding and resources. Furthermore, IBE will facilitate dramatic advances in cognitive neuroscience, artificial intelligence, and robotics through studies performed using virtual insects.
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Affiliation(s)
- Logan T Collins
- Department of Psychology and Neuroscience, University of Colorado, Boulder, 2860 Wilderness Place, Boulder, CO, 80301, USA.
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15
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Barranca VJ, Zhou D. Compressive Sensing Inference of Neuronal Network Connectivity in Balanced Neuronal Dynamics. Front Neurosci 2019; 13:1101. [PMID: 31680835 PMCID: PMC6811502 DOI: 10.3389/fnins.2019.01101] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2019] [Accepted: 09/30/2019] [Indexed: 12/30/2022] Open
Abstract
Determining the structure of a network is of central importance to understanding its function in both neuroscience and applied mathematics. However, recovering the structural connectivity of neuronal networks remains a fundamental challenge both theoretically and experimentally. While neuronal networks operate in certain dynamical regimes, which may influence their connectivity reconstruction, there is widespread experimental evidence of a balanced neuronal operating state in which strong excitatory and inhibitory inputs are dynamically adjusted such that neuronal voltages primarily remain near resting potential. Utilizing the dynamics of model neurons in such a balanced regime in conjunction with the ubiquitous sparse connectivity structure of neuronal networks, we develop a compressive sensing theoretical framework for efficiently reconstructing network connections by measuring individual neuronal activity in response to a relatively small ensemble of random stimuli injected over a short time scale. By tuning the network dynamical regime, we determine that the highest fidelity reconstructions are achievable in the balanced state. We hypothesize the balanced dynamics observed in vivo may therefore be a result of evolutionary selection for optimal information encoding and expect the methodology developed to be generalizable for alternative model networks as well as experimental paradigms.
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Affiliation(s)
- Victor J Barranca
- Department of Mathematics and Statistics, Swarthmore College, Swarthmore, PA, United States
| | - Douglas Zhou
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China.,Ministry of Education Key Laboratory of Scientific and Engineering Computing, Shanghai Jiao Tong University, Shanghai, China.,Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China
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16
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Wang H, Zhu Q, Ding L, Shen Y, Yang CY, Xu F, Shu C, Guo Y, Xiong Z, Shan Q, Jia F, Su P, Yang QR, Li B, Cheng Y, He X, Chen X, Wu F, Zhou JN, Xu F, Han H, Lau PM, Bi GQ. Scalable volumetric imaging for ultrahigh-speed brain mapping at synaptic resolution. Natl Sci Rev 2019; 6:982-992. [PMID: 34691959 PMCID: PMC8291554 DOI: 10.1093/nsr/nwz053] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Revised: 03/25/2019] [Accepted: 04/15/2019] [Indexed: 12/31/2022] Open
Abstract
The speed of high-resolution optical imaging has been a rate-limiting factor for meso-scale mapping of brain structures and functional circuits, which is of fundamental importance for neuroscience research. Here, we describe a new microscopy method of Volumetric Imaging with Synchronized on-the-fly-scan and Readout (VISoR) for high-throughput, high-quality brain mapping. Combining synchronized scanning beam illumination and oblique imaging over cleared tissue sections in smooth motion, the VISoR system effectively eliminates motion blur to obtain undistorted images. By continuously imaging moving samples without stopping, the system achieves high-speed 3D image acquisition of an entire mouse brain within 1.5 hours, at a resolution capable of visualizing synaptic spines. A pipeline is developed for sample preparation, imaging, 3D image reconstruction and quantification. Our approach is compatible with immunofluorescence methods, enabling flexible cell-type specific brain mapping and is readily scalable for large biological samples such as primate brains. Using this system, we examined behaviorally relevant whole-brain neuronal activation in 16 c-Fos-shEGFP mice under resting or forced swimming conditions. Our results indicate the involvement of multiple subcortical areas in stress response. Intriguingly, neuronal activation in these areas exhibits striking individual variability among different animals, suggesting the necessity of sufficient cohort size for such studies.
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Affiliation(s)
- Hao Wang
- Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China.,CAS Key Laboratory of Brain Function and Disease, and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China
| | - Qingyuan Zhu
- Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China
| | - Lufeng Ding
- CAS Key Laboratory of Brain Function and Disease, and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China
| | - Yan Shen
- CAS Key Laboratory of Brain Function and Disease, and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China
| | - Chao-Yu Yang
- CAS Key Laboratory of Brain Function and Disease, and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China
| | - Fang Xu
- CAS Key Laboratory of Brain Function and Disease, and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China
| | - Chang Shu
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yujie Guo
- Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China
| | - Zhiwei Xiong
- School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China.,National Engineering Laboratory for Brain-inspired Intelligence Technology and Application, University of Science and Technology of China, Hefei 230027, China
| | - Qinghong Shan
- CAS Key Laboratory of Brain Function and Disease, and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China
| | - Fan Jia
- Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, China
| | - Peng Su
- Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, China
| | - Qian-Ru Yang
- CAS Key Laboratory of Brain Function and Disease, and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China
| | - Bing Li
- CAS Key Laboratory of Brain Function and Disease, and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China
| | - Yuxiao Cheng
- CAS Key Laboratory of Brain Function and Disease, and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China
| | - Xiaobin He
- Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, China
| | - Xi Chen
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Feng Wu
- School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China.,National Engineering Laboratory for Brain-inspired Intelligence Technology and Application, University of Science and Technology of China, Hefei 230027, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai 200031, China
| | - Jiang-Ning Zhou
- CAS Key Laboratory of Brain Function and Disease, and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai 200031, China
| | - Fuqiang Xu
- Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai 200031, China
| | - Hua Han
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai 200031, China
| | - Pak-Ming Lau
- CAS Key Laboratory of Brain Function and Disease, and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China
| | - Guo-Qiang Bi
- Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei 230027, China.,National Engineering Laboratory for Brain-inspired Intelligence Technology and Application, University of Science and Technology of China, Hefei 230027, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai 200031, China
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17
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Kleinfeld D, Luan L, Mitra PP, Robinson JT, Sarpeshkar R, Shepard K, Xie C, Harris TD. Can One Concurrently Record Electrical Spikes from Every Neuron in a Mammalian Brain? Neuron 2019; 103:1005-1015. [PMID: 31495645 PMCID: PMC6763354 DOI: 10.1016/j.neuron.2019.08.011] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Revised: 06/30/2019] [Accepted: 08/03/2019] [Indexed: 12/26/2022]
Abstract
The classic approach to measure the spiking response of neurons involves the use of metal electrodes to record extracellular potentials. Starting over 60 years ago with a single recording site, this technology now extends to ever larger numbers and densities of sites. We argue, based on the mechanical and electrical properties of existing materials, estimates of signal-to-noise ratios, assumptions regarding extracellular space in the brain, and estimates of heat generation by the electronic interface, that it should be possible to fabricate rigid electrodes to concurrently record from essentially every neuron in the cortical mantle. This will involve fabrication with existing yet nontraditional materials and procedures. We further emphasize the need to advance materials for improved flexible electrodes as an essential advance to record from neurons in brainstem and spinal cord in moving animals.
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Affiliation(s)
- David Kleinfeld
- Section of Neurobiology, University of California, San Diego, CA, USA; Department of Physics, University of California, San Diego, CA, USA.
| | - Lan Luan
- Department of Biomedical Engineering, University of Texas, Austin, TX, USA
| | - Partha P Mitra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Jacob T Robinson
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Rahul Sarpeshkar
- Department of Engineering, Dartmouth, Hanover, NH, USA; Department of Microbiology and Immunology, Dartmouth, Hanover, NH, USA; Department of Molecular and Systems Biology, Dartmouth, Hanover, NH, USA; Department of Physics, Dartmouth, Hanover, NH, USA
| | - Kenneth Shepard
- Department of Electrical Engineering, Columbia University, New York, NY, USA
| | - Chong Xie
- Department of Biomedical Engineering, University of Texas, Austin, TX, USA
| | - Timothy D Harris
- Howard Hughes Medical Institutes, Janelia Research Campus, Ashburn, VA, USA; Department of Bioengineering, Johns Hopkins University, Baltimore, MD, USA.
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18
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Reimann MW, Gevaert M, Shi Y, Lu H, Markram H, Muller E. A null model of the mouse whole-neocortex micro-connectome. Nat Commun 2019; 10:3903. [PMID: 31467291 PMCID: PMC6715727 DOI: 10.1038/s41467-019-11630-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 07/25/2019] [Indexed: 11/27/2022] Open
Abstract
In connectomics, the study of the network structure of connected neurons, great advances are being made on two different scales: that of macro- and meso-scale connectomics, studying the connectivity between populations of neurons, and that of micro-scale connectomics, studying connectivity between individual neurons. We combine these two complementary views of connectomics to build a first draft statistical model of the micro-connectome of a whole mouse neocortex based on available data on region-to-region connectivity and individual whole-brain axon reconstructions. This process reveals a targeting principle that allows us to predict the innervation logic of individual axons from meso-scale data. The resulting connectome recreates biological trends of targeting on all scales and predicts that an established principle of scale invariant topological organization of connectivity can be extended down to the level of individual neurons. It can serve as a powerful null model and as a substrate for whole-brain simulations.
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Affiliation(s)
- Michael W Reimann
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
| | - Michael Gevaert
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Ying Shi
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Huanxiang Lu
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Henry Markram
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Eilif Muller
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
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19
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Curto C, Morrison K. Relating network connectivity to dynamics: opportunities and challenges for theoretical neuroscience. Curr Opin Neurobiol 2019; 58:11-20. [PMID: 31319287 DOI: 10.1016/j.conb.2019.06.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 06/22/2019] [Indexed: 11/29/2022]
Abstract
We review recent work relating network connectivity to the dynamics of neural activity. While concepts stemming from network science provide a valuable starting point, the interpretation of graph-theoretic structures and measures can be highly dependent on the dynamics associated to the network. Properties that are quite meaningful for linear dynamics, such as random walk and network flow models, may be of limited relevance in the neuroscience setting. Theoretical and computational neuroscience are playing a vital role in understanding the relationship between network connectivity and the nonlinear dynamics associated to neural networks.
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Affiliation(s)
- Carina Curto
- The Pennsylvania State University, PA 16802, United States.
| | - Katherine Morrison
- School of Mathematical Sciences, University of Northern Colorado, Greeley, CO 80639, USA
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20
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Recanatesi S, Ocker GK, Buice MA, Shea-Brown E. Dimensionality in recurrent spiking networks: Global trends in activity and local origins in connectivity. PLoS Comput Biol 2019; 15:e1006446. [PMID: 31299044 PMCID: PMC6655892 DOI: 10.1371/journal.pcbi.1006446] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 07/24/2019] [Accepted: 04/03/2019] [Indexed: 11/25/2022] Open
Abstract
The dimensionality of a network's collective activity is of increasing interest in neuroscience. This is because dimensionality provides a compact measure of how coordinated network-wide activity is, in terms of the number of modes (or degrees of freedom) that it can independently explore. A low number of modes suggests a compressed low dimensional neural code and reveals interpretable dynamics [1], while findings of high dimension may suggest flexible computations [2, 3]. Here, we address the fundamental question of how dimensionality is related to connectivity, in both autonomous and stimulus-driven networks. Working with a simple spiking network model, we derive three main findings. First, the dimensionality of global activity patterns can be strongly, and systematically, regulated by local connectivity structures. Second, the dimensionality is a better indicator than average correlations in determining how constrained neural activity is. Third, stimulus evoked neural activity interacts systematically with neural connectivity patterns, leading to network responses of either greater or lesser dimensionality than the stimulus.
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Affiliation(s)
- Stefano Recanatesi
- Center for Computational Neuroscience, University of Washington, Seattle, Washington, United States of America
| | - Gabriel Koch Ocker
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Michael A. Buice
- Center for Computational Neuroscience, University of Washington, Seattle, Washington, United States of America
- Allen Institute for Brain Science, Seattle, Washington, United States of America
- Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America
| | - Eric Shea-Brown
- Center for Computational Neuroscience, University of Washington, Seattle, Washington, United States of America
- Allen Institute for Brain Science, Seattle, Washington, United States of America
- Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America
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21
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Synchronization dependent on spatial structures of a mesoscopic whole-brain network. PLoS Comput Biol 2019; 15:e1006978. [PMID: 31013267 PMCID: PMC6499430 DOI: 10.1371/journal.pcbi.1006978] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Revised: 05/03/2019] [Accepted: 03/26/2019] [Indexed: 11/20/2022] Open
Abstract
Complex structural connectivity of the mammalian brain is believed to underlie the versatility of neural computations. Many previous studies have investigated properties of small subsystems or coarse connectivity among large brain regions that are often binarized and lack spatial information. Yet little is known about spatial embedding of the detailed whole-brain connectivity and its functional implications. We focus on closing this gap by analyzing how spatially-constrained neural connectivity shapes synchronization of the brain dynamics based on a system of coupled phase oscillators on a mammalian whole-brain network at the mesoscopic level. This was made possible by the recent development of the Allen Mouse Brain Connectivity Atlas constructed from viral tracing experiments together with a new mapping algorithm. We investigated whether the network can be compactly represented based on the spatial dependence of the network topology. We found that the connectivity has a significant spatial dependence, with spatially close brain regions strongly connected and distal regions weakly connected, following a power law. However, there are a number of residuals above the power-law fit, indicating connections between brain regions that are stronger than predicted by the power-law relationship. By measuring the sensitivity of the network order parameter, we show how these strong connections dispersed across multiple spatial scales of the network promote rapid transitions between partial synchronization and more global synchronization as the global coupling coefficient changes. We further demonstrate the significance of the locations of the residual connections, suggesting a possible link between the network complexity and the brain’s exceptional ability to swiftly switch computational states depending on stimulus and behavioral context. In a previous study, a data-driven large-scale model of mouse brain connectivity was constructed. This mouse brain connectivity model is estimated by a simplified model which only takes in account anatomy and distance dependence of connection strength which is best fit by a power law. The distance dependence model captures the connection strengths of the mouse whole-brain network well. But can it capture the dynamics? In this study, we show that a small number of connections which are missed by the simple spatial model lead to significant differences in dynamics. The presence of a small number of strong connections over longer distances increases sensitivity of synchronization to perturbations. Unlike the data-driven network, the network without these long-range connections, as well as the network in which these long range connections are shuffled, lose global synchronization while maintaining localized synchrony, underlining the significance of the exact topology of these distal connections in the data-driven brain network.
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22
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Martins NRB, Angelica A, Chakravarthy K, Svidinenko Y, Boehm FJ, Opris I, Lebedev MA, Swan M, Garan SA, Rosenfeld JV, Hogg T, Freitas RA. Human Brain/Cloud Interface. Front Neurosci 2019; 13:112. [PMID: 30983948 PMCID: PMC6450227 DOI: 10.3389/fnins.2019.00112] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 01/30/2019] [Indexed: 12/25/2022] Open
Abstract
The Internet comprises a decentralized global system that serves humanity's collective effort to generate, process, and store data, most of which is handled by the rapidly expanding cloud. A stable, secure, real-time system may allow for interfacing the cloud with the human brain. One promising strategy for enabling such a system, denoted here as a "human brain/cloud interface" ("B/CI"), would be based on technologies referred to here as "neuralnanorobotics." Future neuralnanorobotics technologies are anticipated to facilitate accurate diagnoses and eventual cures for the ∼400 conditions that affect the human brain. Neuralnanorobotics may also enable a B/CI with controlled connectivity between neural activity and external data storage and processing, via the direct monitoring of the brain's ∼86 × 109 neurons and ∼2 × 1014 synapses. Subsequent to navigating the human vasculature, three species of neuralnanorobots (endoneurobots, gliabots, and synaptobots) could traverse the blood-brain barrier (BBB), enter the brain parenchyma, ingress into individual human brain cells, and autoposition themselves at the axon initial segments of neurons (endoneurobots), within glial cells (gliabots), and in intimate proximity to synapses (synaptobots). They would then wirelessly transmit up to ∼6 × 1016 bits per second of synaptically processed and encoded human-brain electrical information via auxiliary nanorobotic fiber optics (30 cm3) with the capacity to handle up to 1018 bits/sec and provide rapid data transfer to a cloud based supercomputer for real-time brain-state monitoring and data extraction. A neuralnanorobotically enabled human B/CI might serve as a personalized conduit, allowing persons to obtain direct, instantaneous access to virtually any facet of cumulative human knowledge. Other anticipated applications include myriad opportunities to improve education, intelligence, entertainment, traveling, and other interactive experiences. A specialized application might be the capacity to engage in fully immersive experiential/sensory experiences, including what is referred to here as "transparent shadowing" (TS). Through TS, individuals might experience episodic segments of the lives of other willing participants (locally or remote) to, hopefully, encourage and inspire improved understanding and tolerance among all members of the human family.
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Affiliation(s)
- Nuno R. B. Martins
- Lawrence Berkeley National Laboratory, Berkeley, CA, United States
- Center for Research and Education on Aging (CREA), University of California, Berkeley and LBNL, Berkeley, CA, United States
| | | | - Krishnan Chakravarthy
- UC San Diego Health Science, San Diego, CA, United States
- VA San Diego Healthcare System, San Diego, CA, United States
| | | | | | - Ioan Opris
- Miami Project to Cure Paralysis, University of Miami, Miami, FL, United States
- Department of Biomedical Engineering, University of Miami, Coral Gables, FL, United States
| | - Mikhail A. Lebedev
- Center for Neuroengineering, Duke University, Durham, NC, United States
- Center for Bioelectric Interfaces of the Institute for Cognitive Neuroscience of the National Research University Higher School of Economics, Moscow, Russia
- Department of Information and Internet Technologies of Digital Health Institute, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Melanie Swan
- Department of Philosophy, Purdue University, West Lafayette, IN, United States
| | - Steven A. Garan
- Lawrence Berkeley National Laboratory, Berkeley, CA, United States
- Center for Research and Education on Aging (CREA), University of California, Berkeley and LBNL, Berkeley, CA, United States
| | - Jeffrey V. Rosenfeld
- Monash Institute of Medical Engineering, Monash University, Clayton, VIC, Australia
- Department of Neurosurgery, Alfred Hospital, Melbourne, VIC, Australia
- Department of Surgery, Monash University, Clayton, VIC, Australia
- Department of Surgery, F. Edward Hébert School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States
| | - Tad Hogg
- Institute for Molecular Manufacturing, Palo Alto, CA, United States
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23
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Düring DN, Rocha MD, Dittrich F, Gahr M, Hahnloser RHR. Expansion Light Sheet Microscopy Resolves Subcellular Structures in Large Portions of the Songbird Brain. Front Neuroanat 2019; 13:2. [PMID: 30766480 PMCID: PMC6365838 DOI: 10.3389/fnana.2019.00002] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 01/11/2019] [Indexed: 12/24/2022] Open
Abstract
Expansion microscopy and light sheet imaging (ExLSM) provide a viable alternative to existing tissue clearing and large volume imaging approaches. The analysis of intact volumes of brain tissue presents a distinct challenge in neuroscience. Recent advances in tissue clearing and light sheet microscopy have re-addressed this challenge and blossomed into a plethora of protocols with diverse advantages and disadvantages. While refractive index matching achieves near perfect transparency and allows for imaging at large depths, the resolution of cleared brains is usually limited to the micrometer range. Moreover, the often long and harsh tissue clearing protocols hinder preservation of native fluorescence and antigenicity. Here we image large expanded brain volumes of zebra finch brain tissue in commercially available light sheet microscopes. Our expansion light sheet microscopy (ExLSM) approach presents a viable alternative to many clearing and imaging methods because it improves on tissue processing times, fluorophore compatibility, and image resolution.
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Affiliation(s)
- Daniel Normen Düring
- Institute of Neuroinformatics, University of Zürich/ETH Zürich, Zurich, Switzerland
- Neuroscience Center Zurich (ZNZ), Zurich, Switzerland
- Department of Behavioral Neurobiology, Max Planck Institute for Ornithology, Seewiesen, Germany
| | - Mariana Diales Rocha
- Department of Behavioral Neurobiology, Max Planck Institute for Ornithology, Seewiesen, Germany
| | - Falk Dittrich
- Department of Behavioral Neurobiology, Max Planck Institute for Ornithology, Seewiesen, Germany
| | - Manfred Gahr
- Department of Behavioral Neurobiology, Max Planck Institute for Ornithology, Seewiesen, Germany
| | - Richard Hans Robert Hahnloser
- Institute of Neuroinformatics, University of Zürich/ETH Zürich, Zurich, Switzerland
- Neuroscience Center Zurich (ZNZ), Zurich, Switzerland
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24
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Guo J, Keller KA, Govyadinov P, Ruchhoeft P, Slater JH, Mayerich D. Accurate flow in augmented networks (AFAN): an approach to generating three-dimensional biomimetic microfluidic networks with controlled flow. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2019; 11:8-16. [PMID: 31490456 PMCID: PMC6336169 DOI: 10.1039/c8ay01798k] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Accepted: 08/30/2018] [Indexed: 05/18/2023]
Abstract
In vivo, microvasculature provides oxygen, nutrients, and soluble factors necessary for cell survival and function. The highly tortuous, densely-packed, and interconnected three-dimensional (3D) architecture of microvasculature ensures that cells receive these crucial components. The ability to duplicate microvascular architecture in tissue-engineered models could provide a means to generate large-volume constructs as well as advanced microphysiological systems. Similarly, the ability to induce realistic flow in engineered microvasculature is crucial to recapitulating in vivo-like flow and transport. Advanced biofabrication techniques are capable of generating 3D, biomimetic microfluidic networks in hydrogels, however, these models can exhibit systemic aberrations in flow due to incorrect boundary conditions. To overcome this problem, we developed an automated method for generating synthetic augmented channels that induce the desired flow properties within three-dimensional microfluidic networks. These augmented inlets and outlets enforce the appropriate boundary conditions for achieving specified flow properties and create a three-dimensional output useful for image-guided fabrication techniques to create biomimetic microvascular networks.
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Affiliation(s)
- Jiaming Guo
- Department of Electrical and Computer Engineering , University of Houston , USA .
| | - Keely A Keller
- Department of Biomedical Engineering , University of Delaware , USA
| | - Pavel Govyadinov
- Department of Electrical and Computer Engineering , University of Houston , USA .
| | - Paul Ruchhoeft
- Department of Electrical and Computer Engineering , University of Houston , USA .
| | - John H Slater
- Department of Biomedical Engineering , University of Delaware , USA
| | - David Mayerich
- Department of Electrical and Computer Engineering , University of Houston , USA .
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25
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Garcia-Marin V, Kelly JG, Hawken MJ. Major Feedforward Thalamic Input Into Layer 4C of Primary Visual Cortex in Primate. Cereb Cortex 2019; 29:134-149. [PMID: 29190326 PMCID: PMC6490972 DOI: 10.1093/cercor/bhx311] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Revised: 09/29/2017] [Accepted: 10/30/2017] [Indexed: 01/28/2023] Open
Abstract
One of the underlying principles of how mammalian circuits are constructed is the relative influence of feedforward to recurrent synaptic drive. It has been dogma in sensory systems that the thalamic feedforward input is relatively weak and that there is a large amplification of the input signal by recurrent feedback. Here we show that in trichromatic primates there is a major feedforward input to layer 4C of primary visual cortex. Using a combination of 3D-electron-microscopy and 3D-confocal imaging of thalamic boutons we found that the average feedforward contribution was about 20% of the total excitatory input in the parvocellular (P) pathway, about 3 times the currently accepted values for primates. In the magnocellular (M) pathway it was around 15%, nearly twice the currently accepted values. New methods showed the total synaptic and cell densities were as much as 150% of currently accepted values. The new estimates of contributions of feedforward synaptic inputs into visual cortex call for a major revision of the design of the canonical cortical circuit.
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Affiliation(s)
| | - Jenna G Kelly
- Center for Neural Science, New York University, 4 Washington Place, New York, USA
| | - Michael J Hawken
- Center for Neural Science, New York University, 4 Washington Place, New York, USA
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26
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Eberle AL, Zeidler D. Multi-Beam Scanning Electron Microscopy for High-Throughput Imaging in Connectomics Research. Front Neuroanat 2018; 12:112. [PMID: 30618653 PMCID: PMC6297274 DOI: 10.3389/fnana.2018.00112] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Accepted: 11/23/2018] [Indexed: 02/06/2023] Open
Abstract
Major progress has been achieved in recent years in three-dimensional microscopy techniques. This applies to the life sciences in general, but specifically the neuroscientific field has been a main driver for developments regarding volume imaging. In particular, scanning electron microscopy offers new insights into the organization of cells and tissues by volume imaging methods, such as serial section array tomography, serial block-face imaging or focused ion beam tomography. However, most of these techniques are restricted to relatively small tissue volumes due to the limited acquisition throughput of most standard imaging techniques. Recently, a novel multi-beam scanning electron microscope technology optimized to the imaging of large sample areas has been developed. Complemented by the commercialization of automated sample preparation robots, the mapping of larger, cubic millimeter range tissue volumes at high-resolution is now within reach. This Mini Review will provide a brief overview of the various approaches to electron microscopic volume imaging, with an emphasis on serial section array tomography and multi-beam scanning electron microscopic imaging.
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27
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Knox JE, Harris KD, Graddis N, Whitesell JD, Zeng H, Harris JA, Shea-Brown E, Mihalas S. High-resolution data-driven model of the mouse connectome. Netw Neurosci 2018; 3:217-236. [PMID: 30793081 PMCID: PMC6372022 DOI: 10.1162/netn_a_00066] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Accepted: 07/31/2018] [Indexed: 11/04/2022] Open
Abstract
Knowledge of mesoscopic brain connectivity is important for understanding inter- and intraregion information processing. Models of structural connectivity are typically constructed and analyzed with the assumption that regions are homogeneous. We instead use the Allen Mouse Brain Connectivity Atlas to construct a model of whole-brain connectivity at the scale of 100 μm voxels. The data consist of 428 anterograde tracing experiments in wild type C57BL/6J mice, mapping fluorescently labeled neuronal projections brain-wide. Inferring spatial connectivity with this dataset is underdetermined, since the approximately 2 × 105 source voxels outnumber the number of experiments. To address this issue, we assume that connection patterns and strengths vary smoothly across major brain divisions. We model the connectivity at each voxel as a radial basis kernel-weighted average of the projection patterns of nearby injections. The voxel model outperforms a previous regional model in predicting held-out experiments and compared with a human-curated dataset. This voxel-scale model of the mouse connectome permits researchers to extend their previous analyses of structural connectivity to much higher levels of resolution, and it allows for comparison with functional imaging and other datasets.
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Affiliation(s)
- Joseph E. Knox
- Allen Institute for Brain Science, Seattle, Washington, USA
- Applied Mathematics, University of Washington, Seattle, Washington, USA
| | - Kameron Decker Harris
- Applied Mathematics, University of Washington, Seattle, Washington, USA
- Computer Science and Engineering, University of Washington, Seattle, Washington, USA
| | - Nile Graddis
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Eric Shea-Brown
- Allen Institute for Brain Science, Seattle, Washington, USA
- Applied Mathematics, University of Washington, Seattle, Washington, USA
| | - Stefan Mihalas
- Allen Institute for Brain Science, Seattle, Washington, USA
- Applied Mathematics, University of Washington, Seattle, Washington, USA
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28
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Sizemore AE, Bassett DS. Dynamic graph metrics: Tutorial, toolbox, and tale. Neuroimage 2018; 180:417-427. [PMID: 28698107 PMCID: PMC5758445 DOI: 10.1016/j.neuroimage.2017.06.081] [Citation(s) in RCA: 90] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Revised: 05/24/2017] [Accepted: 06/29/2017] [Indexed: 11/23/2022] Open
Abstract
The central nervous system is composed of many individual units - from cells to areas - that are connected with one another in a complex pattern of functional interactions that supports perception, action, and cognition. One natural and parsimonious representation of such a system is a graph in which nodes (units) are connected by edges (interactions). While applicable across spatiotemporal scales, species, and cohorts, the traditional graph approach is unable to address the complexity of time-varying connectivity patterns that may be critically important for an understanding of emotional and cognitive state, task-switching, adaptation and development, or aging and disease progression. Here we survey a set of tools from applied mathematics that offer measures to characterize dynamic graphs. Along with this survey, we offer suggestions for visualization and a publicly-available MATLAB toolbox to facilitate the application of these metrics to existing or yet-to-be acquired neuroimaging data. We illustrate the toolbox by applying it to a previously published data set of time-varying functional graphs, but note that the tools can also be applied to time-varying structural graphs or to other sorts of relational data entirely. Our aim is to provide the neuroimaging community with a useful set of tools, and an intuition regarding how to use them, for addressing emerging questions that hinge on accurate and creative analyses of dynamic graphs.
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Affiliation(s)
- Ann E Sizemore
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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29
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Abstract
The central nervous system is composed of many individual units - from cells to areas - that are connected with one another in a complex pattern of functional interactions that supports perception, action, and cognition. One natural and parsimonious representation of such a system is a graph in which nodes (units) are connected by edges (interactions). While applicable across spatiotemporal scales, species, and cohorts, the traditional graph approach is unable to address the complexity of time-varying connectivity patterns that may be critically important for an understanding of emotional and cognitive state, task-switching, adaptation and development, or aging and disease progression. Here we survey a set of tools from applied mathematics that offer measures to characterize dynamic graphs. Along with this survey, we offer suggestions for visualization and a publicly-available MATLAB toolbox to facilitate the application of these metrics to existing or yet-to-be acquired neuroimaging data. We illustrate the toolbox by applying it to a previously published data set of time-varying functional graphs, but note that the tools can also be applied to time-varying structural graphs or to other sorts of relational data entirely. Our aim is to provide the neuroimaging community with a useful set of tools, and an intuition regarding how to use them, for addressing emerging questions that hinge on accurate and creative analyses of dynamic graphs.
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Affiliation(s)
- Ann E Sizemore
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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30
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Dehghani N. Theoretical Principles of Multiscale Spatiotemporal Control of Neuronal Networks: A Complex Systems Perspective. Front Comput Neurosci 2018; 12:81. [PMID: 30349469 PMCID: PMC6187923 DOI: 10.3389/fncom.2018.00081] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Accepted: 09/11/2018] [Indexed: 01/14/2023] Open
Abstract
Success in the fine control of the nervous system depends on a deeper understanding of how neural circuits control behavior. There is, however, a wide gap between the components of neural circuits and behavior. We advance the idea that a suitable approach for narrowing this gap has to be based on a multiscale information-theoretic description of the system. We evaluate the possibility that brain-wide complex neural computations can be dissected into a hierarchy of computational motifs that rely on smaller circuit modules interacting at multiple scales. In doing so, we draw attention to the importance of formalizing the goals of stimulation in terms of neural computations so that the possible implementations are matched in scale to the underlying circuit modules.
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Affiliation(s)
- Nima Dehghani
- Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, United States
- Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, MA, United States
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31
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Lefebvre J, Delafontaine-Martel P, Pouliot P, Girouard H, Descoteaux M, Lesage F. Fully automated dual-resolution serial optical coherence tomography aimed at diffusion MRI validation in whole mouse brains. NEUROPHOTONICS 2018; 5:045004. [PMID: 30681668 PMCID: PMC6215086 DOI: 10.1117/1.nph.5.4.045004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Accepted: 10/12/2018] [Indexed: 06/09/2023]
Abstract
An automated dual-resolution serial optical coherence tomography (2R-SOCT) scanner is developed. The serial histology system combines a low-resolution ( 25 μ m / voxel ) 3 × OCT with a high-resolution ( 1.5 μ m / voxel ) 40 × OCT to acquire whole mouse brains at low resolution and to target specific regions of interest (ROIs) at high resolution. The 40 × ROIs positions are selected either manually by the microscope operator or using an automated ROI positioning selection algorithm. Additionally, a multimodal and multiresolution registration pipeline is developed in order to align the 2R-SOCT data onto diffusion MRI (dMRI) data acquired in the same ex vivo mouse brains prior to automated histology. Using this imaging system, 3 whole mouse brains are imaged, and 250 high-resolution 40 × three-dimensional ROIs are acquired. The capability of this system to perform multimodal imaging studies is demonstrated by labeling the ROIs using a mouse brain atlas and by categorizing the ROIs based on their associated dMRI measures. This reveals a good correspondence of the tissue microstructure imaged by the high-resolution OCT with various dMRI measures such as fractional anisotropy, number of fiber orientations, apparent fiber density, orientation dispersion, and intracellular volume fraction.
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Affiliation(s)
- Joël Lefebvre
- École Polytechnique de Montréal, Laboratoire d’imagerie optique et moléculaire, Montreal, Canada
| | | | - Philippe Pouliot
- École Polytechnique de Montréal, Laboratoire d’imagerie optique et moléculaire, Montreal, Canada
- Université de Montréal, Research Centre, Montréal Heart Institute, Montreal, Canada
| | - Hélène Girouard
- Université de Montréal, Institut universitaire de gériatrie de Montréal, Montreal, Canada
| | - Maxime Descoteaux
- Université de Sherbrooke, Sherbrooke Connectivity Imaging Laboratory, Sherbrooke, Canada
| | - Frédéric Lesage
- École Polytechnique de Montréal, Laboratoire d’imagerie optique et moléculaire, Montreal, Canada
- Université de Montréal, Research Centre, Montréal Heart Institute, Montreal, Canada
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32
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On Curiosity: A Fundamental Aspect of Personality, a Practice of Network Growth. PERSONALITY NEUROSCIENCE 2018; 1:e13. [PMID: 32435732 PMCID: PMC7219889 DOI: 10.1017/pen.2018.3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Accepted: 01/06/2018] [Indexed: 12/15/2022]
Abstract
Human personality is reflected in patterns-or networks-of behavior, either in thought or action. Curiosity is an oft-treasured component of one's personality, commonly associated with information-seeking proclivities with distinct neurophysiological correlates. The markers of curiosity can differ substantially across people, suggesting the possibility that personality also determines the architectural style of one's curiosity. Yet progress in defining those styles, and marking their neurophysiological basis, has been hampered by fairly fundamental difficulties in defining curiosity itself. Here, we offer and exercise a definition of the practice of curiosity as knowledge network building, one particular pattern of thought behavior. To unpack this definition and motivate its utility, we begin with a short primer on network science and describe how the mathematical object of a network can be used to map items and relations that are characteristic of bodies of knowledge. Next, we turn to a discussion of how networks grow, how their growth can be modeled, and how the practice of curiosity can be formalized as a process of network growth. We pay particular attention to how individuals may differ in how they build their knowledge networks, and discuss how the sort, manner, and action of building can be modulated by experience. We discuss how this definition of the practice of curiosity motivates new experiments and theory development at the interdisciplinary intersection of network science, personality neuroscience, education, and curiosity studies. We close with a note on the potential of network science to inform studies of other domains of personality, and the patterns of thought- or action-behavior characteristic thereof.
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33
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Varando G, Benavides-Piccione R, Muñoz A, Kastanauskaite A, Bielza C, Larrañaga P, DeFelipe J. MultiMap: A Tool to Automatically Extract and Analyse Spatial Microscopic Data From Large Stacks of Confocal Microscopy Images. Front Neuroanat 2018; 12:37. [PMID: 29875639 PMCID: PMC5974206 DOI: 10.3389/fnana.2018.00037] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 04/24/2018] [Indexed: 11/13/2022] Open
Abstract
The development of 3D visualization and reconstruction methods to analyse microscopic structures at different levels of resolutions is of great importance to define brain microorganization and connectivity. MultiMap is a new tool that allows the visualization, 3D segmentation and quantification of fluorescent structures selectively in the neuropil from large stacks of confocal microscopy images. The major contribution of this tool is the posibility to easily navigate and create regions of interest of any shape and size within a large brain area that will be automatically 3D segmented and quantified to determine the density of puncta in the neuropil. As a proof of concept, we focused on the analysis of glutamatergic and GABAergic presynaptic axon terminals in the mouse hippocampal region to demonstrate its use as a tool to provide putative excitatory and inhibitory synaptic maps. The segmentation and quantification method has been validated over expert labeled images of the mouse hippocampus and over two benchmark datasets, obtaining comparable results to the expert detections.
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Affiliation(s)
- Gherardo Varando
- Computational Intelligence Group, Department of Artificial Intelligence, Universidad Politécnica de Madrid, Madrid, Spain
| | - Ruth Benavides-Piccione
- Departamento de Neurobiología Funcional y de Sistemas, Instituto Cajal (CSIC), Madrid, Spain.,Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
| | - Alberto Muñoz
- Departamento de Neurobiología Funcional y de Sistemas, Instituto Cajal (CSIC), Madrid, Spain.,Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain.,Departamento de Biología Celular, Universidad Complutense, Madrid, Spain
| | - Asta Kastanauskaite
- Departamento de Neurobiología Funcional y de Sistemas, Instituto Cajal (CSIC), Madrid, Spain.,Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
| | - Concha Bielza
- Computational Intelligence Group, Department of Artificial Intelligence, Universidad Politécnica de Madrid, Madrid, Spain
| | - Pedro Larrañaga
- Computational Intelligence Group, Department of Artificial Intelligence, Universidad Politécnica de Madrid, Madrid, Spain
| | - Javier DeFelipe
- Departamento de Neurobiología Funcional y de Sistemas, Instituto Cajal (CSIC), Madrid, Spain.,Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
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34
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Ocker GK, Hu Y, Buice MA, Doiron B, Josić K, Rosenbaum R, Shea-Brown E. From the statistics of connectivity to the statistics of spike times in neuronal networks. Curr Opin Neurobiol 2017; 46:109-119. [PMID: 28863386 DOI: 10.1016/j.conb.2017.07.011] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Revised: 07/21/2017] [Accepted: 07/27/2017] [Indexed: 10/19/2022]
Abstract
An essential step toward understanding neural circuits is linking their structure and their dynamics. In general, this relationship can be almost arbitrarily complex. Recent theoretical work has, however, begun to identify some broad principles underlying collective spiking activity in neural circuits. The first is that local features of network connectivity can be surprisingly effective in predicting global statistics of activity across a network. The second is that, for the important case of large networks with excitatory-inhibitory balance, correlated spiking persists or vanishes depending on the spatial scales of recurrent and feedforward connectivity. We close by showing how these ideas, together with plasticity rules, can help to close the loop between network structure and activity statistics.
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Affiliation(s)
| | - Yu Hu
- Center for Brain Science, Harvard University, United States
| | - Michael A Buice
- Allen Institute for Brain Science, United States; Department of Applied Mathematics, University of Washington, United States
| | - Brent Doiron
- Department of Mathematics, University of Pittsburgh, United States; Center for the Neural Basis of Cognition, Pittsburgh, United States
| | - Krešimir Josić
- Department of Mathematics, University of Houston, United States; Department of Biology and Biochemistry, University of Houston, United States; Department of BioSciences, Rice University, United States
| | - Robert Rosenbaum
- Department of Mathematics, University of Notre Dame, United States
| | - Eric Shea-Brown
- Allen Institute for Brain Science, United States; Department of Applied Mathematics, University of Washington, United States; Department of Physiology and Biophysics, and University of Washington Institute for Neuroengineering, United States.
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35
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On the Structure of Cortical Microcircuits Inferred from Small Sample Sizes. J Neurosci 2017; 37:8498-8510. [PMID: 28760860 DOI: 10.1523/jneurosci.0984-17.2017] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Revised: 06/23/2017] [Accepted: 07/18/2017] [Indexed: 02/05/2023] Open
Abstract
The structure in cortical microcircuits deviates from what would be expected in a purely random network, which has been seen as evidence of clustering. To address this issue, we sought to reproduce the nonrandom features of cortical circuits by considering several distinct classes of network topology, including clustered networks, networks with distance-dependent connectivity, and those with broad degree distributions. To our surprise, we found that all of these qualitatively distinct topologies could account equally well for all reported nonrandom features despite being easily distinguishable from one another at the network level. This apparent paradox was a consequence of estimating network properties given only small sample sizes. In other words, networks that differ markedly in their global structure can look quite similar locally. This makes inferring network structure from small sample sizes, a necessity given the technical difficulty inherent in simultaneous intracellular recordings, problematic. We found that a network statistic called the sample degree correlation (SDC) overcomes this difficulty. The SDC depends only on parameters that can be estimated reliably given small sample sizes and is an accurate fingerprint of every topological family. We applied the SDC criterion to data from rat visual and somatosensory cortex and discovered that the connectivity was not consistent with any of these main topological classes. However, we were able to fit the experimental data with a more general network class, of which all previous topologies were special cases. The resulting network topology could be interpreted as a combination of physical spatial dependence and nonspatial, hierarchical clustering.SIGNIFICANCE STATEMENT The connectivity of cortical microcircuits exhibits features that are inconsistent with a simple random network. Here, we show that several classes of network models can account for this nonrandom structure despite qualitative differences in their global properties. This apparent paradox is a consequence of the small numbers of simultaneously recorded neurons in experiment: when inferred via small sample sizes, many networks may be indistinguishable despite being globally distinct. We develop a connectivity measure that successfully classifies networks even when estimated locally with a few neurons at a time. We show that data from rat cortex is consistent with a network in which the likelihood of a connection between neurons depends on spatial distance and on nonspatial, asymmetric clustering.
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36
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Peikon ID, Kebschull JM, Vagin VV, Ravens DI, Sun YC, Brouzes E, Corrêa IR, Bressan D, Zador AM. Using high-throughput barcode sequencing to efficiently map connectomes. Nucleic Acids Res 2017; 45:e115. [PMID: 28449067 PMCID: PMC5499584 DOI: 10.1093/nar/gkx292] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2017] [Revised: 03/20/2017] [Accepted: 04/13/2017] [Indexed: 01/16/2023] Open
Abstract
The function of a neural circuit is determined by the details of its synaptic connections. At present, the only available method for determining a neural wiring diagram with single synapse precision-a 'connectome'-is based on imaging methods that are slow, labor-intensive and expensive. Here, we present SYNseq, a method for converting the connectome into a form that can exploit the speed and low cost of modern high-throughput DNA sequencing. In SYNseq, each neuron is labeled with a unique random nucleotide sequence-an RNA 'barcode'-which is targeted to the synapse using engineered proteins. Barcodes in pre- and postsynaptic neurons are then associated through protein-protein crosslinking across the synapse, extracted from the tissue, and joined into a form suitable for sequencing. Although our failure to develop an efficient barcode joining scheme precludes the widespread application of this approach, we expect that with further development SYNseq will enable tracing of complex circuits at high speed and low cost.
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Affiliation(s)
- Ian D. Peikon
- Watson School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Justus M. Kebschull
- Watson School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Vasily V. Vagin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Diana I. Ravens
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Yu-Chi Sun
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Eric Brouzes
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | | | - Dario Bressan
- Watson School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge CB2 0RE, UK
| | - Anthony M. Zador
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
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37
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Chambers AR, Rumpel S. A stable brain from unstable components: Emerging concepts and implications for neural computation. Neuroscience 2017; 357:172-184. [PMID: 28602920 DOI: 10.1016/j.neuroscience.2017.06.005] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Revised: 06/02/2017] [Accepted: 06/05/2017] [Indexed: 11/28/2022]
Abstract
Neuroscientists have often described the adult brain in similar terms to an electronic circuit board- dependent on fixed, precise connectivity. However, with the advent of technologies allowing chronic measurements of neural structure and function, the emerging picture is that neural networks undergo significant remodeling over multiple timescales, even in the absence of experimenter-induced learning or sensory perturbation. Here, we attempt to reconcile the parallel observations that critical brain functions are stably maintained, while synapse- and single-cell properties appear to be reformatted regularly throughout adult life. In this review, we discuss experimental evidence at multiple levels ranging from synapses to neuronal ensembles, suggesting that many parameters are maintained in a dynamic equilibrium. We highlight emerging hypotheses that could explain how stable brain functions may be generated from dynamic elements. Furthermore, we discuss the impact of dynamic circuit elements on neural computations, and how they could provide living neural circuits with computational abilities a fixed structure cannot offer. Taken together, recent evidence indicates that continuous dynamics are a fundamental property of neural circuits compatible with macroscopically stable behaviors. In addition, they may be a unique advantage imparting robustness and flexibility throughout life.
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Affiliation(s)
- Anna R Chambers
- Focus Program Translational Neuroscience (FTN), Johannes Gutenberg University, Mainz, Germany; Institute of Physiology, Johannes Gutenberg University, Mainz, Germany
| | - Simon Rumpel
- Focus Program Translational Neuroscience (FTN), Johannes Gutenberg University, Mainz, Germany; Institute of Physiology, Johannes Gutenberg University, Mainz, Germany.
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Micheva KD, Wolman D, Mensh BD, Pax E, Buchanan J, Smith SJ, Bock DD. A large fraction of neocortical myelin ensheathes axons of local inhibitory neurons. eLife 2016; 5. [PMID: 27383052 PMCID: PMC4972537 DOI: 10.7554/elife.15784] [Citation(s) in RCA: 167] [Impact Index Per Article: 20.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2016] [Accepted: 07/05/2016] [Indexed: 12/30/2022] Open
Abstract
Myelin is best known for its role in increasing the conduction velocity and metabolic efficiency of long-range excitatory axons. Accordingly, the myelin observed in neocortical gray matter is thought to mostly ensheath excitatory axons connecting to subcortical regions and distant cortical areas. Using independent analyses of light and electron microscopy data from mouse neocortex, we show that a surprisingly large fraction of cortical myelin (half the myelin in layer 2/3 and a quarter in layer 4) ensheathes axons of inhibitory neurons, specifically of parvalbumin-positive basket cells. This myelin differs significantly from that of excitatory axons in distribution and protein composition. Myelin on inhibitory axons is unlikely to meaningfully hasten the arrival of spikes at their pre-synaptic terminals, due to the patchy distribution and short path-lengths observed. Our results thus highlight the need for exploring alternative roles for myelin in neocortical circuits.
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Affiliation(s)
- Kristina D Micheva
- Department of Molecular and Cellular Physiology, Stanford University, Stanford, United States
| | - Dylan Wolman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Brett D Mensh
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Elizabeth Pax
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - JoAnn Buchanan
- Department of Molecular and Cellular Physiology, Stanford University, Stanford, United States
| | - Stephen J Smith
- Department of Molecular and Cellular Physiology, Stanford University, Stanford, United States
| | - Davi D Bock
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
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Krzyzanowski MC, Woldemariam S, Wood JF, Chaubey AH, Brueggemann C, Bowitch A, Bethke M, L’Etoile ND, Ferkey DM. Aversive Behavior in the Nematode C. elegans Is Modulated by cGMP and a Neuronal Gap Junction Network. PLoS Genet 2016; 12:e1006153. [PMID: 27459302 PMCID: PMC4961389 DOI: 10.1371/journal.pgen.1006153] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Accepted: 06/08/2016] [Indexed: 01/03/2023] Open
Abstract
All animals rely on their ability to sense and respond to their environment to survive. However, the suitability of a behavioral response is context-dependent, and must reflect both an animal's life history and its present internal state. Based on the integration of these variables, an animal's needs can be prioritized to optimize survival strategies. Nociceptive sensory systems detect harmful stimuli and allow for the initiation of protective behavioral responses. The polymodal ASH sensory neurons are the primary nociceptors in C. elegans. We show here that the guanylyl cyclase ODR-1 functions non-cell-autonomously to downregulate ASH-mediated aversive behaviors and that ectopic cGMP generation in ASH is sufficient to dampen ASH sensitivity. We define a gap junction neural network that regulates nociception and propose that decentralized regulation of ASH signaling can allow for rapid correlation between an animal's internal state and its behavioral output, lending modulatory flexibility to this hard-wired nociceptive neural circuit.
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Affiliation(s)
- Michelle C. Krzyzanowski
- Department of Biological Sciences, University at Buffalo, The State University of New York, Buffalo, New York, United States of America
| | - Sarah Woldemariam
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, California, United States of America
| | - Jordan F. Wood
- Department of Biological Sciences, University at Buffalo, The State University of New York, Buffalo, New York, United States of America
| | - Aditi H. Chaubey
- Department of Biological Sciences, University at Buffalo, The State University of New York, Buffalo, New York, United States of America
| | - Chantal Brueggemann
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, California, United States of America
| | - Alexander Bowitch
- Department of Biological Sciences, University at Buffalo, The State University of New York, Buffalo, New York, United States of America
| | - Mary Bethke
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, California, United States of America
| | - Noelle D. L’Etoile
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, California, United States of America
| | - Denise M. Ferkey
- Department of Biological Sciences, University at Buffalo, The State University of New York, Buffalo, New York, United States of America
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40
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Roberts DG, Johnsonbaugh HB, Spence RD, MacKenzie-Graham A. Optical Clearing of the Mouse Central Nervous System Using Passive CLARITY. J Vis Exp 2016. [PMID: 27404319 DOI: 10.3791/54025] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Traditionally, tissue visualization has required that the tissue of interest be serially sectioned and imaged, subjecting each tissue section to unique non-linear deformations, dramatically hampering one's ability to evaluate cellular morphology, distribution and connectivity in the central nervous system (CNS). However, optical clearing techniques are changing the way tissues are visualized. These approaches permit one to probe deeply into intact organ preparations, providing tremendous insight into the structural organization of tissues in health and disease. Techniques such as Clear Lipid-exchanged Acrylamide-hybridized Rigid Imaging-compatible Tissue-hYdrogel (CLARITY) achieve this goal by providing a matrix that binds important biomolecules while permitting light-scattering lipids to freely diffuse out. Lipid removal, followed by refractive index matching, renders the tissue transparent and readily imaged in 3 dimensions (3D). Nevertheless, the electrophoretic tissue clearing (ETC) used in the original CLARITY protocol can be challenging to implement successfully and the use of a proprietary refraction index matching solution makes it expensive to use the technique routinely. This report demonstrates the implementation of a simple and inexpensive optical clearing protocol that combines passive CLARITY for improved tissue integrity and 2,2'-thiodiethanol (TDE), a previously described refractive index matching solution.
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Affiliation(s)
- Dustin G Roberts
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles
| | - Hadley B Johnsonbaugh
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles
| | - Rory D Spence
- W.M. Keck Science Department, Claremont McKenna, Pitzer & Scripps Colleges
| | - Allan MacKenzie-Graham
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles;
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41
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Goense J, Bohraus Y, Logothetis NK. fMRI at High Spatial Resolution: Implications for BOLD-Models. Front Comput Neurosci 2016; 10:66. [PMID: 27445782 PMCID: PMC4923185 DOI: 10.3389/fncom.2016.00066] [Citation(s) in RCA: 83] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2015] [Accepted: 06/15/2016] [Indexed: 11/13/2022] Open
Abstract
As high-resolution functional magnetic resonance imaging (fMRI) and fMRI of cortical layers become more widely used, the question how well high-resolution fMRI signals reflect the underlying neural processing, and how to interpret laminar fMRI data becomes more and more relevant. High-resolution fMRI has shown laminar differences in cerebral blood flow (CBF), volume (CBV), and neurovascular coupling. Features and processes that were previously lumped into a single voxel become spatially distinct at high resolution. These features can be vascular compartments such as veins, arteries, and capillaries, or cortical layers and columns, which can have differences in metabolism. Mesoscopic models of the blood oxygenation level dependent (BOLD) response therefore need to be expanded, for instance, to incorporate laminar differences in the coupling between neural activity, metabolism and the hemodynamic response. Here we discuss biological and methodological factors that affect the modeling and interpretation of high-resolution fMRI data. We also illustrate with examples from neuropharmacology and the negative BOLD response how combining BOLD with CBF- and CBV-based fMRI methods can provide additional information about neurovascular coupling, and can aid modeling and interpretation of high-resolution fMRI.
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Affiliation(s)
- Jozien Goense
- Department of Psychology, Institute of Neuroscience and Psychology, University of Glasgow Glasgow, UK
| | - Yvette Bohraus
- Department of Physiology of Cognitive Processes, Max-Planck Institute for Biological Cybernetics Tübingen, Germany
| | - Nikos K Logothetis
- Department of Physiology of Cognitive Processes, Max-Planck Institute for Biological CyberneticsTübingen, Germany; Divison of Imaging Science and Biomedical Engineering, University of ManchesterManchester, UK
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42
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Owald D, Lin S, Waddell S. Light, heat, action: neural control of fruit fly behaviour. Philos Trans R Soc Lond B Biol Sci 2016; 370:20140211. [PMID: 26240426 PMCID: PMC4528823 DOI: 10.1098/rstb.2014.0211] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
The fruit fly Drosophila melanogaster has emerged as a popular model to investigate fundamental principles of neural circuit operation. The sophisticated genetics and small brain permit a cellular resolution understanding of innate and learned behavioural processes. Relatively recent genetic and technical advances provide the means to specifically and reproducibly manipulate the function of many fly neurons with temporal resolution. The same cellular precision can also be exploited to express genetically encoded reporters of neural activity and cell-signalling pathways. Combining these approaches in living behaving animals has great potential to generate a holistic view of behavioural control that transcends the usual molecular, cellular and systems boundaries. In this review, we discuss these approaches with particular emphasis on the pioneering studies and those involving learning and memory.
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Affiliation(s)
- David Owald
- Centre for Neural Circuits and Behaviour, University of Oxford, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK
| | - Suewei Lin
- Centre for Neural Circuits and Behaviour, University of Oxford, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK
| | - Scott Waddell
- Centre for Neural Circuits and Behaviour, University of Oxford, Tinsley Building, Mansfield Road, Oxford OX1 3SR, UK
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43
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Sukhinin DI, Engel AK, Manger P, Hilgetag CC. Building the Ferretome. Front Neuroinform 2016; 10:16. [PMID: 27242503 PMCID: PMC4861729 DOI: 10.3389/fninf.2016.00016] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Accepted: 04/14/2016] [Indexed: 11/13/2022] Open
Abstract
Databases of structural connections of the mammalian brain, such as CoCoMac (cocomac.g-node.org) or BAMS (https://bams1.org), are valuable resources for the analysis of brain connectivity and the modeling of brain dynamics in species such as the non-human primate or the rodent, and have also contributed to the computational modeling of the human brain. Another animal model that is widely used in electrophysiological or developmental studies is the ferret; however, no systematic compilation of brain connectivity is currently available for this species. Thus, we have started developing a database of anatomical connections and architectonic features of the ferret brain, the Ferret(connect)ome, www.Ferretome.org. The Ferretome database has adapted essential features of the CoCoMac methodology and legacy, such as the CoCoMac data model. This data model was simplified and extended in order to accommodate new data modalities that were not represented previously, such as the cytoarchitecture of brain areas. The Ferretome uses a semantic parcellation of brain regions as well as a logical brain map transformation algorithm (objective relational transformation, ORT). The ORT algorithm was also adopted for the transformation of architecture data. The database is being developed in MySQL and has been populated with literature reports on tract-tracing observations in the ferret brain using a custom-designed web interface that allows efficient and validated simultaneous input and proofreading by multiple curators. The database is equipped with a non-specialist web interface. This interface can be extended to produce connectivity matrices in several formats, including a graphical representation superimposed on established ferret brain maps. An important feature of the Ferretome database is the possibility to trace back entries in connectivity matrices to the original studies archived in the system. Currently, the Ferretome contains 50 reports on connections comprising 20 injection reports with more than 150 labeled source and target areas, the majority reflecting connectivity of subcortical nuclei and 15 descriptions of regional brain architecture. We hope that the Ferretome database will become a useful resource for neuroinformatics and neural modeling, and will support studies of the ferret brain as well as facilitate advances in comparative studies of mesoscopic brain connectivity.
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Affiliation(s)
- Dmitrii I Sukhinin
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf Hamburg, Germany
| | - Andreas K Engel
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf Hamburg, Germany
| | - Paul Manger
- School of Anatomical Science, University of the Witwatersrand Johannesburg, South Africa
| | - Claus C Hilgetag
- Department of Computational Neuroscience, University Medical Center Hamburg-EppendorfHamburg, Germany; Department of Health Sciences, Boston University, BostonMA, USA
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44
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Valenzuela RA, Micheva KD, Kiraly M, Li D, Madison DV. Array tomography of physiologically-characterized CNS synapses. J Neurosci Methods 2016; 268:43-52. [PMID: 27141856 DOI: 10.1016/j.jneumeth.2016.04.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Revised: 04/15/2016] [Accepted: 04/22/2016] [Indexed: 01/11/2023]
Abstract
BACKGROUND The ability to correlate plastic changes in synaptic physiology with changes in synaptic anatomy has been very limited in the central nervous system because of shortcomings in existing methods for recording the activity of specific CNS synapses and then identifying and studying the same individual synapses on an anatomical level. NEW METHOD We introduce here a novel approach that combines two existing methods: paired neuron electrophysiological recording and array tomography, allowing for the detailed molecular and anatomical study of synapses with known physiological properties. RESULTS The complete mapping of a neuronal pair allows determining the exact number of synapses in the pair and their location. We have found that the majority of close appositions between the presynaptic axon and the postsynaptic dendrite in the pair contain synaptic specializations. The average release probability of the synapses between the two neurons in the pair is low, below 0.2, consistent with previous studies of these connections. Other questions, such as receptor distribution within synapses, can be addressed more efficiently by identifying only a subset of synapses using targeted partial reconstructions. In addition, time sensitive events can be captured with fast chemical fixation. COMPARISON WITH EXISTING METHODS Compared to existing methods, the present approach is the only one that can provide detailed molecular and anatomical information of electrophysiologically-characterized individual synapses. CONCLUSIONS This method will allow for addressing specific questions about the properties of identified CNS synapses, even when they are buried within a cloud of millions of other brain circuit elements.
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Affiliation(s)
- Ricardo A Valenzuela
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305-5345, USA
| | - Kristina D Micheva
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305-5345, USA
| | - Marianna Kiraly
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305-5345, USA
| | - Dong Li
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305-5345, USA
| | - Daniel V Madison
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305-5345, USA.
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45
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Davis MA, Gagnon L, Boas DA, Dunn AK. Sensitivity of laser speckle contrast imaging to flow perturbations in the cortex. BIOMEDICAL OPTICS EXPRESS 2016; 7:759-75. [PMID: 27231587 PMCID: PMC4866454 DOI: 10.1364/boe.7.000759] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Revised: 01/27/2016] [Accepted: 01/28/2016] [Indexed: 05/18/2023]
Abstract
Laser speckle contrast imaging has become a ubiquitous tool for imaging blood flow in a variety of tissues. However, due to its widefield imaging nature, the measured speckle contrast is a depth integrated quantity and interpretation of baseline values and the depth dependent sensitivity of those values to changes in underlying flow has not been thoroughly evaluated. Using dynamic light scattering Monte Carlo simulations, the sensitivity of the autocorrelation function and speckle contrast to flow changes in the cerebral cortex was extensively examined. These simulations demonstrate that the sensitivity of the inverse autocorrelation time, [Formula: see text], varies across the field of view: directly over surface vessels [Formula: see text] is strongly localized to the single vessel, while parenchymal ROIs have a larger sensitivity to flow changes at depths up to 500 μm into the tissue and up to 200 μm lateral to the ROI. It is also shown that utilizing the commonly used models the relate [Formula: see text] to flow resulted in nearly the same sensitivity to the underlying flow, but fail to accurately relate speckle contrast values to absolute [Formula: see text].
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Affiliation(s)
- Mitchell A. Davis
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712,
USA
| | - Louis Gagnon
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts 02129,
USA
| | - David A. Boas
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts 02129,
USA
| | - Andrew K. Dunn
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712,
USA
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46
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Super-Resolution Mapping of Neuronal Circuitry With an Index-Optimized Clearing Agent. Cell Rep 2016; 14:2718-32. [DOI: 10.1016/j.celrep.2016.02.057] [Citation(s) in RCA: 178] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2015] [Revised: 01/11/2016] [Accepted: 02/09/2016] [Indexed: 11/18/2022] Open
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47
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Aljadeff J, Renfrew D, Vegué M, Sharpee TO. Low-dimensional dynamics of structured random networks. Phys Rev E 2016; 93:022302. [PMID: 26986347 DOI: 10.1103/physreve.93.022302] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Indexed: 01/12/2023]
Abstract
Using a generalized random recurrent neural network model, and by extending our recently developed mean-field approach [J. Aljadeff, M. Stern, and T. Sharpee, Phys. Rev. Lett. 114, 088101 (2015)], we study the relationship between the network connectivity structure and its low-dimensional dynamics. Each connection in the network is a random number with mean 0 and variance that depends on pre- and postsynaptic neurons through a sufficiently smooth function g of their identities. We find that these networks undergo a phase transition from a silent to a chaotic state at a critical point we derive as a function of g. Above the critical point, although unit activation levels are chaotic, their autocorrelation functions are restricted to a low-dimensional subspace. This provides a direct link between the network's structure and some of its functional characteristics. We discuss example applications of the general results to neuroscience where we derive the support of the spectrum of connectivity matrices with heterogeneous and possibly correlated degree distributions, and to ecology where we study the stability of the cascade model for food web structure.
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Affiliation(s)
- Johnatan Aljadeff
- Department of Neurobiology, University of Chicago, Chicago, Illinois, USA.,Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California, USA
| | - David Renfrew
- Department of Mathematics, University of California Los Angeles, Los Angeles, California, USA
| | - Marina Vegué
- Centre de Recerca Matemàtica, Campus de Bellaterra, Barcelona, Spain.,Departament de Matemàtiques, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Tatyana O Sharpee
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California, USA
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48
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Economo MN, Clack NG, Lavis LD, Gerfen CR, Svoboda K, Myers EW, Chandrashekar J. A platform for brain-wide imaging and reconstruction of individual neurons. eLife 2016; 5:e10566. [PMID: 26796534 PMCID: PMC4739768 DOI: 10.7554/elife.10566] [Citation(s) in RCA: 253] [Impact Index Per Article: 31.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2015] [Accepted: 11/18/2015] [Indexed: 12/19/2022] Open
Abstract
The structure of axonal arbors controls how signals from individual neurons are routed within the mammalian brain. However, the arbors of very few long-range projection neurons have been reconstructed in their entirety, as axons with diameters as small as 100 nm arborize in target regions dispersed over many millimeters of tissue. We introduce a platform for high-resolution, three-dimensional fluorescence imaging of complete tissue volumes that enables the visualization and reconstruction of long-range axonal arbors. This platform relies on a high-speed two-photon microscope integrated with a tissue vibratome and a suite of computational tools for large-scale image data. We demonstrate the power of this approach by reconstructing the axonal arbors of multiple neurons in the motor cortex across a single mouse brain.
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Affiliation(s)
- Michael N Economo
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Nathan G Clack
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Luke D Lavis
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Charles R Gerfen
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
- Laboratory of Systems Neuroscience, National Institute of Mental Health, Bethesda, United States
| | - Karel Svoboda
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
| | - Eugene W Myers
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
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49
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Sporns O. Connectome Networks: From Cells to Systems. MICRO-, MESO- AND MACRO-CONNECTOMICS OF THE BRAIN 2016. [DOI: 10.1007/978-3-319-27777-6_8] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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50
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Schmidt-Kastner R. Genomic approach to selective vulnerability of the hippocampus in brain ischemia–hypoxia. Neuroscience 2015; 309:259-79. [DOI: 10.1016/j.neuroscience.2015.08.034] [Citation(s) in RCA: 74] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2015] [Revised: 08/12/2015] [Accepted: 08/17/2015] [Indexed: 01/06/2023]
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