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Abstract
Brain scientists are now capable of collecting more data in a single experiment than researchers a generation ago might have collected over an entire career. Indeed, the brain itself seems to thirst for more and more data. Such digital information not only comprises individual studies but is also increasingly shared and made openly available for secondary, confirmatory, and/or combined analyses. Numerous web resources now exist containing data across spatiotemporal scales. Data processing workflow technologies running via cloud-enabled computing infrastructures allow for large-scale processing. Such a move toward greater openness is fundamentally changing how brain science results are communicated and linked to available raw data and processed results. Ethical, professional, and motivational issues challenge the whole-scale commitment to data-driven neuroscience. Nevertheless, fueled by government investments into primary brain data collection coupled with increased sharing and community pressure challenging the dominant publishing model, large-scale brain and data science is here to stay.
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Affiliation(s)
- John Darrell Van Horn
- Department of Psychology, University of Virginia, Charlottesville, Virginia, USA
- School of Data Science, University of Virginia, Charlottesville, Virginia, USA
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2
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Anderson KR, Harris JA, Ng L, Prins P, Memar S, Ljungquist B, Fürth D, Williams RW, Ascoli GA, Dumitriu D. Highlights from the Era of Open Source Web-Based Tools. J Neurosci 2021; 41:927-936. [PMID: 33472826 PMCID: PMC7880282 DOI: 10.1523/jneurosci.1657-20.2020] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 11/22/2020] [Accepted: 11/29/2020] [Indexed: 12/20/2022] Open
Abstract
High digital connectivity and a focus on reproducibility are contributing to an open science revolution in neuroscience. Repositories and platforms have emerged across the whole spectrum of subdisciplines, paving the way for a paradigm shift in the way we share, analyze, and reuse vast amounts of data collected across many laboratories. Here, we describe how open access web-based tools are changing the landscape and culture of neuroscience, highlighting six free resources that span subdisciplines from behavior to whole-brain mapping, circuits, neurons, and gene variants.
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Affiliation(s)
- Kristin R Anderson
- Departments of Pediatrics and Psychiatry, Columbia University, New York, New York 10032
- Division of Developmental Psychobiology, New York State Psychiatric Institute, New York, New York 10032
- The Sackler Institute for Developmental Psychobiology, Columbia University, New York, New York 10032
- Columbia Population Research Center, Columbia University, New York, New York 10027
- Zuckerman Institute, Columbia University, New York, New York 10027
| | - Julie A Harris
- Allen Institute for Brain Science, Seattle, Washington 98109
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, Washington 98109
| | - Pjotr Prins
- Department of Genetics, Genomics and Informatics, Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, Tennessee 38163
| | - Sara Memar
- Robarts Research Institute, BrainsCAN, Schulich School of Medicine & Dentistry, Western University, London, Ontario N6A 3K7, Canada
| | - Bengt Ljungquist
- Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study; and Department of Bioengineering, Volgenau School of Engineering, George Mason University, Fairfax, Virginia 22030
| | - Daniel Fürth
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724
| | - Robert W Williams
- Department of Genetics, Genomics and Informatics, Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, Tennessee 38163
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study; and Department of Bioengineering, Volgenau School of Engineering, George Mason University, Fairfax, Virginia 22030
| | - Dani Dumitriu
- Departments of Pediatrics and Psychiatry, Columbia University, New York, New York 10032
- Division of Developmental Psychobiology, New York State Psychiatric Institute, New York, New York 10032
- The Sackler Institute for Developmental Psychobiology, Columbia University, New York, New York 10032
- Columbia Population Research Center, Columbia University, New York, New York 10027
- Zuckerman Institute, Columbia University, New York, New York 10027
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3
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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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4
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Affiliation(s)
- Giorgio A Ascoli
- Bioengineering Department, George Mason University, Fairfax, VA, USA.
- Neuroscience Program, George Mason University, Fairfax, VA, USA.
- Center for Neural Informatics, Structures, & Plasticity, George Mason University, Fairfax, VA, USA.
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA.
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Akram MA, Nanda S, Maraver P, Armañanzas R, Ascoli GA. An open repository for single-cell reconstructions of the brain forest. Sci Data 2018; 5:180006. [PMID: 29485626 PMCID: PMC5827689 DOI: 10.1038/sdata.2018.6] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Accepted: 01/08/2018] [Indexed: 02/06/2023] Open
Abstract
NeuroMorpho.Org was launched in 2006 to provide unhindered access to any and all digital tracings of neuronal morphology that researchers were willing to share freely upon request. Today this database is the largest public inventory of cellular reconstructions in neuroscience with a content of over 80,000 neurons and glia from a representative diversity of animal species, anatomical regions, and experimental methods. Datasets continuously contributed by hundreds of laboratories worldwide are centrally curated, converted into a common non-proprietary format, morphometrically quantified, and annotated with comprehensive metadata. Users download digital reconstructions for a variety of scientific applications including visualization, classification, analysis, and simulations. With more than 1,000 peer-reviewed publications describing data stored in or utilizing data retrieved from NeuroMorpho.Org, this ever-growing repository can already be considered a mature resource for neuroscience.
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Affiliation(s)
- Masood A. Akram
- Center for Neural Informatics, Structures & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA
| | - Sumit Nanda
- Center for Neural Informatics, Structures & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA
| | - Patricia Maraver
- Center for Neural Informatics, Structures & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA
| | - Rubén Armañanzas
- Center for Neural Informatics, Structures & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA
| | - Giorgio A. Ascoli
- Center for Neural Informatics, Structures & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA
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6
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An evolutionary developmental approach for generation of 3D neuronal morphologies using gene regulatory networks. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2017.08.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Bower JM. The 40-year history of modeling active dendrites in cerebellar Purkinje cells: emergence of the first single cell "community model". Front Comput Neurosci 2015; 9:129. [PMID: 26539104 PMCID: PMC4611061 DOI: 10.3389/fncom.2015.00129] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2014] [Accepted: 10/02/2015] [Indexed: 11/13/2022] Open
Abstract
The subject of the effects of the active properties of the Purkinje cell dendrite on neuronal function has been an active subject of study for more than 40 years. Somewhat unusually, some of these investigations, from the outset have involved an interacting combination of experimental and model-based techniques. This article recounts that 40-year history, and the view of the functional significance of the active properties of the Purkinje cell dendrite that has emerged. It specifically considers the emergence from these efforts of what is arguably the first single cell "community" model in neuroscience. The article also considers the implications of the development of this model for future studies of the complex properties of neuronal dendrites.
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Abstract
Routine data sharing is greatly benefiting several scientific disciplines, such as molecular biology, particle physics, and astronomy. Neuroscience data, in contrast, are still rarely shared, greatly limiting the potential for secondary discovery and the acceleration of research progress. Although the attitude toward data sharing is non-uniform across neuroscience subdomains, widespread adoption of data sharing practice will require a cultural shift in the community. Digital reconstructions of axonal and dendritic morphology constitute a particularly "sharable" kind of data. The popularity of the public repository NeuroMorpho.Org demonstrates that data sharing can benefit both users and contributors. Increased data availability is also catalyzing the grassroots development and spontaneous integration of complementary resources, research tools, and community initiatives. Even in this rare successful subfield, however, more data are still unshared than shared. Our experience as developers and curators of NeuroMorpho.Org suggests that greater transparency regarding the expectations and consequences of sharing (or not sharing) data, combined with public disclosure of which datasets are shared and which are not, may expedite the transition to community-wide data sharing.
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Affiliation(s)
- Giorgio A. Ascoli
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia, United States of America
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Pronk TE, Wiersma PH, van Weerden A, Schieving F. A game theoretic analysis of research data sharing. PeerJ 2015; 3:e1242. [PMID: 26401453 PMCID: PMC4579014 DOI: 10.7717/peerj.1242] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2015] [Accepted: 08/24/2015] [Indexed: 11/20/2022] Open
Abstract
While reusing research data has evident benefits for the scientific community as a whole, decisions to archive and share these data are primarily made by individual researchers. In this paper we analyse, within a game theoretical framework, how sharing and reuse of research data affect individuals who share or do not share their datasets. We construct a model in which there is a cost associated with sharing datasets whereas reusing such sets implies a benefit. In our calculations, conflicting interests appear for researchers. Individual researchers are always better off not sharing and omitting the sharing cost, at the same time both sharing and not sharing researchers are better off if (almost) all researchers share. Namely, the more researchers share, the more benefit can be gained by the reuse of those datasets. We simulated several policy measures to increase benefits for researchers sharing or reusing datasets. Results point out that, although policies should be able to increase the rate of sharing researchers, and increased discoverability and dataset quality could partly compensate for costs, a better measure would be to directly lower the cost for sharing, or even turn it into a (citation-) benefit. Making data available would in that case become the most profitable, and therefore stable, strategy. This means researchers would willingly make their datasets available, and arguably in the best possible way to enable reuse.
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Affiliation(s)
- Tessa E. Pronk
- Utrecht University Library, Utrecht University, Utrecht, The Netherlands
| | - Paulien H. Wiersma
- Utrecht University Library, Utrecht University, Utrecht, The Netherlands
| | - Anne van Weerden
- Utrecht University Library, Utrecht University, Utrecht, The Netherlands
| | - Feike Schieving
- Ecology and Biodiversity, Utrecht University, Utrecht, The Netherlands
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10
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Polavaram S, Gillette TA, Parekh R, Ascoli GA. Statistical analysis and data mining of digital reconstructions of dendritic morphologies. Front Neuroanat 2014; 8:138. [PMID: 25538569 PMCID: PMC4255610 DOI: 10.3389/fnana.2014.00138] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2014] [Accepted: 11/06/2014] [Indexed: 11/21/2022] Open
Abstract
Neuronal morphology is diverse among animal species, developmental stages, brain regions, and cell types. The geometry of individual neurons also varies substantially even within the same cell class. Moreover, specific histological, imaging, and reconstruction methodologies can differentially affect morphometric measures. The quantitative characterization of neuronal arbors is necessary for in-depth understanding of the structure-function relationship in nervous systems. The large collection of community-contributed digitally reconstructed neurons available at NeuroMorpho.Org constitutes a “big data” research opportunity for neuroscience discovery beyond the approaches typically pursued in single laboratories. To illustrate these potential and related challenges, we present a database-wide statistical analysis of dendritic arbors enabling the quantification of major morphological similarities and differences across broadly adopted metadata categories. Furthermore, we adopt a complementary unsupervised approach based on clustering and dimensionality reduction to identify the main morphological parameters leading to the most statistically informative structural classification. We find that specific combinations of measures related to branching density, overall size, tortuosity, bifurcation angles, arbor flatness, and topological asymmetry can capture anatomically and functionally relevant features of dendritic trees. The reported results only represent a small fraction of the relationships available for data exploration and hypothesis testing enabled by sharing of digital morphological reconstructions.
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Affiliation(s)
- Sridevi Polavaram
- Department of Molecular Neuroscience, Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University Fairfax, VA, USA
| | - Todd A Gillette
- Department of Molecular Neuroscience, Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University Fairfax, VA, USA
| | - Ruchi Parekh
- Department of Molecular Neuroscience, Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University Fairfax, VA, USA
| | - Giorgio A Ascoli
- Department of Molecular Neuroscience, Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University Fairfax, VA, USA
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11
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Parekh R, Ascoli GA. Quantitative investigations of axonal and dendritic arbors: development, structure, function, and pathology. Neuroscientist 2014; 21:241-54. [PMID: 24972604 DOI: 10.1177/1073858414540216] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The branching structures of neurons are a long-standing focus of neuroscience. Axonal and dendritic morphology affect synaptic signaling, integration, and connectivity, and their diversity reflects the computational specialization of neural circuits. Altered neuronal morphology accompanies functional changes during development, experience, aging, and disease. Technological improvements continuously accelerate high-throughput tissue processing, image acquisition, and morphological reconstruction. Digital reconstructions of neuronal morphologies allow for complex quantitative analyses that are unattainable from raw images or two-dimensional tracings. Furthermore, digitized morphologies enable computational modeling of biophysically realistic neuronal dynamics. Additionally, reconstructions generated to address specific scientific questions have the potential for continued investigations beyond the original reason for their acquisition. Facilitating multiple reuse are repositories like NeuroMorpho.Org, which ease the sharing of reconstructions. Here, we review selected scientific literature reporting the reconstruction of axonal or dendritic morphology with diverse goals including establishment of neuronal identity, examination of physiological properties, and quantification of developmental or pathological changes. These reconstructions, deposited in NeuroMorpho.Org, have since been used by other investigators in additional research, of which we highlight representative examples. This cycle of data generation, analysis, sharing, and reuse reveals the vast potential of digital reconstructions in quantitative investigations of neuronal morphology.
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Affiliation(s)
- Ruchi Parekh
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Giorgio A Ascoli
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
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12
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López-Cruz PL, Larrañaga P, DeFelipe J, Bielza C. Bayesian network modeling of the consensus between experts: An application to neuron classification. Int J Approx Reason 2014. [DOI: 10.1016/j.ijar.2013.03.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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13
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Parekh R, Ascoli GA. Neuronal morphology goes digital: a research hub for cellular and system neuroscience. Neuron 2013; 77:1017-38. [PMID: 23522039 PMCID: PMC3653619 DOI: 10.1016/j.neuron.2013.03.008] [Citation(s) in RCA: 117] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/06/2013] [Indexed: 02/07/2023]
Abstract
The importance of neuronal morphology in brain function has been recognized for over a century. The broad applicability of "digital reconstructions" of neuron morphology across neuroscience subdisciplines has stimulated the rapid development of numerous synergistic tools for data acquisition, anatomical analysis, three-dimensional rendering, electrophysiological simulation, growth models, and data sharing. Here we discuss the processes of histological labeling, microscopic imaging, and semiautomated tracing. Moreover, we provide an annotated compilation of currently available resources in this rich research "ecosystem" as a central reference for experimental and computational neuroscience.
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Affiliation(s)
- Ruchi Parekh
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, 22030, USA
| | - Giorgio A. Ascoli
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, 22030, USA
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14
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Budd JML, Kisvárday ZF. Communication and wiring in the cortical connectome. Front Neuroanat 2012; 6:42. [PMID: 23087619 PMCID: PMC3472565 DOI: 10.3389/fnana.2012.00042] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2012] [Accepted: 09/24/2012] [Indexed: 11/23/2022] Open
Abstract
In cerebral cortex, the huge mass of axonal wiring that carries information between near and distant neurons is thought to provide the neural substrate for cognitive and perceptual function. The goal of mapping the connectivity of cortical axons at different spatial scales, the cortical connectome, is to trace the paths of information flow in cerebral cortex. To appreciate the relationship between the connectome and cortical function, we need to discover the nature and purpose of the wiring principles underlying cortical connectivity. A popular explanation has been that axonal length is strictly minimized both within and between cortical regions. In contrast, we have hypothesized the existence of a multi-scale principle of cortical wiring where to optimize communication there is a trade-off between spatial (construction) and temporal (routing) costs. Here, using recent evidence concerning cortical spatial networks we critically evaluate this hypothesis at neuron, local circuit, and pathway scales. We report three main conclusions. First, the axonal and dendritic arbor morphology of single neocortical neurons may be governed by a similar wiring principle, one that balances the conservation of cellular material and conduction delay. Second, the same principle may be observed for fiber tracts connecting cortical regions. Third, the absence of sufficient local circuit data currently prohibits any meaningful assessment of the hypothesis at this scale of cortical organization. To avoid neglecting neuron and microcircuit levels of cortical organization, the connectome framework should incorporate more morphological description. In addition, structural analyses of temporal cost for cortical circuits should take account of both axonal conduction and neuronal integration delays, which appear mostly of the same order of magnitude. We conclude the hypothesized trade-off between spatial and temporal costs may potentially offer a powerful explanation for cortical wiring patterns.
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Affiliation(s)
- Julian M. L. Budd
- Department of Informatics, University of SussexFalmer, East Sussex, UK
| | - Zoltán F. Kisvárday
- Laboratory for Cortical Systems Neuroscience, Department of Anatomy, Histology and Embryology, University of DebrecenDebrecen, Hungary
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15
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Abstract
Neuron morphology is crucial for neuronal connectivity and brain information processing. Computational models are important tools for studying dendritic morphology and its role in brain function. We applied a class of probabilistic graphical models called Bayesian networks to generate virtual dendrites from layer III pyramidal neurons from three different regions of the neocortex of the mouse. A set of 41 morphological variables were measured from the 3D reconstructions of real dendrites and their probability distributions used in a machine learning algorithm to induce the model from the data. A simulation algorithm is also proposed to obtain new dendrites by sampling values from Bayesian networks. The main advantage of this approach is that it takes into account and automatically locates the relationships between variables in the data instead of using predefined dependencies. Therefore, the methodology can be applied to any neuronal class while at the same time exploiting class-specific properties. Also, a Bayesian network was defined for each part of the dendrite, allowing the relationships to change in the different sections and to model heterogeneous developmental factors or spatial influences. Several univariate statistical tests and a novel multivariate test based on Kullback-Leibler divergence estimation confirmed that virtual dendrites were similar to real ones. The analyses of the models showed relationships that conform to current neuroanatomical knowledge and support model correctness. At the same time, studying the relationships in the models can help to identify new interactions between variables related to dendritic morphology.
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16
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Abstract
The study of the structure and function of neuronal cells and networks is of crucial importance in the endeavor to understand how the brain works. A key component in this process is the extraction of neuronal morphology from microscopic imaging data. In the past four decades, many computational methods and tools have been developed for digital reconstruction of neurons from images, with limited success. As witnessed by the growing body of literature on the subject, as well as the organization of challenging competitions in the field, the quest for a robust and fully automated system of more general applicability still continues. The aim of this work, is to contribute by surveying recent developments in the field for anyone interested in taking up the challenge. Relevant aspects discussed in the article include proposed image segmentation methods, quantitative measures of neuronal morphology, currently available software tools for various related purposes, and morphology databases. (c) 2010 International Society for Advancement of Cytometry.
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Affiliation(s)
- Erik Meijering
- Biomedical Imaging Group Rotterdam, Erasmus MC, University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands
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17
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Anderson K, Yamamoto E, Kaplan J, Hannan M, Jacobs B. Neurolucida Lucivid versus Neurolucida camera: A quantitative and qualitative comparison of three-dimensional neuronal reconstructions. J Neurosci Methods 2009; 186:209-14. [PMID: 19963008 DOI: 10.1016/j.jneumeth.2009.11.024] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2009] [Revised: 11/16/2009] [Accepted: 11/30/2009] [Indexed: 11/26/2022]
Abstract
A critical issue in quantitative neuromorphology is the accuracy and subsequent reliability of the tracing techniques employed to characterize neuronal components. Historically, the camera lucida was the only option for such investigations. In 1987, MBF Bioscience, Inc. (Williston, VT) developed the integrative Neurolucida computer-microscope system, replacing the camera lucida drawing tube with a Lucivid cathode ray tube, thereby allowing computer overlays directly on the view through microscope oculars. Subsequent advances in digital cameras have allowed the Lucivid system to be replaced so that microscope images can be traced by viewing the digital image on a computer monitor. Indeed, the camera systems now outsell Lucivid systems 9 to 1 (J. Glaser, personal communication, 08/2008). Nevertheless, researchers seldom note which of these configurations are being used (which may confound the accuracy of data sharing), and there have been no published comparisons of the Lucivid and camera configurations. The present study thus assesses the relative accuracy of these two hardware configurations by examining reconstructions of human pyramidal neurons. We report significant differences with respect to dendritic spines, with the camera estimates of spine counts being greater than those obtained with the Lucivid system. Potential underlying reasons (e.g., magnification, illumination, and resolution, as well as observer ergonomic differences between the two systems) for these quantitative findings are explored here, along with qualitative observations on the relative strengths of each configuration.
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Affiliation(s)
- Kaeley Anderson
- Laboratory of Quantitative Neuromorphology, Psychology, The Colorado College, 14 E. Cache La Poudre, Colorado Springs, CO 80903, USA
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Heumann H, Wittum G. The Tree-Edit-Distance, a Measure for Quantifying Neuronal Morphology. Neuroinformatics 2009; 7:179-90. [DOI: 10.1007/s12021-009-9051-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2008] [Accepted: 05/13/2009] [Indexed: 02/04/2023]
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Puzzling challenges in contemporary neuroscience: insights from complexity and emergence in epileptogenic circuits. Epilepsy Behav 2009; 14 Suppl 1:54-63. [PMID: 18835370 DOI: 10.1016/j.yebeh.2008.09.010] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2008] [Revised: 09/02/2008] [Accepted: 09/03/2008] [Indexed: 11/24/2022]
Abstract
The brain is a complex system that, in the normal condition, has emergent properties like those associated with activity-dependent plasticity in learning and memory, and in pathological situations, manifests abnormal long-term phenomena like the epilepsies. Data from our laboratory and from the literature were classified qualitatively as sources of complexity and emergent properties from behavior to electrophysiological, cellular, molecular, and computational levels. We used such models as brainstem-dependent acute audiogenic seizures and forebrain-dependent kindled audiogenic seizures. Additionally we used chemical or electrical experimental models of temporal lobe epilepsy that induce status epilepticus with behavioral, anatomical, and molecular sequelae such as spontaneous recurrent seizures and long-term plastic changes. Current computational neuroscience tools will help the interpretation, storage, and sharing of the exponential growth of information derived from those studies. These strategies are considered solutions to deal with the complexity of brain pathologies such as the epilepsies.
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Vasilkoski Z, Stepanyants A. Detection of the optimal neuron traces in confocal microscopy images. J Neurosci Methods 2008; 178:197-204. [PMID: 19059434 DOI: 10.1016/j.jneumeth.2008.11.008] [Citation(s) in RCA: 43] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2008] [Revised: 11/05/2008] [Accepted: 11/07/2008] [Indexed: 11/26/2022]
Abstract
Quantitative methods of analysis of neural circuits rely on large datasets of neurons reconstructed accurately in three dimensions (3D). Due to the complexity of neuronal arbors, large datasets of reconstructed neurons must be generated with automated algorithms. Here, we attempted to automate the process of neuron tracing from sparsely labeled 3D stacks of confocal microscopy images. Our algorithm involves two steps. In the first step, the segmented image of neurites in the stack is voxel-coded. Centers of intensity of consecutively coded wave fronts are connected into a branched structure, which represents a coarse trace of the neurites. In the second step, this trace is optimized with the modified active contour method, which tends to maximize the intensity along the trace while keeping it under tension. To assess the performance of the algorithm we used manual reconstructions of neurons and converted them into artificial stacks of intensity images. These images were traced using the developed algorithm and quantitatively compared to the corresponding manual traces. The optimal traces were on average 6.0% shorter than the manual traces. This reduction in length resulted from the smoothness of the optimal traces, which, in comparison to the manual ones, were built out of shorter segments, and, as a result, were 3.3% less tortuous. The average distance between the optimal and the manual traces was 0.14 microm, and the average distance between their corresponding branch-points was 2.2 microm, illustrating good agreement between the traces.
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Affiliation(s)
- Zlatko Vasilkoski
- Department of Physics and Center for Interdisciplinary Research on Complex Systems, Northeastern University, Boston, MA 02115, USA
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Torben-Nielsen B, Vanderlooy S, Postma EO. Non-parametric algorithmic generation of neuronal morphologies. Neuroinformatics 2008; 6:257-77. [PMID: 18797828 DOI: 10.1007/s12021-008-9026-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2008] [Accepted: 08/26/2008] [Indexed: 02/02/2023]
Abstract
Generation algorithms allow for the generation of Virtual Neurons (VNs) from a small set of morphological properties. The set describes the morphological properties of real neurons in terms of statistical descriptors such as the number of branches and segment lengths (among others). The majority of reconstruction algorithms use the observed properties to estimate the parameters of a priori fixed probability distributions in order to construct statistical descriptors that fit well with the observed data. In this article, we present a non-parametric generation algorithm based on kernel density estimators (KDEs). The new algorithm is called KDE-NEURON: and has three advantages over parametric reconstruction algorithms: (1) no a priori specifications about the distributions underlying the real data, (2) peculiarities in the biological data will be reflected in the VNs, and (3) ability to reconstruct different cell types. We experimentally generated motor neurons and granule cells, and statistically validated the obtained results. Moreover, we assessed the quality of the prototype data set and observed that our generated neurons are as good as the prototype data in terms of the used statistical descriptors. The opportunities and limitations of data-driven algorithmic reconstruction of neurons are discussed.
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Halavi M, Polavaram S, Donohue DE, Hamilton G, Hoyt J, Smith KP, Ascoli GA. NeuroMorpho.Org implementation of digital neuroscience: dense coverage and integration with the NIF. Neuroinformatics 2008; 6:241-52. [PMID: 18949582 PMCID: PMC2655120 DOI: 10.1007/s12021-008-9030-1] [Citation(s) in RCA: 53] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2008] [Accepted: 09/22/2008] [Indexed: 02/03/2023]
Abstract
Neuronal morphology affects network connectivity, plasticity, and information processing. Uncovering the design principles and functional consequences of dendritic and axonal shape necessitates quantitative analysis and computational modeling of detailed experimental data. Digital reconstructions provide the required neuromorphological descriptions in a parsimonious, comprehensive, and reliable numerical format. NeuroMorpho.Org is the largest web-accessible repository service for digitally reconstructed neurons and one of the integrated resources in the Neuroscience Information Framework (NIF). Here we describe the NeuroMorpho.Org approach as an exemplary experience in designing, creating, populating, and curating a neuroscience digital resource. The simple three-tier architecture of NeuroMorpho.Org (web client, web server, and relational database) encompasses all necessary elements to support a large-scale, integrate-able repository. The data content, while heterogeneous in scientific scope and experimental origin, is unified in format and presentation by an in house standardization protocol. The server application (MRALD) is secure, customizable, and developer-friendly. Centralized processing and expert annotation yields a comprehensive set of metadata that enriches and complements the raw data. The thoroughly tested interface design allows for optimal and effective data search and retrieval. Availability of data in both original and standardized formats ensures compatibility with existing resources and fosters further tool development. Other key functions enable extensive exploration and discovery, including 3D and interactive visualization of branching, frequently measured morphometrics, and reciprocal links to the original PubMed publications. The integration of NeuroMorpho.Org with version-1 of the NIF (NIFv1) provides the opportunity to access morphological data in the context of other relevant resources and diverse subdomains of neuroscience, opening exciting new possibilities in data mining and knowledge discovery. The outcome of such coordination is the rapid and powerful advancement of neuroscience research at both the conceptual and technological level.
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Affiliation(s)
- Maryam Halavi
- Center for Neural Informatics, Structure, & Plasticity, and Molecular Neuroscience Department, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
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