1
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Tecuatl C, Ljungquist B, Ascoli GA. Accelerating the continuous community sharing of digital neuromorphology data. FASEB Bioadv 2024; 6:207-221. [PMID: 38974113 PMCID: PMC11226999 DOI: 10.1096/fba.2024-00048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 05/28/2024] [Accepted: 06/03/2024] [Indexed: 07/09/2024] Open
Abstract
The tree-like morphology of neurons and glia is a key cellular determinant of circuit connectivity and metabolic function in the nervous system of essentially all animals. To elucidate the contribution of specific cell types to both physiological and pathological brain states, it is important to access detailed neuroanatomy data for quantitative analysis and computational modeling. NeuroMorpho.Org is the largest online collection of freely available digital neural reconstructions and related metadata and is continuously updated with new uploads. Earlier in the project, we released multiple datasets together yearly, but this process caused an average delay of several months in making the data public. Moreover, in the past 5 years, >80% of invited authors agreed to share their data with the community via NeuroMorpho.Org, up from <20% in the first 5 years of the project. In the same period, the average number of reconstructions per publication increased 600%, creating the need for automatic processing to release more reconstructions in less time. The progressive automation of our pipeline enabled the transition to agile releases of individual datasets as soon as they are ready. The overall time from data identification to public sharing decreased by 63.7%; 78% of the datasets are now released in less than 3 months with an average workflow duration below 40 days. Furthermore, the mean processing time per reconstruction dropped from 3 h to 2 min. With these continuous improvements, NeuroMorpho.Org strives to forge a positive culture of open data. Most importantly, the new, original research enabled through reuse of datasets across the world has a multiplicative effect on science discovery, benefiting both authors and users.
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Affiliation(s)
- Carolina Tecuatl
- Bioengineering Department and Center for Neural Informatics, Structures and Plasticity, College of Engineering and ComputingGeorge Mason UniversityFairfaxVirginiaUSA
| | - Bengt Ljungquist
- Bioengineering Department and Center for Neural Informatics, Structures and Plasticity, College of Engineering and ComputingGeorge Mason UniversityFairfaxVirginiaUSA
| | - Giorgio A. Ascoli
- Bioengineering Department and Center for Neural Informatics, Structures and Plasticity, College of Engineering and ComputingGeorge Mason UniversityFairfaxVirginiaUSA
- Interdisciplinary Program in Neuroscience, College of ScienceGeorge Mason UniversityFairfaxVirginiaUSA
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2
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Leite J, Nhoatto F, Jacob A, Santana R, Lobato F. Computational Tools for Neuronal Morphometric Analysis: A Systematic Search and Review. Neuroinformatics 2024; 22:353-377. [PMID: 38922389 DOI: 10.1007/s12021-024-09674-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/08/2024] [Indexed: 06/27/2024]
Abstract
Morphometry is fundamental for studying and correlating neuronal morphology with brain functions. With increasing computational power, it is possible to extract morphometric characteristics automatically, including features such as length, volume, and number of neuron branches. However, to the best of our knowledge, there is no mapping of morphometric tools yet. In this context, we conducted a systematic search and review to identify and analyze tools within the scope of neuron analysis. Thus, the work followed a well-defined protocol and sought to answer the following research questions: What open-source tools are available for neuronal morphometric analysis? What morphometric characteristics are extracted by these tools? For this, aiming for greater robustness and coverage, the study was based on the paper analysis as well as the study of documentation and tests with the tools available in repositories. We analyzed 1,586 papers and mapped 23 tools, where NeuroM, L-Measure, and NeuroMorphoVis extract the most features. Furthermore, we contribute to the body of knowledge with the unprecedented presentation of 150 unique morphometric features whose terminologies were categorized and standardized. Overall, the study contributes to advancing the understanding of the complex mechanisms underlying the brain.
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Affiliation(s)
- Jéssica Leite
- Institute of Engineering and Geosciences, Federal University of Western Pará, Santarém, Pará, Brazil
| | - Fabiano Nhoatto
- Institute of Engineering and Geosciences, Federal University of Western Pará, Santarém, Pará, Brazil
| | - Antonio Jacob
- Department of Computer Engineering, State University of Maranhão, São Luís, Maranhão, Brazil
| | - Roberto Santana
- Department of Computer Science and Artificial Intelligence, University of the Basque Country, Donostia/San Sebastián, Guipúzcoa, Spain
| | - Fábio Lobato
- Institute of Engineering and Geosciences, Federal University of Western Pará, Santarém, Pará, Brazil.
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3
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Tecuatl C, Ljungquist B, Ascoli GA. Accelerating the continuous community sharing of digital neuromorphology data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.15.585306. [PMID: 38562736 PMCID: PMC10983892 DOI: 10.1101/2024.03.15.585306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The tree-like morphology of neurons and glia is a key cellular determinant of circuit connectivity and metabolic function in the nervous system of essentially all animals. To elucidate the contribution of specific cell types to both physiological and pathological brain states, it is important to access detailed neuroanatomy data for quantitative analysis and computational modeling. NeuroMorpho.Org is the largest online collection of freely available digital neural reconstructions and related metadata and is continuously updated with new uploads. Earlier in the project, we released multiple datasets together yearly, but this process caused an average delay of several months in making the data public. Moreover, in the past 5 years, >80% of invited authors agreed to share their data with the community via NeuroMorpho.Org, up from <20% in the first 5 years of the project. In the same period, the average number of reconstructions per publication increased 600%, creating the need for automatic processing to release more reconstructions in less time. The progressive automation of our pipeline enabled the transition to agile releases of individual datasets as soon as they are ready. The overall time from data identification to public sharing decreased by 63.7%; 78% of the datasets are now released in less than 3 months with an average workflow duration below 40 days. Furthermore, the mean processing time per reconstruction dropped from 3 hours to 2 minutes. With these continuous improvements, NeuroMorpho.Org strives to forge a positive culture of open data. Most importantly, the new, original research enabled through reuse of datasets across the world has a multiplicative effect on science discovery, benefiting both authors and users.
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Affiliation(s)
- Carolina Tecuatl
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
| | - Bengt Ljungquist
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
| | - Giorgio A. Ascoli
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
- Interdisciplinary Program in Neuroscience; College of Science; George Mason University, Fairfax, VA, USA
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4
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Mehta K, Ljungquist B, Ogden J, Nanda S, Ascoli RG, Ng L, Ascoli GA. Online conversion of reconstructed neural morphologies into standardized SWC format. Nat Commun 2023; 14:7429. [PMID: 37973857 PMCID: PMC10654402 DOI: 10.1038/s41467-023-42931-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 10/26/2023] [Indexed: 11/19/2023] Open
Abstract
Digital reconstructions provide an accurate and reliable way to store, share, model, quantify, and analyze neural morphology. Continuous advances in cellular labeling, tissue processing, microscopic imaging, and automated tracing catalyzed a proliferation of software applications to reconstruct neural morphology. These computer programs typically encode the data in custom file formats. The resulting format heterogeneity severely hampers the interoperability and reusability of these valuable data. Among these many alternatives, the SWC file format has emerged as a popular community choice, coalescing a rich ecosystem of related neuroinformatics resources for tracing, visualization, analysis, and simulation. This report presents a standardized specification of the SWC file format. In addition, we introduce xyz2swc, a free online service that converts all 26 reconstruction formats (and 72 variations) described in the scientific literature into the SWC standard. The xyz2swc service is available open source through a user-friendly browser interface ( https://neuromorpho.org/xyz2swc/ui/ ) and an Application Programming Interface (API).
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Affiliation(s)
- Ketan Mehta
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - Bengt Ljungquist
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - James Ogden
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - Sumit Nanda
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - Ruben G Ascoli
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA.
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5
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Bijari K, Zoubi Y, Ascoli GA. Assisted neuroscience knowledge extraction via machine learning applied to neural reconstruction metadata on NeuroMorpho.Org. Brain Inform 2022; 9:26. [PMID: 36344713 PMCID: PMC9640520 DOI: 10.1186/s40708-022-00174-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 10/06/2022] [Indexed: 11/09/2022] Open
Abstract
The amount of unstructured text produced daily in scholarly journals is enormous. Systematically identifying, sorting, and structuring information from such a volume of data is increasingly challenging for researchers even in delimited domains. Named entity recognition is a fundamental natural language processing tool that can be trained to annotate, structure, and extract information from scientific articles. Here, we harness state-of-the-art machine learning techniques and develop a smart neuroscience metadata suggestion system accessible by both humans through a user-friendly graphical interface and machines via Application Programming Interface. We demonstrate a practical application to the public repository of neural reconstructions, NeuroMorpho.Org, thus expanding the existing web-based metadata management system currently in use. Quantitative analysis indicates that the suggestion system reduces personnel labor by at least 50%. Moreover, our results show that larger training datasets with the same software architecture are unlikely to further improve performance without ad-hoc heuristics due to intrinsic ambiguities in neuroscience nomenclature. All components of this project are released open source for community enhancement and extensions to additional applications.
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Affiliation(s)
- Kayvan Bijari
- College of Science, Neuroscience Program, George Mason University, Fairfax, USA
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, USA
| | - Yasmeen Zoubi
- College of Science, Neuroscience Program, George Mason University, Fairfax, USA
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, USA
| | - Giorgio A. Ascoli
- College of Science, Neuroscience Program, George Mason University, Fairfax, USA
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, USA
- Bioengineering Department, Volgenau School of Engineering, George Mason University, Fairfax, USA
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6
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Ljungquist B, Akram MA, Ascoli GA. Large scale similarity search across digital reconstructions of neural morphology. Neurosci Res 2022; 181:39-45. [PMID: 35580795 PMCID: PMC9960175 DOI: 10.1016/j.neures.2022.05.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 04/12/2022] [Accepted: 05/12/2022] [Indexed: 01/18/2023]
Abstract
Most functions of the nervous system depend on neuronal and glial morphology. Continuous advances in microscopic imaging and tracing software have provided an increasingly abundant availability of 3D reconstructions of arborizing dendrites, axons, and processes, allowing their detailed study. However, efficient, large-scale methods to rank neural morphologies by similarity to an archetype are still lacking. Using the NeuroMorpho.Org database, we present a similarity search software enabling fast morphological comparison of hundreds of thousands of neural reconstructions from any species, brain regions, cell types, and preparation protocols. We compared the performance of different morphological measurements: 1) summary morphometrics calculated by L-Measure, 2) persistence vectors, a vectorized descriptor of branching structure, 3) the combination of the two. In all cases, we also investigated the impact of applying dimensionality reduction using principal component analysis (PCA). We assessed qualitative performance by gauging the ability to rank neurons in order of visual similarity. Moreover, we quantified information content by examining explained variance and benchmarked the ability to identify occasional duplicate reconstructions of the same specimen. We also compared two different methods for selecting the number of principal components using this benchmark. The results indicate that combining summary morphometrics and persistence vectors with applied PCA using maximum likelihood based automatic dimensionality selection provides an information rich characterization that enables efficient and precise comparison of neural morphology. We have deployed the similarity search as open-source online software both through a user-friendly graphical interface and as an API for programmatic access.
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Affiliation(s)
- Bengt Ljungquist
- Center for Neural Informatics, Structures, & Plasticity and Bioengineering Department, George Mason University, Mail Stop 2A1, 4400 University Dr, Fairfax, VA, United States of America
| | - Masood A Akram
- Center for Neural Informatics, Structures, & Plasticity and Bioengineering Department, George Mason University, Mail Stop 2A1, 4400 University Dr, Fairfax, VA, United States of America
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures, & Plasticity and Bioengineering Department, George Mason University, Mail Stop 2A1, 4400 University Dr, Fairfax, VA, United States of America.
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7
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Efficient metadata mining of web-accessible neural morphologies. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2022; 168:94-102. [PMID: 34022302 PMCID: PMC8602463 DOI: 10.1016/j.pbiomolbio.2021.05.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 04/09/2021] [Accepted: 05/12/2021] [Indexed: 01/03/2023]
Abstract
Advancements in neuroscience research have led to steadily accelerating data production and sharing. The online community repository of neural reconstructions NeuroMorpho.Org grew from fewer than 1000 digitally traced neurons in 2006 to more than 140,000 cells today, including glia that now constitute 10.1% of the content. Every reconstruction consists of a detailed 3D representation of branch geometry and connectivity in a standardized format, from which a collection of morphometric features is extracted and stored. Moreover, each entry in the database is accompanied by rich metadata annotation describing the animal subject, anatomy, and experimental details. The rapid expansion of this resource in the past decade was accompanied by a parallel rise in the complexity of the available information, creating both opportunities and challenges for knowledge mining. Here, we introduce a new summary reporting functionality, allowing NeuroMorpho.Org users to efficiently download digests of metadata and morphometrics from multiple groups of similar cells for further analysis. We demonstrate the capabilities of the tool for both glia and neurons and present an illustrative statistical analysis of the resulting data.
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8
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Sullivan AE, Tappan SJ, Angstman PJ, Rodriguez A, Thomas GC, Hoppes DM, Abdul-Karim MA, Heal ML, Glaser JR. A Comprehensive, FAIR File Format for Neuroanatomical Structure Modeling. Neuroinformatics 2022; 20:221-240. [PMID: 34601704 PMCID: PMC8975944 DOI: 10.1007/s12021-021-09530-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/01/2021] [Indexed: 01/09/2023]
Abstract
With advances in microscopy and computer science, the technique of digitally reconstructing, modeling, and quantifying microscopic anatomies has become central to many fields of biological research. MBF Bioscience has chosen to openly document their digital reconstruction file format, the Neuromorphological File Specification, available at www.mbfbioscience.com/filespecification (Angstman et al., 2020). The format, created and maintained by MBF Bioscience, is broadly utilized by the neuroscience community. The data format's structure and capabilities have evolved since its inception, with modifications made to keep pace with advancements in microscopy and the scientific questions raised by worldwide experts in the field. More recent modifications to the neuromorphological file format ensure it abides by the Findable, Accessible, Interoperable, and Reusable (FAIR) data principles promoted by the International Neuroinformatics Coordinating Facility (INCF; Wilkinson et al., Scientific Data, 3, 160018,, 2016). The incorporated metadata make it easy to identify and repurpose these data types for downstream applications and investigation. This publication describes key elements of the file format and details their relevant structural advantages in an effort to encourage the reuse of these rich data files for alternative analysis or reproduction of derived conclusions.
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9
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Reed JD, Blackwell KT. Prediction of Neural Diameter From Morphology to Enable Accurate Simulation. Front Neuroinform 2021; 15:666695. [PMID: 34149388 PMCID: PMC8209307 DOI: 10.3389/fninf.2021.666695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 05/10/2021] [Indexed: 11/29/2022] Open
Abstract
Accurate neuron morphologies are paramount for computational model simulations of realistic neural responses. Over the last decade, the online repository NeuroMorpho.Org has collected over 140,000 available neuron morphologies to understand brain function and promote interaction between experimental and computational research. Neuron morphologies describe spatial aspects of neural structure; however, many of the available morphologies do not contain accurate diameters that are essential for computational simulations of electrical activity. To best utilize available neuron morphologies, we present a set of equations that predict dendritic diameter from other morphological features. To derive the equations, we used a set of NeuroMorpho.org archives with realistic neuron diameters, representing hippocampal pyramidal, cerebellar Purkinje, and striatal spiny projection neurons. Each morphology is separated into initial, branching children, and continuing nodes. Our analysis reveals that the diameter of preceding nodes, Parent Diameter, is correlated to diameter of subsequent nodes for all cell types. Branching children and initial nodes each required additional morphological features to predict diameter, such as path length to soma, total dendritic length, and longest path to terminal end. Model simulations reveal that membrane potential response with predicted diameters is similar to the original response for several tested morphologies. We provide our open source software to extend the utility of available NeuroMorpho.org morphologies, and suggest predictive equations may supplement morphologies that lack dendritic diameter and improve model simulations with realistic dendritic diameter.
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Affiliation(s)
- Jonathan D Reed
- Krasnow Institute of Advanced Study, George Mason University, Fairfax, VA, United States.,Department of Biology, George Mason University, Fairfax, VA, United States
| | - Kim T Blackwell
- Krasnow Institute of Advanced Study, George Mason University, Fairfax, VA, United States.,Department of Bioengineering, Volgenau School of Engineering, George Mason University, Fairfax, VA, United States
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10
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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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11
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Comprehensive Estimates of Potential Synaptic Connections in Local Circuits of the Rodent Hippocampal Formation by Axonal-Dendritic Overlap. J Neurosci 2020; 41:1665-1683. [PMID: 33361464 DOI: 10.1523/jneurosci.1193-20.2020] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 10/19/2020] [Accepted: 12/13/2020] [Indexed: 12/12/2022] Open
Abstract
A quantitative description of the hippocampal formation synaptic architecture is essential for understanding the neural mechanisms of episodic memory. Yet the existing knowledge of connectivity statistics between different neuron types in the rodent hippocampus only captures a mere 5% of this circuitry. We present a systematic pipeline to produce first-approximation estimates for most of the missing information. Leveraging the www.Hippocampome.org knowledge base, we derive local connection parameters between distinct pairs of morphologically identified neuron types based on their axonal-dendritic overlap within every layer and subregion of the hippocampal formation. Specifically, we adapt modern image analysis technology to determine the parcel-specific neurite lengths of every neuron type from representative morphologic reconstructions obtained from either sex. We then compute the average number of synapses per neuron pair using relevant anatomic volumes from the mouse brain atlas and ultrastructurally established interaction distances. Hence, we estimate connection probabilities and number of contacts for >1900 neuron type pairs, increasing the available quantitative assessments more than 11-fold. Connectivity statistics thus remain unknown for only a minority of potential synapses in the hippocampal formation, including those involving long-range (23%) or perisomatic (6%) connections and neuron types without morphologic tracings (7%). The described approach also yields approximate measurements of synaptic distances from the soma along the dendritic and axonal paths, which may affect signal attenuation and delay. Overall, this dataset fills a substantial gap in quantitatively describing hippocampal circuits and provides useful model specifications for biologically realistic neural network simulations, until further direct experimental data become available.SIGNIFICANCE STATEMENT The hippocampal formation is a crucial functional substrate for episodic memory and spatial representation. Characterizing the complex neuron type circuit of this brain region is thus important to understand the cellular mechanisms of learning and navigation. Here we present the first numerical estimates of connection probabilities, numbers of contacts per connected pair, and synaptic distances from the soma along the axonal and dendritic paths, for more than 1900 distinct neuron type pairs throughout the dentate gyrus, CA3, CA2, CA1, subiculum, and entorhinal cortex. This comprehensive dataset, publicly released online at www.Hippocampome.org, constitutes an unprecedented quantification of the majority of the local synaptic circuit for a prominent mammalian neural system and provides an essential foundation for data-driven, anatomically realistic neural network models.
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12
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Sielemann K, Hafner A, Pucker B. The reuse of public datasets in the life sciences: potential risks and rewards. PeerJ 2020; 8:e9954. [PMID: 33024631 PMCID: PMC7518187 DOI: 10.7717/peerj.9954] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 08/25/2020] [Indexed: 12/13/2022] Open
Abstract
The 'big data' revolution has enabled novel types of analyses in the life sciences, facilitated by public sharing and reuse of datasets. Here, we review the prodigious potential of reusing publicly available datasets and the associated challenges, limitations and risks. Possible solutions to issues and research integrity considerations are also discussed. Due to the prominence, abundance and wide distribution of sequencing data, we focus on the reuse of publicly available sequence datasets. We define 'successful reuse' as the use of previously published data to enable novel scientific findings. By using selected examples of successful reuse from different disciplines, we illustrate the enormous potential of the practice, while acknowledging the respective limitations and risks. A checklist to determine the reuse value and potential of a particular dataset is also provided. The open discussion of data reuse and the establishment of this practice as a norm has the potential to benefit all stakeholders in the life sciences.
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Affiliation(s)
- Katharina Sielemann
- Genetics and Genomics of Plants, Center for Biotechnology (CeBiTec) & Faculty of Biology, Bielefeld University, Bielefeld, Germany
- Graduate School DILS, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Bielefeld University, Bielefeld, Germany
| | - Alenka Hafner
- Genetics and Genomics of Plants, Center for Biotechnology (CeBiTec) & Faculty of Biology, Bielefeld University, Bielefeld, Germany
- Current Affiliation: Intercollege Graduate Degree Program in Plant Biology, Penn State University, University Park, State College, PA, United States of America
| | - Boas Pucker
- Genetics and Genomics of Plants, Center for Biotechnology (CeBiTec) & Faculty of Biology, Bielefeld University, Bielefeld, Germany
- Evolution and Diversity, Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom
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13
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Nguyen VT, Uchida R, Warling A, Sloan LJ, Saviano MS, Wicinski B, Hård T, Bertelsen MF, Stimpson CD, Bitterman K, Schall M, Hof PR, Sherwood CC, Manger PR, Spocter MA, Jacobs B. Comparative neocortical neuromorphology in felids: African lion, African leopard, and cheetah. J Comp Neurol 2020; 528:1392-1422. [DOI: 10.1002/cne.24823] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 11/18/2019] [Accepted: 11/18/2019] [Indexed: 02/03/2023]
Affiliation(s)
- Vivian T. Nguyen
- Laboratory of Quantitative Neuromorphology, Neuroscience Program, Department of PsychologyColorado College Colorado Springs Colorado
| | - Riri Uchida
- Laboratory of Quantitative Neuromorphology, Neuroscience Program, Department of PsychologyColorado College Colorado Springs Colorado
| | - Allysa Warling
- Laboratory of Quantitative Neuromorphology, Neuroscience Program, Department of PsychologyColorado College Colorado Springs Colorado
| | - Lucy J. Sloan
- Laboratory of Quantitative Neuromorphology, Neuroscience Program, Department of PsychologyColorado College Colorado Springs Colorado
| | - Mark S. Saviano
- Laboratory of Quantitative Neuromorphology, Neuroscience Program, Department of PsychologyColorado College Colorado Springs Colorado
| | - Bridget Wicinski
- Nash Family Department of Neuroscience and Friedman Brain InstituteIcahn School of Medicine at Mount Sinai New York New York
| | | | - Mads F. Bertelsen
- Center for Zoo and Wild Animal HealthCopenhagen Zoo Frederiksberg Denmark
| | - Cheryl D. Stimpson
- Department of Anthropology and Center for the Advanced Study of Human PaleobiologyThe George Washington University Washington District of Columbia
| | - Kathleen Bitterman
- School of Anatomical Sciences, Faculty of Health SciencesUniversity of the Witwatersrand Johannesburg South Africa
| | - Matthew Schall
- Laboratory of Quantitative Neuromorphology, Neuroscience Program, Department of PsychologyColorado College Colorado Springs Colorado
| | - Patrick R. Hof
- Nash Family Department of Neuroscience and Friedman Brain InstituteIcahn School of Medicine at Mount Sinai New York New York
| | - Chet C. Sherwood
- Department of Anthropology and Center for the Advanced Study of Human PaleobiologyThe George Washington University Washington District of Columbia
| | - Paul R. Manger
- School of Anatomical Sciences, Faculty of Health SciencesUniversity of the Witwatersrand Johannesburg South Africa
| | - Muhammad A. Spocter
- School of Anatomical Sciences, Faculty of Health SciencesUniversity of the Witwatersrand Johannesburg South Africa
- Department of AnatomyDes Moines University Des Moines Iowa
| | - Bob Jacobs
- Laboratory of Quantitative Neuromorphology, Neuroscience Program, Department of PsychologyColorado College Colorado Springs Colorado
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14
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Bijari K, Akram MA, Ascoli GA. An open-source framework for neuroscience metadata management applied to digital reconstructions of neuronal morphology. Brain Inform 2020; 7:2. [PMID: 32219575 PMCID: PMC7098402 DOI: 10.1186/s40708-020-00103-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 03/14/2020] [Indexed: 12/21/2022] Open
Abstract
Research advancements in neuroscience entail the production of a substantial amount of data requiring interpretation, analysis, and integration. The complexity and diversity of neuroscience data necessitate the development of specialized databases and associated standards and protocols. NeuroMorpho.Org is an online repository of over one hundred thousand digitally reconstructed neurons and glia shared by hundreds of laboratories worldwide. Every entry of this public resource is associated with essential metadata describing animal species, anatomical region, cell type, experimental condition, and additional information relevant to contextualize the morphological content. Until recently, the lack of a user-friendly, structured metadata annotation system relying on standardized terminologies constituted a major hindrance in this effort, limiting the data release pace. Over the past 2 years, we have transitioned the original spreadsheet-based metadata annotation system of NeuroMorpho.Org to a custom-developed, robust, web-based framework for extracting, structuring, and managing neuroscience information. Here we release the metadata portal publicly and explain its functionality to enable usage by data contributors. This framework facilitates metadata annotation, improves terminology management, and accelerates data sharing. Moreover, its open-source development provides the opportunity of adapting and extending the code base to other related research projects with similar requirements. This metadata portal is a beneficial web companion to NeuroMorpho.Org which saves time, reduces errors, and aims to minimize the barrier for direct knowledge sharing by domain experts. The underlying framework can be progressively augmented with the integration of increasingly autonomous machine intelligence components.
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Affiliation(s)
- Kayvan Bijari
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA USA
| | - Masood A. Akram
- 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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15
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Armañanzas R. Revealing post-transcriptional microRNA-mRNA regulations in Alzheimer's disease through ensemble graphs. BMC Genomics 2018; 19:668. [PMID: 30255799 PMCID: PMC6157163 DOI: 10.1186/s12864-018-5025-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND In silico investigations on the integration of multiple datasets are in need of higher statistical power methods to unveil secondary findings that were hidden from the initial analyses. We present here a novel method for the network analysis of messenger RNA post-translational regulation by microRNA molecules. The method integrates expression data and sequence binding predictions through a set of sound machine learning techniques, forwarding all results to an ensemble graph of regulations. RESULTS Bayesian network classifiers are induced based on a pool of ensemble graphs with ascending order of complexity. Individual goodness-of-fit and classification performances are evaluated for each learned model. As a testbed, four Alzheimer's disease datasets are integrated using the new approach, achieving top values of 0.9794 ± 0.01 for the area under the receiver operating characteristic curve and 0.9439 ± 0.0234 for the prediction accuracy. CONCLUSIONS Post-transcriptional regulations found by the optimal network classifier concur with previous literature findings. Furthermore, additional network structures suggest previously unreported regulations in the state of the art of Alzheimer's research. The quantitative performance as well as sound biological findings provide confidence in the ensemble approach and encourage similar integrative analyses for other conditions.
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Affiliation(s)
- Rubén Armañanzas
- Department of Bioengineering, Krasnow Institute for Advanced Study, George Mason University, 4400 University Dr, MS2A1, Fairfax, 22030, VA, USA.
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16
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Mining Big Neuron Morphological Data. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2018; 2018:8234734. [PMID: 30034462 PMCID: PMC6035829 DOI: 10.1155/2018/8234734] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2018] [Revised: 05/09/2018] [Accepted: 05/24/2018] [Indexed: 11/26/2022]
Abstract
The advent of automatic tracing and reconstruction technology has led to a surge in the number of neurons 3D reconstruction data and consequently the neuromorphology research. However, the lack of machine-driven annotation schema to automatically detect the types of the neurons based on their morphology still hinders the development of this branch of science. Neuromorphology is important because of the interplay between the shape and functionality of neurons and the far-reaching impact on the diagnostics and therapeutics in neurological disorders. This survey paper provides a comprehensive research in the field of automatic neurons classification and presents the existing challenges, methods, tools, and future directions for automatic neuromorphology analytics. We summarize the major automatic techniques applicable in the field and propose a systematic data processing pipeline for automatic neuron classification, covering data capturing, preprocessing, analyzing, classification, and retrieval. Various techniques and algorithms in machine learning are illustrated and compared to the same dataset to facilitate ongoing research in the field.
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17
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López-Cabrera JD, Lorenzo-Ginori JV. Feature selection for the classification of traced neurons. J Neurosci Methods 2018; 303:41-54. [DOI: 10.1016/j.jneumeth.2018.04.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Revised: 03/19/2018] [Accepted: 04/04/2018] [Indexed: 10/17/2022]
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18
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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: 51] [Impact Index Per Article: 8.5] [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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19
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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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20
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Polavaram S, Ascoli GA. An ontology-based search engine for digital reconstructions of neuronal morphology. Brain Inform 2017; 4:123-134. [PMID: 28337675 PMCID: PMC5413594 DOI: 10.1007/s40708-017-0062-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Accepted: 03/13/2017] [Indexed: 11/27/2022] Open
Abstract
Neuronal morphology is extremely diverse across and within animal species, developmental stages, brain regions, and cell types. This diversity is functionally important because neuronal structure strongly affects synaptic integration, spiking dynamics, and network connectivity. Digital reconstructions of axonal and dendritic arbors are thus essential to quantify and model information processing in the nervous system. NeuroMorpho.Org is an established repository containing tens of thousands of digitally reconstructed neurons shared by several hundred laboratories worldwide. Each neuron is annotated with specific metadata based on the published references and additional details provided by data owners. The number of represented metadata concepts has grown over the years in parallel with the increase of available data. Until now, however, the lack of standardized terminologies and of an adequately structured metadata schema limited the effectiveness of user searches. Here we present a new organization of NeuroMorpho.Org metadata grounded on a set of interconnected hierarchies focusing on the main dimensions of animal species, anatomical regions, and cell types. We have comprehensively mapped each metadata term in NeuroMorpho.Org to this formal ontology, explicitly resolving all ambiguities caused by synonymy and homonymy. Leveraging this consistent framework, we introduce OntoSearch, a powerful functionality that seamlessly enables retrieval of morphological data based on expert knowledge and logical inferences through an intuitive string-based user interface with auto-complete capability. In addition to returning the data directly matching the search criteria, OntoSearch also identifies a pool of possible hits by taking into consideration incomplete metadata annotation.
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Affiliation(s)
- Sridevi Polavaram
- Center for Neural Informatics, Structures and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA.
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Abstract
Most neuroscientists have yet to embrace a culture of data sharing. Using our decade-long experience at NeuroMorpho.Org as an example, we discuss how publicly available repositories may benefit data producers and end-users alike. We outline practical recipes for resource developers to maximize the research impact of data sharing platforms for both contributors and users.
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Affiliation(s)
- Giorgio A Ascoli
- Center for Neural Informatics, Structures, and Plasticity Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia, USA
| | - Patricia Maraver
- Center for Neural Informatics, Structures, and Plasticity Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia, USA
| | - Sumit Nanda
- Center for Neural Informatics, Structures, and Plasticity Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia, USA
| | - Sridevi Polavaram
- Center for Neural Informatics, Structures, and Plasticity Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia, USA
| | - Rubén Armañanzas
- Center for Neural Informatics, Structures, and Plasticity Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia, USA
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Affiliation(s)
- Hanchuan Peng
- Allen Institute for Brain Science, Seattle, WA, USA,
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23
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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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24
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Gillette TA, Ascoli GA. Topological characterization of neuronal arbor morphology via sequence representation: I--motif analysis. BMC Bioinformatics 2015; 16:216. [PMID: 26156313 PMCID: PMC4496917 DOI: 10.1186/s12859-015-0604-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2015] [Accepted: 04/30/2015] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND The morphology of neurons offers many insights into developmental processes and signal processing. Numerous reports have focused on metrics at the level of individual branches or whole arbors; however, no studies have attempted to quantify repeated morphological patterns within neuronal trees. We introduce a novel sequential encoding of neurite branching suitable to explore topological patterns. RESULTS Using all possible branching topologies for comparison we show that the relative abundance of short patterns of up to three bifurcations, together with overall tree size, effectively capture the local branching patterns of neurons. Dendrites and axons display broadly similar topological motifs (over-represented patterns) and anti-motifs (under-represented patterns), differing most in their proportions of bifurcations with one terminal branch and in select sub-sequences of three bifurcations. In addition, pyramidal apical dendrites reveal a distinct motif profile. CONCLUSIONS The quantitative characterization of topological motifs in neuronal arbors provides a thorough description of local features and detailed boundaries for growth mechanisms and hypothesized computational functions.
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Affiliation(s)
- Todd A Gillette
- Department of Molecular Neuroscience, Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study (MS2A1), 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 (MS2A1), George Mason University, Fairfax, VA, USA.
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25
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Affiliation(s)
- Christopher S von Bartheld
- Department of Physiology and Cell Biology, Center of Biomedical Research Excellence in Cell Biology, University of Nevada School of Medicine, Mailstop 352, Reno, NV, 89557, USA,
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