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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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Ji Z, Guo S, Qiao Y, McDougal RA. Automating literature screening and curation with applications to computational neuroscience. J Am Med Inform Assoc 2024; 31:1463-1470. [PMID: 38722233 PMCID: PMC11187430 DOI: 10.1093/jamia/ocae097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 03/19/2024] [Accepted: 04/16/2024] [Indexed: 06/21/2024] Open
Abstract
OBJECTIVE ModelDB (https://modeldb.science) is a discovery platform for computational neuroscience, containing over 1850 published model codes with standardized metadata. These codes were mainly supplied from unsolicited model author submissions, but this approach is inherently limited. For example, we estimate we have captured only around one-third of NEURON models, the most common type of models in ModelDB. To more completely characterize the state of computational neuroscience modeling work, we aim to identify works containing results derived from computational neuroscience approaches and their standardized associated metadata (eg, cell types, research topics). MATERIALS AND METHODS Known computational neuroscience work from ModelDB and identified neuroscience work queried from PubMed were included in our study. After pre-screening with SPECTER2 (a free document embedding method), GPT-3.5, and GPT-4 were used to identify likely computational neuroscience work and relevant metadata. RESULTS SPECTER2, GPT-4, and GPT-3.5 demonstrated varied but high abilities in identification of computational neuroscience work. GPT-4 achieved 96.9% accuracy and GPT-3.5 improved from 54.2% to 85.5% through instruction-tuning and Chain of Thought. GPT-4 also showed high potential in identifying relevant metadata annotations. DISCUSSION Accuracy in identification and extraction might further be improved by dealing with ambiguity of what are computational elements, including more information from papers (eg, Methods section), improving prompts, etc. CONCLUSION Natural language processing and large language model techniques can be added to ModelDB to facilitate further model discovery, and will contribute to a more standardized and comprehensive framework for establishing domain-specific resources.
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Affiliation(s)
- Ziqing Ji
- Biostatistics, Yale School of Public Health, Yale University, New Haven, CT 06510, United States
| | - Siyan Guo
- Biostatistics, Yale School of Public Health, Yale University, New Haven, CT 06510, United States
| | - Yujie Qiao
- Biostatistics, Yale School of Public Health, Yale University, New Haven, CT 06510, United States
- Integrative Genomics, Princeton University, Princeton, NJ 08540, United States
| | - Robert A McDougal
- Biostatistics, Yale School of Public Health, Yale University, New Haven, CT 06510, United States
- Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT 06510, United States
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06510, United States
- Wu Tsai Institute, Yale University, New Haven, CT 06510, United States
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Anger M, Wendelborn C, Schickhardt C. German funders' data sharing policies-A qualitative interview study. PLoS One 2024; 19:e0296956. [PMID: 38330079 PMCID: PMC10852319 DOI: 10.1371/journal.pone.0296956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 12/21/2023] [Indexed: 02/10/2024] Open
Abstract
BACKGROUND Data sharing is commonly seen as beneficial for science but is not yet common practice. Research funding agencies are known to play a key role in promoting data sharing, but German funders' data sharing policies appear to lag behind in international comparison. This study aims to answer the question of how German data sharing experts inside and outside funding agencies perceive and evaluate German funders' data sharing policies and overall efforts to promote data sharing. METHODS This study is based on sixteen guided expert interviews with representatives of German funders and German research data experts from stakeholder organisations, who shared their perceptions of German' funders efforts to promote data sharing. By applying the method of qualitative content analysis to our interview data, we categorise and describe noteworthy aspects of the German data sharing policy landscape and illustrate our findings with interview passages. RESULTS We present our findings in five sections to distinguish our interviewees' perceptions on a) the status quo of German funders' data sharing policies, b) the role of funders in promoting data sharing, c) current and potential measures by funders to promote data sharing, d) general barriers to those measures, and e) the implementation of more binding data sharing requirements. DISCUSSION AND CONCLUSION Although funders are perceived to be important promoters and facilitators of data sharing throughout our interviews, only few German funding agencies have data sharing policies in place. Several interviewees stated that funders could do more, for example by providing incentives for data sharing or by introducing more concrete policies. Our interviews suggest the academic freedom of grantees is widely perceived as an obstacle for German funders in introducing mandatory data sharing requirements. However, some interviewees stated that stricter data sharing requirements could be justified if data sharing is a part of good scientific practice.
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Affiliation(s)
- Michael Anger
- Section for Translational Medical Ethics, Clinical Cooperation Unit Applied Tumor Immunity, National Center for Tumor Diseases (NCT) Heidelberg, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christian Wendelborn
- Section for Translational Medical Ethics, Clinical Cooperation Unit Applied Tumor Immunity, National Center for Tumor Diseases (NCT) Heidelberg, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christoph Schickhardt
- Section for Translational Medical Ethics, Clinical Cooperation Unit Applied Tumor Immunity, National Center for Tumor Diseases (NCT) Heidelberg, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Emissah H, Ljungquist B, Ascoli GA. Bibliometric analysis of neuroscience publications quantifies the impact of data sharing. Bioinformatics 2023; 39:btad746. [PMID: 38070153 PMCID: PMC10733721 DOI: 10.1093/bioinformatics/btad746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/01/2023] [Accepted: 12/07/2023] [Indexed: 12/19/2023] Open
Abstract
SUMMARY Neural morphology, the branching geometry of brain cells, is an essential cellular substrate of nervous system function and pathology. Despite the accelerating production of digital reconstructions of neural morphology, the public accessibility of data remains a core issue in neuroscience. Deficiencies in the availability of existing data create redundancy of research efforts and limit synergy. We carried out a comprehensive bibliometric analysis of neural morphology publications to quantify the impact of data sharing in the neuroscience community. Our findings demonstrate that sharing digital reconstructions of neural morphology via NeuroMorpho.Org leads to a significant increase of citations to the original article, thus directly benefiting authors. The rate of data reusage remains constant for at least 16 years after sharing (the whole period analyzed), altogether nearly doubling the peer-reviewed discoveries in the field. Furthermore, the recent availability of larger and more numerous datasets fostered integrative applications, which accrue on average twice the citations of re-analyses of individual datasets. We also released an open-source citation tracking web-service allowing researchers to monitor reusage of their datasets in independent peer-reviewed reports. These results and tools can facilitate the recognition of shared data reuse for merit evaluations and funding decisions. AVAILABILITY AND IMPLEMENTATION The application is available at: http://cng-nmo-dev3.orc.gmu.edu:8181/. The source code at https://github.com/HerveEmissah/nmo-authors-app and https://github.com/HerveEmissah/nmo-bibliometric-analysis.
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Affiliation(s)
- Herve Emissah
- Bioinformatics Program, College of Science, George Mason University, Fairfax, VA 22030, United States
- Center for Neural Informatics, Structures, & Plasticity (CN3) and Bioengineering Department, College of Engineering & Computing, George Mason University, Fairfax, VA 22030, United States
| | - Bengt Ljungquist
- Center for Neural Informatics, Structures, & Plasticity (CN3) and Bioengineering Department, College of Engineering & Computing, George Mason University, Fairfax, VA 22030, United States
| | - Giorgio A Ascoli
- Bioinformatics Program, College of Science, George Mason University, Fairfax, VA 22030, United States
- Center for Neural Informatics, Structures, & Plasticity (CN3) and Bioengineering Department, College of Engineering & Computing, George Mason University, Fairfax, VA 22030, United States
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Amra LN, Mächler P, Fomin-Thunemann N, Kılıç K, Saisan P, Devor A, Thunemann M. Tissue Oxygen Depth Explorer: an interactive database for microscopic oxygen imaging data. Front Neuroinform 2023; 17:1278787. [PMID: 38088985 PMCID: PMC10711099 DOI: 10.3389/fninf.2023.1278787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 11/07/2023] [Indexed: 02/01/2024] Open
Affiliation(s)
- Layth N. Amra
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
| | - Philipp Mächler
- Department of Physics, University of California, San Diego, La Jolla, CA, United States
| | | | - Kıvılcım Kılıç
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
| | - Payam Saisan
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Anna Devor
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
- Harvard Medical School, Martinos Center for Biomedical Imaging, MGH, Charlestown, MA, United States
| | - Martin Thunemann
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
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Emissah H, Ljungquist B, Ascoli GA. Bibliometric analysis of neuroscience publications quantifies the impact of data sharing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.12.557386. [PMID: 37745378 PMCID: PMC10515804 DOI: 10.1101/2023.09.12.557386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Motivation Neural morphology, the branching geometry of neurons and glia in the nervous system, is an essential cellular substrate of brain function and pathology. Despite the accelerating production of digital reconstructions of neural morphology in laboratories worldwide, the public accessibility of data remains a core issue in neuroscience. Deficiencies in the availability of existing data create redundancy of research efforts and prevent researchers from building on others' work. Data sharing complements the development of computational resources and literature mining tools to accelerate scientific discovery. Results We carried out a comprehensive bibliometric analysis of neural morphology publications to quantify the impact of data sharing in the neuroscience community. Our findings demonstrate that sharing digital reconstructions of neural morphology via the NeuroMorpho.Org online repository leads to a significant increase of citations to the original article, thus directly benefiting the authors. Moreover, the rate of data reusage remains constant for at least 16 years after sharing (the whole period analyzed), altogether nearly doubling the peer-reviewed discoveries in the field. Furthermore, the recent availability of larger and more numerous datasets fostered integrative meta-analysis applications, which accrue on average twice the citations of re-analyses of individual datasets. We also designed and deployed an open-source citation tracking web-service that allows researchers to monitor reusage of their datasets in independent peer-reviewed reports. These results and the released tool can facilitate the recognition of shared data reuse for promotion and tenure considerations, merit evaluations, and funding decisions.
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Affiliation(s)
- Herve Emissah
- Bioinformatics Program, College of Science, George Mason University
| | - Bengt Ljungquist
- Center for Neural Informatics, Structures, and Plasticity, College of Engineering & Computing, George Mason University
| | - Giorgio A. Ascoli
- Bioinformatics Program, College of Science, George Mason University
- Center for Neural Informatics, Structures, and Plasticity, College of Engineering & Computing, George Mason University
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Maraver P, Tecuatl C, Ascoli GA. Automatic identification of scientific publications describing digital reconstructions of neural morphology. Brain Inform 2023; 10:23. [PMID: 37684527 PMCID: PMC10491540 DOI: 10.1186/s40708-023-00202-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 08/06/2023] [Indexed: 09/10/2023] Open
Abstract
The increasing number of peer-reviewed publications constitutes a challenge for biocuration. For example, NeuroMorpho.Org, a sharing platform for digital reconstructions of neural morphology, must evaluate more than 6000 potentially relevant articles per year to identify data of interest. Here, we describe a tool that uses natural language processing and deep learning to assess the likelihood of a publication to be relevant for the project. The tool automatically identifies articles describing digitally reconstructed neural morphologies with high accuracy. Its processing rate of 900 publications per hour is not only amply sufficient to autonomously track new research, but also allowed the successful evaluation of older publications backlogged due to limited human resources. The number of bio-entities found since launching the tool almost doubled while greatly reducing manual labor. The classification tool is open source, configurable, and simple to use, making it extensible to other biocuration projects.
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Affiliation(s)
- Patricia Maraver
- Bioengineering Department; College of Engineering and Computing, George Mason University, Fairfax, VA, USA
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Carolina Tecuatl
- Bioengineering Department; College of Engineering and Computing, George Mason University, Fairfax, VA, USA
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Giorgio A Ascoli
- Bioengineering Department; College of Engineering and Computing, George Mason University, Fairfax, VA, USA.
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA.
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Maraver P, Tecuatl C, Ascoli GA. Automatic identification of scientific publications describing digital reconstructions of neural morphology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.14.527522. [PMID: 36824882 PMCID: PMC9949101 DOI: 10.1101/2023.02.14.527522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Motivation The increasing number of peer-reviewed publications constitutes a challenge for biocuration. For example, NeuroMorpho.Org, a sharing platform for digital reconstructions of neural morphology, must evaluate more than 6000 potentially relevant articles per year to identify data of interest. Here, we describe a tool that uses natural language processing and deep learning to assess the likelihood of a publication to be relevant for the project. Results The tool automatically identifies articles describing digitally reconstructed neural morphologies with high accuracy. Its processing rate of 900 publications per hour is not only amply sufficient to autonomously track new research, but also allowed the successful evaluation of older publications backlogged due to limited human resources. The number of bio-entities found since launching the tool almost doubled while greatly reducing manual labor. The classification tool is open source, configurable, and simple to use, making it extensible to other biocuration projects. Availability https://github.com/Joindbre/TextRelevancy. Contact ascoli@gmu.edu. Supplementary information Supplementary information, tool installation, and API usage are available at https://docs.joindbre.com.
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Affiliation(s)
- Patricia Maraver
- Bioengineering Department; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
| | - Carolina Tecuatl
- Bioengineering Department; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
| | - Giorgio A. Ascoli
- Bioengineering Department; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
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Hendriks S, Ramos KM, Grady C. Survey of Investigators About Sharing Human Research Data in the Neurosciences. Neurology 2022; 99:e1314-e1325. [PMID: 35981894 PMCID: PMC9576293 DOI: 10.1212/wnl.0000000000200886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/11/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES In the neurosciences, significant opportunities for sharing individual-level data are underexploited. Commentators suggest various barriers to data sharing, which may need to be addressed. Investigators' perspectives on the main barriers are unclear. Furthermore, bioethicists have raised concerns about the potential misuse of neuroscience data, although discussions are hampered by uncertainty about the potential risks. It is unclear how common sensitive data are obtained and whether investigators judge them as sensitive. METHODS An online survey was disseminated among 1,190 principal investigators (PIs) of active National Institute of Neurological Disorders and Stroke, National Institute of Mental Health, or NIH Brain Research Through Advancing Innovative Neurotechnologies Initiative grants involving human subject research. RESULTS A total of 397 investigators responded to the survey (response rate 33%). Most investigators (84%) support efforts to increase sharing of deidentified individual-level data. However, investigators perceive many barriers to data sharing. The largest barriers were costs and time; limited interpretation of the data without understanding the context of data collection; lack of incentives; limited standardization and norms for data acquisition, formatting, and description; and heterogeneity of data types. Several types of data described as sensitive in the literature are common among neuroscience studies, for example, neural correlates of behavior, emotions, or decision making (71%) and/or predictive data (54%). Although most investigators consider it unlikely or extremely unlikely for their research data to be misused to harm individual research participants (82%), the majority were at least slightly concerned about potential harm to individuals if their research data were misused (65%). Investigators with more easily reidentifiable data, data from vulnerable groups, and neural data were more concerned about the likelihood of misuse and/or magnitude of harm of misuse of their research data. DISCUSSION We hope these data help prioritize the development of tools and strategies to overcome the main barriers to data sharing. Furthermore, these data provide input on what may be sensitive data for which additional safeguards should be considered.
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Affiliation(s)
- Saskia Hendriks
- From the The Office of Neuroscience Communications and Engagement (S.H., K.M.R.), National Institute of Neurological Disorders and Stroke; and Department of Bioethics (S.H., C.G.), Clinical Center National Institute of Health, Bethesda, MD.
| | - Khara M Ramos
- From the The Office of Neuroscience Communications and Engagement (S.H., K.M.R.), National Institute of Neurological Disorders and Stroke; and Department of Bioethics (S.H., C.G.), Clinical Center National Institute of Health, Bethesda, MD
| | - Christine Grady
- From the The Office of Neuroscience Communications and Engagement (S.H., K.M.R.), National Institute of Neurological Disorders and Stroke; and Department of Bioethics (S.H., C.G.), Clinical Center National Institute of Health, Bethesda, MD
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Anger M, Wendelborn C, Winkler EC, Schickhardt C. Neither carrots nor sticks? Challenges surrounding data sharing from the perspective of research funding agencies—A qualitative expert interview study. PLoS One 2022; 17:e0273259. [PMID: 36070283 PMCID: PMC9451069 DOI: 10.1371/journal.pone.0273259] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 08/04/2022] [Indexed: 11/19/2022] Open
Abstract
Background Data Sharing is widely recognised as crucial for accelerating scientific research and improving its quality. However, data sharing is still not a common practice. Funding agencies tend to facilitate the sharing of research data by both providing incentives and requiring data sharing as part of their policies and conditions for awarding grants. The goal of our article is to answer the following question: What challenges do international funding agencies see when it comes to their own efforts to foster and implement data sharing through their policies? Methods We conducted a series of sixteen guideline-based expert interviews with representatives of leading international funding agencies. As contact persons for open science at their respective agencies, they offered their perspectives and experiences concerning their organisations’ data sharing policies. We performed a qualitative content analysis of the interviews and categorised the challenges perceived by funding agencies. Results We identify and illustrate six challenges surrounding data sharing policies as perceived by leading funding agencies: The design of clear policies, monitoring of compliance, sanctions for non-compliance, incentives, support, and limitations for funders’ own capabilities. However, our interviews also show how funders approach potential solutions to overcome these challenges, for example by coordinating with other agencies or adjusting grant evaluation metrics to incentivise data sharing. Discussion and conclusion Our interviews point to existing flaws in funders’ data sharing policies, such as a lack of clarity, a lack of monitoring of funded researchers’ data sharing behaviour, and a lack of incentives. A number of agencies could suggest potential solutions but often struggle with the overall complexity of data sharing and the implementation of these measures. Funders cannot solve each challenge by themselves, but they can play an active role and lead joint efforts towards a culture of data sharing.
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Affiliation(s)
- Michael Anger
- Section for Translational Medical Ethics, National Center for Tumor Diseases (NCT) Heidelberg, German Cancer Research Center (DKFZ), Heidelberg, Germany
- * E-mail:
| | - Christian Wendelborn
- Section for Translational Medical Ethics, National Center for Tumor Diseases (NCT) Heidelberg, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Eva C. Winkler
- Section for Translational Medical Ethics/Department of Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg, University Hospital Heidelberg, Heidelberg, Germany
| | - Christoph Schickhardt
- Section for Translational Medical Ethics, National Center for Tumor Diseases (NCT) Heidelberg, German Cancer Research Center (DKFZ), Heidelberg, Germany
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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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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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Hintiryan H, Bowman I, Johnson DL, Korobkova L, Zhu M, Khanjani N, Gou L, Gao L, Yamashita S, Bienkowski MS, Garcia L, Foster NN, Benavidez NL, Song MY, Lo D, Cotter KR, Becerra M, Aquino S, Cao C, Cabeen RP, Stanis J, Fayzullina M, Ustrell SA, Boesen T, Tugangui AJ, Zhang ZG, Peng B, Fanselow MS, Golshani P, Hahn JD, Wickersham IR, Ascoli GA, Zhang LI, Dong HW. Connectivity characterization of the mouse basolateral amygdalar complex. Nat Commun 2021; 12:2859. [PMID: 34001873 PMCID: PMC8129205 DOI: 10.1038/s41467-021-22915-5] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 03/25/2021] [Indexed: 11/08/2022] Open
Abstract
The basolateral amygdalar complex (BLA) is implicated in behaviors ranging from fear acquisition to addiction. Optogenetic methods have enabled the association of circuit-specific functions to uniquely connected BLA cell types. Thus, a systematic and detailed connectivity profile of BLA projection neurons to inform granular, cell type-specific interrogations is warranted. Here, we apply machine-learning based computational and informatics analysis techniques to the results of circuit-tracing experiments to create a foundational, comprehensive BLA connectivity map. The analyses identify three distinct domains within the anterior BLA (BLAa) that house target-specific projection neurons with distinguishable morphological features. We identify brain-wide targets of projection neurons in the three BLAa domains, as well as in the posterior BLA, ventral BLA, posterior basomedial, and lateral amygdalar nuclei. Inputs to each nucleus also are identified via retrograde tracing. The data suggests that connectionally unique, domain-specific BLAa neurons are associated with distinct behavior networks.
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Affiliation(s)
- Houri Hintiryan
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
| | - Ian Bowman
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - David L Johnson
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Laura Korobkova
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Muye Zhu
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Neda Khanjani
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Lin Gou
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Lei Gao
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Seita Yamashita
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Michael S Bienkowski
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Luis Garcia
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Nicholas N Foster
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Nora L Benavidez
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Monica Y Song
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Darrick Lo
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Kaelan R Cotter
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Marlene Becerra
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sarvia Aquino
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Chunru Cao
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Ryan P Cabeen
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jim Stanis
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Marina Fayzullina
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sarah A Ustrell
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Tyler Boesen
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Amanda J Tugangui
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Zheng-Gang Zhang
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Physiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Bo Peng
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Michael S Fanselow
- Brain Research Institute, Department of Psychology, University of California, Los Angeles, CA, USA
| | - Peyman Golshani
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- West Los Angeles Veterans Administration Medical Center, Los Angeles, CA, USA
| | - Joel D Hahn
- Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
| | - Ian R Wickersham
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Giorgio A Ascoli
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Li I Zhang
- Center for Neural Circuitry & Sensory Processing Disorders, Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hong-Wei Dong
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
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14
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Devriendt T, Shabani M, Borry P. Data Sharing in Biomedical Sciences: A Systematic Review of Incentives. Biopreserv Biobank 2021; 19:219-227. [PMID: 33926229 DOI: 10.1089/bio.2020.0037] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background: The lack of incentives has been described as the rate-limiting step for data sharing. Currently, the evaluation of scientific productivity by academic institutions and funders has been heavily reliant upon the number of publications and citations, raising questions about the adequacy of such mechanisms to reward data generation and sharing. This article provides a systematic review of the current and proposed incentive mechanisms for researchers in biomedical sciences and discusses their strengths and weaknesses. Methods: PubMed, Web of Science, and Google Scholar were queried for original research articles, editorials, and opinion articles on incentives for data sharing. Articles were included if they discussed incentive mechanisms for data sharing, were applicable to biomedical sciences, and were written in English. Results: Although coauthorship in return for the sharing of data is common, this might be incompatible with authorship guidelines and raise concerns over the ability of secondary analysts to contest the proposed research methods or conclusions that are drawn. Data publication, citation, and altmetrics have been proposed as alternative routes to credit data generators, which could address these disadvantages. Their primary downsides are that they are not well-established, it is difficult to acquire evidence to support their implementation, and that they could be gamed or give rise to novel forms of research misconduct. Conclusions: Alternative recognition mechanisms need to be more commonly used to generate evidence on their power to stimulate data sharing, and to assess where they fall short. There is ample discussion in policy documents on alternative crediting systems to work toward Open Science, which indicates that that there is an interest in working out more elaborate metascience programs.
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Affiliation(s)
- Thijs Devriendt
- Department of Public Health and Primary Care, Centre for Biomedical Ethics and Law, KU Leuven, Leuven, Belgium
| | - Mahsa Shabani
- Department of Public Health and Primary Care, Centre for Biomedical Ethics and Law, KU Leuven, Leuven, Belgium.,Metamedica, Faculty of Law and Criminology, Ghent University, Gent, Belgium
| | - Pascal Borry
- Department of Public Health and Primary Care, Centre for Biomedical Ethics and Law, KU Leuven, Leuven, Belgium
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15
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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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16
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Kirch C, Gollo LL. Single-neuron dynamical effects of dendritic pruning implicated in aging and neurodegeneration: towards a measure of neuronal reserve. Sci Rep 2021; 11:1309. [PMID: 33446683 PMCID: PMC7809359 DOI: 10.1038/s41598-020-78815-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 11/13/2020] [Indexed: 12/13/2022] Open
Abstract
Aging is a main risk factor for neurodegenerative disorders including Alzheimer's disease. It is often accompanied by reduced cognitive functions, gray-matter volume, and dendritic integrity. Although age-related brain structural changes have been observed across multiple scales, their functional implications remain largely unknown. Here we simulate the aging effects on neuronal morphology as dendritic pruning and characterize its dynamical implications. Utilizing a detailed computational modeling approach, we simulate the dynamics of digitally reconstructed neurons obtained from Neuromorpho.org. We show that dendritic pruning affects neuronal integrity: firing rate is reduced, causing a reduction in energy consumption, energy efficiency, and dynamic range. Pruned neurons require less energy but their function is often impaired, which can explain the diminished ability to distinguish between similar experiences (pattern separation) in older people. Our measures indicate that the resilience of neuronal dynamics is neuron-specific, heterogeneous, and strongly affected by dendritic topology and the position of the soma. Based on the emergent neuronal dynamics, we propose to classify the effects of dendritic deterioration, and put forward a topological measure of “neuronal reserve” that quantifies the resilience of neuronal dynamics to dendritic pruning. Moreover, our findings suggest that increasing dendritic excitability could partially mitigate the dynamical effects of aging.
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Affiliation(s)
- Christoph Kirch
- QIMR Berghofer Medical Research Institute, Brisbane, Australia.,The Queensland University of Technology, Brisbane, Australia
| | - Leonardo L Gollo
- QIMR Berghofer Medical Research Institute, Brisbane, Australia. .,The Queensland University of Technology, Brisbane, Australia. .,The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash Biomedical Imaging, Monash University, Melbourne, Australia.
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17
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Kirch C, Gollo LL. Spatially resolved dendritic integration: towards a functional classification of neurons. PeerJ 2020; 8:e10250. [PMID: 33282551 PMCID: PMC7694565 DOI: 10.7717/peerj.10250] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 10/06/2020] [Indexed: 01/19/2023] Open
Abstract
The vast tree-like dendritic structure of neurons allows them to receive and integrate input from many neurons. A wide variety of neuronal morphologies exist, however, their role in dendritic integration, and how it shapes the response of the neuron, is not yet fully understood. Here, we study the evolution and interactions of dendritic spikes in excitable neurons with complex real branch structures. We focus on dozens of digitally reconstructed illustrative neurons from the online repository NeuroMorpho.org, which contains over 130,000 neurons. Yet, our methods can be promptly extended to any other neuron. This approach allows us to estimate and map specific and heterogeneous patterns of activity observed across extensive dendritic trees with thousands of compartments. We propose a classification of neurons based on the location of the soma (centrality) and the number of branches connected to the soma. These are key topological factors in determining the neuron's energy consumption, firing rate, and the dynamic range, which quantifies the range in synaptic input rate that can be reliably encoded by the neuron's firing rate. Moreover, we find that bifurcations, the structural building blocks of complex dendrites, play a major role in increasing the dynamic range of neurons. Our results provide a better understanding of the effects of neuronal morphology in the diversity of neuronal dynamics and function.
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Affiliation(s)
- Christoph Kirch
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Queensland University of Technology, Brisbane, QLD, Australia
| | - Leonardo L. Gollo
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Queensland University of Technology, Brisbane, QLD, Australia
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, VIC, Australia
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18
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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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19
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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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20
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Zhang DF, Xu M, Bi R, Yao YG. Genetic Analyses of Alzheimer's Disease in China: Achievements and Perspectives. ACS Chem Neurosci 2019; 10:890-901. [PMID: 30698408 DOI: 10.1021/acschemneuro.8b00435] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Since 2010, the Chinese have become one of the most aged populations in the world, leading to a severe burden of neurodegenerative disorders. Alzheimer's disease (AD) is the most prevalent neurodegenerative disease and has a high genetic heritability. In the past two decades, numerous genetic analyses, from linkage analyses and candidate gene studies to genome-wide association studies (GWASs) and next-generation sequencing studies, have identified dozens of AD susceptibility or causal genes. These studies have provided a comprehensive genetic view and contributed to the understanding of the pathological and molecular mechanisms of the disease. However, most of the recognized AD genetic risk factors have been reported in studies based on European populations or populations of European ancestry, and data about the genetics of AD from other populations has been very limited. As China has the largest AD population in the world and because of the remarkable genetic differences between the East and the West, deciphering the genetic basis and molecular mechanism in Chinese patients with AD may add key points to the full characterization of AD. In this review, we present an overview of the current state of AD genetic research in China, with an emphasis on genome-level studies. We also describe the challenges and opportunities for future advances, especially for in-depth collaborations, brain bank construction, and primate animal modeling. There is an urgent need to promote public awareness and increase our collaborations and data sharing.
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Affiliation(s)
- Deng-Feng Zhang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan 650223, China
| | - Min Xu
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
| | - Rui Bi
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan 650223, China
| | - Yong-Gang Yao
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
- Kunming Institute of Zoology−Chinese University of Hong Kong Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
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21
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Automated Metadata Suggestion During Repository Submission. Neuroinformatics 2018; 17:361-371. [PMID: 30382537 DOI: 10.1007/s12021-018-9403-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
Knowledge discovery via an informatics resource is constrained by the completeness of the resource, both in terms of the amount of data it contains and in terms of the metadata that exists to describe the data. Increasing completeness in one of these categories risks reducing completeness in the other because manually curating metadata is time consuming and is restricted by familiarity with both the data and the metadata annotation scheme. The diverse interests of a research community may drive a resource to have hundreds of metadata tags with few examples for each making it challenging for humans or machine learning algorithms to learn how to assign metadata tags properly. We demonstrate with ModelDB, a computational neuroscience model discovery resource, that using manually-curated regular-expression based rules can overcome this challenge by parsing existing texts from data providers during user data entry to suggest metadata annotations and prompt them to suggest other related metadata annotations rather than leaving the task to a curator. In the ModelDB implementation, analyzing the abstract identified 6.4 metadata tags per abstract at 79% precision. Using the full-text produced higher recall with low precision (41%), and the title alone produced few (1.3) metadata annotations per entry; we thus recommend data providers use their abstract during upload. Grouping the possible metadata annotations into categories (e.g. cell type, biological topic) revealed that precision and recall for the different text sources varies by category. Given this proof-of-concept, other bioinformatics resources can likewise improve the quality of their metadata by adopting our approach of prompting data uploaders with relevant metadata at the minimal cost of formalizing rules for each potential metadata annotation.
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22
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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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23
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Alp M, Cucinotta FA. Track structure model of microscopic energy deposition by protons and heavy ions in segments of neuronal cell dendrites represented by cylinders or spheres. LIFE SCIENCES IN SPACE RESEARCH 2017; 13:27-38. [PMID: 28554507 PMCID: PMC5495005 DOI: 10.1016/j.lssr.2017.03.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Accepted: 03/29/2017] [Indexed: 05/20/2023]
Abstract
Changes to cognition, including memory, following radiation exposure are a concern for cosmic ray exposures to astronauts and in Hadron therapy with proton and heavy ion beams. The purpose of the present work is to develop computational methods to evaluate microscopic energy deposition (ED) in volumes representative of neuron cell structures, including segments of dendrites and spines, using a stochastic track structure model. A challenge for biophysical models of neuronal damage is the large sizes (> 100µm) and variability in volumes of possible dendritic segments and pre-synaptic elements (spines and filopodia). We consider cylindrical and spherical microscopic volumes of varying geometric parameters and aspect ratios from 0.5 to 5 irradiated by protons, and 3He and 12C particles at energies corresponding to a distance of 1cm to the Bragg peak, which represent particles of interest in Hadron therapy as well as space radiation exposure. We investigate the optimal axis length of dendritic segments to evaluate microscopic ED and hit probabilities along the dendritic branches at a given macroscopic dose. Because of large computation times to analyze ED in volumes of varying sizes, we developed an analytical method to find the mean primary dose in spheres that can guide numerical methods to find the primary dose distribution for cylinders. Considering cylindrical segments of varying aspect ratio at constant volume, we assess the chord length distribution, mean number of hits and ED profiles by primary particles and secondary electrons (δ-rays). For biophysical modeling applications, segments on dendritic branches are proposed to have equal diameters and axes lengths along the varying diameter of a dendritic branch.
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Affiliation(s)
- Murat Alp
- Department of Health Physics and Diagnostic Sciences, University of Nevada, Las Vegas, NV, USA
| | - Francis A Cucinotta
- Department of Health Physics and Diagnostic Sciences, University of Nevada, Las Vegas, NV, USA.
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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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McDougal RA, Morse TM, Carnevale T, Marenco L, Wang R, Migliore M, Miller PL, Shepherd GM, Hines ML. Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience. J Comput Neurosci 2017; 42:1-10. [PMID: 27629590 PMCID: PMC5279891 DOI: 10.1007/s10827-016-0623-7] [Citation(s) in RCA: 127] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Revised: 08/17/2016] [Accepted: 08/30/2016] [Indexed: 11/29/2022]
Abstract
Neuron modeling may be said to have originated with the Hodgkin and Huxley action potential model in 1952 and Rall's models of integrative activity of dendrites in 1964. Over the ensuing decades, these approaches have led to a massive development of increasingly accurate and complex data-based models of neurons and neuronal circuits. ModelDB was founded in 1996 to support this new field and enhance the scientific credibility and utility of computational neuroscience models by providing a convenient venue for sharing them. It has grown to include over 1100 published models covering more than 130 research topics. It is actively curated and developed to help researchers discover and understand models of interest. ModelDB also provides mechanisms to assist running models both locally and remotely, and has a graphical tool that enables users to explore the anatomical and biophysical properties that are represented in a model. Each of its capabilities is undergoing continued refinement and improvement in response to user experience. Large research groups (Allen Brain Institute, EU Human Brain Project, etc.) are emerging that collect data across multiple scales and integrate that data into many complex models, presenting new challenges of scale. We end by predicting a future for neuroscience increasingly fueled by new technology and high performance computation, and increasingly in need of comprehensive user-friendly databases such as ModelDB to provide the means to integrate the data for deeper insights into brain function in health and disease.
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Affiliation(s)
- Robert A McDougal
- Department of Neuroscience, Yale University, PO Box 208001, New Haven, CT, 06520-8001, USA.
| | - Thomas M Morse
- Department of Neuroscience, Yale University, PO Box 208001, New Haven, CT, 06520-8001, USA
| | - Ted Carnevale
- Department of Neuroscience, Yale University, PO Box 208001, New Haven, CT, 06520-8001, USA
| | - Luis Marenco
- Department of Neuroscience, Yale University, PO Box 208001, New Haven, CT, 06520-8001, USA
- VA Connecticut Healthcare System, West Haven, CT, 06516, USA
- Center for Medical Informatics, Yale University, New Haven, CT, 06520, USA
| | - Rixin Wang
- Center for Medical Informatics, Yale University, New Haven, CT, 06520, USA
- Department of Anesthesiology, Yale University, New Haven, CT, 06520, USA
| | - Michele Migliore
- Department of Neuroscience, Yale University, PO Box 208001, New Haven, CT, 06520-8001, USA
- Institute of Biophysics, National Research Council, Palermo, Italy
| | - Perry L Miller
- VA Connecticut Healthcare System, West Haven, CT, 06516, USA
- Center for Medical Informatics, Yale University, New Haven, CT, 06520, USA
- Department of Anesthesiology, Yale University, New Haven, CT, 06520, USA
| | - Gordon M Shepherd
- Department of Neuroscience, Yale University, PO Box 208001, New Haven, CT, 06520-8001, USA
| | - Michael L Hines
- Department of Neuroscience, Yale University, PO Box 208001, New Haven, CT, 06520-8001, USA
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26
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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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McDougal RA, Bulanova AS, Lytton WW. Reproducibility in Computational Neuroscience Models and Simulations. IEEE Trans Biomed Eng 2016; 63:2021-35. [PMID: 27046845 PMCID: PMC5016202 DOI: 10.1109/tbme.2016.2539602] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
OBJECTIVE Like all scientific research, computational neuroscience research must be reproducible. Big data science, including simulation research, cannot depend exclusively on journal articles as the method to provide the sharing and transparency required for reproducibility. METHODS Ensuring model reproducibility requires the use of multiple standard software practices and tools, including version control, strong commenting and documentation, and code modularity. RESULTS Building on these standard practices, model-sharing sites and tools have been developed that fit into several categories: 1) standardized neural simulators; 2) shared computational resources; 3) declarative model descriptors, ontologies, and standardized annotations; and 4) model-sharing repositories and sharing standards. CONCLUSION A number of complementary innovations have been proposed to enhance sharing, transparency, and reproducibility. The individual user can be encouraged to make use of version control, commenting, documentation, and modularity in development of models. The community can help by requiring model sharing as a condition of publication and funding. SIGNIFICANCE Model management will become increasingly important as multiscale models become larger, more detailed, and correspondingly more difficult to manage by any single investigator or single laboratory. Additional big data management complexity will come as the models become more useful in interpreting experiments, thus increasing the need to ensure clear alignment between modeling data, both parameters and results, and experiment.
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28
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Spires-Jones TL, Poirazi P, Grubb MS. Opening up: open access publishing, data sharing, and how they can influence your neuroscience career. Eur J Neurosci 2016; 43:1413-9. [PMID: 26950407 DOI: 10.1111/ejn.13234] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- T L Spires-Jones
- FENS-Kavli Network of Excellence, Europe-wide.,Centre for Cognitive and Neural Systems, Euan MacDonald Centre, and the Centre for Dementia Prevention, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - P Poirazi
- FENS-Kavli Network of Excellence, Europe-wide.,Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology-Hellas (FORTH), Crete, Greece
| | - M S Grubb
- FENS-Kavli Network of Excellence, Europe-wide.,Department of Developmental Neurobiology, King's College London, London, SE1 1UL, UK
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