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Bannach-Brown A, Rackoll T, Macleod MR, McCann SK. Building a synthesis-ready research ecosystem: fostering collaboration and open science to accelerate biomedical translation. BMC Med Res Methodol 2025; 25:66. [PMID: 40065205 PMCID: PMC11892198 DOI: 10.1186/s12874-025-02524-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2024] [Accepted: 02/27/2025] [Indexed: 03/14/2025] Open
Abstract
In this review article, we provide a comprehensive overview of current practices and challenges associated with research synthesis in preclinical biomedical research. We identify critical barriers and roadblocks that impede effective identification, utilisation, and integration of research findings to inform decision making in research translation. We examine practices at each stage of the research lifecycle, including study design, conduct, and publishing, that can be optimised to facilitate the conduct of timely, accurate, and comprehensive evidence synthesis. These practices are anchored in open science and engaging with the broader research community to ensure evidence is accessible and useful to all stakeholders. We underscore the need for collective action from researchers, synthesis specialists, institutions, publishers and journals, funders, infrastructure providers, and policymakers, who all play a key role in fostering an open, robust and synthesis-ready research environment, for an accelerated trajectory towards integrated biomedical research and translation.
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Affiliation(s)
- Alexandra Bannach-Brown
- QUEST Center for Responsible Research, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Torsten Rackoll
- QUEST Center for Responsible Research, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Malcolm R Macleod
- Centre for Clinical Brain Sciences, The University of Edinburgh Medical School, Edinburgh, UK
| | - Sarah K McCann
- QUEST Center for Responsible Research, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
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2
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Sinha A, Gleeson P, Marin B, Dura-Bernal S, Panagiotou S, Crook S, Cantarelli M, Cannon RC, Davison AP, Gurnani H, Silver RA. The NeuroML ecosystem for standardized multi-scale modeling in neuroscience. eLife 2025; 13:RP95135. [PMID: 39792574 PMCID: PMC11723582 DOI: 10.7554/elife.95135] [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] [Indexed: 01/12/2025] Open
Abstract
Data-driven models of neurons and circuits are important for understanding how the properties of membrane conductances, synapses, dendrites, and the anatomical connectivity between neurons generate the complex dynamical behaviors of brain circuits in health and disease. However, the inherent complexity of these biological processes makes the construction and reuse of biologically detailed models challenging. A wide range of tools have been developed to aid their construction and simulation, but differences in design and internal representation act as technical barriers to those who wish to use data-driven models in their research workflows. NeuroML, a model description language for computational neuroscience, was developed to address this fragmentation in modeling tools. Since its inception, NeuroML has evolved into a mature community standard that encompasses a wide range of model types and approaches in computational neuroscience. It has enabled the development of a large ecosystem of interoperable open-source software tools for the creation, visualization, validation, and simulation of data-driven models. Here, we describe how the NeuroML ecosystem can be incorporated into research workflows to simplify the construction, testing, and analysis of standardized models of neural systems, and supports the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles, thus promoting open, transparent and reproducible science.
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Affiliation(s)
- Ankur Sinha
- Department of Neuroscience, Physiology and Pharmacology, University College LondonLondonUnited Kingdom
| | - Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College LondonLondonUnited Kingdom
| | - Bóris Marin
- Universidade Federal do ABCSão Bernardo do CampoBrazil
| | - Salvador Dura-Bernal
- SUNY Downstate Medical CenterBrooklynUnited States
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric ResearchOrangeburgUnited States
| | | | | | | | | | | | | | - Robin Angus Silver
- Department of Neuroscience, Physiology and Pharmacology, University College LondonLondonUnited Kingdom
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3
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Ekhtiari H, Zare-Bidoky M, Sangchooli A, Valyan A, Abi-Dargham A, Cannon DM, Carter CS, Garavan H, George TP, Ghobadi-Azbari P, Juchem C, Krystal JH, Nichols TE, Öngür D, Pernet CR, Poldrack RA, Thompson PM, Paulus MP. Reporting checklists in neuroimaging: promoting transparency, replicability, and reproducibility. Neuropsychopharmacology 2024; 50:67-84. [PMID: 39242922 PMCID: PMC11525976 DOI: 10.1038/s41386-024-01973-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 08/08/2024] [Accepted: 08/12/2024] [Indexed: 09/09/2024]
Abstract
Neuroimaging plays a crucial role in understanding brain structure and function, but the lack of transparency, reproducibility, and reliability of findings is a significant obstacle for the field. To address these challenges, there are ongoing efforts to develop reporting checklists for neuroimaging studies to improve the reporting of fundamental aspects of study design and execution. In this review, we first define what we mean by a neuroimaging reporting checklist and then discuss how a reporting checklist can be developed and implemented. We consider the core values that should inform checklist design, including transparency, repeatability, data sharing, diversity, and supporting innovations. We then share experiences with currently available neuroimaging checklists. We review the motivation for creating checklists and whether checklists achieve their intended objectives, before proposing a development cycle for neuroimaging reporting checklists and describing each implementation step. We emphasize the importance of reporting checklists in enhancing the quality of data repositories and consortia, how they can support education and best practices, and how emerging computational methods, like artificial intelligence, can help checklist development and adherence. We also highlight the role that funding agencies and global collaborations can play in supporting the adoption of neuroimaging reporting checklists. We hope this review will encourage better adherence to available checklists and promote the development of new ones, and ultimately increase the quality, transparency, and reproducibility of neuroimaging research.
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Affiliation(s)
- Hamed Ekhtiari
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA.
- Laureate Institute for Brain Research, Tulsa, OK, USA.
| | - Mehran Zare-Bidoky
- Iranian National Center for Addiction Studies, Tehran University of Medical Sciences, Tehran, Iran
| | - Arshiya Sangchooli
- Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, VIC, Australia
| | - Alireza Valyan
- Iranian National Center for Addiction Studies, Tehran University of Medical Sciences, Tehran, Iran
| | - Anissa Abi-Dargham
- Department of Psychiatry and Behavioral Health, Stony Brook University Renaissance School of Medicine, Stony Brook, NY, USA
- Department of Psychiatry, Columbia University Vagelos School of Medicine and New York State Psychiatric Institute, New York, NY, USA
| | - Dara M Cannon
- Clinical Neuroimaging Laboratory, Center for Neuroimaging, Cognition & Genomics, College of Medicine, Nursing & Health Sciences, University of Galway, Galway, Ireland
| | - Cameron S Carter
- Department of Psychiatry and Human Behavior, University of California at Irvine, Irvine, CA, USA
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, VT, USA
| | - Tony P George
- Institute for Mental Health Policy and Research at CAMH, Toronto, ON, Canada
- Department of Psychiatry, Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Peyman Ghobadi-Azbari
- Iranian National Center for Addiction Studies, Tehran University of Medical Sciences, Tehran, Iran
| | - Christoph Juchem
- Department of Biomedical Engineering, Columbia University Fu Foundation, School of Engineering and Applied Science, New York, NY, USA
- Department of Radiology, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Thomas E Nichols
- Nuffield Department of Population Health, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Dost Öngür
- McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Cyril R Pernet
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | | | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
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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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6
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Blixhavn CH, Reiten I, Kleven H, Øvsthus M, Yates SC, Schlegel U, Puchades MA, Schmid O, Bjaalie JG, Bjerke IE, Leergaard TB. The Locare workflow: representing neuroscience data locations as geometric objects in 3D brain atlases. Front Neuroinform 2024; 18:1284107. [PMID: 38421771 PMCID: PMC10884250 DOI: 10.3389/fninf.2024.1284107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 01/24/2024] [Indexed: 03/02/2024] Open
Abstract
Neuroscientists employ a range of methods and generate increasing amounts of data describing brain structure and function. The anatomical locations from which observations or measurements originate represent a common context for data interpretation, and a starting point for identifying data of interest. However, the multimodality and abundance of brain data pose a challenge for efforts to organize, integrate, and analyze data based on anatomical locations. While structured metadata allow faceted data queries, different types of data are not easily represented in a standardized and machine-readable way that allow comparison, analysis, and queries related to anatomical relevance. To this end, three-dimensional (3D) digital brain atlases provide frameworks in which disparate multimodal and multilevel neuroscience data can be spatially represented. We propose to represent the locations of different neuroscience data as geometric objects in 3D brain atlases. Such geometric objects can be specified in a standardized file format and stored as location metadata for use with different computational tools. We here present the Locare workflow developed for defining the anatomical location of data elements from rodent brains as geometric objects. We demonstrate how the workflow can be used to define geometric objects representing multimodal and multilevel experimental neuroscience in rat or mouse brain atlases. We further propose a collection of JSON schemas (LocareJSON) for specifying geometric objects by atlas coordinates, suitable as a starting point for co-visualization of different data in an anatomical context and for enabling spatial data queries.
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Affiliation(s)
- Camilla H. Blixhavn
- Neural Systems Laboratory, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Ingrid Reiten
- Neural Systems Laboratory, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Heidi Kleven
- Neural Systems Laboratory, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Martin Øvsthus
- Neural Systems Laboratory, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Sharon C. Yates
- Neural Systems Laboratory, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Ulrike Schlegel
- Neural Systems Laboratory, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Maja A. Puchades
- Neural Systems Laboratory, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | | | - Jan G. Bjaalie
- Neural Systems Laboratory, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Ingvild E. Bjerke
- Neural Systems Laboratory, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Trygve B. Leergaard
- Neural Systems Laboratory, Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
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7
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Martone ME. The past, present and future of neuroscience data sharing: a perspective on the state of practices and infrastructure for FAIR. Front Neuroinform 2024; 17:1276407. [PMID: 38250019 PMCID: PMC10796549 DOI: 10.3389/fninf.2023.1276407] [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: 08/11/2023] [Accepted: 10/31/2023] [Indexed: 01/23/2024] Open
Abstract
Neuroscience has made significant strides over the past decade in moving from a largely closed science characterized by anemic data sharing, to a largely open science where the amount of publicly available neuroscience data has increased dramatically. While this increase is driven in significant part by large prospective data sharing studies, we are starting to see increased sharing in the long tail of neuroscience data, driven no doubt by journal requirements and funder mandates. Concomitant with this shift to open is the increasing support of the FAIR data principles by neuroscience practices and infrastructure. FAIR is particularly critical for neuroscience with its multiplicity of data types, scales and model systems and the infrastructure that serves them. As envisioned from the early days of neuroinformatics, neuroscience is currently served by a globally distributed ecosystem of neuroscience-centric data repositories, largely specialized around data types. To make neuroscience data findable, accessible, interoperable, and reusable requires the coordination across different stakeholders, including the researchers who produce the data, data repositories who make it available, the aggregators and indexers who field search engines across the data, and community organizations who help to coordinate efforts and develop the community standards critical to FAIR. The International Neuroinformatics Coordinating Facility has led efforts to move neuroscience toward FAIR, fielding several resources to help researchers and repositories achieve FAIR. In this perspective, I provide an overview of the components and practices required to achieve FAIR in neuroscience and provide thoughts on the past, present and future of FAIR infrastructure for neuroscience, from the laboratory to the search engine.
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Affiliation(s)
- Maryann E. Martone
- Department of Neurosciences, University of California, San Diego, CA, United States
- San Francisco Veterans Administration Hospital, San Francisco, CA, United States
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8
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Bernier A, Knoppers BM, Bermudez P, Beauvais MJS, Thorogood A. Open Data governance at the Canadian Open Neuroscience Platform (CONP): From the Walled Garden to the Arboretum. Gigascience 2024; 13:giad114. [PMID: 38217404 PMCID: PMC10787360 DOI: 10.1093/gigascience/giad114] [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: 07/17/2023] [Revised: 11/14/2023] [Accepted: 12/10/2023] [Indexed: 01/15/2024] Open
Abstract
Scientific research communities pursue dual imperatives in implementing strategies to share their data. These communities attempt to maximize the accessibility of biomedical data for downstream research use, in furtherance of open science objectives. Simultaneously, such communities safeguard the interests of research participants through data stewardship measures and the integration of suitable risk disclosures to the informed consent process. The Canadian Open Neuroscience Platform (CONP) convened an Ethics and Governance Committee composed of experts in bioethics, neuroethics, and law to develop holistic policy tools, organizational approaches, and technological supports to align the open governance of data with ethical and legal norms. The CONP has adopted novel platform governance methods that favor full data openness, legitimated through the use of robust deidentification processes and informed consent practices. The experience of the CONP is articulated as a potential template for other open science efforts to further build upon. This experience highlights informed consent guidance, deidentification practices, ethicolegal metadata, platform-level norms, and commercialization and publication policies as the principal pillars of a practicable approach to the governance of open data. The governance approach adopted by the CONP stands as a viable model for the broader neuroscience and open science communities to adopt for sharing data in full open access.
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Affiliation(s)
- Alexander Bernier
- Centre of Genomics and Policy, Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, 740, Dr Penfield Ave, suite 5200, Montréal, Québec H3A 0G1, Canada
| | - Bartha M Knoppers
- Centre of Genomics and Policy, Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, 740, Dr Penfield Ave, suite 5200, Montréal, Québec H3A 0G1, Canada
| | - Patrick Bermudez
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec H3A 2B4, Canada
| | - Michael J S Beauvais
- Faculty of Law, University of Toronto, Falconer Hall, 84 Queens Park, Toronto, Ontario M5S 2C5, Canada
| | - Adrian Thorogood
- The Terry Fox Research Institute, 110 Pine Ave W, Montreal, Quebec H2W IR7, Canada
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Van Horn JD. Editorial: On the Economics of Neuroscientific Data Sharing. Neuroinformatics 2024; 22:1-4. [PMID: 37966621 DOI: 10.1007/s12021-023-09649-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Affiliation(s)
- John Darrell Van Horn
- Department of Psychology, University of Virginia, Charlottesville, VA, USA.
- School of Data Science, University of Virginia, Charlottesville, VA, USA.
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10
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Thorogood A. Population Neuroscience: Strategies to Promote Data Sharing While Protecting Privacy. Curr Top Behav Neurosci 2024; 68:53-66. [PMID: 38509403 DOI: 10.1007/7854_2024_467] [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] [Indexed: 03/22/2024]
Abstract
Population neuroscience aims to advance our understanding of how genetic and environmental factors influence brain development and brain health over the life span, by integrating genomics, epidemiology, and neuroscience at population scale. This big data approach depends on data sharing strategies at both the micro- and macro-level, as well as attention to effective data management and protection of participant privacy. At the micro-level, researchers participate in international consortia that support collaboration, standards, and data sharing. They also seek to link together cohort studies, administrative health databases, and measures of the physical, built, and social environment in creative ways. Large-scale, longitudinal, and multi-modal cohorts are being designed to support explorations of genetic and environmental impacts on the brain. At a macro-level, funding agency policies now require data across health research domains to be managed according to the FAIR (findable, accessible, interoperable, and re-useable) Data principles and made available to the research community in a timely manner to support reproducibility and re-use. Data repositories provide technical infrastructure for storing, accessing, and increasingly also analyzing rich population-level data. Federated and cloud-based approaches are being leveraged to improve the security, remote accessibility, and performance of repositories. Finally, legal frameworks are being developed to facilitate secure health data access, integration, and analysis, providing new opportunities for the field.
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Radabaugh HL, Ferguson AR, Bramlett HM, Dietrich WD. Increasing Rigor of Preclinical Research to Maximize Opportunities for Translation. Neurotherapeutics 2023; 20:1433-1445. [PMID: 37525025 PMCID: PMC10684440 DOI: 10.1007/s13311-023-01400-5] [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] [Accepted: 06/09/2023] [Indexed: 08/02/2023] Open
Abstract
The use of animal models in pre-clinical research has significantly broadened our understanding of the pathologies that underlie traumatic brain injury (TBI)-induced damage and deficits. However, despite numerous pre-clinical studies reporting the identification of promising neurotherapeutics, translation of these therapies to clinical application has so far eluded the TBI research field. A concerted effort to address this lack of translatability is long overdue. Given the inherent heterogeneity of TBI and the replication crisis that continues to plague biomedical research, this is a complex task that will require a multifaceted approach centered around rigor and reproducibility. Here, we discuss the role of three primary focus areas for better aligning pre-clinical research with clinical TBI management. These focus areas are (1) reporting and standardization of protocols, (2) replication of prior knowledge including the confirmation of expected pharmacodynamics, and (3) the broad application of open science through inter-center collaboration and data sharing. We further discuss current efforts that are establishing the core framework needed for successfully addressing the translatability crisis of TBI.
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Affiliation(s)
- Hannah L Radabaugh
- Brain and Spinal Injury Center, Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Adam R Ferguson
- Brain and Spinal Injury Center, Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- San Francisco Veterans Affairs Healthcare System, San Francisco, CA, USA
| | - Helen M Bramlett
- Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, USA
| | - W Dalton Dietrich
- Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, USA.
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Gado S, Lingelbach K, Wirzberger M, Vukelić M. Decoding Mental Effort in a Quasi-Realistic Scenario: A Feasibility Study on Multimodal Data Fusion and Classification. SENSORS (BASEL, SWITZERLAND) 2023; 23:6546. [PMID: 37514840 PMCID: PMC10383122 DOI: 10.3390/s23146546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/17/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023]
Abstract
Humans' performance varies due to the mental resources that are available to successfully pursue a task. To monitor users' current cognitive resources in naturalistic scenarios, it is essential to not only measure demands induced by the task itself but also consider situational and environmental influences. We conducted a multimodal study with 18 participants (nine female, M = 25.9 with SD = 3.8 years). In this study, we recorded respiratory, ocular, cardiac, and brain activity using functional near-infrared spectroscopy (fNIRS) while participants performed an adapted version of the warship commander task with concurrent emotional speech distraction. We tested the feasibility of decoding the experienced mental effort with a multimodal machine learning architecture. The architecture comprised feature engineering, model optimisation, and model selection to combine multimodal measurements in a cross-subject classification. Our approach reduces possible overfitting and reliably distinguishes two different levels of mental effort. These findings contribute to the prediction of different states of mental effort and pave the way toward generalised state monitoring across individuals in realistic applications.
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Affiliation(s)
- Sabrina Gado
- Experimental Clinical Psychology, Department of Psychology, Julius-Maximilians-University of Würzburg, 97070 Würzburg, Germany
| | - Katharina Lingelbach
- Applied Neurocognitive Systems, Fraunhofer Institute for Industrial Engineering IAO, 70569 Stuttgart, Germany
- Applied Neurocognitive Psychology Lab, Department of Psychology, Carl von Ossietzky University, 26129 Oldenburg, Germany
| | - Maria Wirzberger
- Department of Teaching and Learning with Intelligent Systems, University of Stuttgart, 70174 Stuttgart, Germany
- LEAD Graduate School & Research Network, University of Tübingen, 72072 Tübingen, Germany
| | - Mathias Vukelić
- Applied Neurocognitive Systems, Fraunhofer Institute for Industrial Engineering IAO, 70569 Stuttgart, Germany
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Shweikh Y, Sekimitsu S, Boland MV, Zebardast N. The Growing Need for Ophthalmic Data Standardization. OPHTHALMOLOGY SCIENCE 2023; 3:100262. [PMID: 36691643 PMCID: PMC9860145 DOI: 10.1016/j.xops.2022.100262] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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14
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Mittal D, Mease R, Kuner T, Flor H, Kuner R, Andoh J. Data management strategy for a collaborative research center. Gigascience 2022; 12:giad049. [PMID: 37401720 PMCID: PMC10318494 DOI: 10.1093/gigascience/giad049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 02/20/2023] [Accepted: 06/11/2023] [Indexed: 07/05/2023] Open
Abstract
The importance of effective research data management (RDM) strategies to support the generation of Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience data grows with each advance in data acquisition techniques and research methods. To maximize the impact of diverse research strategies, multidisciplinary, large-scale neuroscience research consortia face a number of unsolved challenges in RDM. While open science principles are largely accepted, it is practically difficult for researchers to prioritize RDM over other pressing demands. The implementation of a coherent, executable RDM plan for consortia spanning animal, human, and clinical studies is becoming increasingly challenging. Here, we present an RDM strategy implemented for the Heidelberg Collaborative Research Consortium. Our consortium combines basic and clinical research in diverse populations (animals and humans) and produces highly heterogeneous and multimodal research data (e.g., neurophysiology, neuroimaging, genetics, behavior). We present a concrete strategy for initiating early-stage RDM and FAIR data generation for large-scale collaborative research consortia, with a focus on sustainable solutions that incentivize incremental RDM while respecting research-specific requirements.
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Affiliation(s)
- Deepti Mittal
- Institute of Pharmacology, Heidelberg University, 69120 Heidelberg, Germany
| | - Rebecca Mease
- Institute of Physiology and Pathophysiology, Heidelberg University, 69120 Heidelberg, Germany
| | - Thomas Kuner
- Institute for Anatomy and Cell Biology, Heidelberg University, 69120 Mannheim, Germany
| | - Herta Flor
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
| | - Rohini Kuner
- Institute of Pharmacology, Heidelberg University, 69120 Heidelberg, Germany
| | - Jamila Andoh
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
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15
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Brůha P, Mouček R, Salamon J, Vacek V. Workflow for health-related and brain data lifecycle. Front Digit Health 2022; 4:1025086. [PMID: 36532611 PMCID: PMC9748096 DOI: 10.3389/fdgth.2022.1025086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 11/01/2022] [Indexed: 09/19/2023] Open
Abstract
Poor lifestyle leads potentially to chronic diseases and low-grade physical and mental fitness. However, ahead of time, we can measure and analyze multiple aspects of physical and mental health, such as body parameters, health risk factors, degrees of motivation, and the overall willingness to change the current lifestyle. In conjunction with data representing human brain activity, we can obtain and identify human health problems resulting from a long-term lifestyle more precisely and, where appropriate, improve the quality and length of human life. Currently, brain and physical health-related data are not commonly collected and evaluated together. However, doing that is supposed to be an interesting and viable concept, especially when followed by a more detailed definition and description of their whole processing lifecycle. Moreover, when best practices are used to store, annotate, analyze, and evaluate such data collections, the necessary infrastructure development and more intense cooperation among scientific teams and laboratories are facilitated. This approach also improves the reproducibility of experimental work. As a result, large collections of physical and brain health-related data could provide a robust basis for better interpretation of a person's overall health. This work aims to overview and reflect some best practices used within global communities to ensure the reproducibility of experiments, collected datasets and related workflows. These best practices concern, e.g., data lifecycle models, FAIR principles, and definitions and implementations of terminologies and ontologies. Then, an example of how an automated workflow system could be created to support the collection, annotation, storage, analysis, and publication of findings is shown. The Body in Numbers pilot system, also utilizing software engineering best practices, was developed to implement the concept of such an automated workflow system. It is unique just due to the combination of the processing and evaluation of physical and brain (electrophysiological) data. Its implementation is explored in greater detail, and opportunities to use the gained findings and results throughout various application domains are discussed.
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Affiliation(s)
- Petr Brůha
- Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic
| | - Roman Mouček
- Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic
- New Technologies for the Information Society, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic
| | - Jaromír Salamon
- Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic
| | - Vítězslav Vacek
- Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic
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16
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Redolfi A, Archetti D, De Francesco S, Crema C, Tagliavini F, Lodi R, Ghidoni R, Gandini Wheeler-Kingshott CAM, Alexander DC, D'Angelo E. Italian, European, and international neuroinformatics efforts: An overview. Eur J Neurosci 2022. [PMID: 36310103 DOI: 10.1111/ejn.15854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/18/2022] [Accepted: 10/18/2022] [Indexed: 12/15/2022]
Abstract
Neuroinformatics is a research field that focusses on software tools capable of identifying, analysing, modelling, organising and sharing multiscale neuroscience data. Neuroinformatics has exploded in the last two decades with the emergence of the Big Data phenomenon, characterised by the so-called 3Vs (volume, velocity and variety), which provided neuroscientists with an improved ability to acquire and process data faster and more cheaply thanks to technical improvements in clinical, genomic and radiological technologies. This situation has led to a 'data deluge', as neuroscientists can routinely collect more study data in a few days than they could in a year just a decade ago. To address this phenomenon, several neuroimaging-focussed neuroinformatics platforms have emerged, funded by national or transnational agencies, with the following goals: (i) development of tools for archiving and organising analytical data (XNAT, REDCap and LabKey); (ii) development of data-driven models evolving from reductionist approaches to multidimensional models (RIN, IVN, HBD, EuroPOND, E-DADS and GAAIN BRAIN); and (iii) development of e-infrastructures to provide sufficient computational power and storage resources (neuGRID, HBP-EBRAINS, LONI and CONP). Although the scenario is still fragmented, there are technological and economical attempts at both national and international levels to introduce high standards for open and Findable, Accessible, Interoperable and Reusable (FAIR) neuroscience worldwide.
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Affiliation(s)
- Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Damiano Archetti
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Silvia De Francesco
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Claudio Crema
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Fabrizio Tagliavini
- Scientific Directorate, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Raffaele Lodi
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.,Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Roberta Ghidoni
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Center, Department of Neuroinflammation, UCL Institute of Neurology, London, UK.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Daniel C Alexander
- Centre for Medical Image Computing, University College London, London, UK.,Department of Computer Science, University College London, London, UK
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
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17
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Ropelewski AJ, Rizzo MA, Swedlow JR, Huisken J, Osten P, Khanjani N, Weiss K, Bakalov V, Engle M, Gridley L, Krzyzanowski M, Madden T, Maiese D, Mandal M, Waterfield J, Williams D, Hamilton CM, Huggins W. Standard metadata for 3D microscopy. Sci Data 2022; 9:449. [PMID: 35896564 PMCID: PMC9329339 DOI: 10.1038/s41597-022-01562-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 07/13/2022] [Indexed: 01/04/2023] Open
Abstract
Recent advances in fluorescence microscopy techniques and tissue clearing, labeling, and staining provide unprecedented opportunities to investigate brain structure and function. These experiments' images make it possible to catalog brain cell types and define their location, morphology, and connectivity in a native context, leading to a better understanding of normal development and disease etiology. Consistent annotation of metadata is needed to provide the context necessary to understand, reuse, and integrate these data. This report describes an effort to establish metadata standards for three-dimensional (3D) microscopy datasets for use by the Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative and the neuroscience research community. These standards were built on existing efforts and developed with input from the brain microscopy community to promote adoption. The resulting 3D Microscopy Metadata Standards (3D-MMS) includes 91 fields organized into seven categories: Contributors, Funders, Publication, Instrument, Dataset, Specimen, and Image. Adoption of these metadata standards will ensure that investigators receive credit for their work, promote data reuse, facilitate downstream analysis of shared data, and encourage collaboration.
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Affiliation(s)
- Alexander J Ropelewski
- Biomedical Applications Group, Pittsburgh Supercomputing Center, 300 S Craig Street, Pittsburgh, PA, 15213, USA
| | - Megan A Rizzo
- Department of Physiology, University of Maryland School of Medicine, 660 West Redwood Street, Baltimore, MD, 21201, USA
| | - Jason R Swedlow
- Centre for Gene Regulation & Expression, Division of Computational Biology, University of Dundee, Nethergate, Dundee, Scotland, DD1 4HN, United Kingdom
| | - Jan Huisken
- Morgridge Institute for Research, 330 N Orchard Street, Madison, WI, 53715, USA
| | - Pavel Osten
- Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY, 11724, USA
| | - Neda Khanjani
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine of University of Southern California, 1975 Zonal Avenue, Los Angeles, CA, 90033, USA
| | - Kurt Weiss
- Morgridge Institute for Research, 330 N Orchard Street, Madison, WI, 53715, USA
| | - Vesselina Bakalov
- Bioinformatics and Computational Biology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, 27709, USA
| | - Michelle Engle
- Bioinformatics and Computational Biology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, 27709, USA
| | - Lauren Gridley
- Bioinformatics and Computational Biology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, 27709, USA
| | - Michelle Krzyzanowski
- Bioinformatics and Computational Biology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, 27709, USA
| | - Tom Madden
- Bioinformatics and Computational Biology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, 27709, USA
| | - Deborah Maiese
- Bioinformatics and Computational Biology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, 27709, USA
| | - Meisha Mandal
- Bioinformatics and Computational Biology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, 27709, USA
| | - Justin Waterfield
- Bioinformatics and Computational Biology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, 27709, USA
| | - David Williams
- Bioinformatics and Computational Biology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, 27709, USA
| | - Carol M Hamilton
- Bioinformatics and Computational Biology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, 27709, USA
| | - Wayne Huggins
- Bioinformatics and Computational Biology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, 27709, USA.
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18
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Eriksson O, Bhalla US, Blackwell KT, Crook SM, Keller D, Kramer A, Linne ML, Saudargienė A, Wade RC, Hellgren Kotaleski J. Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows. eLife 2022; 11:e69013. [PMID: 35792600 PMCID: PMC9259018 DOI: 10.7554/elife.69013] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 05/13/2022] [Indexed: 12/22/2022] Open
Abstract
Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data - such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles - also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock-Cooper-Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.
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Affiliation(s)
- Olivia Eriksson
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of TechnologyStockholmSweden
| | - Upinder Singh Bhalla
- National Center for Biological Sciences, Tata Institute of Fundamental ResearchBangaloreIndia
| | - Kim T Blackwell
- Department of Bioengineering, Volgenau School of Engineering, George Mason UniversityFairfaxUnited States
| | - Sharon M Crook
- School of Mathematical and Statistical Sciences, Arizona State UniversityTempeUnited States
| | - Daniel Keller
- Blue Brain Project, École Polytechnique Fédérale de LausanneLausanneSwitzerland
| | - Andrei Kramer
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of TechnologyStockholmSweden
- Department of Neuroscience, Karolinska InstituteStockholmSweden
| | - Marja-Leena Linne
- Faculty of Medicine and Health Technology, Tampere UniversityTampereFinland
| | - Ausra Saudargienė
- Neuroscience Institute, Lithuanian University of Health SciencesKaunasLithuania
- Department of Informatics, Vytautas Magnus UniversityKaunasLithuania
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS)HeidelbergGermany
- Center for Molecular Biology (ZMBH), ZMBH-DKFZ Alliance, University of HeidelbergHeidelbergGermany
- Interdisciplinary Center for Scientific Computing (IWR), Heidelberg UniversityHeidelbergGermany
| | - Jeanette Hellgren Kotaleski
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of TechnologyStockholmSweden
- Department of Neuroscience, Karolinska InstituteStockholmSweden
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19
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Yan J, Chen L, Yu Y, Xu H, Xu Z, Sheng Y, Chen J. Neuroimaging-ITM: A Text Mining Pipeline Combining Deep Adversarial Learning with Interaction Based Topic Modeling for Enabling the FAIR Neuroimaging Study. Neuroinformatics 2022; 20:701-726. [PMID: 35235184 DOI: 10.1007/s12021-022-09571-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/04/2022] [Indexed: 12/31/2022]
Abstract
Sharing various neuroimaging digital resources have received widespread attention in FAIR (Findable, Accessible, Interoperable and Reusable) neuroscience. In order to support a comprehensive understanding of brain cognition, neuroimaging provenance should be constructed to characterize both research processes and results, and integrates various digital resources for quick replication and open cooperation. This brings new challenges to neuroimaging text mining, including fragmented information, lack of labelled corpora, and vague topics. This paper proposes a text mining pipeline for enabling the FAIR neuroimaging study. In order to avoid fragmented information, the Brain Informatics provenance model is redesigned based on NIDM (Neuroimaging Data Model) and FAIR facets. It can systematically capture the provenance requests from the FAIR neuroimaging study and then transform them into a group of text mining tasks. A neuroimaging text mining pipeline combining deep adversarial learning with interaction based topic modeling, called neuroimaging interaction topic model (Neuroimaging-ITM), is proposed to automatically extract neuroimaging provenance and identify research topics in the few-shot scenario. Finally, a group of experiments is completed by using real data from the journal PloS One. The experimental results show that Neuroimaging-ITM can systematically and accurately extract provenance information and obtain high-quality research topics from the full text of neuroimaging articles. Most of the mean F1 values of provenance extraction exceed 0.9. The topic coherence and KL (Kullback-Leibler) divergence reach 9.95 and 0.96 respectively. The results are obviously better than baseline methods.
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Affiliation(s)
- Jianzhuo Yan
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.,Engineering Research Center of Digital Community, Beijing University of Technology, Beijing, 100124, China
| | - Lihong Chen
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.,Engineering Research Center of Digital Community, Beijing University of Technology, Beijing, 100124, China
| | - Yongchuan Yu
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.,Engineering Research Center of Digital Community, Beijing University of Technology, Beijing, 100124, China
| | - Hongxia Xu
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.,Engineering Research Center of Digital Community, Beijing University of Technology, Beijing, 100124, China
| | - Zhe Xu
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| | - Ying Sheng
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| | - Jianhui Chen
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China. .,Beijing International Collaboration Base On Brain Informatics and Wisdom Services, Beijing University of Technology, Beijing, 100124, China. .,Beijing Key Laboratory of MRI and Brain Informatics, Beijing University of Technology, Beijing, 100124, China.
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20
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Amunts K, DeFelipe J, Pennartz C, Destexhe A, Migliore M, Ryvlin P, Furber S, Knoll A, Bitsch L, Bjaalie JG, Ioannidis Y, Lippert T, Sanchez-Vives MV, Goebel R, Jirsa V. Linking Brain Structure, Activity, and Cognitive Function through Computation. eNeuro 2022; 9:ENEURO.0316-21.2022. [PMID: 35217544 PMCID: PMC8925650 DOI: 10.1523/eneuro.0316-21.2022] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 01/11/2022] [Accepted: 01/17/2022] [Indexed: 01/19/2023] Open
Abstract
Understanding the human brain is a "Grand Challenge" for 21st century research. Computational approaches enable large and complex datasets to be addressed efficiently, supported by artificial neural networks, modeling and simulation. Dynamic generative multiscale models, which enable the investigation of causation across scales and are guided by principles and theories of brain function, are instrumental for linking brain structure and function. An example of a resource enabling such an integrated approach to neuroscientific discovery is the BigBrain, which spatially anchors tissue models and data across different scales and ensures that multiscale models are supported by the data, making the bridge to both basic neuroscience and medicine. Research at the intersection of neuroscience, computing and robotics has the potential to advance neuro-inspired technologies by taking advantage of a growing body of insights into perception, plasticity and learning. To render data, tools and methods, theories, basic principles and concepts interoperable, the Human Brain Project (HBP) has launched EBRAINS, a digital neuroscience research infrastructure, which brings together a transdisciplinary community of researchers united by the quest to understand the brain, with fascinating insights and perspectives for societal benefits.
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Affiliation(s)
- Katrin Amunts
- Institute of Neurosciences and Medicine (INM-1), Research Centre Jülich, Jülich 52425, Germany
- C. & O. Vogt Institute for Brain Research, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf 40225, Germany
| | - Javier DeFelipe
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid 28223, Spain
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Madrid 28002, Spain
| | - Cyriel Pennartz
- Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, 1098 XH, The Netherlands
| | - Alain Destexhe
- Centre National de la Recherche Scientifique, Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Gif sur Yvette 91400, France
| | - Michele Migliore
- Institute of Biophysics, National Research Council, Palermo 90146, Italy
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, Lausanne CH-1011, Switzerland
| | - Steve Furber
- Department of Computer Science, The University of Manchester, Manchester M13 9PL, United Kingdom
| | - Alois Knoll
- Department of Informatics, Technical University of Munich, Garching 385748, Germany
| | - Lise Bitsch
- The Danish Board of Technology Foundation, Copenhagen, 2650 Hvidovre, Denmark
| | - Jan G Bjaalie
- Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Yannis Ioannidis
- ATHENA Research & Innovation Center, Athena 12125, Greece
- Department of Informatics & Telecom, Nat'l and Kapodistrian University of Athens, 157 84 Athens, Greece
| | - Thomas Lippert
- Institute for Advanced Simulation (IAS), Jülich Supercomputing Centre (JSC), Research Centre Jülich, Jülich 52425, Germany
| | - Maria V Sanchez-Vives
- ICREA and Systems Neuroscience, Institute of Biomedical Investigations August Pi i Sunyer, Barcelona 08036, Spain
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht 6229 EV, The Netherlands
| | - Viktor Jirsa
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
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21
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Newmaster KT, Kronman FA, Wu YT, Kim Y. Seeing the Forest and Its Trees Together: Implementing 3D Light Microscopy Pipelines for Cell Type Mapping in the Mouse Brain. Front Neuroanat 2022; 15:787601. [PMID: 35095432 PMCID: PMC8794814 DOI: 10.3389/fnana.2021.787601] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 12/02/2021] [Indexed: 12/14/2022] Open
Abstract
The brain is composed of diverse neuronal and non-neuronal cell types with complex regional connectivity patterns that create the anatomical infrastructure underlying cognition. Remarkable advances in neuroscience techniques enable labeling and imaging of these individual cell types and their interactions throughout intact mammalian brains at a cellular resolution allowing neuroscientists to examine microscopic details in macroscopic brain circuits. Nevertheless, implementing these tools is fraught with many technical and analytical challenges with a need for high-level data analysis. Here we review key technical considerations for implementing a brain mapping pipeline using the mouse brain as a primary model system. Specifically, we provide practical details for choosing methods including cell type specific labeling, sample preparation (e.g., tissue clearing), microscopy modalities, image processing, and data analysis (e.g., image registration to standard atlases). We also highlight the need to develop better 3D atlases with standardized anatomical labels and nomenclature across species and developmental time points to extend the mapping to other species including humans and to facilitate data sharing, confederation, and integrative analysis. In summary, this review provides key elements and currently available resources to consider while developing and implementing high-resolution mapping methods.
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Affiliation(s)
- Kyra T Newmaster
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
| | - Fae A Kronman
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
| | - Yuan-Ting Wu
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
| | - Yongsoo Kim
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
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22
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Das S, Abou-Haidar R, Rabalais H, Sun SDLW, Rosli Z, Chatpar K, Boivin MN, Tabatabaei M, Rogers C, Legault M, Lo D, Degroot C, Dagher A, Dyke SOM, Durcan TM, Seyller A, Doyon J, Poupon V, Fon EA, Genge A, Rouleau GA, Karamchandani J, Evans AC. The C-BIG Repository: an Institution-Level Open Science Platform. Neuroinformatics 2022; 20:139-153. [PMID: 34003431 PMCID: PMC9537233 DOI: 10.1007/s12021-021-09516-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/16/2021] [Indexed: 01/07/2023]
Abstract
In January 2016, the Montreal Neurological Institute-Hospital (The Neuro) declared itself an Open Science organization. This vision extends beyond efforts by individual scientists seeking to release individual datasets, software tools, or building platforms that provide for the free dissemination of such information. It involves multiple stakeholders and an infrastructure that considers governance, ethics, computational resourcing, physical design, workflows, training, education, and intra-institutional reporting structures. The C-BIG repository was built in response as The Neuro's institutional biospecimen and clinical data repository, and collects biospecimens as well as clinical, imaging, and genetic data from patients with neurological disease and healthy controls. It is aimed at helping scientific investigators, in both academia and industry, advance our understanding of neurological diseases and accelerate the development of treatments. As many neurological diseases are quite rare, they present several challenges to researchers due to their small patient populations. Overcoming these challenges required the aggregation of datasets from various projects and locations. The C-BIG repository achieves this goal and stands as a scalable working model for institutions to collect, track, curate, archive, and disseminate multimodal data from patients. In November 2020, a Registered Access layer was made available to the wider research community at https://cbigr-open.loris.ca , and in May 2021 fully open data will be released to complement the Registered Access data. This article outlines many of the aspects of The Neuro's transition to Open Science by describing the data to be released, C-BIG's full capabilities, and the design aspects that were implemented for effective data sharing.
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Affiliation(s)
- Samir Das
- grid.14709.3b0000 0004 1936 8649McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada ,grid.416102.00000 0004 0646 3639Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada ,grid.416102.00000 0004 0646 3639McConnell Brain Imaging Centre, Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada
| | - Rida Abou-Haidar
- grid.14709.3b0000 0004 1936 8649McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada ,grid.416102.00000 0004 0646 3639Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada
| | - Henri Rabalais
- grid.14709.3b0000 0004 1936 8649McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada ,grid.416102.00000 0004 0646 3639Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada
| | - Sonia Denise Lai Wing Sun
- grid.416102.00000 0004 0646 3639Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada ,grid.14709.3b0000 0004 1936 8649Clinical Research Unit, McGill University, Montreal, Quebec, Canada
| | - Zaliqa Rosli
- grid.14709.3b0000 0004 1936 8649McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada ,grid.416102.00000 0004 0646 3639Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada
| | - Krishna Chatpar
- grid.14709.3b0000 0004 1936 8649McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada ,grid.416102.00000 0004 0646 3639Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada
| | - Marie-Noëlle Boivin
- grid.416102.00000 0004 0646 3639Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada
| | - Mahdieh Tabatabaei
- grid.416102.00000 0004 0646 3639Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada
| | - Christine Rogers
- grid.14709.3b0000 0004 1936 8649McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada ,grid.416102.00000 0004 0646 3639Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada
| | - Melanie Legault
- grid.14709.3b0000 0004 1936 8649McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada ,grid.416102.00000 0004 0646 3639Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada
| | - Derek Lo
- grid.14709.3b0000 0004 1936 8649McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada ,grid.416102.00000 0004 0646 3639Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada
| | - Clotilde Degroot
- grid.416102.00000 0004 0646 3639Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada
| | - Alain Dagher
- grid.416102.00000 0004 0646 3639Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada ,grid.416102.00000 0004 0646 3639McConnell Brain Imaging Centre, Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada
| | - Stephanie O. M. Dyke
- grid.14709.3b0000 0004 1936 8649McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada ,grid.416102.00000 0004 0646 3639Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada ,grid.14709.3b0000 0004 1936 8649Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Thomas M. Durcan
- grid.14709.3b0000 0004 1936 8649Clinical Research Unit, McGill University, Montreal, Quebec, Canada ,grid.416102.00000 0004 0646 3639Early Drug Discovery Unit (EDDU), Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada
| | - Annabel Seyller
- grid.416102.00000 0004 0646 3639Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada
| | - Julien Doyon
- grid.416102.00000 0004 0646 3639Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada ,grid.416102.00000 0004 0646 3639McConnell Brain Imaging Centre, Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada
| | - Viviane Poupon
- grid.416102.00000 0004 0646 3639Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada
| | - Edward A. Fon
- grid.416102.00000 0004 0646 3639Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada ,grid.14709.3b0000 0004 1936 8649Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Angela Genge
- grid.416102.00000 0004 0646 3639Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada ,grid.14709.3b0000 0004 1936 8649Clinical Research Unit, McGill University, Montreal, Quebec, Canada
| | - Guy A. Rouleau
- grid.416102.00000 0004 0646 3639Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada
| | - Jason Karamchandani
- grid.416102.00000 0004 0646 3639Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada ,grid.14709.3b0000 0004 1936 8649Clinical Research Unit, McGill University, Montreal, Quebec, Canada
| | - Alan C. Evans
- grid.14709.3b0000 0004 1936 8649McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada ,grid.416102.00000 0004 0646 3639Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada ,grid.416102.00000 0004 0646 3639McConnell Brain Imaging Centre, Montreal Neurological Institute-Hospital, Montreal, Quebec, Canada
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23
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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: 3] [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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24
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Barch DM, Norris SMP, Martone ME. Using Cloud-Based Resources for Neuroimaging Research: A Practical Approach. NAM Perspect 2021; 2021:202107b. [PMID: 34611606 DOI: 10.31478/202107b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Affiliation(s)
| | | | - Maryann E Martone
- University of California, San Diego, and the International Neuroinformatics Coordinating Facility
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