*Result*: Sharing Neuroimaging Data with Squirrel - A Relational Data Format to Store Raw to Analyzed Data and Everything in Between.
Title:
Sharing Neuroimaging Data with Squirrel - A Relational Data Format to Store Raw to Analyzed Data and Everything in Between.
Authors:
Book GA; Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT, USA. gregory.book@hhchealth.org., Calhoun VD; School of Electrical and Computer Engineering, Georgia Tech, Atlanta, GA, USA., Stevens MC; Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT, USA.; Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA., Pearlson GD; Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT, USA.; Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
Source:
Neuroinformatics [Neuroinformatics] 2025 Jul 03; Vol. 23 (3), pp. 37. Date of Electronic Publication: 2025 Jul 03.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Humana Press, Inc Country of Publication: United States NLM ID: 101142069 Publication Model: Electronic Cited Medium: Internet ISSN: 1559-0089 (Electronic) Linking ISSN: 15392791 NLM ISO Abbreviation: Neuroinformatics Subsets: MEDLINE
Imprint Name(s):
Original Publication: Totowa, NJ : Humana Press, Inc., c2003-
MeSH Terms:
References:
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Bao, S., et al. (2022). Integrating the BIDS neuroimaging data format and workflow optimization for large-scale medical image analysis. Journal of Digital Imaging, 35(6), 1576–1589. (PMID: 10.1007/s10278-022-00679-8359227009712842)
Bartnik, A., et al. (2024). An Automated Tool to Classify and Transform Unstructured MRI Data into BIDS Datasets. Neuroinformatics.
Book, G. A., et al. (2013). Neuroinformatics Database (NiDB)–a modular, portable database for the storage, analysis, and sharing of neuroimaging data. Neuroinformatics, 11(4), 495–505. (PMID: 10.1007/s12021-013-9194-123912507)
Book, G. A., et al. (2016). Neuroimaging data sharing on the neuroinformatics database platform. NeuroImage, 124(Pt B), 1089–1092. (PMID: 10.1016/j.neuroimage.2015.04.02225888923)
Bourget, M. H., et al. (2022). Microscopy-BIDS: An extension to the brain imaging data structure for microscopy data. Frontiers in Neuroscience, 16, 871228. (PMID: 10.3389/fnins.2022.871228355168119063519)
Clement, P., et al. (2022). ASL-BIDS, the brain imaging data structure extension for arterial spin labeling. Sci Data, 9(1), 543. (PMID: 10.1038/s41597-022-01615-9360682319448788)
Covitz, S., et al. (2022). Curation of BIDS (CuBIDS): A workflow and software package for streamlining reproducible curation of large BIDS datasets. NeuroImage, 263, 119609. (PMID: 10.1016/j.neuroimage.2022.11960936064140)
Demuru, M., et al. (2022). A practical workflow for organizing clinical intraoperative and long-term iEEG data in BIDS. Neuroinformatics, 20(3), 727–736. (PMID: 10.1007/s12021-022-09567-6352448559440951)
Di Martino, A., et al. (2014). The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19(6), 659–667. (PMID: 10.1038/mp.2013.7823774715)
Fostering reproducible fMRI research (2017). Nature Communications, 8, 14748.
Ganz, M., & Poldrack, R. A. (2023). Data sharing in neuroimaging: Experiences from the BIDS project. Nature Reviews Neuroscience, 24(12), 729–730. (PMID: 10.1038/s41583-023-00762-137875580)
Gehrke, L., et al. (2024). Visuo-haptic prediction errors: A multimodal dataset (EEG, motion) in BIDS format indexing mismatches in haptic interaction. Front Neuroergon, 5, 1411305. (PMID: 10.3389/fnrgo.2024.14113053890390511188399)
Gorgolewski, K. J., et al. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data, 3, 160044. (PMID: 10.1038/sdata.2016.44273265424978148)
Gorgolewski, K. J., et al. (2017). BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLoS Computational Biology, 13(3), e1005209. (PMID: 10.1371/journal.pcbi.1005209282782285363996)
Halchenko, Y. O., et al. (2021). DataLad: Distributed system for joint management of code, data, and their relationship. J Open Source Softw, 6(63), 3262. (PMID: 10.21105/joss.032623946914711514317)
Hatlestad-Hall, C., Rygvold, T. W., & Andersson, S. (2022). BIDS-structured resting-state electroencephalography (EEG) data extracted from an experimental paradigm. Data in Brief, 45, 108647. (PMID: 10.1016/j.dib.2022.108647364259649679478)
Holdgraf, C., et al. (2019). iEEG-BIDS, extending the brain imaging data structure specification to human intracranial electrophysiology. Sci Data, 6(1), 102. (PMID: 10.1038/s41597-019-0105-7312394386592874)
Jeung, S., et al. (2024). Motion-BIDS: An extension to the brain imaging data structure to organize motion data for reproducible research. Sci Data, 11(1), 716. (PMID: 10.1038/s41597-024-03559-83895607111219788)
Jou, J. H., & Fischer, P. C. (1982). The complexity of recognizing 3NF relation schemes. Information Processing Letters, 14(4), 187–190. (PMID: 10.1016/0020-0190(82)90034-5)
Karakuzu, A., et al. (2022). qMRI-BIDS: An extension to the brain imaging data structure for quantitative magnetic resonance imaging data. Sci Data, 9(1), 517. (PMID: 10.1038/s41597-022-01571-4360024449402561)
Kim, Y., et al. (2023) BrainSuite BIDS App: Containerized Workflows for MRI Analysis. bioRxiv.
Lang, C., et al. (2023). DICOM(R) integrated EEG data: A first clinical implementation of the new DICOM standard for neurophysiology data. Clinical Neurophysiology, 155, 107–112. (PMID: 10.1016/j.clinph.2023.07.00837634966)
Larobina, M., & Murino, L. (2014). Medical image file formats. Journal of Digital Imaging, 27(2), 200–206. (PMID: 10.1007/s10278-013-9657-924338090)
Moreau, C. A., et al. (2020). The genetics-BIDS extension: Easing the search for genetic data associated with human brain imaging. Gigascience, 9(10).
Mueller, S. G., et al. (2005). The Alzheimer’s disease neuroimaging initiative. Neuroimaging Clin N Am, 15(4), 869–77. xi-xii. (PMID: 10.1016/j.nic.2005.09.008164434972376747)
NEMA. (2022). The DICOM Standard. Available from: https://www.dicomstandard.org/current .
Nichols, T. E., et al. (2017). Best practices in data analysis and sharing in neuroimaging using MRI. Nature Neuroscience, 20(3), 299–303. (PMID: 10.1038/nn.4500282308465685169)
Nichols, E. S., et al. (2023). Funcmasker-flex: An Automated BIDS-App for Brain Segmentation of Human Fetal Functional MRI data. Neuroinformatics, 21(3), 565–573. (PMID: 10.1007/s12021-023-09629-337000360)
Niso, G., et al. (2018). MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Sci Data, 5, 180110. (PMID: 10.1038/sdata.2018.110299170166007085)
Niso, G., et al. (2022). Open and reproducible neuroimaging: From study inception to publication. NeuroImage, 263, 119623. (PMID: 10.1016/j.neuroimage.2022.11962336100172)
Norgaard, M., et al. (2022). PET-BIDS, an extension to the brain imaging data structure for positron emission tomography. Sci Data, 9(1), 65. (PMID: 10.1038/s41597-022-01164-1352368468891322)
Pernet, C. R., et al. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Sci Data, 6(1), 103. (PMID: 10.1038/s41597-019-0104-8312394356592877)
Poldrack, R. A., et al. (2024). The Past, Present, and Future of the Brain Imaging Data Structure (BIDS). ArXiv.
Rorden, C., & Brett, M. (2000). Stereotaxic display of brain lesions. Behavioural Neurology, 12(4), 191–200. (PMID: 10.1155/2000/42171911568431)
Rovai, A., et al. (2024). CVRmap-a complete cerebrovascular reactivity mapping post-processing BIDS toolbox. Science and Reports, 14(1), 7252. (PMID: 10.1038/s41598-024-57572-3)
Saborit-Torres, J. M., et al. (2022). Beyond the brain: MIDS extends BIDS to multiple modalities and anatomical regions. Stud Health Technol Inform, 294, 413–414. (PMID: 35612110)
Standardization, I. O. f. (2015). Information Technology, Document Container File., ISO/IEC. p. 16.
The Brain Imaging Data Structure. (2022). Available from: https://bids-specification.readthedocs.io/en/stable/ .
Thompson, P. M., et al. (2014). The ENIGMA Consortium: Large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging and Behavior, 8(2), 153–182. (PMID: 10.1007/s11682-013-9269-5243993584008818)
Torabian, S., et al. (2023). The PyMVPA BIDS-App: A robust multivariate pattern analysis pipeline for fMRI data. Frontiers in Neuroscience, 17, 1233416. (PMID: 10.3389/fnins.2023.12334163769412310483824)
Vanden Bulcke, C., et al. (2022). BMAT: An open-source BIDS managing and analysis tool. Neuroimage Clin, 36, 103252. (PMID: 10.1016/j.nicl.2022.103252364513579723304)
Yarkoni, T., et al. (2019). PyBIDS: Python tools for BIDS datasets. J Open Source Softw, 4(40), 1294. (PMID: 10.21105/joss.01294327759557409983)
Zhao, C., et al. (2023). A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps. bioRxiv.
Bao, S., et al. (2022). Integrating the BIDS neuroimaging data format and workflow optimization for large-scale medical image analysis. Journal of Digital Imaging, 35(6), 1576–1589. (PMID: 10.1007/s10278-022-00679-8359227009712842)
Bartnik, A., et al. (2024). An Automated Tool to Classify and Transform Unstructured MRI Data into BIDS Datasets. Neuroinformatics.
Book, G. A., et al. (2013). Neuroinformatics Database (NiDB)–a modular, portable database for the storage, analysis, and sharing of neuroimaging data. Neuroinformatics, 11(4), 495–505. (PMID: 10.1007/s12021-013-9194-123912507)
Book, G. A., et al. (2016). Neuroimaging data sharing on the neuroinformatics database platform. NeuroImage, 124(Pt B), 1089–1092. (PMID: 10.1016/j.neuroimage.2015.04.02225888923)
Bourget, M. H., et al. (2022). Microscopy-BIDS: An extension to the brain imaging data structure for microscopy data. Frontiers in Neuroscience, 16, 871228. (PMID: 10.3389/fnins.2022.871228355168119063519)
Clement, P., et al. (2022). ASL-BIDS, the brain imaging data structure extension for arterial spin labeling. Sci Data, 9(1), 543. (PMID: 10.1038/s41597-022-01615-9360682319448788)
Covitz, S., et al. (2022). Curation of BIDS (CuBIDS): A workflow and software package for streamlining reproducible curation of large BIDS datasets. NeuroImage, 263, 119609. (PMID: 10.1016/j.neuroimage.2022.11960936064140)
Demuru, M., et al. (2022). A practical workflow for organizing clinical intraoperative and long-term iEEG data in BIDS. Neuroinformatics, 20(3), 727–736. (PMID: 10.1007/s12021-022-09567-6352448559440951)
Di Martino, A., et al. (2014). The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19(6), 659–667. (PMID: 10.1038/mp.2013.7823774715)
Fostering reproducible fMRI research (2017). Nature Communications, 8, 14748.
Ganz, M., & Poldrack, R. A. (2023). Data sharing in neuroimaging: Experiences from the BIDS project. Nature Reviews Neuroscience, 24(12), 729–730. (PMID: 10.1038/s41583-023-00762-137875580)
Gehrke, L., et al. (2024). Visuo-haptic prediction errors: A multimodal dataset (EEG, motion) in BIDS format indexing mismatches in haptic interaction. Front Neuroergon, 5, 1411305. (PMID: 10.3389/fnrgo.2024.14113053890390511188399)
Gorgolewski, K. J., et al. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data, 3, 160044. (PMID: 10.1038/sdata.2016.44273265424978148)
Gorgolewski, K. J., et al. (2017). BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLoS Computational Biology, 13(3), e1005209. (PMID: 10.1371/journal.pcbi.1005209282782285363996)
Halchenko, Y. O., et al. (2021). DataLad: Distributed system for joint management of code, data, and their relationship. J Open Source Softw, 6(63), 3262. (PMID: 10.21105/joss.032623946914711514317)
Hatlestad-Hall, C., Rygvold, T. W., & Andersson, S. (2022). BIDS-structured resting-state electroencephalography (EEG) data extracted from an experimental paradigm. Data in Brief, 45, 108647. (PMID: 10.1016/j.dib.2022.108647364259649679478)
Holdgraf, C., et al. (2019). iEEG-BIDS, extending the brain imaging data structure specification to human intracranial electrophysiology. Sci Data, 6(1), 102. (PMID: 10.1038/s41597-019-0105-7312394386592874)
Jeung, S., et al. (2024). Motion-BIDS: An extension to the brain imaging data structure to organize motion data for reproducible research. Sci Data, 11(1), 716. (PMID: 10.1038/s41597-024-03559-83895607111219788)
Jou, J. H., & Fischer, P. C. (1982). The complexity of recognizing 3NF relation schemes. Information Processing Letters, 14(4), 187–190. (PMID: 10.1016/0020-0190(82)90034-5)
Karakuzu, A., et al. (2022). qMRI-BIDS: An extension to the brain imaging data structure for quantitative magnetic resonance imaging data. Sci Data, 9(1), 517. (PMID: 10.1038/s41597-022-01571-4360024449402561)
Kim, Y., et al. (2023) BrainSuite BIDS App: Containerized Workflows for MRI Analysis. bioRxiv.
Lang, C., et al. (2023). DICOM(R) integrated EEG data: A first clinical implementation of the new DICOM standard for neurophysiology data. Clinical Neurophysiology, 155, 107–112. (PMID: 10.1016/j.clinph.2023.07.00837634966)
Larobina, M., & Murino, L. (2014). Medical image file formats. Journal of Digital Imaging, 27(2), 200–206. (PMID: 10.1007/s10278-013-9657-924338090)
Moreau, C. A., et al. (2020). The genetics-BIDS extension: Easing the search for genetic data associated with human brain imaging. Gigascience, 9(10).
Mueller, S. G., et al. (2005). The Alzheimer’s disease neuroimaging initiative. Neuroimaging Clin N Am, 15(4), 869–77. xi-xii. (PMID: 10.1016/j.nic.2005.09.008164434972376747)
NEMA. (2022). The DICOM Standard. Available from: https://www.dicomstandard.org/current .
Nichols, T. E., et al. (2017). Best practices in data analysis and sharing in neuroimaging using MRI. Nature Neuroscience, 20(3), 299–303. (PMID: 10.1038/nn.4500282308465685169)
Nichols, E. S., et al. (2023). Funcmasker-flex: An Automated BIDS-App for Brain Segmentation of Human Fetal Functional MRI data. Neuroinformatics, 21(3), 565–573. (PMID: 10.1007/s12021-023-09629-337000360)
Niso, G., et al. (2018). MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Sci Data, 5, 180110. (PMID: 10.1038/sdata.2018.110299170166007085)
Niso, G., et al. (2022). Open and reproducible neuroimaging: From study inception to publication. NeuroImage, 263, 119623. (PMID: 10.1016/j.neuroimage.2022.11962336100172)
Norgaard, M., et al. (2022). PET-BIDS, an extension to the brain imaging data structure for positron emission tomography. Sci Data, 9(1), 65. (PMID: 10.1038/s41597-022-01164-1352368468891322)
Pernet, C. R., et al. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Sci Data, 6(1), 103. (PMID: 10.1038/s41597-019-0104-8312394356592877)
Poldrack, R. A., et al. (2024). The Past, Present, and Future of the Brain Imaging Data Structure (BIDS). ArXiv.
Rorden, C., & Brett, M. (2000). Stereotaxic display of brain lesions. Behavioural Neurology, 12(4), 191–200. (PMID: 10.1155/2000/42171911568431)
Rovai, A., et al. (2024). CVRmap-a complete cerebrovascular reactivity mapping post-processing BIDS toolbox. Science and Reports, 14(1), 7252. (PMID: 10.1038/s41598-024-57572-3)
Saborit-Torres, J. M., et al. (2022). Beyond the brain: MIDS extends BIDS to multiple modalities and anatomical regions. Stud Health Technol Inform, 294, 413–414. (PMID: 35612110)
Standardization, I. O. f. (2015). Information Technology, Document Container File., ISO/IEC. p. 16.
The Brain Imaging Data Structure. (2022). Available from: https://bids-specification.readthedocs.io/en/stable/ .
Thompson, P. M., et al. (2014). The ENIGMA Consortium: Large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging and Behavior, 8(2), 153–182. (PMID: 10.1007/s11682-013-9269-5243993584008818)
Torabian, S., et al. (2023). The PyMVPA BIDS-App: A robust multivariate pattern analysis pipeline for fMRI data. Frontiers in Neuroscience, 17, 1233416. (PMID: 10.3389/fnins.2023.12334163769412310483824)
Vanden Bulcke, C., et al. (2022). BMAT: An open-source BIDS managing and analysis tool. Neuroimage Clin, 36, 103252. (PMID: 10.1016/j.nicl.2022.103252364513579723304)
Yarkoni, T., et al. (2019). PyBIDS: Python tools for BIDS datasets. J Open Source Softw, 4(40), 1294. (PMID: 10.21105/joss.01294327759557409983)
Zhao, C., et al. (2023). A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps. bioRxiv.
Contributed Indexing:
Keywords: BIDS; Data format; Data sharing; Neuroimaging; Neuroinformatics; Squirrel
Entry Date(s):
Date Created: 20250703 Date Completed: 20250703 Latest Revision: 20251016
Update Code:
20260130
DOI:
10.1007/s12021-025-09732-7
PMID:
40610663
Database:
MEDLINE
*Further Information*
*Reproducibility of neuroimaging analyses and aggregation of heterogenous datasets are significant challenges in human subjects imaging research. This stems in part from a lack of an easy to use and universal data format that encompasses all steps of neuroimaging. The BIDS format has become widely adopted, however it is increasingly complex to implement as features are added, with the documentation now exceeding 500 pages. As such, there is a need for standards that can handle the complexity of the data while minimizing the complexity of the format. Here we present a simple but generalizable data sharing specification, called the squirrel format (not related to the squirrel programming language), to share imaging data in a simple, but flexible, specification. It is so named because squirrels are effective at storing significant quantities of food and knowing exactly where and when to find it. The design objectives of the format specification are to 1) store subject information, experimental parameters, raw data, analyzed data, and analysis methods 2) organize data in a human-readable hierarchy 3) enable easy sharing and dissemination of data packages. We developed a relational hierarchy with a structured representation of all steps of neuroimaging data collection and analysis, and a generalizable specification to store any modality of neuroimaging data, which satisfies the design objectives. Additionally, redundancy is minimized by using relational database principles. The specification allows all research data to be classified into one of ten object types, thus simplifying the sharing of neuroimaging data. Like how squirrels employ 'chunking', the squirrel format chunks data into a manageable number of object types. The squirrel format was developed to share neuroimaging data but can be generalized to share any imaging research.
(© 2025. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)*
*Declarations. Ethical Approval: N/A. Competing Interests: The authors declare no competing interests.*