Abstract
Neuroimaging research requires purpose-built analysis software, which is challenging to install and may produce different results across computing environments. The community-oriented, open-source Neurodesk platform (https://www.neurodesk.org/) harnesses a comprehensive and growing suite of neuroimaging software containers. Neurodesk includes a browser-accessible virtual desktop, command-line interface and computational notebook compatibility, allowing for accessible, flexible, portable and fully reproducible neuroimaging analysis on personal workstations, high-performance computers and the cloud.
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Data availability
The data that support the findings of the case study are available from the ICBM database (https://www.loni.usc.edu/). There are restrictions that apply to the availability of these data, which were used under approved permission for the current study, and thus are not publicly available but are available from ICBM upon request. Source data are provided with this paper.
Code availability
The code for this project is publicly available on GitHub, across multiple repositories under the https://github.com/NeuroDesk/ organization. It has also been archived on Zenodo at https://doi.org/10.5281/zenodo.8053090. The code is licensed under the MIT License.
All stages of development, from the initial conception as a hackathon project, through to the most current iteration of Neurodesk, with up-to-date community-built Neurocontainer recipes, are documented publicly across the project’s GitHub repository and the platform’s website; which contains descriptions of how code is organized on the GitHub repository, and how to contribute to the project (https://www.neurodesk.org/).
Any issues can be logged at https://github.com/orgs/NeuroDesk/discussions/. Contributions can be made by any community member with a GitHub account and the eagerness to create pull requests.
References
Halchenko, Y. & Hanke, M. Open is not enough. let’s take the next step: an integrated, community-driven computing platform for neuroscience. Front. Neuroinform. 6, 22 (2012).
Hanke, M. & Halchenko, Y. Neuroscience runs on GNU/Linux. Front. Neuroinform. 5, 8 (2011).
Niso, G. et al. Open and reproducible neuroimaging: from study inception to publication. NeuroImage 263, 119623 (2022).
Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).
Kurtzer, G. M., Sochat, V. & Bauer, M. W. Singularity: scientific containers for mobility of compute. PLoS ONE 12, e0177459 (2017).
Van Gorp, P. & Mazanek, S. SHARE: a web portal for creating and sharing executable research papers. Procedia Comput. Sci. 4, 589–597 (2011).
Poline, J. -B. et al. Is neuroscience FAIR? a call for collaborative standardisation of neuroscience data. Neuroinformatics 20, 507–512 (2022).
Silberzahn, R. et al. Many analysts, one data set: making transparent how variations in analytic choices affect results. Adv. Methods Pract. Psychol. Sci. 1, 337–356 (2018).
Tapera, T. M. et al. FlywheelTools: data curation and manipulation on the Flywheel platform. Front. Neuroinform. 15, 678403 (2021).
Routier, A. et al. Clinica: an open-source software platform for reproducible clinical neuroscience studies. Front. Neuroinform. 15, 689675 (2021).
Abe, T. et al. Neuroscience cloud analysis as a service: an open-source platform for scalable, reproducible data analysis. Neuron 110, 2771–2789 (2022).
Goodman, S. N., Fanelli, D. & Ioannidis, J. P. A. What does research reproducibility mean? Sci. Transl. Med. 8, 341ps12–341ps12 (2016).
Nosek, B. A. et al. Replicability, robustness, and reproducibility in psychological science. Annu. Rev. Psychol. 73, 719–748 (2022).
Plesser, H. E. Reproducibility vs. replicability: a brief history of a confused terminology. Front. Neuroinform. 11, 76 (2018).
Nosek, B. A. et al. Promoting an open research culture. Science 348, 1422–1425 (2015).
Boettiger, C. An introduction to Docker for reproducible research. ACM SIGOPS Oper. Syst. Rev. 49, 71–79 (2015).
Trunov, A. S., Voronova, L. I., Voronov, V. I. & Ayrapetov, D. P. Container cluster model development for legacy applications integration in scientific software system. in 2018 IEEE International Conference ‘Quality Management, Transport and Information Security, Information Technologies’ (IT QM IS) 815–819 https://doi.org/10.1109/ITMQIS.2018.8525120 (2018).
Thomas, T. et al. Jupyter Notebooks—a publishing format for reproducible computational workflows. In Positioning and Power in Academic Publishing: Players, Agents and Agendas (eds Loizides, F. & Schmid, B.) 87–90 (IOS Press, 2016).
Glatard, T. et al. Reproducibility of neuroimaging analyses across operating systems. Front. Neuroinform. 9, 12 (2015).
Gronenschild, E. H. et al. The effects of FreeSurfer version, workstation type, and Macintosh operating system version on anatomical volume and cortical thickness measurements. PLoS ONE 7, e38234 (2012).
Krefting, D. et al. Reliability of quantitative neuroimage analysis using freesurfer in distributed environments. In MICCAI Workshop on High-Performance and Distributed Computing for Medical Imaging (2011).
Fischl, B. FreeSurfer. NeuroImage 62, 774–781 (2012).
DuPre, E. et al. Beyond advertising: new infrastructures for publishing integrated research objects. PLoS Comput. Biol. 18, e1009651 (2022).
Karakuzu, A. et al. NeuroLibre: a preprint server for full-fledged reproducible neuroscience. Preprint at OSF https://doi.org/10.31219/osf.io/h89js (2022).
Gau, R. et al. Brainhack: developing a culture of open, inclusive, community-driven neuroscience. Neuron 109, 1769–1775 (2021).
Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 46, W537–W544 (2018).
Sinha, A. et al. Comp-NeuroFedora, a free/open source operating system for computational neuroscience: download, install, research. BMC Neurosci. 21, 1 (2020).
Hayashi, S. et al. brainlife.io: a decentralized and open source cloud platform to support neuroscience research. Preprint at arXiv https://doi.org/10.48550/arXiv.2306.02183 (2023).
Gorgolewski, K. J. et al. BIDS apps: improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLoS Comput. Biol. 13, e1005209 (2017).
Herrick, R. et al. XNAT Central: open sourcing imaging research data. NeuroImage 124, 1093–1096 (2016).
Staubitz, T., Klement, H., Teusner, R., Renz, J. & Meinel, C. CodeOcean—a versatile platform for practical programming excercises in online environments. In 2016 IEEE Global Engineering Education Conference (EDUCON) 314–323 https://doi.org/10.1109/EDUCON.2016.7474573 (2016).
Sherif, T. et al. CBRAIN: a web-based, distributed computing platform for collaborative neuroimaging research. Front. Neuroinform. 8, 54 (2014).
da Veiga Leprevost, F. et al. BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics 33, 2580–2582 (2017).
Blomer, J. et al. Micro-CernVM: slashing the cost of building and deploying virtual machines. J. Phys. Conf. Ser. 513, 032009 (2014).
Jupyter, P. et al. Binder 2.0—reproducible, interactive, sharable environments for science at scale. in Proceedings of the 17th Python in Science Conference 113–120 https://doi.org/10.25080/Majora-4af1f417-011 (2018).
Atilgan, H. et al. Functional relevance of the extrastriate body area for visual and haptic object recognition: a preregistered fMRI-guided TMS study. Cereb. Cortex Commun. 4, tgad005 (2023).
Chang, J. et al. Open-source hypothalamic-ForniX (OSHy-X) atlases and segmentation tool for 3T and 7T. J. Open Source Softw. 7, 4368 (2022).
Stewart, A. W. et al. QSMxT: robust masking and artifact reduction for quantitative susceptibility mapping. Magn. Reson. Med. 87, 1289–1300 (2022).
Biondetti, E. et al. Multi-echo quantitative susceptibility mapping: how to combine echoes for accuracy and precision at 3 Tesla. Magn. Reson. Med. 88, 2101–2116 (2022).
Kaczmarzyk, J. et al. ReproNim/neurodocker: 0.9.5. https://doi.org/10.5281/zenodo.7929032 (2023).
Gorgolewski, K. et al. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. Front. Neuroinform. 5, 13 (2011).
Adebimpe, A. et al. ASLPrep: a platform for processing of arterial spin labeled MRI and quantification of regional brain perfusion. Nat. Methods 19, 683–686 (2022).
Esteban, O. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat. Methods 16, 111–116 (2019).
Esteban, O. et al. MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PLoS ONE 12, e0184661 (2017).
Li, X., Morgan, P. S., Ashburner, J., Smith, J. & Rorden, C. The first step for neuroimaging data analysis: DICOM to NIfTI conversion. J. Neurosci. Methods 264, 47–56 (2016).
Zwiers, M. P., Moia, S. & Oostenveld, R. BIDScoin: a user-friendly application to convert source data to brain imaging data structure. Front. Neuroinform. 15, 770608 (2022).
Gorgolewski, K. J. et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci. Data 3, 160044 (2016).
Yushkevich, P. A. et al. User-guided segmentation of multi-modality medical imaging datasets with ITK-SNAP. Neuroinformatics 17, 83–102 (2019).
Wang, R., Benner, T., Sorensen, A. G. & Wedeen, V. J. Diffusion toolkit: a software package for diffusion imaging data processing and tractography. Proc. Intl Soc. Mag. Reson. Med. 15, 3720 (2007).
Yeh, F. -C. Population-based tract-to-region connectome of the human brain and its hierarchical topology. Nat. Commun. 13, 4933 (2022).
Tournier, J. -D., Calamante, F. & Connelly, A. MRtrix: diffusion tractography in crossing fiber regions. Int. J. Imaging Syst. Technol. 22, 53–66 (2012).
Pallast, N. et al. Processing pipeline for atlas-based imaging data analysis of structural and functional mouse brain MRI (AIDAmri). Front. Neuroinform. 13, 42 (2019).
Desrosiers-Gregoire, G. et al. Rodent Automated Bold Improvement of EPI Sequences (RABIES): a standardized image processing and data quality platform for rodent fMRI. Preprint at bioRxiv https://doi.org/10.1101/2022.08.20.504597 (2022).
Hangel, G. et al. Ultra-high resolution brain metabolite mapping at 7T by short-TR Hadamard-encoded FID-MRSI. NeuroImage 168, 199–210 (2018).
Cox, R. W. AFNI: what a long strange trip it’s been. NeuroImage 62, 743–747 (2012).
Avants, B. B., Tustison, N. & Johnson, H. Advanced Normalization Tools (ANTS). Insight J. 2, 1–35 (2009).
Wisse, L. E. M. et al. Automated hippocampal subfield segmentation at 7T MRI. Am. J. Neuroradiol. 37, 1050–1057 (2016).
Gaser, C. et al. CAT—a computational anatomy toolbox for the analysis of structural MRI data. Preprint at bioRxiv https://doi.org/10.1101/2022.06.11.495736 (2022).
Eckstein, K. et al. Improved susceptibility weighted imaging at ultra-high field using bipolar multi-echo acquisition and optimized image processing: CLEAR-SWI. NeuroImage 237, 118175 (2021).
Whitfield-Gabrieli, S. & Nieto-Castanon, A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect 2, 125–141 (2012).
Marcus, D. S. et al. Human Connectome Project informatics: quality control, database services, and data visualization. NeuroImage 80, 202–219 (2013).
Estrada, S. et al. FatSegNet: a fully automated deep learning pipeline for adipose tissue segmentation on abdominal dixon MRI. Magn. Reson. Med. 83, 1471–1483 (2020).
Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W. & Smith, S. M. FSL. NeuroImage 62, 782–790 (2012).
Isensee, F. et al. Automated brain extraction of multisequence MRI using artificial neural networks. Hum. Brain Mapp. 40, 4952–4964 (2019).
Shaw, T., York, A., Ziaei, M., Barth, M. & Bollmann, S. Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) using multi-contrast MRI. NeuroImage 218, 116798 (2020).
Huber, L. R. et al. LayNii: a software suite for layer-fMRI. NeuroImage 237, 118091 (2021).
Vincent, R. D. et al. MINC 2.0: a flexible format for multi-modal images. Front. Neuroinformatics 10, 35 (2016).
Grussu, F. et al. Multi-parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising. NeuroImage 217, 116884 (2020).
Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M. & Nichols, T. E. Permutation inference for the general linear model. NeuroImage 92, 381–397 (2014).
Kasper, L. et al. The PhysIO toolbox for modeling physiological noise in fMRI data. J. Neurosci. Methods 276, 56–72 (2017).
Dymerska, B. et al. Phase unwrapping with a rapid opensource minimum spanning tree algorithm (ROMEO). Magn. Reson. Med. 85, 2294–2308 (2021).
Fedorov, A. et al. 3D slicer as an image computing platform for the quantitative imaging network. Magn. Reson. Imaging 30, 1323–1341 (2012).
De Leener, B. et al. SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data. NeuroImage 145, 24–43 (2017).
Ashburner, J. Computational anatomy with the SPM software. Magn. Reson. Imaging 27, 1163–1174 (2009).
Langkammer, C. et al. Fast quantitative susceptibility mapping using 3D EPI and total generalized variation. NeuroImage 111, 622–630 (2015).
Klein, S., Staring, M., Murphy, K., Viergever, M. A. & Pluim, J. elastix: a toolbox for intensity-based medical image Registration. IEEE Trans. Med. Imaging 29, 196–205 (2010).
Shamonin, D. et al. Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer’s disease. Front. Neuroinform. 7, 50 (2014).
Civier, O., Sourty, M. & Calamante, F. MFCSC: novel method to calculate mismatch between functional and structural brain connectomes, and its application for detecting hemispheric functional specialisations. Sci. Rep. 13, 3485 (2023).
Tadel, F., Baillet, S., Mosher, J. C., Pantazis, D. & Leahy, R. M. Brainstorm: a user-friendly application for MEG/EEG analysis. Comput. Intell. Neurosci. 2011, 879716 (2011).
Brunner, C., Delorme, A. & Makeig, S. Eeglab—an open source MATLAB toolbox for electrophysiological research. Biomed. Tech. 58, 1 (2013).
Oostenveld, R., Fries, P., Maris, E. & Schoffelen, J. -M. FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. 2011, 156869 (2011).
Gramfort, A. et al. MNE software for processing MEG and EEG data. NeuroImage 86, 446–460 (2014).
Brunner, C., Breitwieser, C. & Müller-Putz, G. R. Sigviewer and Signalserver—open source software projects for biosignal analysis. Biomed. Eng. Tech. 58, 1 (2013).
Ihaka, R. & Gentleman, R. R: a language for data analysis and graphics. J. Comput. Graph. Stat. 5, 299–314 (1996).
Ribeiro, F. L., Bollmann, S. & Puckett, A. M. Predicting the retinotopic organization of human visual cortex from anatomy using geometric deep learning. NeuroImage 244, 118624 (2021).
Mishra, P., Lehmkuhl, R., Srinivasan, A., Zheng, W. & Popa, R. A. Delphi: a cryptographic inference service for neural networks. In 29th USENIX Security Symposium (USENIX Security 20) 2505–2522 (2020).
Still, M. The definitive guide to ImageMagick. vol. 1 (Springer, 2006).
Rorden, C. & Brett, M. Stereotaxic display of brain lesions. Behav. Neurol. 12, 191–200 (2000).
Rorden, C. rordenlab/MRIcroGL: version 20-July-2022 (v1.2.20220720) https://doi.org/10.5281/ZENODO.7533834 (2022).
Vicory, J. et al. SlicerSALT: Shape AnaLysis Toolbox. In Shape in Medical Imaging (eds. Reuter, M. et al.) vol. 11167, 65–72 (Springer International Publishing, 2018).
Rorden, C. & Hanayik, T. neurolabusc/surf-ice: version 6-October-2021 (v1.0.20211006). https://doi.org/10.5281/ZENODO.7533772 (2021)
Bumgarner, J. R. & Nelson, R. J. Open-source analysis and visualization of segmented vasculature datasets with VesselVio. Cell Rep. Methods 2, 100189 (2022).
Cusack, R. et al. Automatic analysis (aa): efficient neuroimaging workflows and parallel processing using Matlab and XML. Front. Neuroinform. 8, 90 (2015).
Liem, F. & Gorgolewski, C. F. BIDS-Apps/baracus: v1.1.2. https://doi.org/10.5281/ZENODO.1018841 (2017).
Kim, Y. et al. BrainSuite BIDS App: containerized workflows for MRI analysis. Preprint at bioRxiv https://doi.org/10.1101/2023.03.14.532686 (2023).
Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage 80, 105–124 (2013).
Smith, S. M. et al. Resting-state fMRI in the Human Connectome Project. NeuroImage 80, 144–168 (2013).
Trott, O. & Olson, A. J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455–461 (2010).
Eberhardt, J., Santos-Martins, D., Tillack, A. F. & Forli, S. AutoDock Vina 1.2.0: new docking methods, expanded force field, and Python bindings. J. Chem. Inf. Model. 61, 3891–3898 (2021).
Acknowledgements
The ARDC invested in Neurodesk’s development through the Australian Electrophysiology Data Analytics Platform project (S.B., A.N., O.C., T.J. and R.S.). We thank Oracle for Research for providing Oracle Cloud credits and related cloud resources to support this project (S.B.) The University of Queensland funded the project via the Knowledge Exchange & Translation Fund and the UQ AI Collaboratory (S.B.). S.B., F.L.R. and A.W.S. acknowledge funding through an ARC Linkage grant (LP200301393). S.B. and A.W.S. acknowledge funding through the Australian Research Council Training Centre for Innovation in Biomedical Imaging Technology (IC170100035). This research was supported by use of the Nectar Research Cloud, a collaborative Australian research platform supported by the National Collaborative Research Infrastructure Strategy-funded ARDC. We acknowledge the facilities and scientific and technical assistance of the National Imaging Facility, a National Collaborative Research Infrastructure Strategy capability. A National Institutes of Health grant (P41EB019936) partially supported J.R.K. and S.S.G. Data collection and sharing for this project was provided by the International Consortium for Brain Mapping (ICBM; Principal Investigator: J. Mazziotta). ICBM funding was provided by the National Institute of Biomedical Imaging and BioEngineering. ICBM data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California. We thank I. C. D. Lenton, E. Cooper-Williams and Y. ‘Sam’ Peng for contributions to the first NeuroDesk precursor ‘Dicom2Cloud’ and the reviewers for the constructive feedback. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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Conceptualization: S.B., A.N., O.C., T.J., D.W., A.R., T.S., R.S., T.C., A.H., G.E., M.G., A.P., F.P., M.G., L.K., G.S., D.A., M.C., N.R., J.R.K., S.G., P.F.S., S.B. and J.B.M. Software: S.B., A.N., T.S., O.C., T.J., D.W., A.N., T.D., A.S., M.G., L.K., J.D.Z., K.E., S.E., X.Y., F.R., J.C., K.L., J.M., R.H., Y.-J.M.-R., J.R.K., A.B., C.R., Y.O.H. and A.S.H. Validation: S.B., A.N., T.J.A., A.R., T.S., O.C., D.W., K.G., T.D., A.S., L.K., J.D.Z., K.E., G.F., M.G., S.E., X.Y., M.S., F.R., J.C., J.K., K.L., L.H., R.S., T.C., M.H., L.K., G.S., D.A., M.C., N.R., M.G., A.P., M.D. and M.L.M. Formal analysis: T.D. Conceptualization of formal analysis: S.B., T.D., A.R., F.R. and T.S. Writing—initial outline: A.R., O.C., P.L. and S.B. Writing—original draft: A.R. Writing—review and editing: all authors. Visualization: A.R. Supervision: S.B., T.J. and A.R. Project administration: S.B., A.N., P.L., T.J., O.C. and B.S. Funding acquisition: S.B., A.N., O.C., T.J., D.W. and R.S.
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Nature Methods thanks Taiga Abe, Agah Karakuzu, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Nina Vogt, in collaboration with the Nature Methods team. Peer reviewer reports are available.
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Renton, A.I., Dao, T.T., Johnstone, T. et al. Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging. Nat Methods 21, 804–808 (2024). https://doi.org/10.1038/s41592-023-02145-x
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DOI: https://doi.org/10.1038/s41592-023-02145-x