[en] 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.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Renton, Angela I ; The University of Queensland, Queensland Brain Institute, St Lucia, Brisbane, Queensland, Australia. angie.renton23@gmail.com ; The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia. angie.renton23@gmail.com
Dao, Thuy T ; The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
Johnstone, Tom; Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
Civier, Oren ; Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
Sullivan, Ryan P ; The University of Sydney, School of Biomedical Engineering, Sydney, New South Wales, Australia
White, David J ; Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
Lyons, Paris; Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
Slade, Benjamin M; Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
Abbott, David F ; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
Amos, Toluwani J ; School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, China
Bollmann, Saskia; The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
Botting, Andy; Australian Research Data Commons (ARDC), Sydney, New South Wales, Australia
Campbell, Megan E J ; School of Psychological Sciences, University of Newcastle, Newcastle, New South Wales, Australia ; Hunter Medical Research Institute Imaging Centre, Newcastle, New South Wales, Australia
Chang, Jeryn ; The University of Queensland, School of Biomedical Sciences, St Lucia, Brisbane, Queensland, Australia
Close, Thomas G; The University of Sydney, School of Biomedical Engineering, Sydney, New South Wales, Australia
Dörig, Monika ; Integrative Spinal Research, Department of Chiropractic Medicine, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
Eckstein, Korbinian ; The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
Egan, Gary F; The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia ; Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
Evas, Stefanie ; School of Psychology, University of Adelaide, Adelaide, South Australia, Australia ; Human Health, Health & Biosecurity, CSIRO, Adelaide, South Australia, Australia
Flandin, Guillaume; Wellcome Centre for Human Neuroimaging, University College London, London, UK
Garner, Kelly G; School of Psychology, University of New South Wales, Sydney, New South Wales, Australia ; The University of Queensland, School of Psychology, St Lucia, Brisbane, Queensland, Australia
Garrido, Marta I ; Melbourne School of Psychological Sciences, he University of Melbourne, Melbourne, Victoria, Australia ; Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
Ghosh, Satrajit S ; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA ; Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
Grignard, Martin ; Université de Liège - ULiège > Département des sciences biomédicales et précliniques
Halchenko, Yaroslav O; Center for Open Neuroscience, Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
Hannan, Anthony J ; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
Heinsfeld, Anibal S; Department of Psychology, Center for Perceptual Systems, Institute for Neuroscience, Center For Learning and Memory, The University of Texas at Austin, Austin, TX, USA
Huber, Laurentius; National Institute of Mental Health (NIMH), National Institutes Health, Bethesda, MD, USA
Hughes, Matthew E; Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
Kaczmarzyk, Jakub R ; Department of Biomedical Informatics, Stony Brook University, New York, NY, USA ; Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, New York, NY, USA
Kasper, Lars ; BRAIN-TO Lab, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada ; Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
Kuhlmann, Levin ; Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia
Lou, Kexin; The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia ; Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China
Mantilla-Ramos, Yorguin-Jose ; Grupo Neuropsicología y Conducta (GRUNECO), Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
Mattingley, Jason B ; The University of Queensland, Queensland Brain Institute, St Lucia, Brisbane, Queensland, Australia ; The University of Queensland, School of Psychology, St Lucia, Brisbane, Queensland, Australia
Meier, Michael L ; Integrative Spinal Research, Department of Chiropractic Medicine, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
Morris, Jo; Australian Research Data Commons (ARDC), Sydney, New South Wales, Australia
Narayanan, Akshaiy; School of Computer Science, The University of Auckland, Auckland, New Zealand
Pestilli, Franco; Department of Psychology, Center for Perceptual Systems, Institute for Neuroscience, Center For Learning and Memory, The University of Texas at Austin, Austin, TX, USA
Puce, Aina; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
Ribeiro, Fernanda L ; The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
Rogasch, Nigel C; The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia ; Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia ; Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia
Rorden, Chris; McCausland Center for Brain Imaging, Department of Psychology, University of South Carolina, Columbia, SC, USA
Schira, Mark M ; School of Psychology, University of Wollongong, Wollongong, New South Wales, Australia
Shaw, Thomas B ; The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia ; The University of Queensland, Centre for Advanced Imaging, St Lucia, Brisbane, Queensland, Australia ; Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia
Sowman, Paul F; Macquarie University, School of Psychological Sciences, Sydney, New South Wales, Australia
Spitz, Gershon; Department of Neuroscience, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia ; Monash-Epworth Rehabilitation Research Centre, Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
Stewart, Ashley W ; The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia ; ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia
Ye, Xincheng ; The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
Zhu, Judy D ; Macquarie University, School of Psychological Sciences, Sydney, New South Wales, Australia
Narayanan, Aswin; The University of Queensland, Centre for Advanced Imaging, St Lucia, Brisbane, Queensland, Australia
Bollmann, Steffen ; The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia. s.bollmann@uq.edu.au ; The University of Queensland, Centre for Advanced Imaging, St Lucia, Brisbane, Queensland, Australia. s.bollmann@uq.edu.au ; ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia. s.bollmann@uq.edu.au ; Queensland Digital Health Centre, The University of Queensland, Brisbane, Queensland, Australia. s.bollmann@uq.edu.au
The ARDC invested in Neurodesk\u2019s 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. \u2018Sam\u2019 Peng for contributions to the first NeuroDesk precursor \u2018Dicom2Cloud\u2019 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.
Y. Halchenko M. Hanke Open is not enough. let’s take the next step: an integrated, community-driven computing platform for neuroscience Front. Neuroinform. 2012 6 22 23055966 3458431 10.3389/fninf.2012.00022
M. Hanke Y. Halchenko Neuroscience runs on GNU/Linux Front. Neuroinform. 2011 5 8 21779243 3133852 10.3389/fninf.2011.00008
G. Niso et al. Open and reproducible neuroimaging: from study inception to publication NeuroImage 2022 263 119623 36100172 10.1016/j.neuroimage.2022.119623
M.D. Wilkinson et al. The FAIR Guiding Principles for scientific data management and stewardship Sci. Data 2016 3 26978244 4792175 10.1038/sdata.2016.18
G.M. Kurtzer V. Sochat M.W. Bauer Singularity: scientific containers for mobility of compute PLoS ONE 2017 12 e0177459 28494014 5426675 10.1371/journal.pone.0177459
P. Van Gorp S. Mazanek SHARE: a web portal for creating and sharing executable research papers Procedia Comput. Sci. 2011 4 589 597 10.1016/j.procs.2011.04.062
J.-B. Poline et al. Is neuroscience FAIR? a call for collaborative standardisation of neuroscience data Neuroinformatics 2022 20 507 512 35061216 9300762 10.1007/s12021-021-09557-0
R. Silberzahn et al. Many analysts, one data set: making transparent how variations in analytic choices affect results Adv. Methods Pract. Psychol. Sci. 2018 1 337 356 10.1177/2515245917747646
T.M. Tapera et al. FlywheelTools: data curation and manipulation on the Flywheel platform Front. Neuroinform. 2021 15 678403 34239433 8258420 10.3389/fninf.2021.678403
A. Routier et al. Clinica: an open-source software platform for reproducible clinical neuroscience studies Front. Neuroinform. 2021 15 689675 34483871 8415107 10.3389/fninf.2021.689675
T. Abe et al. Neuroscience cloud analysis as a service: an open-source platform for scalable, reproducible data analysis Neuron 2022 110 2771 2789 1:CAS:528:DC%2BB38XhvFWmsrjM 35870448 9464703 10.1016/j.neuron.2022.06.018
S.N. Goodman D. Fanelli J.P.A. Ioannidis What does research reproducibility mean? Sci. Transl. Med. 2016 8 341ps12 341ps12 27252173 10.1126/scitranslmed.aaf5027
B.A. Nosek et al. Replicability, robustness, and reproducibility in psychological science Annu. Rev. Psychol. 2022 73 719 748 34665669 10.1146/annurev-psych-020821-114157
H.E. Plesser Reproducibility vs. replicability: a brief history of a confused terminology Front. Neuroinform. 2018 11 76 29403370 5778115 10.3389/fninf.2017.00076
B.A. Nosek et al. Promoting an open research culture Science 2015 348 1422 1425 1:CAS:528:DC%2BC2MXhtVOitL7J 26113702 4550299 10.1126/science.aab2374
C. Boettiger An introduction to Docker for reproducible research ACM SIGOPS Oper. Syst. Rev. 2015 49 71 79 10.1145/2723872.2723882
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).
T. Glatard et al. Reproducibility of neuroimaging analyses across operating systems Front. Neuroinform. 2015 9 12 25964757 4408913 10.3389/fninf.2015.00012
E.H. Gronenschild et al. The effects of FreeSurfer version, workstation type, and Macintosh operating system version on anatomical volume and cortical thickness measurements PLoS ONE 2012 7 e38234 1:CAS:528:DC%2BC38XosVekt74%3D 22675527 3365894 10.1371/journal.pone.0038234
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).
B. Fischl FreeSurfer NeuroImage 2012 62 774 781 22248573 10.1016/j.neuroimage.2012.01.021
E. DuPre et al. Beyond advertising: new infrastructures for publishing integrated research objects PLoS Comput. Biol. 2022 18 e1009651 1:CAS:528:DC%2BB38XhtVGgs7g%3D 34990466 8735620 10.1371/journal.pcbi.1009651
Karakuzu, A. et al. NeuroLibre: a preprint server for full-fledged reproducible neuroscience. Preprint at OSFhttps://doi.org/10.31219/osf.io/h89js (2022).
R. Gau et al. Brainhack: developing a culture of open, inclusive, community-driven neuroscience Neuron 2021 109 1769 1775 1:CAS:528:DC%2BB3MXhtVWjsrnF 33932337 9153215 10.1016/j.neuron.2021.04.001
E. Afgan et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update Nucleic Acids Res. 2018 46 W537 W544 1:CAS:528:DC%2BC1MXosVyrur8%3D 29790989 6030816 10.1093/nar/gky379
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 arXivhttps://doi.org/10.48550/arXiv.2306.02183 (2023).
K.J. Gorgolewski et al. BIDS apps: improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods PLoS Comput. Biol. 2017 13 e1005209 28278228 5363996 10.1371/journal.pcbi.1005209
R. Herrick et al. XNAT Central: open sourcing imaging research data NeuroImage 2016 124 1093 1096 26143202 10.1016/j.neuroimage.2015.06.076
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).
T. Sherif et al. CBRAIN: a web-based, distributed computing platform for collaborative neuroimaging research Front. Neuroinform. 2014 8 54 24904400 4033081 10.3389/fninf.2014.00054
F. da Veiga Leprevost et al. BioContainers: an open-source and community-driven framework for software standardization Bioinformatics 2017 33 2580 2582 28379341 5870671 10.1093/bioinformatics/btx192
J. Blomer et al. Micro-CernVM: slashing the cost of building and deploying virtual machines J. Phys. Conf. Ser. 2014 513 032009 10.1088/1742-6596/513/3/032009
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).
H. Atilgan et al. Functional relevance of the extrastriate body area for visual and haptic object recognition: a preregistered fMRI-guided TMS study Cereb. Cortex Commun. 2023 4 tgad005 37188067 10176024 10.1093/texcom/tgad005
J. Chang et al. Open-source hypothalamic-ForniX (OSHy-X) atlases and segmentation tool for 3T and 7T J. Open Source Softw. 2022 7 4368 10.21105/joss.04368
A.W. Stewart et al. QSMxT: robust masking and artifact reduction for quantitative susceptibility mapping Magn. Reson. Med. 2022 87 1289 1300 34687073 10.1002/mrm.29048
E. Biondetti et al. Multi-echo quantitative susceptibility mapping: how to combine echoes for accuracy and precision at 3 Tesla Magn. Reson. Med. 2022 88 2101 2116 35766450 9545116 10.1002/mrm.29365
Kaczmarzyk, J. et al. ReproNim/neurodocker: 0.9.5. https://doi.org/10.5281/zenodo.7929032 (2023).
K. Gorgolewski et al. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python Front. Neuroinform. 2011 5 13 21897815 3159964 10.3389/fninf.2011.00013
A. Adebimpe et al. ASLPrep: a platform for processing of arterial spin labeled MRI and quantification of regional brain perfusion Nat. Methods 2022 19 683 686 1:CAS:528:DC%2BB38XhsFGisLbL 35689029 10548890 10.1038/s41592-022-01458-7
O. Esteban et al. fMRIPrep: a robust preprocessing pipeline for functional MRI Nat. Methods 2019 16 111 116 1:CAS:528:DC%2BC1cXisVyhurnN 30532080 10.1038/s41592-018-0235-4
O. Esteban et al. MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites PLoS ONE 2017 12 e0184661 28945803 5612458 10.1371/journal.pone.0184661
X. Li P.S. Morgan J. Ashburner J. Smith C. Rorden The first step for neuroimaging data analysis: DICOM to NIfTI conversion J. Neurosci. Methods 2016 264 47 56 26945974 10.1016/j.jneumeth.2016.03.001
M.P. Zwiers S. Moia R. Oostenveld BIDScoin: a user-friendly application to convert source data to brain imaging data structure Front. Neuroinform. 2022 15 770608 35095452 8792932 10.3389/fninf.2021.770608
K.J. Gorgolewski et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments Sci. Data 2016 3 27326542 4978148 10.1038/sdata.2016.44
P.A. Yushkevich et al. User-guided segmentation of multi-modality medical imaging datasets with ITK-SNAP Neuroinformatics 2019 17 83 102 29946897 6310114 10.1007/s12021-018-9385-x
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).
F.-C. Yeh Population-based tract-to-region connectome of the human brain and its hierarchical topology Nat. Commun. 2022 13 1:CAS:528:DC%2BB38Xit1SksbrF 35995773 9395399 10.1038/s41467-022-32595-4
J.-D. Tournier F. Calamante A. Connelly MRtrix: diffusion tractography in crossing fiber regions Int. J. Imaging Syst. Technol. 2012 22 53 66 10.1002/ima.22005
N. Pallast et al. Processing pipeline for atlas-based imaging data analysis of structural and functional mouse brain MRI (AIDAmri) Front. Neuroinform. 2019 13 42 31231202 6559195 10.3389/fninf.2019.00042
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 bioRxivhttps://doi.org/10.1101/2022.08.20.504597 (2022).
G. Hangel et al. Ultra-high resolution brain metabolite mapping at 7T by short-TR Hadamard-encoded FID-MRSI NeuroImage 2018 168 199 210 27825954 10.1016/j.neuroimage.2016.10.043
R.W. Cox AFNI: what a long strange trip it’s been NeuroImage 2012 62 743 747 21889996 10.1016/j.neuroimage.2011.08.056
B.B. Avants N. Tustison H. Johnson Advanced Normalization Tools (ANTS) Insight J. 2009 2 1 35
L.E.M. Wisse et al. Automated hippocampal subfield segmentation at 7T MRI Am. J. Neuroradiol. 2016 37 1050 1057 1:STN:280:DC%2BC28nnslOrug%3D%3D 26846925 4907820 10.3174/ajnr.A4659
Gaser, C. et al. CAT—a computational anatomy toolbox for the analysis of structural MRI data. Preprint at bioRxivhttps://doi.org/10.1101/2022.06.11.495736 (2022).
K. Eckstein et al. Improved susceptibility weighted imaging at ultra-high field using bipolar multi-echo acquisition and optimized image processing: CLEAR-SWI NeuroImage 2021 237 118175 34000407 10.1016/j.neuroimage.2021.118175
S. Whitfield-Gabrieli A. Nieto-Castanon Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks Brain Connect 2012 2 125 141 22642651 10.1089/brain.2012.0073
D.S. Marcus et al. Human Connectome Project informatics: quality control, database services, and data visualization NeuroImage 2013 80 202 219 23707591 10.1016/j.neuroimage.2013.05.077
S. Estrada et al. FatSegNet: a fully automated deep learning pipeline for adipose tissue segmentation on abdominal dixon MRI Magn. Reson. Med. 2020 83 1471 1483 31631409 10.1002/mrm.28022
M. Jenkinson C.F. Beckmann T.E.J. Behrens M.W. Woolrich S.M. Smith FSL NeuroImage 2012 62 782 790 21979382 10.1016/j.neuroimage.2011.09.015
F. Isensee et al. Automated brain extraction of multisequence MRI using artificial neural networks Hum. Brain Mapp. 2019 40 4952 4964 31403237 6865732 10.1002/hbm.24750
T. Shaw A. York M. Ziaei M. Barth S. Bollmann Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) using multi-contrast MRI NeuroImage 2020 218 116798 32311467 10.1016/j.neuroimage.2020.116798
L.R. Huber et al. LayNii: a software suite for layer-fMRI NeuroImage 2021 237 118091 33991698 10.1016/j.neuroimage.2021.118091
R.D. Vincent et al. MINC 2.0: a flexible format for multi-modal images Front. Neuroinformatics 2016 10 35 10.3389/fninf.2016.00035
F. Grussu et al. Multi-parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising NeuroImage 2020 217 116884 32360689 10.1016/j.neuroimage.2020.116884
A.M. Winkler G.R. Ridgway M.A. Webster S.M. Smith T.E. Nichols Permutation inference for the general linear model NeuroImage 2014 92 381 397 24530839 10.1016/j.neuroimage.2014.01.060
L. Kasper et al. The PhysIO toolbox for modeling physiological noise in fMRI data J. Neurosci. Methods 2017 276 56 72 27832957 10.1016/j.jneumeth.2016.10.019
B. Dymerska et al. Phase unwrapping with a rapid opensource minimum spanning tree algorithm (ROMEO) Magn. Reson. Med. 2021 85 2294 2308 33104278 10.1002/mrm.28563
A. Fedorov et al. 3D slicer as an image computing platform for the quantitative imaging network Magn. Reson. Imaging 2012 30 1323 1341 22770690 3466397 10.1016/j.mri.2012.05.001
B. De Leener et al. SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data NeuroImage 2017 145 24 43 27720818 10.1016/j.neuroimage.2016.10.009
J. Ashburner Computational anatomy with the SPM software Magn. Reson. Imaging 2009 27 1163 1174 19249168 10.1016/j.mri.2009.01.006
C. Langkammer et al. Fast quantitative susceptibility mapping using 3D EPI and total generalized variation NeuroImage 2015 111 622 630 25731991 10.1016/j.neuroimage.2015.02.041
S. Klein M. Staring K. Murphy M.A. Viergever J. Pluim elastix: a toolbox for intensity-based medical image Registration IEEE Trans. Med. Imaging 2010 29 196 205 19923044 10.1109/TMI.2009.2035616
D. Shamonin et al. Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer’s disease Front. Neuroinform. 2014 7 50 24474917 3893567
O. Civier M. Sourty F. Calamante MFCSC: novel method to calculate mismatch between functional and structural brain connectomes, and its application for detecting hemispheric functional specialisations Sci. Rep. 2023 13 1:CAS:528:DC%2BB3sXkvVKjsLc%3D 36882426 9992688 10.1038/s41598-022-17213-z
F. Tadel S. Baillet J.C. Mosher D. Pantazis R.M. Leahy Brainstorm: a user-friendly application for MEG/EEG analysis Comput. Intell. Neurosci. 2011 2011 879716 21584256 3090754 10.1155/2011/879716
Brunner, C., Delorme, A. & Makeig, S. Eeglab—an open source MATLAB toolbox for electrophysiological research. Biomed. Tech. 58, 1 (2013).
R. Oostenveld P. Fries E. Maris J.-M. Schoffelen FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data Comput. Intell. Neurosci. 2011 2011 156869 21253357 10.1155/2011/156869
A. Gramfort et al. MNE software for processing MEG and EEG data NeuroImage 2014 86 446 460 24161808 10.1016/j.neuroimage.2013.10.027
Brunner, C., Breitwieser, C. & Müller-Putz, G. R. Sigviewer and Signalserver—open source software projects for biosignal analysis. Biomed. Eng. Tech. 58, 1 (2013).
R. Ihaka & Gentleman, R. R: a language for data analysis and graphics J. Comput. Graph. Stat. 1996 5 299 314 10.1080/10618600.1996.10474713
F.L. Ribeiro S. Bollmann A.M. Puckett Predicting the retinotopic organization of human visual cortex from anatomy using geometric deep learning NeuroImage 2021 244 118624 34607019 10.1016/j.neuroimage.2021.118624
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).
C. Rorden M. Brett Stereotaxic display of brain lesions Behav. Neurol. 2000 12 191 200 11568431 10.1155/2000/421719
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)
J.R. Bumgarner R.J. Nelson Open-source analysis and visualization of segmented vasculature datasets with VesselVio Cell Rep. Methods 2022 2 100189 35497491 9046271 10.1016/j.crmeth.2022.100189
R. Cusack et al. Automatic analysis (aa): efficient neuroimaging workflows and parallel processing using Matlab and XML Front. Neuroinform. 2015 8 90 25642185 4295539 10.3389/fninf.2014.00090
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 bioRxivhttps://doi.org/10.1101/2023.03.14.532686 (2023).
M.F. Glasser et al. The minimal preprocessing pipelines for the Human Connectome Project NeuroImage 2013 80 105 124 23668970 10.1016/j.neuroimage.2013.04.127
S.M. Smith et al. Resting-state fMRI in the Human Connectome Project NeuroImage 2013 80 144 168 23702415 10.1016/j.neuroimage.2013.05.039
O. Trott A.J. Olson AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading J. Comput. Chem. 2010 31 455 461 1:CAS:528:DC%2BD1MXhsFGnur3O 19499576 3041641 10.1002/jcc.21334
J. Eberhardt D. Santos-Martins A.F. Tillack S. Forli AutoDock Vina 1.2.0: new docking methods, expanded force field, and Python bindings J. Chem. Inf. Model. 2021 61 3891 3898 1:CAS:528:DC%2BB3MXhsFGqtb%2FP 34278794 10683950 10.1021/acs.jcim.1c00203