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Our Toolbox is available in Python and Matlab and complemented with expandable online documentation (http://enigma-toolbox.readthedocs.io). Derivative data (for example, summary statistics from published ENIGMA studies) and codes are openly accessible under the terms of the BSD-3-Clause license at http://github.com/MICA-MNI/ENIGMA. Requests to work on a project with subject-level data can be proposed to the working group via the chairs (http://enigma.ini.usc.edu/). Users seeking help are encouraged to subscribe and post their questions to the ENIGMA Toolbox mailing list at https://groups.google.com/g/enigma-toolbox.
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Many scientists around the world contributed to ENIGMA but did not take part in the writing of this report. A full list of contributors to ENIGMA is available at http://enigma.ini.usc.edu/about-2/consortium/members/. The authors would like to express their gratitude to the open science initiatives that made this work possible: (i) the ENIGMA Consortium (core funding for ENIGMA was provided by the NIH Big Data to Knowledge (BD2K) program under consortium grant U54 EB020403 to P.M.T.), (ii) The Allen Human Brain Atlas and the abagen toolbox (https://doi.org/10.5281/zenodo.4984124), (iii) BigBrain/HIBALL, and (iv) the Human Connectome Project (principal investigators David Van Essen and Kamil Ugurbil; 1U54MH091657), funded by the 16 NIH institutes and centers that support the NIH Blueprint for Neuroscience Research and by the McDonnell Center for Systems Neuroscience at Washington University. S.L. acknowledges funding from Fonds de la Recherche du Québec – Santé (FRQ-S) and the Canadian Institutes of Health Research (CIHR). C.P. was funded through a postdoctoral FRQ-S fellowship. O.B. was funded by a Healthy Brains for Healthy Lives (HBHL) postdoctoral fellowship. B.-y.P. was funded by the National Research Foundation of Korea (NRF-2020R1A6A3A03037088), a Molson Neuro-Engineering fellowship from the Montreal Neurological Institute and Hospital, and FRQ-S. J.R. was supported by CIHR. R.V.d.W. was funded by studentships from the Savoy Foundation for Epilepsy and the Richard and Ann Sievers award. S.L.V. was supported by the Otto Hahn award of the Max Planck Society. M.K. acknowledges funding from the Swiss National Science Foundation (P2SKP3_178175). S.M.S. was supported by the Epilepsy Society, UK. Part of this work was undertaken at University College London Hospitals, which received a proportion of funding from the NIHR Biomedical Research Centres funding scheme. C.R.M. acknowledges funding from the National Institutes of Health (NINDS R01NS065838 and R21 NS107739). B.C.B. acknowledges research funding from the SickKids Foundation (NI17-039), the Natural Sciences and Engineering Research Council of Canada (NSERC; Discovery-1304413), CIHR (FDN-154298, PJT-174995), the Azrieli Center for Autism Research (ACAR), an MNI-Cambridge collaboration grant, salary support from FRQ-S (Chercheur-Boursier), BrainCanada, the Helmholtz BigBrain Analytics and Learning Lab (Hiball) and the Canada Research Chairs (CRC) Program.
P.M.T. received partial grant support from Biogen, Inc., and consulting payments from Kairos Venture Capital, Inc., for work unrelated to ENIGMA and this manuscript.
Peer review information Nature Methods thanks Oscar Esteban, David Van Essen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Larivière, S., Paquola, C., Park, By. et al. The ENIGMA Toolbox: multiscale neural contextualization of multisite neuroimaging datasets. Nat Methods 18, 698–700 (2021). https://doi.org/10.1038/s41592-021-01186-4