Garcia-Dias, RafaelScarpazza, CristinaBaecker, LeaVieira, SandraPinaya, Walter H LCorvin, AidenRedolfi, AlbertoNelson, BarnabyCrespo-Facorro, BenedictoMcDonald, ColmTordesillas-Gutiérrez, DianaCannon, DaraMothersill, DavidHernaus, DennisMorris, DerekSetien-Suero, EstherDonohoe, GaryFrisoni, GiovanniTronchin, GiuliaSato, JoãoMarcelis, MachteldKempton, Matthewvan Haren, Neeltje E MGruber, OliverMcGorry, PatrickAmminger, PaulMcGuire, PhilipGong, QiyongKahn, René SAyesa-Arriola, Rosavan Amelsvoort, ThereseOrtiz-García de la Foz, VictorCalhoun, VinceCahn, WiepkeMechelli, Andrea2023-02-092023-02-092020-07-04http://hdl.handle.net/10668/15889• We present Neuroharmony, a harmonization tool for images from unseen scanners. • We developed Neuroharmony using a total of 15,026 sMRI images. • The tool was able to reduce scanner-related bias from unseen scans. • Neuroharmony represents a significant step towards imaging-based clinical tools. • Neuroharmony is available at https://github.com/garciadias/Neuroharmony.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/AdolescentAdultAgedBrainFemaleHumansImage Processing, Computer-AssistedMachine LearningMagnetic Resonance ImagingMaleMiddle AgedNeuroimagingSoftwareYoung Adultresearch article32634595open access10.1016/j.neuroimage.2020.1171271095-9572PMC7573655https://doi.org/10.1016/j.neuroimage.2020.117127https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7573655/pdf