Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners

Rafael Garcia-Dias; Cristina Scarpazza; Lea Baecker; Sandra Vieira; Walter H L Pinaya; Aiden Corvin; Alberto Redolfi; Barnaby Nelson; Benedicto Crespo-Facorro; Colm McDonald; Diana Tordesillas-Gutiérrez; Dara Cannon; David Mothersill; Dennis Hernaus; Derek Morris; Esther Setien-Suero; Gary Donohoe; Giovanni Frisoni; Giulia Tronchin; João Sato; Machteld Marcelis; Matthew Kempton; Neeltje E M van Haren; Oliver Gruber; Patrick McGorry; Paul Amminger; Philip McGuire; Qiyong Gong; René S Kahn; Rosa Ayesa-Arriola; Therese van Amelsvoort; Victor Ortiz-García de la Foz; Vince Calhoun; Wiepke Cahn; Andrea Mechelli

doi:10.1016/j.neuroimage.2020.117127

Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners

Rafael Garcia-Dias, Cristina Scarpazza, Lea Baecker, Sandra Vieira, Walter H L Pinaya, Aiden Corvin, Alberto Redolfi, Barnaby Nelson, Benedicto Crespo-Facorro, Colm McDonald, Diana Tordesillas-Gutiérrez, Dara Cannon, David Mothersill, Dennis Hernaus, Derek Morris, Esther Setien-Suero, Gary Donohoe, Giovanni Frisoni, Giulia Tronchin, João SatoMachteld Marcelis, Matthew Kempton, Neeltje E M van Haren, Oliver Gruber, Patrick McGorry, Paul Amminger, Philip McGuire, Qiyong Gong, René S Kahn, Rosa Ayesa-Arriola, Therese van Amelsvoort, Victor Ortiz-García de la Foz, Vince Calhoun, Wiepke Cahn, Andrea Mechelli

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Original language	English
Article number	117127
Journal	NeuroImage
Volume	220
Early online date	4 Jul 2020
DOIs	https://doi.org/10.1016/j.neuroimage.2020.117127
Publication status	Published - 15 Oct 2020

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10.1016/j.neuroimage.2020.117127

1-s2.0-S1053811920306133-mainFinal published version, 6.19 MBLicence: CC BY

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Garcia-Dias, R., Scarpazza, C., Baecker, L., Vieira, S., Pinaya, W. H. L., Corvin, A., Redolfi, A., Nelson, B., Crespo-Facorro, B., McDonald, C., Tordesillas-Gutiérrez, D., Cannon, D., Mothersill, D., Hernaus, D., Morris, D., Setien-Suero, E., Donohoe, G., Frisoni, G., Tronchin, G., ... Mechelli, A. (2020). Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners. NeuroImage, 220, Article 117127. https://doi.org/10.1016/j.neuroimage.2020.117127

@article{e67ba7fb42c246be98abd4e667c33a93,

title = "Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners",

author = "Rafael Garcia-Dias and Cristina Scarpazza and Lea Baecker and Sandra Vieira and Pinaya, {Walter H L} and Aiden Corvin and Alberto Redolfi and Barnaby Nelson and Benedicto Crespo-Facorro and Colm McDonald and Diana Tordesillas-Guti{\'e}rrez and Dara Cannon and David Mothersill and Dennis Hernaus and Derek Morris and Esther Setien-Suero and Gary Donohoe and Giovanni Frisoni and Giulia Tronchin and Jo{\~a}o Sato and Machteld Marcelis and Matthew Kempton and {van Haren}, {Neeltje E M} and Oliver Gruber and Patrick McGorry and Paul Amminger and Philip McGuire and Qiyong Gong and Kahn, {Ren{\'e} S} and Rosa Ayesa-Arriola and {van Amelsvoort}, Therese and {Ortiz-Garc{\'i}a de la Foz}, Victor and Vince Calhoun and Wiepke Cahn and Andrea Mechelli",

note = "Funding Information: The imaging data and demographic information was collected and shared by [University of Iowa, University of Minnesota, University of New Mexico, Massachusetts General Hospital] the Mind Research Network supported by the Department of Energy under Award Number DE-FG02-08ER64581. Funding Information: This research has been conducted using the UK Biobank Resource (Project Number 40323) and has been supported by a Wellcome Trust{\textquoteright}s Innovator Award ( 208519/Z/17/Z ) to Andrea Mechelli. The present work was carried out within the scope of the research program Dipartimenti di Eccellenza (art.1, commi 314-337 legge 232/2016), which was supported by a grant from MIUR to the Department of General Psychology, University of Padua . The data from UCLA, LOSS AVERSION, EMOTIONREGULATION, FALSEBELIEFS, MATURATIONAL CHANGES, ASSOCIATIVE LEARNING, HARMAVOIDANCE, PLACEBO, MORAL JUDGEMENT, CYBERBALL, ROUTE LEARNING, SEQUENTIAL INFERENCE VBM, WASHINGTON UNIVERSITY datasets were obtained from the OpenfMRI database. Their accession numbers are ds000030, ds000053, ds000108, ds000109, ds000119, ds000168, ds000202, ds000208, ds000212, ds000214, ds000217, ds000222, and ds000243, respectively. The acquisition of dataset HMRRC was supported by the National Natural Science Foundation of China to Prof. Qiyong Gong ( 81220108013 , 8122010801 , 81621003 , 81761128023 and 81227002 ). Part of the data used in this article (NITRC) have been funded in whole or in part with Federal funds from the Department of Health and Human Services, National Institute of Biomedical Imaging and Bioengineering , the National Institute of Neurological Disorders and Stroke , under the following NIH grants: 1R43NS074540 , 2R44NS074540 , and 1U24EB023398 and previously GSA Contract No. GS-00F-0034P , Order Number HHSN268200100090U . This research has been conducted using the UK Biobank Resource. Part of the data used in preparation of this article were obtained from the Alzheimer{\textquoteright}s Disease Repository Without Borders (ARWiBo – www.arwibo.it ). The overall goal of ARWiBo is to contribute, thorough synergy with neuGRID ( https://neugrid2.eu ), to global data sharing and analysis in order to develop effective therapies, prevention methods and a cure for Alzheimer{\textquoteright} and other neurodegenerative diseases. Part of the data used in this article was downloaded from the Collaborative Informatics and Neuroimaging Suite Data Exchange tool (COINS; http://coins.mrn.org/dx ) and data collection was performed at the Mind Research Network and funded by a Center of Biomedical Research Excellence (COBRE) grant 5P20RR021938/P20GM103472 from the NIH to Dr. Vince Calhoun. Part of the data used for this study were downloaded from the Function BIRN Data Repository ( http://fbirnbdr.birncommunity.org:8080/BDR/ ), supported by grants to the Function BIRN (U24-RR021992) Testbed funded by the National Center for Research Resources at the National Institutes of Health , U.S.A. Part of the data used in the preparation of this work were obtained from the Mind Clinical Imaging Consortium database through the Mind Research Network ( www.mrn.org ). The MCIC project was supported by the Department of Energy under Award Number DE-FG02-08ER64581 . MCIC is the result of efforts of co-investigators from University of Iowa, University of Minnesota, University of New Mexico, Massachusetts General Hospital. CLING/HMS: The CliNG study sample was partially supported by the Deutsche Forschungsgemeinschaft (DFG) via the Clinical Research Group 241 {\textquoteleft}Genotype-phenotype relationships and neurobiology of the longitudinal course of psychosis{\textquoteright}, TP2 ( PI Gruber; http://www.kfo241.de ; grant number GR 1950/5-1 ). Part of the data used in preparation of this article were obtained from the NU Schizophrenia Data and Software Tool (NUSDAST) database ( http://central.xnat.org/REST/projects/NUDataSharing ) As such, the investigators within NUSDAST contributed to the design and implementation of NUSDAST and/or provided data but did not participate in analysis or writing of this report. Part of the data used in the preparation of this article were obtained from the Parkinson{\textquoteright}s Progression Markers Initiative (PPMI) database ( www.ppmi-info.org/data ). For up-to-date information on the study, visit www.ppmi-info.org . PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson{\textquoteright}s Research and funding partners, including [list the full names of all of the PPMI funding partners found at www.ppmi-info.org/fundingpartners ]. Part of the data used in preparation of this article were obtained from the SchizConnect database ( http://schizconnect.org ). As such, the investigators within SchizConnect contributed to the design and implementation of SchizConnect and/or provided data but did not participate in analysis or writing of this report. Data collection and sharing for this project was funded by NIMH cooperative agreement 1U01 MH097435 . Jo{\~a}o Sato is supported by Sao Paulo Research Foundation ( FAPESP , Brazil) Grants 2018/04654-9 and 2018/21934-5 . Funding Information: This research has been conducted using the UK Biobank Resource (Project Number 40323) and has been supported by a Wellcome Trust's Innovator Award (208519/Z/17/Z) to Andrea Mechelli. The present work was carried out within the scope of the research program Dipartimenti di Eccellenza (art.1, commi 314-337 legge 232/2016), which was supported by a grant from MIUR to the Department of General Psychology, University of Padua. The data from UCLA, LOSS AVERSION, EMOTIONREGULATION, FALSEBELIEFS, MATURATIONAL CHANGES, ASSOCIATIVE LEARNING, HARMAVOIDANCE, PLACEBO, MORAL JUDGEMENT, CYBERBALL, ROUTE LEARNING, SEQUENTIAL INFERENCE VBM, WASHINGTON UNIVERSITY datasets were obtained from the OpenfMRI database. Their accession numbers are ds000030, ds000053, ds000108, ds000109, ds000119, ds000168, ds000202, ds000208, ds000212, ds000214, ds000217, ds000222, and ds000243, respectively. The acquisition of dataset HMRRC was supported by the National Natural Science Foundation of China to Prof. Qiyong Gong (81220108013, 8122010801, 81621003, 81761128023 and 81227002). Part of the data used in this article (NITRC) have been funded in whole or in part with Federal funds from the Department of Health and Human Services, National Institute of Biomedical Imaging and Bioengineering, the National Institute of Neurological Disorders and Stroke, under the following NIH grants: 1R43NS074540, 2R44NS074540, and 1U24EB023398and previously GSA Contract No. GS-00F-0034P, Order Number HHSN268200100090U. This research has been conducted using the UK Biobank Resource. Part of the data used in preparation of this article were obtained from the Alzheimer's Disease Repository Without Borders (ARWiBo ? www.arwibo.it). The overall goal of ARWiBo is to contribute, thorough synergy with neuGRID (https://neugrid2.eu), to global data sharing and analysis in order to develop effective therapies, prevention methods and a cure for Alzheimer? and other neurodegenerative diseases. Part of the data used in this article was downloaded from the Collaborative Informatics and Neuroimaging Suite Data Exchange tool (COINS; http://coins.mrn.org/dx) and data collection was performed at the Mind Research Network and funded by a Center of Biomedical Research Excellence (COBRE) grant 5P20RR021938/P20GM103472 from the NIH to Dr. Vince Calhoun. Part of the data used for this study were downloaded from the Function BIRN Data Repository (http://fbirnbdr.birncommunity.org:8080/BDR/), supported by grants to the Function BIRN (U24-RR021992) Testbed funded by the National Center for Research Resources at the National Institutes of Health, U.S.A. Part of the data used in the preparation of this work were obtained from the Mind Clinical Imaging Consortium database through the Mind Research Network (www.mrn.org). The MCIC project was supported by the Department of Energy under Award Number DE-FG02-08ER64581. MCIC is the result of efforts of co-investigators from University of Iowa, University of Minnesota, University of New Mexico, Massachusetts General Hospital. CLING/HMS: The CliNG study sample was partially supported by the Deutsche Forschungsgemeinschaft (DFG) via the Clinical Research Group 241 ?Genotype-phenotype relationships and neurobiology of the longitudinal course of psychosis?, TP2 (PI Gruber; http://www.kfo241.de; grant number GR 1950/5-1). Part of the data used in preparation of this article were obtained from the NU Schizophrenia Data and Software Tool (NUSDAST) database (http://central.xnat.org/REST/projects/NUDataSharing) As such, the investigators within NUSDAST contributed to the design and implementation of NUSDAST and/or provided data but did not participate in analysis or writing of this report. Part of the data used in the preparation of this article were obtained from the Parkinson's Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data). For up-to-date information on the study, visit www.ppmi-info.org. PPMI ? a public-private partnership ? is funded by the Michael J. Fox Foundation for Parkinson's Research and funding partners, including [list the full names of all of the PPMI funding partners found at www.ppmi-info.org/fundingpartners]. Part of the data used in preparation of this article were obtained from the SchizConnect database (http://schizconnect.org). As such, the investigators within SchizConnect contributed to the design and implementation of SchizConnect and/or provided data but did not participate in analysis or writing of this report. Data collection and sharing for this project was funded by NIMH cooperative agreement 1U01 MH097435. Jo?o Sato is supported by Sao Paulo Research Foundation (FAPESP, Brazil) Grants 2018/04654-9 and 2018/21934-5.",

year = "2020",

month = oct,

day = "15",

doi = "10.1016/j.neuroimage.2020.117127",

language = "English",

volume = "220",

journal = "NeuroImage",

issn = "1053-8119",

publisher = "Academic Press Inc.",

}

Garcia-Dias, R, Scarpazza, C, Baecker, L, Vieira, S, Pinaya, WHL, Corvin, A, Redolfi, A, Nelson, B, Crespo-Facorro, B, McDonald, C, Tordesillas-Gutiérrez, D, Cannon, D, Mothersill, D, Hernaus, D, Morris, D, Setien-Suero, E, Donohoe, G, Frisoni, G, Tronchin, G, Sato, J, Marcelis, M, Kempton, M, van Haren, NEM, Gruber, O, McGorry, P, Amminger, P, McGuire, P, Gong, Q, Kahn, RS, Ayesa-Arriola, R, van Amelsvoort, T, Ortiz-García de la Foz, V, Calhoun, V, Cahn, W & Mechelli, A 2020, 'Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners', NeuroImage, vol. 220, 117127. https://doi.org/10.1016/j.neuroimage.2020.117127

TY - JOUR

T1 - Neuroharmony

T2 - A new tool for harmonizing volumetric MRI data from unseen scanners

AU - Garcia-Dias, Rafael

AU - Scarpazza, Cristina

AU - Baecker, Lea

AU - Vieira, Sandra

AU - Pinaya, Walter H L

AU - Corvin, Aiden

AU - Redolfi, Alberto

AU - Nelson, Barnaby

AU - Crespo-Facorro, Benedicto

AU - McDonald, Colm

AU - Tordesillas-Gutiérrez, Diana

AU - Cannon, Dara

AU - Mothersill, David

AU - Hernaus, Dennis

AU - Morris, Derek

AU - Setien-Suero, Esther

AU - Donohoe, Gary

AU - Frisoni, Giovanni

AU - Tronchin, Giulia

AU - Sato, João

AU - Marcelis, Machteld

AU - Kempton, Matthew

AU - van Haren, Neeltje E M

AU - Gruber, Oliver

AU - McGorry, Patrick

AU - Amminger, Paul

AU - McGuire, Philip

AU - Gong, Qiyong

AU - Kahn, René S

AU - Ayesa-Arriola, Rosa

AU - van Amelsvoort, Therese

AU - Ortiz-García de la Foz, Victor

AU - Calhoun, Vince

AU - Cahn, Wiepke

AU - Mechelli, Andrea

N1 - Funding Information: The imaging data and demographic information was collected and shared by [University of Iowa, University of Minnesota, University of New Mexico, Massachusetts General Hospital] the Mind Research Network supported by the Department of Energy under Award Number DE-FG02-08ER64581. Funding Information: This research has been conducted using the UK Biobank Resource (Project Number 40323) and has been supported by a Wellcome Trust’s Innovator Award ( 208519/Z/17/Z ) to Andrea Mechelli. The present work was carried out within the scope of the research program Dipartimenti di Eccellenza (art.1, commi 314-337 legge 232/2016), which was supported by a grant from MIUR to the Department of General Psychology, University of Padua . The data from UCLA, LOSS AVERSION, EMOTIONREGULATION, FALSEBELIEFS, MATURATIONAL CHANGES, ASSOCIATIVE LEARNING, HARMAVOIDANCE, PLACEBO, MORAL JUDGEMENT, CYBERBALL, ROUTE LEARNING, SEQUENTIAL INFERENCE VBM, WASHINGTON UNIVERSITY datasets were obtained from the OpenfMRI database. Their accession numbers are ds000030, ds000053, ds000108, ds000109, ds000119, ds000168, ds000202, ds000208, ds000212, ds000214, ds000217, ds000222, and ds000243, respectively. The acquisition of dataset HMRRC was supported by the National Natural Science Foundation of China to Prof. Qiyong Gong ( 81220108013 , 8122010801 , 81621003 , 81761128023 and 81227002 ). Part of the data used in this article (NITRC) have been funded in whole or in part with Federal funds from the Department of Health and Human Services, National Institute of Biomedical Imaging and Bioengineering , the National Institute of Neurological Disorders and Stroke , under the following NIH grants: 1R43NS074540 , 2R44NS074540 , and 1U24EB023398 and previously GSA Contract No. GS-00F-0034P , Order Number HHSN268200100090U . This research has been conducted using the UK Biobank Resource. Part of the data used in preparation of this article were obtained from the Alzheimer’s Disease Repository Without Borders (ARWiBo – www.arwibo.it ). The overall goal of ARWiBo is to contribute, thorough synergy with neuGRID ( https://neugrid2.eu ), to global data sharing and analysis in order to develop effective therapies, prevention methods and a cure for Alzheimer’ and other neurodegenerative diseases. Part of the data used in this article was downloaded from the Collaborative Informatics and Neuroimaging Suite Data Exchange tool (COINS; http://coins.mrn.org/dx ) and data collection was performed at the Mind Research Network and funded by a Center of Biomedical Research Excellence (COBRE) grant 5P20RR021938/P20GM103472 from the NIH to Dr. Vince Calhoun. Part of the data used for this study were downloaded from the Function BIRN Data Repository ( http://fbirnbdr.birncommunity.org:8080/BDR/ ), supported by grants to the Function BIRN (U24-RR021992) Testbed funded by the National Center for Research Resources at the National Institutes of Health , U.S.A. Part of the data used in the preparation of this work were obtained from the Mind Clinical Imaging Consortium database through the Mind Research Network ( www.mrn.org ). The MCIC project was supported by the Department of Energy under Award Number DE-FG02-08ER64581 . MCIC is the result of efforts of co-investigators from University of Iowa, University of Minnesota, University of New Mexico, Massachusetts General Hospital. CLING/HMS: The CliNG study sample was partially supported by the Deutsche Forschungsgemeinschaft (DFG) via the Clinical Research Group 241 ‘Genotype-phenotype relationships and neurobiology of the longitudinal course of psychosis’, TP2 ( PI Gruber; http://www.kfo241.de ; grant number GR 1950/5-1 ). Part of the data used in preparation of this article were obtained from the NU Schizophrenia Data and Software Tool (NUSDAST) database ( http://central.xnat.org/REST/projects/NUDataSharing ) As such, the investigators within NUSDAST contributed to the design and implementation of NUSDAST and/or provided data but did not participate in analysis or writing of this report. Part of the data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database ( www.ppmi-info.org/data ). For up-to-date information on the study, visit www.ppmi-info.org . PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including [list the full names of all of the PPMI funding partners found at www.ppmi-info.org/fundingpartners ]. Part of the data used in preparation of this article were obtained from the SchizConnect database ( http://schizconnect.org ). As such, the investigators within SchizConnect contributed to the design and implementation of SchizConnect and/or provided data but did not participate in analysis or writing of this report. Data collection and sharing for this project was funded by NIMH cooperative agreement 1U01 MH097435 . João Sato is supported by Sao Paulo Research Foundation ( FAPESP , Brazil) Grants 2018/04654-9 and 2018/21934-5 . Funding Information: This research has been conducted using the UK Biobank Resource (Project Number 40323) and has been supported by a Wellcome Trust's Innovator Award (208519/Z/17/Z) to Andrea Mechelli. The present work was carried out within the scope of the research program Dipartimenti di Eccellenza (art.1, commi 314-337 legge 232/2016), which was supported by a grant from MIUR to the Department of General Psychology, University of Padua. The data from UCLA, LOSS AVERSION, EMOTIONREGULATION, FALSEBELIEFS, MATURATIONAL CHANGES, ASSOCIATIVE LEARNING, HARMAVOIDANCE, PLACEBO, MORAL JUDGEMENT, CYBERBALL, ROUTE LEARNING, SEQUENTIAL INFERENCE VBM, WASHINGTON UNIVERSITY datasets were obtained from the OpenfMRI database. Their accession numbers are ds000030, ds000053, ds000108, ds000109, ds000119, ds000168, ds000202, ds000208, ds000212, ds000214, ds000217, ds000222, and ds000243, respectively. The acquisition of dataset HMRRC was supported by the National Natural Science Foundation of China to Prof. Qiyong Gong (81220108013, 8122010801, 81621003, 81761128023 and 81227002). Part of the data used in this article (NITRC) have been funded in whole or in part with Federal funds from the Department of Health and Human Services, National Institute of Biomedical Imaging and Bioengineering, the National Institute of Neurological Disorders and Stroke, under the following NIH grants: 1R43NS074540, 2R44NS074540, and 1U24EB023398and previously GSA Contract No. GS-00F-0034P, Order Number HHSN268200100090U. This research has been conducted using the UK Biobank Resource. Part of the data used in preparation of this article were obtained from the Alzheimer's Disease Repository Without Borders (ARWiBo ? www.arwibo.it). The overall goal of ARWiBo is to contribute, thorough synergy with neuGRID (https://neugrid2.eu), to global data sharing and analysis in order to develop effective therapies, prevention methods and a cure for Alzheimer? and other neurodegenerative diseases. Part of the data used in this article was downloaded from the Collaborative Informatics and Neuroimaging Suite Data Exchange tool (COINS; http://coins.mrn.org/dx) and data collection was performed at the Mind Research Network and funded by a Center of Biomedical Research Excellence (COBRE) grant 5P20RR021938/P20GM103472 from the NIH to Dr. Vince Calhoun. Part of the data used for this study were downloaded from the Function BIRN Data Repository (http://fbirnbdr.birncommunity.org:8080/BDR/), supported by grants to the Function BIRN (U24-RR021992) Testbed funded by the National Center for Research Resources at the National Institutes of Health, U.S.A. Part of the data used in the preparation of this work were obtained from the Mind Clinical Imaging Consortium database through the Mind Research Network (www.mrn.org). The MCIC project was supported by the Department of Energy under Award Number DE-FG02-08ER64581. MCIC is the result of efforts of co-investigators from University of Iowa, University of Minnesota, University of New Mexico, Massachusetts General Hospital. CLING/HMS: The CliNG study sample was partially supported by the Deutsche Forschungsgemeinschaft (DFG) via the Clinical Research Group 241 ?Genotype-phenotype relationships and neurobiology of the longitudinal course of psychosis?, TP2 (PI Gruber; http://www.kfo241.de; grant number GR 1950/5-1). Part of the data used in preparation of this article were obtained from the NU Schizophrenia Data and Software Tool (NUSDAST) database (http://central.xnat.org/REST/projects/NUDataSharing) As such, the investigators within NUSDAST contributed to the design and implementation of NUSDAST and/or provided data but did not participate in analysis or writing of this report. Part of the data used in the preparation of this article were obtained from the Parkinson's Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data). For up-to-date information on the study, visit www.ppmi-info.org. PPMI ? a public-private partnership ? is funded by the Michael J. Fox Foundation for Parkinson's Research and funding partners, including [list the full names of all of the PPMI funding partners found at www.ppmi-info.org/fundingpartners]. Part of the data used in preparation of this article were obtained from the SchizConnect database (http://schizconnect.org). As such, the investigators within SchizConnect contributed to the design and implementation of SchizConnect and/or provided data but did not participate in analysis or writing of this report. Data collection and sharing for this project was funded by NIMH cooperative agreement 1U01 MH097435. Jo?o Sato is supported by Sao Paulo Research Foundation (FAPESP, Brazil) Grants 2018/04654-9 and 2018/21934-5.

PY - 2020/10/15

Y1 - 2020/10/15

UR - http://www.scopus.com/inward/record.url?scp=85088386927&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2020.117127

DO - 10.1016/j.neuroimage.2020.117127

M3 - Article

C2 - 32634595

SN - 1053-8119

VL - 220

JO - NeuroImage

JF - NeuroImage

M1 - 117127

ER -

Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners

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