Privacy-preserving harmonization via distributed ComBat
Challenges in clinical data sharing and the need to protect data privacy have led to the development and popularization of methods that do not require directly transferring patient data. In neuroimaging, integration of data across multiple institutions also introduces unwanted biases driven by scann...
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| Vydáno v: | NeuroImage (Orlando, Fla.) Ročník 248; s. 118822 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Elsevier Inc
01.03.2022
Elsevier Limited Elsevier |
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| ISSN: | 1053-8119, 1095-9572, 1095-9572 |
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| Abstract | Challenges in clinical data sharing and the need to protect data privacy have led to the development and popularization of methods that do not require directly transferring patient data. In neuroimaging, integration of data across multiple institutions also introduces unwanted biases driven by scanner differences. These scanner effects have been shown by several research groups to severely affect downstream analyses. To facilitate the need of removing scanner effects in a distributed data setting, we introduce distributed ComBat, an adaptation of a popular harmonization method for multivariate data that borrows information across features. We present our fast and simple distributed algorithm and show that it yields equivalent results using data from the Alzheimer’s Disease Neuroimaging Initiative. Our method enables harmonization while ensuring maximal privacy protection, thus facilitating a broad range of downstream analyses in functional and structural imaging studies. |
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| AbstractList | Challenges in clinical data sharing and the need to protect data privacy have led to the development and popularization of methods that do not require directly transferring patient data. In neuroimaging, integration of data across multiple institutions also introduces unwanted biases driven by scanner differences. These scanner effects have been shown by several research groups to severely affect downstream analyses. To facilitate the need of removing scanner effects in a distributed data setting, we introduce distributed ComBat, an adaptation of a popular harmonization method for multivariate data that borrows information across features. We present our fast and simple distributed algorithm and show that it yields equivalent results using data from the Alzheimer’s Disease Neuroimaging Initiative. Our method enables harmonization while ensuring maximal privacy protection, thus facilitating a broad range of downstream analyses in functional and structural imaging studies. Challenges in clinical data sharing and the need to protect data privacy have led to the development and popularization of methods that do not require directly transferring patient data. In neuroimaging, integration of data across multiple institutions also introduces unwanted biases driven by scanner differences. These scanner effects have been shown by several research groups to severely affect downstream analyses. To facilitate the need of removing scanner effects in a distributed data setting, we introduce distributed ComBat, an adaptation of a popular harmonization method for multivariate data that borrows information across features. We present our fast and simple distributed algorithm and show that it yields equivalent results using data from the Alzheimer's Disease Neuroimaging Initiative. Our method enables harmonization while ensuring maximal privacy protection, thus facilitating a broad range of downstream analyses in functional and structural imaging studies.Challenges in clinical data sharing and the need to protect data privacy have led to the development and popularization of methods that do not require directly transferring patient data. In neuroimaging, integration of data across multiple institutions also introduces unwanted biases driven by scanner differences. These scanner effects have been shown by several research groups to severely affect downstream analyses. To facilitate the need of removing scanner effects in a distributed data setting, we introduce distributed ComBat, an adaptation of a popular harmonization method for multivariate data that borrows information across features. We present our fast and simple distributed algorithm and show that it yields equivalent results using data from the Alzheimer's Disease Neuroimaging Initiative. Our method enables harmonization while ensuring maximal privacy protection, thus facilitating a broad range of downstream analyses in functional and structural imaging studies. |
| ArticleNumber | 118822 |
| Author | Shinohara, Russell T. Shou, Haochang Chen, Yong Chen, Andrew A. Luo, Chongliang |
| Author_xml | – sequence: 1 givenname: Andrew A. surname: Chen fullname: Chen, Andrew A. email: andrewac@pennmedicine.upenn.edu organization: Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, United States – sequence: 2 givenname: Chongliang surname: Luo fullname: Luo, Chongliang organization: Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, United States – sequence: 3 givenname: Yong surname: Chen fullname: Chen, Yong organization: Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, United States – sequence: 4 givenname: Russell T. surname: Shinohara fullname: Shinohara, Russell T. organization: Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, United States – sequence: 5 givenname: Haochang surname: Shou fullname: Shou, Haochang organization: Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, United States |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34958950$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1016_j_neuroimage_2022_119297 crossref_primary_10_1016_j_neuroimage_2023_120125 crossref_primary_10_1371_journal_pone_0321631 crossref_primary_10_1007_s43032_025_01917_4 crossref_primary_10_1002_hbm_26708 crossref_primary_10_1016_j_dcn_2024_101464 crossref_primary_10_1038_s41597_023_02421_7 crossref_primary_10_1016_j_waojou_2025_101120 crossref_primary_10_1016_j_biopsych_2025_09_003 crossref_primary_10_1089_neu_2024_0128 crossref_primary_10_1093_biomtc_ujae003 crossref_primary_10_1146_annurev_biodatasci_020722_100353 crossref_primary_10_1002_eng2_70153 crossref_primary_10_1162_imag_a_00011 |
| Cites_doi | 10.3233/JAD-190283 10.1016/j.neuroimage.2017.08.047 10.1016/j.neuroimage.2020.117129 10.1016/j.neuroimage.2009.11.006 10.1093/jamia/ocz199 10.1093/jamia/ocaa044 10.1016/j.neuroimage.2008.10.037 10.1016/j.neuroimage.2016.02.036 10.1038/s41386-018-0122-9 10.1002/hbm.24241 10.1016/j.neuroimage.2008.12.016 10.1016/j.neuroimage.2017.11.024 10.1016/j.neuroimage.2019.116450 10.1002/jmri.22003 10.1093/biostatistics/kxj037 10.1016/j.dcn.2019.100706 10.1007/s12021-011-9109-y 10.1016/j.datak.2007.03.015 10.3174/ajnr.A5254 10.1016/j.neuroimage.2006.02.051 10.1002/hbm.20511 10.1109/TMI.2010.2046908 10.1016/j.jalz.2010.03.004 10.1198/jasa.2009.tm08651 |
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| Copyright | 2021 Copyright © 2021. Published by Elsevier Inc. Copyright Elsevier Limited Mar 2022 |
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| DOI | 10.1016/j.neuroimage.2021.118822 |
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| Keywords | Privacy-preserving Distributed analysis Site effect ComBat Harmonization |
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