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Exploring the limits of ComBat method for multi-site diffusion MRI harmonization

View ORCID ProfileSuheyla Cetin-Karayumak, Katharina Stegmayer, Sebastian Walther, Philip R. Szeszko, Tim Crow, Anthony James, Matcheri Keshavan, Marek Kubicki, Yogesh Rathi
doi: https://doi.org/10.1101/2020.11.20.390120
Suheyla Cetin-Karayumak
aDepartment of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
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  • ORCID record for Suheyla Cetin-Karayumak
  • For correspondence: skarayumak@bwh.harvard.edu
Katharina Stegmayer
cUniversity of Bern, University Hospital of Psychiatry, Switzerland
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Sebastian Walther
cUniversity of Bern, University Hospital of Psychiatry, Switzerland
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Philip R. Szeszko
dMental Illness Research, Education and Clinical Center, James J, Peters VA Medical Center, Bronx, NY, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, NY, NY, USA
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Tim Crow
eDepartment of Psychiatry, SANE POWIC, Warneford Hospital, University of Oxford, UK
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Anthony James
eDepartment of Psychiatry, SANE POWIC, Warneford Hospital, University of Oxford, UK
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Matcheri Keshavan
fDepartment of Psychiatry, Beth Israel Deaconess Medical Centre, Harvard Medical School, Boston, Massachusetts, USA
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Marek Kubicki
aDepartment of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
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Yogesh Rathi
aDepartment of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
bDepartment of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
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Abstract

The findings from diffusion-weighted magnetic resonance imaging (dMRI) studies often show inconsistent and sometimes contradictory results due to small sample sizes as well as differences in acquisition parameters and pre-/post-processing methods. To address these challenges, collaborative multi-site initiatives have provided an opportunity to collect larger and more diverse groups of subjects, including those with neuropsychiatric disorders, leading to increased power and findings that may be more representative at the group and individual level. With the availability of these datasets openly, the ability of joint analysis of multi-site dMRI data has become more important than ever. However, intrinsic- or acquisition-related variability in scanner models, acquisition protocols, and reconstruction settings hinder pooling multi-site dMRI directly. One powerful and fast statistical harmonization method called ComBat (https://github.com/Jfortin1/ComBatHarmonization) was developed to mitigate the “batch effect” in gene expression microarray data and was adapted for multi-site dMRI harmonization to reduce scanner/site effect. Our goal is to evaluate this commonly used harmonization approach using a large diffusion MRI dataset involving 542 individuals from 5 sites. We investigated two important aspects of using ComBat for harmonization of fractional anisotropy (FA) across sites: First, we assessed how well ComBat preserves the inter-subject biological variability (measured by the effect sizes of between-group FA differences) after harmonization. Second, we evaluated the effect of minor differences in pre-processing on ComBat’s performance. While the majority of effect sizes are mostly preserved in some sites after harmonization, they are not well-preserved at other sites where non-linear scanner contributions exist. Further, even minor differences in pre-processing can yield unwanted effects during ComBat harmonization. Thus, our findings suggest paying careful attention to the data being harmonized as well as using the same processing pipeline while using ComBat for data harmonization.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 21, 2020.
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Exploring the limits of ComBat method for multi-site diffusion MRI harmonization
Suheyla Cetin-Karayumak, Katharina Stegmayer, Sebastian Walther, Philip R. Szeszko, Tim Crow, Anthony James, Matcheri Keshavan, Marek Kubicki, Yogesh Rathi
bioRxiv 2020.11.20.390120; doi: https://doi.org/10.1101/2020.11.20.390120
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Exploring the limits of ComBat method for multi-site diffusion MRI harmonization
Suheyla Cetin-Karayumak, Katharina Stegmayer, Sebastian Walther, Philip R. Szeszko, Tim Crow, Anthony James, Matcheri Keshavan, Marek Kubicki, Yogesh Rathi
bioRxiv 2020.11.20.390120; doi: https://doi.org/10.1101/2020.11.20.390120

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