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Detecting and harmonizing scanner differences in the ABCD study - annual release 1.0

View ORCID ProfileDylan M. Nielson, View ORCID ProfileFrancisco Pereira, View ORCID ProfileCharles Y. Zheng, View ORCID ProfileNino Migineishvili, View ORCID ProfileJohn A. Lee, View ORCID ProfileAdam G. Thomas, View ORCID ProfilePeter A. Bandettini
doi: https://doi.org/10.1101/309260
Dylan M. Nielson
1Data Science and Sharing Team, DIRP, NIMH, NIH, Bethesda, MD
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Francisco Pereira
2Machine Learning Team, DIRP, NIMH, NIH, Bethesda, MD
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Charles Y. Zheng
2Machine Learning Team, DIRP, NIMH, NIH, Bethesda, MD
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Nino Migineishvili
1Data Science and Sharing Team, DIRP, NIMH, NIH, Bethesda, MD
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John A. Lee
1Data Science and Sharing Team, DIRP, NIMH, NIH, Bethesda, MD
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Adam G. Thomas
1Data Science and Sharing Team, DIRP, NIMH, NIH, Bethesda, MD
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Peter A. Bandettini
3Functional MRI Facility, DIRP, NIMH, NIH, Bethesda, MD
4Section on Functional Imaging Methods, DIRP, NIMH, NIH, Bethesda, MD
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Abstract

In order to obtain the sample sizes needed for robustly reproducible effects, it is often necessary to acquire data at multiple sites using different MRI scanners. This poses a challenge for investigators to account for the variance due to scanner, as balanced sampling is often not an option. Similarly, longitudinal studies must deal with known and unknown changes to scanner hardware and software over time. In this manuscript, we have explored scanner-related differences in the dataset recently released by the Adolescent Brain Cognitive Development (ABCD) project, a multi-site, longitudinal study of children age 9-10. We demonstrate that scanner manufacturer, model, as well as the individual scanner itself, are detectable in the resting and task-based fMRI results of the ABCD dataset. We further demonstrate that these differences can be harmonized using an empirical Bayes approach known as ComBat. We argue that accounting for scanner variance, including even minor differences in scanner hardware or software, is crucial for any analysis.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license.
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Posted May 02, 2018.
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Detecting and harmonizing scanner differences in the ABCD study - annual release 1.0
Dylan M. Nielson, Francisco Pereira, Charles Y. Zheng, Nino Migineishvili, John A. Lee, Adam G. Thomas, Peter A. Bandettini
bioRxiv 309260; doi: https://doi.org/10.1101/309260
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Detecting and harmonizing scanner differences in the ABCD study - annual release 1.0
Dylan M. Nielson, Francisco Pereira, Charles Y. Zheng, Nino Migineishvili, John A. Lee, Adam G. Thomas, Peter A. Bandettini
bioRxiv 309260; doi: https://doi.org/10.1101/309260

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