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

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.