Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias

Abstract

When collecting large amounts of neuroimaging data associated with psychiatric disorders, images must be acquired from multiple sites because of the limited capacity of a single site. However, site differences represent a barrier when acquiring multisite neuroimaging data. We utilized a traveling-subject dataset in conjunction with a multisite, multidisorder dataset to demonstrate that site differences are composed of biological sampling bias and engineering measurement bias. The effects on resting-state functional MRI connectivity based on pairwise correlations because of both bias types were greater than or equal to psychiatric disorder differences. Furthermore, our findings indicated that each site can sample only from a subpopulation of participants. This result suggests that it is essential to collect large amounts of neuroimaging data from as many sites as possible to appropriately estimate the distribution of the grand population. Finally, we developed a novel harmonization method that removed only the measurement bias by using a traveling-subject dataset and achieved the reduction of the measurement bias by 29% and improvement of the signal-to-noise ratios by 40%. Our results provide fundamental knowledge regarding site effects, which is important for future research using multisite, multidisorder resting-state functional MRI data.

Nonreplication of functional connectivity differences in autism spectrum disorder across multiple sites and denoising strategies

“…Fortunately, these factors, while they do contribute to across-site variance, tend to be small in terms of effect size (Brown et al, 2011;Dansereau et al, 2017;Noble et al, 2017) or result in localized differences (Nair et al, 2018), consistent with our finding that group-averaged connectomes were highly reliable across sites. To further increase chances of replication, either a priori coordination and standardization of procedures (Glover et al, 2012) or the implementation of postprocessing methods designed to increase multisite data harmonization would both be possibilities (Yamashita et al, 2019;Yu et al, 2018).…”

“…Further complicating the picture is that site effects, or variation across different scanning sites, have been reported in several studies of both task-based and resting-state fMRI (Brown et al, 2011;Dansereau et al, 2017;Noble et al, 2017;Turner et al, 2013;Yamashita et al, 2019;Yan, Craddock, Zuo, Zang, & Milham, 2013;Yu et al, 2018). Different sites present many potential sources of variation, including differences in participant (i.e., cohort) characteristics, image acquisition parameters, scanners, scan procedures, and more.…”

Non-replication of functional connectivity differences in autism spectrum disorder across multiple sites and denoising strategies

“…On the data collection side, it is possible that uncontrolled factors (including some that remain uncontrolled in the present study) contribute to this lack of replication. These factors include scanner and acquisition parameter differences (e.g., pulse sequence, voxel size, phase encoding directions, scanner manufacturer, etc; Yamashita et al, 2019), as well as experimental procedural differences (e.g., eyes open or closed, experiences immediately preceding the functional scan; Nair et al, 2018). Fortunately, these factors, while they do contribute to across-site variance, tend to be small in terms of effect size (Brown et al, 2011;Dansereau et al, 2017;Noble et al, 2017) or result in localized differences (Nair et al, 2018), consistent with our finding that group-average connectomes were highly reliable across sites.…”

“…Fortunately, these factors, while they do contribute to across-site variance, tend to be small in terms of effect size (Brown et al, 2011;Dansereau et al, 2017;Noble et al, 2017) or result in localized differences (Nair et al, 2018), consistent with our finding that group-average connectomes were highly reliable across sites. To further increase chances of replication, either a priori coordination and standardization of procedures (Glover et al, 2012) or the implementation of post-processing methods designed to increase multisite data harmonization would both be possibilities (Yamashita et al, 2019;Yu et al, 2018).…”

“…Further complicating the picture is that data site effects, or variation across different scanning sites, have been reported in several studies of both task-based and resting-state fMRI (Brown et al, 2011;Dansereau et al, 2017;Noble et al, 2017;Turner et al, 2013;Yamashita et al, 2019;Yan, Craddock, Zuo, Zang, & Milham, 2013;Yu et al, 2018). Different data sites present many potential sources of variation, including differences in participant (i.e., cohort) characteristics, image acquisition parameters, scanners, scan procedures, and more.…”