ABSTRACT
Background and Purpose Source-based morphometry (SBM) is a data-driven multivariate approach for interrogating covariation in structural brain patterns (SBPs) across subjects and quantifying the subject-specific loading parameters of these patterns. This approach has been used in multi-centre studies pooling magnetic resonance imaging (MRI) data across different scanners to advance the reproducibility of neuroscience research. In the present study, we developed an analysis strategy for Scanner-Specific Detection (SS-Detect) of SBPs in multiscanner studies, and evaluated its performance relative to a conventional strategy.
Methods We conducted two simulation experiments. In the first experiment, the SimTB toolbox was used to generate simulation datasets mimicking twenty different scanners with common and scanner-specific SBPs. In the second experiment, we generated one simulated SBP from empirical datasets from two different scanners.
Results The outputs of the conventional strategy were limited to whole-sample-level results across all scanners; the outputs of SS-Detect included whole-sample-level and scanner-specific results. In the first simulation experiment, SS-Detect successfully estimated all simulated SBPs, including the common and scanner-specific SBPs whereas the conventional strategy detected only some of the whole-sample SBPs. The second simulation experiment showed that both strategies could detect the simulated SBP. Quantitative evaluations of both experiments demonstrated greater accuracy of the SS-Detect in estimating spatial SBPs and subject-specific loading parameters.
Conclusions SS-Detect outperformed the conventional strategy in terms of accurately estimating spatial SBPs and loading parameters both at whole-sample and scanner-specific levels and can be considered advantageous when SBM is applied to a multi-scanner study.
Competing Interest Statement
RG, SD, TK, and SF declare no potential conflict of interest with respect to the research, authorship, and/or publication of this article. Dr. Honer has received consulting fees or sat on paid advisory boards for: AlphaSights, Guidepoint, In Silico, Translational Life Sciences, Otsuka, Lundbeck, and Newron, and holds shares in Translational Life Sciences and Eli Lilly. FVR reports research grants from CIHR, Brain Canada, Michael Smith Foundation for Health Research, and Vancouver Coastal Health Research Institute; reports receiving in-kind equipment support for this investigator-initiated trial from MagVenture; and consulting honoraria from Janssen pharmaceutical.