SS-Detect: Development and Validation of a New Strategy for Source-Based Morphometry in Multi-Scanner Studies

Abstract

Background and PurposeSource-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.MethodsWe 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.ResultsThe 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.ConclusionsSS-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.