RT Journal Article
SR Electronic
T1 SS-Detect: Development and Validation of a New Strategy for Source-Based Morphometry in Multi-Scanner Studies
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.09.03.282236
DO 10.1101/2020.09.03.282236
A1 Ruiyang Ge
A1 Shiqing Ding
A1 Tyler Keeling
A1 William G. Honer
A1 Sophia Frangou
A1 Fidel Vila-Rodriguez
YR 2020
UL http://biorxiv.org/content/early/2020/10/28/2020.09.03.282236.abstract
AB Background and Purpose Source-based morphometry (SBM) 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 multi-scanner studies, and evaluated its performance relative to a conventional strategy.Methods In the first experiment, the SimTB toolbox was used to generate simulated datasets mimicking twenty different scanners with common and scanner-specific SBPs. In the second experiment, we generated one simulated SBP from empirical gray matter volume (GMV) datasets from two different scanners. Moreover, we applied two strategies to compare SBPs between schizophrenia patients’ and healthy controls’ GMV 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. In the third experiment, SS-Detect detected more significant between-group SBPs, and these SBPs corresponded with the results from voxel-based morphometry analysis, suggesting that SS-Detect has higher sensitivity in detecting between-group differences.Conclusions SS-Detect outperformed the conventional strategy and can be considered advantageous when SBM is applied to a multi-scanner study.Competing Interest StatementWe sincerely thank the study participants and MRI technologists at the UBC MRI Research Centre; we also sincerely thank the Center for Biomedical Research Excellence (COBRE) for sharing their data. 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.