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

Ruiyang Ge; Shiqing Ding; Tyler Keeling; William G Honer; Sophia Frangou; Fidel Vila-Rodriguez

doi:10.1111/jon.12814

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

J Neuroimaging. 2021 Mar;31(2):261-271. doi: 10.1111/jon.12814. Epub 2020 Dec 3.

Authors

Ruiyang Ge¹, Shiqing Ding¹, Tyler Keeling¹, William G Honer², Sophia Frangou^{2

3}, Fidel Vila-Rodriguez¹

Affiliations

¹ Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada.
² Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada.
³ Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, New York, US.

PMID: 33270962
DOI: 10.1111/jon.12814

Abstract

Background and purpose: Source-based morphometry(SBM) has been used in multicenter studies pooling magnetic resonance imaging 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: In the first experiment, the SimTB toolbox was used to generate simulated datasets mimicking 20 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 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 multiscanner study.

Keywords: Simulation; T1-weighted MRI; multiscanner study; source-based morphometry; structural brain pattern.

Publication types

Validation Study

MeSH terms

Adult
Case-Control Studies
Gray Matter / diagnostic imaging
Humans
Magnetic Resonance Imaging / instrumentation*
Male
Reproducibility of Results
Schizophrenia / diagnostic imaging