SS‐Detect: Development and Validation of a New Strategy for Source‐Based Morphometry in Multiscanner Studies. Issue 2 (3rd December 2020)

• Record Type: Journal Article
• Authors: Ge, Ruiyang; Ding, Shiqing; Keeling, Tyler; Honer, William G.; Frangou, Sophia; Vila‐Rodriguez, Fidel
• Is Part Of: Journal of neuroimaging. Volume 31:Issue 2(2021)
• Page Start: 261
• Page End: 271
• Publication Date: 2020-12-03
• Subjects: Simulation -- source‐based morphometry -- structural brain pattern -- T1‐weighted MRI -- multiscanner study; Diagnostic imaging -- Periodicals; Nervous system -- Diseases -- Diagnosis -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Système nerveux -- Maladies -- Diagnostic -- Périodiques; Imagerie médicale; Neuroimagerie; Neurologie; Système nerveux; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.804754
• DOI: 10.1111/jon.12814
• Languages: English
• ISSNs: 1051-2284

Abstract

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 S canner‐S pecific D etection (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.