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

Ruiyang Ge1, Shiqing Ding1, Tyler Keeling1

  • 1Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada.

Journal of Neuroimaging : Official Journal of the American Society of Neuroimaging
|December 3, 2020
PubMed
Summary

A new Scanner-Specific Detection (SS-Detect) method improves the analysis of neuroimaging data across multiple scanners. This strategy enhances the detection of subtle brain patterns (SBPs) and increases reproducibility in multicenter neuroscience research.

Keywords:
SimulationT1-weighted MRImultiscanner studysource-based morphometrystructural brain pattern

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Area of Science:

  • Neuroimaging analysis
  • Computational neuroscience
  • Statistical modeling

Background:

  • Multicenter neuroimaging studies rely on pooling data from diverse scanners.
  • Source-based morphometry (SBM) aims to improve research reproducibility but faces challenges with scanner variability.
  • Existing methods may not adequately account for scanner-specific effects in SBM.

Purpose of the Study:

  • To develop and evaluate a novel analysis strategy, Scanner-Specific Detection (SS-Detect), for SBM in multiscanner studies.
  • To compare the performance of SS-Detect against a conventional SBM strategy.
  • To enhance the detection and characterization of subtle brain patterns (SBPs) in diverse datasets.

Main Methods:

  • Simulated datasets were generated using the SimTB toolbox to mimic 20 different scanners, including common and scanner-specific SBPs.
  • Empirical gray matter volume (GMV) datasets from two scanners were used to simulate an SBP.
  • Two strategies (conventional and SS-Detect) were applied to compare SBPs between patient and control groups across scanners.

Main Results:

  • SS-Detect successfully identified all simulated common and scanner-specific SBPs, outperforming the conventional strategy which only detected some.
  • Quantitative evaluations showed SS-Detect's superior accuracy in estimating spatial SBPs and subject-specific loading parameters.
  • SS-Detect demonstrated higher sensitivity in detecting significant between-group differences in SBPs, aligning with voxel-based morphometry results.

Conclusions:

  • The developed SS-Detect strategy significantly outperforms the conventional approach for SBM in multiscanner studies.
  • SS-Detect offers improved accuracy and sensitivity for detecting subtle brain patterns and group differences.
  • This method enhances the reliability and reproducibility of neuroimaging research utilizing multicenter data.