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Moderated t-tests for group-level fMRI analysis.

Guoqing Wang1, John Muschelli1, Martin A Lindquist1

  • 1Department of Biostatistics, Johns Hopkins University, Baltimore, MD, United States.

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|May 7, 2021
PubMed
Summary
This summary is machine-generated.

Small sample functional magnetic resonance imaging (fMRI) studies often lack power. A moderated t-statistic improves reliability in fMRI group analyses with fewer than 40 subjects.

Keywords:
Group analysisLIMMALIMMIModerated t-testfMRI

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Area of Science:

  • Neuroimaging
  • Statistical Analysis
  • Brain Imaging

Background:

  • Small sample sizes in functional magnetic resonance imaging (fMRI) studies lead to low statistical power and replicability issues.
  • High scanning costs contribute to the continued use of small sample sizes in fMRI research.
  • Published fMRI results from small samples are frequently unreproducible and potentially false.

Purpose of the Study:

  • To investigate the efficacy of a moderated t-statistic for group-level fMRI analysis in small sample sizes.
  • To address the statistical power and reliability concerns associated with small sample fMRI studies.
  • To introduce a novel R-package, LIMMI, for applying this statistical approach to medical imaging data.

Main Methods:

  • Utilized task-based fMRI data from the Human Connectome Project (HCP).
  • Compared the moderated t-statistic (from the LIMMA package) against standard and pseudo t-statistics.
  • Evaluated performance using both voxel-based and cluster-based thresholding methods.

Main Results:

  • The moderated t-statistic significantly outperformed standard and pseudo t-statistics for fMRI studies with sample sizes under 40 subjects.
  • Consistent results were observed across both voxel-based and cluster-based thresholding techniques.
  • The LIMMA package's moderated t-statistic approach, adapted for fMRI, proved effective.

Conclusions:

  • The moderated t-statistic offers a viable solution to enhance statistical power and reliability in small sample fMRI studies.
  • The LIMMI R-package provides a user-friendly tool for implementing this improved statistical method.
  • This approach can help mitigate the problem of unreproducible findings in neuroimaging research.