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Evaluation of Second-Level Inference in fMRI Analysis.

Sanne P Roels1, Tom Loeys1, Beatrijs Moerkerke1

  • 1Department of Data Analysis, Ghent University, H. Dunantlaan 1, 9000 Ghent, Belgium.

Computational Intelligence and Neuroscience
|January 29, 2016
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Summary

The two-step cluster-based thresholding method enhances reproducibility in functional magnetic resonance imaging (fMRI) second-level analysis. Familywise error rate correction offers moderate stability, while False Discovery Rate (FDR) correction yields the most variable results.

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Area of Science:

  • Neuroimaging
  • Statistical analysis in neuroscience

Background:

  • Reproducibility in functional magnetic resonance imaging (fMRI) is crucial for reliable scientific findings.
  • Second-level analysis in fMRI involves inferential processes over subjects, impacting result validity.
  • Key challenges include balancing false positives and negatives and ensuring data-analytical stability.

Purpose of the Study:

  • To investigate the impact of different second-level inferential decisions on fMRI result reproducibility.
  • To evaluate how modeling within-subject variability affects statistical outcomes.
  • To compare the performance of various multiple testing correction procedures.

Main Methods:

  • The study employed a mass univariate approach for second-level fMRI analysis.
  • Evaluated general linear models with and without first-level subject variability.
  • Compared parametric inference with permutation-based inference.
  • Assessed three multiple testing correction methods: familywise error rate, False Discovery Rate (FDR), and a two-step cluster size procedure.

Main Results:

  • The two-step procedure with minimal cluster size yielded the most stable results, followed by familywise error rate correction.
  • False Discovery Rate (FDR) correction resulted in the highest variability across inference methods.
  • Modeling subject-specific variability improved the balance of false positives and negatives with parametric inference.

Conclusions:

  • The choice of multiple testing correction significantly influences the stability and reproducibility of fMRI second-level analyses.
  • The two-step cluster size method is recommended for enhancing data-analytical stability.
  • Incorporating subject-specific variability can optimize statistical inference in fMRI studies.