WIAS Preprint No. 2806, (2021)

Utilizing anatomical information for signal detection in functional magnetic resonance imaging



Authors

  • Neumann, André
  • Peitek, Norman
  • Brechmann, André
  • Tabelow, Karsten
    ORCID: 0000-0003-1274-9951
  • Dickhaus, Thorsten

2020 Mathematics Subject Classification

  • 62J15 62P10 62-07

Keywords

  • Aparc label, combination test, false discovery rate, family-wise error rate, mass-univariate linear model, multiple testing, program comprehension

DOI

10.20347/WIAS.PREPRINT.2806

Abstract

We are considering the statistical analysis of functional magnetic resonance imaging (fMRI) data. As demonstrated in previous work, grouping voxels into regions (of interest) and carrying out a multiple test for signal detection on the basis of these regions typically leads to a higher sensitivity when compared with voxel-wise multiple testing approaches. In the case of a multi-subject study, we propose to define the regions for each subject separately based on their individual brain anatomy, represented, e.g., by so-called Aparc labels. The aggregation of the subject-specific evidence for the presence of signals in the different regions is then performed by means of a combination function for p-values. We apply the proposed methodology to real fMRI data and demonstrate that our approach can perform comparably to a two-stage approach for which two independent experiments are needed, one for defining the regions and one for actual signal detection.

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