• Medientyp: Bericht; E-Book; Sonstige Veröffentlichung
  • Titel: Utilizing anatomical information for signal detection in functional magnetic resonance imaging
  • Beteiligte: Neumann, André [VerfasserIn]; Peitek, Norman [VerfasserIn]; Brechmann, André [VerfasserIn]; Tabelow, Karsten [VerfasserIn]; Dickhaus, Thorsten [VerfasserIn]
  • Erschienen: Weierstrass Institute for Applied Analysis and Stochastics publication server, 2021
  • Sprache: Englisch
  • DOI: https://doi.org/10.20347/WIAS.PREPRINT.2806
  • Schlagwörter: 62-07 ; Aparc label -- combination test -- false discovery rate -- family-wise error rate -- mass-univariate linear model -- multiple testing -- program comprehension ; article ; 62P10 ; 62J15
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  • Beschreibung: 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.
  • Zugangsstatus: Freier Zugang