Peak p-values and false discovery rate inference in neuroimaging

Armin Schwartzman; Fabian Telschow

doi:10.1016/j.neuroimage.2019.04.041

Peak p-values and false discovery rate inference in neuroimaging

Neuroimage. 2019 Aug 15:197:402-413. doi: 10.1016/j.neuroimage.2019.04.041. Epub 2019 Apr 24.

Authors

Armin Schwartzman¹, Fabian Telschow²

Affiliations

¹ Division of Biostatistics, University of California, San Diego, USA. Electronic address: [email protected].
² Division of Biostatistics, University of California, San Diego, USA. Electronic address: [email protected].

Abstract

Peaks are a mainstay of neuroimage analysis for reporting localization results. The current peak detection procedure in SPM12 requires a pre-threshold for approximating p-values and a false discovery rate (FDR) nominal level for inference. However, the pre-threshold is an undesirable feature, while the FDR level is meaningless if the null hypothesis is not properly defined. This article provides: 1) a peak height distribution for smooth Gaussian error fields, which does not require a screening pre-threshold; 2) a signal-plus-noise model where FDR of peaks can be controlled and properly interpreted. Matlab code for calculation of p-values using the exact peak height distribution is available as an SPM extension.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Brain / physiology*
Brain Mapping / methods*
Data Interpretation, Statistical
Humans
Image Processing, Computer-Assisted / methods
Neuroimaging / methods*
Normal Distribution
Reproducibility of Results

Abstract

Publication types

MeSH terms

Grants and funding