Simultaneous control of error rates in fMRI data analysis

Hakmook Kang*, Jeffrey Blume, Hernando Ombao, David Badre

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

The key idea of statistical hypothesis testing is to fix, and thereby control, the Type I error (false positive) rate across samples of any size. Multiple comparisons inflate the global (family-wise) Type I error rate and the traditional solution to maintaining control of the error rate is to increase the local (comparison-wise) Type II error (false negative) rates. However, in the analysis of human brain imaging data, the number of comparisons is so large that this solution breaks down: the local Type II error rate ends up being so large that scientifically meaningful analysis is precluded. Here we propose a novel solution to this problem: allow the Type I error rate to converge to zero along with the Type II error rate. It works because when the Type I error rate per comparison is very small, the accumulation (or global) Type I error rate is also small. This solution is achieved by employing the likelihood paradigm, which uses likelihood ratios to measure the strength of evidence on a voxel-by-voxel basis. In this paper, we provide theoretical and empirical justification for a likelihood approach to the analysis of human brain imaging data. In addition, we present extensive simulations that show the likelihood approach is viable, leading to "cleaner"-looking brain maps and operational superiority (lower average error rate). Finally, we include a case study on cognitive control related activation in the prefrontal cortex of the human brain.

Original languageEnglish (US)
Pages (from-to)102-113
Number of pages12
JournalNeuroImage
Volume123
DOIs
StatePublished - Dec 1 2015

Keywords

  • Functional magnetic resonance imaging
  • Likelihood paradigm
  • Likelihood ratio
  • Multiple comparison

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

Fingerprint

Dive into the research topics of 'Simultaneous control of error rates in fMRI data analysis'. Together they form a unique fingerprint.

Cite this