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Simultaneous control of error rates in fMRI data analysis.

Hakmook Kang1, Jeffrey Blume2, Hernando Ombao3

  • 1Department of Biostatistics, Vanderbilt University, Nashville, TN 37203, USA; Center for Quantitative Sciences, Vanderbilt University, Nashville, TN 37232, USA.

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|August 15, 2015
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Summary

This study introduces a novel likelihood approach for analyzing human brain imaging data. It controls both Type I (false positive) and Type II (false negative) errors, enabling more scientifically meaningful results.

Keywords:
Functional magnetic resonance imagingLikelihood paradigmLikelihood ratioMultiple comparison

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Area of Science:

  • Neuroimaging
  • Statistical Analysis
  • Cognitive Neuroscience

Background:

  • Traditional statistical hypothesis testing struggles with the high number of comparisons in human brain imaging.
  • Multiple comparisons inflate the global Type I error rate, forcing an increase in Type II error rates, which hinders meaningful analysis.

Purpose of the Study:

  • To propose a novel solution for controlling statistical errors in human brain imaging data analysis.
  • To introduce a likelihood-based approach that allows Type I and Type II error rates to converge to zero.

Main Methods:

  • Employing the likelihood paradigm using likelihood ratios to measure evidence on a voxel-by-voxel basis.
  • Theoretical and empirical justification for the likelihood approach.
  • Extensive simulations to validate the method's viability and performance.

Main Results:

  • The likelihood approach effectively controls both Type I and Type II error rates.
  • Simulations demonstrate the approach leads to "cleaner" brain maps and operational superiority.
  • The method is shown to be viable for analyzing human brain imaging data.

Conclusions:

  • The proposed likelihood approach offers a robust solution to the multiple comparisons problem in brain imaging.
  • This method enhances the scientific meaningfulness of brain imaging analyses by improving error control.
  • The approach is validated through simulations and a case study on prefrontal cortex activation.