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False positives in neuroimaging genetics using voxel-based morphometry data.

Matt Silver1, Giovanni Montana, Thomas E Nichols

  • 1Department of Mathematics, Imperial College London, London, UK.

Neuroimage
|September 21, 2010
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Summary

This study on brain imaging genetics found that cluster size inference tests control false positive rates well with high thresholds and smoothing. However, lower thresholds or less smoothing led to inflated false positives, suggesting caution and alternative methods.

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Area of Science:

  • Neuroimaging
  • Statistical Genetics
  • Brain Imaging Analysis

Background:

  • Voxel-wise statistical inference is standard in brain imaging studies.
  • Cluster size inference increases statistical power for detecting effects.
  • Concerns exist regarding false positive control in imaging genetics using cluster inference.

Purpose of the Study:

  • To evaluate false positive rates in imaging genetics using cluster size inference.
  • To assess the impact of cluster-forming thresholds and image smoothing on false positive rates.
  • To provide recommendations for robust statistical inference in voxel-based morphometry (VBM) studies.

Main Methods:

  • Investigated false positive rates using 700 null single nucleotide polymorphisms (SNPs) and grey matter volume in 181 mild cognitive impairment subjects from ADNI.
  • Employed both non-stationary and stationary cluster size tests due to spatially varying smoothness in VBM data.
  • Analyzed false positive rates with varying cluster-forming thresholds (α(c)=0.001, 0.01, 0.05) and Gaussian kernel smoothing (6mm, 12mm).

Main Results:

  • False positive rates were well-controlled (3.9-5.6%) at a 5% significance level with a high cluster-forming threshold (α(c)=0.001) and 12mm smoothing.
  • Tests became anticonservative at lower thresholds (α(c)=0.01, 0.05) and with 6mm smoothing, yielding false positive rates from 9.8% to 67.6%.
  • Simulated data analysis showed well-controlled false positive rates across various conditions.

Conclusions:

  • Parametric cluster size inference in VBM studies requires high cluster-forming thresholds and substantial image smoothing for adequate false positive control.
  • Lower thresholds or reduced smoothing increase the risk of false positives, potentially impacting findings in imaging genetics and other VBM research.
  • Nonparametric methods are recommended when conditions do not permit high thresholds and smoothing, ensuring more reliable statistical inference.