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False positive rates in surface-based anatomical analysis.

Douglas N Greve1, Bruce Fischl1

  • 1Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Radiology Department, Boston, MA, USA.

Neuroimage
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PubMed
Summary
This summary is machine-generated.

Non-parametric permutation testing effectively controls false positive rates (FPRs) in brain imaging analyses, unlike parametric methods which are susceptible to inflated FPRs due to non-Gaussian spatial correlations in cortical thickness, surface area, and volume data.

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Area of Science:

  • Neuroimaging
  • Brain Anatomy
  • Statistical Analysis

Background:

  • Surface-based morphometry is crucial for analyzing brain structure.
  • Accurate statistical correction for multiple comparisons is essential in neuroimaging studies.
  • Parametric methods in group analysis may yield inflated false positive rates (FPRs).

Purpose of the Study:

  • To evaluate the false positive rates (FPRs) of parametric and non-parametric clusterwise correction methods.
  • To assess the impact of smoothing and cluster-forming thresholds on FPRs in surface-based analyses.
  • To compare the performance of these methods across cortical thickness, surface area, and volume.

Main Methods:

  • Real neuroimaging data were analyzed using surface-based group analysis.
  • Parametric and non-parametric clusterwise correction methods were applied.
  • Analyses included whole cortical surface and interhemispheric asymmetry measures.
  • Varying smoothing levels and cluster-forming thresholds (CFT) were tested.

Main Results:

  • Parametric methods showed inflated FPRs for cortical thickness (10% vs. 5%), surface area (20-30%), and volume.
  • Non-parametric permutation testing consistently controlled FPRs across all measures.
  • Inflated parametric FPRs were linked to violations of Gaussian spatial correlation assumptions, stemming from individual anatomical variations.

Conclusions:

  • Non-parametric permutation testing is a reliable method for controlling FPRs in surface-based neuroimaging analyses.
  • Parametric methods are vulnerable to inflated FPRs when spatial correlation assumptions are violated.
  • Cortical thickness analysis demonstrated better performance than surface area and volume due to the absence of Jacobian correction.