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A statistical analysis of brain morphology using wild bootstrapping.

Hongtu Zhu1, Joseph G Ibrahim, Niansheng Tang

  • 1Department of Biostatistics and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC 27599-7420, USA. hzhu@bios.unc.edu

IEEE Transactions on Medical Imaging
|July 26, 2007
PubMed
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New statistical methods enhance brain morphology analysis. Wild bootstrapping offers a robust, computationally simple approach for detecting significant associations in neuroimaging data, controlling errors effectively.

Area of Science:

  • Neuroimaging
  • Statistical analysis
  • Brain morphology

Background:

  • Voxel-based and surface-based morphometry analyze brain structure associations with covariates.
  • Standard analysis involves voxel-wise modeling and multiple testing correction.

Purpose of the Study:

  • To introduce novel statistical methods for brain morphology analysis.
  • To develop a robust test procedure for assessing associations between brain structure and covariates.

Main Methods:

  • Utilized a heteroscedastic linear model for voxel-wise association testing.
  • Developed a wild bootstrapping resampling method for robust statistical significance assessment.
  • Applied methods to analyze hippocampal morphology differences over time across gender groups.

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Main Results:

  • The wild bootstrapping procedure accurately controls the family-wise error rate.
  • Demonstrated application in detecting significant hippocampal morphology differences in healthy subjects.

Conclusions:

  • The proposed robust test procedure is computationally simple and widely applicable to neuroimaging data.
  • This method enhances the reliability of detecting structure-covariate associations in brain morphology studies.