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Updated: May 19, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

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Bootstrapping GEE models for fMRI regional connectivity.

Gina M D'Angelo1, Nicole A Lazar, Gongfu Zhou

  • 1Division of Biostatistics, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO 63110, USA. gina@wubios.wustl.edu

Neuroimage
|August 22, 2012
PubMed
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Generalized estimating equation (GEE) transition models offer superior statistical properties for analyzing Alzheimer's disease functional connectivity. These models effectively detect group differences in brain region correlations, outperforming traditional methods.

Area of Science:

  • Neuroimaging
  • Biostatistics
  • Alzheimer's Disease Research

Background:

  • Functional connectivity analysis using resting-state fMRI is crucial for understanding brain alterations in Alzheimer's disease.
  • Existing methods for comparing inter-regional correlations between groups have limitations.
  • The ApoE4 genotype is a significant risk factor for late-onset Alzheimer's disease.

Purpose of the Study:

  • To evaluate and compare statistical methods for detecting differences in inter-regional brain correlations between groups, motivated by Alzheimer's disease research.
  • To assess the performance of Generalized Estimating Equation (GEE) marginal and transition models against the standard pooling Fisher-z approach.
  • To validate these methods using simulation studies and a real-world functional connectivity dataset.

Main Methods:

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Last Updated: May 19, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Published on: July 1, 2014

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

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Published on: October 13, 2023

  • Application of Generalized Estimating Equation (GEE) marginal and transition models.
  • Comparison with the standard pooling Fisher-z method.
  • Evaluation using simulation studies and analysis of resting-state fMRI data from cognitively normal ApoE4+ and ApoE4- participants.

Main Results:

  • GEE transition models demonstrated the best statistical properties for analyzing group differences in functional connectivity.
  • Both model-based and bootstrap methods for estimating standard errors performed comparably.
  • The study successfully applied these methods to a cohort from the Washington University Knight Alzheimer's Disease Research Center.

Conclusions:

  • GEE transition models provide a robust statistical framework for analyzing group differences in functional brain connectivity.
  • The findings support the utility of GEE models in Alzheimer's disease research and other neuroimaging studies.
  • Accurate estimation of standard errors is achievable through both theoretical and empirical approaches.