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Related Experiment Video

Updated: Sep 22, 2025

Basics of Multivariate Analysis in Neuroimaging Data
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Parsimonious model for mass-univariate vertexwise analysis.

Baptiste Couvy-Duchesne1,2, Futao Zhang1, Kathryn E Kemper1

  • 1University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland, Australia.

Journal of Medical Imaging (Bellingham, Wash.)
|May 25, 2022
PubMed
Summary

Linear mixed models (LMMs) offer more precise brain network mapping than general linear models (GLMs) by reducing false positive associations in neuroimaging studies. LMMs provide parsimonious results, controlling for confounding factors in large-scale brain data analysis.

Keywords:
associationbrain mappinglinear mixed modelstructural brain MRIvertex-wise processing

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Area of Science:

  • Neuroimaging
  • Brain Network Analysis
  • Statistical Genetics

Background:

  • Covariance in gray-matter measurements can reveal brain networks but is susceptible to confounding factors like age and head size.
  • Confounding factors can reduce mapping precision and introduce false positives in vertexwise analyses.
  • General linear models (GLMs) are commonly used but may be influenced by these confounding effects.

Purpose of the Study:

  • To evaluate the impact of confounding factors on mass-univariate vertexwise analyses.
  • To compare the performance of GLMs with linear mixed models (LMMs) in neuroimaging association studies.
  • To assess the precision and false positive rates of LMMs versus GLMs using simulated and real brain data.

Main Methods:

  • Simulated vertex-wise gray matter data (thickness, surface area) from real data.
  • Mass-univariate analyses using the general linear model (GLM).
  • Linear mixed models (LMMs) applied to simulated and real UK Biobank data (N=8662).

Main Results:

  • GLMs on large samples showed inflated false positive rates (cluster FDR > 0.6).
  • LMMs produced more parsimonious results with smaller clusters and reduced false positives, despite higher computational cost.
  • Analysis of five UK Biobank traits revealed fewer, more localized associations with LMMs, identifying 19 significant clusters for age, sex, and BMI.

Conclusions:

  • Published neuroimaging literature may contain numerous redundant or confounded associations.
  • LMMs effectively prevent local and distal redundant associations by controlling for joint effects across all vertices.
  • LMMs offer a more robust and precise approach for brain network analysis compared to traditional GLMs.