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Generative modeling of brain maps with spatial autocorrelation.

Joshua B Burt1, Markus Helmer2, Maxwell Shinn3

  • 1Yale University, Department of Physics, USA.

Neuroimage
|June 26, 2020
PubMed
Summary

This study introduces a new software tool to create realistic brain maps for statistical analysis. This helps researchers distinguish true brain organization patterns from random chance, improving the reliability of neuroscience findings.

Keywords:
Gene set enrichment analysisGenerative null modelingLarge-scale gradientsSpatial autocorrelation

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Statistical Modeling

Background:

  • Large-scale brain organization studies reveal spatial gradient relationships across modalities.
  • Statistical significance requires null hypothesis expectations, often unmet by conventional tests due to spatial autocorrelation (SA).

Purpose of the Study:

  • To develop a generative null model for creating synthetic brain maps that preserve empirical spatial autocorrelation.
  • To provide an open-access software platform for generating these surrogate brain maps.
  • To improve statistical rigor in analyzing large-scale brain organization.

Main Methods:

  • Generative modeling to create synthetic brain maps.
  • Matching spatial autocorrelation (SA) of surrogate maps to target brain maps.
  • Simulation of cortical, subcortical, parcellated, and dense brain maps.

Main Results:

  • The open-access software generates surrogate brain maps preserving empirical SA.
  • Characterization of SA's impact on p-values in pairwise brain map comparisons.
  • Demonstration of SA-preserving surrogates in gene set enrichment analyses for brain map topography.

Conclusions:

  • SA-preserving surrogate maps are valuable for hypothesis testing in complex neuroimaging analyses.
  • This method helps disambiguate meaningful brain organization relationships from chance associations.
  • The tool enhances statistical validity in large-scale brain organization studies.