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Discovering prominent differences in structural and functional connectomes using a multinomial stochastic block

Nina Braad Iskov1, Anders Stevnhoved Olsen1, Kristoffer Hougaard Madsen1,2

  • 1Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark.

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|December 30, 2024
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Summary
This summary is machine-generated.

The multinomial stochastic block model (MSBM) reveals key differences between human brain

Keywords:
Bayesian inferenceDifference modelingFunctional connectivityMultinomial stochastic block modelStructural connectivity

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Medical Imaging

Background:

  • Human brain connectivity research extensively studies functional and structural differences.
  • Existing methods face challenges in precisely characterizing these distinctions.

Purpose of the Study:

  • To apply a novel multinomial stochastic block model (MSBM) for identifying structure-function differences in brain connectivity.
  • To analyze and compare structural and functional connectomes using advanced modeling techniques.

Main Methods:

  • Utilized the multinomial stochastic block model (MSBM) on structural and functional connectomes.
  • Analyzed high-resolution diffusion-weighted MRI and fMRI data from the Human Connectome Project (n=250 subjects).
  • Group connectivity analysis performed across 50 subjects at varying resolutions (K={3,4} and K≥25).

Main Results:

  • MSBM identified consistent, spatially homogeneous brain partitions, highlighting structure-function differences.
  • Low-resolution analysis (K={3,4}) showed weak functional connectivity in anterior temporal lobes.
  • High-resolution analysis (K≥25) revealed stronger interhemispheric functional than structural connectivity.

Conclusions:

  • Significant differences exist between high-resolution functional and structural connectomes.
  • Challenges include tracking fibers via the corpus callosum and fMRI noise in anterior temporal lobes.
  • MSBM is a valuable tool for graph analysis and connectomics, offering insights into modality-specific distinctions.