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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Applying multilayer analysis to morphological, structural, and functional brain networks to identify relevant

Jordi Casas-Roma1, Eloy Martinez-Heras2, Albert Solé-Ribalta3

  • 1e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain.

Network Neuroscience (Cambridge, Mass.)
|January 6, 2023
PubMed
Summary

This study introduces a novel multilayer network framework integrating brain morphology, structure, and function. This approach identifies synchronized connectivity deterioration in brain regions, particularly in multiple sclerosis patients.

Keywords:
Functional connectivityGray matter networksMultilayerMultiple sclerosisStructural connectivity

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Area of Science:

  • Neuroimaging
  • Network Neuroscience
  • Computational Biology

Background:

  • Current MRI network analysis often focuses on single modalities (e.g., resting-state fMRI, diffusion MRI).
  • Previous studies have combined at most two types of brain networks, lacking a comprehensive approach.
  • A unified framework is needed to integrate diverse neuroimaging data for a holistic brain analysis.

Purpose of the Study:

  • To develop and validate a novel framework for creating multilayer brain networks.
  • To integrate morphological, structural, and functional connectivity data into a single analytical model.
  • To explore the utility of this multilayer network approach in identifying brain alterations in neurological conditions.

Main Methods:

  • Designed and developed a framework to merge morphological (T1-derived gray matter probability), structural (diffusion MRI), and functional (resting-state fMRI) brain networks.
  • Adapted graph theory metrics for analysis within the multilayer network context.
  • Applied the framework to a cohort of individuals with multiple sclerosis.

Main Results:

  • The developed multilayer network framework successfully integrated diverse neuroimaging data.
  • Analysis revealed synchronized connectivity deterioration in specific brain regions within the multiple sclerosis cohort.
  • The framework demonstrated the ability to identify complex patterns of brain network alterations.

Conclusions:

  • The proposed multilayer network perspective offers an advantageous approach for jointly analyzing multiple relational data types from brain imaging.
  • This framework provides a powerful tool for understanding brain connectivity and identifying disease-specific network disruptions.
  • The findings highlight the potential of integrated network analysis for advancing neurological research and diagnostics.