Applying multilayer analysis to morphological, structural, and functional brain networks to identify relevant...

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.