Multilayer network analysis across cortical depths in 7-T resting-state fMRI

Area of Science:

• Neuroscience
• Graph Theory
• Connectomics

Background:

• Multilayer networks model complex systems with interconnected topological levels.
• Cortical depth (laminae) stratification is a key organizational principle in the brain.
• Ultrahigh-resolution functional MRI (fMRI) enables noninvasive investigation of human cortical connectivity.

Purpose of the Study:

• To apply multilayer graph theory to human functional connectivity across cortical depths.
• To compare multilayer network analysis with layer-by-layer approaches.
• To investigate how functional network properties differ across cortical laminae.

Main Methods:

• Utilized 7-Tesla fMRI with 1-mm³ voxels in 30 participants.
• Derived Blood Oxygenation Level Dependent (BOLD) signals from five cortical depths.
• Compared layer-by-layer matrices with a comprehensive multilayer matrix using graph theory metrics.

Main Results:

• Multilayer connectomics enhanced detection of functional differences between cortical depths.
• Superficial cortical depths were dominant in information transfer.
• Deeper cortical depths were associated with increased network clustering, particularly in frontotemporal and limbic regions.

Conclusions:

• Functional connectivity across cortical depths contains neurophysiologically relevant information.
• Multilayer connectomics provides a robust framework for studying information flow across cortical strata.
• This approach advances our understanding of brain network organization and function.