Accurately Modeling the Resting Brain Functional Correlations Using Wave Equation With Spatiotemporal Varying...

Area of Science:

• Neuroscience
• Complex Systems
• Computational Biology

Background:

• Understanding spontaneous brain neural activity emergence from structural connectivity (SC) is a long-standing challenge.
• Traditional graph models of SC are static and cannot capture dynamic interactions or connections between unconnected regions.
• Existing models fail to account for negative functional correlations in brain activity.

Purpose of the Study:

• To develop a novel model for spontaneous neural activity propagation in the human brain.
• To investigate the dynamic relationship between structural connectivity and functional couplings.
• To explain the emergence of functional connectivity (FC) from underlying neural dynamics.

Main Methods:

• Extended static graph models to a spatiotemporal varying hypergraph Laplacian diffusion (STV-HGLD) model.
• Incorporated the Laplacian of the hypergraph representation of the structural connectome (h SC) into a wave equation.
• Analyzed theoretical solutions and empirical data from four brain connectome datasets.

Main Results:

• Theoretical solutions demonstrate dynamic functional couplings fluctuate as an exponential wave regulated by the spatiotemporal varying Laplacian of h SC.
• Cortical waves exhibit resonance with SC through excitation-inhibition interplay, propagating with harmonics bound by SC natural frequencies.
• Functional connectivity (FC) emerges just before the cortical wave reaches a steady state.

Conclusions:

• The STV-HGLD model provides a new framework for understanding brain neural activity and connectivity.
• Dynamic functional couplings are intrinsically linked to the spatiotemporal variations in the hypergraph Laplacian of SC.
• The study elucidates the self-organizing principles governing brain activity and the emergence of FC.