Modeling brain network flexibility in networks of coupled oscillators: a feasibility study

Area of Science:

• Neuroscience
• Computational Neuroscience
• Network Science

Background:

• Human brain functionality modeling is a key neuroscience goal.
• Brain flexibility, linked to working memory and cognitive functions, is characterized by changes in co-active brain region clusters.
• Understanding brain dynamics and network structure is vital for neuroscience research.

Purpose of the Study:

• To model brain flexibility using computational approaches.
• To investigate the relationship between brain network structure and flexibility.
• To propose a novel, efficient metric for measuring brain flexibility.

Main Methods:

• Modeling brain flexibility with coupled FitzHugh-Nagumo oscillators.
• Utilizing Diffusion Tensor Imaging (DTI) to obtain human brain network structure.
• Deriving a macroscopic measure from the Pearson distance of functional brain matrices.

Main Results:

• Brain flexibility can be effectively modeled by coupled FitzHugh-Nagumo oscillators with DTI-derived network structures.
• A novel macroscopic metric shows similarities to established brain flexibility patterns.
• Network structure and connection strength in working memory regions influence observed flexibility patterns.

Conclusions:

• The study provides a modeling strategy for brain flexibility, enhancing understanding of brain network structure-function interplay.
• The proposed metric offers a computationally efficient alternative for assessing brain flexibility.
• Investigating network properties associated with working memory deepens insights into cognitive brain dynamics.