Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and...

Area of Science:

• Neuroscience
• Computational Modeling
• Network Science

Background:

• Recent advances in brain connectomics have detailed structural and functional connectivity.
• Resting-state functional networks dynamically emerge and dissipate.
• The relationship between static structure and dynamic function remains unclear.

Purpose of the Study:

• To review computational models explaining the emergence of resting-state functional networks from structural connectomes.
• To highlight the need for refined models incorporating fine-grained temporal dynamics.

Main Methods:

• Overview of computational models proposed in the last decade.
• Analysis from the perspective of complex network dynamics and dynamical systems theory.

Main Results:

• Several mechanistic scenarios have been proposed to explain functional network emergence.
• Current models require further constraints to fully capture brain dynamics.

Conclusions:

• Bottom-up computational models are crucial for understanding brain activity origins.
• Integrating fine temporal scale data (e.g., MEG) and functional connectivity dynamics is essential for advancing connectomics models.