A perturbative approach to study information communication in brain networks

Area of Science:

• Neuroscience
• Network Science
• Computational Biology

Background:

• Understanding neuronal communication is crucial for neuroscience.
• Brain communication arises from interactions between node activities and structural networks.
• Current methods face challenges in describing complex brain dynamics and network-dynamical interactions.

Purpose of the Study:

• To develop a method for studying network-dynamical interactions and their impact on information flow without prior dynamic assumptions.
• To investigate how brain information flow emerges from underlying structural properties.
• To apply and validate a perturbation-based network analysis method in a neuroscientific context.

Main Methods:

• Adaptation of a perturbation-based network analysis method for neuroscientific applications.
• Application of the method to in silico whole-brain models for proof-of-concept.
• Analysis of network-dynamical interactions, including 'net influence' and 'flow' metrics, and their functional implications.

Main Results:

• Demonstration that information flow can arise from structural properties of the brain network.
• Characterization of network-dynamical interactions using novel metrics.
• Application of the method to resting-state networks.

Conclusions:

• The developed method offers a simple yet powerful approach to study brain network dynamics and information flow.
• This method can be directly translated to experimental and clinical settings.
• Potential applications include identifying targets for stimulation studies and therapeutic interventions.