Network dynamics under neuronal degradation: A comparison between modular and small-world topologies

Area of Science:

• Neuroscience
• Computational Biology
• Network Science

Background:

• The human brain integrates local and global information via modular and small-world network topologies.
• Demyelination and neuronal loss disrupt neural architecture, impairing signal transmission.
• The precise role of network topology in functional decline is not fully understood.

Purpose of the Study:

• To investigate how different network topologies influence neural system resilience and functional degradation.
• To compare the impact of progressive damage on modular versus small-world neural networks.

Main Methods:

• Simulated two network models: one modular, one small-world.
• Applied progressive demyelination and neuronal loss to both models.
• Analyzed functional metrics including mean firing rate, responder fraction, and latency.

Main Results:

• Small-world networks exhibited gradual, diffuse degradation and maintained higher temporal efficiency.
• Modular networks showed topology-dependent functional transitions with sharper declines and greater variability.
• Both network types displayed distinct degradation patterns under progressive damage.

Conclusions:

• Network topology critically modulates functional deterioration patterns during demyelination.
• Findings provide insights into the heterogeneous progression of neurological conditions like multiple sclerosis.
• Understanding topology-based resilience is key for neurological disorder research.