Frustrated hierarchical synchronization and emergent complexity in the human connectome network

Area of Science:

• Neuroscience
• Complex Systems
• Network Science

Background:

• Neural synchronization is crucial for brain function.
• Anomalies in synchronization are linked to neurological pathologies.
• Understanding brain dynamics requires studying its network structure.

Purpose of the Study:

• To analytically and computationally study synchronization dynamics on the human brain connectome.
• To identify novel phases of synchronization beyond standard synchronous and asynchronous states.
• To elucidate the role of network architecture in complex brain dynamics.

Main Methods:

• Employed a parsimonious (mesoscopic) approach.
• Utilized Kuramoto synchronization dynamics.
• Analyzed the actual human-brain connectome network.
• Applied spectral graph theory.

Main Results:

• Discovered a novel intermediate phase between synchronous and asynchronous states.
• Identified frustrated synchronization, metastability, and chimera-like states.
• Revealed that hierarchical modularity, bottlenecks, and frequency heterogeneities drive complex dynamics.
• Uncovered the origin of dynamic freezing in these patterns.

Conclusions:

• The brain's complex synchronization patterns arise from its hierarchical organization.
• These dynamics enable robust yet flexible access to diverse functional states.
• The findings offer insights into brain function and dysfunction without critical point fine-tuning.