Beyond modularity: Fine-scale mechanisms and rules for brain network reconfiguration.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Network Science

Background:

• The human brain exhibits dynamic functional connectivity, with transient communication between areas supporting cognition.
• Functional brain networks evolve over time, organizing into systems that facilitate cognitive processes.

Purpose of the Study:

• To uncover the organizing principles of dynamic functional brain networks.
• To investigate the spatial and temporal rules governing brain region interactions.

Main Methods:

• Applied non-negative matrix factorization (NMF), an unsupervised machine learning technique.
• Analyzed time-evolving, resting-state functional networks from 20 healthy subjects.
• Grouped temporally co-varying functional interactions into subgraphs representing topological modes.

Main Results:

• Identified subgraphs stratified by modular organization and topographical distance.
• Observed subgraphs largely within modules and others spanning between modules with varying temporal expression.
• Demonstrated that time-varying subgraph expression explains inter-individual differences in module reorganization.

Conclusions:

• Subgraph organization is critical for constraining the topography and topology of functional brain networks.
• The machine learning approach provides new insights into dynamic functional network architecture.
• This method can probe network alterations in disease states.