Algebraic Topology of Multi-Brain Connectivity Networks Reveals Dissimilarity in Functional Patterns during Spoken...

Area of Science:

• Neuroscience
• Graph Theory
• Algebraic Topology

Background:

• Human behavior is linked to brain connectivity patterns represented by functional brain networks.
• Graph theory analysis has advanced understanding of brain functions, but multi-brain structures in social behavior remain under-explored.

Purpose of the Study:

• To analyze multi-brain network structures and connections during spoken communication.
• To explore the application of algebraic topology for characterizing higher-order topological complexes in functional brain networks.
• To identify novel measures for brain activity patterns and inter-brain synchronization.

Main Methods:

• Analysis of aggregate graphs mapping electroencephalography (EEG) signal coordination during spoken communication between speakers and listeners.
• Application of algebraic topology to analyze higher-order topological complexes formed by functional connections.
• Quantification of topological features and network-to-network distances.

Main Results:

• Topological quantifiers reveal differences in brain activity and inter-brain synchronization between speakers and listeners.
• Higher topological complexity correlates with listener concentration and perceived closeness to the speaker's brain activity.
• Multi-brain community topology exceeds individual contributions and reflects listener ratings of the speaker and subject.

Conclusions:

• Algebraic topology offers valuable measures beyond standard graph theory for characterizing functional brain networks.
• Higher-order topological structures are relevant for understanding brain network dynamics during social interactions.
• This approach provides new insights into the neural basis of attention and social cognition during communication.