Flexible Reconfigurations of Brain Networks During Decisions With Predefined Versus Self-Generated Options.

Area of Science:

• Neuroscience
• Cognitive Science
• Network Science

Background:

• Decision-making involves evaluating external options or generating internal ones.
• Brain networks must flexibly reconfigure to support these distinct cognitive demands.
• Understanding network dynamics is crucial for explaining adaptive decision-making.

Purpose of the Study:

• To investigate how large-scale brain networks dynamically reorganize for different decision-making types.
• To test the hypothesis that network reconfiguration recruits specific modules for external versus internal option evaluation.
• To explore the hierarchical organization of brain networks during decision-making.

Main Methods:

• Applied graph-theoretic tools to functional neuroimaging data.
• Analyzed data from three conditions: external-menu choices (EMC), internal-menu choices (IMC), and semantic fluency (SF).
• Utilized categorical multi-slice community detection to analyze network modularity.

Main Results:

• Distinct cognitive demands led to specific reconfigurations of hierarchically organized modular brain networks.
• Global network organization, particularly sensory input, differentiated EMC from internally oriented tasks (IMC, SF).
• IMC showed stronger interactions between semantic retrieval and valuation networks compared to SF.

Conclusions:

• Brain network organization dynamically adapts to the cognitive demands of decision-making.
• A hierarchical architecture supports adaptive decision-making, with modules interacting at multiple levels.
• Network flexibility allows for the integration of sensory processing and semantic retrieval as needed.