Intrinsic network static and dynamic functional connectivity associated with induced affect.

Area of Science:

• Neuroscience
• Cognitive Neuroscience
• Network Neuroscience

Background:

• Affective processing research has evolved from localization to network-based approaches.
• Understanding the neural basis of emotions requires examining brain network dynamics.

Purpose of the Study:

• To investigate the topology of brain networks during an affect-induction task.
• To explore static and dynamic functional integration and within-network coherence.

Main Methods:

• Utilized fMRI with a novel affect-induction task involving visualization of acquaintances.
• Applied graph theory metrics (participation coefficient, within-module degree) to analyze network topology.
• Examined five key brain networks: salience, default mode, ventral attention, dorsal attention, and frontoparietal.

Main Results:

• Static dorsal attention network within-module degree distinguished negative from neutral conditions.
• Dynamic ventral attention network participation coefficient differentiated negative from positive conditions.
• Findings highlight network flexibility in affective processing.

Conclusions:

• Emotions arise from adaptable brain network configurations, supporting the constructionist account.
• Results contribute to a network-based understanding of affective processing.
• Demonstrates the utility of dynamic network analysis in affective neuroscience.