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Related Experiment Video

Updated: Feb 22, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

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Published on: March 21, 2019

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

Ruofan Ma¹, Taylor N West¹, Yuritza Y Escalante¹

¹University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.

Social Cognitive and Affective Neuroscience

|February 20, 2026

Summary

This summary is machine-generated.

Brain network activity during emotion processing differs based on valence. Dynamic network configurations support the constructionist view of emotions, advancing network neuroscience understanding.

Keywords:

constructionism emotion functional connectivity intrinsic networks network neuroscience

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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.