The neural component-process architecture of endogenously generated emotion

Area of Science:

• Neuroscience
• Psychology
• Affective Science

Background:

• Endogenous emotions are fundamental to human experience, influencing resilience and psychopathology.
• The neural mechanisms underlying emotion generation remain largely uncharacterized.

Purpose of the Study:

• To propose and test a neural component process model for the endogenous generation of emotion (EGE).
• To elucidate the specific roles of distinct brain networks in generating subjective emotional states.

Main Methods:

• Utilized functional magnetic resonance imaging (fMRI) in two experiments with participants actively generating emotions.
• Employed self-chosen emotion generation methods to explore diverse internal representation strategies.
• Analyzed network activation, functional associations, and dynamic integration of salience (SN), default mode (DMN), and frontoparietal control (FPCN) networks.

Main Results:

• EGE engaged SN, DMN, and FPCN, with distinct roles for each network.
• SN, particularly midbrain nodes, correlated with affect intensity and distinguished positive/negative valence.
• DMN and insula supported diverse generation methods and elaborated representations, while FPCN coordinated the process.

Conclusions:

• The study presents a novel neural model for endogenous emotion generation.
• Identified specific network contributions to core affect, representation, and executive control in emotion.
• Provides a framework for future research on emotion generation in health and disease.