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The physiology of emotions is a multifaceted process involving the autonomic nervous system, brain structures, hormones, and neurotransmitters. This intricate interplay dictates how emotions manifest in the body and influence behavior.
Autonomic Nervous System
The autonomic nervous system (ANS) plays a critical role in emotional responses by regulating involuntary physiological functions. It consists of two main components: the sympathetic and parasympathetic systems. The sympathetic system...
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Brain Imaging Investigation of the Neural Correlates of Emotion Regulation
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EEG Functional Connectivity Underlying Emotional Valance and Arousal Using Minimum Spanning Trees.

Rui Cao1, Yan Hao1, Xin Wang2

  • 1College of Software Engineering, Taiyuan University of Technology, Taiyuan, China.

Frontiers in Neuroscience
|May 28, 2020
PubMed
Summary
This summary is machine-generated.

Brain network analysis using minimum spanning trees reveals distinct patterns in emotional states. Low arousal emotions exhibit more linear brain network structures, while negative emotions show increased randomness and activation.

Keywords:
emotionfunctional connectivitygraph theoryminimum spanning treenegative bias

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Affective Computing

Background:

  • Traditional electroencephalogram (EEG) analysis methods like power spectrum and amplitude analysis have limitations in capturing complex brain activity during emotional states.
  • Brain network analysis offers a more comprehensive approach to understanding brain activity, with minimum spanning tree (MST) methods effectively identifying key information flow and network organization.

Purpose of the Study:

  • To investigate emotional electroencephalogram (EEG) patterns using brain network analysis with minimum spanning trees (MSTs).
  • To explore how different arousal and valence levels influence brain network topology and dynamics.

Main Methods:

  • Utilized the DEAP dataset containing EEG data for four emotional categories: high arousal/high valence (HAHV), high arousal/low valence (HALV), low arousal/high valence (LAHV), and low arousal/low valence (LALV).
  • Calculated Phase Lag Index (PLI) weighted matrices across five frequency bands.
  • Constructed minimum spanning trees (MSTs) from PLI matrices to analyze brain network topology.

Main Results:

  • In the gamma (γ) band, emotions with high arousal (HAHV, HALV) showed significantly higher mean PLI (MPLI), maximum degree (Degreemax), and leaf fraction, alongside lower diameter and eccentricity compared to low-arousal states (LAHV, LALV).
  • At the same arousal level, high arousal/low valence (HALV) exhibited higher MPLI, Degreemax, and leaf fraction, with lower diameter and eccentricity than high arousal/high valence (HAHV).
  • Low-arousal states displayed more line-shaped brain network configurations, while high-arousal states, particularly negative emotions (HALV), showed a trend towards more star-shaped, random networks.

Conclusions:

  • Brain network topology differs significantly across emotional states, with arousal and valence levels modulating network structure.
  • Low arousal is associated with more linear brain networks, whereas high arousal, especially negative valence, correlates with increased network randomness and activation.
  • These findings provide a network-based perspective on emotional processing, supporting the concept of negative bias and increased brain activation in response to negative emotions.