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Emotions are processed across the entire brain, not just specific regions. This study used functional magnetic resonance imaging (fMRI) to show whole-brain patterns predict valence and arousal, supporting a distributed model for affective neuroscience.

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Area of Science:

  • Affective Neuroscience
  • Cognitive Neuroscience
  • Neuroimaging

Background:

  • Emerging evidence suggests emotions are represented across distributed neural networks.
  • Understanding emotion regulation and potential neuromodulation targets requires knowledge of neural representations.
  • The precise distribution of neural representations for affective dimensions like valence and arousal remains under investigation.

Purpose of the Study:

  • To test the distribution of neural representations for the affective dimensions of valence and arousal.
  • To investigate whether whole-brain patterns or specific regions better predict these affective dimensions.
  • To explore implications for emotion therapeutics and affective neuroscience.

Main Methods:

  • Utilized multi-voxel pattern classification (MVPC) on functional magnetic resonance imaging (fMRI) data.
  • Analyzed neural activation patterns predicting valence (positive/negative) and arousal (high/low) for visual stimuli.
  • Employed inter-subject leave-one-out cross-validation and general linear modeling (GLM) on a sample of 32 healthy adults.

Main Results:

  • Whole-brain MVPC significantly predicted valence (59% accuracy) and arousal (56% accuracy) above chance.
  • Classifiers using only identified regions of interest (ROIs) performed significantly worse than whole-brain analysis.
  • Performance varied across affective dimensions and was poorest for stimuli with moderate arousal and extreme valence.

Conclusions:

  • Findings strongly support a highly distributed neural processing model for valence and arousal.
  • Whole-brain analysis is superior to ROI-based or all-ROI approaches for decoding these affective dimensions.
  • Highlights new avenues for characterizing affective processing for mechanistic and therapeutic applications in neuroscience.