A short review on emotion processing: a lateralized network of neuronal networks

• Published in: Brain Structure and Function 2022-03, Vol.227 (2), p.673-684
• Main Authors: Palomero-Gallagher, Nicola, Amunts, Katrin
• Format: Article
• Language: English
• Subjects: Amygdala; Biomedical and Life Sciences; Biomedicine; Cell Biology; Cortex (cingulate); Emotions; External stimuli; Functional Laterality; Hemispheric laterality; Hypotheses; Neural networks; Neurology; Neurons; Neurosciences; Review
• ISSN: 1863-2653; 1863-2661; 1863-2661; 0340-2061
• DOI: 10.1007/s00429-021-02331-7

Emotions are valenced mental responses and associated physiological reactions that occur spontaneously and automatically in response to internal or external stimuli, and can influence our behavior, and can themselves be modulated to a certain degree voluntarily or by external stimuli. They are subserved by large-scale integrated neuronal networks with epicenters in the amygdala and the hippocampus, and which overlap in the anterior cingulate cortex. Although emotion processing is accepted as being lateralized, the specific role of each hemisphere remains an issue of controversy, and two major hypotheses have been proposed. In the right-hemispheric dominance hypothesis, all emotions are thought to be processed in the right hemisphere, independent of their valence or of the emotional feeling being processed. In the valence lateralization hypothesis, the left is thought to be dominant for the processing of positively valenced stimuli, or of stimuli inducing approach behaviors, whereas negatively valenced stimuli, or stimuli inducing withdrawal behaviors, would be processed in the right hemisphere. More recent research points at the existence of multiple interrelated networks, each associated with the processing of a specific component of emotion generation, i.e., its generation, perception, and regulation. It has thus been proposed to move from hypotheses supporting an overall hemispheric specialization for emotion processing toward dynamic models incorporating multiple interrelated networks which do not necessarily share the same lateralization patterns.