Summary: Whole-brain functional connectivity models successfully classified six basic emotions from neutral expressions.
Source: Science China Press
Emotions are an important part of human intelligence. The identification of specific emotional categories from complex neural patterns (i.e. the neural decoding of emotional information) is a key question in current emotion research.
Categorical emotional models have suggested a set of basic emotional units (e.g., anger, disgust, fear, happiness, sadness, and surprise) that have specialized and independent neural circuits in the brain to support the expression of different emotional information.
Accordingly, different regions of the brain are specifically involved in the processing of specific basic emotions. In recent years, a growing body of evidence suggests that representations of basic emotions may be supported by large-scale functional connectivity (FC) networks in the brain.
Recently, an article titled “Decoding Six Basic Emotions from Functional Brain Connectivity Patterns” was published online in Science Life Sciences in China by Dr. Fang Fang’s group at Peking University’s School of Psychological and Cognitive Sciences.
This study analyzed the neural mechanism of emotional information represented by brain network models from a data-driven perspective. By leveraging the sliding window technique and the random forest model, this study built the model for decoding emotional brain networks and provided evidence that functional connectivity patterns contain the basic emotion representation information.
Professor Fang’s team collected whole-brain fMRI data from human participants as they looked at images of faces expressing one of six basic emotions (anger, disgust, fear, happiness, sadness and surprise ) or showing neutral expressions.
They obtained FC patterns for each emotion in brain regions across the whole brain with the Harvard-Oxford Atlas, and applied multivariate pattern decoding to decode six basic emotions from the neutral expressions.
Results showed that whole-brain FC schemas successfully classified all six basic emotions from neutral expressions. By analyzing the contribution ratio of each brain region when identifying emotions, the spatial distribution locations of the top 10 contributing brain nodes for each basic emotion were revealed.
This data-driven research method not only identified important regions that were previously relevant to the study of the face and emotion processing, such as the fusiform gyrus, right amygdala under fear, but also identified some regions of the brain that have rarely been claimed to contribute. representations of emotions, such as the supramarginal gyrus, the supracalcarin cortex, and other brain regions of the limbic system.
Moreover, the brain network-based decoding model showed superior decoding performance than the traditional voxelwise activation-based decoding model, both on whole-brain regions and on the 10 most contributing brain regions.
In conclusion, the results of this study further indicate that brain network models contain more useful information for decoding emotions than voxel activation models, and imply that there is great potential to study emotion recognition. from the functional connectivity between brain regions.
About this emotion and neuroscience research news
Author: Press office
Source: Science China Press
Contact: Press Office – Science China Press
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Original research: Access closed.
“Decoding Six Basic Emotions from Functional Brain Connectivity Patterns” by Chunyu Liu et al. Science Life Sciences in China
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Decoding Six Basic Emotions from Functional Brain Connectivity Patterns
Although distinctive neural and physiological states are suggested to underlie the six basic emotions, the basic emotions are often indistinguishable from voxelwise (VA) activation patterns of functional magnetic resonance imaging (fMRI).
Here, we hypothesize that functional connectivity (FC) patterns in brain regions may contain information about emotion representation beyond VA patterns.
We collected whole-brain fMRI data while human participants viewed images of faces expressing one of six basic emotions (i.e., anger, disgust, fear, happiness, sadness and surprise) or showing neutral expressions.
We obtained FC models for each emotion in brain regions across the whole brain and applied multivariate model decoding to decode emotions in the FC model representation space.
Our results showed that whole-brain FC schemas successfully classified not only the six basic emotions from neutral expressions, but also every basic emotion from other emotions.
An emotion representation network for each basic emotion that extended beyond the classical brain regions for emotion processing was identified. Finally, we demonstrated that in the same brain regions, FC-based decoding performed consistently better than VA-based decoding.
Taken together, our results revealed that CF models contained emotional information and advocated paying more attention to CF’s contribution to emotion processing.
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