Classification of emotions based on functional connectivity patterns of the human brain

Abstract

Neurophysiological and psychological models posit that emotions depend on connections across widespread corticolimbic circuits. While previous studies using pattern recognition on neuroimaging data have shown differences between various discrete emotions in brain activity patterns, less is known about the differences in functional connectivity. Thus, we employed multivariate pattern analysis on functional magnetic resonance imaging data (i) to develop a pipeline for applying pattern recognition in functional connectivity data, and (ii) to test whether connectivity signatures differ across emotions. Six emotions (anger, fear, disgust, happiness, sadness, and surprise) and a neutral state were induced in 16 participants using one-minute-long emotional narratives with natural prosody while brain activity was measured with functional magnetic resonance imaging (fMRI). We computed emotion-wise connectivity matrices both for whole-brain connections and for 10 previously defined functionally connected brain subnetworks, and trained an across-participant classifier to categorize the emotional states based on whole-brain data and for each subnetwork separately. The whole-brain classifier performed above chance level with all emotions except sadness, suggesting that different emotions are characterized by differences in large-scale connectivity patterns. When focusing on the connectivity within the 10 subnetworks, classification was successful within the default mode system and for all emotions. We conclude that functional connectivity patterns consistently differ across different emotions particularly within the default mode system.

Specific and segregated changes to the functional connectome evoked by the processing of emotional faces: A task-based connectome study

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Large-scale functional brain networks for consciousness

“…For example, a proximal threat can activate periaqueductal gray matter and central nuclei of the amygdala [ 83 ], but a distant threat activates the VMPFC. A recent study that used multivariate pattern classification showed that distinct subnetworks representing different emotions are observed in the DMN [ 84 ].…”

When self comes to a wandering mind: Brain representations and dynamics of self-generated concepts in spontaneous thought

“…The pattern similarity of the unthresholded predictive weights within the MPFC between the two models was low, r = 0.064. In addition to the default mode and limbic network regions, many voxels within the somatomotor and ventral and dorsal attention networks were among the important features of the models, suggesting that the information about the levels of self-relevance and valence involves many brain regions distributed across multiple brain systems 78,[86][87][88][89][90] .…”