Classification of emotion categories based on functional connectivity patterns of the human brain
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
NeuroImage, Volume 247
AbstractNeurophysiological and psychological models posit that emotions depend on connections across wide-spread 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 patterns differ across emotion categories. 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 brainsubnetworks 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 thus show preliminary evidence for consistently different sustained functional connectivity patterns for instances of emotion categories particularly within the default mode system.
| openaire: EC/H2020/313000/EU//SOCIAL BRAIN Funding Information: This work was supported by Academy of Finland (#265917 to L.N. and #138145 to I.P.J.), ERC Starting Grant (#313000 to L.N.); Finnish Cultural Foundation (#00140220 to H.S.), Kordelin Foundation (#160387 to H.S.), and by the International Laboratory of Social Neurobiology ICN HSE RF Government grant ag. No. 075–15- 2019–1930 (to I.P.J and E.G.). We thank Marita Kattelus for herhelp with the data acquisition. We acknowledge the computational resources provided by the Aalto Science-IT project. Publisher Copyright: © 2021
Emotion, fMRI, Functional connectivity, MVPA, Pattern classification
Saarimäki , H , Glerean , E , Smirnov , D , Mynttinen , H , Jääskeläinen , I P , Sams , M & Nummenmaa , L 2022 , ' Classification of emotion categories based on functional connectivity patterns of the human brain ' , NeuroImage , vol. 247 , 118800 , pp. 1-10 . https://doi.org/10.1016/j.neuroimage.2021.118800