Classification of emotion categories based on functional connectivity patterns of the human brain
Heini Saarimäki,
Enrico Glerean,
Dmitry Smirnov,
Henri Mynttinen,
Iiro P. Jääskeläinen,
Mikko Sams,
Lauri Nummenmaa
Affiliations
Heini Saarimäki
Faculty of Social Sciences, Tampere University, FI-33014 Tampere University, Tampere, Finland; Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland; Corresponding author at: Faculty of Social Sciences, Tampere University, FI-33014 Tampere University, Tampere, Finland.
Enrico Glerean
Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland; Advanced Magnetic Imaging (AMI) Centre, Aalto NeuroImaging, School of Science, Aalto University, Espoo, Finland; Turku PET Centre and Department of Psychology, University of Turku, Turku, Finland; Department of Computer Science, School of Science, Aalto University, Espoo, Finland; International Laboratory of Social Neurobiology, Institute for Cognitive Neuroscience, HSE University, Moscow, Russian Federation
Dmitry Smirnov
Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland
Henri Mynttinen
Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland
Iiro P. Jääskeläinen
Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland; International Laboratory of Social Neurobiology, Institute for Cognitive Neuroscience, HSE University, Moscow, Russian Federation
Mikko Sams
Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland; Department of Computer Science, School of Science, Aalto University, Espoo, Finland
Lauri Nummenmaa
Turku PET Centre and Department of Psychology, University of Turku, Turku, Finland
Neurophysiological 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 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 thus show preliminary evidence for consistently different sustained functional connectivity patterns for instances of emotion categories particularly within the default mode system.
