Music and emotion: an EEG connectivity study in patients with disorders of consciousness.

G. Varotto, P. Fazio, D. Rossi Sebastiano, G. Avanzini, S. Franceschetti, F. Panzica, [No Value] CRC

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Abstract

Human emotion perception is a topic of great interest for both cognitive and clinical neuroscience, but its electrophysiological correlates are still poorly understood. The present study is aimed at evaluating if measures of synchronization and indexes based on graph-theory are a tool suitable to study and quantify electrophysiological changes due to emotional stimuli perception. In particular, our study is aimed at evaluating if different EEG connectivity patterns can be induced by pleasant (consonant) or unpleasant (dissonant) music, in a population of healthy subjects, and in patients with severe disorders of consciousness (DOCs), namely vegetative state (VS) patients. In the control group, pleasant music induced an increase in network number of connections, compared with the resting condition, while no changes were caused by the unpleasant stimuli. However, clustering coefficient and path length, two indexes derived from graph theory, able to characterise segregation and integration properties of a network, were not affected by the stimuli, neither pleasant nor unpleasant. In the VS group, changes were found only in those patients with the less severe consciousness impairment, according to the clinical assessment. In these patients a stronger synchronization was found during the unpleasant condition; moreover we observed changes in the network topology, with decreased values of clustering coefficient and path length during both musical stimuli.Our results show that measures of synchronization can provide new insights into the study of the electro physiological correlates of emotion perception, indicating that these tools can be used to study patients with DOCs, in whom the issue of objective measures and quantification of the degree of impairment is still an open and unsolved question.

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

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