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Previous research has shown that empathy, a fundamental component of human social functioning, is engaged when listening to music. Neuroimaging studies of empathy processing in music have, however, been limited. fMRI analysis methods based on graph theory have recently gained popularity as they are capable of illustrating global patterns of functional connectivity, which could be very useful in studying complex traits such as empathy. The current study examines the role of trait empathy, including cognitive and affective facets, on whole-brain functional network centrality in 36 participants listening to music in a naturalistic setting. Voxel-wise eigenvector centrality mapping was calculated as it provides us with an understanding of globally distributed centres of coordination associated with the processing of empathy. Partial correlation between Eigenvector centrality and measures of empathy showed that cognitive empathy is associated with higher centrality in the sensorimotor regions responsible for motor mimicry while affective empathy showed higher centrality in regions related to auditory affect processing. Results are discussed in relation to various theoretical models of empathy and music cognition.
Original language | English |
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Title of host publication | Brain informatics |
Editors | Mufti Mahmud, Stefano Vassanelli, M. Shamim Kaiser, Ning Zhong |
Number of pages | 11 |
Place of publication | Cham |
Publisher | Springer |
Publication year | 2020 |
Pages | 107-117 |
ISBN (print) | 978-3-030-59276-9 |
ISBN (Electronic) | 978-3-030-59277-6 |
DOIs | |
Publication status | Published - 2020 |
Event | 13th International Conference on Brain Informatics, BI 2020 - Padua, Italy Duration: 19 Sept 2020 → 19 Sept 2020 |
Conference | 13th International Conference on Brain Informatics, BI 2020 |
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Land | Italy |
By | Padua |
Periode | 19/09/2020 → 19/09/2020 |
Series | Lecture Notes in Computer Science |
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Volume | 12241 |
ISSN | 0302-9743 |
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
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ID: 210853486