Emergence of the default-mode network from resting-state to activation-state in reciprocal social interaction via eye contact

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

The default-mode network has been identified as a resting state BOLD response that is often associated with self-referential or sensory task-passive processes. Many recent studies reveal that this vaguely defined network often plays an essential role in many pervasive mental diseases. By taking advantage of the recent development of dyadic fMRI, this study presents the initial experimental evidence that the default-mode network emerges from resting-state to activation-state in social interaction during live eye contact. Moreover, by comparing the BOLD responses between dyadic fMRI and monadic fMRI, it suggests that live eye contact excites empathy networks in the exogenous system which further activates the default mode network in endogenous system; whereas seeing eyes in face pictures activates completely different brain responses in which the default-mode network remains in resting-state.

Original languageEnglish (US)
Title of host publication2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1821-1824
Number of pages4
ISBN (Electronic)9781424492718
DOIs
StatePublished - Nov 4 2015
Externally publishedYes
Event37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy
Duration: Aug 25 2015Aug 29 2015

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2015-November
ISSN (Print)1557-170X

Conference

Conference37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Country/TerritoryItaly
CityMilan
Period8/25/158/29/15

ASJC Scopus subject areas

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

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