2014
DOI: 10.1016/j.neuroimage.2014.07.063
|View full text |Cite
|
Sign up to set email alerts
|

Emotional speech synchronizes brains across listeners and engages large-scale dynamic brain networks

Abstract: Speech provides a powerful means for sharing emotions. Here we implement novel intersubject phase synchronization and whole-brain dynamic connectivity measures to show that networks of brain areas become synchronized across participants who are listening to emotional episodes in spoken narratives. Twenty participants' hemodynamic brain activity was measured with functional magnetic resonance imaging (fMRI) while they listened to 45-s narratives describing unpleasant, neutral, and pleasant events spoken in neut… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

12
126
0

Year Published

2015
2015
2022
2022

Publication Types

Select...
6
1

Relationship

4
3

Authors

Journals

citations
Cited by 130 publications
(141 citation statements)
references
References 61 publications
12
126
0
Order By: Relevance
“…Scenes showing highest differences in ISC timecourses also contained moments of high suspense. In the GLM analysis, suspense was associated with activity in corticolimbic areas whose ISC and BOLD‐GLM responses are modulated by affective arousal [Nummenmaa et al, ], which is an important component of suspense experience [Lehne and Koelsch, ]. However, due to the slowness of the signals, the parametric test was very liberal.…”
Section: Discussionmentioning
confidence: 99%
“…Scenes showing highest differences in ISC timecourses also contained moments of high suspense. In the GLM analysis, suspense was associated with activity in corticolimbic areas whose ISC and BOLD‐GLM responses are modulated by affective arousal [Nummenmaa et al, ], which is an important component of suspense experience [Lehne and Koelsch, ]. However, due to the slowness of the signals, the parametric test was very liberal.…”
Section: Discussionmentioning
confidence: 99%
“…In general, auditory perception tasks deactivate the DMN (Abbott, Kim, Sponheim, Bustillo, & Calhoun, 2010; Binder et al, 1999; McKiernan et al, 2003). However, it has been reported that several factors influence the degree of the deactivation in this network, including the load of the task, the discriminability and presentation rate of stimuli, and the emotive valence and salience of the stimuli (Antrobus, 1968; Antrobus, Singer, & Greenberg, 1966; Binder et al, 1999; Giambra 1995; McGuire et al, 1996; McKiernan, Kaufman, Kucera-Thompson, & Binder, 2003; Nummenmaa et al, 2014; Pope & Singer, 1976; Shulman et al, 1997; Teasdale, Proctor, Lloyd, & Baddeley, 1993). In our study, infant crying exerted more interference than other sounds in women, capturing attentional resources from the SOT, and deactivation of the PCC reflected the disengagement from the task.…”
Section: Discussionmentioning
confidence: 99%
“…The observed weak deactivations in men might reflect a resource competition to process female adult crying and simultaneously count syllables. In fact, the DMN is partially activated in response to exogenous stimuli that have self-relevance (Li, Mai, & Liu, 2014; Nummenmaa et al, 2014; Vessel, Starr, & Rubin, 2013). The DMN is also positively activated when individuals evaluate their own (and others’) beliefs, perspectives, and mental states (Li et al, 2014).…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…To examine the relationship between the synchronization time series resulting from the ICA with each task condition we used the convolved block regressors of the GLM as reference functions. As noted by others (Nummenmaa et al, 2014), conventional double-gamma HRF convolved regressors attempt to model a late undershoot of the HRF, which would not presumably be present in the case of a voxel synchronization time course (which would exhibit no undershoot). Thus, to get the suitable reference functions, we computed another GLM model using the gamma HRF in FSL to account for HRF lag and width, without an undershoot.…”
Section: Association Of Task Block Regressors With Synchronization Timentioning
confidence: 99%