Literature DB >> 30009875

Neural substrates of emotional interference: A quantitative EEG study.

T Batabyal1, S P Muthukrishnan2, R Sharma3, P Tayade4, S Kaur5.   

Abstract

Emotional stimuli are known to capture attention and disrupt the executive functioning. However, the dynamic interplay of neural substrates of emotion and executive attentional network is widely unexplored. The present study attempts to elucidate the areas implicated during emotional interference condition. Fifteen right handed individuals [24.64 ± 2.63 years] performed two emotional interference tasks - Face Word Interference and Word Face Interference. Single trial EEG was recorded during baseline (eyes open) and during the tasks. The activity of the cortical sources was compared between the tasks and baseline for 66 gyri using sLORETA software. Eighteen gyri in Face Word Interference and fifty-four gyri in Word Face Interference have shown significantly decreased activity [p < 0.05/66] with respect to baseline respectively. Interestingly, in both the interference tasks, there was disengagement of fronto-parietal attentional networks (implicating the probable ability of emotional stimuli to disrupt cognition) and the areas associated with default mode network. Further, during baseline there was significant activity in premotor cortical areas, which may be due to active inhibition of motor movements associated with response.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Amygdala; Cortical sources; Emotional interference; Frontoparietal attentional networks; Inhibition

Mesh:

Year:  2018        PMID: 30009875     DOI: 10.1016/j.neulet.2018.07.019

Source DB:  PubMed          Journal:  Neurosci Lett        ISSN: 0304-3940            Impact factor:   3.046


  2 in total

1.  Accurate emotion recognition using Bayesian model based EEG sources as dynamic graph convolutional neural network nodes.

Authors:  Shiva Asadzadeh; Tohid Yousefi Rezaii; Soosan Beheshti; Saeed Meshgini
Journal:  Sci Rep       Date:  2022-06-18       Impact factor: 4.996

2.  Utility of Cognitive Neural Features for Predicting Mental Health Behaviors.

Authors:  Ryosuke Kato; Pragathi Priyadharsini Balasubramani; Dhakshin Ramanathan; Jyoti Mishra
Journal:  Sensors (Basel)       Date:  2022-04-19       Impact factor: 3.847

  2 in total

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