Literature DB >> 28215469

Depression and implicit emotion processing: An EEG study.

Andrey V Bocharov1, Gennady G Knyazev2, Alexander N Savostyanov3.   

Abstract

OBJECTIVES: Depression is one of the most prevalent mental illnesses and is associated with changes in emotion processing. The aim of this study was to determine the influence of depressive symptoms on EEG oscillatory dynamics accompanying implicit processing of angry and happy facial expressions in 46 healthy subjects.
METHODS: The Beck Depression Inventory was used to assess the presence of depressive symptoms in normal subjects. During the experiment, they were told to categorize the gender of angry, neutral, or happy faces presented to them, while high-resolution EEG was recorded. Analysis of the event-related spectral perturbations and the analysis of dipoles were carried out on EEG recordings using the EEGLAB toolbox.
RESULTS: High depression (HD) and low depression (LD) groups did not differ on error rate and reaction time during categorization of gender. The perception of happy faces was accompanied by higher theta synchronization in the LD than the HD group. In contrast, theta synchronization was higher in the HD than the LD group during perception of angry faces.
CONCLUSION: These findings imply that even at preclinical stages, HD scorers evidence increased emotional arousal to negative and decreased emotional arousal to positive stimuli during implicit emotion processing.
Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Entities:  

Keywords:  Analyse en composantes indépendantes; Depression; Dépression; EEG; Implicit emotion processing; Independent component analysis; Traitement des émotions implicites

Mesh:

Year:  2017        PMID: 28215469     DOI: 10.1016/j.neucli.2017.01.009

Source DB:  PubMed          Journal:  Neurophysiol Clin        ISSN: 0987-7053            Impact factor:   3.734


  7 in total

1.  EEG time-frequency analysis reveals blunted tendency to approach and increased processing of unpleasant stimuli in dysphoria.

Authors:  Carola Dell'Acqua; Elisa Dal Bò; Tania Moretta; Daniela Palomba; Simone Messerotti Benvenuti
Journal:  Sci Rep       Date:  2022-05-17       Impact factor: 4.996

2.  Amygdala-pons connectivity is hyperactive and associated with symptom severity in depression.

Authors:  Jing Jun Wong; Nichol M L Wong; Dorita H F Chang; Tatia M C Lee; Di Qi; Lin Chen
Journal:  Commun Biol       Date:  2022-06-10

Review 3.  Recent Advances in Non-invasive Brain Stimulation for Major Depressive Disorder.

Authors:  Shui Liu; Jiyao Sheng; Bingjin Li; Xuewen Zhang
Journal:  Front Hum Neurosci       Date:  2017-11-06       Impact factor: 3.169

4.  Comparison of Ecological Micro-Expression Recognition in Patients with Depression and Healthy Individuals.

Authors:  Chuanlin Zhu; Xinyun Chen; Jianxin Zhang; Zhiying Liu; Zhen Tang; Yuting Xu; Didi Zhang; Dianzhi Liu
Journal:  Front Behav Neurosci       Date:  2017-10-17       Impact factor: 3.558

5.  Influence of depressive feelings in the brain processing of women with fibromyalgia: An EEG study.

Authors:  Santos Villafaina; Carolina Sitges; Daniel Collado-Mateo; Juan P Fuentes-García; Narcis Gusi
Journal:  Medicine (Baltimore)       Date:  2019-05       Impact factor: 1.817

6.  Theta oscillations: A rhythm difference comparison between major depressive disorder and anxiety disorder.

Authors:  Yu Zhang; Lei Lei; Ziwei Liu; Mingxue Gao; Zhifen Liu; Ning Sun; Chunxia Yang; Aixia Zhang; Yikun Wang; Kerang Zhang
Journal:  Front Psychiatry       Date:  2022-08-03       Impact factor: 5.435

7.  Data Augmentation for EEG-Based Emotion Recognition Using Generative Adversarial Networks.

Authors:  Guangcheng Bao; Bin Yan; Li Tong; Jun Shu; Linyuan Wang; Kai Yang; Ying Zeng
Journal:  Front Comput Neurosci       Date:  2021-12-09       Impact factor: 2.380

  7 in total

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