Literature DB >> 32871681

EEG Frontal Asymmetry and Theta Power in Unipolar and Bipolar Depression.

Kristin Koller-Schlaud1, Andreas Ströhle2, Elisabeth Bärwolf2, Joachim Behr3, Johannes Rentzsch4.   

Abstract

BACKGROUND: Distinguishing between unipolar and bipolar depression is of high clinical relevance. However, there is sparse research directly comparing these groups in terms of EEG activity.
METHOD: We investigated 87 participants' left and right EEG frontal alpha-1, alpha-2, and theta activity related to happy and sad face stimuli in unipolar (UD, n=33) and bipolar (BD, n=22) depressed participants, and controls without depression (HC, n=32).
RESULTS: Post-hoc analysis of an observed hemisphere x group interaction (p< .037) showed significant differences in alpha-1 asymmetry only for the comparison of UD and HC (p< .006). Further analysis of a significant emotion x group interaction (p= .001) revealed a differential impact of stimulus valence on theta power between the groups (p< .001). The valence dependent theta power of the BD differed from that of the UD (p< .0002) and the HC (p< .004). Alpha-1 asymmetry classified HC and both depressed groups with an accuracy of .69. Valence-related theta classified BD from UD with an accuracy of .83. Leave-one-out cross validation resulted in slightly reduced accuracy. LIMITATIONS: Important limitations were the small sample size and that subjects were not medication-free.
CONCLUSIONS: Our results demonstrate the value of simple, task related EEG activity for differentiating not only healthy individuals from those with depression, but also individuals with unipolar depression from those with bipolar depression.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Year:  2020        PMID: 32871681     DOI: 10.1016/j.jad.2020.07.011

Source DB:  PubMed          Journal:  J Affect Disord        ISSN: 0165-0327            Impact factor:   4.839


  4 in total

1.  The novel frontal alpha asymmetry factor and its association with depression, anxiety, and personality traits.

Authors:  Alessandra Monni; Katherine L Collison; Kaylin E Hill; Belel Ait Oumeziane; Dan Foti
Journal:  Psychophysiology       Date:  2022-05-26       Impact factor: 4.348

2.  Automatic Identification of Depression Using Facial Images with Deep Convolutional Neural Network.

Authors:  Xinru Kong; Yan Yao; Cuiying Wang; Yuangeng Wang; Jing Teng; Xianghua Qi
Journal:  Med Sci Monit       Date:  2022-07-10

3.  A Depression Prediction Algorithm Based on Spatiotemporal Feature of EEG Signal.

Authors:  Wei Liu; Kebin Jia; Zhuozheng Wang; Zhuo Ma
Journal:  Brain Sci       Date:  2022-05-11

4.  A Depression Diagnosis Method Based on the Hybrid Neural Network and Attention Mechanism.

Authors:  Zhuozheng Wang; Zhuo Ma; Wei Liu; Zhefeng An; Fubiao Huang
Journal:  Brain Sci       Date:  2022-06-26
  4 in total

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