Increased functional connectivity within alpha and theta frequency bands in dysphoria: A resting-state EEG study

功能连接 脑电图 静息状态功能磁共振成像 烦躁 心理学 阿尔法(金融) 神经科学 听力学 临床心理学 医学 精神科 心理测量学 焦虑 结构效度
作者
Carola Dell’Acqua,Shadi Ghiasi,Simone Messerotti Benvenuti,Alberto Greco,Claudio Gentili,Gaetano Valenza
出处
期刊:Journal of Affective Disorders [Elsevier BV]
卷期号:281: 199-207 被引量:42
标识
DOI:10.1016/j.jad.2020.12.015
摘要

Abstract Background: The understanding of neurophysiological correlates underlying the risk of developing depression may have a significant impact on its early and objective identification. Research has identified abnormal resting-state electroencephalography (EEG) power and functional connectivity patterns in major depression. However, the entity of dysfunctional EEG dynamics in dysphoria is yet unknown. Methods: 32-channel EEG was recorded in 26 female individuals with dysphoria and in 38 age-matched, female healthy controls. EEG power spectra and alpha asymmetry in frontal and posterior channels were calculated in a 4-minute resting condition. An EEG functional connectivity analysis was conducted through phase locking values, particularly mean phase coherence. Results: While individuals with dysphoria did not differ from controls in EEG spectra and asymmetry, they exhibited dysfunctional brain connectivity. Particularly, in the theta band (4-8 Hz), participants with dysphoria showed increased connectivity between right frontal and central areas and right temporal and left occipital areas. Moreover, in the alpha band (8-12 Hz), dysphoria was associated with increased connectivity between right and left prefrontal cortex and between frontal and central-occipital areas bilaterally. Limitations: All participants belonged to the female gender and were relatively young. Mean phase coherence did not allow to compute the causal and directional relation between brain areas. Conclusions: An increased EEG functional connectivity in the theta and alpha bands characterizes dysphoria. These patterns may be associated with the excessive self-focus and ruminative thinking that typifies depressive symptoms. EEG connectivity patterns may represent a promising measure to identify individuals with a higher risk of developing depression.
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