Functional role of frontal electroencephalogram alpha asymmetry in the resting state in patients with depression: A review

Abstract

Depression is a psychological disorder that affects the general public worldwide. It is particularly important to make an objective and accurate diagnosis of depression, and the measurement methods of brain activity have gradually received increasing attention. Resting electroencephalogram (EEG) alpha asymmetry in patients with depression shows changes in activation of the alpha frequency band of the left and right frontal cortices. In this paper, we review the findings of the relationship between frontal EEG alpha asymmetry in the resting state and depression. Based on worldwide studies, we found the following: (1) Compared with individuals without depression, those with depression showed greater right frontal EEG alpha asymmetry in the resting state. However, the pattern of frontal EEG alpha asymmetry in the resting state in depressive individuals seemed to disappear with age; (2) Compared with individuals without maternal depression, those with maternal depression showed greater right frontal EEG alpha asymmetry in the resting state, which indicated that genetic or experience-based influences have an impact on frontal EEG alpha asymmetry at rest; and (3) Frontal EEG alpha asymmetry in the resting state was stable, and little or no change occurred after antidepressant treatment. Finally, we concluded that the contrasting results may be due to differences in methodology, clinical characteristics, and participant characteristics.

EEG-based major depressive disorder recognition by neural oscillation and asymmetry

“…The observed imbalances may indicate disrupted interhemispheric communication, a factor implicated in the pathophysiology of depression (Uhlhaas and Singer, 2010). This lateralization has been noted in EEG studies, where alpha-band asymmetries correlated with emotional processing and depression severity (Bruder et al, 2001;Xie et al, 2023).…”

Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder

“…Machine learning algorithms have demonstrated potential in analyzing neuroimaging data to identify patterns and features that can be used to differentiate between GAD and DD [ 14 , 15 , 16 ]. Algorithms can extract intricate information from neuroimaging modalities such as electroencephalography (EEG) [ 17 ], enabling a deeper understanding of the unique brain signatures associated with each disorder [ 18 , 19 , 20 ]. By leveraging these advancements, the field of mental health research is making strides toward objective and quantifiable measures.…”