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Alpha peak activity in resting-state EEG is associated with depressive score.
- Source :
-
Frontiers in neuroscience [Front Neurosci] 2023 Mar 07; Vol. 17, pp. 1057908. Date of Electronic Publication: 2023 Mar 07 (Print Publication: 2023). - Publication Year :
- 2023
-
Abstract
- Introduction: Depression is a serious psychiatric disorder characterized by prolonged sadness, loss of interest or pleasure. The dominant alpha peak activity in resting-state EEG is suggested to be an intrinsic neural marker for diagnosis of mental disorders.<br />Methods: To investigate an association between alpha peak activity and depression severity, the present study recorded resting-state EEG (EGI 128 channels, off-line average reference, source reconstruction by a distributed inverse method with the sLORETA normalization, parcellation of 68 Desikan-Killiany regions) from 155 patients with depression (42 males, mean age 35 years) and acquired patients' scores of Self-Rating Depression Scales. We measured both the alpha peak amplitude that is more related to synchronous neural discharging and the alpha peak frequency that is more associated with brain metabolism.<br />Results: The results showed that over widely distributed brain regions, individual patients' alpha peak amplitudes were negatively correlated with their depressive scores, and individual patients' alpha peak frequencies were positively correlated with their depressive scores.<br />Discussion: These results reveal that alpha peak amplitude and frequency are associated with self-rating depressive score in different manners, and the finding suggests the potential of alpha peak activity in resting-state EEG acting as an important neural factor in evaluation of depression severity in supplement to diagnosis.<br />Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.<br /> (Copyright © 2023 Zhou, Wu, Zhan, Guo, Wang, Wang, Yang, Lin, Zhang, Liu, Lin, Fu and Wu.)
Details
- Language :
- English
- ISSN :
- 1662-4548
- Volume :
- 17
- Database :
- MEDLINE
- Journal :
- Frontiers in neuroscience
- Publication Type :
- Academic Journal
- Accession number :
- 36960170
- Full Text :
- https://doi.org/10.3389/fnins.2023.1057908