Decreased Resting-State Alpha Self-Synchronization in Depressive Disorder.

Autor:	Mohammadi Y; Integrative Neuroscience, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark., Kafraj MS; Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany., Graversen C; Integrative Neuroscience, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.; Department of Health Science and Technology, Integrative Neuroscience Group, Center for Neuroplasticity and Pain (CNAP), Aalborg University, Aalborg, Denmark., Moradi MH; Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Islamic Republic of Iran.
Jazyk:	angličtina
Zdroj:	Clinical EEG and neuroscience [Clin EEG Neurosci] 2024 Mar; Vol. 55 (2), pp. 185-191. Date of Electronic Publication: 2023 Mar 21.
DOI:	10.1177/15500594231163958
Abstrakt:	Background . Depression disorder has been associated with altered oscillatory brain activity. The common methods to quantify oscillatory activity are Fourier and wavelet transforms. Both methods have difficulties distinguishing synchronized oscillatory activity from nonrhythmic and large-amplitude artifacts. Here we proposed a method called self-synchronization index (SSI) to quantify synchronized oscillatory activities in neural data. The method considers temporal characteristics of neural oscillations, amplitude, and cycles, to estimate the synchronization value for a specific frequency band. Method . The recorded electroencephalography (EEG) data of 45 depressed and 55 healthy individuals were used. The SSI method was applied to each EEG electrode filtered in the alpha frequency band (8-13 Hz). The multiple linear regression model was used to predict depression severity (Beck Depression Inventory-II scores) using alpha SSI values. Results. Patients with severe depression showed a lower alpha SSI than those with moderate depression and healthy controls in all brain regions. Moreover, the alpha SSI values negatively correlated with depression severity in all brain regions. The regression model showed a significant performance of depression severity prediction using alpha SSI. Conclusion. The findings support the SSI measure as a powerful tool for quantifying synchronous oscillatory activity. The data examined in this article support the idea that there is a strong link between the synchronization of alpha oscillatory neural activities and the level of depression. These findings yielded an objective and quantitative depression severity prediction. Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Databáze:	MEDLINE
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