抑郁症患者静息态阿尔法自同步性降低

IF 1.6 4区 医学 Q3 CLINICAL NEUROLOGY Clinical EEG and Neuroscience Pub Date : 2024-03-01 Epub Date: 2023-03-21 DOI:10.1177/15500594231163958
Yousef Mohammadi, Mohadeseh Shafiei Kafraj, Carina Graversen, Mohammad Hassan Moradi
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引用次数: 0

摘要

背景。抑郁症与大脑振荡活动的改变有关。量化振荡活动的常用方法是傅立叶变换和小波变换。这两种方法都难以区分同步振荡活动与非节律性和大振幅伪像。在此,我们提出了一种名为自同步指数(SSI)的方法来量化神经数据中的同步振荡活动。该方法考虑了神经振荡的时间特征、振幅和周期,以估算特定频段的同步值。方法使用 45 名抑郁症患者和 55 名健康人的脑电图(EEG)记录数据。在α频段(8-13 Hz)对每个脑电图电极进行滤波后,采用 SSI 方法。采用多元线性回归模型,利用α SSI 值预测抑郁严重程度(贝克抑郁清单-II 评分)。结果显示在所有脑区,重度抑郁症患者的阿尔法SSI值均低于中度抑郁症患者和健康对照组。此外,所有脑区的α SSI值与抑郁症严重程度呈负相关。回归模型显示,使用阿尔法SSI预测抑郁症严重程度的效果显著。结论研究结果表明,SSI 是量化同步振荡活动的有力工具。本文研究的数据支持了阿尔法振荡神经活动的同步性与抑郁程度之间存在紧密联系的观点。这些发现提供了一种客观、定量的抑郁严重程度预测方法。
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Decreased Resting-State Alpha Self-Synchronization in Depressive Disorder.

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.

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来源期刊
Clinical EEG and Neuroscience
Clinical EEG and Neuroscience 医学-临床神经学
CiteScore
5.20
自引率
5.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Clinical EEG and Neuroscience conveys clinically relevant research and development in electroencephalography and neuroscience. Original articles on any aspect of clinical neurophysiology or related work in allied fields are invited for publication.
期刊最新文献
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