Integrative Neuroscience, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany.
Clin EEG Neurosci. 2024 Mar;55(2):185-191. doi: 10.1177/15500594231163958. Epub 2023 Mar 21.
. 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. . 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. 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. 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.
抑郁障碍与脑振荡活动改变有关。量化振荡活动的常用方法是傅里叶和小波变换。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迹。在这里，我们提出了一种称为自同步指数（SSI）的方法，用于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、幅度和周期，以估计特定频带的同步值。使用了 45 名抑郁患者和 55 名健康个体的记录脑电图（EEG）数据。将 SSI 方法应用于在 alpha 频带（8-13 Hz）滤波的每个 EEG 电极。使用多元线性回归模型，使用 alpha SSI 值预测抑郁严重程度（贝克抑郁量表 II 评分）。严重抑郁患者在所有脑区的 alpha SSI 均低于中度抑郁患者和健康对照组。此外，alpha SSI 值与所有脑区的抑郁严重程度呈负相关。回归模型显示使用 alpha SSI 预测抑郁严重程度具有显著性能。研究结果支持 SSI 测量作为量化同步振荡活动的有力工具。本文中检查的数据支持这样一种观点，即 alpha 振荡神经活动的同步性与抑郁程度之间存在很强的联系。这些发现产生了一种客观和定量的抑郁严重程度预测方法。
