Qi Xuchen, Xu Wanxiu, Li Gang
Department of Neurosurgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China.
Department of Neurosurgery, Shaoxing People's Hospital, Shaoxing 312000, China.
Brain Sci. 2023 Sep 4;13(9):1282. doi: 10.3390/brainsci13091282.
Generalized anxiety disorder (GAD) and depressive disorder (DD) are distinct mental disorders, which are characterized by complex and unique neuroelectrophysiological mechanisms in psychiatric neurosciences. The understanding of the brain functional differences between GAD and DD is crucial for the accurate diagnosis and clinical efficacy evaluation. The aim of this study was to reveal the differences in functional brain imaging between GAD and DD based on multidimensional electroencephalogram (EEG) characteristics. To this end, 10 min resting-state EEG signals were recorded from 38 GAD and 34 DD individuals. Multidimensional EEG features were subsequently extracted, which include power spectrum density (PSD), fuzzy entropy (FE), and phase lag index (PLI). Then, a direct statistical analysis (i.e., ANOVA) and three ensemble learning models (i.e., Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost)) were used on these EEG features for the differential recognitions. Our results showed that DD has significantly higher PSD values in the alpha1 and beta band, and a higher FE in the beta band, in comparison with GAD, along with the aberrant functional connections in all four bands between GAD and DD. Moreover, machine learning analysis further revealed that the distinct features predominantly occurred in the beta band and functional connections. Here, we show that DD has higher power and more complex brain activity patterns in the beta band and reorganized brain functional network structures in all bands compared to GAD. In sum, these findings move towards the practical identification of brain functional differences between GAD and DD.
广泛性焦虑障碍（GAD）和抑郁障碍（DD）是不同的精神障碍，在精神神经科学中具有复杂且独特的神经电生理机制。了解GAD和DD之间的脑功能差异对于准确诊断和临床疗效评估至关重要。本研究的目的是基于多维脑电图（EEG）特征揭示GAD和DD之间的功能脑成像差异。为此，记录了38名GAD患者和34名DD患者10分钟的静息态EEG信号。随后提取了多维EEG特征，包括功率谱密度（PSD）、模糊熵（FE）和相位滞后指数（PLI）。然后，对这些EEG特征进行直接统计分析（即方差分析）和三种集成学习模型（即随机森林（RF）、轻量级梯度提升机（LightGBM）、极端梯度提升（XGBoost））以进行差异识别。我们的结果表明，与GAD相比，DD在α1和β波段的PSD值显著更高，在β波段的FE更高，并且GAD和DD在所有四个波段中均存在异常的功能连接。此外，机器学习分析进一步表明，不同特征主要出现在β波段和功能连接中。在此，我们表明与GAD相比，DD在β波段具有更高的功率和更复杂的脑活动模式，并且在所有波段中脑功能网络结构均发生了重组。总之，这些发现朝着实际识别GAD和DD之间的脑功能差异迈进。
