|Research on EEG-based function brain network analysis and depression recognition
|Place of Conferral
|脑电图 Electroencephalography 功能脑网络 function brain network 抑郁识别 depression recognition 生物标志物 biomarkers 注意力机制 attention mechanism
第二，针对目前功能脑网络研究只在单一尺度或水平上进行分析的缺点，本研究提出了一种基于改进经验模式分解（Empirical mode decomposition，EMD）的功能网络分解模型，该模型适用于功能脑网络的时频分析。该方法一方面解决了传统EMD方法处理脑电数据引起的模式混叠问题。另一方面，基于不同固有模态函数分别构建功能连接矩阵，实现对抑郁症患者功能脑网络的时频分析。相比于传统的分频段分析，实验结果发现抑郁症患者大脑功能连通性在不同固有模态函数上表现出更加显著的变化，而且基于网络度量的统计结果表明，抑郁组与正常对照组之间存在显著的统计学差异，这些都证实了抑郁症患者存在异常的网络拓扑结构。基于功能脑网络的时频分析可能为抑郁症患者的脑功能连通性分析提供了新的思路。
The implementation of brain functions mainly relies on the large-scale network formed by the interaction between neurons. In recent years, research on functional brain networks based on neuroimaging data has gradually become a focal point in the field of neuroscience. Depression is a serious mental disorder characterized by impairments of individual emotional and cognitive functions. Numerous neuroimaging studies have provided evidence indicating that emotional and cognitive impairments in depression are associated with abnormal function and structure in certain brain regions. Specifically, research on functional brain networks based on electroencephalogram (EEG) has identified abnormal network topology and connectivity in depressed patients, which is important for understanding and uncovering the underlying neural mechanisms of depression. However, there are still some challenges and limitations in research on EEG-based functional brain networks analysis and depression identification. These challenges mainly include: (1) there is no recognized "gold standard" for constructing functional brain networks based on EEG, which is difficult to accurately evaluate and reflect brain activity; (2) current researchers mainly focus on a single scale or level, making it difficult to simultaneously obtain relevant information on brain functions from multiple scales and levels; (3) most clinical intervention studies on depression lack objective and effective biological indicators for validation, which makes its pathological interpretability slightly insufficient; (4) EEG-based depression identification models have mostly overlook the regional distribution characteristics of EEG electrode channels, which hinders the development and optimization of new computational models. In response to these problems and challenges, this study attempts to solve them through complementary approaches of theory-driven and data-driven methods. The main work is summarized as follows:
First, since there is no "gold standard" for constructing functional brain networks based on EEG, this study attempts to establish a reliable and accurate method for constructing functional brain networks, which are applied to the analysis of functional brain networks in depressed individuals. This mainly includes two tasks: first, through a systematic exploration of nine traditional functional connectivity methods and experiments, it was found that coherence-based functional networks almost always achieved the best classification performance in depression identification. Second, a weighted functional hypernetwork method was proposed to characterize high-order relationships in EEG signals. Firstly, a sparse regression method was used to construct hyper-network connections; Secondly, the Pearson correlation coefficients between each pair of hyper-edge nodes were aggregated and used as the weight of the hyper-edge to better integrate hyper-edge information, thereby constructing a weighted hyper-network that includes hyper-edge information. Finally, network metrics features were extracted for classification. The experimental results showed that the classification accuracy of the hyper-network method is superior to traditional functional network methods. This study attempts to explore EEG-based functional brain network construction methods from the perspective of comparing existing coupling methods and developing hyper-network methods, which may provide valuable reference for the study of functional brain network in patients with depression.
Second, addressing the limitation of current functional brain network research that only focuses on analysis at a single scale or level, this study proposes a functional network decomposition model based on improved Empirical Mode Decomposition (EMD) for time-frequency analysis of functional brain networks. On the one hand, this method solves the problem of pattern aliasing caused by traditional EMD methods in processing EEG data. On the other hand, by constructing brain functional networks with different intrinsic mode functions (IMF), the study achieves time-frequency analysis of the functional brain networks for depression. Compared to traditional frequency band analysis, the experimental results found that the connectivity of brain function in depression patients showed more significant changes in different intrinsic mode functions. More statistically significant results based on network measures suggested that there were significant differences between the depression group and the normal control group. These findings confirm the abnormal network topology in patients with depression. The time-frequency analysis based on functional brain networks may provide new ideas for the analysis of brain functional connectivity in patients with depression.
Third, a limitation of many clinical intervention studies is the lack of objective biological markers to evaluate treatment efficacy. This study is the first to analyze the impact of nitrous oxide (also known as laughing gas) treatment on patients with treatment-resistant depression from the perspective of brain functional connectivity. Firstly, we explore the changes in event-related potential components (P1, N1, P2, N2, P3) at different time points before and after treatment in patients with treatment-resistant depression. The experimental results show significant differences in the amplitude and latency of P1, N1, P2, and N2 components before and after nitrous oxide treatment in patients with treatment-resistant depression. Secondly, we examine changes in functional brain networks before and after nitrous oxide treatment, and correlates changes in network metric indicators with clinical features. The experimental results show that nitrous oxide treatment increases functional connectivity in the brains of patients to a certain extent. In addition, the study found a significant correlation between the increase in clustering coefficient in the nitrous oxide intervention group and the decrease in depression severity. These findings provide neuroimaging evidence for understanding how nitrous oxide regulates the brain function of patients with treatment-resistant depression and provide reference for its clinical application.
Fourth, previous studies on functional brain networks have found that regional information from different brain regions may be used to explore the potential physiological characteristics of depression. However, most existing studies on depression identification have overlooked the regional distribution characteristics of EEG electrode channels. Therefore, this study proposes a depression recognition method based on attention mechanism. By designing a Divide-general Structure Transformer model to capture depression specific information in EEG signals, the accuracy of depression recognition can be improved. The performance of the new model was compared with other models using leave-one-out cross-validation. Experimental results showed that the new model achieved the best classification performance of 89.7% on the same dataset compared with its variants and baseline methods. By using the spatial distribution characteristics of EEG data to guide the flow of data within the model, this approach may provide new insights for future depression recognition model research.
In summary, this study systematically explores the methods for analyzing functional brain networks and depression identifying based on EEG data from both a theoretical and data-driven perspective. Firstly, it various EEG-based functional network construction methods were explored, and abnormal connection patterns of functional networks in depression patients were analyzed at different dimensions and multi-scale levels to reveal the abnormal neural mechanisms of depression patients; In addition, combining pathological principles to evaluate the therapeutic effect of depression patients and explore their potential neural pathways; Finally, it optimizes depression recognition modeling based on the spatiotemporal characteristics of EEG, in order to provide more reliable methods and biomarkers for auxiliary diagnosis of depression patients.
|MOST Discipline Catalogue
|工学 - 计算机科学与技术（可授工学、理学学位） - 计算机应用技术
|邵学晓. 基于脑电图的功能脑网络分析方法及抑郁识别研究[D]. 兰州. 兰州大学,2023.
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