基于脑功能网络的抑郁症识别研究

Research on depression recognition based on brain function network

传统基于脑电(EEG)的抑郁症研究将电极视为孤立节点,忽略了它们之间的关联性,难以发掘抑郁症患者异常大脑拓扑改变。为此,本文提出一种基于脑功能网络(BFN)的抑郁症识别框架,为避免容积导体效应,相位延迟指数用于构建BFN;以加权与二值化BFN信息互补为基础,选取"小世界"特性密切相关及最小生成树特定脑区BFN指标,采用递进式指标分析策略寻找抑郁症识别潜在标识物。本文以48名受试者静息态EEG数据用于验证方案,结果表明组间同步性在左颞、右顶枕、右额脑区明显改变;加权BFN最短路径长度和聚类系数,二值化BFN左颞和右额的叶子分数及右顶枕的直径与患者健康问卷9项(PHQ-9)之间具有相关性,且获得最高94.11%的识别率。此外,研究发现相对于健康对照者,抑郁症患者的信息处理能力明显下降。通过上述结论,期望本研究结果可为BFN构建与分析提供新的思路,为抑郁症识别潜在标识物的发掘提供新的方法。

Traditional depression research based on electroencephalogram(EEG)regards electrodes as isolated nodes and ignores the correlation between them.So it is difficult to discover abnormal brain topology alters in patients with depression.To resolve this problem,this paper proposes a framework for depression recognition based on brain function network(BFN).To avoid the volume conductor effect,the phase lag index is used to construct BFN.BFN indexes closely related to the characteristics of“small world”and specific brain regions of minimum spanning tree were selected based on the information complementarity of weighted and binary BFN and then potential biomarkers of depression recognition are found based on the progressive index analysis strategy.The resting state EEG data of 48 subjects was used to verify this scheme.The results showed that the synchronization between groups was significantly changed in the left temporal,right parietal occipital and right frontal,the shortest path length and clustering coefficient of weighted BFN,the leaf scores of left temporal and right frontal and the diameter of right parietal occipital of binary BFN were correlated with patient health questionnaire 9-items(PHQ-9),and the highest recognition rate was 94.11%.In addition,the study found that compared with healthy controls,the information processing ability of patients with depression reduced significantly.The results of this study provide a new idea for the construction and analysis of BFN and a new method for exploring the potential markers of depression recognition.