摘要
目的:为了分析帕金森病患者大脑的默认模式网络(default mode network,DMN)潜在变化的原因和其临床特点的关系,以及探讨如何提取其脑电信号特征并对其进行准确有效地分类.方法:选择帕金森病患者和健康对照组各26名被试作为实验对象,将部分定向相干(partial directed coherence,PDC)应用于DMN相关电极的数据序列,获得对照组和帕金森病受试者的有效连接.通过统计分析后,得出6条具有显著差异的PDC连接,对它们进行深入讨论.进一步地,将这些连接值组成特征集并进行分类.结果:与对照组相比,帕金森病患者中有关注意力控制之间的连接降低,在涉及有关工作记忆的连接中,帕金森病相比于健康组患者的一些连接都有不同程度的增加.同时,使用XGBoost算法对特征集进行分类,得到76.5%的平均测试准确率.结论:静息状态下帕金森病患者的非运动症状与DMN网络存在显著性的关系,表现在注意力控制与记忆功能上,这与DMN中BA区的受损有很大的关系.随后对两类受试者的分类也验证了PDC算法用于DMN分析的有效性,为帕金森病人的测试和预防提供了一种新途径.
Objective:In order to analyze the causes of potential changes of default mode network(DMN)in the brain of patients with Parkinson’s disease and the relationship between its clinical characteristics,and to explore how to extract its EEG characteristics and classify them accurately and effectively.Methods:26 subjects in Parkinson’s disease group and 26 subjects in healthy control group were selected as experimental objects.Partial directed coherence(PDC)was applied to the data sequence of DMN related electrodes to obtain the effective connection between the control group and Parkinson’s disease subjects.After statistical analysis,the six PDC connections with significant differences are obtained discussed in depth.Further,these connection values are grouped in feature sets and classfied.Results:Compared with the control group,the connections related to attention control decreased in patients with Parkinson’s disease,and some connections related to working memory increased in patients with Parkinson’s disease compared with healthy patients.At the same time,XGBoost algorithm is used to classify the feature set,and the average test accuracy is 76.5%.Conclusion:There is a significant relationship between non motor symptoms and DMN network in patients with Parkinson’s disease at rest,which is manifested in attention control and memory function,which is closely related to the damage of BA area in DMN.Subsequently,the classification of the two categories of subjects also verified the effectiveness of PDC algorithm in DMN analysis,and provided a new way for the testing and prevention of Parkinson’s patients.
作者
曾宣威
湛慧苗
吕浩铵
高军峰
ZENG Xuanwei;ZHAN Huimiao;LV Haoan;GAO Junfeng(South-Central Minzu University,School of Biomedical Engineering,Wuhan 430074,China;South-Central Minzu University,Key Laboratory of Cognitive Science of State Ethnic Affairs Commission,Wuhan 430074,China)
出处
《中南民族大学学报（自然科学版）》
CAS
2024年第1期119-125,共7页
Journal of South-Central University for Nationalities：Natural Science Edition
基金
国家自然科学基金资助项目(61773408)
中央高校基本科研业务费专项资金资助项目(CZZ23011,CZY20039)。
关键词
帕金森病
部分定向相干
脑电信号
默认模式网络
XGBoost算法
Parkinson’s disease
partial directional coherence
electroencephalogram
default mode network
XGBoost algorithm
