摘要
帕金森氏轻度认知障碍(PDMCI)是帕金森氏症患者痴呆的先兆,这对使用神经评分量表和医生经验等传统方法进行准确诊断提出了挑战。利用26名PDMCI患者和23名正常人的脑电信号,基于定向传递函数构建了Delta、Theta、Alpha、Beta和Gamma频段的脑功能网络。引入了一种新颖的图论特征——效率密度来捕获网络密度和传输效率。研究结果揭示了独特的连接模式,Delta和Theta波段的连接更紧密,而Alpha、Beta和Gamma波段的连接更稀疏。帕金森病(PD)患者与对照组之间的Theta、Alpha、Beta和Gamma频带存在显著差异(p<0.05)。因此,脑功能网络可以有效反映PD脑功能异常状态,效率密度特征可以反映PD脑功能异常活动的特征量。
Parkinson's mild cognitive impairment(PDMCI)is a precursor to dementia in Parkinson's patients,posing challenges for accurate diagnosis using conventional methods such as neurological rating scales and doctors'experience.By using the EEG signals of 26 PDMCI patients and 23 normal subjects,the brain function networks of Delta,Theta,Alpha,Beta and Gamma bands were constructed based on the directional transfer function.A novel graph theory feature,efficiency density,is introduced to capture both network density and transmission efficiency.The findings reveal distinctive connectivity patterns,with tighter connections in Delta and Theta bands and sparser connections in Alpha,Beta,and Gamma bands.Significant differences between PD patients and the control group are observed in Theta,Alpha,Beta,and Gamma bands(p<0.05).Therefore,the brain function network can effectively reflect the abnormal brain function status of PD,and the efficiency density characteristic can reflect the characteristic amount of abnormal brain activity in PD.
作者
李昕
张晴
张莹
谢平
尹立勇
LI Xin;ZHANG Qing;ZHANG Ying;XIE Ping;YIN Liyong(School of Electrical Engineering,Yanshan University,Qinhuangdao,Hebei 066004,China;Measurement Technology and Instrumentation Key Lab of Hebei Province,Qinhuangdao,Hebei 066004,China;Institute of Health and Wellness Industry Technology,Yanshan University,Qinhuangdao,Hebei 066004,China;Qinhuangdao First Hospital,Qinhuangdao,Hebei 066004,China)
出处
《计量学报》
CSCD
北大核心
2024年第1期135-144,共10页
Acta Metrologica Sinica
基金
国家自然科学基金(62076216)
河北省自然科学基金(F2019203515,F2022203005)
燕山大学与秦皇岛市第一医院医工交叉特色专项(UY202201)
河北省科技计划项目(236Z2004G)。
关键词
智能信息处理
帕金森轻度认知障碍
脑电
脑功能网络
特征提取
效率密度
intelligent information processing
Parkinson's mild cognitive impairment
EEG
brain function network
feature extraction
efficiency density
