Betweenness Centrality in Resting-State Functional Networks Distinguishes Parkinson's Disease
Betweenness Centrality in Resting-State Functional Networks Distinguishes Parkinson's Disease
复制标题
DOI：
10.1109/embc48229.2022.9870988
复制
发表时间：
2022-07
期刊：
影响因子：
--
通讯作者：
S. Avvaru;K. Parhi
中科院分区：
文献类型：
--
作者：
S. Avvaru;K. Parhi
本文的目的是利用无创脑电图（EEG）衍生的图论网络测量来开发神经解码器，以区分帕金森病（PD）患者和健康对照（HC）。分析了来自新墨西哥州的27例患者和27例人口统计学匹配的对照者的脑电图信号，估计了他们的功能网络。患者在左旋多巴开启和关闭期间记录的数据被纳入分析以进行比较。我们使用估计功能网络的中间中心性对HC和PD组进行分类。分类器使用留一交叉验证进行评估。我们观察到PD患者（服药和停药）与健康对照的区分准确率为89%，比同一数据集的最新水平高出约4%。这项工作表明，使用颅外静息状态脑电图进行脑网络分析可以发现指示PD的相互作用模式。这种方法也可以扩展到其他神经系统疾病。
The goal of this paper is to use graph theory network measures derived from non-invasive electroencephalography (EEG) to develop neural decoders that can differentiate Parkinson's disease (PD) patients from healthy controls (HC). EEG signals from 27 patients and 27 demographically matched controls from New Mexico were analyzed by estimating their functional networks. Data recorded from the patients during ON and OFF levodopa sessions were included in the analysis for comparison. We used betweenness centrality of estimated functional networks to classify the HC and PD groups. The classifiers were evaluated using leave-one-out cross-validation. We observed that the PD patients (on and off medication) could be distinguished from healthy controls with 89% accuracy – approximately 4% higher than the state-of-the-art on the same dataset. This work shows that brain network analysis using extracranial resting-state EEG can discover patterns of interactions indicative of PD. This approach can also be extended to other neurological disorders.