背景:原发性震颤(ET)和帕金森病(PD)是两种最常见的运动障碍,共享几个重叠的震颤临床特征。尽管越来越多的证据指出,类似大脑网络节点的变化与这两种疾病有关,脑网络的拓扑特性还不是很清楚。
目的:图论分析与机器学习(ML)算法的结合为揭示ET和震颤显性PD(tPD)中的拓扑发病机理提供了一种有希望的方法。
方法:从86例ET患者的静息状态功能图像中提取拓扑指标,86例TPD患者,和86名年龄和性别匹配的健康对照(HCs)。对特征降维进行了三个步骤,并采用了四个常用分类器来区分ET,tPD,和HCs。
结果:支持向量机分类器在区分ET方面取得了四个分类器的最佳分类性能,tPD,和HCs,平均准确率为89.0%(mACC),用于二元分类。特别是,ET与ET之间的二元分类性能tPD,ETvs.HC,和tPDvs.HC的mACC为94.2%,86.0%mACC,和86.3%的mACC,分别。最强大的区分功能主要位于默认,额叶-顶叶,Cingulo-opercular,感觉运动,和小脑网络。相关分析结果显示,2个拓扑特征与临床特征呈负相关,1个与临床特征呈正相关。
结论:这些结果表明,将拓扑度量与ML算法结合起来不仅可以实现对ET的高分类精度,tPD,同时也有助于揭示ET和tPD中潜在的脑拓扑网络发病机制。
BACKGROUND: Essential tremor (ET) and Parkinson\'s disease (PD) are the two most prevalent movement disorders, sharing several overlapping tremor clinical features. Although growing evidence pointed out that changes in similar brain network nodes are associated with these two diseases, the brain network topological properties are still not very clear.
OBJECTIVE: The combination of graph theory analysis with machine learning (ML) algorithms provides a promising way to reveal the topological pathogenesis in ET and tremor-dominant PD (tPD).
METHODS: Topological metrics were extracted from Resting-state functional images of 86 ET patients, 86 tPD patients, and 86 age- and sex-matched healthy controls (HCs). Three steps were conducted to feature dimensionality reduction and four frequently used classifiers were adopted to discriminate ET, tPD, and HCs.
RESULTS: A support vector machine classifier achieved the best classification performance of four classifiers for discriminating ET, tPD, and HCs with 89.0% mean accuracy (mACC) and was used for binary classification. Particularly, the binary classification performances among ET vs. tPD, ET vs. HCs, and tPD vs. HCs were with 94.2% mACC, 86.0% mACC, and 86.3% mACC, respectively. The most power discriminative features were mainly located in the default, frontal-parietal, cingulo-opercular, sensorimotor, and cerebellum networks. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with clinical characteristics.
CONCLUSIONS: These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET, tPD, and HCs but also help to reveal the potential brain topological network pathogenesis in ET and tPD.