This paper presented a novel complex network with one-way ANOVA F-test feature selection to diagnose early-stage Parkinson’s disease (PD) on quantitative susceptibility mapping (QSM). Experimental results on QSM images of 30 early-stage PD patients and 27 healthy controls (HC) proved that the F-test feature selection scheme was effective and achieved good classification results. The accuracy, AUC, sensitivity and specificity of our method were 0.96, 0.97, 0.99 and 0.95, respectively, which were improved by 15%, 4%, 29% and 2%, respectively by comparison with the commonly used region of interest (ROI) based method. Meanwhile, according to the feature importance, the potential brain regions affected by PD were arranged orderly. The affected regions were distributed as follows: 61% of them are located in right hemisphere and 39% in the left hemisphere. Particularly, frontal lobe, parietal lobe, temporal lobe and occipital lobe accounted for 24%, 20%, 5% and 14%, respectively, and striatum and the dorsal thalamus accounted for 16%. It concludes that the complex network with one-way ANOVA F-test feature selection can greatly improve the diagnostic performance of early-stage PD based on QSM, as well as provide a new way to study the effect of PD on brain in the future.

中文翻译：

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使用具有单向方差分析 F 检验特征选择的复杂网络的定量敏感性绘图诊断早期帕金森病

本文提出了一种具有单向方差分析 F 检验特征选择的新型复杂网络，用于在定量敏感性映射 (QSM) 上诊断早期帕金森病 (PD)。对 30 名早期 PD 患者和 27 名健康对照者（HC）的 QSM 图像的实验结果证明，F 检验特征选择方案是有效的，并取得了良好的分类效果。我们方法的准确性、AUC、敏感性和特异性分别为0.96、0.97、0.99和0.95，与常用的感兴趣区域（ROI）相比，分别提高了15%、4%、29%和2%。 ) 基于方法。同时，根据特征重要性，将受PD影响的潜在脑区有序排列。受影响地区分布如下：其中61%位于右半球，39%位于左半球。其中额叶、顶叶、颞叶和枕叶分别占24%、20%、5%和14%，纹状体和背丘脑占16%。得出的结论是，具有单向方差分析 F 检验特征选择的复杂网络可以大大提高基于 QSM 的早期 PD 的诊断性能，并为未来研究 PD 对大脑的影响提供新的途径。