Zhang Bing, Peng Jingjing, Chen Hong, Hu Wenbin
Graduate School of Anhui University of Chinese Medicine,230012, China.
Affiliated Hospital of Institute of Neurology, Anhui University of Chinese Medicine,230031, China.
Heliyon. 2023 Jul 7;9(7):e18087. doi: 10.1016/j.heliyon.2023.e18087. eCollection 2023 Jul.
Wilson's disease (WD) is a genetic disorder with the A7P7B gene mutations. It is difficult to diagnose in clinic. The purpose of this study was to confirm whether amplitude of low-frequency fluctuations (ALFF) is one of the potential biomarkers for the diagnosis of WD. The study enrolled 30 healthy controls (HCs) and 37 WD patients (WDs) to obtain their resting-state functional magnetic resonance imaging (rs-fMRI) data. ALFF was obtained through preprocessing of the rs-fMRI data. To distinguish between patients with WDs and HCs, four clusters with abnormal ALFF-z values were identified through between-group comparisons. Based on these clusters, three machine learning models were developed, including Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR). Abnormal ALFF z-values were also combined with volume information, clinical variables, and imaging features to develop machine learning models. There were 4 clusters where the ALFF z-values of the WDs were significantly higher than that of the HCs. Cluster1 was in the cerebellar region, Cluster2 was in the left caudate nucleus, Cluster3 was in the bilateral thalamus, and Cluster4 was in the right caudate nucleus. In the training set and test set, the models trained with Cluster2, Cluster3, and Cluster4 achieved area of curve (AUC) greater than 0.80. In the Delong test, only the AUC values of models trained with Cluster4 exhibited statistical significance. The AUC values of the Logit model (P = 0.04) and RF model (P = 0.04) were significantly higher than those of the SVM model. In the test set, the LR model and RF model trained with Cluster3 had high specificity, sensitivity, and accuracy. By conducting the Delong test, we discovered that there was no statistically significant inter-group difference in AUC values between the model that integrates multi-modal information and the model before fusion. The LR models trained with multimodal information and Cluster 4, as well as the LR and RF models trained with multimodal information and Cluster 3, have demonstrated high accuracy, specificity, and sensitivity. Overall, these findings suggest that using ALFF based on the thalamus or caudate nucleus as markers can effectively differentiate between WDs and HCs. The fusion of multimodal information did not significantly improve the classification performance of the models before fusion.
威尔逊病（WD）是一种由ATP7B基因突变引起的遗传性疾病。临床上难以诊断。本研究的目的是确认低频振幅波动（ALFF）是否是诊断WD的潜在生物标志物之一。该研究招募了30名健康对照者（HCs）和37名WD患者（WDs），以获取他们的静息态功能磁共振成像（rs-fMRI）数据。通过对rs-fMRI数据进行预处理来获得ALFF。为了区分WD患者和HCs，通过组间比较确定了四个ALFF-z值异常的簇。基于这些簇，开发了三种机器学习模型，包括随机森林（RF）、支持向量机（SVM）和逻辑回归（LR）。异常的ALFF z值还与体积信息、临床变量和影像特征相结合，以开发机器学习模型。有4个簇中WDs的ALFF z值显著高于HCs。簇1位于小脑区域，簇2位于左侧尾状核，簇3位于双侧丘脑，簇4位于右侧尾状核。在训练集和测试集中，用簇2、簇3和簇4训练的模型的曲线下面积（AUC）大于0.80。在德龙检验中，只有用簇4训练的模型的AUC值具有统计学意义。逻辑模型（P = 0.04）和RF模型（P = 0.04）的AUC值显著高于SVM模型。在测试集中，用簇3训练的LR模型和RF模型具有较高的特异性、敏感性和准确性。通过进行德龙检验，我们发现整合多模态信息的模型与融合前的模型之间的AUC值在组间差异无统计学意义。用多模态信息和簇4训练的LR模型，以及用多模态信息和簇3训练的LR和RF模型，均表现出较高的准确性、特异性和敏感性。总体而言，这些发现表明，以丘脑或尾状核为基础的ALFF作为标志物可以有效区分WD患者和HCs。多模态信息的融合并没有显著提高融合前模型的分类性能。
