Machine learning models for diagnosis of essential tremor and dystonic tremor using grey matter morphological networks

作者全名：Gui, Honge; Xiao, Pan; Xu, Bintao; Zhao, Xiaole; Wang, Hongyu; Tao, Li; Zhang, Xiaoyu; Li, Qin; Zhang, Xueyan; Chen, Huiyue; Wang, Hansheng; Lv, Fajin; Luo, Tianyou; Cheng, Oumei; Luo, Jin; Man, Yun; Xiao, Zheng; Fang, Weidong

作者地址：[Gui, Honge; Xiao, Pan; Xu, Bintao; Zhao, Xiaole; Wang, Hongyu; Tao, Li; Zhang, Xiaoyu; Li, Qin; Zhang, Xueyan; Chen, Huiyue; Wang, Hansheng; Lv, Fajin; Luo, Tianyou; Fang, Weidong] Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, 1 Youyi Rd, Chongqing 400016, Peoples R China; [Cheng, Oumei; Luo, Jin; Man, Yun; Xiao, Zheng] Chongqing Med Univ, Affiliated Hosp 1, Dept Neurol, Chongqing, Peoples R China

通信作者：Fang, WD (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, 1 Youyi Rd, Chongqing 400016, Peoples R China.

来源：PARKINSONISM & RELATED DISORDERS

ESI学科分类：NEUROSCIENCE & BEHAVIOR

WOS号：WOS:001240386000001

JCR分区：Q2

影响因子：3.1

年份：2024

卷号：124

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：Essential tremor; Dystonic tremor; Machine learning; Structural magnetic resonance imaging; Grey matter morphological networks

摘要：Background: Essential tremor (ET) and dystonic tremor (DT) are the two most common tremor disorders, and misdiagnoses are very common due to similar tremor symptoms. In this study, we explore the structural network mechanisms of ET and DT using brain grey matter (GM) morphological networks and combine those with machine learning models. Methods: 3D -T1 structural images of 75 ET patients, 71 DT patients, and 79 healthy controls (HCs) were acquired. We used voxel-based morphometry to obtain GM images and constructed GM morphological networks based on the Kullback-Leibler divergence -based similarity (KLS) method. We used the GM volumes, morphological relations, and global topological properties of GM-KLS morphological networks as input features. We employed three classifiers to perform the classification tasks. Moreover, we conducted correlation analysis between discriminative features and clinical characteristics. Results: 16 morphological relations features and 1 global topological metric were identified as the discriminative features, and mainly involved the cerebello-thalamo-cortical circuits and the basal ganglia area. The Random Forest (RF) classifier achieved the best classification performance in the three -classification task, achieving a mean accuracy (mACC) of 78.7%, and was subsequently used for binary classification tasks. Specifically, the RF classifier demonstrated strong classification performance in distinguishing ET vs. HCs, ET vs. DT, and DT vs. HCs, with mACCs of 83.0 %, 95.2 %, and 89.3 %, respectively. Correlation analysis demonstrated that four discriminative features were significantly associated with the clinical characteristics. Conclusion: This study offers new insights into the structural network mechanisms of ET and DT. It demonstrates the effectiveness of combining GM-KLS morphological networks with machine learning models in distinguishing between ET, DT, and HCs.

基金机构：National Natural Science Founda-tion of China [NSFC: 81671663]; Natural Science Foundation of Chongqing (NSFCQ) [cstc2014jcyjA10047]

基金资助正文：This study was supported by the National Natural Science Founda-tion of China (NSFC: 81671663) and the Natural Science Foundation of Chongqing (NSFCQ: cstc2014jcyjA10047) .