Combined brain topological metrics with machine learning to distinguish essential tremor and tremor-dominant Parkinson's disease

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

作者地址："[Xiao, Pan; Li, Qin; Gui, Honge; Xu, Bintao; Zhao, Xiaole; Wang, Hongyu; Tao, Li; 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：NEUROLOGICAL SCIENCES

ESI学科分类：NEUROSCIENCE & BEHAVIOR

WOS号：WOS:001190459100001

JCR分区：Q2

影响因子：2.7

年份：2024

卷号：　

期号：　

开始页：　

结束页：　

文献类型：Article; Early Access

关键词：Essential tremor; Tremor-dominant Parkinson's disease; Graph theory; Multiple thresholds; Machine learning; Resting-state functional magnetic resonance imaging

摘要："BackgroundEssential 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.ObjectiveThe 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).MethodsTopological 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.ResultsA 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.ConclusionsThese 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."

基金机构：National Natural Science Foundation of China

基金资助正文：The authors thank all participants for their participation.