Using histogram analysis of the intrinsic brain activity mapping to identify essential tremor

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

作者地址："[Xiao, Pan; Tao, Li; Zhang, Xiaoyu; Li, Qin; Gui, Honge; Xu, Bintao; Zhang, Xueyan; He, Wanlin; Chen, Huiyue; Wang, Hansheng; Lv, Fajin; Luo, Tianyou; Fang, Weidong] Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, Chongqing, 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, Chongqing, Peoples R China."

来源：FRONTIERS IN NEUROLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001017823900001

JCR分区：Q2

影响因子：2.7

年份：2023

卷号：14

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：essential tremor; machine learning; Radiomics; resting-state fMRI; amplitude of low-frequency fluctuation

摘要："BackgroundEssential tremor (ET) is one of the most common movement disorders. Histogram analysis based on brain intrinsic activity imaging is a promising way to identify ET patients from healthy controls (HCs) and further explore the spontaneous brain activity change mechanisms and build the potential diagnostic biomarker in ET patients. MethodsThe histogram features based on the Resting-state functional magnetic resonance imaging (Rs-fMRI) data were extracted from 133 ET patients and 135 well-matched HCs as the input features. Then, a two-sample t-test, the mutual information, and the least absolute shrinkage and selection operator methods were applied to reduce the feature dimensionality. Support vector machine (SVM), logistic regression (LR), random forest (RF), and k-nearest neighbor (KNN) were used to differentiate ET and HCs, and classification performance of the established models was evaluated by the mean area under the curve (AUC). Moreover, correlation analysis was carried out between the selected histogram features and clinical tremor characteristics. ResultsEach classifier achieved a good classification performance in training and testing sets. The mean accuracy and area under the curve (AUC) of SVM, LR, RF, and KNN in the testing set were 92.62%, 0.948; 92.01%, 0.942; 93.88%, 0.941; and 92.27%, 0.939, respectively. The most power-discriminative features were mainly located in the cerebello-thalamo-motor and non-motor cortical pathways. Correlation analysis showed that there were two histogram features negatively and one positively correlated with tremor severity. ConclusionOur findings demonstrated that the histogram analysis of the amplitude of low-frequency fluctuation (ALFF) images with multiple machine learning algorithms could identify ET patients from HCs and help to understand the spontaneous brain activity pathogenesis mechanisms in ET patients."

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

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