Department of Psychiatry, Beijing Children's Hospital, Beijing, China.
Department of Psychiatry, Beijing Children's Hospital, Beijing, China
BMJ Open. 2022 May 16;12(5):e047343. doi: 10.1136/bmjopen-2020-047343.
Tic disorder (TD) is a common neurodevelopmental disorder in children, and it can be categorised into three subtypes: provisional tic disorder (PTD), chronic motor or vocal TD (CMT or CVT), and Tourette syndrome (TS). An early diagnostic classification among these subtypes is not possible based on a new-onset tic symptom. Machine learning tools have been widely used for early diagnostic classification based on functional MRI (fMRI). However, few machine learning models have been built for the diagnostic classification of patients with TD. Therefore, in the present study, we will provide a study protocol that uses the machine learning model to make early classifications of the three different types of TD.
We planned to recruit 200 children aged 6-9 years with new-onset tic symptoms and 100 age-matched and sex-matched healthy controls under resting-state MRI scanning. Based on the neuroimaging data of resting-state fMRI, the support vector machine (SVM) model will be built. We planned to construct an SVM model based on functional connectivity for the early diagnosis classification of TD subtypes (including PTD, CMT/CVT, TS).
This study was approved by the ethics committee of Beijing Children's Hospital. The trial results will be submitted to peer-reviewed journals for publication.
ChiCTR2000033257.
抽动障碍（TD）是儿童常见的神经发育障碍，可分为三种亚型：暂时性抽动障碍（PTD）、慢性运动或发声 TD（CMT 或 CVT）和妥瑞氏综合征（TS）。根据新发抽动症状，这些亚型之间不可能进行早期诊断分类。机器学习工具已广泛用于基于功能磁共振成像（fMRI）的早期诊断分类。然而，针对 TD 患者的诊断分类，很少有机器学习模型被构建。因此，在本研究中，我们将提供一个研究方案，使用机器学习模型对三种不同类型的 TD 进行早期分类。
我们计划招募 200 名年龄在 6-9 岁之间的新发抽动症状儿童和 100 名年龄和性别匹配的健康对照者，在静息状态下进行 MRI 扫描。基于静息态 fMRI 的神经影像学数据，我们将构建支持向量机（SVM）模型。我们计划构建一个基于功能连接的 SVM 模型，用于早期诊断分类 TD 亚型（包括 PTD、CMT/CVT、TS）。
本研究得到了北京儿童医院伦理委员会的批准。试验结果将提交给同行评议的期刊发表。
ChiCTR2000033257。
