Classification of tic disorders based on functional MRI by machine learning: a study protocol

Abstract

Introduction: 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.

Methods and analysis: 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).

Ethics and dissemination: 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.

Trial registration number: ChiCTR2000033257.

Keywords: Child & adolescent psychiatry; PSYCHIATRY; Protocols & guidelines.

Figure 1

The procedure for building the SVM model. ALFF, altered amplitude of low-frequency fluctuation; CTD, chronic tic disorder; FC, functional connectivity; PTD, provisional tic disorder; ReHo, regional homogeneity; rs-fMRI, resting-state functional MRI; SVM, support vector machine; TS, Tourette syndrome.