Next move in movement disorders: neuroimaging protocols for hyperkinetic movement disorders - fMRI Data Quality Metrics

Introduction: The Next Move in Movement Disorders (NEMO) study is an initiative aimed at advancing our understanding and the classification of hyperkinetic movement disorders, including tremor, myoclonus, dystonia, and myoclonus-dystonia. The study has two main objectives: (a) to develop a computer-aided tool for precise and consistent classification of these movement disorder phenotypes, and (b) to deepen our understanding of brain pathophysiology through advanced neuroimaging techniques. This protocol review details the neuroimaging data acquisition and preprocessing procedures employed by the NEMO team to achieve these goals. Methods and analysis: To meet the study's objectives, NEMO utilizes multiple imaging techniques, including T1-weighted structural MRI, resting-state fMRI, motor task fMRI, and 18F-FDG PET scans. We will outline our efforts over the past four years to enhance the quality of our collected data, and address challenges such as head movements during image acquisition, choosing acquisition parameters and constructing data preprocessing pipelines. This study is the first to employ these neuroimaging modalities in a standardized approach contributing to more uniformity in the analyses of future studies comparing these patient groups. The data collected will contribute to the development of a machine learning-based classification tool and improve our understanding of disorder-specific neurobiological factors. Ethics and dissemination: Ethical approval has been obtained from the relevant local ethics committee. The NEMO study is designed to pioneer the application of machine learning of movement disorders. We expect to publish articles in multiple related fields of research and patients will be informed of important results via patient associations and press releases. The current dataset contains the fMRI data quality statistics used in the manuscript for Figure 4 and Table 1. To assess spatial and temporal signal quality for the two fMRI protocols across rest and hand movement tasks, we compared data quality metrics in a small cohort of hyperkinetic movement disorder patients (1 dystonia, 7 myoclonus, 2 tremor) and 13 healthy controls using framewise displacement (FD), derivative of the root mean square variance over voxels (DVARS), spatial SNR, and temporal (t)SNR.引言：运动障碍领域新进展研究（Next Move in Movement Disorders, NEMO）是一项旨在推动运动过多性运动障碍认知与分类的研究项目，涵盖震颤（tremor）、肌阵挛（myoclonus）、肌张力障碍（dystonia）及肌阵挛-肌张力障碍（myoclonus-dystonia）四类疾病。本研究包含两大核心目标：一是开发可对上述运动障碍表型进行精准且一致分类的计算机辅助工具；二是借助先进神经影像技术深化对脑病理生理学的理解。本方案综述详细阐述了NEMO研究团队为实现上述目标所采用的神经影像数据采集与预处理流程。 方法与分析：为达成研究目标，NEMO研究采用了多种影像技术，包括T1加权结构磁共振成像（T1-weighted structural MRI）、静息态功能磁共振成像（resting-state fMRI）、运动任务态功能磁共振成像（motor task fMRI）以及18F-氟代脱氧葡萄糖正电子发射断层扫描（18F-FDG PET scans）。本研究将梳理团队近四年来为提升采集数据质量所开展的工作，并针对影像采集过程中的头部运动、采集参数选择及构建数据预处理流程等核心挑战提出应对方案。本研究首次采用标准化方案整合上述神经影像模态，可为未来针对此类患者队列的比较研究提供更统一的分析基础。所采集的数据将助力基于机器学习的分类工具开发，并加深对疾病特异性神经生物学机制的认知。 伦理与传播：本研究已获得当地相关伦理委员会的伦理审查批准。NEMO研究旨在开创运动障碍领域机器学习应用的先河。团队计划在多个相关研究领域发表学术论文，并将通过患者协会与新闻发布渠道向受试者通报重要研究结果。 本数据集包含本论文中图4与表1所使用的功能磁共振成像（fMRI）数据质量统计结果。为评估两种功能磁共振成像方案在静息态与手部运动任务下的空间与时域信号质量，本研究针对一小队列运动过多性运动障碍患者（含1例肌张力障碍患者、7例肌阵挛患者、2例震颤患者）及13名健康对照者，采用逐帧位移（framewise displacement, FD）、体素均方根方差导数（derivative of the root mean square variance over voxels, DVARS）、空间信噪比（spatial SNR）及时域信噪比（temporal (t)SNR）等指标开展数据质量对比分析。