Lopez-de-Ipina Karmele, Solé-Casals Jordi, Sánchez-Méndez José Ignacio, Romero-Garcia Rafael, Fernandez Elsa, Requejo Catalina, Poologaindran Anujan, Faúndez-Zanuy Marcos, Martí-Massó José Félix, Bergareche Alberto, Suckling John
Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.
EleKin Research Group, Department of System Engineering and Automation, University of the Basque Country UPV/EHU, Donostia-San Sebastian, Spain.
Front Hum Neurosci. 2021 Jun 8;15:648573. doi: 10.3389/fnhum.2021.648573. eCollection 2021.
Essential tremor (ET) is a highly prevalent neurological disorder characterized by action-induced tremors involving the hand, voice, head, and/or face. Importantly, hand tremor is present in nearly all forms of ET, resulting in impaired fine motor skills and diminished quality of life. To advance early diagnostic approaches for ET, automated handwriting tasks and magnetic resonance imaging (MRI) offer an opportunity to develop early essential clinical biomarkers. In this study, we present a novel approach for the early clinical diagnosis and monitoring of ET based on integrating handwriting and neuroimaging analysis. We demonstrate how the analysis of fine motor skills, as measured by an automated Archimedes' spiral task, is correlated with neuroimaging biomarkers for ET. Together, we present a novel modeling approach that can serve as a complementary and promising support tool for the clinical diagnosis of ET and a large range of tremors.
特发性震颤（ET）是一种高度常见的神经系统疾病，其特征是由动作诱发的震颤，涉及手部、声音、头部和/或面部。重要的是，几乎所有形式的特发性震颤都存在手部震颤，导致精细运动技能受损和生活质量下降。为了推进特发性震颤的早期诊断方法，自动化手写任务和磁共振成像（MRI）为开发早期重要临床生物标志物提供了机会。在本研究中，我们提出了一种基于整合手写和神经影像分析的特发性震颤早期临床诊断和监测的新方法。我们展示了通过自动化阿基米德螺旋任务测量的精细运动技能分析如何与特发性震颤的神经影像生物标志物相关联。我们共同提出了一种新颖的建模方法，可作为特发性震颤和多种震颤临床诊断的补充性且有前景的支持工具。
