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.This work was supported in part by the Universidad del País Vasco/Euskal Herriko Unibertsitatea, the University of Cambridge, PPG 17/51 and GIU 092/19, the Basque government (Saiotek SA-2010/00028, ELEKIN, Engineering and Society and Bioengineering Research Groups, GIC18/136, and ELKARTEK 18/99, 20/81), ‘‘Ministerio de Ciencia e Innovación’’ (SAF201677758R), FEDER funds, DomusVi Foundation (FP18/76), and the government of Gipuzkoa (HELENA, SABRINA, DG18/14-23, DG19/29, DG20/25 projects). This work is also based upon the work from COST Actions CA18106 and CA15225, supported by COST (European Cooperation in Science and Technology)
