AUTHOR=Dalenberg Jelle R. , Peretti Debora E. , Marapin Lenny R. , van der Stouwe A. M. Madelein , Renken Remco J. , Tijssen Marina A. J. TITLE=Next move in movement disorders: neuroimaging protocols for hyperkinetic movement disorders JOURNAL=Frontiers in Human Neuroscience VOLUME=18 YEAR=2024 URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2024.1406786 DOI=10.3389/fnhum.2024.1406786 ISSN=1662-5161 ABSTRACT=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 4 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.