Pallidal Recordings in Chronically Implanted Dystonic Patients: Mitigation of Tremor-Related Artifacts

Author:

Del Vecchio Del Vecchio Jasmin1,Hanafi Ibrahem1ORCID,Pozzi Nicoló Gabriele1,Capetian Philipp1ORCID,Isaias Ioannis U.12ORCID,Haufe Stefan345,Palmisano Chiara1

Affiliation:

1. Department of Neurology, University Hospital of Würzburg and Julius-Maximilian-University Würzburg, 97080 Würzburg, Germany

2. Centro Parkinson e Parkinsonismi, ASST G. Pini-CTO, 20122 Milano, Italy

3. Uncertainty, Inverse Modeling and Machine Learning Group, Technische Universität Berlin, 10623 Berlin, Germany

4. Physikalisch-Technische Bundesanstalt Braunschweig und Berlin, 10587 Berlin, Germany

5. Berlin Center for Advanced Neuroimaging, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany

Abstract

Low-frequency oscillatory patterns of pallidal local field potentials (LFPs) have been proposed as a physiomarker for dystonia and hold the promise for personalized adaptive deep brain stimulation. Head tremor, a low-frequency involuntary rhythmic movement typical of cervical dystonia, may cause movement artifacts in LFP signals, compromising the reliability of low-frequency oscillations as biomarkers for adaptive neurostimulation. We investigated chronic pallidal LFPs with the PerceptTM PC (Medtronic PLC) device in eight subjects with dystonia (five with head tremors). We applied a multiple regression approach to pallidal LFPs in patients with head tremors using kinematic information measured with an inertial measurement unit (IMU) and an electromyographic signal (EMG). With IMU regression, we found tremor contamination in all subjects, whereas EMG regression identified it in only three out of five. IMU regression was also superior to EMG regression in removing tremor-related artifacts and resulted in a significant power reduction, especially in the theta-alpha band. Pallido-muscular coherence was affected by a head tremor and disappeared after IMU regression. Our results show that the Percept PC can record low-frequency oscillations but also reveal spectral contamination due to movement artifacts. IMU regression can identify such artifact contamination and be a suitable tool for its removal.

Funder

Deutsche Forschungsgemeinschaft

Fondazione Grigioni per il Morbo di Parkinson

New York University School of Medicine

Marlene

Paolo Fresco Institute for Parkinson’s and Movement Disorders

Open Access Publication Fund of the University of Würzburg

Graduate School of Life Sciences, University of Würzburg

German Academic Exchange Service

European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program

Publisher

MDPI AG

Subject

Bioengineering

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