A Pilot Study on Data-Driven Adaptive Deep Brain Stimulation in Chronically Implanted Essential Tremor Patients.

Autor:	Castaño-Candamil S; Brain State Decoding Lab, Department of Computer Science, BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg im Breisgau, Germany., Ferleger BI; BioRobotics Lab, Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States., Haddock A; BioRobotics Lab, Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States., Cooper SS; BioRobotics Lab, Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States., Herron J; Department of Neurological Surgery, University of Washington Medical Center, Seattle, WA, United States., Ko A; Department of Neurological Surgery, University of Washington Medical Center, Seattle, WA, United States., Chizeck HJ; BioRobotics Lab, Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States., Tangermann M; Brain State Decoding Lab, Department of Computer Science, BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg im Breisgau, Germany.; Autonomous Intelligent Systems, Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany.; Artificial Cognitive Systems Lab, Artificial Intelligence Department, Faculty of Social Sciences, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands.
Jazyk:	angličtina
Zdroj:	Frontiers in human neuroscience [Front Hum Neurosci] 2020 Nov 05; Vol. 14, pp. 541625. Date of Electronic Publication: 2020 Nov 05 (Print Publication: 2020).
DOI:	10.3389/fnhum.2020.541625
Abstrakt:	Deep brain stimulation (DBS) is an established therapy for Parkinson's disease (PD) and essential-tremor (ET). In adaptive DBS (aDBS) systems, online tuning of stimulation parameters as a function of neural signals may improve treatment efficacy and reduce side-effects. State-of-the-art aDBS systems use symptom surrogates derived from neural signals-so-called neural markers (NMs)-defined on the patient-group level, and control strategies assuming stationarity of symptoms and NMs. We aim at improving these aDBS systems with (1) a data-driven approach for identifying patient- and session-specific NMs and (2) a control strategy coping with short-term non-stationary dynamics. The two building blocks are implemented as follows: (1) The data-driven NMs are based on a machine learning model estimating tremor intensity from electrocorticographic signals. (2) The control strategy accounts for local variability of tremor statistics. Our study with three chronically implanted ET patients amounted to five online sessions. Tremor quantified from accelerometer data shows that symptom suppression is at least equivalent to that of a continuous DBS strategy in 3 out-of 4 online tests, while considerably reducing net stimulation (at least 24%). In the remaining online test, symptom suppression was not significantly different from either the continuous strategy or the no treatment condition. We introduce a novel aDBS system for ET. It is the first aDBS system based on (1) a machine learning model to identify session-specific NMs, and (2) a control strategy coping with short-term non-stationary dynamics. We show the suitability of our aDBS approach for ET, which opens the door to its further study in a larger patient population. (Copyright © 2020 Castaño-Candamil, Ferleger, Haddock, Cooper, Herron, Ko, Chizeck and Tangermann.)
Databáze:	MEDLINE
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