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Multivariable closed-loop control of deep brain stimulation for Parkinson’s disease

Abstract:
We present a nonlinear data-driven Model Predictive Control (MPC) algorithm for deep brain stimulation (DBS) for the treatment of Parkinson’s disease (PD). Although DBS is typically implemented in open-loop, closed-loop DBS (CLDBS) uses the amplitude of neural oscillations in specific frequency bands (e.g. beta 13-30 Hz) as a feedback signal, resulting in improved treatment outcomes with reduced side effects and slower rates of patient habituation to stimulation. To date, CLDBS has only been implemented in vivo with simple algorithms such as proportional, proportional-integral, and thresholded switching control. Our approach employs a multi-step predictor based on differences of input-convex neural networks to model the future evolution of beta oscillations. The use of a multi-step predictor enhances prediction accuracy over the optimization horizon and simplifies online computation. In tests using a simulated model of beta-band activity response and data from PD patients, we achieve reductions of more than 20% in both tracking error and control activity in comparison with existing CLDBS algorithms. The proposed control strategy provides a generalizable data-driven technique that can be applied to the treatment of PD and other diseases targeted by CLDBS, as well as to other neuromodulation techniques
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1088/1741-2552/acfbfa

Authors

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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0003-1969-3188
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Role:
Author
ORCID:
0000-0002-4732-3697
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Role:
Author
ORCID:
0000-0001-6743-360X


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Funder identifier:
10.13039/100010663
Grant:
ERC-2014-CoG-646923-DBSModel


Publisher:
IOP Publishing
Journal:
Journal of Neural Engineering More from this journal
Volume:
20
Issue:
5
Pages:
056029-056029
Publication date:
2023-09-21
DOI:
EISSN:
1741-2552
ISSN:
1741-2560


Language:
English
Keywords:
Pubs id:
1537417
Local pid:
pubs:1537417
Source identifiers:
W4386910397
Deposit date:
2026-05-17
ARK identifier:
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