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Optimal closed-loop deep brain stimulation using multiple independently controlled contacts

Authors :
Rafal Bogacz
Gihan Weerasinghe
Benoit Duchet
Christian Bick
Mathematics
Amsterdam Neuroscience - Systems & Network Neuroscience
Source :
Weerasinghe, G, Duchet, B, Bick, C & Bogacz, R 2021, ' Optimal closed-loop deep brain stimulation using multiple independently controlled contacts ', PLoS Computational Biology, vol. 17, no. 8, e1009281 . https://doi.org/10.1371/journal.pcbi.1009281, PLoS Computational Biology, Vol 17, Iss 8, p e1009281 (2021), PLoS Computational Biology, PLoS Computational Biology, 17(8):e1009281. Public Library of Science
Publication Year :
2021

Abstract

Deep brain stimulation (DBS) is a well-established treatment option for a variety of neurological disorders, including Parkinson’s disease and essential tremor. The symptoms of these disorders are known to be associated with pathological synchronous neural activity in the basal ganglia and thalamus. It is hypothesised that DBS acts to desynchronise this activity, leading to an overall reduction in symptoms. Electrodes with multiple independently controllable contacts are a recent development in DBS technology which have the potential to target one or more pathological regions with greater precision, reducing side effects and potentially increasing both the efficacy and efficiency of the treatment. The increased complexity of these systems, however, motivates the need to understand the effects of DBS when applied to multiple regions or neural populations within the brain. On the basis of a theoretical model, our paper addresses the question of how to best apply DBS to multiple neural populations to maximally desynchronise brain activity. Central to this are analytical expressions, which we derive, that predict how the symptom severity should change when stimulation is applied. Using these expressions, we construct a closed-loop DBS strategy describing how stimulation should be delivered to individual contacts using the phases and amplitudes of feedback signals. We simulate our method and compare it against two others found in the literature: coordinated reset and phase-locked stimulation. We also investigate the conditions for which our strategy is expected to yield the most benefit.<br />Author summary In this paper we use computer models of brain tissue to derive an optimal control algorithm for a recently developed new generation of deep brain stimulation (DBS) devices. DBS is a treatment for a variety of neurological disorders including Parkinson’s disease, essential tremor, depression and pain. There is a growing amount of evidence to suggest that delivering stimulation according to feedback from patients, or closed-loop, has the potential to improve the efficacy, efficiency and side effects of the treatment. An important recent development in DBS technology are electrodes with multiple independently controllable contacts and this paper is a theoretical study into the effects of using this new technology. On the basis of a theoretical model, we devise a closed-loop strategy and address the question of how to best apply DBS across multiple contacts to maximally desynchronise neural populations. We demonstrate using numerical simulation that, for the systems we consider, our methods are more effective than two well-known alternatives, namely phase-locked stimulation and coordinated reset. We also predict that the benefits of using multiple contacts should depend strongly on the intrinsic neuronal response. The insights from this work should lead to a better understanding of how to implement and optimise closed-loop multi-contact DBS systems which in turn should lead to more effective and efficient DBS treatments.

Details

Language :
English
ISSN :
1553734X
Database :
OpenAIRE
Journal :
Weerasinghe, G, Duchet, B, Bick, C & Bogacz, R 2021, ' Optimal closed-loop deep brain stimulation using multiple independently controlled contacts ', PLoS Computational Biology, vol. 17, no. 8, e1009281 . https://doi.org/10.1371/journal.pcbi.1009281, PLoS Computational Biology, Vol 17, Iss 8, p e1009281 (2021), PLoS Computational Biology, PLoS Computational Biology, 17(8):e1009281. Public Library of Science
Accession number :
edsair.doi.dedup.....128cd2f1cacdb2cd5529ebb89aab96d3