ArticleFrontiers in neuroscience2021
Adaptive Parameter Modulation of Deep Brain Stimulation Based on Improved Supervisory Algorithm.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- From trade-offs to translation: An interdisciplinary roadmap for neurotechnology.Science advances · 2026Review
- Closing the loop in DBS: A data-driven approach.Parkinsonism & related disorders · 2025Review
- Suppression of seizure in childhood absence epilepsy using robust control of deep brain stimulation: a simulation study.Scientific reports · 2023Article
- Predictive neuromodulation of cingulo-frontal neural dynamics in major depressive disorder using a brain-computer interface system: A simulation study.Frontiers in computational neuroscience · 2023Article
- Delayed closed-loop neurostimulation for the treatment of pathological brain rhythms in mental disorders: a computational study.Frontiers in neuroscience · 2023Article
- Toward noninvasive brain stimulation 2.0 in Alzheimer's disease.Ageing research reviews · 2022Review
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
Clinically deployed deep brain stimulation (DBS) for the treatment of Parkinson's disease operates in an open loop with fixed stimulation parameters, and this may result in high energy consumption and suboptimal therapy. The objective of this manuscript is to establish, through simulation in a computational model, a closed-loop control system that can automatically adjust the stimulation parameters to recover normal activity in model neurons. Exaggerated beta band activity is recognized as a hallmark of Parkinson's disease and beta band activity in model neurons of the globus pallidus internus (GPi) was used as the feedback signal to control DBS of the GPi. Traditional proportional controller and proportional-integral controller were not effective in eliminating the error between the target level of beta power and the beta power under Parkinsonian conditions. To overcome the difficulties in tuning the controller parameters and improve tracking performance in the case of changes in the plant, a supervisory control algorithm was implemented by introducing a Radial Basis Function (RBF) network to build the inverse model of the plant. Simulation results show the successful tracking of target beta power in the presence of changes in Parkinsonian state as well as during dynamic changes in the target level of beta power. Our computational study suggests the feasibility of the RBF network-driven supervisory control algorithm for real-time modulation of DBS parameters for the treatment of Parkinson's disease.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.