ArticlePLoS computational biology2018
Bayesian adaptive dual control of deep brain stimulation in a computational model of Parkinson's disease.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- Corticocortical evoked potential amplitude is altered by cortical stimulation in epilepsy.Epilepsia · 2026Article
- Bayesian-enhanced closed-loop optimization of ultrasound protocols for targeted and precise neuromodulation.bioRxiv : the preprint server for biology · 2026Article
- Understanding the Effects of Conductive Polymer Electrode Coating on Recorded Neural Signals.Advanced healthcare materials · 2026Article
- Movement-responsive deep brain stimulation for Parkinson's disease using a remotely optimized neural decoder.Nature biomedical engineering · 2026Article
- Real-time Bayesian optimization of deep brain stimulation for personalized cognitive control enhancement.bioRxiv : the preprint server for biology · 2025Article
- Modeling and optimizing deep brain stimulation to enhance gait in Parkinson's disease: personalized treatment with neurophysiological insights.NPJ Parkinson's disease · 2025Article
- Failure modes and mitigations for Bayesian optimization of neuromodulation parameters.Journal of neural engineering · 2025Review
- Are we ready for automated deep brain stimulation programming?Parkinsonism & related disorders · 2025Review
- A framework for translational therapy development in deep brain stimulation.NPJ Parkinson's disease · 2024Review
- Refining Brain Stimulation Therapies: An Active Learning Approach to Personalization.Research square · 2024Article
- Refining Brain Stimulation Therapies: An Active Learning Approach to Personalization.bioRxiv : the preprint server for biology · 2024Article
- Multispecies initial numerical validation of an efficient algorithm prototype for auditory brainstem response hearing threshold estimation.The Journal of the Acoustical Society of America · 2024Article
- Practical Bayesian Inference in Neuroscience: Or How I Learned to Stop Worrying and Embrace the Distribution.eNeuro · 2024Article
- Reinforcement learning for closed-loop regulation of cardiovascular system with vagus nerve stimulation: a computational study.Journal of neural engineering · 2024Article
- Practical Bayesian Inference in Neuroscience: Or How I Learned To Stop Worrying and Embrace the Distribution.bioRxiv : the preprint server for biology · 2024Article
- Closing the loop in psychiatric deep brain stimulation: physiology, psychometrics, and plasticity.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Model-based closed-loop control of thalamic deep brain stimulation.Frontiers in network physiology · 2024Article
- From dawn till dusk: Time-adaptive bayesian optimization for neurostimulation.PLoS computational biology · 2023Article
- Open Hardware Implementation of Real-Time Phase and Amplitude Estimation for Neurophysiologic Signals.Bioengineering (Basel, Switzerland) · 2023Article
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3 authors.
Funding
Abstract
In this paper, we present a novel Bayesian adaptive dual controller (ADC) for autonomously programming deep brain stimulation devices. We evaluated the Bayesian ADC's performance in the context of reducing beta power in a computational model of Parkinson's disease, in which it was tasked with finding the set of stimulation parameters which optimally reduced beta power as fast as possible. Here, the Bayesian ADC has dual goals: (a) to minimize beta power by exploiting the best parameters found so far, and (b) to explore the space to find better parameters, thus allowing for better control in the future. The Bayesian ADC is composed of two parts: an inner parameterized feedback stimulator and an outer parameter adjustment loop. The inner loop operates on a short time scale, delivering stimulus based upon the phase and power of the beta oscillation. The outer loop operates on a long time scale, observing the effects of the stimulation parameters and using Bayesian optimization to intelligently select new parameters to minimize the beta power. We show that the Bayesian ADC can efficiently optimize stimulation parameters, and is superior to other optimization algorithms. The Bayesian ADC provides a robust and general framework for tuning stimulation parameters, can be adapted to use any feedback signal, and is applicable across diseases and stimulator designs.
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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.