ArticlePLoS computational biology2019
Predicting the effects of deep brain stimulation using a reduced coupled oscillator model.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 58 citations in OpenAlex.
- Cortico-thalamic tremor circuits and their associations with deep brain stimulation effects in essential tremor.Brain : a journal of neurology · 2025Article
- Evoked resonant neural activity long-term dynamics can be reproduced by a computational model with vesicle depletion.Neurobiology of disease · 2024Article
- How to design optimal brain stimulation to modulate phase-amplitude coupling?Journal of neural engineering · 2024Article
- Article
- Dynamics of parkinsonian oscillations mediated by transmission delays in a mean-field model of the basal ganglia.Frontiers in cellular neuroscience · 2024Article
- From dawn till dusk: Time-adaptive bayesian optimization for neurostimulation.PLoS computational biology · 2023Article
- Mean-Field Approximations With Adaptive Coupling for Networks With Spike-Timing-Dependent Plasticity.Neural computation · 2023Article
- How to entrain a selected neuronal rhythm but not others: open-loop dithered brain stimulation for selective entrainment.Journal of neural engineering · 2023Article
- Decoupling of interacting neuronal populations by time-shifted stimulation through spike-timing-dependent plasticity.PLoS computational biology · 2023Article
- Transcranial electrical stimulation: How can a simple conductor orchestrate complex brain activity?PLoS biology · 2023Article
- Deep brain-machine interfaces: sensing and modulating the human deep brain.National science review · 2022Review
- Stimulating at the right time to recover network states in a model of the cortico-basal ganglia-thalamic circuit.PLoS computational biology · 2022Article
- Essential tremor amplitude modulation by median nerve stimulation.Scientific reports · 2021Article
- Optimal closed-loop deep brain stimulation using multiple independently controlled contacts.PLoS computational biology · 2021Article
- Optimizing deep brain stimulation based on isostable amplitude in essential tremor patient models.Journal of neural engineering · 2021Article
- Review
- Phase-Dependent Deep Brain Stimulation: A Review.Brain sciences · 2021Review
- Electrophysiological Signature and the Prediction of Deep Brain Stimulation Withdrawal and Insertion Effects.Frontiers in neurology · 2021Article
- Controlling Clinical States Governed by Different Temporal Dynamics With Closed-Loop Deep Brain Stimulation: A Principled Framework.Frontiers in neuroscience · 2021Article
- Understanding the dynamics of biological and neural oscillator networks through exact mean-field reductions: a review.Journal of mathematical neuroscience · 2020Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Deep brain stimulation (DBS) is known to be an effective treatment for a variety of neurological disorders, including Parkinson's disease and essential tremor (ET). At present, it involves administering a train of pulses with constant frequency via electrodes implanted into the brain. New 'closed-loop' approaches involve delivering stimulation according to the ongoing symptoms or brain activity and have the potential to provide improvements in terms of efficiency, efficacy and reduction of side effects. The success of closed-loop DBS depends on being able to devise a stimulation strategy that minimizes oscillations in neural activity associated with symptoms of motor disorders. A useful stepping stone towards this is to construct a mathematical model, which can describe how the brain oscillations should change when stimulation is applied at a particular state of the system. Our work focuses on the use of coupled oscillators to represent neurons in areas generating pathological oscillations. Using a reduced form of the Kuramoto model, we analyse how a patient should respond to stimulation when neural oscillations have a given phase and amplitude, provided a number of conditions are satisfied. For such patients, we predict that the best stimulation strategy should be phase specific but also that stimulation should have a greater effect if applied when the amplitude of brain oscillations is lower. We compare this surprising prediction with data obtained from ET patients. In light of our predictions, we also propose a new hybrid strategy which effectively combines two of the closed-loop strategies found in the literature, namely phase-locked and adaptive DBS.
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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.