ArticleJournal of mathematical neuroscience2020
Phase-dependence of response curves to deep brain stimulation and their relationship: from essential tremor patient data to a Wilson-Cowan model.
Article in Journal of mathematical neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Phase-dependent stimulation response is shaped by the brain's dynamic functional connectivity.Network neuroscience (Cambridge, Mass.) · 2026Article
- Predicting neural responses to intra- and extra-cranial electric brain stimulation by means of the reciprocity theorem.PLoS computational biology · 2025Article
- Targeting the fundamentals for tremors: the frequency and amplitude coding in essential tremor.Journal of biomedical science · 2025Review
- 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
- Dynamics of parkinsonian oscillations mediated by transmission delays in a mean-field model of the basal ganglia.Frontiers in cellular neuroscience · 2024Article
- Feedback control of collective dynamics in an oscillator population with time-dependent connectivity.Frontiers in network physiology · 2024Article
- 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
- Dynamical Mechanism Analysis of Three Neuroregulatory Strategies on the Modulation of Seizures.International journal of molecular sciences · 2022Article
- Automated deep brain stimulation programming with safety constraints for tremor suppression in patients with Parkinson's disease and essential tremor.Journal of neural engineering · 2022Article
- Quantitative comparison of the mean-return-time phase and the stochastic asymptotic phase for noisy oscillators.Biological cybernetics · 2022Article
- Developing control-theoretic objectives for large-scale brain dynamics and cognitive enhancement.Annual reviews in control · 2022Article
- Average beta burst duration profiles provide a signature of dynamical changes between the ON and OFF medication states in Parkinson's disease.PLoS computational biology · 2021Article
- Optimizing deep brain stimulation based on isostable amplitude in essential tremor patient models.Journal of neural engineering · 2021Article
- Closing the Loop With Cortical Sensing: The Development of Adaptive Deep Brain Stimulation for Essential Tremor Using the Activa PC+S.Frontiers in neuroscience · 2021Review
- Predicting the effects of deep brain stimulation using a reduced coupled oscillator model.PLoS computational biology · 2019Article
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6 authors.
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Abstract
Essential tremor manifests predominantly as a tremor of the upper limbs. One therapy option is high-frequency deep brain stimulation, which continuously delivers electrical stimulation to the ventral intermediate nucleus of the thalamus at about 130 Hz. Constant stimulation can lead to side effects, it is therefore desirable to find ways to stimulate less while maintaining clinical efficacy. One strategy, phase-locked deep brain stimulation, consists of stimulating according to the phase of the tremor. To advance methods to optimise deep brain stimulation while providing insights into tremor circuits, we ask the question: can the effects of phase-locked stimulation be accounted for by a canonical Wilson-Cowan model? We first analyse patient data, and identify in half of the datasets significant dependence of the effects of stimulation on the phase at which stimulation is provided. The full nonlinear Wilson-Cowan model is fitted to datasets identified as statistically significant, and we show that in each case the model can fit to the dynamics of patient tremor as well as to the phase response curve. The vast majority of top fits are stable foci. The model provides satisfactory prediction of how patient tremor will react to phase-locked stimulation by predicting patient amplitude response curves although they were not explicitly fitted. We also approximate response curves of the significant datasets by providing analytical results for the linearisation of a stable focus model, a simplification of the Wilson-Cowan model in the stable focus regime. We report that the nonlinear Wilson-Cowan model is able to describe response to stimulation more precisely than the linearisation.
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