Predicting the effects of deep brain stimulation using a reduced coupled oscillator model

PLoS Computational Biology
Gihan WeerasingheRafal Bogacz

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 t...Continue Reading

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Citations

May 29, 2020·Journal of Mathematical Neuroscience·Christian BickErik A Martens
Apr 4, 2021·Brain Sciences·Lekshmy Sudha Kumari, Abbas Z Kouzani
Apr 4, 2021·Biosensors·Jiancheng Mo, Ronny Priefer
Apr 7, 2021·Journal of Neural Engineering·Benoit DuchetRafal Bogacz
Aug 7, 2021·PLoS Computational Biology·Gihan WeerasingheRafal Bogacz
Sep 8, 2021·Scientific Reports·Carolina ReisHayriye Cagnan

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