Optimal control of directional deep brain stimulation in the parkinsonian neuronal network

Fan, Denggui; Wang, Zhihui; Wang, Qingyun

doi:10.1016/j.cnsns.2015.12.005

Cited by 23 publications

(10 citation statements)

References 81 publications

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“…From the system perspective, DBS is an external local control input into the network, which causes several problems. (i) DBS generally uses a single nucleus as the target, and prolonged stimulation will cause specific physical damage [46] . (ii) The battery needs to be replaced by clinical surgery [47][48] .…”

Section: Dbs Therapymentioning

confidence: 99%

“…Figure 5(b) shows that EI decreases gradually between 40 Hz and 100 Hz. However, such unbalanced single-phase stimulation currents can damage nerve tissues and cause serious side effects [46] . Therefore, charge-balanced bi-phasic (CBBP) pulses are generally used in actual surgical operations.…”

Section: Optimization Of Dbs: Waveform Target and Electrodementioning

confidence: 99%

“…Therefore, charge-balanced bi-phasic (CBBP) pulses are generally used in actual surgical operations. Fan et al [46] modeled the CBBP pulse. In order to be consistent with the electrophysiological experiment, a short-duration positive-phase anode pulse of 60 µs was used in each unit stimulation cycle accompanied by a longer-duration negative-phase pulse.…”

Section: Optimization Of Dbs: Waveform Target and Electrodementioning

confidence: 99%

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A review of computational modeling and deep brain stimulation: applications to Parkinson’s disease

Wang

2020

Appl. Math. Mech.-Engl. Ed.

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Biophysical computational models are complementary to experiments and theories, providing powerful tools for the study of neurological diseases. The focus of this review is the dynamic modeling and control strategies of Parkinson’s disease (PD). In previous studies, the development of parkinsonian network dynamics modeling has made great progress. Modeling mainly focuses on the cortex-thalamus-basal ganglia (CTBG) circuit and its sub-circuits, which helps to explore the dynamic behavior of the parkinsonian network, such as synchronization. Deep brain stimulation (DBS) is an effective strategy for the treatment of PD. At present, many studies are based on the side effects of the DBS. However, the translation from modeling results to clinical disease mitigation therapy still faces huge challenges. Here, we introduce the progress of DBS improvement. Its specific purpose is to develop novel DBS treatment methods, optimize the treatment effect of DBS for each patient, and focus on the study in closed-loop DBS. Our goal is to review the inspiration and insights gained by combining the system theory with these computational models to analyze neurodynamics and optimize DBS treatment.

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Section: Dbs Therapymentioning

confidence: 99%

Section: Optimization Of Dbs: Waveform Target and Electrodementioning

confidence: 99%

Section: Optimization Of Dbs: Waveform Target and Electrodementioning

confidence: 99%

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A review of computational modeling and deep brain stimulation: applications to Parkinson’s disease

Wang

2020

Appl. Math. Mech.-Engl. Ed.

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“…That is to say, perfect synchronization is realized when the synchronization factor is close to 1 and non-perfect synchronization is reached when the factor of synchronization is close to 0. Recently, a large number of neural stimulation shapes, including deep brain stimulation [60][61][62], coordinated reset stimulation paradigm [63], charge-balanced biphasic pulses [64] have been proposed either for the treatment of Parkinson's disease [60][61][62][63] or for the treatment of epileptiform activity [64]. In this study a new pathway based on both continuous and periodic stimulations of neural activity is proposed to control such brain seizures, especially using DNA dynamics as a potential neurostimulator.…”

Section: From Asynchronous To Synchronous Neural Networkmentioning

confidence: 99%

Neuronal firing and DNA dynamics in a neural network

Etémé

Tabi²,

Ateba

et al. 2018

J. Phys. Commun.

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We study the mutual effects of neuronal electrical activity and DNA dynamics in a network of electrically coupled neurons with nearest neighbors communication. For that we develop by means of the coupling function method, a mathematical DNA-neuron model that can be considered as an extension of the simplified Hodgkin-Huxley models. Numerically, we explore the biological properties of the model with emphasis on local phase transitions and synchronous neural activity. Our results suggest that firing and coherent behavior of neural network are controlled by DNA dynamics. Conversely, neuronal activity induces DNA electrochemical denaturation. As a result, we expect that the prominent DNA-neuron model would be worth in controlling gene expression and neurodegenerative diseases in the sensory thalamus.

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“…In the end, we will introduce the DBS, which can be described as a periodic step function with the following form (Fan et al, 2016),

D B S (t) = a \times H (s i n (\frac{2 π t}{ρ})) (1 - H (\frac{s i n (2 π (t + δ))}{ρ})) .

…”

Section: Description Of Modelsmentioning

confidence: 99%

Eliminating Absence Seizures through the Deep Brain Stimulation to Thalamus Reticular Nucleus

Wang

2017

Front. Comput. Neurosci.

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Deep brain stimulation (DBS) can play a crucial role in the modulation of absence seizures, yet relevant biophysical mechanisms are not completely established. In this paper, on the basis of a biophysical mean-field model, we investigate a typical absence epilepsy activity by introducing slow kinetics of GABAB receptors on thalamus reticular nucleus (TRN). We find that the region of spike and slow-wave discharges (SWDs) can be reduced greatly when we add the DBS to TRN. Furthermore, we systematically explore how the corresponding stimulation parameters including frequency, amplitude and positive input duration suppress the SWDs under certain conditions. It is shown that the SWDs can be controlled as key stimulation parameters are suitably chosen. The results in this paper can be helpful for researchers to understand the thalamus stimulation in treating epilepsy patients, and provide theoretical basis for future experimental and clinical studies.

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Optimal control of directional deep brain stimulation in the parkinsonian neuronal network

Cited by 23 publications

References 81 publications

A review of computational modeling and deep brain stimulation: applications to Parkinson’s disease

A review of computational modeling and deep brain stimulation: applications to Parkinson’s disease

Neuronal firing and DNA dynamics in a neural network

Eliminating Absence Seizures through the Deep Brain Stimulation to Thalamus Reticular Nucleus

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