Dominant efficiency of nonregular patterns of subthalamic nucleus deep brain stimulation for Parkinson’s disease and obsessive-compulsive disorder in a data-driven computational model

Karamintziou, Sofia D; Deligiannis, Nick G; Piallat, Brigitte; Polosan, Mircea; Chabardès, Stéphan; David, Olivier; Stathis, Pantelis; Tagaris, G.; Boviatsis, Efstathios; Sakas, Damianos E.; Polychronaki, G E; Tsirogiannis, George L.; Nikita, Konstantina S.

doi:10.1088/1741-2560/13/1/016013

Cited by 19 publications

(16 citation statements)

References 96 publications

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“…We used a previously published stochastic phase-reduced model [ 40 ], inclusively allowing for the phase-response dynamics of a bursting neuron in both weak and strong perturbation regimes [ 46 , 47 ]. The phase-reduced model is described by the following Ito stochastic differential equation (derivation of Eq (6) is provided in S1 Text ): where and .…”

Section: Methodsmentioning

confidence: 99%

“…Variable ϕ ∈ [0,1) denotes the oscillator’s phase, ω is its natural frequency, while K , r and ψ symbolize the coupling strength, the degree of synchrony and the mean phase of the oscillators, respectively, in the surrounding neural population. These parameters were evaluated based on the processing of the MERs, as described in [ 28 , 40 ]. Parameter α represents the phase shift inherent to nonlinear coupling.…”

Section: Methodsmentioning

confidence: 99%

“…In our analysis, we opt for a phase-reduced bursting neuron model [ 40 , 46 , 47 ]. Our motivation for this particular selection is twofold.…”

Section: Introductionmentioning

confidence: 99%

“…Thereby, the ability of the model to simulate realistic neuronal dynamics is further enhanced. A data-driven phase-reduced model of subthalamic neuronal activity was employed in [ 40 ], where we adopted a measure of the invariant density (steady-state phase distribution) of the simulated dynamical system, as a quantity inversely related to the desynchronizing effect of temporally alternative patterns of stimulation, and further provided evidence for a possible correlation of this measure with clinical effectiveness of stimulation in PD. Determination of the precise optimal parameters of stimulation is accomplished through the application of a derivative-free optimization algorithm, in particular a model-based pattern search method guided by simplex derivatives [ 54 , 55 ].…”

Section: Introductionmentioning

confidence: 99%

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Algorithmic design of a noise-resistant and efficient closed-loop deep brain stimulation system: A computational approach

Karamintziou¹,

Custódio²,

Piallat³

et al. 2017

PLoS ONE

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Advances in the field of closed-loop neuromodulation call for analysis and modeling approaches capable of confronting challenges related to the complex neuronal response to stimulation and the presence of strong internal and measurement noise in neural recordings. Here we elaborate on the algorithmic aspects of a noise-resistant closed-loop subthalamic nucleus deep brain stimulation system for advanced Parkinson’s disease and treatment-refractory obsessive-compulsive disorder, ensuring remarkable performance in terms of both efficiency and selectivity of stimulation, as well as in terms of computational speed. First, we propose an efficient method drawn from dynamical systems theory, for the reliable assessment of significant nonlinear coupling between beta and high-frequency subthalamic neuronal activity, as a biomarker for feedback control. Further, we present a model-based strategy through which optimal parameters of stimulation for minimum energy desynchronizing control of neuronal activity are being identified. The strategy integrates stochastic modeling and derivative-free optimization of neural dynamics based on quadratic modeling. On the basis of numerical simulations, we demonstrate the potential of the presented modeling approach to identify, at a relatively low computational cost, stimulation settings potentially associated with a significantly higher degree of efficiency and selectivity compared with stimulation settings determined post-operatively. Our data reinforce the hypothesis that model-based control strategies are crucial for the design of novel stimulation protocols at the backstage of clinical applications.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…In our analysis, we opt for a phase-reduced bursting neuron model [ 40 , 46 , 47 ]. Our motivation for this particular selection is twofold.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Algorithmic design of a noise-resistant and efficient closed-loop deep brain stimulation system: A computational approach

Karamintziou¹,

Custódio²,

Piallat³

et al. 2017

PLoS ONE

Self Cite

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“…Thus, detecting and disrupting this network of oscillatory neurons is potentially another strategy for feedback control. 25 Popovych et al presented a delayed feedback stimulation method, which combined high-frequency DBS stimulation and pulsatile delayed feedback to desynchronize abnormal neuronal activity. 46,47 Such methods include pulsatile linear delayed feedback (LDF) or pulsatile nonlinear delayed feedback as methods to use to counteract abnormal neuronal synchronization present in PD.…”

Section: Amplitude Responsementioning

confidence: 99%

Approaches to closed-loop deep brain stimulation for movement disorders

Kuo

White-Dzuro

2018

Neurosurgical Focus

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OBJECTIVEDeep brain stimulation (DBS) is a safe and effective therapy for movement disorders, such as Parkinson’s disease (PD), essential tremor (ET), and dystonia. There is considerable interest in developing “closed-loop” DBS devices capable of modulating stimulation in response to sensor feedback. In this paper, the authors review related literature and present selected approaches to signal sources and approaches to feedback being considered for deployment in closed-loop systems.METHODSA literature search using the keywords “closed-loop DBS” and “adaptive DBS” was performed in the PubMed database. The search was conducted for all articles published up until March 2018. An in-depth review was not performed for publications not written in the English language, nonhuman studies, or topics other than Parkinson’s disease or essential tremor, specifically epilepsy and psychiatric conditions.RESULTSThe search returned 256 articles. A total of 71 articles were primary studies in humans, of which 50 focused on treatment of movement disorders. These articles were reviewed with the aim of providing an overview of the features of closed-loop systems, with particular attention paid to signal sources and biomarkers, general approaches to feedback control, and clinical data when available.CONCLUSIONSClosed-loop DBS seeks to employ biomarkers, derived from sensors such as electromyography, electrocorticography, and local field potentials, to provide real-time, patient-responsive therapy for movement disorders. Most studies appear to focus on the treatment of Parkinson’s disease. Several approaches hold promise, but additional studies are required to determine which approaches are feasible, efficacious, and efficient.

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Automatic identification of sweet spots from MERs for electrodes implantation in STN-DBS

Xiao

Wang

et al. 2021

Int J CARS

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Dominant efficiency of nonregular patterns of subthalamic nucleus deep brain stimulation for Parkinson’s disease and obsessive-compulsive disorder in a data-driven computational model

Cited by 19 publications

References 96 publications

Algorithmic design of a noise-resistant and efficient closed-loop deep brain stimulation system: A computational approach

Algorithmic design of a noise-resistant and efficient closed-loop deep brain stimulation system: A computational approach

Approaches to closed-loop deep brain stimulation for movement disorders

Automatic identification of sweet spots from MERs for electrodes implantation in STN-DBS

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