Model-based rational feedback controller design for closed-loop deep brain stimulation of Parkinson's disease

Gorzelic, Patrick; Schiff, Steven J.; Sinha, Alok

doi:10.1088/1741-2560/10/2/026016

Cited by 88 publications

(52 citation statements)

References 16 publications

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“…Our results show that while the open-loop controller performs well, the amplitude of the stimuli using the closed-loop controller is smaller than when using open-loop controller, leading to considerable power savings. Similar results were observed in a study by Gorzelic et al (2013) that investigated optimization techniques for a model-based proportional-integrate-derivative controller in application to Parkinson's disease.…”

Section: Discussionsupporting

confidence: 86%

“…In the last decade, there has been increasing interest from the research community and clinicians in implementing closed-loop stimulation strategies in neurobionic devices such as visual prostheses, cochlear implants, and spinal cord stimulation to relieve pain (Abbas and Chizeck 1991;Gorzelic et al 2013;Fountas et al 2005;Nelson et al 2011;Parker et al 2012;Rosin et al 2011;Vetter et al 2010;Zimmermann and Jackson 2014). These schemes adjust stimulation levels dynamically based on the response of neurons in real time.…”

Section: Introductionmentioning

confidence: 99%

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Spike history neural response model

et al. 2015

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There is a potential for improved efficacy of neural stimulation if stimulation levels can be modified dynamically based on the responses of neural tissue in real time. A neural model is developed that describes the response of neurons to electrical stimulation and that is suitable for feedback control neuroprosthetic stimulation. Experimental data from NZ white rabbit retinae is used with a data-driven technique to model neural dynamics. The linear-nonlinear approach is adapted to incorporate spike history and to predict the neural response of ganglion cells to electrical stimulation. To validate the fitness of the model, the penalty term is calculated based on the time difference between each simulated spike and the closest spike in time in the experimentally recorded train. The proposed model is able to robustly predict experimentally observed spike trains.

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Section: Discussionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Spike history neural response model

et al. 2015

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“…Approaches using closed-loop stimulation are inherently state dependent and require computational neurostimulation (Cheng and Anderson, 2015;Gluckman et al, 2001;Gorzelic et al, 2013;Grahn et al, 2014;Liu et al, 2013;Priori et al, 2013;Shamir et al, 2015). As relevant and practical for any given approach, feedback can be based on output at any of the four stages: (1) recording of current flow patterns for a given dose (Datta et al, 2013b), (2) monitoring of cellular responses such as unit firing rat, (3) changes in network activity such as local field potentials (Bergey et al, 2015;Gluckman et al, 2001;Merlet et al, 2013), and (4) behavior (Shamir et al, 2015).…”

Section: Dealing With Unknowns and Multiscale Approachesmentioning

confidence: 99%

Modeling sequence and quasi-uniform assumption in computational neurostimulation

Bikson¹,

Truong²,

Mourdoukoutas³

et al. 2015

Progress in Brain Research

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“…Additional non-linear analytical tools need to be integrated in order to better identify non-linear hallmarks in the neuronal and global signals. The identification of such patterns could become relevant in future algorithms for closed-loop devices aimed to deliver on-demand anti-parkinsonian treatments (85). An additional research orientation would be to investigate whether the delivery of non-linear complex patterns could improve the benefit of DBS.…”

Section: Discussionmentioning

confidence: 99%

Non-Linear Dynamics in Parkinsonism

et al. 2013

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Over the last 30 years, the functions (and dysfunctions) of the sensory-motor circuitry have been mostly conceptualized using linear modelizations which have resulted in two main models: the “rate hypothesis” and the “oscillatory hypothesis.” In these two models, the basal ganglia data stream is envisaged as a random temporal combination of independent simple patterns issued from its probability distribution of interval interspikes or its spectrum of frequencies respectively. More recently, non-linear analyses have been introduced in the modelization of motor circuitry activities, and they have provided evidences that complex temporal organizations exist in basal ganglia neuronal activities. Regarding movement disorders, these complex temporal organizations in the basal ganglia data stream differ between conditions (i.e., parkinsonism, dyskinesia, healthy control) and are responsive to treatments (i.e., l-DOPA, deep brain stimulation). A body of evidence has reported that basal ganglia neuronal entropy (a marker for complexity/irregularity in time series) is higher in hypokinetic state. In line with these findings, an entropy-based model has been recently formulated to introduce basal ganglia entropy as a marker for the alteration of motor processing and a factor of motor inhibition. Importantly, non-linear features have also been identified as a marker of condition and/or treatment effects in brain global signals (EEG), muscular activities (EMG), or kinetic of motor symptoms (tremor, gait) of patients with movement disorders. It is therefore warranted that the non-linear dynamics of motor circuitry will contribute to a better understanding of the neuronal dysfunctions underlying the spectrum of parkinsonian motor symptoms including tremor, rigidity, and hypokinesia.

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Model-based rational feedback controller design for closed-loop deep brain stimulation of Parkinson's disease

Abstract: Our findings point to the potential value of model-based rational design of feedback controllers for Parkinson's disease.

Cited by 88 publications

References 16 publications

Spike history neural response model

Spike history neural response model

Modeling sequence and quasi-uniform assumption in computational neurostimulation

Non-Linear Dynamics in Parkinsonism

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