Towards automated patient-specific optimization of deep brain stimulation for movement disorders

Sarikhani, Parisa; Miocinovic, Svjetlana; Mahmoudi, Babak

doi:10.1109/embc.2019.8857736

Cited by 9 publications

(9 citation statements)

References 4 publications

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“…Cortical gamma power has also been proposed as a biomarker of dyskinesias (a side effect) [11]. Non-electrophysiologic biomarkers of DBS efficacy include kinematic measurements [21], which can serve as a surrogate for tremor severity, and thereby used as an objective function for closed-loop optimization techniques in patients with PD or essential tremor [21,41]. Similarly, while we used a relatively slow 10 Hz stimulation for a short 12 s duration to measure the spatial spread of stimulation effects and activation of the hyperdirect pathway, applying stimulation using a more clinically relevant 130 Hz stimulation or stimulation for longer durations may influence the morphology of the evoked potential and provide additional information about the effect of stimulation on pathologic neural activity within the motor network [22].…”

Section: Biomarkers For Optimizing Dbsmentioning

confidence: 99%

Multi-objective data-driven optimization for improving deep brain stimulation in Parkinson’s disease

et al. 2021

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Objective. Deep brain stimulation (DBS) is an effective treatment for Parkinson’s disease (PD) but its success depends on a time-consuming process of trial-and-error to identify the optimal stimulation settings for each individual patient. Data-driven optimization algorithms have been proposed to efficiently find the stimulation setting that maximizes a quantitative biomarker of symptom relief. However, these algorithms cannot efficiently take into account stimulation settings that may control symptoms but also cause side effects. Here we demonstrate how multi-objective data-driven optimization can be used to find the optimal trade-off between maximizing symptom relief and minimizing side effects. Approach. Cortical and motor evoked potential data collected from PD patients during intraoperative stimulation of the subthalamic nucleus were used to construct a framework for designing and prototyping data-driven multi-objective optimization algorithms. Using this framework, we explored how these techniques can be applied clinically, and characterized the design features critical for solving this optimization problem. Our two optimization objectives were to maximize cortical evoked potentials, a putative biomarker of therapeutic benefit, and to minimize motor potentials, a biomarker of motor side effects. Main Results. Using this in silico design framework, we demonstrated how the optimal trade-off between two objectives can substantially reduce the stimulation parameter space by 61 ± 19%. The best algorithm for identifying the optimal trade-off between the two objectives was a Bayesian optimization approach with an area under the receiver operating characteristic curve of up to 0.94 ± 0.02, which was possible with the use of a surrogate model and a well-tuned acquisition function to efficiently select which stimulation settings to sample. Significance. These findings show that multi-objective optimization is a promising approach for identifying the optimal trade-off between symptom relief and side effects in DBS. Moreover, these approaches can be readily extended to newly discovered biomarkers, adapted to DBS for disorders beyond PD, and can scale with the development of more complex DBS devices.

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Section: Biomarkers For Optimizing Dbsmentioning

confidence: 99%

Multi-objective data-driven optimization for improving deep brain stimulation in Parkinson’s disease

et al. 2021

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“…Data-driven optimization through direct interaction is a well-developed field of engineering that is gaining traction as an approach to neural modulation. Promising work has used data-driven optimization for problems including, selecting audio-visual stimuli for fMRI experiments [36], optimizing stimulation for Parkinson's disease [37,38], evoking targeted motor outputs through cortical stimulation [39], and others [20,40,41]. A data-driven optimization problem is defined by an objective function (a quantitative measure; for example, the performance on a behavioral assay or the value of an electrophysiological feature), and a search space (the set of all possible combinations of stimulation parameters).…”

Section: Introductionmentioning

confidence: 99%

A framework for designing data-driven optimization systems for neural modulation

et al. 2020

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Objective. Neural modulation is a fundamental tool for understanding and treating neurological and psychiatric diseases. However, due to the high-dimensional space, subject-specific responses, and variability within each subject, it is a major challenge to select the stimulation parameters that have the desired effect. Data-driven optimization provides a range of different algorithms and tools for addressing this challenge, but each of these algorithms has specific strengths and limitations, and therefore must be carefully designed for a given neural modulation problem. Here we present a framework for designing data-driven optimization algorithms for neural modulation. Approach. We develop this framework using an optogenetic medial septum stimulation model, where the goal is to find the stimulation parameters that modulate hippocampal gamma power to a desired value. This framework proceeds in four steps: (a) collecting stimulation data, (b) creating high-throughput simulation models, (c) prototyping a range of different data-driven optimization algorithms and evaluating their performance, and (d) deploying the best performing algorithm in vivo. Main results. Following this framework, we prototype and design an algorithm specifically for finding the medial septum optogenetic stimulation parameters that maximize hippocampal gamma power. Building on this, we then change our objective function to find the stimulation parameters that modulate gamma to a specific setpoint, use the framework to understand and anticipate the results before deploying in vivo. Significance. We show that this framework can be used to design an effective optimization solution for a specific neural modulation problem, and discuss how it can potentially be applied beyond the optogenetic medial septum stimulation model.

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“…An automated data driven approach to determining optimal stimulation settings would reduce the burden on clinicians and likely improve patient outcomes. This is true for epilepsy patients, and for patients receiving or exploring electrical stimulation for a range of neurological disorders and disabilities, including Parkinson’s 61,76 , Essential Tremor 63,85 , chronic pain 86,87 , Obsessive Compulsive Disorder 88,89 , and Depression 90,91 . Our work highlights the potential benefits of a Bayesian optimization approach.…”

Section: Discussionmentioning

confidence: 99%

Optimization of closed-loop electrical stimulation enables robust cerebellar-directed seizure control

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Krook-Magnuson

et al. 2021

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Additional treatment options for temporal lobe epilepsy are needed, and potential interventions targeting the cerebellum are of interest. Previous animal work has shown strong inhibition of hippocampal seizures through on-demand optogenetic manipulation of the cerebellum. However, decades of work examining electrical stimulation - a more immediately translatable approach - targeting the cerebellum has produced very mixed results. We were therefore interested in exploring the impact that stimulation parameters may have on seizure outcomes. Using a mouse model of temporal lobe epilepsy, we conducted on-demand electrical stimulation of the cerebellar cortex, and varied stimulation charge, frequency, and pulse width, resulting in over a thousand different potential combinations of settings. To explore this parameter space in an efficient, data-driven, manner, we utilized Bayesian optimization with Gaussian process regression, implemented in Matlab with an Expected Improvement Plus acquisition function. We examined two different fitting conditions and two different electrode orientations. Following the optimization process, we conducted additional on-demand experiments to test the effectiveness of selected settings. Across all animals, we found that Bayesian optimization allowed identification of effective intervention settings. Additionally, generally similar optimal settings were identified across animals, suggesting that personalized optimization may not always be necessary. While optimal settings were consistently effective, stimulation with settings predicted from the Gaussian process regression to be ineffective failed to provide seizure control. Taken together, our results provide a blueprint for exploration of a large parameter space for seizure control, and illustrate that robust inhibition of seizures can be achieved with electrical stimulation of the cerebellum, but only if the correct stimulation parameters are used.

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Towards automated patient-specific optimization of deep brain stimulation for movement disorders

Cited by 9 publications

References 4 publications

Multi-objective data-driven optimization for improving deep brain stimulation in Parkinson’s disease

Multi-objective data-driven optimization for improving deep brain stimulation in Parkinson’s disease

A framework for designing data-driven optimization systems for neural modulation

Optimization of closed-loop electrical stimulation enables robust cerebellar-directed seizure control

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