Self-Tuning Deep Brain Stimulation Controller for Suppression of Beta Oscillations: Analytical Derivation and Numerical Validation

Fleming, John E.; Orlowski, Jakub; Lowery, Madeleine M.; Chaillet, Antoine

doi:10.3389/fnins.2020.00639

Cited by 22 publications

(31 citation statements)

References 81 publications

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“…There is mounting evidence that local field potentials recorded through the lead can be of value to determine the timing and patterns of stimulation to be delivered (17,18). Technologies that help transmit the information to the medical team will become more relevant as closedloop systems become available (19).…”

Section: Technologymentioning

confidence: 99%

“…Among the disadvantages of neuromodulation is the need for more appointments for programming and device assessments, imposing time and cost demands on patients and caregivers that can be disproportionate in rural areas. More recently, in the face of Coronavirus Disease 2019 (COVID- 19) and the subsequent pandemic and worldwide quarantine, the need for a viable distance health program for patients with DBS became more salient.…”

Section: Introductionmentioning

confidence: 99%

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The Need for Digital Health Solutions in Deep Brain Stimulation for Parkinson’s Disease in the Time of COVID-19 and Beyond

Rammo

Gostkowski

Rasmussen

et al. 2021

Neuromodulation: Technology at the Neural Interface

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Objectives Deep brain stimulation (DBS) is a well‐established therapy for the management of patients with advanced Parkinson's disease and other movement disorders. Patients implanted with DBS require life‐long management of the medical device as well as medications. Patients are often challenged to frequently visit the specialized DBS centers and such challenges are aggravated depending on geography, socioeconomic factors, and support systems. We discuss the need for digital health solutions to overcome these barriers to better and safely take care of patients, especially in the current COVID‐19 pandemic. Materials and Methods A review of the literature was conducted for technology and logistics necessary in forming a digital health program. Results Digital health encounters can take place in both a synchronous and asynchronous manner. Factors involving patients include cognitive capacity, physical safety, physical capacity, connectivity, and technological security. Physician factors include examining the patient, system diagnostics, and adjusting stimulation or medications. Technology is focused on bridging the gap between patient and physician through integrating the DBS lead, implantable pulse generator (IPG), programmer, novel devices/applications to grade motor function, and teleconference modalities. Conclusions For patients with Parkinson's disease, digital health has the potential to drastically change the landscape after DBS surgery. Furthermore, technology is fundamental in connectivity, diagnostic evaluation, and security in order to create stable and useful patient‐focused care.

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Section: Technologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Need for Digital Health Solutions in Deep Brain Stimulation for Parkinson’s Disease in the Time of COVID-19 and Beyond

Rammo

Gostkowski

Rasmussen

et al. 2021

Neuromodulation: Technology at the Neural Interface

View full text Add to dashboard Cite

show abstract

“…All existing algorithmic approaches for closed-loop control proposed over the past years would easily be integrated under such structure. Weerasinghe et al, 2019;Fleming et al, 2020b) and to suggest possible control strategies for closed-loop DBS (Holt et al, 2016;Duchet et al, 2021b).…”

Section: Low-level Controllers: Targeting Individual Manifestationsmentioning

confidence: 99%

“…Few feedforward components that use predictive models have been included in real-life applications, even though biophysical or data-driven black-box models may greatly improve accuracy ( Gorzelic et al, 2013 ; Su et al, 2019 ), especially for slowly changing biomarkers. To date, modeling has predominantly been used to better understand the dynamics of manifestations ( Holgado et al, 2010 ; Fleming et al, 2020a ), the impact that DBS may have on the circuits ( Hahn and McIntyre, 2010 ; Weerasinghe et al, 2019 ; Fleming et al, 2020b ) and to suggest possible control strategies for closed-loop DBS ( Holt et al, 2016 ; Duchet et al, 2021b ).…”

Section: Leveraging Temporal Dynamics To Enable Multi-objective Closed-loop Dbsmentioning

confidence: 99%

Controlling Clinical States Governed by Different Temporal Dynamics With Closed-Loop Deep Brain Stimulation: A Principled Framework

Tinkhauser

Moraud

2021

Front. Neurosci.

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Closed-loop strategies for deep brain stimulation (DBS) are paving the way for improving the efficacy of existing neuromodulation therapies across neurological disorders. Unlike continuous DBS, closed-loop DBS approaches (cl-DBS) optimize the delivery of stimulation in the temporal domain. However, clinical and neurophysiological manifestations exhibit highly diverse temporal properties and evolve over multiple time-constants. Moreover, throughout the day, patients are engaged in different activities such as walking, talking, or sleeping that may require specific therapeutic adjustments. This broad range of temporal properties, along with inter-dependencies affecting parallel manifestations, need to be integrated in the development of therapies to achieve a sustained, optimized control of multiple symptoms over time. This requires an extended view on future cl-DBS design. Here we propose a conceptual framework to guide the development of multi-objective therapies embedding parallel control loops. Its modular organization allows to optimize the personalization of cl-DBS therapies to heterogeneous patient profiles. We provide an overview of clinical states and symptoms, as well as putative electrophysiological biomarkers that may be integrated within this structure. This integrative framework may guide future developments and become an integral part of next-generation precision medicine instruments.

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“…By adapting the control gain in real time, the σ-modification strategy employed here is expected to offer better battery life, fewer side effects, and better adaptation to changing conditions than purely proportional control. While the present study focuses on analytical results on a neuronal population model, an extensive performance comparison between the two approaches was recently made on a detailed numerical model comprised of individual neurons [6].…”

Section: Introductionmentioning

confidence: 99%

Adaptive control of Lipschitz time-delay systems by sigma modification with application to neuronal population dynamics

Orlowski

Chaillet

Destexhe

et al. 2022

Systems & Control Letters

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Self-Tuning Deep Brain Stimulation Controller for Suppression of Beta Oscillations: Analytical Derivation and Numerical Validation

Cited by 22 publications

References 81 publications

The Need for Digital Health Solutions in Deep Brain Stimulation for Parkinson’s Disease in the Time of COVID-19 and Beyond

The Need for Digital Health Solutions in Deep Brain Stimulation for Parkinson’s Disease in the Time of COVID-19 and Beyond

Controlling Clinical States Governed by Different Temporal Dynamics With Closed-Loop Deep Brain Stimulation: A Principled Framework

Adaptive control of Lipschitz time-delay systems by sigma modification with application to neuronal population dynamics

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