Chronic electrocorticography for sensing movement intention and closed-loop deep brain stimulation with wearable sensors in an essential tremor patient

Deep brain stimulation (DBS) has become a widespread and valuable treatment for patients with movement disorders such as essential tremor (ET). However, current DBS treatment constantly delivers stimulation in an open loop, which can be inefficient. Closing the loop with sensors to provide feedback may increase power efficiency and reduce side effects for patients. New implantable neuromodulation platforms, such as the Medtronic Activa PC+S DBS system, offer important data sources by providing chronic neural sensing capabilities and a means of investigating dynamic stimulation based on symptom measurements. The authors implanted in a single patient with ET an Activa PC+S system, a cortical strip of electrodes on the hand sensorimotor cortex, and therapeutic electrodes in the ventral intermediate nucleus of the thalamus. In this paper they describe the effectiveness of the platform when sensing cortical movement intentions while the patient actually performed and imagined performing movements. Additionally, they demonstrate dynamic closed-loop DBS based on several wearable sensor measurements of tremor intensity.

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Chronic embedded cortico-thalamic closed-loop deep brain stimulation for the treatment of essential tremor

Science Translational Medicine ◽

10.1126/scitranslmed.aay7680 ◽

2020 ◽

Vol 12 (572) ◽

pp. eaay7680

Author(s):

Enrico Opri ◽

Stephanie Cernera ◽

Rene Molina ◽

Robert S. Eisinger ◽

Jackson N. Cagle ◽

...

Keyword(s):

Deep Brain Stimulation ◽

Essential Tremor ◽

Brain Stimulation ◽

Closed Loop ◽

Motor Behavior ◽

Open Loop ◽

Therapeutic Window ◽

Patient Specific ◽

Stimulation Paradigm ◽

Deep Brain

Deep brain stimulation (DBS) is an approved therapy for the treatment of medically refractory and severe movement disorders. However, most existing neurostimulators can only apply continuous stimulation [open-loop DBS (OL-DBS)], ignoring patient behavior and environmental factors, which consequently leads to an inefficient therapy, thus limiting the therapeutic window. Here, we established the feasibility of a self-adjusting therapeutic DBS [closed-loop DBS (CL-DBS)], fully embedded in a chronic investigational neurostimulator (Activa PC + S), for three patients affected by essential tremor (ET) enrolled in a longitudinal (6 months) within-subject crossover protocol (DBS OFF, OL-DBS, and CL-DBS). Most patients with ET experience involuntary limb tremor during goal-directed movements, but not during rest. Hence, the proposed CL-DBS paradigm explored the efficacy of modulating the stimulation amplitude based on patient-specific motor behavior, suppressing the pathological tremor on-demand based on a cortical electrode detecting upper limb motor activity. Here, we demonstrated how the proposed stimulation paradigm was able to achieve clinical efficacy and tremor suppression comparable with OL-DBS in a range of movements (cup reaching, proximal and distal posture, water pouring, and writing) while having a consistent reduction in energy delivery. The proposed paradigm is an important step toward a behaviorally modulated fully embedded DBS system, capable of delivering stimulation only when needed, and potentially mitigating pitfalls of OL-DBS, such as DBS-induced side effects and premature device replacement.

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Seizure Suppression Efficacy of Closed-Loop Versus Open-Loop Deep Brain Stimulation in a Rodent Model of Epilepsy

IEEE Transactions on Neural Systems and Rehabilitation Engineering ◽

10.1109/tnsre.2015.2498973 ◽

2016 ◽

Vol 24 (6) ◽

pp. 710-719 ◽

Cited By ~ 35

Author(s):

M. Tariqus Salam ◽

Jose Luis Perez Velazquez ◽

Roman Genov

Keyword(s):

Deep Brain Stimulation ◽

Brain Stimulation ◽

Closed Loop ◽

Open Loop ◽

Rodent Model ◽

Deep Brain ◽

Seizure Suppression

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Practical Closed-Loop Strategies for Deep Brain Stimulation: Lessons From Chronic Pain

Frontiers in Neuroscience ◽

10.3389/fnins.2021.762097 ◽

2021 ◽

Vol 15 ◽

Author(s):

Jordan Prosky ◽

Jackson Cagle ◽

Kristin K. Sellers ◽

Ro’ee Gilron ◽

Cora de Hemptinne ◽

...

Keyword(s):

Chronic Pain ◽

Deep Brain Stimulation ◽

Brain Stimulation ◽

Closed Loop ◽

Open Loop ◽

Neural Signals ◽

Variable Success ◽

Control Stimulation ◽

Tonic Stimulation ◽

Deep Brain

Deep brain stimulation (DBS) is a plausible therapy for various neuropsychiatric disorders, though continuous tonic stimulation without regard to underlying physiology (open-loop) has had variable success. Recently available DBS devices can sense neural signals which, in turn, can be used to control stimulation in a closed-loop mode. Closed-loop DBS strategies may mitigate many drawbacks of open-loop stimulation and provide more personalized therapy. These devices contain many adjustable parameters that control how the closed-loop system operates, which need to be optimized using a combination of empirically and clinically informed decision making. We offer a practical guide for the implementation of a closed-loop DBS system, using examples from patients with chronic pain. Focusing on two research devices from Medtronic, the Activa PC+S and Summit RC+S, we provide pragmatic details on implementing closed- loop programming from a clinician’s perspective. Specifically, by combining our understanding of chronic pain with data-driven heuristics, we describe how to tune key parameters to handle feature selection, state thresholding, and stimulation artifacts. Finally, we discuss logistical and practical considerations that clinicians must be aware of when programming closed-loop devices.

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Predicting the effects of deep brain stimulation using a reduced coupled oscillator model

10.1101/448290 ◽

2018 ◽

Cited By ~ 2

Author(s):

Gihan Weerasinghe ◽

Benoit Duchet ◽

Hayriye Cagnan ◽

Peter Brown ◽

Christian Bick ◽

...

Keyword(s):

Deep Brain Stimulation ◽

Essential Tremor ◽

Brain Stimulation ◽

Neurological Disorders ◽

Closed Loop ◽

Kuramoto Model ◽

Brain Oscillations ◽

Hybrid Strategy ◽

The Brain ◽

Deep Brain

AbstractDeep brain stimulation (DBS) is known to be an effective treatment for a variety of neurological disorders, including Parkinson’s disease and essential tremor (ET). At present, it involves administering a train of pulses with constant frequency via electrodes implanted into the brain. New ‘closed-loop’ approaches involve delivering stimulation according to the ongoing symptoms or brain activity and have the potential to provide improvements in terms of efficiency, efficacy and reduction of side effects. The success of closed-loop DBS depends on being able to devise a stimulation strategy that minimizes oscillations in neural activity associated with symptoms of motor disorders. A useful stepping stone towards this is to construct a mathematical model, which can describe how the brain oscillations should change when stimulation is applied at a particular state of the system. Our work focuses on the use of coupled oscillators to represent neurons in areas generating pathological oscillations. Using a reduced form of the Kuramoto model, we analyse how a patient should respond to stimulation when neural oscillations have a given phase and amplitude. We predict that, provided certain conditions are satisfied, the best stimulation strategy should be phase specific but also that stimulation should have a greater effect if applied when the amplitude of brain oscillations is lower. We compare this surprising prediction with data obtained from ET patients. In light of our predictions, we also propose a new hybrid strategy which effectively combines two of the strategies found in the literature, namely phase-locked and adaptive DBS.Author summaryDeep brain stimulation (DBS) involves delivering electrical impulses to target sites within the brain and is a proven therapy for a variety of neurological disorders. Closed loop DBS is a promising new approach where stimulation is applied according to the state of a patient. Crucial to the success of this approach is being able to predict how a patient should respond to stimulation. Our work focusses on DBS as applied to patients with essential tremor (ET). On the basis of a theoretical model, which describes neurons as oscillators that respond to stimulation and have a certain tendency to synchronize, we provide predictions for how a patient should respond when stimulation is applied at a particular phase and amplitude of the ongoing tremor oscillations. Previous experimental studies of closed loop DBS provided stimulation either on the basis of ongoing phase or amplitude of pathological oscillations. Our study suggests how both of these measurements can be used to control stimulation. As part of this work, we also look for evidence for our theories in experimental data and find our predictions to be satisfied in one patient. The insights obtained from this work should lead to a better understanding of how to optimise closed loop DBS strategies.

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Classifier-based closed-loop deep brain stimulation for essential tremor

2017 8th International IEEE/EMBS Conference on Neural Engineering (NER) ◽

10.1109/ner.2017.8008354 ◽

2017 ◽

Cited By ~ 4

Author(s):

Brady C. Houston ◽

Margaret C. Thompson ◽

Jeffrey G. Ojemann ◽

Andrew L. Ko ◽

Howard J. Chizeck

Keyword(s):

Deep Brain Stimulation ◽

Essential Tremor ◽

Brain Stimulation ◽

Closed Loop ◽

Deep Brain

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Approaches to closed-loop deep brain stimulation for movement disorders

Neurosurgical FOCUS ◽

10.3171/2018.5.focus18173 ◽

2018 ◽

Vol 45 (2) ◽

pp. E2 ◽

Cited By ~ 17

Author(s):

Chao-Hung Kuo ◽

Gabrielle A. White-Dzuro ◽

Andrew L. Ko

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Deep Brain Stimulation ◽

Essential Tremor ◽

Brain Stimulation ◽

Movement Disorders ◽

Closed Loop ◽

Closed Loop Systems ◽

Signal Sources ◽

Deep Brain

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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Closing the Loop With Cortical Sensing: The Development of Adaptive Deep Brain Stimulation for Essential Tremor Using the Activa PC+S

Frontiers in Neuroscience ◽

10.3389/fnins.2021.749705 ◽

2021 ◽

Vol 15 ◽

Author(s):

Tomasz M. Fra̧czek ◽

Benjamin I. Ferleger ◽

Timothy E. Brown ◽

Margaret C. Thompson ◽

Andrew J. Haddock ◽

...

Keyword(s):

Deep Brain Stimulation ◽

Essential Tremor ◽

Brain Stimulation ◽

Computational Models ◽

Open Loop ◽

Learning Approaches ◽

Loop Design ◽

Stimulation System ◽

Neural Feedback ◽

Deep Brain

Deep Brain Stimulation (DBS) is an important tool in the treatment of pharmacologically resistant neurological movement disorders such as essential tremor (ET) and Parkinson's disease (PD). However, the open-loop design of current systems may be holding back the true potential of invasive neuromodulation. In the last decade we have seen an explosion of activity in the use of feedback to “close the loop” on neuromodulation in the form of adaptive DBS (aDBS) systems that can respond to the patient's therapeutic needs. In this paper we summarize the accomplishments of a 5-year study at the University of Washington in the use of neural feedback from an electrocorticography strip placed over the sensorimotor cortex. We document our progress from an initial proof of hardware all the way to a fully implanted adaptive stimulation system that leverages machine-learning approaches to simplify the programming process. In certain cases, our systems out-performed current open-loop approaches in both power consumption and symptom suppression. Throughout this effort, we collaborated with neuroethicists to capture patient experiences and take them into account whilst developing ethical aDBS approaches. Based on our results we identify several key areas for future work. “Graded” aDBS will allow the system to smoothly tune the stimulation level to symptom severity, and frequent automatic calibration of the algorithm will allow aDBS to adapt to the time-varying dynamics of the disease without additional input from a clinician. Additionally, robust computational models of the pathophysiology of ET will allow stimulation to be optimized to the nuances of an individual patient's symptoms. We also outline the unique advantages of using cortical electrodes for control and the remaining hardware limitations that need to be overcome to facilitate further development in this field. Over the course of this study we have verified the potential of fully-implanted, cortically driven aDBS as a feasibly translatable treatment for pharmacologically resistant ET.

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Neural Closed Loop Deep Brain Stimulation for Freezing of Gait

10.1101/19011858 ◽

2019 ◽

Author(s):

Matthew N. Petrucci ◽

Raumin S. Neuville ◽

M. Furqan Afzal ◽

Anca Velisar ◽

Chioma M. Anidi ◽

...

Keyword(s):

Deep Brain Stimulation ◽

Brain Stimulation ◽

Closed Loop ◽

Freezing Of Gait ◽

Control Variable ◽

Field Potential ◽

Preliminary Evidence ◽

Open Loop ◽

Percent Time ◽

Deep Brain

AbstractFreezing of gait (FOG), a devastating symptom of Parkinson’s disease (PD), can be refractory to current treatments such as medication and open-loop deep brain stimulation (olDBS). Recent evidence suggests that closed-loop DBS (clDBS), using beta local field potential power from the subthalamic nucleus (STN) as the control variable, can improve tremor and bradykinesia; however, no study has investigated the use of clDBS for the treatment of FOG. In this study, we provide preliminary evidence that clDBS was superior to olDBS in reducing percent time freezing and in reducing freezing behavior (gait arrhythmicity) in a person with PD and FOG. These findings warrant further investigation into the use of clDBS to treat FOG while also minimizing the total energy delivered to maintain a therapeutic effect.

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