Closed-loop Deep Brain Stimulation Research Articles

A miniature closed-loop deep brain stimulation device.

This paper presents a miniature light-weight closed-loop deep brain stimulation (DBS) device that delivers on-demand stimulation current pulses by monitoring and analysing local field potentials. The device includes monitoring and DBS units, each designed and fabricated on a separate small round circuit board. The closed-loop DBS device has been successfully validated by injecting a pre-recorded neural signal into its input, and collecting and analysing its output. The monitoring unit has an amplification gain of 113 dB in frequency range of 0.7-50 Hz. The DBS unit gives on-demand stimulation current pulses of duration 90 μs, frequency 130 Hz, and amplitude 200 μA. The total weight of the device including a 3V coin battery is 1.41 g. The diameter of the device is 11.4 mm. This portable head-mountable device is suitable for use in pre-clinical trials with small laboratory animals.

Brain-computer interface-based control of closed-loop brain stimulation: attitudes and ethical considerations

Patients who have undergone deep brain stimulation (DBS) for emerging indications have unique perspectives on ethical challenges that may shape trial design and identify key design features for BCI-driven DBS systems. DBS research in cognitive and emotional disorders has generated significant ethical interest. Much of this work has focused on developing ethical guidelines and recommendations for open-loop DBS systems. While early trials of open-loop DBS for depression gave disappointing results, research is moving toward clinical trials with closed-loop or patient-controllable DBS systems that may modulate aspects of personality and emotion. Though user-centered design is an increasingly important principle in neurotechnology, the perspectives of implanted individuals on ethical issues raised by DBS are poorly understood. We solicited those perspectives through a focus group and set of qualitative interviews of participants in trials of DBS for depression and obsessive-compulsive disorder. We identified four major themes: control over device function, authentic self, relationship effects, and meaningful consent. Each has implications for the design of closed-loop systems for non-motor disorders.

Closed-Loop Deep Brain Stimulation Effects on Parkinsonian Motor Symptoms in a Non-Human Primate - Is Beta Enough?

Incorporating feedback controls based on real-time measures of pathological brain activity may improve deep brain stimulation (DBS) approaches for the treatment of Parkinson's disease (PD). Excessive beta oscillations in subthalamic nucleus (STN) local field potentials (LFP) have been proposed as a potential biomarker for closed-loop DBS (CL-DBS). In a non-human primate PD model we compared CL-DBS, which delivered stimulation only when STN LFP beta activity was elevated, to traditional continuous DBS (tDBS). Therapeutic effects of CL-DBS and tDBS relative to the Off-DBS condition were evaluated via a clinical rating scale and objective measures of movement speed during a cued reaching task. CL-DBS was comparable to tDBS at reducing rigidity, while reducing the amount of time DBS was on by ≈50%; however, only tDBS improved bradykinesia during the reaching behavior. This was likely due to reach-related reductions in beta amplitude that influence the timing and duration of stimulation in the CL-DBS condition. These results illustrate the potential utility of closed-loop DBS devices for PD based on STN beta LFP levels. They also point to possible consequences in behavioral tasks when restricting real-time sensing to a single LFP frequency that itself is modulated during performance of such tasks. The present study provides data that suggest alternate algorithms or more than one physiological biomarker may be required to optimize the performance of behavioral tasks and demonstrates the value of using multiple objective measures when evaluating the efficacy of closed-loop DBS systems.

Detecting a Cortical Fingerprint of Parkinson's Disease for Closed-Loop Neuromodulation.

Recent evidence suggests that deep brain stimulation (DBS) of the subthalamic nucleus (STN) in Parkinson's disease (PD) mediates its clinical effects by modulating cortical oscillatory activity, presumably via a direct cortico-subthalamic connection. This observation might pave the way for novel closed-loop approaches comprising a cortical sensor. Enhanced beta oscillations (13-35 Hz) have been linked to the pathophysiology of PD and may serve as such a candidate marker to localize a cortical area reliably modulated by DBS. However, beta-oscillations are widely distributed over the cortical surface, necessitating an additional signal source for spotting the cortical area linked to the pathologically synchronized cortico-subcortical motor network. In this context, both cortico-subthalamic coherence and cortico-muscular coherence (CMC) have been studied in PD patients. Whereas, the former requires invasive recordings, the latter allows for non-invasive detection, but displays a rather distributed cortical synchronization pattern in motor tasks. This distributed cortical representation may conflict with the goal of detecting a cortical localization with robust biomarker properties which is detectable on a single subject basis. We propose that this limitation could be overcome when recording CMC at rest. We hypothesized that—unlike healthy subjects—PD would show CMC at rest owing to the enhanced beta oscillations observed in PD. By performing source space analysis of beta CMC recorded during resting-state magnetoencephalography, we provide preliminary evidence in one patient for a cortical hot spot that is modulated most strongly by subthalamic DBS. Such a spot would provide a prominent target region either for direct neuromodulation or for placing a potential sensor in closed-loop DBS approaches, a proposal that requires investigation in a larger cohort of PD patients.

Nonlinear interactions in the thalamocortical loop in essential tremor: A model-based frequency domain analysis

There is increasing evidence to suggest that essential tremor has a central origin. Different structures appear to be part of the central tremorogenic network, including the motor cortex, the thalamus and the cerebellum. Some studies using electroencephalogram (EEG) and magnetoencephalography (MEG) show linear association in the tremor frequency between the motor cortex and the contralateral tremor electromyography (EMG). Additionally, high thalamomuscular coherence is found with the use of thalamic local field potential (LFP) recordings and tremulous EMG in patients undergoing surgery for deep brain stimulation (DBS). Despite a well-established reciprocal anatomical connection between the thalamus and cortex, the functional association between the two structures during “tremor-on” periods remains elusive. Thalamic (Vim) LFPs, ipsilateral scalp EEG from the sensorimotor cortex and contralateral tremor arm EMG recordings were obtained from two patients with essential tremor who had undergone successful surgery for DBS. Coherence analysis shows a strong linear association between thalamic LFPs and contralateral tremor EMG, but the relationship between the EEG and the thalamus is much less clear. These measurements were then analyzed by constructing a novel parametric nonlinear autoregressive with exogenous input (NARX) model. This new approach uncovered two distinct and not overlapping frequency “channels” of communication between Vim thalamus and the ipsilateral motor cortex, defining robustly “tremor-on” versus “tremor-off” states. The associated estimated nonlinear time lags also showed non-overlapping values between the two states, with longer corticothalamic lags (exceeding 50ms) in the tremor active state, suggesting involvement of an indirect multisynaptic loop. The results reveal the importance of the nonlinear interactions between cortical and subcortical areas in the central motor network of essential tremor. This work is important because it demonstrates for the first time that in essential tremor the functional interrelationships between the cortex and thalamus should not be sought exclusively within individual frequencies but more importantly between cross-frequency nonlinear interactions. Should our results be successfully reproduced on a bigger cohort of patients with essential tremor, our approach could be used to create an on-demand closed-loop DBS device, able to automatically activate when the tremor is on.

Factors influencing the outcome of deep brain stimulation: Placebo, nocebo, lessebo, and lesion effects.

Deep brain stimulation (DBS) is a well-established treatment option for movement disorders, especially for Parkinson's disease (PD). There is a need to determine the role of expectation of benefit and the use of placebo to better understand the effects of electrode placement including the (micro)lesion effect. These factors must be understood to better interpret and attribute the therapeutic value of DBS. In this review, we critically present currently available data on the placebo, nocebo, lessebo, and lesion effects in the context of DBS. We provide a discussion of strategies that have the potential for controlling these effects in the setting of future DBS trials. We conclude that there is a need to standardize definitions for nocebo and (micro)lesion effects and that there are intrinsic limitations in defining the effect of expectation of benefit in DBS. These issues will be challenging to overcome especially with current technology and available study designs. New stimulation paradigms, better study designs, and the use of adaptive closed-loop DBS devices may facilitate a more accurate assessment of the placebo, nocebo, and lessebo effects in future DBS trials.

Closed-loop DBS in the non-human primate model of Parkinsonism

High-frequency deep brain stimulation (DBS) is a widely used therapy for advanced Parkinson’s disease (PD) management. However, the mechanisms by which DBS exerts its alleviating effect remain to be described. Furthermore, due to its non-adaptive nature, standard DBS is not ideally suited for the treatment of this highly dynamic and progressive disorder. We have devised several closed-loop stimulation strategies for PD and tested them in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) primate model of PD. These included stimulation of the internal pallidal segment (GPi) according to a predefined trigger in the ongoing neuronal activity. Application of pallido-pallidal closed-loop stimulation leads to dissociation between changes in basal ganglia (BG) discharge rates and patterns, providing insights into PD pathophysiology. Notably, cortico-pallidal closed-loop stimulation has a significantly greater effect on akinesia and on cortical and pallidal discharge patterns than standard open-loop DBS and matched control stimulation paradigms. These results demonstrate that closed-loop DBS paradigms, by modulating pathological oscillatory activity rather than the discharge rate of the BG-cortical networks, may afford more effective management of advanced PD. Such strategies have the potential to be effective in additional brain disorders in which a pathological neuronal discharge pattern can be recognized.

Spatiotemporal dynamics of cortical perfusion in response to thalamic deep brain stimulation

Deep brain stimulation (DBS) has revolutionized the treatment of movement disorders. The parameters of electrical stimulation are important to its therapeutic effect and remain a source of clinical controversy. DBS exerts its actions not only locally at the site of stimulation but also remotely through afferent and efferent connections, which are vital to its clinical effects. Yet, only a few studies have examined how cortical activity changes in response to various electrical parameters. Here, we investigated how the parameters of thalamic DBS alter cortical perfusion in rats using intrinsic optical imaging. We hypothesized that thalamic DBS will increase perfusion in primary motor cortex (M1), proportional to amplitude, pulse width, or frequency of the stimulation applied. We applied 45 different combinations of amplitude, pulse width and frequency in the ventro-lateral (VL) nucleus of the thalamus in anesthetized rats while measuring perfusion in M1. VL thalamic DBS reduced cortical reflectance, which corresponds to an increase in cortical perfusion. We computed the maximum change in reflectance (MCR) as well as the spatial spread of MCR in each trial. Both MCR and spatial spread increased linearly with increases in current amplitude or pulse width of stimulation; however, the effect of frequency was non-linear. Stimulation at 20Hz was significantly different from that at higher frequencies while stimulation at higher frequencies did not differ significantly from each other. Moreover, the effect of pulse width on MCR was larger than the effect of amplitude. The proportional increase in M1 perfusion due to increase in amplitude or pulse width suggests that both activate more neural elements and increase the volume of tissue activated. These results should help clinicians set parameters of DBS. The use of optical imaging to monitor effects of DBS on M1 may not only help understand DBS mechanisms, but may also provide feedback for closed loop DBS devices.

A Fuzzy Inference System for Closed-Loop Deep Brain Stimulation in Parkinson’s Disease

Parkinsons disease is a complex neurodegenerative disorder for which patients present many symptoms, tremor being the main one. In advanced stages of the disease, Deep Brain Stimulation is a generalized therapy which can significantly improve the motor symptoms. However despite its beneficial effects on treating the symptomatology, the technique can be improved. One of its main limitations is that the parameters are fixed, and the stimulation is provided uninterruptedly, not taking into account any fluctuation in the patients state. A closed-loop system which provides stimulation by demand would adjust the stimulation to the variations in the state of the patient, stimulating only when it is necessary. It would not only perform a more intelligent stimulation, capable of adapting to the changes in real time, but also extending the devices battery life, thereby avoiding surgical interventions. In this work we design a tool that learns to recognize the principal symptom of Parkinsons disease and particularly the tremor. The goal of the designed system is to detect the moments the patient is suffering from a tremor episode and consequently to decide whether stimulation is needed or not. For that, local field potentials were recorded in the subthalamic nucleus of ten Parkinsonian patients, who were diagnosed with tremor-dominant Parkinsons disease and who underwent surgery for the implantation of a neurostimulator. Electromyographic activity in the forearm was simultaneously recorded, and the relation between both signals was evaluated using two different synchronization measures. The results of evaluating the synchronization indexes on each moment represent the inputs to the designed system. Finally, a fuzzy inference system was applied with the goal of identifying tremor episodes. Results are favourable, reaching accuracies of higher 98.7% in 70% of the patients.

Beta oscillations in freely moving Parkinson's subjects are attenuated during deep brain stimulation.

Investigations into the effect of deep brain stimulation (DBS) on subthalamic (STN) beta (13-30 Hz) oscillations have been performed in the perioperative period with the subject tethered to equipment. Using an embedded sensing neurostimulator, this study investigated whether beta power was similar in different resting postures and during forward walking in freely moving subjects with Parkinson's disease (PD) and whether STN DBS attenuated beta power in a voltage-dependent manner. Subthalamic local field potentials were recorded from the DBS lead, using a sensing neurostimulator (Activa(®) PC+S, Medtronic, Inc., Food and Drug Administration- Investigational Device Exemption (IDE)-, institutional review board-approved) from 15 PD subjects (30 STNs) off medication during lying, sitting, and standing, during forward walking, and during randomized periods of 140 Hz DBS at 0 V, 1 V, and 2.5/3 V. Continuous video, limb angular velocity, and forearm electromyography recordings were synchronized with neural recordings. Data were parsed to avoid any movement or electrical artifact during resting states. Beta power was similar during lying, sitting, and standing (P = 0.077, n = 28) and during forward walking compared with the averaged resting state (P = 0.466, n = 24), although akinetic rigid PD subjects tended to exhibit decreased beta power when walking. Deep brain stimulation at 3 V and at 1 V attenuated beta power compared with 0 V (P < 0.003, n = 14), and this was voltage dependent (P < 0.001). Beta power was conserved during resting and forward walking states and was attenuated in a voltage-dependent manner during 140-Hz DBS. Phenotype may be an important consideration if this is used for closed-loop DBS.

Long-term detection of Parkinsonian tremor activity from subthalamic nucleus local field potentials.

Current deep brain stimulation paradigms deliver continuous stimulation to deep brain structures to ameliorate the symptoms of Parkinson's disease. This continuous stimulation has undesirable side effects and decreases the lifespan of the unit's battery, necessitating earlier replacement. A closed-loop deep brain stimulator that uses brain signals to determine when to deliver stimulation based on the occurrence of symptoms could potentially address these drawbacks of current technology. Attempts to detect Parkinsonian tremor using brain signals recorded during the implantation procedure have been successful. However, the ability of these methods to accurately detect tremor over extended periods of time is unknown. Here we use local field potentials recorded during a deep brain stimulation clinical follow-up visit 1 month after initial programming to build a tremor detection algorithm and use this algorithm to detect tremor in subsequent visits up to 8 months later. Using this method, we detected the occurrence of tremor with accuracies between 68-93%. These results demonstrate the potential of tremor detection methods for efficacious closed-loop deep brain stimulation over extended periods of time.

Towards fully automated closed-loop Deep Brain Stimulation in Parkinson's disease patients: A LAMSTAR-based tremor predictor.

This paper describes the application of the LAMSTAR (LArge Memory STorage and Retrieval) neural network for prediction of onset of tremor in Parkinson's disease (PD) patients to allow for on-off adaptive control of Deep Brain Stimulation (DBS). Currently, the therapeutic treatment of PD by DBS is an open-loop system where continuous stimulation is applied to a target area in the brain. This work demonstrates a fully automated closed-loop DBS system so that stimulation can be applied on-demand only when needed to treat PD symptoms. The proposed LAMSTAR network uses spectral, entropy and recurrence rate parameters for prediction of the advent of tremor after the DBS stimulation is switched off. These parameters are extracted from non-invasively collected surface electromyography and accelerometry signals. The LAMSTAR network has useful characteristics, such as fast retrieval of patterns and ability to handle large amount of data of different types, which make it attractive for medical applications. Out of 21 trials blue from one subject, the average ratio of delay in prediction of tremor to the actual delay in observed tremor from the time stimulation was switched off achieved by the proposed LAMSTAR network is 0.77. Moreover, sensitivity of 100% and overall performance better than previously proposed Back Propagation neural networks is obtained.

Wake/Sleep Identification Based on Body Movement for Parkinson’s Disease Patients

This paper presents a method for identifying a patient’s wake/sleep state for closed-loop deep brain stimulation (DBS). The method uses a real-time wake/sleep identification algorithm that includes posture analysis based on the movement of the chest below the clavicle, which is the location of the subcutaneous pulse generator. A single micro-accelerometer was used to monitor the movement of the wrist and the chest of thirteen healthy adults and twelve patients with Parkinson’s disease for nine continuous hours. The wake/sleep state identification for the chest algorithm had accuracy, sensitivity, and specificity values of 85.78, 84.21, and 82.08 %, respectively, compared to video recordings for patients with DBS ON, and 82.74, 82.68, and 82.28 %, respectively, for patients with DBS OFF. The algorithm performance for the chest is comparable to that of the commonly used location on the wrist. The real-time wake/sleep identification algorithms were proved to be effective. This research provides a practical method for closed-loop DBS, which will greatly benefit patients with Parkinson’s disease.

Motor task event detection using Subthalamic Nucleus Local Field Potentials.

Deep Brain Stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson's disease. Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and DBS side effects. In such systems, DBS parameters are adjusted based on patient's behavior, which means that behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local Field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. A practical behavior detection method should be able to detect behaviors asynchronously meaning that it should not use any prior knowledge of behavior onsets. In this paper, we introduce a behavior detection method that is able to asynchronously detect the finger movements of Parkinson patients. As a result of this study, we learned that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We used non-linear regression method to measure this connectivity and use it to detect the finger movements. Performance of this method is evaluated using Receiver Operating Characteristic (ROC).

Are we close to the advent of closed loop deep brain stimulation in Parkinson's disease?

Are we close to the advent of closed loop deep brain stimulation in Parkinson's disease?

Conceptualization and validation of an open-source closed-loop deep brain stimulation system in rat.

Conventional deep brain stimulation (DBS) applies constant electrical stimulation to specific brain regions to treat neurological disorders. Closed-loop DBS with real-time feedback is gaining attention in recent years, after proved more effective than conventional DBS in terms of pathological symptom control clinically. Here we demonstrate the conceptualization and validation of a closed-loop DBS system using open-source hardware. We used hippocampal theta oscillations as system input, and electrical stimulation in the mesencephalic reticular formation (mRt) as controller output. It is well documented that hippocampal theta oscillations are highly related to locomotion, while electrical stimulation in the mRt induces freezing. We used an Arduino open-source microcontroller between input and output sources. This allowed us to use hippocampal local field potentials (LFPs) to steer electrical stimulation in the mRt. Our results showed that closed-loop DBS significantly suppressed locomotion compared to no stimulation, and required on average only 56% of the stimulation used in open-loop DBS to reach similar effects. The main advantages of open-source hardware include wide selection and availability, high customizability, and affordability. Our open-source closed-loop DBS system is effective, and warrants further research using open-source hardware for closed-loop neuromodulation.

帕金森状态的慢变量反馈模糊控制

Closed-loop deep brain stimulation is an effective method for controlling the Parkinsonian state. However, the twin issues of how to obtain a suitable feedback variable and design a high-performance control strategy are still unresolved. This paper proposes a variable universe fuzzy closed-loop control method based on slow variable to modulate the abnormal Parkinsonian state. For highly nonlinear neural systems, in order to achieve energy optimization of the control signal, this paper designs a closed-loop control strategy of thalamic neurons by combining unscented Kalman filter with variable universe fuzzy control, with the objective of improving the firing patterns of thalamic neurons via external stimuli with lower energy consumption. Using a slow variable as the feedback variable significantly decreases the fluctuations and energy expenditure of the stimuli. Qualitative and quantitative analyses conducted demonstrate that the proposed variable universe fuzzy closed-loop control strategy based on slow variable is effective.

A personalised, closed-loop DBS system based on a cranial neurostimulator for treating Parkinson’s disease and other disorders

A personalised, closed-loop DBS system based on a cranial neurostimulator for treating Parkinson’s disease and other disorders

NeuroBi: A Highly Configurable Neurostimulator for a Retinal Prosthesis and Other Applications.

To evaluate the efficacy of a suprachoroidal retinal prosthesis, a highly configurable external neurostimulator is required. In order to meet functional and safety specifications, it was necessary to develop a custom device. A system is presented which can deliver charge-balanced, constant-current biphasic pulses, with widely adjustable parameters, to arbitrary configurations of output electrodes. This system is shown to be effective in eliciting visual percepts in a patient with approximately 20 years of light perception vision only due to retinitis pigmentosa, using an electrode array implanted in the suprachoroidal space of the eye. The flexibility of the system also makes it suitable for use in a number of other emerging clinical neurostimulation applications, including epileptic seizure suppression and closed-loop deep brain stimulation. Clinical trial registration number NCT01603576 (www.clinicaltrials.gov).

A Fully Self-Contained Logarithmic Closed-Loop Deep Brain Stimulation SoC With Wireless Telemetry and Wireless Power Management

Although closed-loop deep brain stimulation (DBS) promises treatment of many neurological disorders, an implantable system-on-chip (SoC) implementing an effective closed-loop DBS algorithm has not been demonstrated. This work introduces a logarithmic, closed-loop DBS system that detects and processes low-frequency brain field signals to control and adapt stimulation currents. The system records and processes neural signals with four low-noise neural amplifier (LNA) channels, a multiplexed logarithmic ADC, and two high-pass and two low-pass digital logarithmic filters. Logarithmic processing saves power and achieves high dynamic range. A logarithmic domain digital signal processor (DSP) and PI-controller controls eight current stimulator channels and enables closed-loop stimulation. An RF transceiver, a clock generator, and a power harvester are also included in the system to achieve a complete implantable SoC. The 4 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 180 nm CMOS prototype consumes a total of 468 μW for recording and processing neural signals, for stimulation, and for two-way wireless communication.