Feasibility of Endovascular Deep Brain Stimulation of Anterior Nucleus of the Thalamus for Refractory Epilepsy.

BACKGROUND AND OBJECTIVES: Deep brain stimulation (DBS) has developed into an effective therapy for several disease states including treatment-resistant Parkinson disease and medically intractable essential tremor, as well as segmental, generalized and cervical dystonia, and obsessive-compulsive disorder (OCD). Dystonia and OCD are approved with Humanitarian Device Exemption. In addition, DBS is also approved for the treatment of epilepsy in the anterior nucleus of the thalamus. Although overall considered an effective treatment for Parkinson disease and epilepsy, a number of specific factors determine the treatment success for DBS including careful patient selection, effective postoperative programming of DBS devices and accurate electrode placement. Furthermore, invasiveness of the procedure is a rate limiter for patient adoption. It is desired to explore a less invasive way to deliver DBS therapy. METHODS: Here, we report for the first time the direct comparison of endovascular and parenchymal DBS in a triplicate ovine model using the anterior nucleus of the thalamus as the parenchymal target for refractory epilepsy. RESULTS: Triplicate ovine studies show comparable sensing resolution and stimulation performance of endovascular DBS with parenchymal DBS. CONCLUSION: The results from this feasibility study opens up a new frontier for minimally invasive DBS therapy.

Fully Closed Loop Test Environment for Adaptive Implantable Neural Stimulators Using Computational Models.

Implantable brain stimulation devices continue to be developed to treat and monitor brain conditions. As the complexity of these devices grows to include adaptive neuromodulation therapy, validating the operation and verifying the correctness of these systems becomes more complicated. The new complexities lie in the functioning of the device being dependent on the interaction with the patient and environmental factors such as noise and artifacts. Here, we present a hardware-in-the-loop (HIL) testing framework that employs computational models of pathological neural dynamics to test adaptive deep brain stimulation (DBS) devices prior to animal or human testing. A brain stimulation and recording electrode array is placed in the saline tank and connected to an adaptive neuromodulation system that measures and processes the synthetic signals and delivers stimulation back into the saline tank. A data acquisition system is used to detect the stimulation and provide feedback to the computational model in order to simulate the effects of stimulation on the neural dynamics. In this study, we used real-time computational models to emulate the dynamics of epileptic seizures observed in the anterior nucleus of the thalamus (ANT) in epilepsy patients and beta band (11-35 Hz) oscillations observed in the subthalamic nucleus (STN) of Parkinson's disease (PD) patients. These models simulated neuronal responses to electrical stimulation pulses and the saline tank tested hardware interactions between the detection algorithms and stimulation interference. We tested and validated the operation of adaptive DBS algorithms for seizure and beta band power suppression embedded in an implantable DBS system (Medtronic Summit RC+S). This study highlights the utility of the proposed hardware-in-the-loop framework to systematically test the adaptive DBS systems in the presence of system aggressors such as environmental noise and stimulation-induced electrical artifacts. This testing procedure can help ensure correctness and robustness of adaptive DBS devices prior to animal and human testing.

Long Term Performance of a Bi-Directional Neural Interface for Deep Brain Stimulation and Recording.

Background: In prior reports, we described the design and initial performance of a fully implantable, bi-directional neural interface system for use in deep brain and other neurostimulation applications. Here we provide an update on the chronic, long-term neural sensing performance of the system using traditional 4-contact leads and extend those results to include directional 8-contact leads. Methods: Seven ovine subjects were implanted with deep brain stimulation (DBS) leads at different nodes within the Circuit of Papez: four with unilateral leads in the anterior nucleus of the thalamus and hippocampus; two with bilateral fornix leads, and one with bilateral hippocampal leads. The leads were connected to either an Activa PC+S® (Medtronic) or Percept PC°ledR (Medtronic) deep brain stimulation and recording device. Spontaneous local field potentials (LFPs), evoked potentials (EPs), LFP response to stimulation, and electrode impedances were monitored chronically for periods of up to five years in these subjects. Results: The morphology, amplitude, and latencies of chronic hippocampal EPs evoked by thalamic stimulation remained stable over the duration of the study. Similarly, LFPs showed consistent spectral peaks with expected variation in absolute magnitude dependent upon behavioral state and other factors, but no systematic degradation of signal quality over time. Electrode impedances remained within expected ranges with little variation following an initial stabilization period. Coupled neural activity between the two nodes within the Papez circuit could be observed in synchronized recordings up to 5 years post-implant. The magnitude of passive LFP power recorded from directional electrode segments was indicative of the contacts that produced the greatest stimulation-induced changes in LFP power within the Papez network. Conclusion: The implanted device performed as designed, providing the ability to chronically stimulate and record neural activity within this network for up to 5 years of follow-up.

Peaks in the beta band of the human subthalamic nucleus: a case for low beta and high beta activity.

OBJECTIVE: Peaks in the beta band of local field potentials (LFPs) may serve as a biological feedback signal for closed-loop deep brain stimulation (DBS) in Parkinson's disease (PD). However, the specific frequency of such peaks and their response to DBS and to different types of movement remains uncertain. In the present study, the authors examined the abundance of discernible peaks in the beta band and the effect of different types of movement and DBS on these peaks. METHODS: Subthalamic nucleus LFPs were analyzed from 38 patients with PD in a frequency range between 10 and 35 Hz, as well as the impact of movement (gait, hand movements) and electrical stimulation on these peaks. The position of the electrode segments from which LFPs were recorded was computed. RESULTS: The authors found a bimodal distribution of peaks in the beta band with discernible high- (27 Hz) and low-frequency (15 Hz) peaks. Movement of either hand had no significant effect on these peaks, whereas walking significantly reduced high-frequency beta peaks but not the peaks in the low beta band. Stimulation caused an amplitude-dependent suppression of both peaks. CONCLUSIONS: DBS suppresses LFP beta peaks of different frequencies, whereas beta suppression caused by movement is dependent on the type of movement and frequency of the peak. These results will support the investigation of distinct LFP spectra for the application of closed-loop DBS.

The development of an implantable deep brain stimulation device with simultaneous chronic electrophysiological recording and stimulation in humans.

Deep brain stimulation (DBS) is used to treat a wide array of neurologic conditions. However, traditional programming of stimulation parameters relies upon short term subjective observation of patient symptoms and undesired stimulation effects while in the clinic. To gain a more objective measure of the neuronal activity that contributes to patient symptoms and response to treatment, there is a clear need for a fully-implantable DBS system capable of chronically recording patient-specific electrophysiological biomarker signals over time. By providing an objective correlate of a patient's disease and response to treatment, this capability has the potential to improve therapeutic benefit while preventing undesirable side effects. Herein, the engineering and capabilities of the Percept PC, the first FDA-approved, fully-implantable DBS device capable of nearly-simultaneous electrophysiological recordings and stimulation, are discussed. The device's ability to chronically record local field potentials (LFPs) at implanted DBS leads was validated in patients with neurological disorders. Lastly, the electrophysiological activity correlates of clinically relevant patient-reported events are presented. While FDA approved for conditions such as Parkinson's disease, essential tremor, dystonia, obsessive-compulsive disorder, and epilepsy, chronic electrophysiological recordings in humans has broad applications within basic science and clinical practice beyond DBS, offering a wealth of information related to normal and abnormal neurophysiology within distinct brain areas.

Industrial perspectives on brain-computer interface technology.

Neuromodulation therapies offer a unique opportunity for translating brain-computer interface (BCI) technologies into a clinical setting. Several diseases such as Parkinson's disease are effectively treated by invasive device stimulation therapies, and the addition of sensing and algorithm technology is an obvious evolutionary expansion of capabilities. In addition, this infrastructure might enable a roadmap of novel BCI technologies. While the initial applications are focused on epilepsy and movement disorders, the technology is potentially transferable to a broader base of disorders, including stroke and rehabilitation. The ultimate potential of BCI technology will be determined by forthcoming chronic evaluation in multiple neurologic disorders.

A Chronically Implantable Neural Coprocessor for Investigating the Treatment of Neurological Disorders.

Developing new tools to better understand disorders of the nervous system, with a goal to more effectively treat them, is an active area of bioelectronic medicine research. Future tools must be flexible and configurable, given the evolving understanding of both neuromodulation mechanisms and how to configure a system for optimal clinical outcomes. We describe a system, the Summit RC+S "neural coprocessor," that attempts to bring the capability and flexibility of a microprocessor to a prosthesis embedded within the nervous system. This paper describes the updated system architecture for the Summit RC+S system, the five custom integrated circuits required for bi-directional neural interfacing, the supporting firmware/software ecosystem, and the verification and validation activities to prepare for human implantation. Emphasis is placed on design changes motivated by experience with the CE-marked Activa PC+S research tool; specifically, enhancement of sense-stim performance for improved bi-directional communication to the nervous system, implementation of rechargeable technology to extend device longevity, and application of MICS-band telemetry for algorithm development and data management. The technology was validated in a chronic treatment paradigm for canines with naturally occurring epilepsy, including free ambulation in the home environment, which represents a typical use case for future human protocols.

Modulation of hippocampal activity with fornix Deep Brain Stimulation.

BACKGROUND: Deep Brain Stimulation (DBS) within the Papez circuit is under investigation as a treatment for epilepsy and Alzheimer's disease. We previously reported the effects of stimulation at nodes within this network (anterior thalamic nucleus and hippocampus) on hippocampal activity in a large animal model, using a chronic implantable, clinical-grade system that permits concurrent stimulation and recording. OBJECTIVE: In this study we extended earlier work to compare the effects of fornix DBS on evoked potentials (EPs) and local field potential (LFP) activity within the hippocampus, and to assess closed-loop stimulation. METHODS: Unilateral fornix and hippocampal DBS leads were implanted in three ovine subjects using image-guided, frameless stereotaxy. Chronic, awake recordings of EPs and LFPs in response to fornix and hippocampal stimulation were collected with the implanted device and analyzed off-line. RESULTS: Stimulation of the fornix produced robust, short latency hippocampal EPs. High frequency fornix stimulation generated parameter-dependent effects. At low amplitudes, short lasting inhibition of LFP activity occurred. Above a specific amplitude threshold, DBS elicited pronounced bursts of theta activity, followed by a marked state shift in hippocampal activity. These effects persisted for minutes post-DBS and were reflected as changes in LFP spectral content and phase-amplitude coupling. Real-time modulation of hippocampal activity via the implanted device was demonstrated using LFPs as the control signal for closed-loop stimulation. CONCLUSIONS: The current results expand earlier findings and demonstrate target-specific effects produced by DBS within this neural circuit. These changes in network activity may provide insights into stimulation targets and parameter selection for clinical investigations.

Local field potential recordings in a non-human primate model of Parkinsons disease using the Activa PC + S neurostimulator.

OBJECTIVE: Using the Medtronic Activa® PC + S system, this study investigated how passive joint manipulation, reaching behavior, and deep brain stimulation (DBS) modulate local field potential (LFP) activity in the subthalamic nucleus (STN) and globus pallidus (GP). APPROACH: Five non-human primates were implanted unilaterally with one or more DBS leads. LFPs were collected in montage recordings during resting state conditions and during motor tasks that facilitate the expression of parkinsonian motor signs. These recordings were made in the naïve state in one subject, in the parkinsonian state in two subjects, and in both naïve and parkinsonian states in two subjects. MAIN RESULTS: LFPs measured at rest were consistent over time for a given recording location and parkinsonian state in a given subject; however, LFPs were highly variable between subjects, between and within recording locations, and across parkinsonian states. LFPs in both naïve and parkinsonian states across all recorded nuclei contained a spectral peak in the beta band (10-30 Hz). Moreover, the spectral content of recorded LFPs was modulated by passive and active movement of the subjects' limbs. LFPs recorded during a cued-reaching task displayed task-related beta desynchronization in STN and GP. The bidirectional capabilities of the Activa® PC + S also allowed for recording LFPs while delivering DBS. The therapeutic effect of STN DBS on parkinsonian rigidity outlasted stimulation for 30-60 s, but there was no correlation with beta band power. SIGNIFICANCE: This study emphasizes (1) the variability in spontaneous LFPs amongst subjects and (2) the value of using the Activa® PC + S system to record neural data in the context of behavioral tasks that allow one to evaluate a subject's symptomatology.

Proceedings of the Second Annual Deep Brain Stimulation Think Tank: What's in the Pipeline.

The proceedings of the 2nd Annual Deep Brain Stimulation Think Tank summarize the most contemporary clinical, electrophysiological, and computational work on DBS for the treatment of neurological and neuropsychiatric disease and represent the insights of a unique multidisciplinary ensemble of expert neurologists, neurosurgeons, neuropsychologists, psychiatrists, scientists, engineers and members of industry. Presentations and discussions covered a broad range of topics, including advocacy for DBS, improving clinical outcomes, innovations in computational models of DBS, understanding of the neurophysiology of Parkinson's disease (PD) and Tourette syndrome (TS) and evolving sensor and device technologies.

Brain stimulation for epilepsy--local and remote modulation of network excitability.

BACKGROUND: The use of Deep Brain Stimulation (DBS) as a potential therapy for treatment resistant epilepsy remains an area of active clinical investigation. We recently reported the first chronic evaluation of an implantable, clinical-grade system that permits concurrent stimulation and recording, in a large animal (ovine) model developed to study DBS for epilepsy. OBJECTIVE: In this study we extended this work to compare the effects of remote (anterior thalamic) and direct (hippocampal) stimulation on local field potential (LFP) activity and network excitability, and to assess closed-loop stimulation within this neural network. METHODS: Following anesthesia and 1.5T MRI acquisition, unilateral anterior thalamic and hippocampal DBS leads were implanted in three subjects using a frameless stereotactic system. Chronic, awake recordings of evoked potentials (EPs) and LFPs in response to thalamic and hippocampal stimulation were collected with the implanted device and analyzed off-line. RESULTS: Consistent with earlier reports, thalamic DBS and direct stimulation of the hippocampus produced parameter-dependent effects on hippocampal activity. LFP suppression could be reliably induced with specific stimulation parameters, and was shown to reflect a state of reduced network excitability, as measured by effects on hippocampal EP amplitudes and after-discharge thresholds. Real-time modulation of network excitability via the implanted device was demonstrated using hippocampal theta-band power level as a control signal for closed-loop stimulation. CONCLUSIONS: The results presented provide evidence of network excitability changes induced by stimulation that could underlie the clinical effects that have been reported with both thalamic and direct cortical stimulation.

Chronic cortical and electromyographic recordings from a fully implantable device: preclinical experience in a nonhuman primate.

OBJECTIVE: Analysis of intra- and perioperatively recorded cortical and basal ganglia local field potentials in human movement disorders has provided great insight into the pathophysiology of diseases such as Parkinson's, dystonia, and essential tremor. However, in order to better understand the network abnormalities and effects of chronic therapeutic stimulation in these disorders, long-term recording from a fully implantable data collection system is needed. APPROACH: A fully implantable investigational data collection system, the Activa® PC + S neurostimulator (Medtronic, Inc., Minneapolis, MN), has been developed for human use. Here, we tested its utility for extended intracranial recording in the motor system of a nonhuman primate. The system was attached to two quadripolar paddle arrays: one covering sensorimotor cortex, and one covering a proximal forelimb muscle, to study simultaneous cortical field potentials and electromyography during spontaneous transitions from rest to movement. MAIN RESULTS: Over 24 months of recording, movement-related changes in physiologically relevant frequency bands were readily detected, including beta and gamma signals at approximately 2.5 µV/[Formula: see text] and 0.7 µV/[Formula: see text], respectively. The system architecture allowed for flexible recording configurations and algorithm triggered data recording. In the course of physiological analyses, sensing artifacts were observed (∼1 µVrms stationary tones at fixed frequency), which were mitigated either with post-processing or algorithm design and did not impact the scientific conclusions. Histological examination revealed no underlying tissue damage; however, a fibrous capsule had developed around the paddles, demonstrating a potential mechanism for the observed signal amplitude reduction. SIGNIFICANCE: This study establishes the usefulness of this system in measuring chronic brain and muscle signals. Use of this system may potentially be valuable in human trials of chronic brain recording in movement disorders, a next step in the design of closed-loop neurostimulation paradigms.

A flexible algorithm framework for closed-loop neuromodulation research systems.

Modulation of neural activity through electrical stimulation of tissue is an effective therapy for neurological diseases such as Parkinson's disease and essential tremor. Researchers are exploring improving therapy through adjustment of stimulation parameters based upon sensed data. This requires classifiers to extract features and estimate patient state. It also requires algorithms to appropriately map the state estimation to stimulation parameters. The latter, known as the control policy algorithm, is the focus of this work. Because the optimal control policy algorithms for the nervous system are not fully characterized at this time, we have implemented a generic control policy framework to facilitate exploratory research and rapid prototyping of new neuromodulation strategies.

Chronic evaluation of a clinical system for deep brain stimulation and recording of neural network activity.

BACKGROUND/AIMS: In conjunction with therapeutic stimulation, next-generation deep brain stimulation (DBS) devices may offer the ability to record and analyze neural signals, providing for unprecedented insight into DBS effects on neural networks. This work was conducted to evaluate an implantable, clinical-grade system that permits concurrent stimulation and recording using a large animal (ovine) model recently developed to study DBS for epilepsy. METHODS: Following anesthesia and 1.5-tesla MRI acquisition, unilateral anterior thalamic and hippocampal DBS leads were implanted (n = 3) using a frameless stereotactic system. Chronic, awake recordings of evoked potentials (EPs) and local field potentials were collected with the implanted device and analyzed off-line. RESULTS: Hippocampal EPs were stable over long-term (>1 year) recording and consistent in morphology and latency with prior acute results. Thalamic and hippocampal DBS produced both excitatory and inhibitory network effects that were stimulation site and parameter dependent. Free roaming recordings illustrated periods of highly correlated activity between these two structures within the circuit of Papez. CONCLUSIONS: These results provide further insight into mechanisms of DBS therapy for epilepsy and an encouraging demonstration of the capabilities of this new technology, which in the future, may afford unique opportunities to study human brain function and neuromodulation mechanism of action.

Design and validation of a fully implantable, chronic, closed-loop neuromodulation device with concurrent sensing and stimulation.

Chronically implantable, closed-loop neuromodulation devices with concurrent sensing and stimulation hold promise for better understanding the nervous system and improving therapies for neurological disease. Concurrent sensing and stimulation are needed to maximize usable neural data, minimize time delays for closed-loop actuation, and investigate the instantaneous response to stimulation. Current systems lack concurrent sensing and stimulation primarily because of stimulation interference to neural signals of interest. While careful design of high performance amplifiers has proved useful to reduce disturbances in the system, stimulation continues to contaminate neural sensing due to biological effects like tissue-electrode impedance mismatch and constraints on stimulation parameters needed to deliver therapy. In this work we describe systematic methods to mitigate the effect of stimulation through a combination of sensing hardware, stimulation parameter selection, and classification algorithms that counter residual stimulation disturbances. To validate these methods we implemented and tested a completely implantable system for over one year in a large animal model of epilepsy. The system proved capable of measuring and detecting seizure activity in the hippocampus both during and after stimulation. Furthermore, we demonstrate an embedded algorithm that actuates neural modulation in response to seizure detection during stimulation, validating the capability to detect bioelectrical markers in the presence of therapy and titrate it appropriately. The capability to detect neural states in the presence of stimulation and optimally titrate therapy is a key innovation required for generalizing closed-loop neural systems for multiple disease states.

A translational platform for prototyping closed-loop neuromodulation systems.

While modulating neural activity through stimulation is an effective treatment for neurological diseases such as Parkinson's disease and essential tremor, an opportunity for improving neuromodulation therapy remains in automatically adjusting therapy to continuously optimize patient outcomes. Practical issues associated with achieving this include the paucity of human data related to disease states, poorly validated estimators of patient state, and unknown dynamic mappings of optimal stimulation parameters based on estimated states. To overcome these challenges, we present an investigational platform including: an implanted sensing and stimulation device to collect data and run automated closed-loop algorithms; an external tool to prototype classifier and control-policy algorithms; and real-time telemetry to update the implanted device firmware and monitor its state. The prototyping system was demonstrated in a chronic large animal model studying hippocampal dynamics. We used the platform to find biomarkers of the observed states and transfer functions of different stimulation amplitudes. Data showed that moderate levels of stimulation suppress hippocampal beta activity, while high levels of stimulation produce seizure-like after-discharge activity. The biomarker and transfer function observations were mapped into classifier and control-policy algorithms, which were downloaded to the implanted device to continuously titrate stimulation amplitude for the desired network effect. The platform is designed to be a flexible prototyping tool and could be used to develop improved mechanistic models and automated closed-loop systems for a variety of neurological disorders.

Emerging technology for advancing the treatment of epilepsy using a dynamic control framework.

We briefly describe a dynamic control system framework for neuromodulation for epilepsy, with an emphasis on its practical challenges and the preliminary validation of key prototype technologies in a chronic animal model. The current state of neuromodulation can be viewed as a classical dynamic control framework such that the nervous system is the classical "plant", the neural stimulator is the controller/actuator, clinical observation, patient diaries and/or measured bio-markers are the sensor, and clinical judgment applied to these sensor inputs forms the state estimator. Technology can potentially address two main factors contributing to the performance limitations of existing systems: "observability," the ability to observe the state of the system from output measurements, and "controllability," the ability to drive the system to a desired state. In addition to improving sensors and actuator performance, methods and tools to better understand disease state dynamics and state estimation are also critical for improving therapy outcomes. We describe our preliminary validation of key "observability" and "controllability" technology blocks using an implanted research tool in an epilepsy disease model. This model allows for testing the key emerging technologies in a representative neural network of therapeutic importance. In the future, we believe these technologies might enable both first principles understanding of neural network behavior for optimizing therapy design, and provide a practical pathway towards clinical translation.

An implantable bi-directional brain-machine interface system for chronic neuroprosthesis research.

An implantable bi-directional brain-machine interface (BMI) prototype is presented. With sensing, algorithm, wireless telemetry, and stimulation therapy capabilities, the system is designed for chronic studies exploring closed-loop and diagnostic opportunities for neuroprosthetics. In particular, we hope to enable fundamental chronic research into the physiology of neurological disorders, define key electrical biomarkers related to disease, and apply this learning to patient-specific algorithms for therapeutic stimulation and diagnostics. The ultimate goal is to provide practical neuroprosthetics with adaptive therapy for improved efficiency and efficacy.