Timothy J. Denison

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 chronic generalized bi-directional brain–machine interface

A bi-directional neural interface (NI) system was designed and prototyped by incorporating a novel neural recording and processing subsystem into a commercial neural stimulator architecture. The NI system prototype leverages the system infrastructure from an existing neurostimulator to ensure reliable operation in a chronic implantation environment. In addition to providing predicate therapy capabilities, the device adds key elements to facilitate chronic research, such as four channels of electrocortigram/local field potential amplification and spectral analysis, a three-axis accelerometer, algorithm processing, event-based data logging, and wireless telemetry for data uploads and algorithm/configuration updates. The custom-integrated micropower sensor and interface circuits facilitate extended operation in a power-limited device. The prototype underwent significant verification testing to ensure reliability, and meets the requirements for a class CF instrument per IEC-60601 protocols. The ability of the device system to process and aid in classifying brain states was preclinically validated using an in vivo non-human primate model for brain control of a computer cursor (i.e. brain-machine interface or BMI). The primate BMI model was chosen for its ability to quantitatively measure signal decoding performance from brain activity that is similar in both amplitude and spectral content to other biomarkers used to detect disease states (e.g. Parkinsons disease). A key goal of this research prototype is to help broaden the clinical scope and acceptance of NI techniques, particularly real-time brain state detection. These techniques have the potential to be generalized beyond motor prosthesis, and are being explored for unmet needs in other neurological conditions such as movement disorders, stroke and epilepsy.

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.

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.

An Implantable Optical Stimulation Delivery System for Actuating an Excitable Biosubstrate

The use of light-activated modulation techniques, such as optogenetics, is growing in popularity for enabling basic neuroscience research. It is also being explored for advancing more applied applications like therapeutic neuromodulation. However, current hardware systems are generally limited to acute measurements or require external tethering of the system to the light source. This paper presents an implantable prototype for use in techniques that modulate neurological state through optically-activated channels and compounds. The prototype system employs a three chip custom IC architecture to manage information flow into the neural substrate, while also handling power dissipation and providing a chronic barrier to the tissue interface. In addition to covering the details of the IC architecture, we discuss system level design constraints and solutions, and in-vitro test results using our prototype system with an optogenetic model. Potential technical limitations for the broader adoption of these techniques will also be considered.

Information, energy, and entropy: Design principles for adaptive, therapeutic modulation of neural circuits

This paper discusses the challenges and opportunities designing technology for deep brain stimulation (DBS). DBS is currently approved for the treatment of movement disorders such as Parkinson Disease, essential tremor and dystonia, and a number of studies are underway to determine its clinical efficacy for the treatment of epilepsy, treatment resistant depression, and obsessive compulsive disorder (OCD). Designing a DBS system is a complex system engineering problem, drawing on such diverse fields as applied physics, circuit design, algorithms and biology. But fundamental to device design is the neurophysiology of the dasiabrain circuitspsila affected by the disease, and how they can be modulated for therapeutic affect. Recent activities are drawing on information theory to help better understand the operation of brain circuits. From that understanding, we hope to clarify the mechanisms by which existing DBS therapy works. In addition, considerations from information theory, and the relationships between concepts like entropy, energy and information flow, can help guide the design of more advanced therapy systems. We briefly review these concepts as applied to brain circuits and disease. We then describe our recent work in designing research tools that allow for exploration of adaptive circuit modulation based on measured electrical biomarkers, which are believed to represent compromised information processing in the brain. Future opportunities are discussed to highlight that electrical engineering, from MEMS to circuits to signal processing, is crucial to enabling the next generation of neurological therapies.

An implantable 5mW/channel dual-wavelength optogenetic stimulator for therapeutic neuromodulation research

While serving as the core technology of many neurological therapies, electrical stimulation suffers from several drawbacks. Constraints on electrode geometry and placement can result in an inability to modulate specific neural populations, and stimulation of non-target networks can cause undesirable side-effects. Conducting electrodes in tissue can also restrict the level of tolerated EM exposure from modalities like MRI and electrosurgery, and large stimulation currents can undermine the ability to simultaneously sense underlying neural activity when implementing a closed-loop therapy system [1]. These drawbacks motivate the need for exploring alternative techniques to therapeutically modulate neural network activity.

Functional electrical stimulation as a neuroprosthetic methodology for enabling closed-loop urinary incontinence treatment

This paper describes a neuroprosthetic methodology using functional electrical stimulation (FES) to treat urinary incontinence. The neuroprosthesis is designed to enable closed-loop urinary incontinence treatment by providing dynamic support to the urethra to restore function. Acute in vivo evaluation of a closed-loop FES control system in a canine model has demonstrated that the neuroprosthesis can significantly increase warning time and bladder capacity to prevent incontinence. The FES can be triggered by the sensation of urgency and bladder contraction to prevent urge urinary incontinence, and by the sudden increase of abdominal pressure to prevent stress urinary incontinence.

A 2μW Three-Axis MEMS-based Accelerometer

This paper describes a prototype acceleration sensor that enables chronic motion sensing in battery powered applications. The design facilitates inertial measurement with minimal area, power penalty, and routing concerns by converting three axes of acceleration into three independent analog output channels in a single package. The sensor includes on-chip memory to store trim codes during production, and built-in electrostatic self-test to enable complete verification of the accelerometer signal chain at any time. The total supply requirement is 1 μA from a 1.8 V supply, with a noise floor of 1 mG/rtHz. A figure of merit, the noise efficiency (NE), is introduced for comparison of sense interface performance for noise and power. Applying this metric, we demonstrate that the sensor architecture explored in this paper represents the state-of-the-art for both absolute power dissipation and power efficiency with respect to the noise floor.

Enabling closed-loop neurostimulation research with downloadable firmware upgrades

We present a simulated case study using investigational, downloadable firmware updates to configure an existing implanted deep brain stimulator. With the upgrade, closed-loop algorithm parameters affecting stimulation state classification and stimulation timing can be embedded within the device to allow for chronic investigation of closed-loop concepts both flexibly and incrementally. The system can be outfitted with either cortical sensing electrodes or depth electrodes to explore a variety of closed-loop paradigms. The prototype case study presented here uses motor intention signals to titrate stimulation, a relevant use case for treatment of movement disorders, though the technology is extensible to other neurological disorders. To highlight the translational capabilities of this tool, the key technology considerations of battery power usage, chronic sensitivity and stability of biomarkers, and parameter tuning are discussed using representative testing and data sources.