Peng Cong

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.

A translational platform for prototyping closed-loop neuromodulation systems

While modulating neural activity through stimulation is an effective treatment for neurological diseases such as Parkinsons 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.

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.

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.

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.

A 32-channel modular bi-directional neural interface system with embedded DSP for closed-loop operation

A prototype bi-directional neural interface system is presented with closed-loop and embedded DSP capabilities. The system includes 32-electrode stimulation capability, eight multiplexed low-noise low-power bio-potential sensing channels with on-chip digital FFT, and a Cortex M3-based microcontroller for implementing closed-loop algorithms. The stimulation subsystem can provide a maximum stimulation current of 12mA with approximately 3% accuracy per channel. The sensing subsystem uses a fully differential chopping amplifier achieving a noise floor <;100nV/rtHz with approximately 1μA current from 2V supply. A 13bit continuous-time sigma-delta ADC is used to sample the amplifier output at the rate of 33kHz. The ADC consumes approximately 100nA while achieving an ENOB of 12b with a 250Hz date rate. A hardware implementation of a cached-FFT is included in the sensing IC to perform spectral analysis of the bio-potential signal. The resulting multiple spectral signatures can be selectively sent to the microcontroller allowing for algorithmic control of the stimulator system. The system partition was designed to minimize overall computational power while providing user flexibility. The sensing performance of the prototype has been demonstrated with 2-D cursor control in a non-human primate brain machine interface (BMI) model using 20 features simultaneously.

Preliminary validation of an implantable bi-directional neural interface for chronic, in vivo investigation of brain networks

This paper describes the preliminary technology validation of a bi-directional neural interface in an awake large animal model (ovine). The device addresses the major requirements of a chronic research system, including operation within the implantable environment and electrical stimulation with concurrent bioelectric sensing. Preliminary chronic measurements of network dynamics demonstrate that a chronically stable bi-directional interface to the nervous system is achievable. This was shown through chronic impedance and evoked potential measurements in the thalamo-cortical circuit of Papez. Characterization of bioelectric sensing in the presence of stimulation was also performed through measurements of the noise floor in the presence and absence of stimulation. Further technology validation was performed by using the prototype to correlate activity within and between structures in the circuit of Papez in the presence and absence of stimulation.

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.