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.

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.