Neuromodulation for refractory epilepsy: biomarkers and stimulation strategies

Abstract

Implantable brain sensing and stimulation devices offer transformative potential for treating neurological disorders such as epilepsy. Currently, electrical stimulation is primarily adjusted and improved through “physician in the loop” control. Harnessing the full potential of these technologies requires appropriate tuning of the stimulation therapy, reliable biomarkers, and closed-loop control strategies with proper neural signal processing. This thesis investigated biomarkers and tuning strategies for further improving current stimulation therapy options as well as the development of a novel real time closed-loop neuromodulation approach.As part of an ongoing anterior nucleus of the thalamus deep brain stimulation clinical trial, we developed and implemented critical in clinic Medtronic Percept™ data collection workflows. Local field potential data processing, analysis, and patient response to different stimulation parameters were estimated at every neurologist followup to determine a research optimized setting that minimized their LFP broadband response. Through retrospective analysis of 11 participants in the trial, a significant association was observed between responders and participants that exhibited a slow gamma oscillation. In 4/6 responders, DBS suppression of slow gamma oscillations was observed within clinical visits under multiple settings. Furthermore, chronic stimulation across visits demonstrated a long term reduction in baseline (“stimulation off”) slow gamma activity in all 6 responders. These data demonstrate the potential for slow gamma oscillations as a biomarker of ANT DBS responder candidacy, effective therapy management, and a potential signal for optimizing stimulation parameters over time. Additionally, we present an Adaptive Real-TIme STate Space (ARTISTS) closed-loop controller based on an Auto-Regressive with eXogenous input (ARX) model and Linear Quadratic Regulator (LQR) for state estimation and optimal stimulation. This low-complexity controller enables dynamic adjustment of stimulation parameters without prior system knowledge and was validated in biologically grounded models of epilepsy and Parkinsonian dynamics where neural activity was significantly suppressed during stimulation. Both findings in ANT DBS and the development of the ARTISTS controller show potential for new closed-loop ANT DBS therapy opportunities and methodologies for improving neuromodulation to be tuned based on quantitative biomarkers, representative of the patient state.