Introduction: The neuromodulation field has seen many early successes in converting therapies
into clinical application, but challenges remain in reducing off-target effects for new indications as
well as targeting therapies to ensure clinical and commercial viability. Challenges have arisen
validating preclinical successes in clinical settings due to the differences between preclinical and
clinical subjects leading to different stimulation parameters required. These challenges have
impeded the expansion of neuromodulation to new indications [Herring 2019]. Herein we aim to
show how the information in the neural signals can be leveraged to quantify and avoid off-target
effects [Ardell 2017] providing better specificity for future therapies.
Outline of the project: We propose utilising existing SPARC data to build an efficient method of
searching the stimulation parameter space using Bayesian Optimisation with Gaussian
Processes as a data-efficient way of modelling the stimulation response function. Furthermore,
we will build a machine learning model for mapping neural signals into a compressed
representation (termed Neural Biomarkers) in order to interpret the body’s neural signals as
effective measures of organ function. Finally, we validate both approaches in acute and chronic
large animal models where stimulation parameters are searched extensively through the input
space and further optimised against neural biomarkers derived from the neural signals.