Modelling frequency-based neural responses to deep brain stimulation in Parkinson’s disease - Sermon, JJ (2024)
Deep Brain Stimulation (DBS) is an established therapy for Parkinson’s disease (PD) when pharmacological measures have been exhausted or following medication- associated motor control side effects. DBS has been implanted in over 100,000 patients with PD, and is also used in other neurological disorders, such as epilepsy and psychiatric conditions. Despite this success and widespread adoption, the underlying methods of DBS therapy have not changed in over a decade and can introduce side effects, for example on coordination and cognition. Improvements in therapeutic outcome as well as side effects can likely be addressed through real time adaptation to both patient and pathology. This could be achieved using closed-loop DBS in sense-stimulation devices. However, there is a lack of understanding of how stimulation artefacts infiltrate the neural signals as well as how DBS affects neural activity, which even limits the application of current open-loop DBS techniques. To investigate observed signal changes from DBS, we analyse and develop computational models of signals whose frequency signature changes in response to neuromodulation. In Chapter 2, we review hypothesised causes of sub-harmonic artefacts and analyse whether these represent feasible sources of the activity seen in data. In addition, we provide methods to confirm physiological origin as well as tools to improve the design of future sense-stimulation protocols. In Chapter 3, we predict regions of medically-induced prokinetic motor cortical rhythm entrainment (frequency locking) to variable stimulation parameters using a computationalmodelandvalidatethepredictionsagainstdata. Wedemonstratethat DBS can produce nonlinear neuronal responses to inform future clinical paradigms. In Chapter 4, we create a network model that can reproduce long-term dynamic frequency responses to DBS. We suggest that single neuronal populations may be able to replicate these observations and that vesicle depletion may be a fundamental neural response to DBS. Together, this thesis advances our understanding of DBS- associated changes in neural recordings and suggests potential neural or electrical underlying mechanisms. Using computational models of these responses to DBS, we look to advance our understanding of the mechanisms of existing open-loop DBS with direct applications to techniques for future closed-loop approaches.