Optimising Transcranial Magnetic Stimulation for treatment of Neurological and Psychiatric Disorders
LEAD SUPERVISOR: Prof Timothy Denison, Nuffield Department of Clinical Neurosciences
Co-supervisor: Prof Charlotte Stagg, Nuffield Department of Clinical Neurosciences
Commercial partner: The Magstim Company Ltd, Whitland
Non-invasive brain stimulation, particularly Transcranial Magnetic Stimulation (TMS) is rapidly evolving to show considerable therapeutic utility in neurological and psychiatric disorders, and already has FDA approval for treatment resistant depression. However, TMS needs improvement: for example, only about 1/3 of treated depressed patients achieve remission. This project aims to increase clinical efficacy by developing concrete solutions to improve response rates and aid patients.
Animal models highlight that optimising stimulation parameters including frequency, pulse duration and temporal pattern can make TMS substantially more effective. However, current technology prevents the application of the most promising protocols in humans, producing a translation bottleneck.
This thesis will exploit the revolutionary new TMS machine built via a current, highly successful iCASE award. The prototype kit allows for almost limitless stimulation parameter optimisation; meaning that we can use the higher frequency, shorter duration, patterned stimulation paradigms optimally effective in inducing neural changes.
Here, we propose a cycle of multidisciplinary studies to maximise the value of our previous iCASE, where optimal parameters from animal studies, in terms of stimulation parameters and underlying neural state, will be applied to healthy adults. We will determine the behavioural effects and neural changes, then optimise the biomedical engineering and trial our enhanced approach again, iteratively optimising our approach.
The motor system offers an ideal model system for this work; it provides robust, quantifiable, objective measurements by which to determine success, representing a high-throughput model to quickly and objectively determine optimal parameters. We will inform our empirical approach with computational models, cyclically refining the model based on the empirical data.
By the end of the iCASE, we will have developed methods for optimising stimulation parameters for individuals, something that will have long-term, substantial clinical and financial benefits.
Apply using course: DPhil in Clinical Neurosciences