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Lead supervisor: Prof Tim Denison, Department of Engineering Science, Oxford

Commercial partner: Magstim, Whitland, Wales


Transcranial Magnetic Stimulation (TMS) is a non-invasive technology to stimulate brain circuits. TMS is a research tool for neuroscience, with promising translation opportunities for clinical treatments. Current TMS clinical applications include major depressive disorder, OCD and migraine. TMS therapies under exploration include post - stroke rehabilitation, central pain, addictions and Tinnitus. However, TMS therapy needs to be improved. For example, in depression, only about 1/3 of patients achieve remission. To further TMS utility, this project will explore complementary two areas of technology.

First, we believe expanding the stimulation parameter space is beneficial. Historically, most TMS applications use frequencies of 1 - 20 Hz, continuously or in simple repetitive bursts. Yet new paradigms are emerging; “Thetaburst Stimulation” recently showed promise to treat depression with shorter treatment intervals than continuous TMS (Blumberger, Lancet, 2018). In addition, alternative neuromodulation therapies like Deep Brain Stimulation use significantly higher frequencies for effect. Pulse shape is another degree of freedom for differentially engaging neural substrates, but is not fully explored, as systems generally operate at a single resonance frequency. These data suggest TMS with expanded parameter capabilities may unlock novel therapeutic uses or improve outcomes.

Second, to make the most of these extended capabilities, we believe researchers need a methodology for systematically searching the parameter space. Even with today’s options, only a small set of clusters are used in practice, and the mechanisms of these settings are not clearly understood (Klimjai, Annals Phys Rehab Med, 2015). Similar to theta burst, other patterns might emerge that provide additional benefit, but the pathway for exploring the space must be tractable. Finally, TMS compatible EEG systems are available but no clear algorithms to measure potentially beneficial or negative effects of TMS have been validated.

This thesis project will design and test an enhanced TMS system that addresses these shortcomings. First, the system will deliver TMS with an expanded parameter set, including higher rates, extended pattern capability, and variations in pulse shaping with tunable circuits. This portion of the project will require systems-level integration of electronics, magnetic coils, and neural activation models. The second component of the thesis will be to design a reinforcement learning algorithm for searching the stimulation parameter space. Physiological sensors will be used to estimate the subject’s brain state in real-time. Perturbations of stimulation will then be applied, and the effect on brain state used to adjust the next parameter run. Using methods from reinforcement (machine) learning, we will explore both model-based and “black box” explorations of the TMS parameter space to in search of more optimal stimulation paradigms.

To demonstrate the utility of the new research system, the thesis will include measurements of Long Term Potentiation and Inhibition of selected neuronal networks. We will use established methods for modulation of motor and speech centers as objective markers for method validation. This work will be coupled with in-vivo testing in large animal models to ensure safety. The final deliverable will be a proof-of-concept instrument and parameter optimization framework for advancing TMS clinical neuroscience research and applications.

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