Automated non-invasive brain stimulation parameter selection using Bayesian optimisation

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The overall aim of my project is to develop a protocol to efficiently alter high-level cognitive functions using non-invasive brain stimulation. Non-invasive brain stimulation is a key technique in modern neuroscientific research, allowing researchers to probe the causal link between brain processes and behaviour, as well as developing new therapeutic treatments for patients suffering from mental illnesses and neurological diseases including Parkinsonism and Alzheimer's. However, a key downside of this technique is that its effects are heavily reliant on the participant's existing brain state, leading to varying stimulation effects between individuals. In this PhD I will aim to combine neuroimaging and non-invasive brain stimulation techniques to develop a more efficient stimulation protocol which adapts to participant brain state. In the main part of this research I will utilise machine-learning techniques to classify trials of a behavioural task according to the participant's neural state. The online classification of behavioural trials by the machine-learning algorithm and the resulting administration of stimulation will be performed in a closed-loop system, i.e. the system, once initialised, will be able to run without any input from the researcher, and any stimulation onset/parameters will be determined by the algorithm (within strict safety limitations) depending on participant brain state. The use of a closed-loop design will allow for a more efficient stimulation protocol, which is better able to adapt to the current behavioural task demands and so transforming non-invasive brain stimulation into a more reliable research and therapeutic tool. This work has the potential to not only improve brain stimulation results in research by reducing the variability of results from brain stimulation experiments, but could also have a potential translation to therapeutic settings where it has the potential to improve the efficacy of stimulation in clinical populations. The completion of this work will involve training in several MRC skill priority areas including 1) quantitative skills in the form of high level statistics including mixed model analysis and machine learning techniques, 2) interdisciplinary skills through interaction with engineers as well as psychologists and neuroscientists, particularly during implementation of machine learning techniques and development of the closed-loop system and 3) whole organ/organism physiology as the project involves both neuroimaging as well as methods to alter brain functions using brain stimulation.