Dynamic modulation of brain states using brain stimulation and neuroadaptive Bayesian optimization

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Like an orchestra that relies on the coordinated efforts of its members, the brain depends on its many regions working together to perform the multitude of cognitive functions that makes us human. These functions allow us to solve problems, retrieve relevant information from memory and select the responses necessary to perform a particular task. In order to do this, the brain must coordinate the interactions between regions located far apart. One of the greatest challenges of modern neuroscience is to understand how these interactions occur, and how their occurrence gives rise to efficient behaviour. A tool capable of influencing the interactions between brain regions could help scientists understand better how a particular pattern of brain activity is associated to efficient behaviour, such as being able to retain information in memory or solve a problem. Such a tool could then be applied to neurological and psychiatric conditions, where the interactions between brain regions might be malfunctioning.The objective of this project is to develop this tool. In order to do this, we will combine functional magnetic resonance imaging (fMRI), non-invasive electrical brain stimulation and machine learning. Each of these techniques brings a critical element to this tool.FMRI is a technique widely used by neuroscientists to provide images with information about brain function. Non-invasive electrical brain stimulation is a technique that applies low-voltage current through the scalp and can change the activity of neurons without requiring surgery to implant electrodes. This technique has been shown to influence brain function and the interactions between brain regions. Electrical brain stimulation, however, can be applied in many different ways, thereby making it difficult to know what would work for to influence a particular interaction between a set of brain regions. In addition, the results of brain stimulation can vary depending on factors such as a person's age, sex, brain anatomy and genetics. This makes creating a tool capable of identifying the stimulation parameters for each individual like 'finding a needle in a haystack'. This is why machine learning is necessary, where a computer program "learns" to identify which brain stimulation parameters optimally engage brain regions involved in cognitive functions in a time frame that would not be possible using conventional methodologies.In essence, our tool will use brain stimulation to influence how brain regions interact, fMRI data analysed while the participant is receiving a certain type of stimulation to inform on how the brain reacts to it, and machine learning to select the next stimulation that should be investigated. By the end of the experiment we will obtain a map with the brain's responses to different stimulation conditions, and a prediction of what the optimal stimulation condition to elicit a brain response is.This tool could then be used in many clinical conditions where inefficient communication between brain regions has been observed, such as psychiatric conditions and during rehabilitation after brain injury.