A novel adaptive sampling technique for mapping brain function

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Diagnostic cognitive tests are used in many clinical situations; for example, cognitive tests assessing memory, attention and other processes can be used to detect early signs of dementia and monitor cognitive impairment after a traumatic brain injury. Such diagnostic tests are thought to activate different brain systems. However, the tests have not been designed for this purpose, and so there may be better, more sensitive tests. We will use a new functional brain imaging technique that "automatically" finds the best cognitive tests that activate specific brain systems. The new technique is called neuroadaptive Bayesian optmisation. It uses neuroimaging (functional MRI) to assess brain function in real-time, guided by a powerful machine learning algorithm. This approach can explore across many cognitive tests and find the ones that are most strongly associated with a specific brain system. This is a "closed-loop" approach: the algorithm suggests a cognitive task, analyses how this affects the brain, and uses this knowledge to update what it "knows" about which cognitive task is best for activating that brain region, all automatically; by taking the human scientist "out-of-the-loop", it is possible to be much more efficient and unbiased and search over many more types of cognitive task very quickly, as our pilot data shows.We will use this technique to develop a new diagnostic battery that we hope will be more sensitive than tests currently in use to distinguish between two important brain systems that have been associated with neurological and psychiatric disorders. If the project is successful, we would work with clinicians to use the tool to improve cognitive testing in a range of clinical situations and help diagnose cognitive problems better in patients.