Unified probabilistic modelling of adaptive spatial-temporal structures in the human brain

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Learning from experience and adapting our behaviour to new situations is a fundamental skill for our everyday interactions. But what are the brain plasticity mechanisms that mediate an individual's ability to make progress during training on complex tasks? What is it that differentiates `good' from `poor' learners in their ability to adapt? Recent advances in functional brain imaging technology provide us with the unique opportunity to study how the human brain changes with learning. However, the existing methods focus predominantly on modelling brain activity data within a single session rather than across training sessions. As such, these methods are not capable of capturing larger scale dependencies emerging in brain activity as training progresses. We will develop a novel methodology that allows holistic unified modelling of a series of brain imaging data measured during the course of learning. Using this methodology we will study brain changes that result from extensive training on complex visual tasks. Our work will offer scientists and practitioners advanced tools for using brain activity measurements to understand the brain learning mechanisms and how they improve our ability to make complex decisions. The proposed methodology may have predictive power for making inferences about 'prototypical' learning patterns that can be used to predict adaptive behaviour in individuals with different learning strategies and design training schemes tailored to the individuals' abilities and needs. Hence our findings have potential implications for the design of dedicated training programmes that take into account an individual's learning capacity. Such programmes may have applications in education or intervention and rehabilitation in normal and pathological development and ageing (e.g. stroke, neurodegenerative disorders).