Brain state variability: understanding how the internal world shapes the external world

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It is widely accepted that the human brain continues to elicit complex fluctuations in activity during rest, yet little is understood of what they represent or how they influence behaviour. Recent research has begun to show that these internal fluctuations are not solely stochastic, and instead form a hierarchy of quasi-stable patterns, now commonly referred to as dynamic brain states. However, current research has focussed on single modalities (e.g. functional magnetic resonance imaging (fMRI)) which potentially faill to fully encapsulate states in their entirety. For example, fMRI alone cannot accurately measure sleep stages. Therefore, this project aims to classify dynamic brain states via hidden Markov models (HMM) in multimodal resting state data sets. Once a valid and reliable set of states is established, various sensory task data will be collected to explore the potential impact of state on neural responses. Lastly, the study will then aim to manipulate these states via sleep deprivation studies. The results of this project will provide a set of states that may serve as a framework for future investigations into brain state. Furthermore, if variation in state does account for a significant amount of the variance within sensory neural responses, it may be possible to use the state information to help "clean" sensory data sets. Overall, the project aims to establish a set of brain states based on multimodal data to investigate the impact of state on sensory processing.