This project utilizes fMRI data and machine learning to predict sleep states, aiming to enhance understanding of sleep patterns and disorders. By analyzing brain activity during different sleep stages, it seeks to improve diagnostics and develop personalized treatments for sleep disorders. The primary goal is to determine whether a participant is asleep or awake using resting-state fMRI data.
Can functional connectivity predict sleep deprivation? This project aims to explore neuroimaging data organization to build a workflow from the acquisition of an open dataset to the visualization of brain connectivity. The pipeline will be detailed and carried out for one subject, using resting state fMRI to compare the result between normal sleep and sleep deprivation (less than 3 hours of sleep the previous night).
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