Different parts of our brains communicate with one another as we learn new information during the day, then continue to communicate “offline” as we sleep. This overnight brain activity helps file memories for long-term storage, but the process is complex and delicate: lots of genes influence brain activity in ways we do not yet understand. We also need to establish why and how this process is disrupted in disorders like schizophrenia, which are associated with impaired sleep-dependent brain activity and memory. This study will investigate links between a set of schizophrenia-associated genes and brain function during sleep.

Participants will be asked to wear a ‘fitbit’-like device for 2 weeks of normal activity, so we can track when and how much they sleep. We will then use a comfortable sleep cap that contains an array of recording devices to monitor EEG brainwaves while participants sleep at home for 2 nights. By analysing the EEG data using machine learning methods (similar to those used for speech recognition), we will identify patterns of activity that make up their personal “sleep fingerprint”. This will allow us to test whether these fingerprints vary according to genetics in a healthy population, without interference from things like medication that complicate patient studies.

The main outcomes will be (1) development of novel analysis methods allowing us to capture brain activity, (2) a deeper understanding of the mechanisms that determine variation in patterns of brain activity during sleep and (3) a route towards understanding mechanisms of schizophrenia liability.