MEG investigation of sleep and sleep-related memory reactivation

• Applicant: Professor Dr. Steffen Gais
• Subject Area: Biological Psychology and Cognitive Neuroscience
• Term: since 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 453986748

Memories are supposed to be strengthened during sleep through reactivation. Animal experiments show that patterns of neuronal activity found during learning are replayed during sleep. In humans, reactivation can be cued with learning-related stimuli, but spontaneous replay has been difficult to pinpoint. Here, we propose experiments in which we record MEG during long-term memory encoding, working memory and sleep in order to detect patterns of neuronal activity that reflect memory reactivation. First, we propose to use machine learning algorithms (particularly support vector machines, SVM) to detect and characterize spontaneous memory processing during the sleep slow oscillation and the sleep spindle. Next, we will characterize the pattern of brain activity that distinguishes memory content in working memory maintenance with a deep convolutional neuronal network (CNN). This activity is supposed to be more similar to stimulus-free reactivation during sleep than stimulus-locked encoding activity. Finally, we will try to use this pattern of activity to detect memory replay during sleep using the representational similarity between the working memory pattern and spontaneous sleep activity. In independent analyses, we will also investigate some aspects of sleep physiology that are related to the functional role of sleep for memory consolidation. Sleep spindles (12 – 16 Hz) and slow oscillations (< 1 Hz) have previously been assumed to be global processes, but more recent studies focussed on their local properties. Our preliminary data indicate that both processes arise locally and propagate throughout the cortex. We will use source reconstruction to describe this local generation and propagation. We propose the use of MEG recordings, because of its advantages compared with the more conventional sleep EEG. MEG has a better signal-to-noise ratio especially in higher frequencies, and the high number of sensors (275 channels) allows transformation from sensor space to source space. Source localization of MEG data with sLORETA proved to be reliable and exact throughout cortical and even subcortical areas in our own preliminary data and other studies. We are therefore confident that we will be able to localize memory reactivation during sleep as well as origination and propagation of sleep oscillations with a much higher precision than has previously been possible.

• DFG Programme: Research Grants