Targeted memory reactivation during sleep elicits neural signals related to learning content

Abstract

Reactivation of learning-related neural activity patterns is thought to drive memory stabilization. However, finding reliable, non-invasive, content-specific indicators of reactivation remains a central challenge. Here, we attempted to decode the content of reactivated memories in the electroencephalogram (EEG) during sleep. During encoding, human participants learned to associate spatial locations of visual objects with left- or right-hand movements, and each object was accompanied by an inherently related sound. During subsequent slow-wave sleep within an afternoon nap, we presented half of the sound cues that were associated (during wake) with left- and right-hand movements before bringing participants back for a final post-nap test. We trained a classifier on sleep EEG data (focusing on lateralized EEG features that discriminated left- vs. right-sided trials during wake) to predict learning content when we reactivated the memories during sleep. Discrimination performance was significantly above chance and predicted subsequent memory, supporting the idea that reactivation leads to memory stabilization. Moreover, these lateralized signals increased with post-cue spindle power, demonstrating that reactivation has a strong relationship with spindles. These results show that lateralized activity related to individual memories can be decoded from sleep EEG, providing an effective indicator of offline reactivation.