Identification of Memory Reactivation during Sleep by EEG Classification

Abstract

Memory reactivation during sleep is critical for consolidation, but also extremely difficult to measure as it is subtle, distributed and temporally unpredictable. This article reports a novel method for detecting such reactivation in standard sleep recordings. During learning, participants produced a complex sequence of finger presses, with each finger cued by a distinct audio-visual stimulus. Auditory cues were then re-played during subsequent sleep to trigger neural reactivation through a method known as targeted memory reactivation (TMR). Next, we used electroencephalography data from the learning session to train a machine learning classifier, and then applied this classifier to sleep data to determine how successfully each tone had elicited memory reactivation. Above chance classification was significantly higher in slow wave sleep than in stage 2, suggesting differential efficacy of TMR in these two sleep stages. Interestingly, classification success reduced across numerous repetitions of the tone cue, suggesting either a gradually reducing responsiveness to such cues or a plasticity-related change in the neural signature as a result of cueing. We believe this method will be invaluable for future investigations of memory consolidation.