RT Journal Article
SR Electronic
T1 Identification of Memory Reactivation during Sleep by EEG Classification
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 200436
DO 10.1101/200436
A1 Suliman Belal
A1 James Cousins
A1 Wael El-Deredy
A1 Laura Parkes
A1 Jules Schneider
A1 Hikaru Tsujimura
A1 Alexia Zoumpoulaki
A1 Marta Perapoch
A1 Penelope Lewis
YR 2017
UL http://biorxiv.org/content/early/2017/10/09/200436.abstract
AB 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.