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. Neural reactivation was classified above chance in all participants when TMR was applied in SWS, and in 5 of the 14 participants to whom TMR was applied in N2. 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 valuable for future investigations of memory consolidation.
Highlights
Learned memories are reactivated in sleep at both neuronal (Ego-Stengel and Wilson, 2010, 2007; Jones and Wilson, 2005; Wilson and McNaughton, 1994) and systems levels (Maquet et al, 2000; Peigneux et al, 2004)
To determine whether more intensive learning was associated with a greater classification rate for targeted memory reactivation (TMR) cued reactivations during subsequent sleep, we computed a measure of initial learning by calculating the change in performance using composite score (CS 1⁄4 speed/accuracy) between the first and last test blocks of the presleep Motor task (Bruyer and Brysbaert, 2011; Jackson et al, 2015)
We have developed a non-invasive method for identification of neural reactivation in sleep, demonstrating as a proof of principle that it is possible to detect TMR cued reactivations of a procedural memory task above chance level using EEG classifiers
Summary
Learned memories are reactivated in sleep at both neuronal (Ego-Stengel and Wilson, 2010, 2007; Jones and Wilson, 2005; Wilson and McNaughton, 1994) and systems levels (Maquet et al, 2000; Peigneux et al, 2004). Several influential models, including Active Systems Consolidation (Rasch and Born, 2013), Synaptic Homeostasis (Tononi and Cirelli, 2014, 2006), Memory Triage (Stickgold and Walker, 2013), and Information Overlap to Abstract (Lewis and Durrant, 2011), have proposed mechanisms by which memory reactivation in sleep could boost memory consolidation, but these ideas have been difficult to test since reactivation is notoriously problematic to detect in humans. We use EEG because of its excellent temporal resolution and appropriateness for sleep studies
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