Targeted Memory Reactivation during Sleep Elicits Neural Signals Related to Learning Content

Abstract

Retrieval of learning-related neural activity patterns is thought to drive memory stabilization. However, finding reliable, noninvasive, content-specific indicators of memory retrieval remains a central challenge. Here, we attempted to decode the content of retrieved memories in the EEG during sleep. During encoding, male and female human subjects 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 subjects back for a final postnap 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 cued the memories during sleep. Discrimination performance was significantly above chance and predicted subsequent memory, supporting the idea that retrieval leads to memory stabilization. Moreover, these lateralized signals increased with postcue sleep spindle power, demonstrating that retrieval 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 retrieval.SIGNIFICANCE STATEMENT Memories are thought to be retrieved during sleep, leading to their long-term stabilization. However, there has been relatively little work in humans linking neural measures of retrieval of individual memories during sleep to subsequent memory performance. This work leverages the prominent electrophysiological signal triggered by lateralized movements to robustly demonstrate the retrieval of specific cued memories during sleep. Moreover, these signals predict subsequent memory and are correlated with sleep spindles, neural oscillations that have previously been implicated in memory stabilization. Together, these findings link memory retrieval to stabilization and provide a powerful tool for investigating memory in a wide range of learning contexts and human populations.

Keywords: episodic memory; memory consolidation; memory reactivation; multivariate pattern analysis; sleep.

Copyright © 2019 the authors.

Figures

Figure 1.

Experimental task and design. Subjects imagined reaching out and touching targets that were associated with auditory cues before executing their movement. Subjects used either their left or right hand depending on the location of the target on the screen (left hand for 32 left-side targets, right hand for 32 right-side targets). Subjects then took a prenap test on the location of each target. During periods of SWS in an afternoon nap, subjects were presented with half of the sounds associated with left-side and half associated with right-side targets. After the nap, subjects returned to the laboratory and took a test on all target and nontarget locations.

Figure 2.

Wake classification and feature selection for sleep. Voltages from lateralized EEG electrodes were subtracted, and midline electrodes were eliminated to create 27 electrode pairs. Next, data from all subjects were combined. ICA was run on the combined data, and we assessed which of the resulting components discriminated between left and right motor imagery during wake. A, The five components (1, 3, 4, 6, and 7) that significantly discriminated between left and right motor imagery. B, Classification accuracy (operationalized as AUC) as a function of time bin for those five components. Error bars indicate the edges of the middle 95th percentile of classification accuracies. x axis values indicate the center time point for each 1000 ms time bin. There is a wider y axis range for component 1 than other components.

Figure 3.

Lateralized retrieval of learning content during sleep related to prenap and postnap memory. Using the lateralized components that discriminated left versus right motor imagery during wake as features, we trained a leave-one-subject-out classifier on sleep data and computed AUC-based classification performance time-locked to TMR cues. We found significant classification performance when using all trials (A). B, Significant across-subject decoding from 1.5 to 4 s. C, Performance when crossing training and testing time. We also found significant classification performance when focusing just on trials that were remembered prenap (D), but not for trials that were not remembered prenap (E). However, there were no significant differences in classification performance between these conditions (F). Using only trials that were remembered before the nap, we then found significant classification performance for trials that were subsequently remembered postnap (G), but not for those forgotten postnap (H), and there were significant differences in classification performance between these conditions (I). In all graphs, classification was performed on time-averaged signals within 1000 ms windows centered on each time point. Error bars indicate the edges of the middle 95th percentile of classification accuracies.

Figure 4.

Retrieval of learning content increased with higher postcue σ power and decreased with higher precue σ power. Subsequent memory differed as a function of postcue (1000 to 4500 ms, baseline-corrected) (A, left) and precue σ power (−2000 to 0 ms, no baseline correction) (B, left) over electrode CPz. We also plotted the full topography across the scalp (A, B, right). We then plotted AUC-based classification performance as a function of postcue and precue σ power. Trials were split by the median RMS power in the σ band of each subject. Classifier accuracy was significantly above chance on trials with above-median postcue σ power (C), but not below-median σ power (D). Moreover, there were significant differences in classifier accuracy between these conditions (E). Conversely, classifier accuracy was not significantly above chance on trials with above-median precue σ power (F), but it was for trials with below-median σ power (G), and there were marginally significant differences in classifier accuracy between these conditions (H). In all graphs, classification was performed using 1000 ms windows centered on each time point. Error bars indicate the edges of the middle 95th percentile of classification accuracies.