Closed-Loop Targeted Memory Reactivation during Sleep Improves Spatial Navigation

Abstract

Sounds associated with newly learned information that are replayed during non-rapid eye movement (NREM) sleep can improve recall in simple tasks. The mechanism for this improvement is presumed to be reactivation of the newly learned memory during sleep when consolidation takes place. We have developed an EEG-based closed-loop system to precisely deliver sensory stimulation at the time of down-state to up-state transitions during NREM sleep. Here, we demonstrate that applying this technology to participants performing a realistic navigation task in virtual reality results in a significant improvement in navigation efficiency after sleep that is accompanied by increases in the spectral power especially in the fast (12–15 Hz) sleep spindle band. Our results show promise for the application of sleep-based interventions to drive improvement in real-world tasks.

Individual Differences in Slow-Wave-Sleep Predict Acquisition of Full Cognitive Maps

“…This result resembled our previous long-term sleep study using the same methodology but a different learning paradigm ( Lerner et al, 2016 ). Others have also implicated N2 in the facilitation of navigational performance ( Wamsley et al, 2010 ; Shimizu et al, 2018 ). N2 shares several physiological characteristics with SWS (e.g., sleep spindles) and both stages are sometimes grouped together as non-REM sleep; however, it is currently not clear if the mechanisms contributing to the N2 and SWS effects on memory are similar.…”

“…Studies show that sleep following practice on a virtual navigation task improves performance on the same task the next waking period (e.g., Peigneux et al, 2004 ; Wamsley et al, 2010 ; Nguyen et al, 2013 ) and that activity patterns in the hippocampus during practice are repeated during SWS and predict later improvement ( Peigneux et al, 2004 ). Moreover, artificially eliciting brain-activity patterns from the practice period during sleep stages N2 and SWS using targeted memory reactivation (TMR) techniques improve next-morning performance even further ( Shimizu et al, 2018 ).…”

The modulatory effect of oscillatory reinstatement during slow-wave sleep on declarative memory consolidation

“…This claim is based on the finding that participants, who received congruent stimulation during encoding and SWS, demonstrated a declarative memory enhancement that was maintained after an additional night of sleep. (Shimizu et al, 2018). Henceforth, the persistence of the current declarative memory enhancement highlights the importance of oscillatory reinstatement for sleep-dependent declarative memory consolidation.…”

“…Secondly, whereas post-learning sleep and TMR have been shown to have limited or no beneficial effects on subsequent recognition performance, free recall tasks tend to elicit pronounced memory enhancements following sleep and TMR (Ashton, Cairney, & Gaskell, 2018;Diekelmann, Born, & Rasch, 2016;Rauchs et al, 2004;Tamminen, Ralph, & Lewis, 2017;Wang et al, 2017). Additionally, post-sleep declarative memory performance was assessed immediately after the 90-minute retention interval and 24 hours later, because further overnight processing has been found to attenuate sleep intervention-related memory benefits (Shimizu et al, 2018). Therefore, the current study aims to determine whether oscillatory reinstatement has a long-term modulatory effect on sleep-dependent declarative memory consolidation, such that differences in declarative memory performance between stimulation conditions are maintained following an additional night of sleep.…”

Shaping memory consolidation via targeted memory reactivation during sleep

“…Altogether, these studies systematically indicate that the coupling between SO and spindles (in particular fast spindles, 12–15 Hz) may be a key mechanism in promoting memory consolidation during sleep, and this mechanism can be boosted by delivering an acoustic stimulus in a specific phase of the SO (i.e., the up‐phase). More recently, Shimizu and colleagues developed a system that integrates ACLS and TMR techniques. Specifically, the system was able to detect ongoing EEG activity and deliver specific cues, which were associated with specific information during wakefulness, during the up‐state of the SO.…”