Getting A’s in your Z’s: Optimizing Memory Reactivation through Real-Time Sleep Spindle Detection

Abstract

Biasing memory reactivation with associative cues during non-rapid eye movement (NREM) sleep has been shown to increase recall and performance on declarative memory tasks. The precise distribution of these cues within NREM, however, particularly with regards to intermediate sleep stages like stage 2, has not been studied previously. Through the use of a real- time sleep spindle detection script, we look into the possibility of controlling cue timing to optimize memory reactivation, manipulating cues to be presented either within the refractory period of a spindle (“early” cues) or outside of it (“late” cues). Using an offline spindle detector as the golden standard, the online script showed reasonable levels of precision and recall in spindle classification (~0.55, ~0.40, respectively) and high efficiency in sound presentation, with a mean processing time on an order of magnitude of -4 after detection. As hypothesized, late cues resulted in improved recall performance over early cues on a sound-picture-location declarative memory task. However, interestingly, cues that appeared just on the limit of a spindle’s refractory window (~3-6 seconds) showed best recall, and those before or after this period do not elicit significantly enhanced memory performance. It is likely that these cues occur during a “spindle-rich period,” in which spindles occur continuously within 3-6 sec intervals of one another. Thus, overall, from these results we can conclude that 1) spindle detection is possible on a real-time scale; 2) cues that occur outside the refractory period of spindles are better recalled than those appearing within the period; and 3) there is a 3-6 second window right after a spindle occurrence in which a cue can occur for optimal memory reactivation and performance, likely during a spindle-rich period of time.