Abstract
Offline reactivation of task-related neural activity has been demonstrated in animals but is difficult to directly observe in humans. We sought to identify potential electroencephalographic (EEG) markers of offline memory processing in human subjects by identifying a set of characteristic EEG topographies (“microstates”) that occurred as subjects learned to navigate a virtual maze. We hypothesized that these task-related microstates would appear during post-task periods of rest and sleep. In agreement with this hypothesis, we found that one task-related microstate was increased in post-training rest and sleep compared to baseline rest, selectively for subjects who actively learned the maze, and not in subjects performing a non-learning control task. Source modeling showed that this microstate was produced by activity in temporal and parietal networks, which are known to be involved in spatial navigation. For subjects who napped after training, the increase in this task-related microstate predicted the magnitude of subsequent change in performance. Our findings demonstrate that task-related EEG patterns re-emerge during post-training rest and sleep.
Highlights
A robust body of animal literature suggests that task-related patterns of brain activity are “replayed” during subsequent periods of quiet rest and sleep
We examined memory processing related to learning a complex spatial navigation task
We applied the technique of “microstate analysis” to this question, identifying spatial patterns of scalp EEG activity associated with training on a virtual maze navigation task, and tracking the persistence of these EEG patterns across subsequent periods of rest and sleep
Summary
A robust body of animal literature suggests that task-related patterns of brain activity are “replayed” during subsequent periods of quiet rest and sleep. Multiple neuroimaging studies have shown that brain activity during periods of rest and sleep is shaped by prior learning experience[7,8]. We examined memory processing related to learning a complex spatial navigation task Previous work with this task suggested an important role for N2 sleep[18]. In combination with evidence that memory reactivation is strongest immediately following experience, we hypothesized that experience-dependent changes in resting state brain activity would be largest during quiet rest and N2 sleep immediately following training on the task. We hypothesized and confirmed that task-related EEG topographies (microstates) recur during subsequent periods of quiet rest and sleep
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