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EEG Signatures of Offline Memory Reactivation and Consolidation


Biography

Overview
States of rest and sleep are critical for optimal human cognitive function. Even during sleep, when sensory input and conscious awareness are minimal, the brain continues to process information from the previous day. Research in animals demonstrates that patterns of neural activity first seen during waking exploration of an environment are later reactivated during quiet rest and sleep. In humans, sleep consolidates and enhances previously formed memories. In parallel with this sleep-dependent memory processing, memories of recent experiences contribute to the images, thoughts, and narratives that we commonly call dreams. Thought to be a critical component of long-term memory formation, post-learning reactivation of memories in humans has not been adequately studied, and is still poorly understood. The research proposed here would provide the first EEG-based characterization of offline memory reactivation in humans, analyzing global spatial patterns of the EEG signal recorded during encoding of a virtual navigation task, and during post-training rest and sleep. In parallel, the sampling of ongoing subjective experience provides a measure of memory reactivation at the cognitive level. Using the technique of microstate analysis, our preliminary findings suggest that learning- related EEG patterns reappear during subsequent sleep, and that the extent of this reactivation predicts post- sleep task improvement. If successful, the proposed research would confirm that offline reactivation underlies memory consolidation in humans, as expressed in both electrophysiological and cognitive measures. In its focus on describing memory consolidation across multiple levels of analysis (integrating measures of behavior, electrophysiology, and conscious experience), this project is strongly in line with the proposed sleep/wake construct in NIMH's new Research Domain Criteria initiative (RDoC).
R21MH098171
STICKGOLD, ROBERT

Time
2013-09-05
2016-07-31
Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.