Cerebral CortexNeurosciencePeer-reviewed journal article
Sleep leads to system-wide neural changes independent of allo- and egocentric spatial training in humans and rats
Anumita SamantaiD, Laurens S van Rongen, Janine I. RossatoiD, Justin JacobseiD, Robby Schoenfeld, Lisa GenzeliD
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- 13
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- top 21%
Abstract
Sleep is important for memory consolidation, especially the process of systems consolidation should occur during sleep. While a significant amount of research has been done in regards to the effect of sleep on behavior and certain mechanisms during sleep, until now evidence is lacking that sleep leads to consolidation across the system. Here, we investigated the role of sleep in consolidation of spatial memory in the watermaze in both rats and humans using allocentric and egocentric based training. Combining behavior with immediate early gene expression analysis in rodents and functional MR imaging in humans, elucidated similar behavioral and neural effects in both species. Rats and humans showed a benefit of sleep on behavior. Interestingly, sleep led to systems-wide retrieval network in both species in both training conditions. Thus, we provide cross-species evidence for memory consolidation on the system-level occurring during sleep. Significance StatementProcesses occurring during sleep such as memory reactivations are proposed to lead to consolidation from the initial hippocampal memory representation to long-lasting cortical representations, this is known as systems consolidation. By combining behavioral measurements in the watermaze with immediate early gene expression analysis in rats and function magnetic resonance imaging in humans, we could show a benefit of sleep on behavioral memory performance. And, sleep lead to systems-wide changes in the retrieval network. These results are the first direct evidence supporting the role of sleep for systems-wide memory consolidation in both rats and humans.
- organism: rat
- organism: human
- assay: bulk RNA-seq
- assay: imaging
- research focus: differential expression
- research focus: cell type annotation
- approach: statistical model
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