Memory reactivation during rapid eye movement sleep promotes its generalization and integration in cortical stores.
; Schmidt, Christina ; et al
in Sleep (2014), 37(6), 1061-751075-1075
STUDY OBJECTIVES: Memory reactivation appears to be a fundamental process in memory consolidation. In this study we tested the influence of memory reactivation during rapid eye movement (REM) sleep on ... [more ▼]
STUDY OBJECTIVES: Memory reactivation appears to be a fundamental process in memory consolidation. In this study we tested the influence of memory reactivation during rapid eye movement (REM) sleep on memory performance and brain responses at retrieval in healthy human participants. PARTICIPANTS: Fifty-six healthy subjects (28 women and 28 men, age [mean +/- standard deviation]: 21.6 +/- 2.2 y) participated in this functional magnetic resonance imaging (fMRI) study. METHODS AND RESULTS: Auditory cues were associated with pictures of faces during their encoding. These memory cues delivered during REM sleep enhanced subsequent accurate recollections but also false recognitions. These results suggest that reactivated memories interacted with semantically related representations, and induced new creative associations, which subsequently reduced the distinction between new and previously encoded exemplars. Cues had no effect if presented during stage 2 sleep, or if they were not associated with faces during encoding. Functional magnetic resonance imaging revealed that following exposure to conditioned cues during REM sleep, responses to faces during retrieval were enhanced both in a visual area and in a cortical region of multisensory (auditory-visual) convergence. CONCLUSIONS: These results show that reactivating memories during REM sleep enhances cortical responses during retrieval, suggesting the integration of recent memories within cortical circuits, favoring the generalization and schematization of the information. [less ▲]Detailed reference viewed: 124 (12 ULg)
The impact of visual perceptual learning on sleep and local slow wave initiation
Mascetti, Laura ; Muto, Vincenzo ; et al
in Journal of Neuroscience (2013)Detailed reference viewed: 77 (28 ULg)
Working memory load modulates time-of-day and chronotype effects on task-related BOLD activity. Abstract Book of the conference.
Schmidt, Christina ; Peigneux, Philippe ; et al
Conference (2010, June)Detailed reference viewed: 6 (0 ULg)
FASST - fMRI Artefact rejection and Sleep Scoring Toolbox
Phillips, Christophe ; Schrouff, Jessica ; Coppieters't Wallant, Dorothe et al
"FASST" stands for "fMRI Artefact rejection and Sleep Scoring Toolbox". This M/EEG toolbox is developed by researchers from the Cyclotron Research Centre, University of Li ege, Belgium, with the financial ... [more ▼]
"FASST" stands for "fMRI Artefact rejection and Sleep Scoring Toolbox". This M/EEG toolbox is developed by researchers from the Cyclotron Research Centre, University of Li ege, Belgium, with the financial support of the Fonds de la Recherche Scienti que-FNRS, the Queen Elizabeth's funding, and the University of Li ege. On Dr. Pierre Maquet's impulse we started writing these tools to analyze our sleep EEG-fMRI data and tackle four crucial issues: * Continuous M/EEG. Long multi-channel recording of M/EEG data can be enormous. These data are cumbersome to handle as it usually involves displaying, exploring, comparing, chunking, appending data sets, etc. * EEG-fMRI. When recording EEG and fMRI data simultaneously, the EEG signal acquired contains, on top of the usual neural and ocular activity, artefacts induced by the gradient switching and high static eld of an MR scanner. The rejection of theses artefacts is not easy especially when dealing with brain spontaneous activity. * Scoring M/EEG. Reviewing and scoring continuous M/EEG recordings, such as is common with sleep recordings, is a tedious task as the scorer has to manually browse through the entire data set and give a \score" to each time-window displayed. * Waves detection. Continuous and triggerless recordings of M/EEG data show specifi c wave patterns, characteristic of the subject's state (e.g., sleep spindles or slow waves). Their automatic detection is thus important to assess those states. [less ▲]Detailed reference viewed: 38 (9 ULg)