Rapid eye movement activity before spontaneous awakening in elderly subjects

Ficca, Gianluca; Scavelli, S.; Fagioli, Igino; Gori, Sara; Murri, Luigi; Salzarulo, Piero

doi:10.1046/j.1365-2869.2003.00376.x

Cited by 14 publications

(7 citation statements)

References 17 publications

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“…The alterations of sleep evoked by noise are non-specific and rather diffuse. Similar alterations occur with increasing age as reported by several authors [11,15,16]. Thus it was plausible to assume a significant correlation between age and the SDI.…”

Section: Validitysupporting

confidence: 88%

Development of a sleep disturbance index (SDI) for the assessment of noise-induced sleep disturbances

et al. 2008

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Development of a sleep disturbance index (SDI) for the assessment of noise-induced sleep disturbances Entwicklung eines Schlafstörungs-indexes zur Bewertung lärmbedingter Schlafstörungen Zusammenfassung Ziel dieser Arbeit war die Entwicklung eines Schlafstörungs-Index (SDI, Sleep Disturbance Index), der eine zuverlässige Bewertung physiologischer Schlafstörungen durch Umwelteinwirkungen, wie etwa Lärm ermöglicht. Sieben Schlafparameter, die sich häufig unter nächtlicher Lärmeinwirkung ändern, wurden aus den in einer Ruhenacht im Labor registrierten Polysomnogrammen von 38 Män-nern und 28 Frauen (Referenzgruppe, 19-34 Jahre) abgeleitet und einer Hauptkomponentenanalyse unterworfen. Die Scores der ersten Hauptkomponente, die 35 % der Varianz erklärt, wurden zur Berechnung des SDI genutzt. Die für je eine Ruhenacht von 82 in zwei weiteren Untersuchungen beobachteten Probanden der gleichen Altersgruppe bestimmten SDI-Werte entsprachen im Mittel dem der Referenzgruppe, was für die Reliabilität des Indexes spricht. Die Validität zeigte sich in signifikant höheren Index-Werten für Nächte mit Lärmeinwirkung (25 Männer, 25 Frauen, 19-28 Jahre) und für die erste Nacht im Labor (37 Männer, 25 Frauen, 19-34 Jahre). Darüber hinaus nahmen die bei 261 Personen im Alter vom 18 -68 Jahren für eine Ruhenacht errechneten SDI-Werte signifikant mit dem Lebensalter zu. Die Anwendung des SDI kann die Interpretation von umweltbedingten Schlafstörungen erleichtern und erhöht die Vergleichbarkeit von entsprechenden Untersuchungen. Schlüsselwörter physiologische Schlafqualität -lärmbedingte Schlafstörungen -Hauptkomponentenanalyse -Validität -ReliabilitäẗSummary This analysis was aimed at the development of a sleep disturbance index (SDI) which allows a reliable estimate of physiological sleep disturbances due to environmental influences such as noise. Seven variables, repeatedly reported to change during noise exposure, were derived from polysomnograms of a reference sample of 38 men and 28 women (19-34 yrs) as recorded under quiet conditions in the laboratory after at least one habituation night. The variables were then submitted to a principal component analysis. The scores of the first principal component that explained 35 % of the variance were used to calculate the SDI.Reliability was ascertained by application of the SDI to a quiet night for each of 82 persons of the same age range from two other laboratory studies where the index value did not differ from that of the reference sample. Validity was verified by significantly lower index values for quiet nights than for noisy nights of 50 persons (25 men, 25 women, 19-28 yrs) and for the first night in the laboratory of 62 persons (37 men, 25 women, 19-34 yrs). Moreover, the index value increased with increasing age of the 261 participants in two laboratory studies and a field study whose age varied between 18 and 68 years.The SDI may facilitate the interpretation of sleep disturbances caused by environmental influences and its application would improve the comparability of studies pe...

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Section: Validitysupporting

confidence: 88%

Development of a sleep disturbance index (SDI) for the assessment of noise-induced sleep disturbances

et al. 2008

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“…REM density was calculated by dividing the number of 1-s periods during REM sleep that contained REMs by the total number of 1-s REM sleep epochs (Ficca et al, 2004). Rapid eye movements were detected automatically as rapid changes in the EOG signal (> 0.8 mV/s) after movement artifact rejection.…”

Section: Methodsmentioning

confidence: 99%

No effect of odor-induced memory reactivation during REM sleep on declarative memory stability

Cordi

Diekelmann

Born

et al. 2014

Front. Syst. Neurosci.

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Memory reactivations in hippocampal brain areas are critically involved in memory consolidation processes during sleep. In particular, specific firing patterns of hippocampal place cells observed during learning are replayed during subsequent sleep and rest in rodents. In humans, experimentally inducing hippocampal memory reactivations during slow-wave sleep (but not during wakefulness) benefits consolidation and immediately stabilizes declarative memories against future interference. Importantly, spontaneous hippocampal replay activity can also be observed during rapid eye movement (REM) sleep and some authors have suggested that replay during REM sleep is related to processes of memory consolidation. However, the functional role of reactivations during REM sleep for memory stability is still unclear. Here, we reactivated memories during REM sleep and examined its consequences for the stability of declarative memories. After 3 h of early, slow-wave sleep (SWS) rich sleep, 16 healthy young adults learned a 2-D object location task in the presence of a contextual odor. During subsequent REM sleep, participants were either re-exposed to the odor or to an odorless vehicle, in a counterbalanced within subject design. Reactivation was followed by an interference learning task to probe memory stability after awakening. We show that odor-induced memory reactivation during REM sleep does not stabilize memories against future interference. We propose that the beneficial effect of reactivation during sleep on memory stability might be critically linked to processes characterizing SWS including, e.g., slow oscillatory activity, sleep spindles, or low cholinergic tone, which are required for a successful redistribution of memories from medial temporal lobe regions to neocortical long-term stores.

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“…Average REM density was calculated by dividing the number of 1-sec periods during REM sleep that contained REMs by the total number of 1-sec REM sleep epochs. 25 REMs during REM sleep were detected automatically and were defined as rapid signal changes in the electrooculography channel (> 0.8 mV/s) after movement artefact rejection and application of a 50-msec moving average. Automatic detection was restricted to REM sleep episodes without artefacts.…”

Section: Rem Analysismentioning

confidence: 99%