Electrophysiological Correlates of Dream Recall During REM Sleep: Evidence from Multiple Awakenings and Within-Subjects Design

快速眼动睡眠期间梦境回忆的电生理相关性:来自多次觉醒和受试者内设计的证据

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Abstract

PURPOSE: In the current study, we aimed to investigate the EEG correlates of dream recall (DR) monitoring both the homeostatic and state-trait like factors. We assessed the influence of the time of night on the EEG correlates of DR from REM sleep. Specifically, we tested the continuity-hypothesis (on the theta EEG band) and the activation-hypothesis (on the delta and beta bands). METHODS: Twenty-seven subjects underwent polysomnography with multiple provoked awakenings during REM sleep. Only the subjects showing combinations of dream recall (REC) and non-REC (NREC) conditions in both first (1st-2nd sleep cycle) and second (3rd-4th sleep cycle) part of the night were included in the analyses. The final sample was composed of 10 subjects (mean age 24±0.70). EEG power spectra of the 5-min of REM sleep preceding each awakening were computed by a fast Fourier transform. The following frequency bands were considered: delta (0.50-4.75 Hz), theta (5.00-7.75 Hz), and beta (16.00-24.75 Hz). We also calculated the delta/beta power ratio as an integrated EEG index of activation. RESULTS: The 2×2 within-subjects ANOVA recall × time revealed: a) no significant effect for time and no interaction; b) significant differences over the occipital area in the beta band; c) significant differences over the parietal area for the activation index values. Overall, the results indicated that DR is associated with higher activation regardless of homeostatic pressure across the night of sleep. CONCLUSION: In line with recent findings, we have shown that DR is predicted by desynchronized EEG activity during REM sleep, providing clear evidence in favor of the activation-hypothesis. We have also confirmed that the EEG pattern of DR can be ascribed to state-like factors. Further studies should assess whether homeostatic modulation may interact with some dream features and the related EEG predictors.

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