Dissociated resting-state functional networks between the dream recall frequency and REM sleep percentage

The route to recall a dream: theoretical considerations and methodological implications

The goal of this paper is to shed new light on the relation between dream recall and dream experiences by providing a thorough analysis of the process that leads to dream reports. Three crucial steps of this process will be distinguished: dream production (the generation of a conscious experience during sleep), dream encoding (storing a trace of this experience in memory) and dream retrieval (accessing the memory trace upon awakening). The first part of the paper will assess how major theories think about the relationship between dream reports and these distinct steps. The second part will systematise how trait and state factors affecting dream recall-given different theoretical assumptions-might interact with dream production, encoding and retrieval. Understanding how the distinct steps of dream recall can be modulated by different factors is crucial for getting a better grip on how to acquire information about these steps empirically and for drawing methodological conclusions with regard to the tools dream research relies on to collect subjective data about dream experiences. The third part of the paper will analyse how laboratory reports, logs and retrospective scales interact with the different factors that affect the distinct steps leading to dream reports and will argue that prospective methods provide more direct access to data regarding dream production and encoding than retrospective methods, which-due to their inability to provide systematic control over the factors affecting the retrieval stage-screen-off the variability in the production and the encoding of dreams.

EEG microstates are correlated with brain functional networks during slow-wave sleep

Electroencephalography (EEG) microstates have been extensively studied in wakefulness and have been described as the "atoms of thought". Previous studies of EEG have found four microstates, i.e., microstates A, B, C and D, that are consistent among participants across the lifespan during the resting state. Studies using simultaneous EEG and functional magnetic resonance imaging (fMRI) have provided evidence for correlations between EEG microstates and fMRI networks during the resting state. Microstates have also been found during non-rapid eye movement (NREM) sleep. Slow-wave sleep (SWS) is considered the most restorative sleep stage and has been associated with the maintenance of sleep. However, the relationship between EEG microstates and brain functional networks during SWS has not yet been investigated. In this study, simultaneous EEG-fMRI data were collected during SWS to test the correspondence between EEG microstates and fMRI networks. EEG microstate-informed fMRI analysis revealed that three out of the four microstates showed significant correlations with fMRI data: 1) fMRI fluctuations in the insula and posterior temporal gyrus positively correlated with microstate B, 2) fMRI signals in the middle temporal gyrus and fusiform gyrus negatively correlated with microstate C, and 3) fMRI fluctuations in the occipital lobe negatively correlated with microstate D, while fMRI signals in the anterior cingulate and cingulate gyrus positively correlated with this microstate. Functional brain networks were then assessed using group independent component analysis based on the fMRI data. The group-level spatial correlation analysis showed that the fMRI auditory network overlapped the fMRI activation map of microstate B, the executive control network overlapped the fMRI deactivation of microstate C, and the visual and salience networks overlapped the fMRI deactivation and activation maps of microstate D. In addition, the subject-level spatial correlations between the general linear model (GLM) beta map of each microstate and the individual maps of each component yielded by dual regression also showed that EEG microstates were closely associated with brain functional networks measured using fMRI during SWS. Overall, the results showed that EEG microstates were closely related to brain functional networks during SWS, which suggested that EEG microstates provide an important electrophysiological basis underlying brain functional networks.

SWS Brain-Wave Music May Improve the Quality of Sleep: An EEG Study

Aim

This study investigated the neural mechanisms of brain-wave music on sleep quality.

Background

Sleep disorders are a common health problem in our society and may result in fatigue, depression, and problems in daytime functioning. Previous studies have shown that brain-wave music generated from electroencephalography (EEG) signals could emotionally affect our nervous system and have positive effects on sleep. However, the neural mechanisms of brain-wave music on the quality of sleep need to be clarified.

Methods

A total of 33 young participants were recruited and randomly divided into three groups. The participants listened to rapid eye movement (REM) brain-wave music (Group 1: 13 subjects), slow-wave sleep (SWS) brain-wave music (Group 2: 11 subjects), or white noise (WN) (Control Group: 9 subjects) for 20 min before bedtime for 6 days. EEG and other physiological signals were recorded by polysomnography.

Results

We found that the sleep efficiency increased in the SWS group but decreased in REM and WN groups. The sleep efficiency in the SWS group was ameliorated [t(10) = −1.943, p = 0.076]. In the EEG power spectral density analysis, the delta power spectral density in the REM group and in the control group increased, while that in the SWS group decreased [F(2,31) = 7.909, p = 0.005]. In the network analysis, the functional connectivity (FC), assessed with Pearson correlation coefficients, showed that the connectivity strength decreased [t(10) = 1.969, p = 0.073] between the left frontal lobe (F3) and left parietal lobe (C3) in the SWS group. In addition, there was a negative correlation between the FC of the left frontal lobe and the left parietal lobe and sleep latency in the SWS group (r = −0.527, p = 0.064).

Conclusion

Slow-wave sleep brain-wave music may have a positive effect on sleep quality, while REM brain-wave music or WN may not have a positive effect. Furthermore, better sleep quality might be caused by a decrease in the power spectral density of the delta band of EEG and an increase in the FC between the left frontal lobe and the left parietal lobe. SWS brain-wave music could be a safe and inexpensive method for clinical use if confirmed by more data.

Detecting resting-state brain activity using OEF-weighted imaging

Traditional resting-state functional magnetic resonance imaging (fMRI) is mainly based on the blood oxygenation level-dependent (BOLD) contrast. The oxygen extraction fraction (OEF) represents an important parameter of brain metabolism and is a key biomarker of tissue viability, detecting the ratio of oxygen utilization to oxygen delivery. Investigating spontaneous fluctuations in the OEF-weighted signal is crucial for understanding the underlying mechanism of brain activity because of the immense energy budget during the resting state. However, due to the poor temporal resolution of OEF mapping, no studies have reported using OEF contrast to assess resting-state brain activity. In this fMRI study, we recorded brain OEF-weighted fluctuations for 10 min in healthy volunteers across two scanning visits, using our recently developed pulse sequence that can acquire whole-brain voxel-wise OEF-weighted signals with a temporal resolution of 3 s. Using both group-independent component analysis and seed-based functional connectivity analysis, we robustly identified intrinsic brain networks, including the medial visual, lateral visual, auditory, default mode and bilateral executive control networks, using OEF contrast. Furthermore, we investigated the resting-state local characteristics of brain activity based on OEF-weighted signals using regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuations (fALFF). We demonstrated that the gray matter regions of the brain, especially those in the default mode network, showed higher ReHo and fALFF values with the OEF contrast. Moreover, voxel-wise test-retest reliability comparisons across the whole brain demonstrated that the reliability of resting-state brain activity based on the OEF contrast was moderate for the network indices and high for the local activity indices, especially for ReHo. Although the reliabilities of the OEF-based indices were generally lower than those based on BOLD, the reliability of OEF-ReHo was slightly higher than that of BOLD-ReHo, with a small effect size, which indicated that OEF-ReHo could be used as a reliable index for characterizing resting-state local brain activity as a complement to BOLD. In conclusion, OEF can be used as an effective contrast to study resting-state brain activity with a medium to high test-retest reliability.

Brain structural basis of individual variability in dream recall frequency

Recent neuroimaging studies have indicated that inter-individual variability in dream recall frequency (DRF) is associated with both resting-state regional cerebral blood flow and task-induced brain activations. However, the brain structure underpinning this inter-individual variability in DRF remains unclear. The aim of the current study is to investigate the relationship between brain structural characteristics and DRF. We collected both T1-weighted and diffusion tensor magnetic resonance imaging data from 43 healthy volunteers. DRF was obtained from a two-week sleep diary with a subjective report of dream recall upon waking every morning. General linear model analysis was used to evaluate the relationship between brain structural characteristics (cortical volume and white matter integrity) and DRF. Not only the cortical volume of the medial portion of the right fusiform gyrus and parahippocampal gyrus but also the fractional anisotropy of white matter fibers connected to these regions were significantly negatively correlated with DRF, and these relationships were not modulated by a regular sleep. These findings provide direct evidence that brain structural characteristics are associated with inter-individual variability in DRF and may help us to better understand the structural mechanisms in the brain underlying dream recall.