Resting brain activity varies with dream recall frequency between subjects

Beyond the neuropsychology of dreaming: Insights into the neural basis of dreaming with new techniques of sleep recording and analysis

Recent advances in electrophysiological [e.g., surface high-density electroencephalographic (hd-EEG) and intracranial recordings], video-polysomnography (video-PSG), transcranial stimulation and neuroimaging techniques allow more in-depth and more accurate investigation of the neural correlates of dreaming in healthy individuals and in patients with brain-damage, neurodegenerative diseases, sleep disorders or parasomnias. Convergent evidence provided by studies using these techniques in healthy subjects has led to a reformulation of several unresolved issues of dream generation and recall [such as the inter- and intra-individual differences in dream recall and the predictivity of specific EEG rhythms, such as theta in rapid eye movement (REM) sleep, for dream recall] within more comprehensive models of human consciousness and its variations across sleep/wake states than the traditional models, which were largely based on the neurophysiology of REM sleep in animals. These studies are casting new light on the neural bases (in particular, the activity of dorsal medial prefrontal cortex regions and hippocampus and amygdala areas) of the inter- and intra-individual differences in dream recall, the temporal location of specific contents or properties (e.g., lucidity) of dream experience and the processing of memories accessed during sleep and incorporated into dream content. Hd-EEG techniques, used on their own or in combination with neuroimaging, appear able to provide further important insights into how the brain generates not only dreaming during sleep but also some dreamlike experiences in waking.

Dopaminergic system and dream recall: An MRI study in Parkinson's disease patients

We investigated the role of the dopamine system [i.e., subcortical-medial prefrontal cortex (mPFC) network] in dreaming, by studying patients with Parkinson's Disease (PD) as a model of altered dopaminergic transmission. Subcortical volumes and cortical thickness were extracted by 3T-MR images of 27 PD patients and 27 age-matched controls, who were asked to fill out a dream diary upon morning awakening for one week. PD patients do not substantially differ from healthy controls with respect to the sleep, dream, and neuroanatomical measures. Multivariate correlational analyses in PD patients show that dopamine agonist dosage is associated to qualitatively impoverished dreams, as expressed by lower bizarreness and lower emotional load values. Visual vividness (VV) of their dream reports positively correlates with volumes of both the amygdalae and with thickness of the left mPFC. Emotional load also positively correlates with hippocampal volume. Beside the replication of our previous finding on the role of subcortical nuclei in dreaming experience of healthy subjects, this represents the first evidence of a specific role of the amygdala-mPFC dopaminergic network system in dream recall. The association in PD patients between higher dopamine agonist dosages and impoverished dream reports, however, and the significant correlations between VV and mesolimbic regions, however, provide an empirical support to the hypothesis that a dopamine network plays a key role in dream generation. The causal relation is however precluded by the intrinsic limitation of assuming the dopamine agonist dosage as a measure of the hypodopaminergic state in PD. Periodicals, Inc.

Sleep spindle and K-complex detection using tunable Q-factor wavelet transform and morphological component analysis

A novel framework for joint detection of sleep spindles and K-complex events, two hallmarks of sleep stage S2, is proposed. Sleep electroencephalography (EEG) signals are split into oscillatory (spindles) and transient (K-complex) components. This decomposition is conveniently achieved by applying morphological component analysis (MCA) to a sparse representation of EEG segments obtained by the recently introduced discrete tunable Q-factor wavelet transform (TQWT). Tuning the Q-factor provides a convenient and elegant tool to naturally decompose the signal into an oscillatory and a transient component. The actual detection step relies on thresholding (i) the transient component to reveal K-complexes and (ii) the time-frequency representation of the oscillatory component to identify sleep spindles. Optimal thresholds are derived from ROC-like curves (sensitivity vs. FDR) on training sets and the performance of the method is assessed on test data sets. We assessed the performance of our method using full-night sleep EEG data we collected from 14 participants. In comparison to visual scoring (Expert 1), the proposed method detected spindles with a sensitivity of 83.18% and false discovery rate (FDR) of 39%, while K-complexes were detected with a sensitivity of 81.57% and an FDR of 29.54%. Similar performances were obtained when using a second expert as benchmark. In addition, when the TQWT and MCA steps were excluded from the pipeline the detection sensitivities dropped down to 70% for spindles and to 76.97% for K-complexes, while the FDR rose up to 43.62 and 49.09%, respectively. Finally, we also evaluated the performance of the proposed method on a set of publicly available sleep EEG recordings. Overall, the results we obtained suggest that the TQWT-MCA method may be a valuable alternative to existing spindle and K-complex detection methods. Paths for improvements and further validations with large-scale standard open-access benchmarking data sets are discussed.

State- or trait-like individual differences in dream recall: preliminary findings from a within-subjects study of multiple nap REM sleep awakenings

We examined the question whether the role of EEG oscillations in predicting presence/absence of dream recall (DR) is explained by “state-” or “trait-like” factors. Six healthy subjects were awakened from REM sleep in a within-subjects design with multiple naps, until a recall and a non-recall condition were obtained. Naps were scheduled in the early afternoon and were separated by 1 week. Topographical EEG data of the 5-min of REM sleep preceding each awakening were analyzed by power spectral analysis [Fast Fourier Transform (FFT)] and by a method to detect oscillatory activity [Better OSCillations (BOSC)]. Both analyses show that REC is associated to higher frontal theta activity (5–7 Hz) and theta oscillations (6.06 Hz) compared to NREC condition, but only the second comparison reached significance. Our pilot study provides support to the notion that sleep and wakefulness share similar EEG correlates of encoding in episodic memories, and supports the “state-like hypothesis”: DR may depend on the physiological state related to the sleep stage from which the subject is awakened rather than on a stable individual EEG pattern.

EEG oscillations during sleep and dream recall: state- or trait-like individual differences?

Dreaming represents a peculiar form of cognitive activity during sleep. On the basis of the well-known relationship between sleep and memory, there has been a growing interest in the predictive role of human brain activity during sleep on dream recall. Neuroimaging studies indicate that rapid eye movement (REM) sleep is characterized by limbic activation and prefrontal cortex deactivation. This pattern could explain the presence of emotional contents in dream reports. Furthermore, the morphoanatomical measures of amygdala and hippocampus predict some features of dream contents (bizarreness, vividness, and emotional load). More relevant for a general view of dreaming mechanisms, empirical data from neuropsychological and electroencephalographic (EEG) studies support the hypothesis that there is a sort of continuity between the neurophysiological mechanisms of encoding and retrieval of episodic memories across sleep and wakefulness. A notable overlap between the electrophysiological mechanisms underlying emotional memory formation and some peculiar EEG features of REM sleep has been suggested. In particular, theta (5-8 Hz) EEG oscillations on frontal regions in the pre-awakening sleep are predictive of dream recall, which parallels the predictive relation during wakefulness between theta activity and successful retrieval of episodic memory. Although some observations support an interpretation more in terms of an intraindividual than interindividual mechanism, the existing empirical evidence still precludes from definitely disentangling if this relation is explained by state- or trait-like differences.

Metacognitive mechanisms underlying lucid dreaming

Lucid dreaming is a state of awareness that one is dreaming, without leaving the sleep state. Dream reports show that self-reflection and volitional control are more pronounced in lucid compared with nonlucid dreams. Mostly on these grounds, lucid dreaming has been associated with metacognition. However, the link to lucid dreaming at the neural level has not yet been explored. We sought for relationships between the neural correlates of lucid dreaming and thought monitoring. Human participants completed a questionnaire assessing lucid dreaming ability, and underwent structural and functional MRI. We split participants based on their reported dream lucidity. Participants in the high-lucidity group showed greater gray matter volume in the frontopolar cortex (BA9/10) compared with those in the low-lucidity group. Further, differences in brain structure were mirrored by differences in brain function. The BA9/10 regions identified through structural analyses showed increases in blood oxygen level-dependent signal during thought monitoring in both groups, and more strongly in the high-lucidity group. Our results reveal shared neural systems between lucid dreaming and metacognitive function, in particular in the domain of thought monitoring. This finding contributes to our understanding of the mechanisms enabling higher-order consciousness in dreams.