The Dream Catcher experiment: blinded analyses failed to detect markers of dreaming consciousness in EEG spectral power

Taking consciousness for real: Increasing the ecological validity of the study of conscious vs. unconscious processes

The study of consciousness has developed well-controlled, rigorous methods for manipulating and measuring consciousness. Yet, in the process, experimental paradigms grew farther away from everyday conscious and unconscious processes, which raises the concern of ecological validity. In this review, we suggest that the field can benefit from adopting a more ecological approach, akin to other fields of cognitive science. There, this approach challenged some existing hypotheses, yielded stronger effects, and enabled new research questions. We argue that such a move is critical for studying consciousness, where experimental paradigms tend to be artificial and small effect sizes are relatively prevalent. We identify three paths for doing so-changing the stimuli and experimental settings, changing the measures, and changing the research questions themselves-and review works that have already started implementing such approaches. While acknowledging the inherent challenges, we call for increasing ecological validity in consciousness studies.

The neurocognition of dreaming: key questions and foci

Until recently, understanding the neurobiology of dreaming has relied upon on correlating a subjective dream report with a measure of brain activity or function sampled from a different occasion. As such, most assumptions about dreaming come from the neuroscience of rapid eye-movement (REM) sleep from which many, but not all, dream reports are recalled. Core features of REM sleep (intense emotional activation, a reduction in activity in most frontal regions, particularly the dorsolateral prefrontal cortex, along with increased dopamine, acetylcholine, cholinergic activation) align with typical dream characteristics (characterised by fear, reduced reality monitoring, increased bizarreness and hyperassociativity, respectively). The default mode network offers a way of understanding the nature of dreaming more independently from a REM sleep context, and electroencephalography methods paired with serial awakenings to elicit dream reports demonstrate how high-frequency activity in posterior regions may be associated with dreaming. Nevertheless, all measures of dreaming rely fundamentally on recall processes, so our understanding of dreaming must embrace and address memory's crucial involvement in dream report production.

EEG Lempel-Ziv complexity varies with sleep stage, but does not seem to track dream experience

In a recent electroencephalography (EEG) sleep study inspired by complexity theories of consciousness, we found that multi-channel signal diversity progressively decreased from wakefulness to slow wave sleep, but failed to find any significant difference between dreaming and non-dreaming awakenings within the same sleep stage (NREM2). However, we did find that multi-channel Lempel-Ziv complexity (LZC) measured over the posterior cortex increased with more perceptual ratings of NREM2 dream experience along a thought-perceptual axis. In this follow-up study, we re-tested our previous findings, using a slightly different approach. Partial sleep-deprivation was followed by evening sleep experiments, with repeated awakenings and immediate dream reports. Participants reported whether they had been dreaming, and were asked to rate how diverse, vivid, perceptual, and thought-like the contents of their dreams were. High density (64 channel) EEG was recorded throughout the experiment, and mean single-channel LZC was calculated for each 30 s sleep epoch. LZC progressively decreased with depth of non-REM sleep. Surprisingly, estimated marginal mean LZC was slightly higher for NREM1 than for wakefulness, but the difference did not remain significant after adjusting for multiple comparisons. We found no significant difference in LZC between dream and non-dream awakenings, nor any significant relationship between LZC and subjective ratings of dream experience, within the same sleep stage (NREM2). The failure to reproduce our own previous finding of a positive correlation between posterior LZC and more perceptual dream experiences, or to find any other correlation between brain signal complexity and subjective experience within NREM2 sleep, raises the question of whether EEG LZC is really a reliable correlate of richness of experience as such, within the same sleep stage.

Theories of consciousness

Recent years have seen a blossoming of theories about the biological and physical basis of consciousness. Good theories guide empirical research, allowing us to interpret data, develop new experimental techniques and expand our capacity to manipulate the phenomenon of interest. Indeed, it is only when couched in terms of a theory that empirical discoveries can ultimately deliver a satisfying understanding of a phenomenon. However, in the case of consciousness, it is unclear how current theories relate to each other, or whether they can be empirically distinguished. To clarify this complicated landscape, we review four prominent theoretical approaches to consciousness: higher-order theories, global workspace theories, re-entry and predictive processing theories and integrated information theory. We describe the key characteristics of each approach by identifying which aspects of consciousness they propose to explain, what their neurobiological commitments are and what empirical data are adduced in their support. We consider how some prominent empirical debates might distinguish among these theories, and we outline three ways in which theories need to be developed to deliver a mature regimen of theory-testing in the neuroscience of consciousness. There are good reasons to think that the iterative development, testing and comparison of theories of consciousness will lead to a deeper understanding of this most profound of mysteries.

The vision of dreams: from ontogeny to dream engineering in blindness

The mechanisms involved in the origin of dreams remain one of the great unknowns in science. In the 21st century, studies in the field have focused on 3 main topics: functional networks that underlie dreaming, neural correlates of dream contents, and signal propagation. We review neuroscientific studies about dreaming processes, focusing on their cortical correlations. The involvement of frontoparietal regions in the dream-retrieval process allows us to discuss it in light of the Global Workspace theory of consciousness. However, dreaming in distinct sleep stages maintains relevant differences, suggesting that multiple generators are implicated. Then, given the strong influence of light perception on sleep regulation and the mostly visual content of dreams, we investigate the effect of blindness on the organization of dreams. Blind individuals represent a worthwhile population to clarify the role of perceptual systems in dream generation, and to make inferences about their top-down and/or bottom-up origin. Indeed, congenitally blind people maintain the ability to produce visual dreams, suggesting that bottom-up mechanisms could be associated with innate body schemes or multisensory integration processes. Finally, we propose the new dream-engineering technique as a tool to clarify the mechanisms of multisensory integration during sleep and related mental activity, presenting possible implications for rehabilitation in sensory-impaired individuals. The Theory of Proto-consciousness suggests that the interaction of brain states underlying waking and dreaming ensures the optimal functioning of both. Therefore, understanding the origin of dreams and capabilities of our brain during a dreamlike state, we could introduce it as a rehabilitative tool.

CITATION

Vitali H, Campus C, De Giorgis V, Signorini S, Gori M. The vision of dreams: from ontogeny to dream engineering in blindness. J Clin Sleep Med. 2022;18(8):2051-2062.

What about dreams? State of the art and open questions

Several studies have tried to identify the neurobiological bases of dream experiences, nevertheless some questions are still at the centre of the debate. Here, we summarise the main open issues concerning the neuroscientific study of dreaming. After overcoming the rapid eye movement (REM) ‐ non‐REM (NREM) sleep dichotomy, investigations have focussed on the specific functional or structural brain features predicting dream experience. On the one hand, some results underlined that specific trait‐like factors are associated with higher dream recall frequency. On the other hand, the electrophysiological milieu preceding dream report upon awakening is a crucial state‐like factor influencing the subsequent recall. Furthermore, dreaming is strictly related to waking experiences. Based on the continuity hypothesis, some findings reveal that dreaming could be modulated through visual, olfactory, or somatosensory stimulations. Also, it should be considered that the indirect access to dreaming remains an intrinsic limitation. Recent findings have revealed a greater concordance between parasomnia‐like events and dream contents. This means that parasomnia episodes might be an expression of the ongoing mental sleep activity and could represent a viable direct access to dream experience. Finally, we provide a picture on nightmares and emphasise the possible role of oneiric activity in psychotherapy. Overall, further efforts in dream science are needed (a) to develop a uniform protocol to study dream experience, (b) to introduce and integrate advanced techniques to better understand whether dreaming can be manipulated, (c) to clarify the relationship between parasomnia events and dreaming, and (d) to determine the clinical valence of dreams.

EEG Signal Diversity Varies With Sleep Stage and Aspects of Dream Experience

Several theories link consciousness to complex cortical dynamics, as suggested by comparison of brain signal diversity between conscious states and states where consciousness is lost or reduced. In particular, Lempel-Ziv complexity, amplitude coalition entropy and synchrony coalition entropy distinguish wakefulness and REM sleep from deep sleep and anesthesia, and are elevated in psychedelic states, reported to increase the range and vividness of conscious contents. Some studies have even found correlations between complexity measures and facets of self-reported experience. As suggested by integrated information theory and the entropic brain hypothesis, measures of differentiation and signal diversity may therefore be measurable correlates of consciousness and phenomenological richness. Inspired by these ideas, we tested three hypotheses about EEG signal diversity related to sleep and dreaming. First, diversity should decrease with successively deeper stages of non-REM sleep. Second, signal diversity within the same sleep stage should be higher for periods of dreaming vs. non-dreaming. Third, specific aspects of dream contents should correlate with signal diversity in corresponding cortical regions. We employed a repeated awakening paradigm in sleep deprived healthy volunteers, with immediate dream report and rating of dream content along a thought-perceptual axis, from exclusively thought-like to exclusively perceptual. Generalized linear mixed models were used to assess how signal diversity varied with sleep stage, dreaming and thought-perceptual rating. Signal diversity decreased with sleep depth, but was not significantly different between dreaming and non-dreaming, even though there was a significant positive correlation between Lempel-Ziv complexity of EEG recorded over the posterior cortex and thought-perceptual ratings of dream contents.

Consciousness among delta waves: a paradox?

A common observation in EEG research is that consciousness vanishes with the appearance of delta (1 - 4 Hz) waves, particularly when those waves are high amplitude. High amplitude delta oscillations are very frequently observed in states of diminished consciousness, including slow wave sleep, anaesthesia, generalised epileptic seizures, and disorders of consciousness such as coma and vegetative state. This strong correlation between loss of consciousness and high amplitude delta oscillations is thought to stem from the widespread cortical deactivation that occurs during the "down states" or troughs of these slow oscillations. Recently, however, many studies have reported the presence of prominent delta activity during conscious states, which casts doubt on the hypothesis that high amplitude delta oscillations are an indicator of unconsciousness. These studies include work in Angelman syndrome, epilepsy, behavioural responsiveness during propofol anaesthesia, postoperative delirium, and states of dissociation from the environment such as dreaming and powerful psychedelic states. The foregoing studies complement an older, yet largely unacknowledged, body of literature that has documented awake, conscious patients with high amplitude delta oscillations in clinical reports from Rett syndrome, Lennox-Gastaut syndrome, schizophrenia, mitochondrial diseases, hepatic encephalopathy, and nonconvulsive status epilepticus. At the same time, a largely parallel body of recent work has reported convincing evidence that the complexity or entropy of EEG and magnetoencephalogram or MEG signals strongly relates to an individual's level of consciousness. Having reviewed this literature, we discuss plausible mechanisms that would resolve the seeming contradiction between high amplitude delta oscillations and consciousness. We also consider implications concerning theories of consciousness, such as integrated information theory and the entropic brain hypothesis. Finally, we conclude that false inferences of unconscious states can be best avoided by examining measures of electrophysiological complexity in addition to spectral power.

Investigation on Neurobiological Mechanisms of Dreaming in the New Decade

Dream research has advanced significantly over the last twenty years, thanks to the new applications of neuroimaging and electrophysiological techniques. Many findings pointed out that mental activity during sleep and wakefulness shared similar neural bases. On the other side, recent studies have highlighted that dream experience is promoted by significant brain activation, characterized by reduced low frequencies and increased rapid frequencies. Additionally, several studies confirmed that the posterior parietal area and prefrontal cortex are responsible for dream experience. Further, early results revealed that dreaming might be manipulated by sensory stimulations that would provoke the incorporation of specific cues into the dream scenario. Recently, transcranial stimulation techniques have been applied to modulate the level of consciousness during sleep, supporting previous findings and adding new information about neural correlates of dream recall. Overall, although multiple studies suggest that both the continuity and activation hypotheses provide a growing understanding of neural processes underlying dreaming, several issues are still unsolved. The impact of state-/trait-like variables, the influence of circadian and homeostatic factors, and the examination of parasomnia-like events to access dream contents are all opened issues deserving further deepening in future research.

How deep is the rift between conscious states in sleep and wakefulness? Spontaneous experience over the sleep-wake cycle

Whether we are awake or asleep is believed to mark a sharp divide between the types of conscious states we undergo in either behavioural state. Consciousness in sleep is often equated with dreaming and thought to be characteristically different from waking consciousness. Conversely, recent research shows that we spend a substantial amount of our waking lives mind wandering, or lost in spontaneous thoughts. Dreaming has been described as intensified mind wandering, suggesting that there is a continuum of spontaneous experience that reaches from waking into sleep. This challenges how we conceive of the behavioural states of sleep and wakefulness in relation to conscious states. I propose a conceptual framework that distinguishes different subtypes of spontaneous thoughts and experiences independently of their occurrence in sleep or waking. I apply this framework to selected findings from dream and mind-wandering research. I argue that to assess the relationship between spontaneous thoughts and experiences and the behavioural states of sleep and wakefulness, we need to look beyond dreams to consider kinds of sleep-related experience that qualify as dreamless. I conclude that if we consider the entire range of spontaneous thoughts and experiences, there appears to be variation in subtypes both within as well as across behavioural states. Whether we are sleeping or waking does not appear to strongly constrain which subtypes of spontaneous thoughts and experiences we undergo in those states. This challenges the conventional and coarse-grained distinction between sleep and waking and their putative relation to conscious states. This article is part of the theme issue 'Offline perception: voluntary and spontaneous perceptual experiences without matching external stimulation'.

Evidence accumulation clustering using combinations of features

Evidence accumulation clustering (EAC) is an ensemble clustering algorithm that can cluster data for arbitrary shapes and numbers of clusters. Here, we present a variant of EAC in which we aimed to better cluster data with a large number of features, many of which may be uninformative. Our new method builds on the existing EAC algorithm by populating the clustering ensemble with clusterings based on combinations of fewer features than the original dataset at a time. Our method also calls for prewhitening the recombined data and weighting the influence of each individual clustering by an estimate of its informativeness. We provide code of an example implementation of the algorithm in Matlab and demonstrate its effectiveness compared to ordinary evidence accumulation clustering with synthetic data.•The clustering ensemble is made by clustering on subset combinations of features from the data•The recombined data may be prewhitened•Evidence accumulation can be improved by weighting the evidence with a goodness-of-clustering measure.