Metacognitive reflections on one's current state of mind are largely absent during dreaming. Lucid dreaming as the exception to this rule is a rare phenomenon; however, its occurrence can be facilitated through cognitive training. A central idea of respective training strategies is to regularly question one's phenomenal experience: is the currently experienced world real , or just a dream? Here, we tested if such lucid dreaming training can be enhanced with dream-like virtual reality (VR): over the course of four weeks, volunteers underwent lucid dreaming training in VR scenarios comprising dream-like elements, classical lucid dreaming training or no training. We found that VR-assisted training led to significantly stronger increases in lucid dreaming compared to the no-training condition. Eye signal-verified lucid dreams during polysomnography supported behavioural results. We discuss the potential mechanisms underlying these findings, in particular the role of synthetic dream-like experiences, incorporation of VR content in dream imagery serving as memory cues, and extended dissociative effects of VR session on subsequent experiences that might amplify lucid dreaming training during wakefulness. This article is part of the theme issue ‘Offline perception: voluntary and spontaneous perceptual experiences without matching external stimulation'.
Citizen science allows the public to participate in various stages of scientific research, including study design, data acquisition, and analysis of the resulting data. Citizen science has a long history in several fields of the natural sciences, and with recent developments in technology, neuroscience has also become more accessible to citizen scientists. This development was largely driven by the development of minimal sensing systems for the consumer market, allowing for do-it-yourself (DIY) or quantified-self (QS) investigations of an individual's brain. While most subfields of neuroscience require sophisticated monitoring devices in the laboratory, the study of sleep characteristics has been widely embraced by citizen neuroscientists, likely due to the strong influence of sleep quality on waking life and an increasingly broad accessibility of relevant non-invasive consumer devices. Here, we introduce into the emerging field of citizen neuroscience, illustrating examples of citizen neuroscience projects in the field of sleep research. We then give an overview on wearable technologies for tracking human neurophysiology, and on open software to run them, each with unique capabilities and intended purposes. Finally, we discuss chances and challenges in citizen neuroscience research, and suggest how to improve studying the human brain outside the laboratory.
Background: Textbooks define the classical sleep cycle as an episode of non-rapid eye movement (non-REM) sleep followed by an episode of REM sleep; sometimes, a REM episode can be "skipped". While sleep cycles are considered fundamental components of sleep, their functional significance remains to a large extent unclear. One of the reasons for a lack of research progress in the field is the absence of a data-driven definition. Here, we propose to reset the scientific definition of sleep cycles on fractal (aperiodic) neural activity as a well-established marker of arousal and sleep stages, arguing that this will considerably advance the field. Methods: We used electroencephalography to compute fractal slopes and explore their temporal dynamics over the course of nocturnal sleep. We defined the "fractal cycle of sleep" as a time interval during which fractal slopes descend from their local maximum to their local minimum and then lead back to the next local maximum. Next, we assessed the correspondence between the "fractal" and "classical" sleep cycles, including "skipped" cycles. Finally, we explored fractal cycles in childhood and adolescence, a life period with ongoing sleep architecture changes, as well as in major depressive disorder, a clinical condition characterized by disturbed sleep architecture. Results: Timings of "fractal" and "classical" cycles coincided in 763/940 (81%) cases and their durations (89±34 min vs 90±25 min) correlated positively (r=0.5, p<0.001). The fractal cycle algorithm detected "skipped" cycles in 53/55 (96%) cases. In adults (range: 18-75 years, n=205), the "fractal" cycle duration and participant's age correlated negatively (r=-0.2, p=0.006). Children and adolescents (range: 8-17 years, n=21) had shorter "fractal" cycles compared to young adults (range: 23-25 years, n=24) (mean: 76±34 vs 94±32 min, p<0.001). 38 unmedicated patients with depression showed shorter "fractal" cycles compared to their own medicated state (92±38 min vs 107±51 min, p<0.001). 111 medicated patients showed longer "fractal" cycles compared to 109 matched controls (104±49 vs 88±31 min, p<0.001). Conclusions: We show that "fractal cycles" are an objective, quantifiable and biologically plausible way to display sleep neural activity and its cycles, able to provide additional information compared to hypnograms. Likewise, "fractal cycles" can be used to study the effect of antidepressants on sleep.
Sleep is an indispensable part of our life and plays a critical role in our physical and mental well-being. During sleep, despite the paucity of behavior, our brain stays active and exhibits a wide range of coupled brain oscillations. This activity in sleep-characteristic brain oscillations has been linked to various functions of sleep. However, whether these sleep oscillations mediate these functions or reflect mere epiphenomena is not yet fully understood. To disentangle the causality of these relationships, experiments utilizing non-invasive stimulation techniques have been essential. In particular, auditory stimulation aligned with neural activity in a closed-loop controlled fashion has drawn increasing attention during the last years due to its specificity and practical advantages. In this review, we summarize closed-loop auditory stimulation experiments that generated evidence to support a causal role of slow oscillations in information reprocessing functions of sleep. Furthermore, we provide technical details and guidelines regarding best practices in closed-loop auditory stimulation, from how to perform the stimulation to analysis strategies. Besides discussing important caveats and open questions, we also touch on various areas in which closed-loop auditory stimulation is applicable, from fundamental investigations on memory processing and endocrine function, to its potential for clinical applications. Eventually, we propose potential topics for future research.
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