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Raphael Vallat, Başak Türker, Alain Nicolas, Perrine Ruby Lyon Neuroscience Research Center (CRNL), INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, F-69000, FranceCorrespondence: Raphael Vallat, Department of Psychology, University of California, 2121 Berkeley Way, Berkeley, CA, 94720-1650, USA, Tel +510-642-5292, Email raphaelvallat@Berkeley.eduIntroduction: Several results suggest that the frequency of dream recall is positively correlated with personality traits such as creativity and openness to experience. In addition, neuroimaging results have evidenced different neurophysiological profiles in high dream recallers (HR) and low dream recallers (LR) during both sleep and wakefulness, specifically within regions of the default mode network (DMN). These findings are consistent with the emerging view that dreaming and mind wandering pertain to the same family of spontaneous mental processes, subserved by the DMN.Methods: To further test this hypothesis, we measured the DMN functional connectivity during resting wakefulness, together with personality and cognitive abilities (including creativity) in 28 HR and 27 LR.Results: As expected, HR demonstrated a greater DMN connectivity than LR, higher scores of creativity, and no significant difference in memory abilities. However, there was no significant correlation between creativity scores and DMN connectivity.Discussion: These results further demonstrate that there are trait neurophysiological and psychological differences between individuals who frequently recall their dreams and those who do not. They support the forebrain and the DMN hypotheses of dreaming and leave open the possibility that increased activity in the DMN promotes creative-thinking during both wakefulness and sleep. Further work is needed to test whether activity in the DMN is causally associated with creative-thinking.Keywords: dream recall, creativity, resting state, functional connectivity, default mode network

Insufficient sleep impairs glucose regulation, increasing the risk of diabetes. However, what it is about the human sleeping brain that regulates blood sugar remains unknown. In an examination of over 600 humans, we demonstrate that the coupling of non-rapid eye movement (NREM) sleep spindles and slow oscillations the night before is associated with improved next-day peripheral glucose control. We further show that this sleep-associated glucose pathway may influence glycemic status through altered insulin sensitivity, rather than through altered pancreatic beta cell function. Moreover, we replicate these associations in an independent dataset of over 1,900 adults. Of therapeutic significance, the coupling between slow oscillations and spindles was the most significant sleep predictor of next-day fasting glucose, even more so than traditional sleep markers, relevant to the possibility of an electroencephalogram (EEG) index of hyperglycemia. Taken together, these findings describe a sleeping-brain-body framework of optimal human glucose homeostasis, offering a potential prognostic sleep signature of glycemic control., Competing Interests: Declaration of interests M.P.W. serves as a consultant for and has equity interest in Bryte, The Sleepfoundation.org, Oura, and StimScience., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Alzheimer's disease (AD) pathology impairs cognitive function. Yet some individuals with high amounts of AD pathology suffer marked memory impairment, while others with the same degree of pathology burden show little impairment. Why is this? One proposed explanation is cognitive reserve i.e., factors that confer resilience against, or compensation for the effects of AD pathology. Deep NREM slow wave sleep (SWS) is recognized to enhance functions of learning and memory in healthy older adults. However, that the quality of NREM SWS (NREM slow wave activity, SWA) represents a novel cognitive reserve factor in older adults with AD pathology, thereby providing compensation against memory dysfunction otherwise caused by high AD pathology burden, remains unknown., Methods: Here, we tested this hypothesis in cognitively normal older adults (N = 62) by combining 11 C-PiB (Pittsburgh compound B) positron emission tomography (PET) scanning for the quantification of β-amyloid (Aβ) with sleep electroencephalography (EEG) recordings to quantify NREM SWA and a hippocampal-dependent face-name learning task., Results: We demonstrated that NREM SWA significantly moderates the effect of Aβ status on memory function. Specifically, NREM SWA selectively supported superior memory function in individuals suffering high Aβ burden, i.e., those most in need of cognitive reserve (B = 2.694, p = 0.019). In contrast, those without significant Aβ pathological burden, and thus without the same  need for cognitive reserve, did not similarly benefit from the presence of NREM SWA (B = -0.115, p = 0.876). This interaction between NREM SWA and Aβ status predicting memory function was significant after correcting for age, sex, Body Mass Index, gray matter atrophy, and previously identified cognitive reserve factors, such as education and physical activity (p = 0.042)., Conclusions: These findings indicate that NREM SWA is a novel cognitive reserve factor providing resilience against the memory impairment otherwise caused by high AD pathology burden. Furthermore, this cognitive reserve function of NREM SWA remained significant when accounting both for covariates, and factors previously linked to resilience, suggesting that sleep might be an independent cognitive reserve resource. Beyond such mechanistic insights are potential therapeutic implications. Unlike many other cognitive reserve factors (e.g., years of education, prior job complexity), sleep is a modifiable factor. As such, it represents an intervention possibility that may aid the preservation of cognitive function in the face of AD pathology, both present moment and longitudinally., (© 2023. The Author(s).)

Objective: Poor sleep is associated with hypertension, a major risk factor for cardiovascular disease. However, the mechanism(s) through which sleep loss affects cardiovascular health remains largely unknown, including the brain and body systems that regulate vascular function., Methods: Sixty-six healthy adults participated in a repeated-measures, crossover, experimental study involving assessments of cardiovascular function and brain connectivity after a night of sleep and a night of sleep deprivation., Results: First, sleep deprivation significantly increased blood pressure-both systolic and diastolic. Interestingly, this change was independent of any increase in heart rate, inferring a vasculature-specific rather than direct cardiac pathway. Second, sleep loss compromised functional brain connectivity within the vascular control network, specifically the insula, anterior cingulate, amygdala, and ventral and medial prefrontal cortices. Third, sleep loss-related changes in brain connectivity and vascular tone were not independent, but significantly interdependent, with changes within the vascular control brain network predicting the sleep-loss shift toward hypertension., Conclusions: These findings establish an embodied framework in which sleep loss confers increased risk of cardiovascular disease through an impact upon central brain control of vascular tone, rather than a direct impact on accelerated heart rate itself., (Copyright © 2022 by the American Psychosomatic Society.)

How people wake up and regain alertness in the hours after sleep is related to how they are sleeping, eating, and exercising. Here, in a prospective longitudinal study of 833 twins and genetically unrelated adults, we demonstrate that how effectively an individual awakens in the hours following sleep is not associated with their genetics, but instead, four independent factors: sleep quantity/quality the night before, physical activity the day prior, a breakfast rich in carbohydrate, and a lower blood glucose response following breakfast. Furthermore, an individual's set-point of daily alertness is related to the quality of their sleep, their positive emotional state, and their age. Together, these findings reveal a set of non-genetic (i.e., not fixed) factors associated with daily alertness that are modifiable., (© 2022. The Author(s).)

Humans help each other. This fundamental feature of homo sapiens has been one of the most powerful forces sculpting the advent of modern civilizations. But what determines whether humans choose to help one another? Across 3 replicating studies, here, we demonstrate that sleep loss represents one previously unrecognized factor dictating whether humans choose to help each other, observed at 3 different scales (within individuals, across individuals, and across societies). First, at an individual level, 1 night of sleep loss triggers the withdrawal of help from one individual to another. Moreover, fMRI findings revealed that the withdrawal of human helping is associated with deactivation of key nodes within the social cognition brain network that facilitates prosociality. Second, at a group level, ecological night-to-night reductions in sleep across several nights predict corresponding next-day reductions in the choice to help others during day-to-day interactions. Third, at a large-scale national level, we demonstrate that 1 h of lost sleep opportunity, inflicted by the transition to Daylight Saving Time, reduces real-world altruistic helping through the act of donation giving, established through the analysis of over 3 million charitable donations. Therefore, inadequate sleep represents a significant influential force determining whether humans choose to help one another, observable across micro- and macroscopic levels of civilized interaction. The implications of this effect may be non-trivial when considering the essentiality of human helping in the maintenance of cooperative, civil society, combined with the reported decline in sufficient sleep in many first-world nations., Competing Interests: The authors have declared that no competing interests exist.

Event-related potentials (ERPs) associated with the involuntary orientation of (bottom-up) attention toward an unexpected sound are of larger amplitude in high dream recallers (HR) than in low dream recallers (LR) during passive listening, suggesting different attentional functioning. We measured bottom-up and top-down attentional performance and their cerebral correlates in 18 HR (11 women, age = 22.7 years, dream recall frequency = 5.3 days with a dream recall per week) and 19 LR (10 women, age = 22.3, DRF = 0.2) using EEG and the Competitive Attention Task. Between-group differences were found in ERPs but not in behavior. The results show that HR present larger ERPs to distracting sounds than LR even during active listening, arguing for enhanced bottom-up processing of irrelevant sounds. HR also presented larger contingent negative variation during target expectancy and P3b to target sounds than LR, speaking for an enhanced recruitment of top-down attention. The attentional balance seems preserved in HR since their performances are not altered, but possibly at a higher resource cost. In HR, increased bottom-up processes would favor dream recall through awakening facilitation during sleep and enhanced top-down processes may foster dream recall through increased awareness and/or short-term memory stability of dream content., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Aims/hypothesis: Sleep, diet and exercise are fundamental to metabolic homeostasis. In this secondary analysis of a repeated measures, nutritional intervention study, we tested whether an individual's sleep quality, duration and timing impact glycaemic response to a breakfast meal the following morning., Methods: Healthy adults' data (N = 953 [41% twins]) were analysed from the PREDICT dietary intervention trial. Participants consumed isoenergetic standardised meals over 2 weeks in the clinic and at home. Actigraphy was used to assess sleep variables (duration, efficiency, timing) and continuous glucose monitors were used to measure glycaemic variation (>8000 meals)., Results: Sleep variables were significantly associated with postprandial glycaemic control (2 h incremental AUC), at both between- and within-person levels. Sleep period time interacted with meal type, with a smaller effect of poor sleep on postprandial blood glucose levels when high-carbohydrate (low fat/protein) (p interaction  = 0.02) and high-fat (p interaction  = 0.03) breakfasts were consumed compared with a reference 75 g OGTT. Within-person sleep period time had a similar interaction (high carbohydrate: p interaction  = 0.001, high fat: p interaction  = 0.02). Within- and between-person sleep efficiency were significantly associated with lower postprandial blood glucose levels irrespective of meal type (both p < 0.03). Later sleep midpoint (time deviation from midnight) was found to be significantly associated with higher postprandial glucose, in both between-person and within-person comparisons (p = 0.035 and p = 0.051, respectively)., Conclusions/interpretation: Poor sleep efficiency and later bedtime routines are associated with more pronounced postprandial glycaemic responses to breakfast the following morning. A person's deviation from their usual sleep pattern was also associated with poorer postprandial glycaemic control. These findings underscore sleep as a modifiable, non-pharmacological therapeutic target for the optimal regulation of human metabolic health. Trial registration ClinicalTrials.gov NCT03479866., (© 2021. The Author(s).)

The clinical and societal measurement of human sleep has increased exponentially in recent years. However, unlike other fields of medical analysis that have become highly automated, basic and clinical sleep research still relies on human visual scoring. Such human-based evaluations are time-consuming, tedious, and can be prone to subjective bias. Here, we describe a novel algorithm trained and validated on +30,000 hr of polysomnographic sleep recordings across heterogeneous populations around the world. This tool offers high sleep-staging accuracy that matches human scoring accuracy and interscorer agreement no matter the population kind. The software is designed to be especially easy to use, computationally low-demanding, open source, and free. Our hope is that this software facilitates the broad adoption of an industry-standard automated sleep staging software package., Competing Interests: RV, MW No competing interests declared, (© 2021, Vallat and Walker.)

Why do some individuals recall dreams every day while others hardly ever recall one? We hypothesized that sleep inertia-the transient period following awakening associated with brain and cognitive alterations-could be a key mechanism to explain interindividual differences in dream recall at awakening. To test this hypothesis, we measured the brain functional connectivity (combined electroencephalography-functional magnetic resonance imaging) and cognition (memory and mental calculation) of high dream recallers (HR, n = 20) and low dream recallers (LR, n = 18) in the minutes following awakening from an early-afternoon nap. Resting-state scans were acquired just after or before a 2 min mental calculation task, before the nap, 5 min after awakening from the nap, and 25 min after awakening. A comic was presented to the participants before the nap with no explicit instructions to memorize it. Dream(s) and comic recall were collected after the first post-awakening scan. As expected, between-group contrasts of the functional connectivity at 5 min post-awakening revealed a pattern of enhanced connectivity in HR within the default mode network (DMN) and between regions of the DMN and regions involved in memory processes. At the behavioral level, a between-group difference was observed in dream recall, but not comic recall. Our results provide the first evidence that brain functional connectivity right after awakening is associated with interindividual trait differences in dream recall and suggest that the brain connectivity of HR at awakening facilitates the maintenance of the short-term memory of the dream during the sleep-wake transition., (© Sleep Research Society 2020. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.)

Why does poor-quality sleep lead to atherosclerosis? In a diverse sample of over 1,600 individuals, we describe a pathway wherein sleep fragmentation raises inflammatory-related white blood cell counts (neutrophils and monocytes), thereby increasing atherosclerosis severity, even when other common risk factors have been accounted for. Improving sleep quality may thus represent one preventive strategy for lowering inflammatory status and thus atherosclerosis risk, reinforcing public health policies focused on sleep health., Competing Interests: The authors have declared that no competing interests exist.

Are you feeling emotionally fragile, moody, unpredictable, even ungenerous to those around you? Here, we review how and why these phenomena can occur as a result of insufficient sleep. Sleep loss disrupts a broad spectrum of affective processes, from basic emotional operations (e.g., recognition, responsivity, expression), through to high-order, complex socio-emotional functioning (e.g., loneliness, helping behavior, abusive behavior, and charisma). Translational insights further emerge regarding the pervasive link between sleep disturbance and psychiatric conditions, including anxiety, depression, and suicidality. More generally, such findings raise concerns regarding society's mental (ill)health and the prevalence of insufficient and disrupted sleep., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

We present Visbrain, a Python open-source package that offers a comprehensive visualization suite for neuroimaging and electrophysiological brain data. Visbrain consists of two levels of abstraction: (1) objects which represent highly configurable neuro-oriented visual primitives (3D brain, sources connectivity, etc.) and (2) graphical user interfaces for higher level interactions. The object level offers flexible and modular tools to produce and automate the production of figures using an approach similar to that of Matplotlib with subplots. The second level visually connects these objects by controlling properties and interactions through graphical interfaces. The current release of Visbrain (version 0.4.2) contains 14 different objects and three responsive graphical user interfaces, built with PyQt: Signal , for the inspection of time-series and spectral properties, Brain for any type of visualization involving a 3D brain and Sleep for polysomnographic data visualization and sleep analysis. Each module has been developed in tight collaboration with end-users, i.e., primarily neuroscientists and domain experts, who bring their experience to make Visbrain as transparent as possible to the recording modalities (e.g., intracranial EEG, scalp-EEG, MEG, anatomical and functional MRI). Visbrain is developed on top of VisPy, a Python package providing high-performance 2D and 3D visualization by leveraging the computational power of the graphics card. Visbrain is available on Github and comes with a documentation, examples, and datasets (http://visbrain.org).

The first minutes following awakening from sleep are typically marked by reduced vigilance, increased sleepiness and impaired performance, a state referred to as sleep inertia. Although the behavioral aspects of sleep inertia are well documented, its cerebral correlates remain poorly understood. The present study aimed at filling this gap by measuring in 34 participants the changes in behavioral performance (descending subtraction task, DST), EEG spectral power, and resting-state fMRI functional connectivity across three time points: before an early-afternoon 45-min nap, 5 min after awakening from the nap and 25 min after awakening. Our results showed impaired performance at the DST at awakening and an intrusion of sleep-specific features (spectral power and functional connectivity) into wakefulness brain activity, the intensity of which was dependent on the prior sleep duration and depth for the functional connectivity (14 participants awakened from N2 sleep, 20 from N3 sleep). Awakening in N3 (deep) sleep induced the most robust changes and was characterized by a global loss of brain functional segregation between task-positive (dorsal attention, salience, sensorimotor) and task-negative (default mode) networks. Significant correlations were observed notably between the EEG delta power and the functional connectivity between the default and dorsal attention networks, as well as between the percentage of mistake at the DST and the default network functional connectivity. These results highlight (1) significant correlations between EEG and fMRI functional connectivity measures, (2) significant correlations between the behavioral aspect of sleep inertia and measures of the cerebral functioning at awakening (both EEG and fMRI), and (3) the important difference in the cerebral underpinnings of sleep inertia at awakening from N2 and N3 sleep., (Copyright © 2018 Elsevier Inc. All rights reserved.)

There is a lack of up-to-date data on sleep and dream habits of college students. To fill in this gap, we used an online questionnaire sent to the student mailing lists of two major universities of Lyon (Lyon 1 and Lyon 2) for the recruitment of an functional magnetic resonance imaging study with sleep disorders as exclusion criteria. In the sample (1,137 French college students, 411 males, mean age = 22.2 ± 2.4 years, body mass index = 22.0 ± 3.2 kg m -2 ), on average, the participants reported spending about 8 hr in bed during weekdays, 9 hr during the weekends, and 90.9% of them reported no difficulty falling asleep. Less than 0.4% of students reported to have sleep-walking episodes regularly, but nearly 7% reported regular sleep-talking episodes. The average dream recall frequency was about 3 mornings per week with a dream in mind. Dream recall frequency was positively correlated with the clarity of dream content and the frequency of lucid dreaming, and was negatively correlated with age. Fourteen percent of the students reported frequent lucid dreams, and 6% reported frequent recurrent dreams. We found a gender effect for several sleep and dream parameters, including dream recall frequency and time in bed, both of which were higher in women than in men. We have also observed differences between academic disciplines, namely humanities students (Lyon 2) reported spending more time in bed than sciences students (Lyon 1). These results confirm a gender difference for several sleep and dream parameters, and suggest a link between academic disciplines and sleep duration., (© 2018 European Sleep Research Society.)

Recent findings indicate that dream recall frequency (DRF) is associated with neurophysiological traits, and notably the regional cerebral blood flow at rest within the medial prefrontal cortex (MPFC) and the temporo-parietal junction (TPJ). To test whether, such physiological traits are rooted in anatomical specificities, we used voxel-based morphometry to compare the white matter and gray matter density in regions related to dream recall (either at the experimental or theoretical level, MPFC, TPJ, hippocampus and amygdala) between 46 high dream recallers (HR, DRF = 5.98 ± 1.25 days per week with a dream report) and 46 low dream recallers (LR, DRF = 0.34 ± 0.29). We found an increased medial prefrontal cortex white-matter density in HR compared to LR but no other significant difference between the two groups. These results are consistent with previous studies showing that lesions within the white matter of medial prefrontal cortex are associated with a partial or total cessation of dream reporting and suggest an implication of this region in dream recall or, more likely, in dream production.

[This corrects the article DOI: 10.1371/journal.pone.0185262.].

Several studies have demonstrated that dream content is related to the waking life of the dreamer. However, the characteristics of the memory sources incorporated into dreams are still unclear. We designed a new protocol to investigate remote memories and memories of trivial experiences, both relatively unexplored in dream content until now. Upon awakening, for 7 days, participants identified the waking life elements (WLEs) related to their dream content and characterized them and their dream content on several scales to assess notably emotional valence. Thanks to this procedure, they could report WLEs from the whole lifespan, and mundane ones before they had been forgotten. Participants (N = 40, 14 males, age = 25.2 ± 7.6) reported 6.2 ± 2.0 dreams on average. For each participant, 83.4% ± 17.8 of the dream reports were related to one or more WLEs. Among all the WLEs incorporated into dreams dated by the participants (79.3 ± 19%), 40.2 ± 30% happened the day before the dream, 26.1 ± 26% the month before (the day before excluded), 15.8 ± 21% the year before the dream (the month before excluded), and 17.9 ± 24% happened more than one year before the dream. As could be expected from previous studies, the majority of the WLEs incorporated into dreams were scored as important by the dreamers. However, this was not true for incorporated WLEs dating from the day before the dream. In agreement with Freud's observations, the majority of the day residues were scored as mundane. Finally, for both positive and negative WLEs incorporated into dreams, the dreamt version of the WLE was rated as emotionally less intense than the original WLE. This result, showing that dreams tend to attenuate the emotional tone of waking-life memories towards a more neutral one, argues in favor of the emotional regulation hypothesis of dreaming.

We introduce Sleep, a new Python open-source graphical user interface (GUI) dedicated to visualization, scoring and analyses of sleep data. Among its most prominent features are: (1) Dynamic display of polysomnographic data, spectrogram, hypnogram and topographic maps with several customizable parameters, (2) Implementation of several automatic detection of sleep features such as spindles, K-complexes, slow waves, and rapid eye movements (REM), (3) Implementation of practical signal processing tools such as re-referencing or filtering, and (4) Display of main descriptive statistics including publication-ready tables and figures. The software package supports loading and reading raw EEG data from standard file formats such as European Data Format, in addition to a range of commercial data formats. Most importantly, Sleep is built on top of the VisPy library, which provides GPU-based fast and high-level visualization. As a result, it is capable of efficiently handling and displaying large sleep datasets. Sleep is freely available (http://visbrain.org/sleep) and comes with sample datasets and an extensive documentation. Novel functionalities will continue to be added and open-science community efforts are expected to enhance the capacities of this module.

High dream recallers (HR) show a larger brain reactivity to auditory stimuli during wakefulness and sleep as compared to low dream recallers (LR) and also more intra-sleep wakefulness (ISW), but no other modification of the sleep macrostructure. To further understand the possible causal link between brain responses, ISW and dream recall, we investigated the sleep microstructure of HR and LR, and tested whether the amplitude of auditory evoked potentials (AEPs) was predictive of arousing reactions during sleep. Participants (18 HR, 18 LR) were presented with sounds during a whole night of sleep in the lab and polysomnographic data were recorded. Sleep microstructure (arousals, rapid eye movements (REMs), muscle twitches (MTs), spindles, KCs) was assessed using visual, semi-automatic and automatic validated methods. AEPs to arousing (awakenings or arousals) and non-arousing stimuli were subsequently computed. No between-group difference in the microstructure of sleep was found. In N2 sleep, auditory arousing stimuli elicited a larger parieto-occipital positivity and an increased late frontal negativity as compared to non-arousing stimuli. As compared to LR, HR showed more arousing stimuli and more long awakenings, regardless of the sleep stage but did not show more numerous or longer arousals. These results suggest that the amplitude of the brain response to stimuli during sleep determine subsequent awakening and that awakening duration (and not arousal) is the critical parameter for dream recall. Notably, our results led us to propose that the minimum necessary duration of an awakening during sleep for a successful encoding of dreams into long-term memory is approximately 2 min.

A new heterodinuclear [Zn(L)Dy](3+) complex, with L being a compartmental Schiff base ligand, exhibits the characteristic Dy(3+) luminescence associated with a single-ion field induced slow relaxation of the magnetisation. This complex may be considered as one of the rare examples of a bifunctional luminescent single-ion magnet.