Sleep habits and their relation to self-reported attention and class climate in preteens.

OBJECTIVES/DESIGN: Sleep is fundamental in daily functioning, especially in teenagers who are in a critical period of their development. Accordingly, a deteriorated sleep, that is increasingly common in this age group, has been related to poorer school performance. The goal of this cross-sectional study was to collect up-to-date sleep data in preteens, and to examine their relation with two important dimensions for school achievement, which are self-reported attention and class climate at school. METHODS: Data were collected at school in 1151 preteens (597 boys; 554 girls; 11.31 ± 0.62 years old) between June 2021 and March 2022. Self-completion questionnaires evaluated attention and class climate. Sleep questionnaires assessed sleep habits for weekdays and weekends, separately. RESULTS: Preteens reported sleeping 8 h and 39 min during weekdays and 9 h and 32 min during weekends. All sleep measures showed a significant change between weekdays and weekends, leading to a longer and better sleep on weekends, likely to compensate for insufficient sleep during the week. In addition, girls woke-up later and spent more time in bed than boys during weekends. Importantly, during weekdays, correlational analysis showed a relationship between sleep measures and both self-reported attention and class climate scores, suggesting that longer and better sleep was related to better attention and perceived class climate at school. CONCLUSION: This study depicted up-to-date sleep habits in preteens, depending on the day of the week and the gender, and highlighted their relation to two potential contributors of academic success, namely attention and class climate.

Reactivation of Motor-Related Gamma Activity in Human NREM Sleep.

Models of memory consolidation posit a central role for reactivation of brain activity patterns during sleep, especially in non-Rapid Eye Movement (NREM) sleep. While such "replay" of recent waking experiences has been well-demonstrated in rodents, electrophysiological evidence of reactivation in human sleep is still largely lacking. In this intracranial study in patients with epilepsy (N = 9) we explored the spontaneous electroencephalographic reactivation during sleep of spatial patterns of brain activity evoked by motor learning. We first extracted the gamma-band (60-140 Hz) patterns underlying finger movements during a tapping task and underlying no-movement during a short rest period just prior to the task, and trained a binary classifier to discriminate between motor movements vs. rest. We then used the trained model on NREM sleep data immediately after the task and on NREM sleep during a control sleep period preceding the task. Compared with the control sleep period, we found, at the subject level, an increase in the detection rate of motor-related patterns during sleep following the task, but without association with performance changes. These data provide electrophysiological support for the reoccurrence in NREM sleep of the neural activity related to previous waking experience, i.e. that a basic tenet of the reactivation theory does occur in human sleep.

Replay of Learned Neural Firing Sequences during Rest in Human Motor Cortex.

The offline "replay" of neural firing patterns underlying waking experience, previously observed in non-human animals, is thought to be a mechanism for memory consolidation. Here, we test for replay in the human brain by recording spiking activity from the motor cortex of two participants who had intracortical microelectrode arrays placed chronically as part of a brain-computer interface pilot clinical trial. Participants took a nap before and after playing a neurally controlled sequence-copying game that consists of many repetitions of one "repeated" sequence sparsely interleaved with varying "control" sequences. Both participants performed repeated sequences more accurately than control sequences, consistent with learning. We compare the firing rate patterns that caused the cursor movements when performing each sequence to firing rate patterns throughout both rest periods. Correlations with repeated sequences increase more from pre- to post-task rest than do correlations with control sequences, providing direct evidence of learning-related replay in the human brain.

The nature of delayed dream incorporation ('dream-lag effect'): Personally significant events persist, but not major daily activities or concerns.

Incorporation of details from waking life events into rapid eye movement (REM) sleep dreams has been found to be highest on the 2 nights after, and then 5-7 nights after, the event. These are termed, respectively, the day-residue and dream-lag effects. This study is the first to categorize types of waking life experiences and compare their incorporation into dreams across multiple successive nights. Thirty-eight participants completed a daily diary each evening and a dream diary each morning for 14 days. In the daily diary, three categories of experiences were reported: major daily activities (MDAs), personally significant events (PSEs) and major concerns (MCs). After the 14-day period each participant identified the correspondence between items in their daily diaries and subsequent dream reports. The day-residue and dream-lag effects were found for the incorporation of PSEs into dreams (effect sizes of .33 and .27, respectively), but only for participants (n = 19) who had a below-median total number of correspondences between daily diary items and dream reports (termed "low-incorporators" as opposed to "high-incorporators"). Neither the day-residue or dream-lag effects were found for MDAs or MCs. This U-shaped timescale of incorporation of events from daily life into dreams has been proposed to reflect REM sleep-dependent memory consolidation, possibly related to emotional memory processing. This study had a larger sample size of dreams than any dream-lag study hitherto with trained participants. Coupled with previous successful replications, there is thus substantial evidence supporting the dream-lag effect and further explorations of its mechanism, including its neural underpinnings, are warranted.

Dream Recall Frequency Is Associated With Medial Prefrontal Cortex White-Matter Density.

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.

Ictal and preictal power changes outside of the seizure focus correlate with seizure generalization.

OBJECTIVE: The treatment of focal epilepsies is largely predicated on the concept that there is a "focus" from which the seizure emanates. Yet, the physiological context that determines if and how ictal activity starts and propagates remains poorly understood. To delineate these phenomena more completely, we studied activity outside the seizure-onset zone prior to and during seizure initiation. METHODS: Stereotactic depth electrodes were implanted in 17 patients with longstanding pharmacoresistant epilepsy for lateralization and localization of the seizure-onset zone. Only seizures with focal onset in mesial temporal structures were used for analysis. Spectral analyses were used to quantify changes in delta, theta, alpha, beta, gamma, and high gamma frequency power, in regions inside and outside the area of seizure onset during both preictal and seizure initiation periods. RESULTS: In the 78 seizures examined, an average of 9.26% of the electrode contacts outside of the seizure focus demonstrated changes in power at seizure onset. Of interest, seizures that were secondarily generalized, on average, showed power changes in a greater number of extrafocus electrode contacts at seizure onset (16.7%) compared to seizures that remained focal (3.8%). The majority of these extrafocus changes occupied the delta and theta bands in electrodes placed in the ipsilateral, lateral temporal lobe. Preictally, we observed extrafocal high-frequency power decrements, which also correlated with seizure spread. SIGNIFICANCE: This widespread activity at and prior to the seizure-onset time further extends the notion of the ictogenic focus and its relationship to seizure spread. Further understanding of these extrafocus, periictal changes might help identify the neuronal dynamics underlying the initiation of seizures and how therapies can be devised to control seizure activity.

Incorporation of recent waking-life experiences in dreams correlates with frontal theta activity in REM sleep.

Rapid eye movement (REM) sleep and its main oscillatory feature, frontal theta, have been related to the processing of recent emotional memories. As memories constitute much of the source material for our dreams, we explored the link between REM frontal theta and the memory sources of dreaming, so as to elucidate the brain activities behind the formation of dream content. Twenty participants were woken for dream reports in REM and slow wave sleep (SWS) while monitored using electroencephalography. Eighteen participants reported at least one REM dream and 14 at least one SWS dream, and they, and independent judges, subsequently compared their dream reports with log records of their previous daily experiences. The number of references to recent waking-life experiences in REM dreams was positively correlated with frontal theta activity in the REM sleep period. No such correlation was observed for older memories, nor for SWS dreams. The emotional intensity of recent waking-life experiences incorporated into dreams was higher than the emotional intensity of experiences that were not incorporated. These results suggest that the formation of wakefulness-related dream content is associated with REM theta activity, and accords with theories that dreaming reflects emotional memory processing taking place in REM sleep.

Daydreams incorporate recent waking life concerns but do not show delayed ('dream-lag') incorporations.

This study investigates the time course of incorporation of waking life experiences into daydreams. Thirty-one participants kept a diary for 10 days, reporting major daily activities (MDAs), personally significant events (PSEs) and major concerns (MCs). They were then cued for daydream, Rapid Eye Movement (REM) and N2 dream reports in the sleep laboratory. There was a higher incorporation into daydreams of MCs from the previous two days (day-residue effect), but no day-residue effect for MDAs or PSEs, supporting a function for daydreams of processing current concerns. A day-residue effect for PSEs and the delayed incorporation of PSEs from 5 to 7 days before the dream (the dream-lag effect) have previously been found for REM dreams. Delayed incorporation was not found in this study for daydreams. Daydreams might thus differ in function from REM sleep dreams. However, the REM dream-lag effect was not replicated here, possibly due to design differences from previous studies.

Sleep: An Open-Source Python Software for Visualization, Analysis, and Staging of Sleep Data.

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.

Increased Evoked Potentials to Arousing Auditory Stimuli during Sleep: Implication for the Understanding of Dream Recall.

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.

Meet Spinky: An Open-Source Spindle and K-Complex Detection Toolbox Validated on the Open-Access Montreal Archive of Sleep Studies (MASS).

Sleep spindles and K-complexes are among the most prominent micro-events observed in electroencephalographic (EEG) recordings during sleep. These EEG microstructures are thought to be hallmarks of sleep-related cognitive processes. Although tedious and time-consuming, their identification and quantification is important for sleep studies in both healthy subjects and patients with sleep disorders. Therefore, procedures for automatic detection of spindles and K-complexes could provide valuable assistance to researchers and clinicians in the field. Recently, we proposed a framework for joint spindle and K-complex detection (Lajnef et al., 2015a) based on a Tunable Q-factor Wavelet Transform (TQWT; Selesnick, 2011a) and morphological component analysis (MCA). Using a wide range of performance metrics, the present article provides critical validation and benchmarking of the proposed approach by applying it to open-access EEG data from the Montreal Archive of Sleep Studies (MASS; O'Reilly et al., 2014). Importantly, the obtained scores were compared to alternative methods that were previously tested on the same database. With respect to spindle detection, our method achieved higher performance than most of the alternative methods. This was corroborated with statistic tests that took into account both sensitivity and precision (i.e., Matthew's coefficient of correlation (MCC), F1, Cohen κ). Our proposed method has been made available to the community via an open-source tool named Spinky (for spindle and K-complex detection). Thanks to a GUI implementation and access to Matlab and Python resources, Spinky is expected to contribute to an open-science approach that will enhance replicability and reliable comparisons of classifier performances for the detection of sleep EEG microstructure in both healthy and patient populations.

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.

Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.

BACKGROUND: Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are promising, there is need for improvement, especially given the time-consuming and tedious nature of visual sleep scoring. NEW METHOD: Here we propose a sleep staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. The performance of the method was evaluated using polysomnographic data from 15 subjects (electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) recordings). The decision tree, or dendrogram, was obtained using a hierarchical clustering technique and a wide range of time and frequency-domain features were extracted. Feature selection was carried out using forward sequential selection and classification was evaluated using k-fold cross-validation. RESULTS: The dendrogram-based SVM (DSVM) achieved mean specificity, sensitivity and overall accuracy of 0.92, 0.74 and 0.88 respectively, compared to expert visual scoring. Restricting DSVM classification to data where both experts' scoring was consistent (76.73% of the data) led to a mean specificity, sensitivity and overall accuracy of 0.94, 0.82 and 0.92 respectively. COMPARISON WITH EXISTING METHODS: The DSVM framework outperforms classification with more standard multi-class "one-against-all" SVM and linear-discriminant analysis. CONCLUSION: The promising results of the proposed methodology suggest that it may be a valuable alternative to existing automatic methods and that it could accelerate visual scoring by providing a robust starting hypnogram that can be further fine-tuned by expert inspection.

Resting brain activity varies with dream recall frequency between subjects.

Dreaming is still poorly understood. Notably, its cerebral underpinning remains unclear. Neuropsychological studies have shown that lesions in the temporoparietal junction (TPJ) and/or the white matter of the medial prefrontal cortex (MPFC) lead to the global cessation of dream reports, suggesting that these regions of the default mode network have key roles in the dreaming process (forebrain 'dream-on' hypothesis). To test this hypothesis, we measured regional cerebral blood flow (rCBF) using [(15)O]H2O positron emission tomography in healthy subjects with high and low dream recall frequencies (DRFs) during wakefulness (rest) and sleep (rapid eye movement (REM) sleep, N2, and N3). Compared with Low recallers (0.5 ± 0.3 dream recall per week in average), High recallers (5.2 ± 1.4) showed higher rCBF in the TPJ during REM sleep, N3, and wakefulness, and in the MPFC during REM sleep and wakefulness. We demonstrate that the resting states of High recallers and Low recallers differ during sleep and wakefulness. It coheres with previous ERP results and confirms that a high/low DRF is associated with a specific functional organization of the brain. These results support the forebrain 'dream-on' hypothesis and suggest that TPJ and MPFC are not only involved in dream recall during wakefulness but also have a role in dreaming during sleep (production and/or encoding). Increased activity in the TPJ and MPFC might promote the mental imagery and/or memory encoding of dreams. Notably, increased activity in TPJ might facilitate attention orienting toward external stimuli and promote intrasleep wakefulness, facilitating the encoding of the dreams in memory.

Brain reactivity differentiates subjects with high and low dream recall frequencies during both sleep and wakefulness.

The neurophysiological correlates of dreaming remain unclear. According to the "arousal-retrieval" model, dream encoding depends on intrasleep wakefulness. Consistent with this model, subjects with high and low dream recall frequency (DRF) report differences in intrasleep awakenings. This suggests a possible neurophysiological trait difference between the 2 groups. To test this hypothesis, we compared the brain reactivity (evoked potentials) of subjects with high (HR, N = 18) and low (LR, N = 18) DRF during wakefulness and sleep. During data acquisition, the subjects were presented with sounds to be ignored (first names randomly presented among pure tones) while they were watching a silent movie or sleeping. Brain responses to first names dramatically differed between the 2 groups during both sleep and wakefulness. During wakefulness, the attention-orienting brain response (P3a) and a late parietal response were larger in HR than in LR. During sleep, we also observed between-group differences at the latency of the P3a during N2 and at later latencies during all sleep stages. Our results demonstrate differences in the brain reactivity of HR and LR during both sleep and wakefulness. These results suggest that the ability to recall dreaming is associated with a particular cerebral functional organization, regardless of the state of vigilance.

Dreams are made of memories, but maybe not for memory.

Llewellyn's claim that rapid eye movement (REM) dream imagery may be related to the processes involved in memory consolidation during sleep is plausible. However, whereas there is voluntary and deliberate intention behind the construction of images in the ancient art of memory (AAOM) method, there is a lack of intentionality in producing dream images. The memory for dreams is also fragile, and dependent on encoding once awake.

Alpha reactivity to complex sounds differs during REM sleep and wakefulness.

We aimed at better understanding the brain mechanisms involved in the processing of alerting meaningful sounds during sleep, investigating alpha activity. During EEG acquisition, subjects were presented with a passive auditory oddball paradigm including rare complex sounds called Novels (the own first name - OWN, and an unfamiliar first name - OTHER) while they were watching a silent movie in the evening or sleeping at night. During the experimental night, the subjects' quality of sleep was generally preserved. During wakefulness, the decrease in alpha power (8-12 Hz) induced by Novels was significantly larger for OWN than for OTHER at parietal electrodes, between 600 and 900 ms after stimulus onset. Conversely, during REM sleep, Novels induced an increase in alpha power (from 0 to 1200 ms at all electrodes), significantly larger for OWN than for OTHER at several parietal electrodes between 700 and 1200 ms after stimulus onset. These results show that complex sounds have a different effect on the alpha power during wakefulness (decrease) and during REM sleep (increase) and that OWN induce a specific effect in these two states. The increased alpha power induced by Novels during REM sleep may 1) correspond to a short and transient increase in arousal; in this case, our study provides an objective measure of the greater arousing power of OWN over OTHER, 2) indicate a cortical inhibition associated with sleep protection. These results suggest that alpha modulation could participate in the selection of stimuli to be further processed during sleep.

Alpha reactivity to first names differs in subjects with high and low dream recall frequency.

Studies in cognitive psychology showed that personality (openness to experience, thin boundaries, absorption), creativity, nocturnal awakenings, and attitude toward dreams are significantly related to dream recall frequency (DRF). These results suggest the possibility of neurophysiological trait differences between subjects with high and low DRF. To test this hypothesis we compared sleep characteristics and alpha reactivity to sounds in subjects with high and low DRF using polysomnographic recordings and electroencephalography (EEG). We acquired EEG from 21 channels in 36 healthy subjects while they were presented with a passive auditory oddball paradigm (frequent standard tones, rare deviant tones and very rare first names) during wakefulness and sleep (intensity, 50 dB above the subject's hearing level). Subjects were selected as High-recallers (HR, DRF = 4.42 ± 0.25 SEM, dream recalls per week) and Low-recallers (LR, DRF = 0.25 ± 0.02) using a questionnaire and an interview on sleep and dream habits. Despite the disturbing setup, the subjects' quality of sleep was generally preserved. First names induced a more sustained decrease in alpha activity in HR than in LR at Pz (1000-1200 ms) during wakefulness, but no group difference was found in REM sleep. The current dominant hypothesis proposes that alpha rhythms would be involved in the active inhibition of the brain regions not involved in the ongoing brain operation. According to this hypothesis, a more sustained alpha decrease in HR would reflect a longer release of inhibition, suggesting a deeper processing of complex sounds than in LR during wakefulness. A possibility to explain the absence of group difference during sleep is that increase in alpha power in HR may have resulted in awakenings. Our results support this hypothesis since HR experienced more intra sleep wakefulness than LR (30 ± 4 vs. 14 ± 4 min). As a whole our results support the hypothesis of neurophysiological trait differences in high and low-recallers.

What is the specificity of the response to the own first-name when presented as a novel in a passive oddball paradigm? An ERP study.

One's own first-name is a special stimulus: one's attention is more likely captured by hearing one's own first-name than by hearing another first-name. Previous event-related potential (ERP) studies demonstrated that this special stimulus produces differential responses both in active and in passive condition. Such results suggest that passively hearing one's own first-name triggers processing levels generally activated by the explicit detection of stimuli. This questions about the particular power of the own first-name to automatically orient attention, but no study investigated the specific response to this special stimulus in a paradigm designed to study automatic attention orienting. In this ERP study, we compared the responses elicited by the own first-name (OWN) and one unfamiliar first-name (OTHER) presented, rarely, randomly and at the same frequency among repetitive tones (i.e., as novel stimuli in an oddball paradigm) while subjects (N=36) were watching a silent movie with subtitles. We tested at what latency the responses to OWN and OTHER diverge, and whether OWN modulates the brain orienting response (novelty P3). Data analysis showed specific responses to OWN after 300 ms. OWN only evoked a central negativity (320 ms) and a parietal positivity (550 ms). However, OWN had no significant effect on the brain orienting response (260 ms). Our results confirm that the own first-name does elicit a late specific brain response. However, they challenge the idea that in passive condition, the own first-name is systematically more powerful than another first-name to orient attention when it is heard unexpectedly.