Jean-Baptiste Eichenlaub

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 [15O]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.

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.

Ones own first-name is a special stimulus: ones attention is more likely captured by hearing ones 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 ones 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.

The dream-lag effect: Selective processing of personally significant events during Rapid Eye Movement sleep, but not during Slow Wave Sleep

Incorporation of details from waking life events into Rapid Eye Movement (REM) sleep dreams has been found to be highest on the night after, and then 5-7 nights after events (termed, respectively, the day-residue and dream-lag effects). In experiment 1, 44 participants kept a daily log for 10 days, reporting major daily activities (MDAs), personally significant events (PSEs), and major concerns (MCs). Dream reports were collected from REM and Slow Wave Sleep (SWS) in the laboratory, or from REM sleep at home. The dream-lag effect was found for the incorporation of PSEs into REM dreams collected at home, but not for MDAs or MCs. No dream-lag effect was found for SWS dreams, or for REM dreams collected in the lab after SWS awakenings earlier in the night. In experiment 2, the 44 participants recorded reports of their spontaneously recalled home dreams over the 10 nights following the instrumental awakenings night, which thus acted as a controlled stimulus with two salience levels, high (sleep lab) and low (home awakenings). The dream-lag effect was found for the incorporation into home dreams of references to the experience of being in the sleep laboratory, but only for participants who had reported concerns beforehand about being in the sleep laboratory. The delayed incorporation of events from daily life into dreams has been proposed to reflect REM sleep-dependent memory consolidation. However, an alternative emotion processing or emotional impact of events account, distinct from memory consolidation, is supported by the finding that SWS dreams do not evidence the dream-lag effect.

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.

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.

Intrinsic Coupling between Gamma Oscillations, Neuronal Discharges, and Slow Cortical Oscillations during Human Slow-Wave Sleep

In the first electrocorticograms, Richard [Caton (1877)][1] connected a mirror galvanometer to exposed rabbit brains and observed slow sleep-related fluctuations. Several decades later, [Loomis et al. (1937)][2] applied the then-fledgling technique of scalp EEG to classify sleep stages in humans,

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.

Dreams are made of memories, but maybe not for memory

Llewellyns 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.

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.

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.