Prediction of general mental ability based on neural oscillation measures of sleep

Sleep spindle detection based on non-experts: A validation study

Accurate and efficient detection of sleep spindles is a methodological challenge. The present study describes a method of using non-experts for manual detection of sleep spindles. We recruited five experts and 168 non-experts to manually identify spindles in stage N2 and stage N3 sleep data using a MATLAB interface. Scorers classified each spindle into definite and indefinite spindle (with weights of 1 and 0.5, respectively). First, a method of optimizing the thresholds of the expert/non-expert group consensus according to the results of experts and non-experts themselves is described. Using this method, we established expert and non-expert group standards from expert and non-expert scorers, respectively, and evaluated the performance of the non-expert group standards by compared with the expert group standard (termed EGS). The results indicated that the highest performance was the non-expert group standard when definite spindles were only considered (termed nEGS-1; F1 score = 0.78 for N2; 0.68 for N3). Second, four automatic spindle detection methods were compared with the EGS. We found that the performance of nEGS-1 versus EGS was higher than that of the four automated methods. Our results also showed positive correlation between the mean F1 score of individual expert in EGS and the F1 score of nEGS-1 versus EGS across 30 segments of stage N2 data (r = 0.61, P < 0.001). Further, we found that six and nine non-experts were needed to manually identify spindles in stages N2 and N3, respectively, while maintaining acceptable performance of nEGS-1 versus EGS (F1 score = 0.79 for N2; 0.64 for N3). In conclusion, this study establishes a detailed process for detection of sleep spindles by non-experts in a crowdsourcing scheme.

Widespread reduction in sleep spindle activity in socially anxious children and adolescents

Social anxiety disorder (SAD) is one of the most prevalent psychiatric diseases typically emerging during childhood and adolescence. Biological vulnerabilities such as a protracted maturation of prefrontal cortex functioning together with heightened reactivity of the limbic system leading to increased emotional reactivity are discussed as factors contributing to the emergence and maintenance of SAD. Sleep slow wave activity (SWA, 0.75-4.5 Hz) and sleep spindle activity (9-16 Hz) reflect processes of brain maturation and emotion regulation. We used high-density electroencephalography to characterize sleep SWA and spindle activity and their relationship to emotional reactivity in children and adolescents suffering from SAD and healthy controls (HC). Subjectively rated arousal was assessed using an emotional picture-word association task. SWA did not differ between socially anxious and healthy participants. We found a widespread reduction in fast spindle activity (13-16 Hz) in SAD patients compared to HC. SAD patients rated negative stimuli to be more arousing and these arousal ratings were negatively correlated with fast spindle activity. These results suggest electrophysiological alterations that are evident at an early stage of psychopathology and that are closely linked to one core symptom of anxiety disorders such as increased emotional reactivity. The role of disturbed GABAergic neurotransmission is discussed as an underlying factor.

Sleep Spindles and Intellectual Ability: Epiphenomenon or Directly Related?

Sleep spindles—short, phasic, oscillatory bursts of activity that characterize non-rapid eye movement sleep—are one of the only electrophysiological oscillations identified as a biological marker of human intelligence (e.g., cognitive abilities commonly assessed using intelligence quotient tests). However, spindles are also important for sleep maintenance and are modulated by circadian factors. Thus, the possibility remains that the relationship between spindles and intelligence quotient may be an epiphenomenon of a putative relationship between good quality sleep and cognitive ability or perhaps modulated by circadian factors such as morningness–eveningness tendencies. We sought to ascertain whether spindles are directly or indirectly related to cognitive abilities using mediation analysis. Here, we show that fast (13.5–16 Hz) parietal but not slow (11–13.5 Hz) frontal spindles in both non-rapid eye movement stage 2 sleep and slow wave sleep are directly related to reasoning abilities (i.e., cognitive abilities that support “fluid intelligence,” such as the capacity to identify complex patterns and relationships and the use of logic to solve novel problems) but not verbal abilities (i.e., cognitive abilities that support “crystalized intelligence”; accumulated knowledge and experience) or cognitive abilities that support STM (i.e., the capacity to briefly maintain information in an available state). The relationship between fast spindles and reasoning abilities is independent of the indicators of sleep maintenance and circadian chronotype, thus suggesting that spindles are indeed a biological marker of cognitive abilities and can serve as a window to further explore the physiological and biological substrates that give rise to human intelligence.

Sleep Changes in Adolescents Following Procedural Task Training

Recent research has suggested that some of the inter-individual variation in sleep spindle activity is due to innate learning ability. Sleep spindles have also been observed to vary following learning in both young and older adults. We examined the effect of procedural task acquisition on sleep stages and on sleep spindles in an adolescent sample. Participants were 32 adolescents (17 females) between the ages of 12 and 19 years. Spindle activity was examined in three different frequency ranges: 11.00–13.50 Hz (slow), 13.51–16.00 Hz (fast), and 16.01–18.50 Hz (superfast). No changes in spindle density were observed after successful learning of the pursuit rotor task. This result was in contrast to a number of studies reporting spindle density increases following successful learning. In the present study, participants who successfully learned the task showed no changes in their sleep stage proportions, but participants who were not successful showed a decrease in the proportion of stage 2 and increases in both SWS and REM sleep. We suggest that these changes in the sleep stages are consistent with the two stage model of sleep and memory proposed by Smith et al. (2004a).

Sleep Spindle Density Predicts the Effect of Prior Knowledge on Memory Consolidation

Information that relates to a prior knowledge schema is remembered better and consolidates more rapidly than information that does not. Another factor that influences memory consolidation is sleep and growing evidence suggests that sleep-related processing is important for integration with existing knowledge. Here, we perform an examination of how sleep-related mechanisms interact with schema-dependent memory advantage. Participants first established a schema over 2 weeks. Next, they encoded new facts, which were either related to the schema or completely unrelated. After a 24 h retention interval, including a night of sleep, which we monitored with polysomnography, participants encoded a second set of facts. Finally, memory for all facts was tested in a functional magnetic resonance imaging scanner. Behaviorally, sleep spindle density predicted an increase of the schema benefit to memory across the retention interval. Higher spindle densities were associated with reduced decay of schema-related memories. Functionally, spindle density predicted increased disengagement of the hippocampus across 24 h for schema-related memories only. Together, these results suggest that sleep spindle activity is associated with the effect of prior knowledge on memory consolidation.

SIGNIFICANCE STATEMENT

Episodic memories are gradually assimilated into long-term memory and this process is strongly influenced by sleep. The consolidation of new information is also influenced by its relationship to existing knowledge structures, or schemas, but the role of sleep in such schema-related consolidation is unknown. We show that sleep spindle density predicts the extent to which schemas influence the consolidation of related facts. This is the first evidence that sleep is associated with the interaction between prior knowledge and long-term memory formation.

Sleep Spindles as an Electrographic Element: Description and Automatic Detection Methods

Sleep spindle is a peculiar oscillatory brain pattern which has been associated with a number of sleep (isolation from exteroceptive stimuli, memory consolidation) and individual characteristics (intellectual quotient). Oddly enough, the definition of a spindle is both incomplete and restrictive. In consequence, there is no consensus about how to detect spindles. Visual scoring is cumbersome and user dependent. To analyze spindle activity in a more robust way, automatic sleep spindle detection methods are essential. Various algorithms were developed, depending on individual research interest, which hampers direct comparisons and meta-analyses. In this review, sleep spindle is first defined physically and topographically. From this general description, we tentatively extract the main characteristics to be detected and analyzed. A nonexhaustive list of automatic spindle detection methods is provided along with a description of their main processing principles. Finally, we propose a technique to assess the detection methods in a robust and comparable way.

Sleep Spindle Characteristics in Children with Neurodevelopmental Disorders and Their Relation to Cognition

Empirical evidence indicates that sleep spindles facilitate neuroplasticity and “off-line” processing during sleep, which supports learning, memory consolidation, and intellectual performance. Children with neurodevelopmental disorders (NDDs) exhibit characteristics that may increase both the risk for and vulnerability to abnormal spindle generation. Despite the high prevalence of sleep problems and cognitive deficits in children with NDD, only a few studies have examined the putative association between spindle characteristics and cognitive function. This paper reviews the literature regarding sleep spindle characteristics in children with NDD and their relation to cognition in light of what is known in typically developing children and based on the available evidence regarding children with NDD. We integrate available data, identify gaps in understanding, and recommend future research directions. Collectively, studies are limited by small sample sizes, heterogeneous populations with multiple comorbidities, and nonstandardized methods for collecting and analyzing findings. These limitations notwithstanding, the evidence suggests that future studies should examine associations between sleep spindle characteristics and cognitive function in children with and without NDD, and preliminary findings raise the intriguing question of whether enhancement or manipulation of sleep spindles could improve sleep-dependent memory and other aspects of cognitive function in this population.

General intelligence predicts memory change across sleep

Psychometric intelligence (g) is often conceptualized as the capability for online information processing but it is also possible that intelligence may be related to offline processing of information. Here, we investigated the relationship between psychometric g and sleep-dependent memory consolidation. Participants studied paired-associates and were tested after a 12-hour retention interval that consisted entirely of wake or included a regular sleep phase. We calculated the number of word-pairs that were gained and lost across the retention interval. In a separate session, participants completed a battery of cognitive ability tests to assess g. In the wake group, g was not correlated with either memory gain or memory loss. In the sleep group, we found that g correlated positively with memory gain and negatively with memory loss. Participants with a higher level of general intelligence showed more memory gain and less memory loss across sleep. Importantly, the correlation between g and memory loss was significantly stronger in the sleep condition than in the wake condition, suggesting that the relationship between g and memory loss across time is specific to time intervals that include sleep. The present research suggests that g not only reflects the capability for online cognitive processing, but also reflects capability for offline processes that operate during sleep.

Nap sleep spindle correlates of intelligence

Sleep spindles are thalamocortical oscillations in non-rapid eye movement (NREM) sleep, that play an important role in sleep-related neuroplasticity and offline information processing. Several studies with full-night sleep recordings have reported a positive association between sleep spindles and fluid intelligence scores, however more recently it has been shown that only few sleep spindle measures correlate with intelligence in females, and none in males. Sleep spindle regulation underlies a circadian rhythm, however the association between spindles and intelligence has not been investigated in daytime nap sleep so far. In a sample of 86 healthy male human subjects, we investigated the correlation between fluid intelligence and sleep spindle parameters in an afternoon nap of 100 minutes. Mean sleep spindle length, amplitude and density were computed for each subject and for each derivation for both slow and fast spindles. A positive association was found between intelligence and slow spindle duration, but not any other sleep spindle parameter. As a positive correlation between intelligence and slow sleep spindle duration in full-night polysomnography has only been reported in females but not males, our results suggest that the association between intelligence and sleep spindles is more complex than previously assumed.

Topography of Slow Sigma Power during Sleep is Associated with Processing Speed in Preschool Children

Cognitive development is influenced by maturational changes in processing speed, a construct reflecting the rapidity of executing cognitive operations. Although cognitive ability and processing speed are linked to spindles and sigma power in the sleep electroencephalogram (EEG), little is known about such associations in early childhood, a time of major neuronal refinement. We calculated EEG power for slow (10-13 Hz) and fast (13.25-17 Hz) sigma power from all-night high-density electroencephalography (EEG) in a cross-sectional sample of healthy preschool children (n = 10, 4.3 ± 1.0 years). Processing speed was assessed as simple reaction time. On average, reaction time was 1409 ± 251 ms; slow sigma power was 4.0 ± 1.5 μV²; and fast sigma power was 0.9 ± 0.2 μV². Both slow and fast sigma power predominated over central areas. Only slow sigma power was correlated with processing speed in a large parietal electrode cluster (p < 0.05, r ranging from -0.6 to -0.8), such that greater power predicted faster reaction time. Our findings indicate regional correlates between sigma power and processing speed that are specific to early childhood and provide novel insights into the neurobiological features of the EEG that may underlie developing cognitive abilities.

Expert and crowd-sourced validation of an individualized sleep spindle detection method employing complex demodulation and individualized normalization

A spindle detection method was developed that: (1) extracts the signal of interest (i.e., spindle-related phasic changes in sigma) relative to ongoing "background" sigma activity using complex demodulation, (2) accounts for variations of spindle characteristics across the night, scalp derivations and between individuals, and (3) employs a minimum number of sometimes arbitrary, user-defined parameters. Complex demodulation was used to extract instantaneous power in the spindle band. To account for intra- and inter-individual differences, the signal was z-score transformed using a 60 s sliding window, per channel, over the course of the recording. Spindle events were detected with a z-score threshold corresponding to a low probability (e.g., 99th percentile). Spindle characteristics, such as amplitude, duration and oscillatory frequency, were derived for each individual spindle following detection, which permits spindles to be subsequently and flexibly categorized as slow or fast spindles from a single detection pass. Spindles were automatically detected in 15 young healthy subjects. Two experts manually identified spindles from C3 during Stage 2 sleep, from each recording; one employing conventional guidelines, and the other, identifying spindles with the aid of a sigma (11-16 Hz) filtered channel. These spindles were then compared between raters and to the automated detection to identify the presence of true positives, true negatives, false positives and false negatives. This method of automated spindle detection resolves or avoids many of the limitations that complicate automated spindle detection, and performs well compared to a group of non-experts, and importantly, has good external validity with respect to the extant literature in terms of the characteristics of automatically detected spindles.

Labile sleep promotes awareness of abstract knowledge in a serial reaction time task

Sleep has been identified as a critical brain state enhancing the probability of gaining insight into covert task regularities. Both non-rapid eye movement (NREM) and rapid eye movement (REM) sleep have been implicated with offline re-activation and reorganization of memories supporting explicit knowledge generation. According to two-stage models of sleep function, offline processing of information during sleep is sequential requiring multiple cycles of NREM and REM sleep stages. However, the role of overnight dynamic sleep macrostructure for insightfulness has not been studied so far. In the present study, we test the hypothesis that the frequency of interactions between NREM and REM sleep stages might be critical for awareness after sleep. For that aim, the rate of sleep stage transitions was evaluated in 53 participants who learned implicitly a serial reaction time task (SRTT) in which a determined sequence was inserted. The amount of explicit knowledge about the sequence was established by verbal recall after a night of sleep following SRTT learning. Polysomnography was recorded in this night and in a control night before and was analyzed to compare the rate of sleep-stage transitions between participants who did or did not gain awareness of task regularity after sleep. Indeed, individual ability of explicit knowledge generation was strongly associated with increased rate of transitions between NREM and REM sleep stages and between light sleep stages and slow wave sleep. However, the rate of NREM-REM transitions specifically predicted the amount of explicit knowledge after sleep in a trait-dependent way. These results demonstrate that enhanced lability of sleep goes along with individual ability of knowledge awareness. Observations suggest that facilitated dynamic interactions between sleep stages, particularly between NREM and REM sleep stages play a role for offline processing which promotes rule extraction and awareness.

Changes in Cognitive Performance Are Associated with Changes in Sleep in Older Adults With Insomnia

The present study examined sleep features associated with cognition in older adults and examined whether sleep changes following insomnia treatment were associated with cognitive improvements. Polysomnography and cognition (recall, working memory, and reasoning) were assessed before and after an insomnia intervention (Brief Behavioral Treatment of Insomnia [BBTI] or information control [IC]) in 77 older adults with insomnia. Baseline wake-after-sleep-onset (WASO) was associated with recall. Greater NREM (nonrapid eye movement) delta power and lower NREM sigma power were associated with greater working memory and reasoning. The insomnia intervention did not improve performance. However, increased absolute delta power and decreased relative sigma power were associated with improved reasoning. Findings suggest that improvements in executive function may occur with changes in NREM architecture.

The Multidimensional Aspects of Sleep Spindles and Their Relationship to Word-Pair Memory Consolidation

STUDY OBJECTIVES

Several studies proposed a link between sleep spindles and sleep dependent memory consolidation in declarative learning tasks. In addition to these state-like aspects of sleep spindles, they have also trait-like characteristics, i.e., were related to general cognitive performance, an important distinction that has often been neglected in correlative studies. Furthermore, from the multitude of different sleep spindle measures, often just one specific aspect was analyzed. Thus, we aimed at taking multidimensional aspects of sleep spindles into account when exploring their relationship to word-pair memory consolidation.

DESIGN

Each subject underwent 2 study nights with all-night high-density electroencephalographic (EEG) recordings. Sleep spindles were automatically detected in all EEG channels. Subjects were trained and tested on a word-pair learning task in the evening, and retested in the morning to assess sleep related memory consolidation (overnight retention). Trait-like aspects refer to the mean of both nights and state-like aspects were calculated as the difference between night 1 and night 2.

SETTING

Sleep laboratory.

PARTICIPANTS

Twenty healthy male subjects (age: 23.3 ± 2.1 y).

MEASUREMENTS AND RESULTS

Overnight retention was negatively correlated with trait-like aspects of fast sleep spindle density and positively with slow spindle density on a global level. In contrast, state-like aspects were observed for integrated slow spindle activity, which was positively related to the differences in overnight retention in specific regions.

CONCLUSION

Our results demonstrate the importance of a multidimensional approach when investigating the relationship between sleep spindles and memory consolidation and thereby provide a more complete picture explaining divergent findings in the literature.

Intelligence measures and stage 2 sleep in typically-developing and autistic children

The relationship between intelligence measures and 2 EEG measures of non-rapid eye movement sleep, sleep spindles and Sigma activity, was examined in 13 typically-developing (TD) and 13 autistic children with normal IQ and no complaints of poor sleep. Sleep spindles and Sigma EEG activity were computed for frontal (Fp1, Fp2) and central (C3, C4) recording sites. Time in stage 2 sleep and IQ was similar in both groups. Autistic children presented less spindles at Fp2 compared to the TD children. TD children showed negative correlation between verbal IQ and sleep spindle density at Fp2. In the autistic group, verbal and full-scale IQ scores correlated negatively with C3 sleep spindle density. The duration of sleep spindles at Fp1 was shorter in the autistic group than in the TD children. The duration of sleep spindles at C4 was positively correlated with verbal IQ only in the TD group. Fast Sigma EEG activity (13.25-15.75 Hz) was lower at C3 and C4 in autistic children compared to the TD children, particularly in the latter part of the night. Only the TD group showed positive correlation between performance IQ and latter part of the night fast Sigma activity at C4. These results are consistent with a relationship between EEG activity during sleep and cognitive processing in children. The difference between TD and autistic children could derive from dissimilar cortical organization and information processing in these 2 groups.

Using a quadratic parameter sinusoid model to characterize the structure of EEG sleep spindles

Sleep spindles are essentially non-stationary signals that display time and frequency-varying characteristics within their envelope, which makes it difficult to accurately identify its instantaneous frequency and amplitude. To allow a better parameterization of the structure of spindle, we propose modeling spindles using a Quadratic Parameter Sinusoid (QPS). The QPS is well suited to model spindle activity as it utilizes a quadratic representation to capture the inherent duration and frequency variations within spindles. The effectiveness of our proposed model and estimation technique was quantitatively evaluated in parameter determination experiments using simulated spindle-like signals and real spindles in the presence of background EEG. We used the QPS parameters to predict the energy and frequency of spindles with a mean accuracy of 92.34 and 97.73% respectively. We also show that the QPS parameters provide a quantification of the amplitude and frequency variations occurring within sleep spindles that can be observed visually and related to their characteristic "waxing and waning" shape. We analyze the variations in the parameters values to present how they can be used to understand the inter- and intra-participant variations in spindle structure. Finally, we present a comparison of the QPS parameters of spindles and non-spindles, which shows a substantial difference in parameter values between the two classes.

Sleep spindles and intelligence: evidence for a sexual dimorphism

Sleep spindles are thalamocortical oscillations in nonrapid eye movement sleep, which play an important role in sleep-related neuroplasticity and offline information processing. Sleep spindle features are stable within and vary between individuals, with, for example, females having a higher number of spindles and higher spindle density than males. Sleep spindles have been associated with learning potential and intelligence; however, the details of this relationship have not been fully clarified yet. In a sample of 160 adult human subjects with a broad IQ range, we investigated the relationship between sleep spindle parameters and intelligence. In females, we found a positive age-corrected association between intelligence and fast sleep spindle amplitude in central and frontal derivations and a positive association between intelligence and slow sleep spindle duration in all except one derivation. In males, a negative association between intelligence and fast spindle density in posterior regions was found. Effects were continuous over the entire IQ range. Our results demonstrate that, although there is an association between sleep spindle parameters and intellectual performance, these effects are more modest than previously reported and mainly present in females. This supports the view that intelligence does not rely on a single neural framework, and stronger neural connectivity manifesting in increased thalamocortical oscillations in sleep is one particular mechanism typical for females but not males.

A comparison of two sleep spindle detection methods based on all night averages: individually adjusted vs. fixed frequencies

Sleep spindles are frequently studied for their relationship with state and trait cognitive variables, and they are thought to play an important role in sleep-related memory consolidation. Due to their frequent occurrence in NREM sleep, the detection of sleep spindles is only feasible using automatic algorithms, of which a large number is available. We compared subject averages of the spindle parameters computed by a fixed frequency (FixF) (11–13 Hz for slow spindles, 13–15 Hz for fast spindles) automatic detection algorithm and the individual adjustment method (IAM), which uses individual frequency bands for sleep spindle detection. Fast spindle duration and amplitude are strongly correlated in the two algorithms, but there is little overlap in fast spindle density and slow spindle parameters in general. The agreement between fixed and manually determined sleep spindle frequencies is limited, especially in case of slow spindles. This is the most likely reason for the poor agreement between the two detection methods in case of slow spindle parameters. Our results suggest that while various algorithms may reliably detect fast spindles, a more sophisticated algorithm primed to individual spindle frequencies is necessary for the detection of slow spindles as well as individual variations in the number of spindles in general.

Sleep spindles predict stress-related increases in sleep disturbances

Background and Aim: Predisposing factors place certain individuals at higher risk for insomnia, especially in the presence of precipitating conditions such as stressful life events. Sleep spindles have been shown to play an important role in the preservation of sleep continuity. Lower spindle density might thus constitute an objective predisposing factor for sleep reactivity to stress. The aim of this study was therefore to evaluate the relationship between baseline sleep spindle density and the prospective change in insomnia symptoms in response to a standardized academic stressor.

Methods: Twelve healthy students had a polysomnography recording during a period of lower stress at the beginning of the academic semester, along with an assessment of insomnia complaints using the insomnia severity index (ISI). They completed a second ISI assessment at the end of the semester, a period coinciding with the week prior to final examinations and thus higher stress. Spindle density, amplitude, duration, and frequency, as well as sigma power were computed from C4–O2 electroencephalography derivation during stages N2–N3 of non-rapid-eye-movement (NREM) sleep, across the whole night and for each NREM sleep period. To test for the relationship between spindle density and changes in insomnia symptoms in response to academic stress, spindle measurements at baseline were correlated with changes in ISI across the academic semester.

Results: Spindle density (as well as spindle amplitude and sigma power), particularly during the first NREM sleep period, negatively correlated with changes in ISI (p < 0.05).

Conclusion: Lower spindle activity, especially at the beginning of the night, prospectively predicted larger increases in insomnia symptoms in response to stress. This result indicates that individual differences in sleep spindle activity contribute to the differential vulnerability to sleep disturbances in the face of precipitating factors.

Correlations between adolescent processing speed and specific spindle frequencies

Sleep spindles are waxing and waning thalamocortical oscillations with accepted frequencies of between 11 and 16 Hz and a minimum duration of 0.5 s. Our research has suggested that there is spindle activity in all of the sleep stages, and thus for the present analysis we examined the link between spindle activity (Stage 2, rapid eye movement (REM) and slow wave sleep (SWS)) and waking cognitive abilities in 32 healthy adolescents. After software was used to filter frequencies outside the desired range, slow spindles (11.00–13.50 Hz), fast spindles (13.51–16.00 Hz) and spindle-like activity (16.01–18.50 Hz) were observed in Stage 2, SWS and REM sleep. Our analysis suggests that these specific EEG frequencies were significantly related to processing speed, which is one of the subscales of the intelligence score, in adolescents. The relationship was prominent in SWS and REM sleep. Further, the spindle-like activity (16.01–18.50 Hz) that occurred during SWS was strongly related to processing speed. Results suggest that the ability of adolescents to respond to tasks in an accurate, efficient and timely manner is related to their sleep quality. These findings support earlier research reporting relationships between learning, learning potential and sleep spindle activity in adults and adolescents.

Exploring the effect of sleep and reduced interference on different forms of declarative memory

STUDY OBJECTIVES

Many studies have found that sleep benefits declarative memory consolidation. However, fundamental questions on the specifics of this effect remain topics of discussion. It is not clear which forms of memory are affected by sleep and whether this beneficial effect is partly mediated by passive protection against interference. Moreover, a putative correlation between the structure of sleep and its memory-enhancing effects is still being discussed.

DESIGN

In three experiments, we tested whether sleep differentially affects various forms of declarative memory. We varied verbal content (verbal/nonverbal), item type (single/associate), and recall mode (recall/recognition, cued/free recall) to examine the effect of sleep on specific memory subtypes. We compared within-subject differences in memory consolidation between intervals including sleep, active wakefulness, or quiet meditation, which reduced external as well as internal interference and rehearsal.

PARTICIPANTS

Forty healthy adults aged 18-30 y, and 17 healthy adults aged 24-55 y with extensive meditation experience participated in the experiments.

RESULTS

All types of memory were enhanced by sleep if the sample size provided sufficient statistical power. Smaller sample sizes showed an effect of sleep if a combined measure of different declarative memory scales was used. In a condition with reduced external and internal interference, performance was equal to one with high interference. Here, memory consolidation was significantly lower than in a sleep condition. We found no correlation between sleep structure and memory consolidation.

CONCLUSIONS

Sleep does not preferentially consolidate a specific kind of declarative memory, but consistently promotes overall declarative memory formation. This effect is not mediated by reduced interference.

Sleep spindling and fluid intelligence across adolescent development: sex matters

Evidence supports the intricate relationship between sleep electroencephalogram (EEG) spindling and cognitive abilities in children and adults. Although sleep EEG changes during adolescence index fundamental brain reorganization, a detailed analysis of sleep spindling and the spindle-intelligence relationship was not yet provided for adolescents. Therefore, adolescent development of sleep spindle oscillations were studied in a home polysomnographic study focusing on the effects of chronological age and developmentally acquired overall mental efficiency (fluid IQ) with sex as a potential modulating factor. Subjects were 24 healthy adolescents (12 males) with an age range of 15-22 years (mean: 18 years) and fluid IQ of 91-126 (mean: 104.12, Raven Progressive Matrices Test). Slow spindles (SSs) and fast spindles (FSs) were analyzed in 21 EEG derivations by using the individual adjustment method (IAM). A significant age-dependent increase in average FS density (r = 0.57; p = 0.005) was found. Moreover, fluid IQ correlated with FS density (r = 0.43; p = 0.04) and amplitude (r = 0.41; p = 0.049). The latter effects were entirely driven by particularly reliable FS-IQ correlations in females [r = 0.80 (p = 0.002) and r = 0.67 (p = 0.012), for density and amplitude, respectively]. Region-specific analyses revealed that these correlations peak in the fronto-central regions. The control of the age-dependence of FS measures and IQ scores did not considerably reduce the spindle-IQ correlations with respect to FS density. The only positive spindle-index of fluid IQ in males turned out to be the frequency of FSs (r = 0.60, p = 0.04). Increases in FS density during adolescence may index reshaped structural connectivity related to white matter maturation in the late developing human brain. The continued development over this age range of cognitive functions is indexed by specific measures of sleep spindling unraveling gender differences in adolescent brain maturation and perhaps cognitive strategy.

Sleep spindle and slow wave frequency reflect motor skill performance in primary school-age children

BACKGROUND AND AIM

The role of sleep in the enhancement of motor skills has been studied extensively in adults. We aimed to determine involvement of sleep and characteristics of spindles and slow waves in a motor skill in children.

HYPOTHESIS

We hypothesized sleep-dependence of skill enhancement and an association of interindividual differences in skill and sleep characteristics.

METHODS

30 children (19 females, 10.7 ± 0.8 years of age; mean ± SD) performed finger sequence tapping tasks in a repeated-measures design spanning 4 days including 1 polysomnography (PSG) night. Initial and delayed performance were assessed over 12 h of wake; 12 h with sleep; and 24 h with wake and sleep. For the 12 h with sleep, children were assigned to one of three conditions: modulation of slow waves and spindles was attempted using acoustic perturbation, and compared to yoked and no-sound control conditions.

ANALYSES

Mixed effect regression models evaluated the association of sleep, its macrostructure and spindles and slow wave parameters with initial and delayed speed and accuracy.

RESULTS AND CONCLUSIONS

Children enhance their accuracy only over an interval with sleep. Unlike previously reported in adults, children enhance their speed independent of sleep, a capacity that may to be lost in adulthood. Individual differences in the dominant frequency of spindles and slow waves were predictive for performance: children performed better if they had less slow spindles, more fast spindles and faster slow waves. On the other hand, overnight enhancement of accuracy was most pronounced in children with more slow spindles and slower slow waves, i.e., the ones with an initial lower performance. Associations of spindle and slow wave characteristics with initial performance may confound interpretation of their involvement in overnight enhancement. Slower frequencies of characteristic sleep events may mark slower learning and immaturity of networks involved in motor skills.

Inter-expert and intra-expert reliability in sleep spindle scoring

OBJECTIVES

To measure the inter-expert and intra-expert agreement in sleep spindle scoring, and to quantify how many experts are needed to build a reliable dataset of sleep spindle scorings.

METHODS

The EEG dataset was comprised of 400 randomly selected 115s segments of stage 2 sleep from 110 sleeping subjects in the general population (57±8, range: 42-72 years). To assess expert agreement, a total of 24 Registered Polysomnographic Technologists (RPSGTs) scored spindles in a subset of the EEG dataset at a single electrode location (C3-M2). Intra-expert and inter-expert agreements were calculated as F1-scores, Cohen's kappa (κ), and intra-class correlation coefficient (ICC).

RESULTS

We found an average intra-expert F1-score agreement of 72±7% (κ: 0.66±0.07). The average inter-expert agreement was 61±6% (κ: 0.52±0.07). Amplitude and frequency of discrete spindles were calculated with higher reliability than the estimation of spindle duration. Reliability of sleep spindle scoring can be improved by using qualitative confidence scores, rather than a dichotomous yes/no scoring system.

CONCLUSIONS

We estimate that 2-3 experts are needed to build a spindle scoring dataset with 'substantial' reliability (κ: 0.61-0.8), and 4 or more experts are needed to build a dataset with 'almost perfect' reliability (κ: 0.81-1).

SIGNIFICANCE

Spindle scoring is a critical part of sleep staging, and spindles are believed to play an important role in development, aging, and diseases of the nervous system.

Slow sleep spindle activity, declarative memory, and general cognitive abilities in children

STUDY OBJECTIVES

Functional interactions between sleep spindle activity, declarative memory consolidation, and general cognitive abilities in school-aged children.

DESIGN

Healthy, prepubertal children (n = 63; mean age 9.56 ± 0.76 y); ambulatory all-night polysomnography (2 nights); investigating the effect of prior learning (word pair association task; experimental night) versus nonlearning (baseline night) on sleep spindle activity; general cognitive abilities assessed using the Wechsler Intelligence Scale for Children-IV (WISC-IV).

MEASUREMENTS AND RESULTS

Analysis of spindle activity during nonrapid eye movement sleep (N2 and N3) evidenced predominant peaks in the slow (11-13 Hz) but not in the fast (13-15 Hz) sleep spindle frequency range (baseline and experimental night). Analyses were restricted to slow sleep spindles. Changes in spindle activity from the baseline to the experimental night were not associated with the overnight change in the number of recalled words reflecting declarative memory consolidation. Children with higher sleep spindle activity as measured at frontal, central, parietal, and occipital sites during both baseline and experimental nights exhibited higher general cognitive abilities (WISC-IV) and declarative learning efficiency (i.e., number of recalled words before and after sleep).

CONCLUSIONS

Slow sleep spindles (11-13 Hz) in children age 8-11 y are associated with inter-individual differences in general cognitive abilities and learning efficiency.

Ongoing network state controls the length of sleep spindles via inhibitory activity

Sleep spindles are major transient oscillations of the mammalian brain. Spindles are generated in the thalamus; however, what determines their duration is presently unclear. Here, we measured somatic activity of excitatory thalamocortical (TC) cells together with axonal activity of reciprocally coupled inhibitory reticular thalamic cells (nRTs) and quantified cycle-by-cycle alterations in their firing in vivo. We found that spindles with different durations were paralleled by distinct nRT activity, and nRT firing sharply dropped before the termination of all spindles. Both initial nRT and TC activity was correlated with spindle length, but nRT correlation was more robust. Analysis of spindles evoked by optogenetic activation of nRT showed that spindle probability, but not spindle length, was determined by the strength of the light stimulus. Our data indicate that during natural sleep a dynamically fluctuating thalamocortical network controls the duration of sleep spindles via the major inhibitory element of the circuits, the nRT.

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Coupled excitatory-inhibitory thalamic populations were recorded during spindles

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Spindle termination is preceded by a drop in nRT activity

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Spindles of different lengths have distinct nRT activity trajectories

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Spindle duration is strongly influenced by the initial network state

Electroencephalographic profiles for differentiation of disorders of consciousness

Background

Electroencephalography (EEG) is best suited for long-term monitoring of brain functions in patients with disorders of consciousness (DOC). Mathematical tools are needed to facilitate efficient interpretation of long-duration sleep-wake EEG recordings.

Methods

Starting with matching pursuit (MP) decomposition, we automatically detect and parametrize sleep spindles, slow wave activity, K-complexes and alpha, beta and theta waves present in EEG recordings, and automatically construct profiles of their time evolution, relevant to the assessment of residual brain function in patients with DOC.

Results

Above proposed EEG profiles were computed for 32 patients diagnosed as minimally conscious state (MCS, 20 patients), vegetative state/unresponsive wakefulness syndrome (VS/UWS, 11 patients) and Locked-in Syndrome (LiS, 1 patient). Their interpretation revealed significant correlations between patients’ behavioral diagnosis and: (a) occurrence of sleep EEG patterns including sleep spindles, slow wave activity and light/deep sleep cycles, (b) appearance and variability across time of alpha, beta, and theta rhythms. Discrimination between MCS and VS/UWS based upon prominent features of these profiles classified correctly 87% of cases.

Conclusions

Proposed EEG profiles offer user-independent, repeatable, comprehensive and continuous representation of relevant EEG characteristics, intended as an aid in differentiation between VS/UWS and MCS states and diagnostic prognosis. To enable further development of this methodology into clinically usable tests, we share user-friendly software for MP decomposition of EEG (http://braintech.pl/svarog) and scripts used for creation of the presented profiles (attached to this article).

Validation of a novel automatic sleep spindle detector with high performance during sleep in middle aged subjects

Many of the automatic sleep spindle detectors currently used to analyze sleep EEG are either validated on young subjects or not validated thoroughly. The purpose of this study is to develop and validate a fast and reliable sleep spindle detector with high performance in middle aged subjects. An automatic sleep spindle detector using a bandpass filtering approach and a time varying threshold was developed. The validation was done on sleep epochs from EEG recordings with manually scored sleep spindles from 13 healthy subjects with a mean age of 57.9 ± 9.7 years. The sleep spindle detector reached a mean sensitivity of 84.6 % and a mean specificity of 95.3 %. The sleep spindle detector can be used to obtain measures of spindle count and density together with quantitative measures such as the mean spindle frequency, mean spindle amplitude, and mean spindle duration.

Altered sleep in Borderline Personality Disorder in relation to the core dimensions of psychopathology

The aim of the study was to review the literature regarding sleep disturbances in Borderline Personality Disorder (BPD) and to relate the reported sleep alterations to the underlying core dimensions of BPD pathology. We present a qualitative and theoretical review regarding the empirical studies that investigated objective and subjective sleep quality in BPD and in different psychiatric conditions showing high co-morbidity with this disorder. We show that disturbed sleep including sleep fragmentation, alterations in Slow Wave Sleep and REM sleep, and dysphoric dreaming are prevalent symptoms in BPD. We provide a framework relating the specific sleep alterations to the core dimensions of BPD pathology in order to clarify the inconsistencies of the different findings. The specific sleep disturbances in BPD seem to be related to different dimensions of psychopathological functioning and may have detrimental consequences on waking affect and cognition. Investigating disturbed sleep in BPD in relation to waking symptoms and underlying neural functioning would shed more light on the nature of this complex disorder. Moreover, a stronger emphasis on sleep disturbances would enrich the treatment protocols of BPD.

About sleep's role in memory

Over more than a century of research has established the fact that sleep benefits the retention of memory. In this review we aim to comprehensively cover the field of "sleep and memory" research by providing a historical perspective on concepts and a discussion of more recent key findings. Whereas initial theories posed a passive role for sleep enhancing memories by protecting them from interfering stimuli, current theories highlight an active role for sleep in which memories undergo a process of system consolidation during sleep. Whereas older research concentrated on the role of rapid-eye-movement (REM) sleep, recent work has revealed the importance of slow-wave sleep (SWS) for memory consolidation and also enlightened some of the underlying electrophysiological, neurochemical, and genetic mechanisms, as well as developmental aspects in these processes. Specifically, newer findings characterize sleep as a brain state optimizing memory consolidation, in opposition to the waking brain being optimized for encoding of memories. Consolidation originates from reactivation of recently encoded neuronal memory representations, which occur during SWS and transform respective representations for integration into long-term memory. Ensuing REM sleep may stabilize transformed memories. While elaborated with respect to hippocampus-dependent memories, the concept of an active redistribution of memory representations from networks serving as temporary store into long-term stores might hold also for non-hippocampus-dependent memory, and even for nonneuronal, i.e., immunological memories, giving rise to the idea that the offline consolidation of memory during sleep represents a principle of long-term memory formation established in quite different physiological systems.

Individual differences in white matter diffusion affect sleep oscillations

The characteristic oscillations of the sleeping brain, spindles and slow waves, show trait-like, within-subject stability and a remarkable interindividual variability that correlates with functionally relevant measures such as memory performance and intelligence. Yet, the mechanisms underlying these interindividual differences are largely unknown. Spindles and slow waves are affected by the recent history of learning and neuronal activation, indicating sensitivity to changes in synaptic strength and thus to the connectivity of the neuronal network. Because the structural backbone of this network is formed by white matter tracts, we hypothesized that individual differences in spindles and slow waves depend on the white matter microstructure across a distributed network. We recorded both diffusion-weighted magnetic resonance images and whole-night, high-density electroencephalography and investigated whether individual differences in sleep spindle and slow wave parameters were associated with diffusion tensor imaging metrics; white matter fractional anisotropy and axial diffusivity were quantified using tract-based spatial statistics. Individuals with higher spindle power had higher axial diffusivity in the forceps minor, the anterior corpus callosum, fascicles in the temporal lobe, and the tracts within and surrounding the thalamus. Individuals with a steeper rising slope of the slow wave had higher axial diffusivity in the temporal fascicle and frontally located white matter tracts (forceps minor, anterior corpus callosum). These results indicate that the profiles of sleep oscillations reflect not only the dynamics of the neuronal network at the synaptic level, but also the localized microstructural properties of its structural backbone, the white matter tracts.

Sleep in the unresponsive wakefulness syndrome and minimally conscious state

The goal of our study was to investigate different aspects of sleep, namely the sleep-wake cycle and sleep stages, in the vegetative state/unresponsive wakefulness syndrome (VS/UWS), and minimally conscious state (MCS). A 24-h polysomnography was performed in 20 patients who were in a UWS (n=10) or in a MCS (n=10) because of brain injury. The data were first tested for the presence of a sleep-wake cycle, and the observed sleep patterns were compared with standard scoring criteria. Sleep spindles, slow wave sleep, and rapid eye movement sleep were quantified and their clinical value was investigated. According to our results, an electrophysiological sleep-wake cycle was identified in five MCS and three VS/UWS patients. Sleep stages did not always match the standard scoring criteria, which therefore needed to be adapted. Sleep spindles were present more in patients who clinically improved within 6 months. Slow wave sleep was present in eight MCS and three VS/UWS patients but never in the ischemic etiology. Rapid eye movement sleep, and therefore dreaming that is a form of consciousness, was present in all MCS and three VS/UWS patients. In conclusion, the presence of alternating periods of eyes-open/eyes-closed cycles does not necessarily imply preserved electrophysiological sleep architecture in the UWS and MCS, contrary to previous definition. The investigation of sleep is a little studied yet simple and informative way to evaluate the integrity of residual brain function in patients with disorders of consciousness with possible clinical diagnostic and prognostic implications.

Triangular relationship between sleep spindle activity, general cognitive ability and the efficiency of declarative learning

EEG sleep spindle activity (SpA) during non-rapid eye movement (NREM) sleep has been reported to be associated with measures of intelligence and overnight performance improvements. The reticular nucleus of the thalamus is generating sleep spindles in interaction with thalamocortical connections. The same system enables efficient encoding and processing during wakefulness. Thus, we examined if the triangular relationship between SpA, measures of intelligence and declarative learning reflect the efficiency of the thalamocortical system. As expected, SpA was associated with general cognitive ability, e.g. information processing speed. SpA was also associated with learning efficiency, however, not with overnight performance improvement in a declarative memory task. SpA might therefore reflect the efficiency of the thalamocortical network and can be seen as a marker for learning during encoding in wakefulness, i.e. learning efficiency.

Consolidation of temporal order in episodic memories

Even though it is known that sleep benefits declarative memory consolidation, the role of sleep in the storage of temporal sequences has rarely been examined. Thus we explored the influence of sleep on temporal order in an episodic memory task followed by sleep or sleep deprivation. Thirty-four healthy subjects (17 men) aged between 19 and 28 years participated in the randomized, counterbalanced, between-subject design. Parameters of interests were NREM/REM cycles, spindle activity and spindle-related EEG power spectra. Participants of both groups (sleep group/sleep deprivation group) performed retrieval in the evening, morning and three days after the learning night. Results revealed that performance in temporal order memory significantly deteriorated over three days only in sleep deprived participants. Furthermore our data showed a positive relationship between the ratios of the (i) first NREM/REM cycle with more REM being associated with delayed temporal order recall. Most interestingly, data additionally indicated that (ii) memory enhancers in the sleep group show more fast spindle related alpha power at frontal electrode sites possibly indicating access to a yet to be consolidated memory trace. We suggest that distinct sleep mechanisms subserve different aspects of episodic memory and are jointly involved in sleep-dependent memory consolidation.

Sleep spindles predict neural and behavioral changes in motor sequence consolidation

The purpose of this study was to investigate the predictive function of sleep spindles in motor sequence consolidation. BOLD responses were acquired in 10 young healthy subjects who were trained on an explicitly known 5-item sequence using their left nondominant hand, scanned at 9:00 pm while performing that same task and then were retested and scanned 12 h later after a night of sleep during which polysomnographic measures were recorded. An automatic algorithm was used to detect sleep spindles and to quantify their characteristics (i.e., density, amplitude, and duration). Analyses revealed significant positive correlations between gains in performance and the amplitude of spindles. Moreover, significant increases in BOLD signal were observed in several motor-related areas, most of which were localized in the right hemisphere, particularly in the right cortico-striatal system. Such increases in BOLD signal also correlated positively with the amplitude of spindles at several derivations. Taken together, our results show that sleep spindles predict neural and behavioral changes in overnight motor sequence consolidation.

High density electroencephalography in sleep research: potential, problems, future perspective

High density EEG (hdEEG) during sleep combines the superior temporal resolution of EEG recordings with high spatial resolution. Thus, this method allows a topographical analysis of sleep EEG activity and thereby fosters the shift from a global view of sleep to a local one. HdEEG allowed to investigate sleep rhythms in terms of their characteristic behavior (e.g., the traveling of slow waves) and in terms of their relationship to cortical functioning (e.g., consciousness and cognitive abilities). Moreover, recent studies successfully demonstrated that hdEEG can be used to study brain functioning in neurological and neuro-developmental disorders, and to evaluate therapeutic approaches. This review highlights the potential, the problems, and future perspective of hdEEG in sleep research.

The role of NREM sleep instability in child cognitive performance

STUDY OBJECTIVES

Based on recent reports of the involvement of cyclic alternating pattern (CAP) in cognitive functioning in adults, we investigated the association between CAP parameters and cognitive performance in healthy children.

DESIGN

Polysomnographic assessment and standardized neurocognitive testing in healthy children.

SETTINGS

Sleep laboratory.

PARTICIPANTS

Forty-two children aged 7.6 ± 2.7 years, with an even distribution of body mass percentile (58.5 ± 25.5) and SES reflective of national norms.

MEASUREMENTS

Analysis of sleep macrostructure following the R&K criteria and of cyclic alternating pattern (CAP). The neurocognitive tests were the Stanford Binet Intelligence Scale (5(th) edition) and a Neuropsychological Developmental Assessment (NEPSY) RESULTS: Fluid reasoning ability was positively associated with CAP rate, particularly during SWS and with A1 total index and A1 index in SWS. Regression analysis, controlling for age and SES, showed that CAP rate in SWS and A1 index in SWS were significant predictors of nonverbal fluid reasoning, explaining 24% and 22% of the variance in test scores, respectively.

CONCLUSION

This study shows that CAP analysis provides important insights on the role of EEG slow oscillations (CAP A1) in cognitive performance. Children with higher cognitive efficiency showed an increase of phase A1 in total sleep and in SWS.

Sleep electroencephalography topography and children's intellectual ability

The aim of the study was to investigate the relationship between regional aspects of the children's sleep electroencephalogram (EEG) (high-density EEG recordings) and their intellectual ability. The spectral power in the α, σ, and β frequency ranges of 109 EEG derivations was correlated with the scores of full-scale intelligence quotient, fluid intelligence quotient, and working memory (14 participants, mean age: 10.5±1.0 years; six girls). The previously reported relationship (derivation C3/A2) between spectral band power and intellectual ability could further be refined, particular spatial patterns over central and parietal areas with positive correlations were found. Thus, neurobiological correlates of intelligence during sleep may exhibit brain region-specific patterns.

Visually detected NREM Stage 2 sleep spindles in kindergarten children are associated with stress challenge and coping strategies

OBJECTIVES

Sleep EEG spindles are linked to efficient cortical-subcortical connectivity and intellectual abilities. The aim of the present study was to investigate the relationship of spindle activity to psychosocial stress response and coping strategies in healthy kindergarten children.

METHODS

In a cross-sectional study of 41 5-year old kindergarten children we examined stress-induced hypothalamic-pituitary-adrenocortical (HPA) system activity by saliva cortisol measurements and sleep regulation by sleep EEG-monitoring. Stress response was measured during the application of a standardized psychological challenge appropriate at this age. NREM S2 sleep EEG spindles were visually scored and put into relation to coping and HPA activity parameters.

RESULTS

An increased total number of NREM S2 sleep spindles correlated positively with increased high ego-involvement strategies such as "positive emotions". By contrast, total number of NREM S2 sleep spindles correlated negatively with low ego-involvement strategies such as "denial" and "avoidance". Stress induced HPA-activity correlated positively with coping strategies with high ego-involvement; while there was no correlation with low ego-involvement strategies.

CONCLUSIONS

Total number of visually detected NREM S2 sleep spindles is elevated in children with coping involving positive, high ego-involvement; in contrast, low ego-involvement during stress is associated with reduced total number of NREM S2 sleep spindles.

Large-scale functional brain networks in human non-rapid eye movement sleep: insights from combined electroencephalographic/functional magnetic resonance imaging studies

This paper reviews the existing body of knowledge on the neural correlates of spontaneous oscillations, functional connectivity and brain plasticity in human non-rapid eye movement (NREM) sleep. The first section reviews the evidence that specific sleep events as slow waves and spindles are associated with transient increases in regional brain activity. The second section describes the changes in functional connectivity during NREM sleep, with a particular focus on changes within a low-frequency, large-scale functional brain network. The third section will discuss the possibility that spontaneous oscillations and differential functional connectivity are related to brain plasticity and systems consolidation, with a particular focus on motor skill acquisition. Implications for the mode of information processing per sleep stage and future experimental studies are discussed.

Corticothalamic feedback controls sleep spindle duration in vivo

Spindle oscillations are commonly observed during stage 2 of non-rapid eye movement sleep. During sleep spindles, the cerebral cortex and thalamus interact through feedback connections. Both initiation and termination of spindle oscillations are thought to originate in the thalamus based on thalamic recordings and computational models, although some in vivo results suggest otherwise. Here, we have used computer modeling and in vivo multisite recordings from the cortex and the thalamus in cats to examine the involvement of the cortex in spindle oscillations. We found that although the propagation of spindles depended on synaptic interaction within the thalamus, the initiation and termination of spindle sequences critically involved corticothalamic influences.