Péter P. Ujma

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.

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.

Reliability and validity of the Hungarian version of the Pittsburgh Sleep Quality Index (PSQI-HUN): comparing psychiatric patients with control subjects.

PurposeThe Pittsburgh Sleep Quality Index is used to evaluate subjective sleep quality, and it is commonly used in clinical research. Subjective sleep quality is also an important clinical measure in patients with psychiatric disorders. The aim of the present study was to evaluate the reliability and validity of the Hungarian version of the Pittsburgh Sleep Quality Index (PSQI-HUN) in both clinical and non-clinical samples.MethodsThe original version of PSQI was translated into Hungarian according to standard guidelines. The PSQI-HUN and the Athens Insomnia Scale (AIS) were subsequently administered to 53 psychiatric patients (schizophrenia, recurrent depressive disorder, mixed anxiety, and depressive disorder) and 178 healthy controls.ResultsInternal consistency as measured by Cronbach’s alpha in the whole sample was 0.79. Pearson’s product-moment correlations between component scores and the global scores were high (0.59–0.88) in the PSQI-HUN indicating the homogeneity of the scale. PSQI-HUN global and component scores differed significantly between psychiatric patients and control subjects. In the psychiatric patient subsample, schizophrenics had lower global scores compared to the other two patient groups. The analysis of convergent validity showed significant correlations between the AIS and the global as well as the component scores of the PSQI-HUN (except the component of sleep latency).ConclusionsThe present study concludes that the PSQI-HUN is a reliable, valid, and standardized measure for assessment of the subjective sleep quality in clinical and research settings.

Age-related changes in sleep EEG are attenuated in highly intelligent individuals

Abstract Impaired sleep is a frequent complaint in ageing and a risk factor for many diseases. Non‐rapid eye movement (NREM) sleep EEG delta power reflects neural plasticity and, in line with age‐related cognitive decline, decreases with age. Individuals with higher general intelligence are less affected by age‐related cognitive decline or other disorders and have longer lifespans. We investigated the correlation between age and EEG power in 159 healthy human subjects (age range: 17–69 years), and compared an average (IQ<120; N=87) with a high (IQ≥120; N=72) intelligence subgroup. We found less age‐related decrease in all‐night relative NREM sleep EEG delta power in the high intelligence subgroup. Our results suggest that highly intelligent individuals are less affected by the sleep‐related effects of biological ageing, and therefore potentially less at risk for age‐related cognitive deficits and other diseases.

Corrigendum: A comparison of two sleep spindle detection methods based on all night averages: individually adjusted vs. fixed frequencies (vol 9, pg 52, 2015)

[This corrects the article on p. 52 in vol. 9, PMID: 25741264.].

The sleep EEG spectrum is a sexually dimorphic marker of general intelligence

The shape of the EEG spectrum in sleep relies on genetic and anatomical factors and forms an individual “EEG fingerprint”. Spectral components of EEG were shown to be connected to mental ability both in sleep and wakefulness. EEG sleep spindle correlates of intelligence, however, exhibit a sexual dimorphism, with a more pronounced association to intelligence in females than males. In a sample of 151 healthy individuals, we investigated how intelligence is related to spectral components of full-night sleep EEG, while controlling for the effects of age. A positive linear association between intelligence and REM anterior beta power was found in females but not males. Transient, spindle-like “REM beta tufts” are described in the EEG of healthy subjects, which may reflect the functioning of a recently described cingular-prefrontal emotion and motor regulation network. REM sleep frontal high delta power was a negative correlate of intelligence. NREM alpha and sigma spectral power correlations with intelligence did not unequivocally remain significant after multiple comparisons correction, but exhibited a similar sexual dimorphism. These results suggest that the neural oscillatory correlates of intelligence in sleep are sexually dimorphic, and they are not restricted to either sleep spindles or NREM sleep.

Individual slow wave morphology is a marker of ageing

Slow wave activity is a hallmark of deep NREM sleep. Scalp slow wave morphology is stereotypical, it is highly correlated with the synchronized onset and cessation of cortical neuronal firing measured from the surface or depth of the cortex, strongly affected by ageing, and these changes are causally associated with age-related cognitive decline. We investigated how normal ageing affects the individual morphology of the slow wave, and whether these changes are captured by the summary slow wave parameters generally used in the literature. We recorded full-night polysomnography in 159 subjects (age 17-69 years) and automatically detected slow waves using six different detection methods to ensure methodological robustness. We established individual slow morphologies at 501 data points for each subject and also calculated the individual average slow wave amplitude, average ascending and descending slope steepness and the total number of slow waves (gross parameters). Using LASSO penalized regression we found that fine-grained slow wave morphology is associated with age beyond gross parameters, with young subjects having faster slow wave polarity reversals, suggesting a more efficient initiation and termination of slow wave down- and upstates. Our results demonstrate the superiority of the high-resolution slow wave morphology as a biomarker of ageing, and highlights state transitions as promising targets of restorative stimulation-based interventions.

Lateralized rhythmic acoustic stimulation during daytime NREM sleep enhances slow waves

Slow wave sleep (SWS) is characterized by the predominance of delta waves and slow oscillations, reflecting the synchronized activity of large cortical neuronal populations. Amongst other functions, SWS plays a crucial role in the restorative capacity of sleep. Rhythmic acoustic stimulation (RAS) during SWS has been shown a cost-effective method to enhance slow wave activity. Slow wave activity can be expressed in a region-specific manner as a function of previous waking activity. However, it is unclear whether slow waves can be enhanced in a region-specific manner using RAS. We investigated the effects of unilaterally presented rhythmic acoustic sound patterns on sleep electroencephalographic (EEG) oscillations. Thirty-five participants received during SWS 12-second long rhythmic bursts of pink noise (at a rate of 1 Hz) that alternated with non-stimulated, silent periods, unilaterally delivered into one of the ears of the participants. As expected, RAS enhanced delta power, especially in its low-frequency components between 0.75 and 2.25 Hz. However, increased slow oscillatory activity was apparent in both hemispheres regardless of the side of the stimulation. The most robust increases in slow oscillatory activity appeared during the first 3-4 seconds of the stimulation period. Furthermore, a short-lasting increase in theta and sigma power was evidenced immediately after the first pulse of the stimulation sequences. Our findings indicate that lateralized RAS has a strong potential to globally enhance slow waves during daytime naps. The lack of localized effects suggests that slow waves are triggered by the ascending reticular system and not directly by specific auditory pathways.

Increased cortical involvement and synchronization during CAP A1 slow waves

Slow waves recorded with EEG in NREM sleep are indicative of the strength and spatial extent of synchronized firing in neuronal assemblies of the cerebral cortex. Slow waves often appear in the A1 part of the cyclic alternating patterns (CAP), which correlate with a number of behavioral and biological parameters, but their physiological significance is not adequately known. We automatically detected slow waves from the scalp recordings of 37 healthy patients, visually identified CAP A1 events and compared slow waves during CAP A1 with those during NCAP. For each slow wave, we computed the amplitude, slopes, frequency, synchronization (synchronization likelihood) between specific cortical areas, as well as the location of origin and scalp propagation of individual waves. CAP A1 slow waves were characterized by greater spatial extent and amplitude, steeper slopes and greater cortical synchronization, but a similar prominence in frontal areas and similar propagation patterns to other areas on the scalp. Our results indicate that CAP A1 represents a period of highly synchronous neuronal firing over large areas of the cortical mantle. This feature may contribute to the role CAP A1 plays in both normal synaptic homeostasis and in the generation of epileptiform phenomena in epileptic patients.