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.

BackgroundInfertility is often associated with a chronic state of stress which may manifest itself in anxiety-related and depressive symptoms. The aim of our study is to assess the psychological state of women with and without fertility problems, and to investigate the background factors of anxiety-related and depressive symptoms in women struggling with infertility.MethodsOur study was conducted with the participation of 225 (134 primary infertile and 91 fertile) women, recruited in a clinical setting and online. We used the following questionnaires: Spielberger Trait Anxiety Inventory (STAI-T), Shortened Beck Depression Inventory (BDI) and Fertility Problem Inventory (FPI). We also interviewed our subjects on the presence of other sources of stress (the quality of the relationship with their mother, financial and illness-related stress), and we described sociodemographic and fertility-specific characteristics. We tested our hypotheses using independent-samples t-tests (M ± SD) and multiple linear regression modelling (ß).ResultsInfertile women were younger (33.30 ± 4.85 vs. 35.74 ± 5.73, p = .001), but had significantly worse psychological well-being (BDI = 14.94 ± 12.90 vs. 8.95 ± 10.49, p < .0001; STAI-T = 48.76 ± 10.96 vs. 41.18 ± 11.26, p < .0001) than fertile subjects. Depressive symptoms and anxiety in infertile women were associated with age, social concern, sexual concern and maternal relationship stress. Trait anxiety was also associated with financial stress. Our model was able to account for 58% of the variance of depressive symptoms and 62% of the variance of trait anxiety.ConclusionsDepressive and anxiety-related symptoms of infertile women are more prominent than those of fertile females. The measurement of these indicators and the mitigation of underlying distress by adequate psychosocial interventions should be encouraged.

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.

Features of sleep were shown to reflect aging, typical sex differences and cognitive abilities of humans. However, these measures are characterized by redundancy and arbitrariness. Our present approach relies on the assumptions that the spontaneous human brain activity as reflected by the scalp-derived electroencephalogram (EEG) during non-rapid eye movement (NREM) sleep is characterized by arrhythmic, scale-free properties and is based on the power law scaling of the Fourier spectra with the additional consideration of the rhythmic, oscillatory waves at specific frequencies, including sleep spindles. Measures derived are the spectral intercept and slope, as well as the maximal spectral peak amplitude and frequency in the sleep spindle range, effectively reducing 191 spectral measures to 4, which were efficient in characterizing known age-effects, sex-differences and cognitive correlates of sleep EEG. Future clinical and basic studies are supposed to be significantly empowered by the efficient data reduction provided by our approach.