Human Prosocial Preferences Are Related to Slow-Wave Activity in Sleep.

Prosocial behavior is crucial for the smooth functioning of the society. Yet, individuals differ vastly in the propensity to behave prosocially. Here, we try to explain these individual differences under normal sleep conditions without any experimental modulation of sleep. Using a portable high-density EEG, we measured the sleep data in 54 healthy adults (28 females) during a normal night's sleep at the participants' homes. To capture prosocial preferences, participants played an incentivized public goods game in which they faced real monetary consequences. The whole-brain analyses showed that a higher relative slow-wave activity (SWA, an indicator of sleep depth) in a cluster of electrodes over the right temporoparietal junction (TPJ) was associated with increased prosocial preferences. Source localization and current source density analyses further support these findings. Recent sleep deprivation studies imply that sleeping enough makes us more prosocial; the present findings suggest that it is not only sleep duration, but particularly sufficient sleep depth in the TPJ that is positively related to prosociality. Because the TPJ plays a central role in social cognitive functions, we speculate that sleep depth in the TPJ, as reflected by relative SWA, might serve as a dispositional indicator of social cognition ability, which is reflected in prosocial preferences. These findings contribute to the emerging framework explaining the link between sleep and prosocial behavior by shedding light on the underlying mechanisms.

Local slow-wave activity over the right prefrontal cortex reveals individual risk preferences.

In everyday life, we have to make decisions under varying degrees of risk. Even though previous research has shown that the manipulation of sleep affects risky decision-making, it remains unknown whether individual, temporally stable neural sleep characteristics relate to individual differences in risk preferences. Here, we collected sleep data under normal conditions in fifty-four healthy adults using a portable high-density EEG at participants' home. Whole-brain corrected for multiple testing, we found that lower slow-wave activity (SWA, an indicator of sleep depth) in a cluster of electrodes over the right prefrontal cortex (PFC) is associated with higher individual risk propensity. Importantly, the association between local sleep depth and risk preferences remained significant when controlling for total sleep time and for time spent in deep sleep, i.e., sleep stages N2 and N3. Moreover, the association between risk preferences and SWA over the right PFC was very similar in all sleep cycles. Because the right PFC plays a central role in cognitive control functions, we speculate that local sleep depth in this area, as reflected by SWA, might serve as a dispositional indicator of self-regulatory ability, which in turn reflects risk preferences.

Different Behavioral Types of Distributional Preferences Are Characterized by Distinct Neural Signatures.

There are many situations where resources are distributed between two parties and where the deciding party has information about the initial distribution and can change its outcome, for example, the allocation of budget for funds or bonuses, where the deciding party might have self-interested motives. Although the neural underpinnings of distributional preferences of resources have been extensively studied, it remains unclear if there are different types of distributional preferences and if these types underlie different disposing neural signatures. We used source-localized resting EEG in combination with a data-driven clustering approach to participants' behavior in a distribution game in order to disentangle the neural sources of the different types of distributional preferences. Our findings revealed four behavioral types: Maximizing types always changed initial distributions to maximize their personal outcomes, and compliant types always left initial distributions unchanged. Disadvantage-averse types only changed initial distributions if they received less than the other party did, and equalizing types primarily changed initial distributions to fair distributions. These behavioral types differed regarding neural baseline activation in the right inferior frontal gyrus. Maximizing and compliant types showed the highest baseline activation, followed by disadvantage-averse types and equalizing types. Furthermore, maximizing types showed significantly higher baseline activation in the left orbitofrontal cortex compared to compliant types. Taken together, our findings show that different types of distributional preferences are characterized by distinct neural signatures, which further imply differences in underlying psychological processes in decision-making.

Diurnal changes in human brain glutamate + glutamine levels in the course of development and their relationship to sleep.

Sleep slow waves during non-rapid eye movement (NREM) sleep play a crucial role in maintaining cortical plasticity, a process that is especially important in the developing brain. Children show a considerably larger overnight decrease in slow wave activity (SWA; the power in the EEG frequency band between 1 and 4.5 â€‹Hz during NREM sleep), which constitutes the primary electrophysiological marker for the restorative function of sleep. We previously demonstrated in adults that this marker correlates with the overnight reduction in cortical glutamate â€‹+ â€‹glutamine (GLX) levels assessed by magnetic resonance spectroscopy (MRS), proposing GLX as a promising biomarker for the interplay between cortical plasticity and SWA. Here, we used a multimodal imaging approach of combined MRS and high-density EEG in a cross-sectional cohort of 46 subjects from 8 to 24 years of age in order to examine age-related changes in GLX and its relation to SWA. Gray matter volume, GLX levels and SWA showed the expected age-dependent decrease. Unexpectedly, the overnight changes in GLX followed opposite directions when comparing children to adults. These age-related changes could neither be explained by the overnight decrease in SWA nor by circadian factors.

Interindividual differences in mindfulness are linked to sleep-electroencephalographic characteristics.

STUDY OBJECTIVES: Mindfulness describes the ability to focus on the presence, including one's thoughts and feelings. Trait mindfulness-a person's inherent tendency to be mindful-has been connected to increased subjective sleep quality, but evidence from objective EEG-based sleep measures is lacking. Here, we investigate whether objective EEG-based sleep parameters explain interindividual differences in trait mindfulness. METHODS: Whole-night polysomnographic data were gathered from 52 healthy adults (27 females; agemean = 21.5 [SE = 0.28]) in their homes using a portable high-density EEG device. Trait mindfulness was assessed using the Five Facet Mindfulness Questionnaire short form (FFMQ-SF). RESULTS: Trait mindfulness was positively correlated at trend level with the percentage of rapid eye movement (REM), but not N1, N2, or slow wave sleep. Additionally, those exhibiting less REM beta/gamma power and NREM beta power displayed higher trait mindfulness and vice versa. Lastly, we replicated findings connecting higher trait mindfulness to better subjective sleep quality. CONCLUSIONS: REM sleep is pivotal for emotional processing. Decreased REM high-frequency activity was suggested to reflect adrenergic reduction that defuses affective experiences. Increased NREM high-frequency activity is a marker for cognitive hyperarousal in insomnia. We speculate that differences in trait mindfulness might be explained by differences in REM- and NREM-sleep functions that promote ideal emotional regulation and prevent hyperarousal.

Wake EEG oscillation dynamics reflect both sleep need and brain maturation across childhood and adolescence.

An objective measure of brain maturation is highly insightful for monitoring both typical and atypical development. Slow wave activity, recorded in the sleep electroencephalogram (EEG), reliably indexes changes in brain plasticity with age, as well as deficits related to developmental disorders such as attention-deficit hyperactivity disorder (ADHD). Unfortunately, measuring sleep EEG is resource-intensive and burdensome for participants. We therefore aimed to determine whether wake EEG could likewise index developmental changes in brain plasticity. We analyzed high-density wake EEG collected from 163 participants 3-25 years old, before and after a night of sleep. We compared two measures of oscillatory EEG activity, amplitudes and density, as well as two measures of aperiodic activity, intercepts and slopes. Furthermore, we compared these measures in patients with ADHD (8-17 y.o., N=58) to neurotypical controls. We found that wake oscillation amplitudes behaved the same as sleep slow wave activity: amplitudes decreased with age, decreased after sleep, and this overnight decrease decreased with age. Oscillation densities were also substantially age-dependent, decreasing overnight in children and increasing overnight in adolescents and adults. While both aperiodic intercepts and slopes decreased linearly with age, intercepts decreased overnight, and slopes increased overnight. Overall, our results indicate that wake oscillation amplitudes track both development and sleep need, and overnight changes in oscillation density reflect some yet-unknown shift in neural activity around puberty. No wake measure showed significant effects of ADHD, thus indicating that wake EEG measures, while easier to record, are not as sensitive as those during sleep.