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 deprivation leads to a loss of functional connectivity in frontal brain regions.

BACKGROUND: The restorative effect of sleep on waking brain activity remains poorly understood. Previous studies have compared overall neural network characteristics after normal sleep and sleep deprivation. To study whether sleep and sleep deprivation might differentially affect subsequent connectivity characteristics in different brain regions, we performed a within-subject study of resting state brain activity using the graph theory framework adapted for the individual electrode level.In balanced order, we obtained high-density resting state electroencephalography (EEG) in 8 healthy participants, during a day following normal sleep and during a day following total sleep deprivation. We computed topographical maps of graph theoretical parameters describing local clustering and path length characteristics from functional connectivity matrices, based on synchronization likelihood, in five different frequency bands. A non-parametric permutation analysis with cluster correction for multiple comparisons was applied to assess significance of topographical changes in clustering coefficient and path length. RESULTS: Significant changes in graph theoretical parameters were only found on the scalp overlying the prefrontal cortex, where the clustering coefficient (local integration) decreased in the alpha frequency band and the path length (global integration) increased in the theta frequency band. These changes occurred regardless, and independent of, changes in power due to the sleep deprivation procedure. CONCLUSIONS: The findings indicate that sleep deprivation most strongly affects the functional connectivity of prefrontal cortical areas. The findings extend those of previous studies, which showed sleep deprivation to predominantly affect functions mediated by the prefrontal cortex, such as working memory. Together, these findings suggest that the restorative effect of sleep is especially relevant for the maintenance of functional connectivity of prefrontal brain regions.

Sleep to the beat: A nap favours consolidation of timing.

Growing evidence suggests that sleep is important for procedural learning, but few studies have investigated the effect of sleep on the temporal aspects of motor skill learning. We assessed the effect of a 90-min day-time nap on learning a motor timing task, using 2 adaptations of a serial interception sequence learning (SISL) task. Forty-two right-handed participants performed the task before and after a 90-min period of sleep or wake. Electroencephalography (EEG) was recorded throughout. The motor task consisted of a sequential spatial pattern and was performed according to 2 different timing conditions, that is, either following a sequential or a random temporal pattern. The increase in accuracy was compared between groups using a mixed linear regression model. Within the sleep group, performance improvement was modeled based on sleep characteristics, including spindle- and slow-wave density. The sleep group, but not the wake group, showed improvement in the random temporal, but especially and significantly more strongly in the sequential temporal condition. None of the sleep characteristics predicted improvement on either general of the timing conditions. In conclusion, a daytime nap improves performance on a timing task. We show that performance on the task with a sequential timing sequence benefits more from sleep than motor timing. More important, the temporal sequence did not benefit initial learning, because differences arose only after an offline period and specifically when this period contained sleep. Sleep appears to aid in the extraction of regularities for optimal subsequent performance. (PsycINFO Database Record

Cold hands, warm feet: sleep deprivation disrupts thermoregulation and its association with vigilance.

STUDY OBJECTIVES: Vigilance is affected by induced and spontaneous skin temperature fluctuations. Whereas sleep deprivation strongly affects vigilance, no previous study examined in detail its effect on human skin temperature fluctuations and their association with vigilance. DESIGN: In a repeated-measures constant routine design, skin temperatures were assessed continuously from 14 locations while performance was assessed using a reaction time task, including eyes-open video monitoring, performed five times a day for 2 days, after a normal sleep or sleep deprivation night. SETTING: Participants were seated in a dimly lit, temperature-controlled laboratory. PATIENTS OR PARTICIPANTS: Eight healthy young adults (five males, age 22.0 ± 1.8 yr (mean ± standard deviation)). INTERVENTION: One night of sleep deprivation. MEASUREMENTS AND RESULTS: Mixed-effect regression models were used to evaluate the effect of sleep deprivation on skin temperature gradients of the upper (ear-mastoid), middle (hand-arm), and lower (foot-leg) body, and on the association between fluctuations in performance and in temperature gradients. Sleep deprivation induced a marked dissociation of thermoregulatory skin temperature gradients, indicative of attenuated heat loss from the hands co-occurring with enhanced heat loss from the feet. Sleep deprivation moreover attenuated the association between fluctuations in performance and temperature gradients; the association was best preserved for the upper body gradient. CONCLUSIONS: Sleep deprivation disrupts coordination of fluctuations in thermoregulatory skin temperature gradients. The dissociation of middle and lower body temperature gradients may therefore be evaluated as a marker for sleep debt, and the upper body gradient as a possible aid in vigilance assessment when sleep debt is unknown. Importantly, our findings suggest that sleep deprivation affects the coordination between skin blood flow fluctuations and the baroreceptor-mediated cardiovascular regulation that prevents venous pooling of blood in the lower limbs when there is the orthostatic challenge of an upright posture.