Giovanni Piantoni

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

BackgroundThe 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.ResultsSignificant 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.ConclusionsThe 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 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.

Does sleep restore the topology of functional brain networks

Previous studies have shown that healthy anatomical as well as functional brain networks have small‐world properties and become less optimal with brain disease. During sleep, the functional brain network becomes more small‐world‐like. Here we test the hypothesis that the functional brain network during wakefulness becomes less optimal after sleep deprivation (SD). Electroencephalography (EEG) was recorded five times a day after a night of SD and after a night of normal sleep in eight young healthy subjects, both during eyes‐closed and eyes‐open resting state. Overall synchronization was determined with the synchronization likelihood (SL) and the phase lag index (PLI). From these coupling strength matrices the normalized clustering coefficient C (a measurement of local clustering) and path length L (a measurement of global integration) were computed. Both measures were normalized by dividing them by their corresponding C‐s and L‐s values of random control networks. SD reduced alpha band C/C‐s and L/L‐s and theta band C/C‐s during eyes‐closed resting state. In contrast, SD increased gamma‐band C/C‐s and L/L‐s during eyes‐open resting state. Functional relevance of these changes in network properties was suggested by their association with sleep deprivation‐induced performance deficits on a sustained attention simple reaction time task. The findings indicate that SD results in a more random network of alpha‐coupling and a more ordered network of gamma‐coupling. The present study shows that SD induces frequency‐specific changes in the functional network topology of the brain, supporting the idea that sleep plays a role in the maintenance of an optimal functional network. Hum Brain Mapp, 2013.

Rotating waves during human sleep spindles organize global patterns of activity that repeat precisely through the night

During sleep, the thalamus generates a characteristic pattern of transient, 11-15 Hz sleep spindle oscillations, which synchronize the cortex through large-scale thalamocortical loops. Spindles have been increasingly demonstrated to be critical for sleep-dependent consolidation of memory, but the specific neural mechanism for this process remains unclear. We show here that cortical spindles are spatiotemporally organized into circular wave-like patterns, organizing neuronal activity over tens of milliseconds, within the timescale for storing memories in large-scale networks across the cortex via spike-time dependent plasticity. These circular patterns repeat over hours of sleep with millisecond temporal precision, allowing reinforcement of the activity patterns through hundreds of reverberations. These results provide a novel mechanistic account for how global sleep oscillations and synaptic plasticity could strengthen networks distributed across the cortex to store coherent and integrated memories. DOI: http://dx.doi.org/10.7554/eLife.17267.001

Modulation of gamma and spindle-range power by slow oscillations in scalp sleep EEG of children

Deep sleep is characterized by slow waves of electrical activity in the cerebral cortex. They represent alternating down states and up states of, respectively, hyperpolarization with accompanying neuronal silence and depolarization during which neuronal firing resumes. The up states give rise to faster oscillations, notably spindles and gamma activity which appear to be of major importance to the role of sleep in brain function and cognition. Unfortunately, while spindles are easily detectable, gamma oscillations are of very small amplitude. No previous sleep study has succeeded in demonstrating modulations of gamma power along the time course of slow waves in human scalp EEG. As a consequence, progress in our understanding of the functional role of gamma modulation during sleep has been limited to animal studies and exceptional human studies, notably those of intracranial recordings in epileptic patients. Because high synaptic density, which peaks some time before puberty depending on the brain region (Huttenlocher and Dabholkar, 1997), generates oscillations of larger amplitude, we considered that the best chance to demonstrate a modulation of gamma power by slow wave phase in regular scalp sleep EEG would be in school-aged children. Sleep EEG was recorded in 30 healthy children (aged 10.7 ± 0.8 years; mean ± s.d.). Time-frequency analysis was applied to evaluate the time course of spectral power along the development of a slow wave. Moreover, we attempted to modify sleep architecture and sleep characteristics through automated acoustic stimulation coupled to the occurrence of slow waves in one subset of the children. Gamma power increased on the rising slope and positive peak of the slow wave. Gamma and spindle activity is strongly suppressed during the negative peak. There were no differences between the groups who received and did not receive acoustic stimulation in the sleep parameters and slow wave-locked time-frequency analysis. Our findings show, for the first time in scalp EEG in humans, that gamma activity is associated with the up-going slope and peak of the slow wave. We propose that studies in children provide a uniquely feasible opportunity to conduct investigations into the role of gamma during sleep.

The Contribution of Thalamocortical Core and Matrix Pathways to Sleep Spindles

Sleep spindles arise from the interaction of thalamic and cortical neurons. Neurons in the thalamic reticular nucleus (TRN) inhibit thalamocortical neurons, which in turn excite the TRN and cortical neurons. A fundamental principle of anatomical organization of the thalamocortical projections is the presence of two pathways: the diffuse matrix pathway and the spatially selective core pathway. Cortical layers are differentially targeted by these two pathways with matrix projections synapsing in superficial layers and core projections impinging on middle layers. Based on this anatomical observation, we propose that spindles can be classified into two classes, those arising from the core pathway and those arising from the matrix pathway, although this does not exclude the fact that some spindles might combine both pathways at the same time. We find evidence for this hypothesis in EEG/MEG studies, intracranial recordings, and computational models that incorporate this difference. This distinction will prove useful in accounting for the multiple functions attributed to spindles, in that spindles of different types might act on local and widespread spatial scales. Because spindle mechanisms are often hijacked in epilepsy and schizophrenia, the classification proposed in this review might provide valuable information in defining which pathways have gone awry in these neurological disorders.

Small vessel disease and cognitive impairment: The relevance of central network connections

Central brain network connections greatly contribute to overall network efficiency. Here we examined whether small vessel disease (SVD) related white matter alterations in central brain network connections have a greater impact on executive functioning than alterations in non‐central brain network connections. Brain networks were reconstructed from diffusion‐weighted MRI scans in 72 individuals (75 ± 8 years) with cognitive impairment and SVD on MRI. The centrality of white matter connections in the network was defined using graph theory. The association between the fractional anisotropy (FA) of central versus non‐central connections, executive functioning, and markers of SVD was evaluated with linear regression and mediation analysis. Lower FA in central network connections was more strongly associated with impairment in executive functioning than FA in non‐central network connections (r = 0.41 vs. r = 0.27; P < 0.05). Results were consistent across varying thresholds to define the central subnetwork (>50%–10% connections). Higher SVD burden was associated with lower FA in central as well as non‐central network connections. However, only central network FA mediated the relationship between white matter hyperintensity volume and executive functioning [change in regression coefficient after mediation (95% CI): −0.15 (−0.35 to −0.02)]. The mediation effect was not observed for FA alterations in non‐central network connections [−0.03 (−0.19 to 0.04)]. These findings suggest that the centrality of network connections, and thus their contribution to global network efficiency, appears to be relevant for understanding the relationship between SVD and cognitive impairment. Hum Brain Mapp 37:2446–2454, 2016.

Spatiotemporal characteristics of sleep spindles depend on cortical location

Abstract Since their discovery almost one century ago, sleep spindles, 0.5–2 s long bursts of oscillatory activity at 9–16 Hz during NREM sleep, have been thought to be global and relatively uniform throughout the cortex. Recent work, however, has brought this concept into question but it remains unclear to what degree spindles are global or local and if their properties are uniform or location‐dependent. We addressed this question by recording sleep in eight patients undergoing evaluation for epilepsy with intracranial electrocorticography, which combines high spatial resolution with extensive cortical coverage. We find that spindle characteristics are not uniform but are strongly influenced by the underlying cortical regions, particularly for spindle density and fundamental frequency. We observe both highly isolated and spatially distributed spindles, but in highly skewed proportions: while most spindles are restricted to one or very few recording channels at any given time, there are spindles that occur over widespread areas, often involving lateral prefrontal cortices and superior temporal gyri. Their co‐occurrence is affected by a subtle but significant propagation of spindles from the superior prefrontal regions and the temporal cortices towards the orbitofrontal cortex. This work provides a brain‐wide characterization of sleep spindles as mostly local graphoelements with heterogeneous characteristics that depend on the underlying cortical area. We propose that the combination of local characteristics and global organization reflects the dual properties of the thalamo‐cortical generators and provides a flexible framework to support the many functions ascribed to sleep in general and spindles specifically. HighlightsSpindles are not uniform but are strongly influenced by underlying cortical regions.Isolated spindles are more common but widespread spindles occur as well.Although isolated, spindles are organized at the brain‐wide level.

Coupling of infraslow fluctuations in autonomic and central vigilance markers: Skin temperature, EEG beta power and ERP P300 latency

Even under thermoneutral conditions, skin temperature fluctuates spontaneously, most prominently at distal parts of the body. These fluctuations were shown to be associated with fluctuations in vigilance: mild manipulation of skin temperature during nocturnal sleep affects sleep depth and the power spectral density of the electroencephalogram (EEG), and fluctuations in skin temperature during daytime wakefulness are related to sleep propensity and task performance. The association of daytime skin temperature fluctuations with EEG markers of vigilance has not previously been investigated. Therefore, the present study aimed to evaluate the association between daytime fluctuations in skin temperature with those in two quantitative EEG measures: the power spectral density of background EEG, and the event related potential (ERP) elicited by visual stimuli. High-density EEG and skin temperature were obtained in eight healthy adults five times a day while they performed a visual sustained-attention task. Assessments were made after a night of normal sleep and after the challenge of a night of total sleep deprivation. Fluctuations in the distal-to-proximal skin temperature gradient measured from the earlobe and mastoid were associated with fluctuations in parieto-occipital high beta band (20-40 Hz) power of the pre-stimulus background EEG, but only after sleep deprivation. The temperature fluctuations were moreover associated with fluctuations in the latency of the P300 elicited by the stimulus. The findings demonstrate close association between fluctuations in an autonomic correlate of the vigilance state (i.e. the distal-to-proximal skin temperature gradient), and fluctuations in central nervous system correlates of the vigilance state (i.e. background EEG and ERP). The findings are of theoretical and practical relevance for the assessment and manipulation of vigilance.