The microstructure of REM sleep: Why phasic and tonic?

Rapid eye movement (REM) sleep is a peculiar neural state that occupies 20-25% of nighttime sleep in healthy human adults and seems to play critical roles in a variety of functions spanning from basic physiological mechanisms to complex cognitive processes. REM sleep exhibits a plethora of transient neurophysiological features, such as eye movements, muscle twitches, and changes in autonomic activity, however, despite its heterogeneous nature, it is usually conceptualized as a homogeneous sleep state. We propose here that differentiating and exploring the fine microstructure of REM sleep, especially its phasic and tonic constituents would provide a novel framework to examine the mechanisms and putative functions of REM sleep. In this review, we show that phasic and tonic REM periods are remarkably different neural states with respect to environmental alertness, spontaneous and evoked cortical activity, information processing, and seem to contribute differently to the dysfunctions of REM sleep in several neurological and psychiatric disorders. We highlight that a distinctive view on phasic and tonic REM microstates would facilitate the understanding of the mechanisms and functions of REM sleep in healthy and pathological conditions.info:eu-repo/semantics/publishe

Loss of consciousness is related to hyper-1 correlated gamma-band activity in anesthetized macaques and sleeping humans

Loss of consciousness can result from a wide range of causes, including natural sleep and pharmacologically induced anesthesia. Important insights might thus come from identifying neuronal mechanisms of loss and re-emergence of consciousness independent of a specific manipulation. Therefore, to seek neuronal signatures of loss of consciousness common to sleep and anesthesia we analyzed spontaneous electrophysiological activity recorded in two experiments. First, electrocorticography (ECoG) acquired from 4 macaque monkeys anesthetized with different anesthetic agents (ketamine, medetomidine, propofol) and, second, stereo-electroencephalography (sEEG) from 10 epilepsy patients in different wake-sleep stages (wakefulness, NREM, REM). Specifically, we investigated co-activation patterns among brain areas, defined as correlations between local amplitudes of gamma-band activity. We found that resting wakefulness was associated with intermediate levels of gamma-band coupling, indicating neither complete dependence, nor full independence among brain regions. In contrast, loss of consciousness during NREM sleep and propofol anesthesia was associated with excessively correlated brain activity, as indicated by a robust increase of number and strength of positive correlations. However, such excessively correlated brain signals were not observed during REM sleep, and were present only to a limited extent during ketamine anesthesia. This might be related to the fact that, despite suppression of behavioral responsiveness, REM sleep and ketamine anesthesia often involve presence of dream-like conscious experiences. We conclude that hyper-correlated gamma-band activity might be a signature of loss of consciousness common across various manipulations and independent of behavioral responsiveness

Studying functional networks in human brain through intracerebral spontaneous EEG

none6G.Arnulfo; A.Pigorini; M.Massimini; L.Nobili; A.Schenone; M.M. FatoArnulfo, Gabriele; Pigorini, A.; Massimini, M.; Nobili, L.; Schenone, Andrea; Fato, MARCO MASSIM

Multi-task multiple kernel learning reveals relevant frequency bands for critical areas localization in focal epilepsy

The localization of epileptic zone in pharmacoresistant focal epileptic patients is a daunting task, typically performed by medical experts through visual inspection over highly sampled neural recordings. For a finer localization of the epileptogenic areas and a deeper understanding of the pathology both the identification of pathogenical biomarkers and the automatic characterization of epileptic signals are desirable. In this work we present a data integration learning method based on multi-level representation of stereo-electroencephalography recordings and multiple kernel learning. To the best of our knowledge, this is the first attempt to tackle both aspects simultaneously, as our approach is devised to classify critical vs. non-critical recordings while detecting the most discriminative frequency bands. The learning pipeline is applied to a data set of 18 patients for a total of 2347 neural recordings analyzed by medical experts. Without any prior knowledge assumption, the data-driven method reveals the most discriminative frequency bands for the localization of epileptic areas in the high-frequency spectrum (>=80 Hz) while showing high performance metric scores (mean balanced accuracy of 0.89 +- 0.03). The promising results may represent a starting point for the automatic search of clinical biomarkers of epileptogenicity

Comparison of methods to identify modules in noisy or incomplete brain networks

open6siCommunity structure, or "modularity," is a fundamentally important aspect in the organization of structural and functional brain networks, but their identification with community detection methods is confounded by noisy or missing connections. Although several methods have been used to account for missing data, the performance of these methods has not been compared quantitatively so far. In this study, we compared four different approaches to account for missing connections when identifying modules in binary and weighted networks using both Louvain and Infomap community detection algorithms. The four methods are "zeros," "row-column mean," "common neighbors," and "consensus clustering." Using Lancichinetti-Fortunato-Radicchi benchmark-simulated binary and weighted networks, we find that "zeros," "row-column mean," and "common neighbors" approaches perform well with both Louvain and Infomap, whereas "consensus clustering" performs well with Louvain but not Infomap. A similar pattern of results was observed with empirical networks from stereotactical electroencephalography data, except that "consensus clustering" outperforms other approaches on weighted networks with Louvain. Based on these results, we recommend any of the four methods when using Louvain on binary networks, whereas "consensus clustering" is superior with Louvain clustering of weighted networks. When using Infomap, "zeros" or "common neighbors" should be used for both binary and weighted networks. These findings provide a basis to accounting for noisy or missing connections when identifying modules in brain networks.openWilliams N.; Arnulfo G.; Wang S.H.; Nobili L.; Palva S.; Palva J.M.Williams, N.; Arnulfo, G.; Wang, S. H.; Nobili, L.; Palva, S.; Palva, J. M

Circadian sleep propensity and alcohol interaction at the wheel

Study Objectives: The study was aimed at estimating the effect of alcohol consumption, time of day, and their interaction on traffic crashes in a real regional context. Methods: Blood alcohol concentration (BAC) data were collected from drivers involved in traffic accidents during one year in an Italian region and in a control group of drivers over the same road network. Mean circadian sleep propensity was estimated from a previous study as function of time of day. Accident risk was analyzed by logistic regression as function of BAC and circadian sleep propensity. Results: BAC values greater than zero were found in 72.0% of the drivers involved in crashes and in 40.4% of the controls. Among the former 23.6% of the drivers exceeded the BAC legal threshold of 0.05 g/dL, while illegal values were found in 10.4% of the controls. The relative risk showed a significant increase with both BAC and circadian sleep propensity (as estimated from time of day) and their interaction was significant. Conclusions: Due to the significant interaction, even low BAC levels strongly increased accident risk when associated with high sleep propensity

Genuine cross-frequency coupling networks in human resting-state electrophysiological recordings

cited By 0Phase synchronization of neuronal oscillations in specific frequency bands coordinates anatomically distributed neuronal processing and communication. Typically, oscillations and synchronization take place concurrently in many distinct frequencies, which serve separate computational roles in cognitive functions. While within-frequency phase synchronization has been studied extensively, less is known about the mechanisms that govern neuronal processing distributed across frequencies and brain regions. Such integration of processing between frequencies could be achieved via cross-frequency coupling (CFC), either by phase-amplitude coupling (PAC) or by n:m-cross-frequency phase synchrony (CFS). So far, studies have mostly focused on local CFC in individual brain regions, whereas the presence and functional organization of CFC between brain areas have remained largely unknown. We posit that interareal CFC may be essential for large-scale coordination of neuronal activity and investigate here whether genuine CFC networks are present in human resting-state (RS) brain activity. To assess the functional organization of CFC networks, we identified brain-wide CFC networks at mesoscale resolution from stereoelectroencephalography (SEEG) and at macroscale resolution from source-reconstructed magnetoencephalography (MEG) data. We developed a novel, to our knowledge, graph-theoretical method to distinguish genuine CFC from spurious CFC that may arise from nonsinusoidal signals ubiquitous in neuronal activity. We show that genuine interareal CFC is present in human RS activity in both SEEG and MEG data. Both CFS and PAC networks coupled theta and alpha oscillations with higher frequencies in large-scale networks connecting anterior and posterior brain regions. CFS and PAC networks had distinct spectral patterns and opposing distribution of low- and high-frequency network hubs, implying that they constitute distinct CFC mechanisms. The strength of CFS networks was also predictive of cognitive performance in a separate neuropsychological assessment. In conclusion, these results provide evidence for interareal CFS and PAC being 2 distinct mechanisms for coupling oscillations across frequencies in large-scale brain networks.Peer reviewe

Violence in sleep

Although generally considered as mutually exclusive, violence and sleep can coexist. Violence related to the sleep period is probably more frequent than generally assumed and can be observed in various conditions including parasomnias (such as arousal disorders and rapid eye movement sleep behaviour disorder), epilepsy (in particular nocturnal frontal lobe epilepsy) and psychiatric diseases (including delirium and dissociative states). Important advances in the fields of genetics, neuroimaging and behavioural neurology have expanded the understanding of the mechanisms underlying violence and its particular relation to sleep. The present review outlines the different sleep disorders associated with violence and aims at providing information on diagnosis, therapy and forensic issues. It also discusses current pathophysiological models, establishing a link between sleep-related violence and violence observed in other setting

Global and local complexity of intracranial EEG decreases during NREM sleep

Key to understanding the neuronal basis of consciousness is the characterisation of the neural signatures of changes in level of consciousness during sleep. Here we analysed three measures of dynamical complexity on spontaneous depth electrode recordings from 10 epilepsy patients during wakeful rest and different stages of sleep: (i) Lempel-Ziv complexity, which is derived from how compressible the data are; (ii) amplitude coalition entropy, which measures the variability over time of the set of channels active above a threshold; (iii) synchrony coalition entropy, which measures the variability over time of the set of synchronous channels. When computed across sets of channels that are broadly distributed across multiple brain regions, all 3 measures decreased substantially in all participants during early-night non-rapid eye movement (NREM) sleep. This decrease was partially reversed during late-night NREM sleep, while the measures scored similar to wakeful rest during rapid eye movement (REM) sleep. This global pattern was in almost all cases mirrored at the local level by groups of channels located in a single region. In testing for differences between regions, we found elevated signal complexity in the frontal lobe. These differences could not be attributed solely to changes in spectral power between conditions. Our results provide further evidence that the level of consciousness correlates with neural dynamical complexity

Bistability breaks-off deterministic responses to intracortical stimulation during non-REM sleep

During non-rapid eye movement (NREM) sleep (stage N3), when consciousness fades, cortico-cortical interactions are impaired while neurons are still active and reactive. Why is this? We compared cortico-cortical evoked-potentials recorded during wakefulness and NREM by means of time-frequency analysis and phase-locking measures in 8 epileptic patients undergoing intra-cerebral stimulations/recordings for clinical evaluation. We observed that, while during wakefulness electrical stimulation triggers a chain of deterministic phase-locked activations in its cortical targets, during NREM the same input induces a slow wave associated with an OFF-period (suppression of power > 20 Hz), possibly reflecting a neuronal down-state. Crucially, after the OFF-period, cortical activity resumes to wakefulness-like levels, but the deterministic effects of the initial input are lost, as indicated by a sharp drop of phase-locked activity. These findings suggest that the intrinsic tendency of cortical neurons to fall into a down-state after a transient activation (i.e. bistability) prevents the emergence of stable patterns of causal interactions among cortical areas during NREM. Besides sleep, the same basic neurophysiological dynamics may play a role in pathological conditions in which thalamo-cortical information integration and consciousness are impaired in spite of preserved neuronal activity. (C) 2015 The Authors. Published by Elsevier Inc.Peer reviewe