The functional connectivity of different EEG bands moves towards small-world network organization during sleep

Inter-hemispheric oscillations in human sleep

Sleep is generally categorized into discrete stages based on characteristic electroencephalogram (EEG) patterns. This traditional approach represents sleep architecture in a static way, but it cannot reflect variations in sleep across time and across the cortex. To investigate these dynamic aspects of sleep, we analyzed sleep recordings in 14 healthy volunteers with a novel, frequency-based EEG analysis. This approach enabled comparison of sleep patterns with low inter-individual variability. We then implemented a new probability dependent, automatic classification of sleep states that agreed closely with conventional manual scoring during consolidated sleep. Furthermore, this analysis revealed a previously unrecognized, interhemispheric oscillation during rapid eye movement (REM) sleep. This quantitative approach provides a new way of examining the dynamic aspects of sleep, shedding new light on the physiology of human sleep.

How the statistical validation of functional connectivity patterns can prevent erroneous definition of small-world properties of a brain connectivity network

The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neuroelectrical signals has provided an important step to the interpretation and statistical analysis of such functional networks. The properties of a network are derived from the adjacency matrix describing a connectivity pattern obtained by one of the available functional connectivity methods. However, no common procedure is currently applied for extracting the adjacency matrix from a connectivity pattern. To understand how the topographical properties of a network inferred by means of graph indices can be affected by this procedure, we compared one of the methods extensively used in Neuroscience applications (i.e. fixing the edge density) with an approach based on the statistical validation of achieved connectivity patterns. The comparison was performed on the basis of simulated data and of signals acquired on a polystyrene head used as a phantom. The results showed (i) the importance of the assessing process in discarding the occurrence of spurious links and in the definition of the real topographical properties of the network, and (ii) a dependence of the small world properties obtained for the phantom networks from the spatial correlation of the neighboring electrodes.

Frontoparietal Connectivity and Hierarchical Structure of the Brain's Functional Network during Sleep

Frontal and parietal regions are associated with some of the most complex cognitive functions, and several frontoparietal resting-state networks can be observed in wakefulness. We used functional magnetic resonance imaging data acquired in polysomnographically validated wakefulness, light sleep, and slow-wave sleep to examine the hierarchical structure of a low-frequency functional brain network, and to examine whether frontoparietal connectivity would disintegrate in sleep. Whole-brain analyses with hierarchical cluster analysis on predefined atlases were performed, as well as regression of inferior parietal lobules (IPL) seeds against all voxels in the brain, and an evaluation of the integrity of voxel time-courses in subcortical regions-of-interest. We observed that frontoparietal functional connectivity disintegrated in sleep stage 1 and was absent in deeper sleep stages. Slow-wave sleep was characterized by strong hierarchical clustering of local submodules. Frontoparietal connectivity between IPL and superior medial and right frontal gyrus was lower in sleep stages than in wakefulness. Moreover, thalamus voxels showed maintained integrity in sleep stage 1, making intrathalamic desynchronization an unlikely source of reduced thalamocortical connectivity in this sleep stage. Our data suggest a transition from a globally integrated functional brain network in wakefulness to a disintegrated network consisting of local submodules in slow-wave sleep, in which frontoparietal inter-modular nodes may play a role, possibly in combination with the thalamus.

Network physiology reveals relations between network topology and physiological function

The human organism is an integrated network where complex physiological systems, each with its own regulatory mechanisms, continuously interact, and where failure of one system can trigger a breakdown of the entire network. Identifying and quantifying dynamical networks of diverse systems with different types of interactions is a challenge. Here we develop a framework to probe interactions among diverse systems, and we identify a physiological network. We find that each physiological state is characterized by a specific network structure, demonstrating a robust interplay between network topology and function. Across physiological states, the network undergoes topological transitions associated with fast reorganization of physiological interactions on time scales of a few minutes, indicating high network flexibility in response to perturbations. The proposed system-wide integrative approach may facilitate the development of a new field, Network Physiology.

Pointwise transinformation distinguishes a recurrent increase of synchronization in the rapid eye movement sleep electroencephalogram

The analysis of electroencephalogram (EEG) coupling patterns is essential for understanding how interrelations between cortical sites change with the wake-sleep cycle. Waking and sleep EEGs of 12 normal sleepers were analyzed by pointwise transinformation (PTI). Stage-dependent differences of PTI were assessed, and a spectral analysis of synchronized events was performed. A pattern of recurrent EEG synchronization was distinguished in all rapid eye movement (REM) sleep phases. The mean coupling of EEG leads differed regionally, with high coupling levels of frontal and occipital derivations and lower midtemporal and central coupling levels. Mean coupling levels were comparable in stages R, W, and N1 but were lower than in N2 and N3. An REM-specific pattern of low EEG synchronization was identified for F7-F8 and T3-T4, with lowest coupling levels during tonic REM sleep. Also, maximal intervals of uncoupled EEG were longer during tonic REM sleep. Because of these results, a new descriptive entity is proposed: the recurrent increase of synchronization in the EEG (RISE). This seems to reflect the dynamic aspects of spatiotemporal EEG synchronization on small time scales. A possibly specific low coupling pattern of the temporal leads may distinguish REM sleep from other states with a "desynchronized" EEG and, to some extent, tonic from phasic REM sleep.

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.

Large-scale functional brain networks in human non-rapid eye movement sleep: insights from combined electroencephalographic/functional magnetic resonance imaging studies

This paper reviews the existing body of knowledge on the neural correlates of spontaneous oscillations, functional connectivity and brain plasticity in human non-rapid eye movement (NREM) sleep. The first section reviews the evidence that specific sleep events as slow waves and spindles are associated with transient increases in regional brain activity. The second section describes the changes in functional connectivity during NREM sleep, with a particular focus on changes within a low-frequency, large-scale functional brain network. The third section will discuss the possibility that spontaneous oscillations and differential functional connectivity are related to brain plasticity and systems consolidation, with a particular focus on motor skill acquisition. Implications for the mode of information processing per sleep stage and future experimental studies are discussed.

On the Influence of Amplitude on the Connectivity between Phases

In recent studies, functional connectivities have been reported to display characteristics of complex networks that have been suggested to concur with those of the underlying structural, i.e., anatomical, networks. Do functional networks always agree with structural ones? In all generality, this question can be answered with "no": for instance, a fully synchronized state would imply isotropic homogeneous functional connections irrespective of the "real" underlying structure. A proper inference of structure from function and vice versa requires more than a sole focus on phase synchronization. We show that functional connectivity critically depends on amplitude variations, which implies that, in general, phase patterns should be analyzed in conjunction with the corresponding amplitude. We discuss this issue by comparing the phase synchronization patterns of interconnected Wilson-Cowan models vis-à-vis Kuramoto networks of phase oscillators. For the interconnected Wilson-Cowan models we derive analytically how connectivity between phases explicitly depends on the generating oscillators' amplitudes. In consequence, the link between neurophysiological studies and computational models always requires the incorporation of the amplitude dynamics. Supplementing synchronization characteristics by amplitude patterns, as captured by, e.g., spectral power in M/EEG recordings, will certainly aid our understanding of the relation between structural and functional organizations in neural networks at large.

Cyclic alternating pattern (CAP): the marker of sleep instability

Cyclic alternating pattern CAP is the EEG marker of unstable sleep, a concept which is poorly appreciated among the metrics of sleep physiology. Besides, duration, depth and continuity, sleep restorative properties depend on the capacity of the brain to create periods of sustained stable sleep. This issue is not confined only to the EEG activities but reverberates upon the ongoing autonomic activity and behavioral functions, which are mutually entrained in a synchronized oscillation. CAP can be identified both in adult and children sleep and therefore represents a sensitive tool for the investigation of sleep disorders across the lifespan. The present review illustrates the story of CAP in the last 25 years, the standardized scoring criteria, the basic physiological properties and how the dimension of sleep instability has provided new insight into pathophysiolology and management of sleep disorders.

Nocturnal epileptiform EEG discharges, nocturnal epileptic seizures, and language impairments in children: review of the literature

This review addresses the effect on language function of nocturnal epileptiform EEG discharges and nocturnal epileptic seizures in children. In clinical practice, language impairment is frequently reported in association with nocturnal epileptiform activity. Vice versa, nocturnal epileptiform EEG abnormalities are a common finding in children with specific language impairment. We suggest a spectrum that is characterized by nocturnal epileptiform activity and language impairment ranging from specific language impairment to rolandic epilepsy, nocturnal frontal lobe epilepsy, electrical status epilepticus of sleep, and Landau-Kleffner syndrome. In this spectrum, children with specific language impairment have the best outcome, and children with electrical status epilepticus of sleep or Landau-Kleffner syndrome, the worst. The exact nature of this relationship and the factors causing this spectrum are unknown. We suggest that nocturnal epileptiform EEG discharges and nocturnal epileptic seizures during development will cause or contribute to diseased neuronal networks involving language. The diseased neuronal networks are less efficient compared with normal neuronal networks. This disorganization may cause language impairments.

Long-term variability of global statistical properties of epileptic brain networks

We investigate the influence of various pathophysiologic and physiologic processes on global statistical properties of epileptic brain networks. We construct binary functional networks from long-term, multichannel electroencephalographic data recorded from 13 epilepsy patients, and the average shortest path length and the clustering coefficient serve as global statistical network characteristics. For time-resolved estimates of these characteristics we observe large fluctuations over time, however, with some periodic temporal structure. These fluctuations can--to a large extent--be attributed to daily rhythms while relevant aspects of the epileptic process contribute only marginally. Particularly, we could not observe clear cut changes in network states that can be regarded as predictive of an impending seizure. Our findings are of particular relevance for studies aiming at an improved understanding of the epileptic process with graph-theoretical approaches.

Comparing brain networks of different size and connectivity density using graph theory

Graph theory is a valuable framework to study the organization of functional and anatomical connections in the brain. Its use for comparing network topologies, however, is not without difficulties. Graph measures may be influenced by the number of nodes (N) and the average degree (k) of the network. The explicit form of that influence depends on the type of network topology, which is usually unknown for experimental data. Direct comparisons of graph measures between empirical networks with different N and/or k can therefore yield spurious results. We list benefits and pitfalls of various approaches that intend to overcome these difficulties. We discuss the initial graph definition of unweighted graphs via fixed thresholds, average degrees or edge densities, and the use of weighted graphs. For instance, choosing a threshold to fix N and k does eliminate size and density effects but may lead to modifications of the network by enforcing (ignoring) non-significant (significant) connections. Opposed to fixing N and k, graph measures are often normalized via random surrogates but, in fact, this may even increase the sensitivity to differences in N and k for the commonly used clustering coefficient and small-world index. To avoid such a bias we tried to estimate the N,k-dependence for empirical networks, which can serve to correct for size effects, if successful. We also add a number of methods used in social sciences that build on statistics of local network structures including exponential random graph models and motif counting. We show that none of the here-investigated methods allows for a reliable and fully unbiased comparison, but some perform better than others.

Development of a large-scale functional brain network during human non-rapid eye movement sleep

Graph theoretical analysis of functional magnetic resonance imaging (fMRI) time series has revealed a small-world organization of slow-frequency blood oxygen level-dependent (BOLD) signal fluctuations during wakeful resting. In this study, we used graph theoretical measures to explore how physiological changes during sleep are reflected in functional connectivity and small-world network properties of a large-scale, low-frequency functional brain network. Twenty-five young and healthy participants fell asleep during a 26.7 min fMRI scan with simultaneous polysomnography. A maximum overlap discrete wavelet transformation was applied to fMRI time series extracted from 90 cortical and subcortical regions in normalized space after residualization of the raw signal against unspecific sources of signal fluctuations; functional connectivity analysis focused on the slow-frequency BOLD signal fluctuations between 0.03 and 0.06 Hz. We observed that in the transition from wakefulness to light sleep, thalamocortical connectivity was sharply reduced, whereas corticocortical connectivity increased; corticocortical connectivity subsequently broke down in slow-wave sleep. Local clustering values were closest to random values in light sleep, whereas slow-wave sleep was characterized by the highest clustering ratio (gamma). Our findings support the hypothesis that changes in consciousness in the descent to sleep are subserved by reduced thalamocortical connectivity at sleep onset and a breakdown of general connectivity in slow-wave sleep, with both processes limiting the capacity of the brain to integrate information across functional modules.

Neurodynamics of an election

Variables influencing decision-making in real settings, as in the case of voting decisions, are uncontrollable and in many times even unknown to the experimenter. In this case, the experimenter has to study the intention to decide (vote) as close as possible in time to the moment of the real decision (election day). Here, we investigated the brain activity associated with the voting intention declared 1 week before the election day of the Brazilian Firearms Control Referendum about prohibiting the commerce of firearms. Two alliances arose in the Congress to run the campaigns for YES (for the prohibition of firearm commerce) and NO (against the prohibition of firearm commerce) voting. Time constraints imposed by the necessity of studying a reasonable number (here, 32) of voters during a very short time (5 days) made the EEG the tool of choice for recording the brain activity associated with voting decision. Recent fMRI and EEG studies have shown decision-making as a process due to the enrollment of defined neuronal networks. In this work, a special EEG technique is applied to study the topology of the voting decision-making networks and is compared to the results of standard ERP procedures. The results show that voting decision-making enrolled networks in charge of calculating the benefits and risks of the decision of prohibiting or allowing firearm commerce and that the topology of such networks was vote- (i.e., YES/NO-) sensitive.

The relationship between sleep and epilepsy: the effect on cognitive functioning in children

AIM

The purpose of this review was to examine the possible pathophysiological links between epilepsy, cognition, sleep macro- and microstructure, and sleep disorders to highlight the contributions and interactions of sleep and epilepsy on cognitive functioning in children with epilepsy.

METHOD

PubMed was used as the medical database source. No language restriction was placed on the literature searches, and citations of relevant studies in the paediatric age range (0-18 y) were checked. Studies including a mixed population but with a high percentage of children were also considered.

RESULTS

The searches identified 223 studies. One reviewer scanned these to eliminate obviously irrelevant studies. Three reviewers scanned the remaining 128 studies and their relevant citations. The review showed that several factors could account for the learning impairment in children with epilepsy: aetiology, electroencephalographic (EEG) discharges, and persistence and circadian distribution of seizures, etc. EEG discharges may affect cognition and sleep, even in the absence of clinical or subclinical seizures. The sleep deprivation and/or sleep disruption affect the neurophysiological and neurochemical mechanisms important for the memory-learning process, but also influence the expression of EEG discharges and seizures. Learning and memory consolidation can take place over extended periods, and sleep has been demonstrated to play a fundamental role in these processes through neuroplastic remodelling of neural networks. Epilepsy and EEG paroxysms may affect sleep structure, interfering with these physiological functions.

INTERPRETATION

Improvement in the long-term cognitive-behavioural prognosis of children with epilepsy requires both good sleep quality and good seizure control. The antiepileptic drug of choice should be the one that interferes least with sleep structure and has the best effect on sleep architecture--thus normalizing sleep instability, especially during non-rapid eye movement sleep.

Human brain networks in health and disease

PURPOSE OF REVIEW

Recent developments in the statistical physics of complex networks have been translated to neuroimaging data in an effort to enhance our understanding of human brain structural and functional networks. This review focuses on studies using graph theoretical measures applied to structural MRI, diffusion MRI, functional MRI, electroencephalography, and magnetoencephalography data.

RECENT FINDINGS

Complex network properties have been identified with some consistency in all modalities of neuroimaging data and over a range of spatial and time scales. Conserved properties include small worldness, high efficiency of information transfer for low wiring cost, modularity, and the existence of network hubs. Structural and functional network metrics have been found to be heritable and to change with normal aging. Clinical studies, principally in Alzheimer's disease and schizophrenia, have identified abnormalities of network configuration in patients. Future work will likely involve efforts to synthesize structural and functional networks in integrated models and to explore the interdependence of network configuration and cognitive performance.

SUMMARY

Graph theoretical analysis of neuroimaging data is growing rapidly and could potentially provide a relatively simple but powerful quantitative framework to describe and compare whole human brain structural and functional networks under diverse experimental and clinical conditions.

Partial directed coherence-based information flow in Parkinson's disease patients performing a visually-guided motor task

We propose a partial directed coherence (PCD) method based on a sparse multivariate autoregressive (mAR) model to investigate patterns of information flow in electroencephalography (EEG) recordings in Parkinson's disease (PD) patients performing a visually-guided motor task. The use of a sparsity constraint on the mAR matrix addresses issues such as sample size, model order selection and number of parameters to be estimated, particularly when the number of EEG channels used is large and the window size is small in order to capture dynamic changes. The proposed PDC-based information flow analysis demonstrated distinctly altered patterns of connectivity between PD patients off medication and healthy subjects, particularly with respect to net information outflow from the left sensorimotor (L Sm) region, which might indicate excessive spreading of activity in the diseased state. Disrupted patterns of connectivity in PD were partially restored by levodopa medication. In addition, PDC-based analysis proved to be more sensitive to temporally-dynamic connectivity changes as compared to traditional spectral analysis, which might be influenced primarily by large-scale changes. We suggest that the proposed sparse-PDC method is a suitable technique to investigate altered connectivity in Parkinson's disease.