Cascading spatial drought network: A complex networks approach to track propagation of meteorological droughts to agricultural droughts.

Meteorological droughts often propagate to agricultural (and other) droughts, both spatially and temporally. The present study proposes a novel complex networks-based cascading spatial drought network to examine the spatial propagation of meteorological droughts in a region to agricultural droughts in other regions. This is done through: (1) establishing stable homogeneous drought communities; (2) investigating inter-community drought propagation; (3) locating drought sources; and (4) evaluating drought connections within major crop belts. The approach is implemented to study droughts in the Indian-subcontinent during the period 1948-2022. Monthly precipitation and root-zone soil moisture data from GLDAS (Global Land Data Assimilation System) are used to compute the standardized precipitation index (SPI) for meteorological droughts and standardized soil moisture index (SSI) for agricultural droughts. Primarily, the drought network is demarcated into several subsets of network communities within which clusters of localized propagation take place. Multi-community subgraphs combining different communities are also formed to understand the long-distance inter-community drought linkages. Using network centrality measures, such as degree, closeness, and clustering coefficient, network properties of scale-freeness, small-worldness, and presence of rich-clubs are checked. Although the overall network does not exhibit any of these properties, certain subgraphs have significant small-worldness, rich-clubs, and partial scale-freeness. Some of the crucial nodes that support these network properties lie in the monsoon pathways (in the Western Ghats), and others have a strong association with El Niño Southern Oscillation (ENSO) teleconnections, thus validating the ability of the drought network to capture seasonal and climatic features. Additionally, subgraphs of nodes with high productivity of different food crops are created to study drought propagation within crop belts. Barring potential shortcomings related to data dependencies, the cascading spatial drought network helps identify an impending agricultural drought that could strengthen our ability to forecast droughts.

Electronic Implementation of a Deterministic Small-World Network: Synchronization and Communication.

In this paper, synchronization and encrypted communication transmissions of analog and digital messages in a deterministic small-world network (DSWN) are presented. In the first instance, we use a network with 3 coupled nodes in a nearest-neighbor (NN) topology, then the amount of nodes is increased until reaching a DSWN with 24 nodes. The synchronization and encrypted communication transmissions using a DSWN are presented experimentally by using Chua's chaotic circuit as node, in both analog and digital electronic implementations, where for the continuous version (CV) we use operational amplifiers (OA), and in the discretized version (DV) we use Euler's numerical algorithm implemented in an embedded system by using an Altera/Intel FPGA and external digital-to-analog converters.

Local inhibition in a model of the indirect pathway globus pallidus network slows and deregularizes background firing, but sharpens and synchronizes responses to striatal input.

The external segment of globus pallidus (GPe) is a network of oscillatory neurons connected by inhibitory synapses. We studied the intrinsic dynamic and the response to a shared brief inhibitory stimulus in a model GPe network. Individual neurons were simulated using a phase resetting model based on measurements from mouse GPe neurons studied in slices. The neurons showed a broad heterogeneity in their firing rates and in the shapes and sizes of their phase resetting curves. Connectivity in the network was set to match experimental measurements. We generated statistically equivalent neuron heterogeneity in a small-world model, in which 99% of connections were made with near neighbors and 1% at random, and in a model with entirely random connectivity. In both networks, the resting activity was slowed and made more irregular by the local inhibition, but it did not show any periodic pattern. Cross-correlations among neuron pairs were limited to directly connected neurons. When stimulated by a shared inhibitory input, the individual neuron responses separated into two groups: one with a short and stereotyped period of inhibition followed by a transient increase in firing probability, and the other responding with a sustained inhibition. Despite differences in firing rate, the responses of the first group of neurons were of fixed duration and were synchronized across cells.

Efficient supervision strategy for illegal dumping of construction and demolition waste: A networked game theory decision-making model.

While the construction industry has brought substantial economic benefits to society, it has also generated substantial construction and demolition waste (CDW). Illegal dumping, which refers to dumping CDW in an unauthorized non-filling location, has become widespread in many countries and regions. Illegally dumping CDW destroys the environment, causing groundwater pollution and forest fires and causing significant economic impacts. However, there is a lack of research on the decision-making behaviours and logical rules of the main participants, construction contractors and the government in the illegal CDW dumping process. This paper constructs an evolutionary game model on a small-world network considering government supervision to portray the decision-making behaviours of illegal dumping participants and conducts a numerical simulation based on empirical equations to propose an effective supervision strategy for the government to manage illegal CDW dumping efficiently. It is found that the illegal dumping behaviours of contractors are mainly affected by the intensity of government supervision, the cost of fines and the income of illegal dumping; while for government, a supervision strategy is found to be necessary, and a supervision intensity of approximately 0.7 is the optimal supervision probability given supervision efficiency. Notably, under a low-level supervision probability, increasing the penalty alone does not curb illegal dumping, and a certain degree of supervision must be maintained. The results show that in addition to setting fines for illegal dumping, the government must enforce a certain level of supervision and purify the market environment to steadily reduce illegal dumping.

LSD alters dynamic integration and segregation in the human brain.

Investigating changes in brain function induced by mind-altering substances such as LSD is a powerful method for interrogating and understanding how mind interfaces with brain, by connecting novel psychological phenomena with their neurobiological correlates. LSD is known to increase measures of brain complexity, potentially reflecting a neurobiological correlate of the especially rich phenomenological content of psychedelic-induced experiences. Yet although the subjective stream of consciousness is a constant ebb and flow, no studies to date have investigated how LSD influences the dynamics of functional connectivity in the human brain. Focusing on the two fundamental network properties of integration and segregation, here we combined graph theory and dynamic functional connectivity from resting-state functional MRI to examine time-resolved effects of LSD on brain networks properties and subjective experiences. Our main finding is that the effects of LSD on brain function and subjective experience are non-uniform in time: LSD makes globally segregated sub-states of dynamic functional connectivity more complex, and weakens the relationship between functional and anatomical connectivity. On a regional level, LSD reduces functional connectivity of the anterior medial prefrontal cortex, specifically during states of high segregation. Time-specific effects were correlated with different aspects of subjective experiences; in particular, ego dissolution was predicted by increased small-world organisation during a state of high global integration. These results reveal a more nuanced, temporally-specific picture of altered brain connectivity and complexity under psychedelics than has previously been reported.

Brain network integration dynamics are associated with loss and recovery of consciousness induced by sevoflurane.

The dynamic interplay of integration and segregation in the brain is at the core of leading theoretical accounts of consciousness. The human brain dynamically alternates between a sub-state where integration predominates, and a predominantly segregated sub-state, with different roles in supporting cognition and behaviour. Here, we combine graph theory and dynamic functional connectivity to compare resting-state functional MRI data from healthy volunteers before, during, and after loss of responsiveness induced with different concentrations of the inhalational anaesthetic, sevoflurane. We show that dynamic states characterised by high brain integration are especially vulnerable to general anaesthesia, exhibiting attenuated complexity and diminished small-world character. Crucially, these effects are reversed upon recovery, demonstrating their association with consciousness. Higher doses of sevoflurane (3% vol and burst-suppression) also compromise the temporal balance of integration and segregation in the human brain. Additionally, we demonstrate that reduced anticorrelations between the brain's default mode and executive control networks dynamically reconfigure depending on the brain's state of integration or segregation. Taken together, our results demonstrate that the integrated sub-state of brain connectivity is especially vulnerable to anaesthesia, in terms of both its complexity and information capacity, whose breakdown represents a generalisable biomarker of loss of consciousness and its recovery.

Small-World Networks and Their Relationship With Hippocampal Glutamine/Glutamate Concentration in Healthy Adults With Varying Genetic Risk for Alzheimer's Disease.

BACKGROUND: Apolipoprotein E ɛ4 allele (ApoE4) is the most common gene polymorphism related to Alzheimer's disease (AD). Impaired synaptic dysfunction occurs in ApoE4 carriers before any clinical symptoms. It remains unknown whether ApoE4 status affects the hippocampal neuromodulation, which further influences brain network topology. PURPOSE: To study the relationship of regional and global network properties by using graph theory analysis and glutamatergic (Glx) neuromodulation in the ApoE isoforms. STUDY TYPE: Prospective. SUBJECTS: Eighty-four cognitively normal adults (26 ApoE4 and 58 non-ApoE4 carriers). FIELD STRENGTH/SEQUENCE: Gradient-echo echo-planar and point resolved spectroscopy sequence at 3 T. ASSESSMENT: Glx concentration in bilateral hippocampi were processed with jMRUI (4.0), and graph theory metrics (global: γ, λ, small-worldness in whole brain; regional: nodal clustering coefficient (Ci ) and nodal characteristic path length (Li )) in top 20% highly connected hubs of subgroups (low-risk: non-ApoE4; high-risk: APOE4) were calculated and compared. STATISTICAL TESTS: Two-sample t test was used to compare metrics between subgroups. Correlations between regional properties and Glx by Pearson's partial correlation with false discovery rate correction. RESULTS: Significant differences (P < 0.05) in Ci between subgroups were found in hubs of left inferior frontal, bilateral inferior temporal, and bilateral precentral gyri, right parahippocampus, and bilateral precuneus. In addition, there was a significant correlation between Glx in the left hippocampus and Ci in inferior frontal gyrus (r = -0.537, P = 0.024), right inferior temporal (r = -0.478, P = 0.043), right parahippocampus (r = -0.629, P = 0.016), left precentral (r = -0.581, P = 0.022), right precentral (r = -0.651, P = 0.003), left precuneus (r = -0.545, P = 0.024), and right precuneus (r = -0.567, P = 0.022); and Li in left precuneus (r = 0.575, P = 0.032) and right precuneus (r = 0.586, P = 0.032) in the high-risk group, but not in the low-risk group. DATA CONCLUSION: Our results suggested that healthy ApoE4 carriers exhibit poorer local interconnectivity. Moreover, the close relationship between glutamate and small-world network properties in ApoE4 carriers might reflect a compensatory response to the impaired network efficiency. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 3.

Structured networks support sparse traveling waves in rodent somatosensory cortex.

Neurons responding to different whiskers are spatially intermixed in the superficial layer 2/3 (L2/3) of the rodent barrel cortex, where a single whisker deflection activates a sparse, distributed neuronal population that spans multiple cortical columns. How the superficial layer of the rodent barrel cortex is organized to support such distributed sensory representations is not clear. In a computer model, we tested the hypothesis that sensory representations in L2/3 of the rodent barrel cortex are formed by activity propagation horizontally within L2/3 from a site of initial activation. The model explained the observed properties of L2/3 neurons, including the low average response probability in the majority of responding L2/3 neurons, and the existence of a small subset of reliably responding L2/3 neurons. Sparsely propagating traveling waves similar to those observed in L2/3 of the rodent barrel cortex occurred in the model only when a subnetwork of strongly connected neurons was immersed in a much larger network of weakly connected neurons.

Small-world networks of prognostic genes associated with lung adenocarcinoma development.

The present study investigates the role of network topology in lung adenocarcinoma (LUAD) development. Analysis of sex- and stage-specific whole-genome expression data revealed that co-expressed and highly connected prognostic genes common to all cancer stages form a small-world network in each stage of LUAD. These small-world networks are present within stage-specific scale-free networks, conserved across the cancer stages, and linked to cancer-specific events. The presence of small-world networks across the cancer stages presents a synchronized system in the disordered environment of cancer, resulting in the evolution of malignancy. Our study reported that these small-world networks are resilient to random and systematic attacks, indicating the least opportunity to introduce perturbations by drugs as a therapeutic intervention. We concluded that highly clustered small-world networks could be controlled through transcriptional modulation for the improved treatment of LUAD.

Small-world effects in a modified epidemiological model with mutation and permanent immune mechanism.

Pandemic with mutation and permanent immune spreading in a small-world network described is studied by a modified SIR model, with consideration of mutation-immune mechanism. First, a novel mutation-immune model is proposed to modify the classical SIR model to simulate the transmission of mutable viruses that can be permanently immunized in small-world networks. Then, the influences of the size, coordination number and disorder parameter of the small-world network on the spread of the epidemic are analyzed in detail. Finally, the influences of mutation cycle and infection rate on epidemic transmission in small-world network are investigated further. The results show that the structure of the small-world network and the virus mutation cycle have an important impact on the spread of the epidemic. For viruses that can be permanently immunized, virus mutation is equivalent to making the immune cycle of human beings from infinite to finite. The dynamical behavior of the modified SIR epidemic model changes from an irregular, low-amplitude evolution at small disorder parameter to a spontaneous state of wide amplitude oscillations at large disorder parameter. Moreover, similar transition can also be found in increasing mutation cycle parameter. The maximum valid variation mutation decreases with the increase of disorder parameter and coordination number, but increase with respect to system size. In addition above, as the infection rate increases, the fraction of the infected increases and then decreases. As the mutation cycle increases, the time-average fraction of the infected and the infection rate corresponding to the maximum time-average fraction of the infected also decrease. As one conclusion, the results could give a deep understanding Pandemic with mutation and permanent immune spreading, from viewpoint of small-world network.

An Extended Correlation Dimension of Complex Networks.

Fractal and self-similarity are important characteristics of complex networks. The correlation dimension is one of the measures implemented to characterize the fractal nature of unweighted structures, but it has not been extended to weighted networks. In this paper, the correlation dimension is extended to the weighted networks. The proposed method uses edge-weights accumulation to obtain scale distances. It can be used not only for weighted networks but also for unweighted networks. We selected six weighted networks, including two synthetic fractal networks and four real-world networks, to validate it. The results show that the proposed method was effective for the fractal scaling analysis of weighted complex networks. Meanwhile, this method was used to analyze the fractal properties of the Newman-Watts (NW) unweighted small-world networks. Compared with other fractal dimensions, the correlation dimension is more suitable for the quantitative analysis of small-world effects.

Connecting EEG signal decomposition and response selection processes using the theory of event coding framework.

The neurophysiological mechanisms underlying the integration of perception and action are an important topic in cognitive neuroscience. Yet, connections between neurophysiology and cognitive theoretical frameworks have rarely been established. The theory of event coding (TEC) details how perceptions and actions are associated (bound) in a common representational domain (the "event file"), but the neurophysiological mechanisms underlying these processes are hardly understood. We used complementary neurophysiological methods to examine the neurophysiology of event file processing (i.e., event-related potentials [ERPs], temporal EEG signal decomposition, EEG source localization, time-frequency decomposition, EEG network analysis). We show that the P3 ERP component and activity modulations in inferior parietal regions (BA40) reflect event file binding processes. The relevance of this parietal region is corroborated by source localization of temporally decomposed EEG data. We also show that temporal EEG signal decomposition reveals a pattern of results suggesting that event file processes can be dissociated from pure stimulus and response-related processes in the EEG signal. Importantly, it is also documented that event file binding processes are reflected by modulations in the network architecture of theta frequency band activity. That is, when stimulus-response bindings in event files hamper response selection this was associated with a less efficient theta network organization. A more efficient organization was evident when stimulus-response binding in event files facilitated response selection. Small-world network measures seem to reflect event file processing. The results show how cognitive-theoretical assumptions of TEC can directly be mapped to the neurophysiology of response selection.

Brain functional network alterations caused by a strong desire to void in healthy adults: a graph theory analysis study.

PURPOSE: This resting-state functional magnetic resonance imaging (fMRI) study determined the functional connectivity (FC) changes and topologic property alterations of the brain functional network provoked by a strong desire to void in healthy adults using a graph theory analysis (GTA). MATERIALS AND METHODS: Thirty-four healthy, right-handed subjects filled their bladders by drinking water. The subjects were scanned under an empty bladder and a strong desire to void states. The Pearson's correlation coefficients were calculated among 90 brain regions in the automated anatomical labeling (AAL) atlas to construct the brain functional network. A paired t test (P < .05, after false discovery rate [FDR] correction) was used to detect significant differences in the FC, topologic properties (small-world parameters [gamma, sigma], Cp, Lp, Eglob, Eloc, and Enodal) between the two states in all subjects. RESULTS: Both the two states showed small-world network properties. The clustering coefficient (Cp) and local efficiency (Eloc) in the whole brain network decreased, while the FC within the default mode network (DMN) increased during the strong desire to void compared with the empty bladder state. Moreover, an increased nodal efficiency (Enodal) was detected in the basal ganglia (BG), DMN, sensorimotor-related network (SMN), and visual network (VN). CONCLUSION: We detected FC changes and topologic property alterations in brain functional networks caused by a strong desire to void in healthy and suggest that the micturition control may be a process dominated by DMN and coordinated by multiple sub-networks (such as, BG, SMN, and VN), which could serve as a baseline for understanding the pathologic process underlying bladder dysfunction and be useful to improve targeted therapy in the future.

Are RNA networks scale-free?

A network is scale-free if its connectivity density function is proportional to a power-law distribution. It has been suggested that scale-free networks may provide an explanation for the robustness observed in certain physical and biological phenomena, since the presence of a few highly connected hub nodes and a large number of small-degree nodes may provide alternate paths between any two nodes on average-such robustness has been suggested in studies of metabolic networks, gene interaction networks and protein folding. A theoretical justification for why many networks appear to be scale-free has been provided by Barabási and Albert, who argue that expanding networks, in which new nodes are preferentially attached to highly connected nodes, tend to be scale-free. In this paper, we provide the first efficient algorithm to compute the connectivity density function for the ensemble of all homopolymer secondary structures of a user-specified length-a highly nontrivial result, since the exponential size of such networks precludes their enumeration. Since existent power-law fitting software, such as powerlaw, cannot be used to determine a power-law fit for our exponentially large RNA connectivity data, we also implement efficient code to compute the maximum likelihood estimate for the power-law scaling factor and associated Kolmogorov-Smirnov p value. Hypothesis tests strongly indicate that homopolymer RNA secondary structure networks are not scale-free; moreover, this appears to be the case for real (non-homopolymer) RNA networks.

A common variant in OXTR rs53576 impacts topological patterns of brain functional networks.

A common variant (rs53576, G/A) in the oxytocin receptor (OXTR) gene is associated with individual differences in social behavior and may increase the risk for neuropsychiatric disorders characterized by social impairment, especially autism. Although recent functional magnetic resonance imaging (fMRI) studies have identified functional connectivity alteration in some brain regions in risk A allele carriers, it is currently unknown whether this dysfunctional connectivity causes disruption of the topological properties of brain functional networks. We applied a graph-theoretical analysis to investigate the topological properties of brain networks derived from resting-state fMRI in relation to AA homozygotes versus G allele carriers in 290 cognitive normal young adults. We found both AA homozygotes and G allele carriers demonstrated small-world properties; however, male AA homozygotes showed lower normalized clustering coefficient, small-worldness, and local efficiency compared with male G allele carriers, no differences survived after Bonferroni correction; and the inter-group differences of all three metrics exhibited an allele-load-dependent trend (AA < AG < GG), indicating a randomization shift of their brain functional networks. No significant results were observed in any global measures in female AA homozygotes as compared to female G allele carriers. Our results suggested that the topological patterns of brain functional networks were altered in OXTR rs53576 male homozygotes for the risk A allele compared with male G allele carriers, providing evidence for the disruption of integrity in large-scale intrinsic brain networks in a sex-dimorphic manner.

Reconstructing high-resolution chromosome three-dimensional structures by Hi-C complex networks.

BACKGROUND: Hi-C data have been widely used to reconstruct chromosomal three-dimensional (3D) structures. One of the key limitations of Hi-C is the unclear relationship between spatial distance and the number of Hi-C contacts. Many methods used a fixed parameter when converting the number of Hi-C contacts to wish distances. However, a single parameter cannot properly explain the relationship between wish distances and genomic distances or the  locations of topologically associating domains (TADs). RESULTS: We have addressed one of the key issues of using Hi-C data, that is, the unclear relationship between spatial distances and the number of Hi-C contacts, which is crucial to understand significant biological functions, such as the enhancer-promoter interactions. Specifically, we developed a new method to infer this converting parameter and pairwise Euclidean distances based on the topology of the Hi-C complex network (HiCNet). The inferred distances were modeled by clustering coefficient and multiple other types of constraints. We found that our inferred distances between bead-pairs within the same TAD were apparently smaller than those distances between bead-pairs from different TADs. Our inferred distances had a higher correlation with fluorescence in situ hybridization (FISH) data, fitted the localization patterns of Xist transcripts on DNA, and better matched 156 pairs of protein-enabled long-range chromatin interactions detected by ChIA-PET. Using the inferred distances and another round of optimization, we further reconstructed 40 kb high-resolution 3D chromosomal structures of mouse male ES cells. The high-resolution structures successfully illustrate TADs and DNA loops (peaks in Hi-C contact heatmaps) that usually indicate enhancer-promoter interactions. CONCLUSIONS: We developed a novel method to infer the wish distances between DNA bead-pairs from Hi-C contacts. High-resolution 3D structures of chromosomes were built based on the newly-inferred wish distances. This whole process has been implemented as a tool named HiCNet, which is publicly available at http://dna.cs.miami.edu/HiCNet/ .

[The research on dynamic properties of the small world neural network based on the synaptic plasticity].

The artificial neural network has the ability of the information processing and storage, good adaptability, strong learning function, association function and fault tolerance function. The research on the artificial neural network is mostly focused on the dynamic properties due to fact that the applications of artificial neural networks are related to its dynamic properties. At present, the researches on the neural network are based on the hierarchical network which can not simulate the real neural network. As a high level of abstraction of real complex systems, the small world network has the properties of biological neural networks. In the study, the small world network was constructed and the optimal parameter of the small word network was chosen based on the complex network theory firstly. And then based on the regulation mechanism of the synaptic plasticity and the topology of the small world network, the small world neural network was constructed and dynamic properties of the neural network were analyzed from the three aspects of the firing properties, dynamic properties of synaptic weights and complex network properties. The experimental results showed that with the increase of the time, the firing patterns of excitatory and inhibitory neurons in the small world neural network didn't change and the firing time of the neurons tended to synchronize; the synaptic weights between the neurons decreased sharply and eventually tended to be steady; the connections in the neural network were weakened and the efficiency of the information transmission was reduced, but the small world attribute was stable. The dynamic properties of the small world neural network vary with time, and the dynamic properties can also interact with each other: the firing synchronization of the neural network can affect the distribution of synaptic weights to the minimum, and then the dynamic changes of the synaptic weights can affect the complex network properties of the small world neural network.

Modeling of synchronization behavior of bursting neurons at nonlinearly coupled dynamical networks.

Synchronization behaviors of bursting neurons coupled through electrical and dynamic chemical synapses are investigated. The Izhikevich model is used with random and small world network of bursting neurons. Various currents which consist of diffusive electrical and time-delayed dynamic chemical synapses are used in the simulations to investigate the influences of synaptic currents and couplings on synchronization behavior of bursting neurons. The effects of parameters, such as time delay, inhibitory synaptic strengths, and decay time on synchronization behavior are investigated. It is observed that in random networks with no delay, bursting synchrony is established with the electrical synapse alone, single spiking synchrony is observed with hybrid coupling. In small world network with no delay, periodic bursting behavior with multiple spikes is observed when only chemical and only electrical synapse exist. Single-spike and multiple-spike bursting are established with hybrid couplings. A decrease in the synchronization measure is observed with zero time delay, as the decay time is increased in random network. For synaptic delays which are above active phase period, synchronization measure increases with an increase in synaptic strength and time delay in small world network. However, in random network, it increases with only an increase in synaptic strength.

The effect of the neural activity on topological properties of growing neural networks.

The connectivity structure in cortical networks defines how information is transmitted and processed, and it is a source of the complex spatiotemporal patterns of network's development, and the process of creation and deletion of connections is continuous in the whole life of the organism. In this paper, we study how neural activity influences the growth process in neural networks. By using a two-dimensional activity-dependent growth model we demonstrated the neural network growth process from disconnected neurons to fully connected networks. For making quantitative investigation of the network's activity influence on its topological properties we compared it with the random growth network not depending on network's activity. By using the random graphs theory methods for the analysis of the network's connections structure it is shown that the growth in neural networks results in the formation of a well-known "small-world" network.

Efficiency of a "small-world" brain network depends on consciousness level: a resting-state FMRI study.

It has been revealed that spontaneous coherent brain activity during rest, measured by functional magnetic resonance imaging (fMRI), self-organizes a "small-world" network by which the human brain could sustain higher communication efficiency across global brain regions with lower energy consumption. However, the state-dependent dynamics of the network, especially the dependency on the conscious state, remain poorly understood. In this study, we conducted simultaneous electroencephalographic recording with resting-state fMRI to explore whether functional network organization reflects differences in the conscious state between an awake state and stage 1 sleep. We then evaluated whole-brain functional network properties with fine spatial resolution (3781 regions of interest) using graph theoretical analysis. We found that the efficiency of the functional network evaluated by path length decreased not only at the global level, but also in several specific regions depending on the conscious state. Furthermore, almost two-thirds of nodes that showed a significant decrease in nodal efficiency during stage 1 sleep were categorized as the default-mode network. These results suggest that brain functional network organizations are dynamically optimized for a higher level of information integration in the fully conscious awake state, and that the default-mode network plays a pivotal role in information integration for maintaining conscious awareness.