Structural differences between non-lucid dreams, lucid dreams and out-of-body experience reports assessed by graph analysis.

Dreaming is a complex phenomenon that occurs during sleep, involving various conscious dream experiences. Lucid dreams (LDs) involve heightened awareness within the dream environment, while out-of-body experiences (OBEs) involve the sensation of being outside one's physical body. OBEs occur during sleep paralysis (SP), where voluntary movements are inhibited during sleep/wake transitions while remaining aware of the surroundings. The relationship between LDs and OBEs is debated, with some viewing them as distinct phenomena and others considering them different manifestations of the same underlying experience. This study aimed to characterize non-lucid dreams, LDs, and OBEs by analyzing dream reports' structural properties. OBE reports displayed a condensed and interconnected network structure compared to non-lucid dreams and LDs. Additionally, OBE reports exhibited a specialized network structure, with specific nodes playing a more central role. These findings suggest that OBE dreams may have a more coherent and unified narrative, with certain nodes being pivotal in the network structure.

Spontaneous and induced out-of-body experiences during sleep paralysis: Emotions, "AURA" recognition, and clinical implications.

Sleep paralysis is characterized by the incapacity to perform voluntary movements during sleep/wake transitions, and could bring great discomfort. During sleep paralysis, out-of-body experiences can occur. Out-of-body experiences refers to the sensation of being outside of the physical body and perceiving the world from this outside perspective; however, they are pleasant in comparison with other sleep paralysis hallucinations. Lucid dreams are dreams in which a subject becomes aware of being dreaming while the dream occurs. Here, we designed an online survey to study the predominant and specific emotions during sleep paralysis and/or out-of-body experience events as well as the somatosensory perceptions that preceded their occurrence. The sample (N = 329) was divided into experimental groups depending on the presence/absence of out-of-body experiences, capacity to induce (or not) out-of-body experiences, and perception/no-perception of the sleep paralysis. We showed that more positive emotions were associated with out-of-body experiences and more negative emotions were associated with sleep paralysis episodes, and for those who claim the ability to induce out-of-body experiences, positive emotions were more frequent in their episodes. We found that subjects perceived auditory, tactile and visual sensations before sleep paralysis episodes, and we proposed that these could be an "aura" of sleep paralysis. Furthermore, subjects that had out-of-body experiences but had never felt the sleep paralysis, perceived tactile and visual sensations to the same extent as subjects with out-of-body experiences that felt the sleep paralysis. Therefore, we proposed that the "aura" recognition could be used under controlled conditions for out-of-body experiences induction in patients with sleep paralysis to diminish the negative symptoms associated with sleep paralysis episodes.

Emergent dynamics in a robotic model based on the Caenorhabditis elegans connectome.

We analyze the neural dynamics and their relation with the emergent actions of a robotic vehicle that is controlled by a neural network numerical simulation based on the nervous system of the nematode Caenorhabditis elegans. The robot interacts with the environment through a sensor that transmits the information to sensory neurons, while motor neurons outputs are connected to wheels. This is enough to allow emergent robot actions in complex environments, such as avoiding collisions with obstacles. Working with robotic models makes it possible to simultaneously keep track of the dynamics of all the neurons and also register the actions of the robot in the environment in real time, while avoiding the complex technicalities of simulating a real environment. This allowed us to identify several relevant features of the neural dynamics associated with the emergent actions of the robot, some of which have already been observed in biological worms. These results suggest that some basic aspects of behaviors observed in living beings are determined by the underlying structure of the associated neural network.

Region-specific changes in Mus musculus brain size and cell composition under chronic nutrient restriction.

Nutrition is one of the most influential environmental factors affecting the development of different tissues and organs. It is suggested that under nutrient restriction the growth of the brain is spared as a result of the differential allocation of resources from other organs. However, it is not clear whether this sparing occurs brain-wide. Here, we analyzed morphological changes and cell composition in different regions of the offspring mouse brain after maternal exposure to nutrient restriction during pregnancy and lactation. Using high-resolution magnetic resonance imaging, we found that brain regions were differentially sensitive to maternal protein restriction and exhibited particular patterns of volume reduction. The cerebellum was reduced in absolute and relative volume, while cortex volume was relatively preserved. Alterations in cell composition (examined by the isotropic fractionator method) and organization of white matter (measured by diffusor tensor images) were also region specific. These changes were not related to the metabolic rate of the regions and were only partially explained by their specific growth trajectories. This study is a first step towards understanding the mechanisms of regional brain sparing at microstructural and macrostructural levels resulting from undernutrition.

Role of the circadian clock in the statistics of locomotor activity in Drosophila.

In many animals the circadian rhythm of locomotor activity is controlled by an endogenous circadian clock. Using custom made housing and video tracking software in order to obtain high spatial and temporal resolution, we studied the statistical properties of the locomotor activity of wild type and two clock mutants of Drosophila melanogaster. We show here that the distributions of activity and quiescence bouts for the clock mutants in light-dark conditions (LD) are very different from the distributions obtained when there are no external cues from the environment (DD). In the wild type these distributions are very similar, showing that the clock controls this aspect of behavior in both regimes (LD and DD). Furthermore, the distributions are very similar to those reported for Wistar rats. For the timing of events we also observe important differences, quantified by how the event rate distributions scale for increasing time windows. We find that for the wild type these distributions can be rescaled by the same function in DD as in LD. Interestingly, the same function has been shown to rescale the rate distributions in Wistar rats. On the other hand, for the clock mutants it is not possible to rescale the rate distributions, which might indicate that the extent of circadian control depends on the statistical properties of activity and quiescence.

Brain Structural Networks in Mouse Exposed to Chronic Maternal Undernutrition.

Brain structural connectivity is known to be altered in cases of intrauterine growth restriction and premature birth, although the specific effect of maternal nutritional restriction, a common burden in human populations, has not been assessed yet. Here we analyze the effects of maternal undernutrition during pregnancy and lactation by establishing three experimental groups of female mice divided according to their diet: control (Co), moderate calorie-protein restriction (MCP) and severe protein restriction (SP). Nutritionally restricted dams gained relatively less weight during pregnancy and the body weight of the offspring was also affected by maternal undernutrition, showing global growth restriction. We performed magnetic resonance imaging (MRI) of the offspring's brains after weaning and analyzed their connectivity patterns using complex graph theory. In general, changes observed in the MCP group were more subtle than in SP. Results indicated that brain structures were not homogeneously affected by early nutritional stress. In particular, the growth of central brain regions, such as the temporo-parietal cortex, and long integrative myelinated tracts were relatively preserved, while the frequency of short tracts was relatively reduced. We also found a differential effect on network parameters: network degree, clustering, characteristic path length and small-worldness remained mainly unchanged, while the rich-club index was lower in nutritionally restricted animals. Rich-club decrease reflects an impairment in the structure by which brain regions with large number of connections tend to be more densely linked among themselves. Overall, the findings presented here support the hypothesis that chronic nutritional stress produces long-term changes in brain structural connectivity.

Modeling spatial patterns in the visual cortex.

We propose a model for the formation of patterns in the visual cortex. The dynamical units of the model are Kuramoto phase oscillators that interact through a complex network structure embedded in two dimensions. In this way the strength of the interactions takes into account the geographical distance between units. We show that for different parameters, clustered or striped patterns emerge. Using the structure factor as an order parameter we are able to quantitatively characterize these patterns and present a phase diagram. Finally, we show that the model is able to reproduce patterns with cardinal preference, as observed in ferrets.

A mathematical model of communication between groups of circadian neurons in Drosophila melanogaster.

In the fruit fly, circadian behavior is controlled by a small number of specialized neurons, whose molecular clocks are relatively well known. However, much less is known about how these neurons communicate among themselves. In particular, only 1 circadian neuropeptide, pigment-dispersing factor (PDF), has been identified, and most aspects of its interaction with the molecular clock remain to be elucidated. Furthermore, it is speculated that many other peptides should contribute to circadian communication. We have developed a relatively detailed model of the 2 main groups of circadian pacemaker neurons (sLNvs and LNds) to investigate these issues. We have proposed many possible mechanisms for the interaction between the synchronization factors and the molecular clock, and we have compared the outputs with the experimental results reported in the literature both for the wild-type and PDF-null mutant. We have studied how different the properties of each neuron should be to account for the observations reported for the sLNvs in the mutant. We have found that only a few mechanisms, mostly related to the slowing down of nuclear entry of a circadian protein, can synchronize neurons that present these differences. Detailed immunofluorescent recordings have suggested that, whereas in the mutant, LNd neurons are synchronized, in the wild-type, a subset of the LNds oscillate faster than the rest. With our model, we find that a more likely explanation for the same observations is that this subset is being driven outside its synchronization range and displays therefore a complex pattern of oscillation.

Modelling the effect of phosphorylation on the circadian clock of Drosophila.

It is by now well known that, at the molecular level, the core of the circadian clock of most living species is a negative feedback loop where some proteins inhibit their own transcription. However, it has recently been shown that post-translational processes, such as phosphorylations, are essential for a correct timing of the clock. Depending on which sites of a circadian protein are phosphorylated, different properties such as degradation, nuclear localization and repressing power can be altered. Furthermore, phosphorylation domains can be related in a positive way, giving rise to consecutive phosphorylations, or in a negative way, hindering phosphorylation at other domains. Here we present a simple mathematical model of a circadian protein having two mutually exclusive domains of phosphorylation. We show that the system has limit cycles that arise from a unique fixed point through a Hopf bifurcation. We find a set of parameters, with realistic values, for which the limit cycle has the same period as the wild type circadian oscillations of the fruit fly. The domains act as a switch, in the sense that alterations in their phosphorylation can alter the period of circadian oscillation in opposite ways, increasing or decreasing the period of the wild type oscillations. In particular, we show that our model is able to reproduce some of the experimental results found for switch-like phosphorylations of the PER protein of the circadian clock of the fly Drosophila melanogaster.

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.

Characterizing the complexity of brain and mind networks.

Recent studies of brain connectivity and language with methods of complex networks have revealed common features of organization. These observations open a window to better understand the intrinsic relationship between the brain and the mind by studying how information is either physically stored or mentally represented. In this paper, we review some of the results in both brain and linguistic networks, and we illustrate how modelling approaches can serve to comprehend the relationship between the structure of the brain and its function. On the one hand, we show that brain and neural networks display dynamical behaviour with optimal complexity in terms of a balance between their capacity to simultaneously segregate and integrate information. On the other hand, we show how principles of neural organization can be implemented into models of memory storage and recognition to reproduce spontaneous transitions between memories, resembling phenomena of memory association studied in psycholinguistic experiments.

Emergence of small-world structure in networks of spiking neurons through STDP plasticity.

In this work, we use a complex network approach to investigate how a neural network structure changes under synaptic plasticity. In particular, we consider a network of conductance-based, single-compartment integrate-and-fire excitatory and inhibitory neurons. Initially the neurons are connected randomly with uniformly distributed synaptic weights. The weights of excitatory connections can be strengthened or weakened during spiking activity by the mechanism known as spike-timing-dependent plasticity (STDP). We extract a binary directed connection matrix by thresholding the weights of the excitatory connections at every simulation step and calculate its major topological characteristics such as the network clustering coefficient, characteristic path length and small-world index. We numerically demonstrate that, under certain conditions, a nontrivial small-world structure can emerge from a random initial network subject to STDP learning.

Modelling hierarchical structure in functional brain networks.

In this work, we focus on a complex-network approach for the study of the brain. In particular, we consider functional brain networks, where the vertices represent different anatomical regions and the links their functional connectivity. First, we build these networks using data obtained with functional magnetic resonance imaging. Then, we analyse the main characteristics of these complex networks, including degree distribution, the presence of modules and hierarchical structure. Finally, we present a network model with dynamical nodes and adaptive links. We show that the model allows for the emergence of complex networks with characteristics similar to those observed in functional brain networks.

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.

An adaptive complex network model for brain functional networks.

Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence) of a hierarchical structure are not robust, and show different intriguing behaviors. In order to understand the basic ingredients necessary for the emergence of these complex network structures we present an adaptive complex network model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions of the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of human brain networks obtained from functional magnetic resonance imaging studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network scale-free non-hierarchical networks with well defined communities emerge. On the other hand, when the dynamical rules restrict the information to a local neighborhood, communities cluster together into larger ones, giving rise to a hierarchical structure, with a truncated power law degree distribution.

Nonequilibrium phase transition in a model for the propagation of innovations among economic agents.

We characterize the different morphological phases that occur in a simple one-dimensional model of propagation of innovations among economic agents [X. Guardiola et al., Phys. Rev E 66, 026121 (2002)]. We show that the model can be regarded as a nonequilibrium surface growth model. This allows us to demonstrate the presence of a continuous roughening transition between a flat (system size independent fluctuations) and a rough phase (system size dependent fluctuations). Finite-size scaling studies at the transition strongly suggest that the dynamic critical transition does not belong to directed percolation and, in fact, critical exponents do not seem to fit in any of the known universality classes of nonequilibrium phase transitions. Finally, we present an explanation for the occurrence of the roughening transition and argue that avalanche driven dynamics is responsible for the novel critical behavior.