Changes In Network Properties Research Articles

Overexpression of Toxic Poly(Glycine-Alanine) Aggregates in Primary Neuronal Cultures Induces Time-Dependent Autophagic and Synaptic Alterations but Subtle Activity Impairments.

The pathogenic expansion of the intronic GGGGCC hexanucleotide located in the non-coding region of the C9orf72 gene represents the most frequent genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). This mutation leads to the accumulation of toxic RNA foci and dipeptide repeats (DPRs), as well as reduced levels of the C9orf72 protein. Thus, both gain and loss of function are coexisting pathogenic aspects linked to C9orf72-ALS/FTD. Synaptic alterations have been largely described in C9orf72 models, but it is still not clear which aspect of the pathology mostly contributes to these impairments. To address this question, we investigated the dynamic changes occurring over time at the synapse upon accumulation of poly(GA), the most abundant DPR. Overexpression of this toxic form induced a drastic loss of synaptic proteins in primary neuron cultures, anticipating autophagic defects. Surprisingly, the dramatic impairment characterizing the synaptic proteome was not fully matched by changes in network properties. In fact, high-density multi-electrode array analysis highlighted only minor reductions in the spike number and firing rate of poly(GA) neurons. Our data show that the toxic gain of function linked to C9orf72 affects the synaptic proteome but exerts only minor effects on the network activity.

White matter microstructure and connectivity changes after surgery in male adults with obstructive sleep apnea: recovery or reorganization?

Obstructive sleep apnea (OSA) is a prevalent clinical problem significantly affecting cognitive functions. Surgical treatment is recommended for those unable to use continuous positive airway pressure. We aimed to investigate the therapeutic effect of upper airway surgery on the white matter (WM) microstructure and brain connectivity in patients with OSA. Twenty-one male patients with moderate-to-severe OSA were recruited for multi-level upper airway surgery. Overnight polysomnography (PSG), neuropsychiatric tests, and brain MRI scans were acquired before and 6.1 ± 0.8 months after surgery. Nineteen male patients with untreated OSA were also included as a reference group. We calculated the longitudinal changes of diffusion tensor imaging (DTI) parameters, including fractional anisotropy (ΔFA) and mean/axial/radial diffusivity (ΔMD/AD/RD). We also assessed changes in network properties based on graph theory. Surgically treated patients showed improvement in PSG parameters and verbal memory after surgery. Globally, ΔFA was significantly higher and ΔRD was lower in the surgery group than in the untreated group. Especially ΔFA of the tracts involved in the limbic system was higher after surgery. In network analysis, higher Δbetweenness and lower Δclustering coefficients were observed in the surgical group than in the untreated group. Finally, the improvement of verbal memory after surgery positively correlated with ΔFA in superior thalamic radiation (p = 0.021), fronto aslant tracts (p = 0.027), and forceps minor tracts (p = 0.032). Surgical treatment of OSA can alleviate alterations in WM integrity and disruptions in local networks, particularly for the tracts involved in the limbic system. These findings may further explain the cognitive improvement observed after the treatment of OSA.

Enhanced rich club connectivity in mild or moderate depression after nonpharmacological treatment: A preliminary study.

It has been suggested that the rich club organization in major depressive disorder (MDD) was altered. However, it remained unclear whether the rich club organization could be served as a biomarker that predicted the improvement of clinical symptoms in MDD. The current study included 29 mild or moderate patients with MDD, who were grouped into a treatment group (receiving cognitive behavioral therapy or real-time fMRI feedback treatment) and a no-treatment group. Resting-state MRI scans were obtained for all participants. Graph theory was employed to investigate the treatment-related changes in network properties and rich club organization. We found that patients in the treatment group had decreased depressive symptom scores and enhanced rich club connectivity following the nonpharmacological treatment. Moreover, the changes in rich club connectivity were significantly correlated with the changes in depressive symptom scores. In addition, the nonpharmacological treatment on patients with MDD increased functional connectivity mainly among the salience network, default mode network, frontoparietal network, and subcortical network. Patients in the no-treatment group did not show significant changes in depressive symptom scores and rich club organization. Those results suggested that the remission of depressive symptoms after nonpharmacological treatment in MDD patients was associated with the increased efficiency of global information processing.

Efficiency of Heterogenous Functional Connectomes Explains Variance in Callous-Unemotional Traits After Computational Lesioning of Cortical Midline and Salience Regions.

Introduction: Callous-unemotional (CU) traits are a youth antisocial phenotype hypothesized to be a result of differences in the integration of multiple brain systems. However, mechanistic insights into these brain systems are a continued challenge. Where prior work describes activation and connectivity, new mechanistic insights into the brain's functional connectome can be derived by removing nodes and quantifying changes in network properties (hereafter referred to as computational lesioning) to characterize connectome resilience and vulnerability. Methods: Here, we study the resilience of connectome integration in CU traits by estimating changes in efficiency after computationally lesioning individual-level connectomes. From resting-state data of 86 participants (48% female, age 14.52 ± 1.31) drawn from the Nathan Kline institute's Rockland study, individual-level connectomes were estimated using graphical lasso. Computational lesioning was conducted both sequentially and by targeting global and local hubs. Elastic net regression was applied to determine how these changes explained variance in CU traits. Follow-up analyses characterized modeled node hubs, examined moderation, determined impact of targeting, and decoded the brain mask by comparing regions to meta-analytic maps. Results: Elastic net regression revealed that computational lesioning of 23 nodes, network modularity, and Tanner stage explained variance in CU traits. Hub assignment of selected hubs differed at higher CU traits. No evidence for moderation between simulated lesioning and CU traits was found. Targeting global hubs increased efficiency and targeting local hubs had no effect at higher CU traits. Identified brain mask meta-analytically associated with more emotion and cognitive terms. Although reliable patterns were found across participants, adolescent brains were heterogeneous even for those with a similar CU traits score. Conclusion: Adolescent brain response to simulated lesioning revealed a pattern of connectome resiliency and vulnerability that explains variance in CU traits, which can aid prediction of youth at greater risk for higher CU traits.

Alcohol Dependence Modifies Brain Networks Activated During Withdrawal and Reaccess: A c-Fos–Based Analysis in Mice

BackgroundHigh-level alcohol consumption causes neuroplastic changes in the brain that promote pathological drinking behavior. Some of these changes have been characterized in defined brain circuits and cell types, but unbiased approaches are needed to explore broader patterns of adaptations. MethodsWe used whole-brain c-Fos mapping and network analysis to assess patterns of neuronal activity during alcohol withdrawal and following reaccess in a well-characterized model of alcohol dependence. Mice underwent 4 cycles of chronic intermittent ethanol to increase voluntary alcohol consumption, and a subset underwent forced swim stress to further escalate consumption. Brains were collected either 24 hours (withdrawal) or immediately following a 1-hour period of alcohol reaccess. c-fos counts were obtained for 110 brain regions using iDISCO and ClearMap. Then, we classified mice as high or low drinkers and used graph theory to identify changes in network properties associated with high-drinking behavior. ResultsDuring withdrawal, chronic intermittent ethanol mice displayed widespread increased c-Fos expression relative to air-exposed mice, independent of forced swim stress. Reaccess drinking reversed this increase. Network modularity, a measure of segregation into communities, was increased in high-drinking mice after alcohol reaccess relative to withdrawal. The cortical amygdala showed increased cross-community coactivation during withdrawal in high-drinking mice, and cortical amygdala silencing in chronic intermittent ethanol mice reduced voluntary drinking. ConclusionsAlcohol withdrawal in dependent mice causes changes in brain network organization that are attenuated by reaccess drinking. Olfactory brain regions, including the cortical amygdala, drive some of these changes and may play an important but underappreciated role in alcohol dependence.

Extinction of biotic interactions due to habitat loss could accelerate the current biodiversity crisis.

Habitat loss disrupts species interactions through local extinctions, potentially orphaning species that depend on interacting partners, via mutualisms or commensalisms, and increasing secondary extinction risk. Orphaned species may become functionally or secondarily extinct, increasing the severity of the current biodiversity crisis. While habitat destruction is a major cause of biodiversity loss, the number of secondary extinctions is largely unknown. We investigate the relationship between habitat loss, orphaned species, and bipartite network properties. Using a real seed dispersal network, we simulate habitat loss to estimate the rate at which species are orphaned. To be able to draw general conclusions, we also simulate habitat loss in synthetic networks to quantify how changes in network properties affect orphan rates across broader parameter space. Both real and synthetic network simulations show that even small amounts of habitat loss can cause up to 10% of species to be orphaned. More area loss, less connected networks, and a greater disparity in the species richness of the network's trophic levels generally result in more orphaned species. As habitat is lost to land-use conversion and climate change, more orphaned species increase the loss of community-level and ecosystem functions. However, the potential severity of repercussions ranges from minimal (no species orphaned) to catastrophic (up to 60% of species within a network orphaned). Severity of repercussions also depends on how much the interaction richness and intactness of the community affects the degree of redundancy within networks. Orphaned species could add substantially to the loss of ecosystem function and secondary extinction worldwide.

Classification of Rajayoga Meditators Based on the Duration of Practice Using Graph Theoretical Measures of Functional Connectivity from Task-Based Functional Magnetic Resonance Imaging.

Functional magnetic resonance imaging (fMRI) studies on mental training techniques such as meditation have reported benefits like increased attention and concentration, better emotional regulation, as well as reduced stress and anxiety. Although several studies have examined functional activation and connectivity in long-term as well as short-term meditators from different meditation traditions, it is unclear if long-term meditation practice brings about distinct changes in network properties of brain functional connectivity that persist during task performance. Indeed, task-based functional connectivity studies of meditators are rare. This study aimed to differentiate between long-term and short-term Rajayoga meditators based on functional connectivity between regions of interest in the brain. Task-based fMRI was captured as the meditators performed an engaging task. The graph theoretical-based functional connectivity measures of task-based fMRI were calculated using CONN toolbox and were used as features to classify the two groups using Machine Learning models. In this study, we recruited two age and sex-matched groups of Rajayoga meditators from the Brahma Kumaris tradition that differed in the duration of their meditation experience: Long-term practitioners (n = 12, mean 13,596 h) and short-term practitioners (n = 10, mean 1095 h). fMRI data were acquired as they performed an engaging task and functional connectivity metrics were calculated from this data. These metrics were used as features in training machine learning algorithms. Specifically, we used adjacency matrices generated from graph measures, global efficiency, and local efficiency, as features. We computed functional connectivity with 132 ROIs as well as 32 network ROIs. Five machine learning models, such as logistic regression, SVM, decision tree, random forest, and gradient boosted tree, were trained to classify the two groups. Accuracy, precision, sensitivity, selectivity, area under the curve receiver operating characteristics curve were used as performance measures. The graph measures were effective features, and tree-based algorithms such as decision tree, random forest, and gradient boosted tree yielded the best performance (test accuracy >84% with 132 ROIs) in classifying the two groups of meditators. Our results support the hypothesis that long-term meditative practices alter brain functional connectivity networks even in nonmeditative contexts. Further, the use of adjacency matrices from graph theoretical measures of high-dimensional fMRI data yields a promising feature set for machine learning classifiers.

Food web structure and community composition: a comparison across space and time in the North Sea

Ecological communities are constantly changing as a response to environmental and anthropogenic pressures. Yet, how changes in community composition influence the structure of food webs over time and space remains elusive. Using ecological network analysis, we assessed how food web structure changed across six distinct areas of the North Sea over a sixteen‐year time‐period. We used multivariate analyses to disentangle and compare spatio‐temporal dynamics in community composition (i.e. changes in species abundances) and food web structure (i.e. changes in network properties). Specifically, we assessed how changes in community composition were reflected in food web structure. Our results revealed a strong spatial coupling between community composition and food web structure along a south–north gradient. However, the temporal covariation between community composition and food web structure depended on the spatial scale. We observed a temporal mismatch at regional scale, but a strong coupling at local scale. In particular, we found that community composition can be influenced by hydro‐climatic events over large areas, with diverse effects manifesting in local food web structure. Our proposed methodological framework quantified and compared spatio‐temporal changes in community composition and food web structure, providing key information to support effective management strategies aimed at conserving the structure and functioning of ecological communities in times of environmental change.

The development of structural covariance networks during the transition from childhood to adolescence

Structural covariance conceptualizes how morphologic properties of brain regions are related to one another (across individuals). It can provide unique information to cortical structure (e.g., thickness) about the development of functionally meaningful networks. The current study investigated how structural covariance networks develop during the transition from childhood to adolescence, a period characterized by marked structural re-organization. Participants (N = 192; scans = 366) completed MRI assessments between 8.5 and 14.5 years of age. A sliding window approach was used to create “age-bins”, and structural covariance networks (based on cortical thickness) were created for each bin. Next, generalized additive models were used to characterize trajectories of age-related changes in network properties. Results revealed nonlinear trajectories with “peaks” in mean correlation and global density that are suggestive of a period of convergence in anatomical properties across the cortex during early adolescence, prior to regional specialization. “Hub” regions in sensorimotor cortices were present by late childhood, but the extent and strength of association cortices as “hubs” increased into mid-adolescence. Moreover, these regional changes were found to be related to rates of thinning across the cortex. In the context of neurocognitive networks, the frontoparietal, default mode, and attention systems exhibited age-related increases in within-network and between-network covariance. These regional and modular developmental patterns are consistent with continued refinement of socioemotional and other complex executive functions that are supported by higher-order cognitive networks during early adolescence.

Shift Toward Randomness in Brain Networks of Patients With Anorexia Nervosa: The Role of Malnutrition.

No study to date investigated structural white matter (WM) connectome characteristics in patients with anorexia nervosa (AN). Previous research in AN found evidence of imbalances in global and regional connectomic brain architecture and highlighted a role of malnutrition in determining structural brain changes. The aim of our study was to explore the characteristics of the WM network architecture in a sample of patients with AN. Thirty-six patients with AN and 36 healthy women underwent magnetic resonance imaging to obtain a high-resolution three-dimensional T1-weighted anatomical image and a diffusion tensor imaging scan. Probabilistic tractography data were extracted and analyzed in their network properties through graph theory tools. In comparison to healthy women, patients with AN showed lower global network segregation (normalized clustering: p = 0.029), an imbalance between global network integration and segregation (i.e., lower small-worldness: p = 0.031), and the loss of some of the most integrative and influential hubs. Both clustering and small-worldness correlated with the lowest lifetime body mass index. A significant relationship was found between the average regional loss of cortical volume and changes in network properties of brain nodes: the more the difference in the cortical volume of brain areas, the more the increase in the centrality of corresponding nodes in the whole brain, and the decrease in clustering and efficiency of the nodes of parietal cortex. Our findings showed an unbalanced connectome wiring in AN patients, which seems to be influenced by malnutrition and loss of cortical volume. The role of this rearrangement in the maintenance and prognosis of AN and its reversibility with clinical improvement needs to be established by future studies.

Intrinsic functional connectivity during continuous maintenance and suppression of emotion in bipolar disorder.

Resting-state connectivity studies, which examine unconstrained low frequency BOLD fluctuations, have reported inconsistent abnormalities in bipolar disorder (BP). In this study, we investigated intrinsic brain connectivity under the constraints of a Continuous Emotion Regulation Task (CERT) in BP patients in depressed (BPD) and manic (BPM) states, along with healthy control participants. Medication-free participants, with either a diagnosis of BP (BPD = 27, BPM = 30) or healthy controls (N = 33) were included. We collected 2 fMRI scans using the CERT paradigm, in which participants continuously watched negative pictures and either maintained emotions (MAINTAIN) or suppressed emotion using reappraisal techniques (SUPPRESS). Network-based statistic and graph theory analyses were examined for (i) the main effect of condition (within-group) and (ii) group and condition interactions. In healthy participants, MAINTAIN largely involved occipital and parietal cortices (p < .001), whereas SUPPRESS also recruited the frontal and cingulate cortices (p = .023). The interaction between group (BPD vs. BPM) and condition revealed a network involving the inferior frontal lobe which was stronger during MAINTAIN for BPD and during SUPPRESS for BPM (p = .037). Graph theory properties (i.e., clustering coefficient) for key nodes also evidenced significant group by condition interactions. We observed BP-related changes in network properties involved in normal and abnormal emotion regulation, which provide insights into the neural bases for affective disturbances in BP.

Tracking the Development of Functional Connectomes for Face Processing.

Face processing capacities become more specialized and advanced during development, but neural underpinnings of these processes are not fully understood. The present study applied graph theory-based network analysis to task-negative (resting blocks) and task-positive (viewing faces) functional magnetic resonance imaging data in children (5-17 years) and adults (18-42 years) to test the hypothesis that the development of a specialized network for face processing is driven by task-positive processing (face viewing) more than by task-negative processing (visual fixation) and by both progressive and regressive changes in network properties. Predictive modeling was used to predict age from node-based network properties derived from task-positive and task-negative states in a whole-brain network (WBN) and a canonical face network (FN). The best-fitting model indicated that FN maturation was marked by both progressive and regressive changes in information diffusion (eigenvector centrality) in the task-positive state, with regressive changes outweighing progressive changes. Hence, FN maturation was characterized by reductions in information diffusion potentially reflecting the development of more specialized modules. In contrast, WBN maturation was marked by a balance of progressive and regressive changes in hub-connectivity (betweenness centrality) in the task-negative state. These findings suggest that the development of specialized networks like the FN depends on dynamic developmental changes associated with domain-specific information (e.g., face processing), but maturation of the brain as a whole can be predicted from task-free states.

Resting-state functional brain networks in first-episode psychosis: A 12-month follow-up study.

Schizophrenia is increasingly conceived as a disorder of brain network connectivity and organization. However, reports of network abnormalities during the early illness stage of psychosis are mixed. This study adopted a data-driven whole-brain approach to investigate functional connectivity and network architecture in a first-episode psychosis cohort relative to healthy controls and whether functional network properties changed abnormally over a 12-month period in first-episode psychosis. Resting-state functional connectivity was performed at two time points. At baseline, 29 first-episode psychosis individuals and 30 healthy controls were assessed, and at 12 months, 14 first-episode psychosis individuals and 20 healthy controls completed follow-up. Whole-brain resting-state functional connectivity networks were mapped for each individual and analyzed using graph theory to investigate whether network abnormalities associated with first-episode psychosis were evident and whether functional network properties changed abnormally over 12 months relative to controls. This study found no evidence of abnormal resting-state functional connectivity or topology in first-episode psychosis individuals relative to healthy controls at baseline or at 12-months follow-up. Furthermore, longitudinal changes in network properties over a 12-month period did not significantly differ between first-episode psychosis individuals and healthy control. Network measures did not significantly correlate with symptomatology, duration of illness or antipsychotic medication. This is the first study to show unaffected resting-state functional connectivity and topology in the early psychosis stage of illness. In light of previous literature, this suggests that a subgroup of first-episode psychosis individuals who have a neurotypical resting-state functional connectivity and topology may exist. Our preliminary longitudinal analyses indicate that there also does not appear to be deterioration in these network properties over a 12-month period. Future research in a larger sample is necessary to confirm our longitudinal findings.

Graph Anomaly Detection Based on Steiner Connectivity and Density

Detecting “hotspots” and “anomalies” is a recurring problem with a wide range of applications, such as social network analysis, epidemiology, finance, and biosurveillance, among others. Networks are a common abstraction in these applications for representing complex relationships. Typically, these networks are dynamic-, i.e., they evolve over time. A number of methods have been proposed for anomaly detection in such dynamic network data sets, which are primarily based on changes in network properties. We provide a survey of the various formulations of anomaly detection in dynamic networks with a focus on “window-based” methods. Window-based methods first define a time window of past network snapshots to model normal behavior and then mark a snapshot as anomalous if it has significantly different patterns from those observed in the time window. We describe two classes of techniques: 1) generalizations of Steiner connectivity; and 2) dense subgraph mining. Both have been used extensively in window-based graph anomaly detection. We summarize the key problem formulations that have been studied using these approaches, and we describe details of some of the main techniques.

Task-Related Modulations of BOLD Low-Frequency Fluctuations within the Default Mode Network.

Spontaneous low-frequency Blood-Oxygenation Level-Dependent (BOLD) signals acquired during resting state are characterized by spatial patterns of synchronous fluctuations, ultimately leading to the identification of robust brain networks. The resting-state brain networks, including the Default Mode Network (DMN), are demonstrated to persist during sustained task execution, but the exact features of task-related changes of network properties are still not well characterized. In this work we sought to examine in a group of 20 healthy volunteers (age 33 ± 6 years, 8 F/12 M) the relationship between changes of spectral and spatiotemporal features of one prominent resting-state network, namely the DMN, during the continuous execution of a working memory n-back task. We found that task execution impacted on both functional connectivity and amplitude of BOLD fluctuations within large parts of the DMN, but these changes correlated between each other only in a small area of the posterior cingulate. We conclude that combined analysis of multiple parameters related to connectivity, and their changes during the transition from resting state to continuous task execution, can contribute to a better understanding of how brain networks rearrange themselves in response to a task.

Trophic mechanisms underlying bentho‐demersal community recovery in the north‐east Atlantic

Summary Bottom trawling is considered one of the greatest and most widespread causes of anthropogenic change in shelf seas, with major and prolonged impacts in areas with a long history of exploitation by fisheries such as the North Atlantic. Here, signs of recovery following the enforcement of regulations are increasingly being reported. We examined the extent to which biological diversity and functionality are restored when fishing pressure is reduced by evaluating changes in species biomass and that of the main functional groups present in the continental platform, as obtained from systematic survey (IBTS) results. Moreover, we examined how this recovery is mirrored in the trophic organization of the affected communities by assessing variations in link density and strength of the main consumer species and investigating whether variations in species richness were paralleled by changes in network properties. Finally, we investigated whether reductions in fishing pressure (fishing mortality) were correlated with the abovementioned variations in community and trophic structure of the bentho‐demersal assemblages. Our results corroborate the apparent recovery of North Atlantic fishing stocks and further substantiate the improved welfare of the bentho‐demersal assemblages of the Southern Bay of Biscay. Specifically, we found an increase in species richness and in the abundance of most functional groups, especially those more closely related to the benthos, following the reduction in fishing mortality. Increases in overall species richness were paralleled by an increase in the number of links and a reduction in mean interaction strength connecting the main consumer species with their prey items. This is in accordance with ecological theory and could explain the mechanism by which bentho‐demersal assemblages restructure their trophic network towards more stable organizations. Synthesis and applications. Detecting patterns of recovery or change to alternative stable states following stress release is essential to unravel the effects of perturbations and to design effective management strategies. Our study shows that trophic network properties provide a convincing tool to evaluate and perceive recovery patterns. The trends shown in our study appear to be related with the decline in fishing mortality resulting from the enforcement of fisheries regulations in the area. They substantiate the efficiency of these regulations as a guarantee for an ecosystem approach to fisheries management and advocate their enforcement at a wider level as a convincing measure to preserve the sustainability of marine resources and their welfare.

Empirical comparison of network sampling: How to choose the most appropriate method?

In the past few years, the storage and the analysis of large-scale and fast evolving networks presents a great challenge. Therefore, a number of different techniques have been proposed for sampling large networks. Studies on network sampling primarily analyze the changes of network properties under the sampling. In general, network exploration techniques approximate the original networks more accurate than random node and link selection. Yet, link selection with additional subgraph induction step outperforms most other techniques. In this paper, we apply subgraph induction also to random walk and forest-fire sampling and evaluate the effects of subgraph induction on the sampling accuracy. We analyze different real-world networks and the changes of their properties introduced by sampling. The results reveal that the techniques with subgraph induction improve the performance of techniques without induction and create denser sample networks with larger average degree. Furthermore, the accuracy of sampling decrease consistently across various sampling techniques, when the sampled networks are smaller. Based on the results of the comparison, we introduce the scheme for selecting the most appropriate technique for network sampling. Overall, the breadth-first exploration sampling proves as the best performing technique.

Neural networks of colored sequence synesthesia.

Synesthesia is a condition in which normal stimuli can trigger anomalous associations. In this study, we exploit synesthesia to understand how the synesthetic experience can be explained by subtle changes in network properties. Of the many forms of synesthesia, we focus on colored sequence synesthesia, a form in which colors are associated with overlearned sequences, such as numbers and letters (graphemes). Previous studies have characterized synesthesia using resting-state connectivity or stimulus-driven analyses, but it remains unclear how network properties change as synesthetes move from one condition to another. To address this gap, we used functional MRI in humans to identify grapheme-specific brain regions, thereby constructing a functional "synesthetic" network. We then explored functional connectivity of color and grapheme regions during a synesthesia-inducing fMRI paradigm involving rest, auditory grapheme stimulation, and audiovisual grapheme stimulation. Using Markov networks to represent direct relationships between regions, we found that synesthetes had more connections during rest and auditory conditions. We then expanded the network space to include 90 anatomical regions, revealing that synesthetes tightly cluster in visual regions, whereas controls cluster in parietal and frontal regions. Together, these results suggest that synesthetes have increased connectivity between grapheme and color regions, and that synesthetes use visual regions to a greater extent than controls when presented with dynamic grapheme stimulation. These data suggest that synesthesia is better characterized by studying global network dynamics than by individual properties of a single brain region.

SIMULATING NETWORK STRUCTURES USING BERNOULLI'S PRINCIPLE

Using the fact that connections between vertices of a network often represent directed and weighted flows, we apply hydraulic principles to develop novel insights into network structure and growth. We develop a network model based on Bernoulli's principle and use it to analyze changes in network properties. Simulation results show that velocity of flow, resistance, fitness and existing connections in a system determine network connections of a vertex as well as overall network structure. We demonstrate how network structure is affected by changes in velocity and resistance, and how one vertex can monopolize connections within a network. Using Bernoulli's principle, we are able to independently reproduce key results in the network literature.

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.