Connectivity Of Large-scale Networks Research Articles

Bayesian network-based vulnerability assessment of a large-scale bridge network using improved ORDER-II-Dijkstra algorithm

Vulnerability analysis has become a recent concern for bridge administration authorities. This paper presents a Bayesian network-based vulnerability evaluation methodology for a large-scale national highway (NH) bridge network, using the improved ORDER-II-Dijkstra algorithm. The NH bridge network consists of 1772 bridges, and includes their spatial locations, types, completion years, and assessment states. The network vulnerability was defined by combining edge failure and its influence on network connectivity. Using the equivalent bridge concept for simplicity, the edge failure probability was determined by a series system composed of all bridges in the same edge, where the failure probability for one bridge could be calculated by incorporating its assessment state and design load. The large-scale bridge network connectivity probability was approximately evaluated using the Bayesian network. To avoid the NP-hard problem, the ORDER-II algorithm evaluated the most probable state combinations of equivalent bridges, by adding or deleting ordered state combinations to the minimum heap. The improved Dijkstra’s algorithm was chosen to determine the network connectivity under each state combination by seeking the shortest paths between node pairs. The application of vulnerability to the bridge network illustrates the effectiveness and accuracy of this method, and can provide guidance for decision-making.

Impacts of FKBP5 variants on large-scale brain network connectivity in healthy adults

BackgroundFK506 binding protein 5 (FKBP5) rs1360780 polymorphism has been identified as a molecular genetic marker associated with the dysfunction of the hypothalamic–pituitary–adrenal (HPA) axis. The impact of FKBP5 rs1360780 on the large-scale brain network connectivity in healthy adults is still unknown. Methods479 healthy volunteers (age: 20-80years) completed MRI scans, neuropsychological assessments and blood analysis.All subjects were divided into CC, CT and TT genotypes. Within and between network connectivities (10 sub-networks) were calculated using resting state functional MRI (rs-fMRI) data. The genetic effects and gene-gender/age interaction on large-scale network connectivity were explored. ResultsCompared with CC and CT groups, TT group showed increased intra-connectivity in default mode network (DMN) and increased inter-connectivity mainly distributed among the network of DMN, salience network (SAN), dorsal attention network (DAN), ventral attention network (VAN), subcortical network (SUB), and visual network (VIS). Gene-by-gender and gene-by-age interaction were found in inter-connectivity of DAN to VIS and DMN to FPN, respectively. The altered connectivities correlated with anxiety status test score. LimitationsPlasma adrenocorticotropic hormone (ACTH) or cortisol were not measured,or else, we could estimate the hypothalamic–pituitary–adrenal (HPA) axis activity which may strengthen our results. ConclusionsFKBP5 rs1360780 modulates the large-scale brain network connectivity in healthy adults. TT carriers showed the increased intra- and inter-connectivities mainly distributed among the network of DMN, SAN, DAN, VAN, SUB and VIS.

Constructing EEG Large-Scale Cortical Functional Network Connectivity Based on Brain Atlas by S Estimator

Large-scale functional network connectivity (FNC) reveals the neural substrate of the cognitive process on a large-scale level. Electroencephalogram (EEG) is cost effective, portable, and noninvasive and is capable of capturing brain activities at a millisecond scale. Brain atlas derived from the anatomy clearly defines reliable functional subnetworks. In this article, we proposed to construct robust EEG FNC based on brain atlas by combining EEG source imaging with multivariate synchronization analysis to mine the brain’s large-scale information exchange. We evaluated the performances of two typical methods, canonical correlation analysis (CCA) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula> estimator, in quantifying the couplings among subnetworks by both simulation and application to real EEG data set. Simulation demonstrated that, compared to CCA, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula> estimator shows high robustness and adaptability to low signal-to-noise ratio (SNR) and short length data. According to the FNC of P300, we further found that the FNC network constructed by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula> estimator may be more consistent with the physiological mechanisms of P300 generation, where the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula> estimator-based approach emphasizes the important role of the cerebellar network that has been proved to be involved in attention-related cognition tasks. This article provides a new tool to probe information processing during the cognition process at a higher hierarchal level.

Experience-dependent neuroplasticity in trained musicians modulates the effects of chronic pain on insula-based networks – A resting-state fMRI study

Recent resting-state fMRI studies associated extensive musical training with increased insula-based connectivity in large-scale networks involved in salience, emotion, and higher-order cognitive processes. Similar changes have also been found in chronic pain patients, suggesting that both types of experiences can have comparable effects on insula circuitries. Based on these observations, the current study asked the question whether, and if so in what way, different forms of experience-dependent neuroplasticity may interact. Here we assessed insula-based connectivity during fMRI resting-state between musicians and non-musicians both with and without chronic pain, and correlated the results with clinical pain duration and intensity. As expected, insula connectivity was increased in chronic pain non-musicians relative to healthy non-musicians (with cingulate cortex and supplementary motor area), yet no differences were found between chronic pain non-musicians and healthy musicians. In contrast, musicians with chronic pain showed decreased insula connectivity relative to both healthy musicians (with sensorimotor and memory regions) and chronic pain non-musicians (with the hippocampus, inferior temporal gyrus, and orbitofrontal cortex), as well as lower pain-related inferences with daily activities. Pain duration correlated positively with insula connectivity only in non-musicians, whereas pain intensity exhibited distinct relationships across groups. We conclude that although music-related sensorimotor training and chronic pain, taken in isolation, can lead to increased insula-based connectivity, their combination may lead to higher-order plasticity (metaplasticity) in chronic pain musicians, engaging brain mechanisms that can modulate the consequences of maladaptive experience-dependent neural reorganization (i.e., pain chronification).

Neuroinflammation and Functional Connectivity in Alzheimer's Disease: Interactive Influences on Cognitive Performance.

Neuroinflammation is a key part of the etio-pathogenesis of Alzheimer's disease (AD). We tested the relationship between neuroinflammation and the disruption of functional connectivity in large-scale networks, and their joint influence on cognitive impairment. We combined [11C]PK11195 positron emission tomography (PET) and resting-state functional magnetic resonance imaging (rs-fMRI) in 28 patients (12 females/16 males) with clinical diagnosis of probable AD or mild cognitive impairment with positive PET biomarker for amyloid, and 14 age-, sex-, and education-matched healthy controls (8 females/6 males). Source-based “inflammetry” was used to extract principal components of [11C]PK11195 PET signal variance across all participants. rs-fMRI data were preprocessed via independent component analyses to classify neuronal and non-neuronal signals. Multiple linear regression models identified sources of signal covariance between neuroinflammation and brain connectivity profiles, in relation to the diagnostic group (patients, controls) and cognitive status.Patients showed significantly higher [11C]PK11195 binding relative to controls, in a distributed spatial pattern including the hippocampus, frontal, and inferior temporal cortex. Patients with enhanced loading on this [11C]PK11195 binding distribution displayed diffuse abnormal functional connectivity. The expression of a stronger association between such abnormal connectivity and higher levels of neuroinflammation correlated with worse cognitive deficits.Our study suggests that neuroinflammation relates to the pathophysiological changes in network function that underlie cognitive deficits in Alzheimer's disease. Neuroinflammation, and its association with functionally-relevant reorganization of brain networks, is proposed as a target for emerging immunotherapeutic strategies aimed at preventing or slowing the emergence of dementia.SIGNIFICANCE STATEMENT Neuroinflammation is an important aspect of Alzheimer's disease (AD), but it was not known whether the influence of neuroinflammation on brain network function in humans was important for cognitive deficit. Our study provides clear evidence that in vivo neuroinflammation in AD impairs large-scale network connectivity; and that the link between neuro inflammation and functional network connectivity is relevant to cognitive impairment. We suggest that future studies should address how neuroinflammation relates to network function as AD progresses, and whether the neuroinflammation in AD is reversible, as the basis of immunotherapeutic strategies to slow the progression of AD.

Transdiagnostic modulation of brain networks by electroconvulsive therapy in schizophrenia and major depression

Major depressive disorder (MDD) and schizophrenia (SCZ) share neurobiological and clinical commonalities. Altered functional connectivity of large-scale brain networks has been associated with both disorders. Electroconvulsive therapy (ECT) has proven to be an effective treatment in severe forms of MDD and SCZ. However, the role of ECT on the modulation of the dynamics of brain networks is still unknown. In this study, we used resting state functional magnetic resonance imaging (rs-fMRI) to investigate functional connectivity in 16 pharmacoresistant patients with SCZ or MDD and a matched group of normal controls. Patients were scanned before and after right-sided unilateral ECT. Group spatial independent component analysis was carried out with a multiple analysis of covariance (MANCOVA) approach to estimate the effects of ECT treatment on intrinsic components (INs). Functional network connectivity (FNC) was calculated between pairs of INs. Patients had reduced connectivity within a striato-thalamic network in the thalamus as well as increased low frequency oscillations in a striatal network. ECT reduced low frequency oscillations (LFOs) on a striatal network along with increasing functional connectivity in the medial prefrontal cortex within the DMN. Following ECT treatment, the FNC of the executive network was reduced with the DMN and increased with the salience network, respectively.Our findings suggest transnosological effects of ECT on the connectivity of large-scale networks as well as at the level of their interplay. Furthermore, they support a transnosological approach for the investigation not only of the neural correlates of the disease but also of the brain mechanism of treatment of mental disorders.

A Data Structure for Real-Time Aggregation Queries of Big Brain Networks.

Recent advances in neuro-imaging allowed big brain-initiatives and consortia to create vast resources of brain data that can be mined by researchers for their individual projects. Exploring the relationship between genes, brain circuitry, and behavior is one of the key elements of neuroscience research. This requires fusion of spatial connectivity data at varying scales, such as whole brain correlated gene expression, structural and functional connectivity. With ever-increasing resolution, these tend to exceed the past state-of-the art in size and complexity by several orders of magnitude. Since current analytical workflows in neuroscience involve time-consuming manual data-aggregation, incorporating efficient techniques for handling big connectivity data is a necessity. We propose a novel data structure enabling the interactive exploration of heterogeneous neurobiological connectivity data with billions of edges. Based on this data structure we realized Aggregation Queries, i.e. the aggregated connectivity from, to or between brain areas allows experts to compare the multimodal networks residing at different scales, or levels of hierarchically organized anatomical atlases. Executed on-demand on volumetric gene expression and connectivity data, they allow an interactive dissection of networks in real-time and based on their spatial context. The data structure is optimized in order to be accessible directly from the hard disk, since connectivity of large-scale networks typically exceeds the memory size of current consumer level PCs. This allows experts to embed and explore their own experimental data in the framework of public data resources without the need for their own large-scale infrastructure. Our data structure outperforms state-of-the-art graph engines in retrieving connectivity of arbitrary user defined local brain areas. We demonstrate the feasibility of our approach by analyzing fear-related functional neuroanatomy in mice. Further, we show its versatility by comparing multimodal brain networks linked to autism. Importantly, we achieve cross-species congruence in retrieving human psychiatric traits networks, which facilitates the selection of neural substrates to be further studied in mouse models.

Rapid Reconfiguration of the Functional Connectome after Chemogenetic Locus Coeruleus Activation

The locus coeruleus (LC) supplies norepinephrine (NE) to the entire forebrain and regulates many fundamental brain functions. Studies in humans have suggested that strong LC activation might shift network connectivity to favor salience processing. To causally test this hypothesis, we use a mouse model to study the effect of LC stimulation on large-scale functional connectivity by combining chemogenetic activation of the LC with resting-state fMRI, an approach we term "chemo-connectomics." We show that LC activation rapidly interrupts ongoing behavior and strongly increases brain-wide connectivity, with the most profound effects in the salience and amygdala networks. Functional connectivity changes strongly correlate with transcript levels of alpha-1 and beta-1 adrenergic receptors across the brain, and functional network connectivity correlates with NE turnover within select brain regions. We propose that these changes in large-scale network connectivity are critical for optimizing neural processing in the context of increased vigilance and threat detection.

Rapid Reconfiguration of the Functional Connectome after Chemogenetic Locus Coeruleus Activation

The locus coeruleus (LC) supplies norepinephrine (NE) to the entire forebrain, regulates many fundamental brain functions, and is implicated in several neuropsychiatric diseases. Although selective manipulation of the LC is not possible in humans, studies have suggested that strong LC activation might shift network connectivity to favor salience processing. To test this hypothesis, we use a mouse model to study the impact of LC stimulation on large-scale functional connectivity by combining chemogenetic activation of the LC with resting-state fMRI, an approach we term “chemo-connectomics”. LC activation rapidly interrupts ongoing behavior and strongly increases brain-wide connectivity, with the most profound effects in the salience and amygdala networks. We reveal a direct correlation between functional connectivity changes and transcript levels of alpha-1, alpha-2, and beta-1 adrenoceptors across the brain, and a positive correlation between NE turnover and functional connectivity within select brain regions. These results represent the first brain-wide functional connectivity mapping in response to LC activation, and demonstrate a causal link between receptor expression, brain states and functionally connected large-scale networks at rest. We propose that these changes in large-scale network connectivity are critical for optimizing neural processing in the context of increased vigilance and threat detection.

Altered Forebrain Functional Connectivity and Neurotransmission in a Kinase-Inactive Met Mouse Model of Autism.

MET, the gene encoding the tyrosine kinase receptor for hepatocyte growth factor, is a susceptibility gene for autism spectrum disorder (ASD). Genetically altered mice with a kinase-inactive Met offer a potential model for understanding neural circuit organization changes in autism. Here, we focus on the somatosensory thalamocortical circuitry because distinct somatosensory sensitivity phenotypes accompany ASD, and this system plays a major role in sensorimotor and social behaviors in mice. We employed resting-state functional magnetic resonance imaging and in vivo high-resolution proton MR spectroscopy to examine neuronal connectivity and neurotransmission of wild-type, heterozygous Met–Emx1, and fully inactive homozygous Met–Emx1 mice. Met–Emx1 brains showed impaired maturation of large-scale somatosensory network connectivity when compared with wild-type controls. Significant sex × genotype interaction in both network features and glutamate/gamma-aminobutyric acid (GABA) balance was observed. Female Met–Emx1 brains showed significant connectivity and glutamate/GABA balance changes in the somatosensory thalamocortical system when compared with wild-type brains. The glutamate/GABA ratio in the thalamus was correlated with the connectivity between the somatosensory cortex and the thalamus in heterozygous Met–Emx1 female brains. The findings support the hypothesis that aberrant functioning of the somatosensory thalamocortical system is at the core of the conspicuous somatosensory behavioral phenotypes observed in Met–Emx1 mice.

Intrinsic brain abnormalities in drug-naive patients with obsessive-compulsive disorder: A resting-state functional MRI study

BackgroundUsing the resting-state functional magnetic resonance imaging (rs-fMRI) in a relatively large sample of drug-naive patients with obsessive-compulsive disorder (OCD), the current study aims to explore alterations in regional and network-level neural function and to determine the association between these alterations in intrinsic neural activity and symptom severity in OCD. MethodsA total of 68 drug-naive OCD patients and 68 healthy control subjects (HCS) were examined using rs-fMRI. Regional cerebral function was evaluated by measuring the fraction of amplitude of low-frequency fluctuation (fALFF). Regions with fALFF alterations were used as seeds in whole-brain functional connectivity (FC) analysis. Statistical analyses of fALFF and FC differences between OCD patients with HCS were performed voxel-by-voxel using a two-sample t-test in Statistical Parametric Mapping version 8 (SPM8). Whole brain correlation analyses were performed to identify the association between functional neural correlates and OCD symptom severity evaluated using Yale-Brown Obsessive Compulsive scale (Y-BOCS) and subscale scores. ResultsRelative to HCS, OCD patients showed higher fALFF in the right putamen and right superior frontal gyrus (P < 0.05, corrected for AlphaSim); higher FC in the limbic-striatal circuit and lower FC in the fronto-temporal and fronto-striato-thalamic networks (P < 0.05, corrected for AlphaSim). FC in striato-thalamic junction was negatively correlated with the Y-BOCS total score (r = -0.493, P < 0.001). ConclusionThese findings of focal spontaneous hyperfunction confirmed the prevailing frontal-striatal model of OCD, and altered brain connectivity in large-scale resting-state networks indicated a connectivity-based pathophysiological process in OCD.

Theory of Mind Skills Are Related to Resting-State Frontolimbic Connectivity in Schizophrenia.

Patients with schizophrenia (SCH) often demonstrate impairment in social-cognitive functions as well as disturbances in large-scale network connectivity. The ventromedial prefrontal cortex (vmPFC) is a core region of the default mode network, with projections to limbic structures. It plays an important role in social and emotional decision-making. We investigated whether resting-state functional connectivity (FC) relates to the cognitive and affective domains of theory of mind (ToM). Twenty-three SCH patients and 19 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging scanning. vmPFC seed connectivity was correlated with behavioral measures assessing ToM domains. SCH performed less well than HCs in both ToM task domains. An analysis of the resting-state FC revealed that SCH had reduced connectivity from the vmPFC to the subcallosal cortex, right amygdala, and right hippocampus as a function of behavioral scores in both ToM domains. Within-group analyses indicated that in HCs, the performance in ToM was positively associated with frontoamygdalar resting-state connectivity, whereas in SCH, the performance in ToM was negatively associated with the frontosubcallosal connectivity. Differences in the pattern of the resting-state frontolimbic connectivity and its associations with performance in ToM tasks between the two study groups might represent a different setup for processing social information in patients with SCH.

Dysconnectivity of Large-Scale Functional Networks in Early Psychosis: A Meta-analysis.

Increasingly, studies have identified abnormalities in the functional connectivity (FC) of large-scale neural networks in early psychosis, but the findings thus far have been inconclusive. Therefore, the aim of this study was to identify robust alterations in FC of the default mode (DMN), salience (SN), and central executive networks (CEN), in patients with first-episode psychosis (FEP) using a meta-analytic approach. Included studies were required to be resting-state, seed-to-whole brain, FC neuroimaging studies, comparing FEP patients to healthy controls (HC), with seeds within the boundaries of the region-of-interest networks. Peak effect coordinates and peak t, z, or p values were meta-analyzed using Seed-based d Mapping software. The DMN seeds primarily displayed within-network hypoconnectivity (largest clusters including the middle orbital gyrus; and ventral anterior cingulate gyrus). The SN seeds displayed hypoconnectivity with regions in the DMN and CEN (largest clusters located in the bilateral middle temporal gyri). Review of the limited CEN data revealed hypo- and hyperconnectivity across the networks. Negative symptoms were positively correlated with all DMN FC abnormalities in the FEP group. Antipsychotic-treated patients displayed greater hypoconnectivity than antipsychotic-naïve patients between both the DMN/SN seeds and prefrontal regions. These findings provide substantial evidence of widespread resting-state FC abnormalities of the DMN, SN, and CEN in early psychosis; particularly implicating DMN and SN dysconnectivity as a core deficit underlying the psychopathology of psychosis. Additionally, we highlight the importance of disentangling connectivity abnormalities resulting from disease processes, from those that result from antipsychotic treatment.

Neural correlates of dynamic emotion perception in schizophrenia and the influence of prior expectations

Impaired emotion perception is a well-established and stable deficit in schizophrenia; however, there is limited knowledge about the underlying aberrant cognitive and brain processes that result in emotion perception deficits. Recent influential work has shown that perceptual deficits in schizophrenia may result from aberrant precision in prior expectations, associated with disrupted activity in frontal regions. In the present study, we investigated the perception of dynamic, multisensory emotion, the influence of prior expectations and the underlying aberrant brain processes in schizophrenia. During a functional Magnetic Resonance Imaging scan, participants completed the Dynamic Emotion Perception task, which induces prior expectations with emotion instruction cues. We delineated neural responses and functional connectivity in whole-brain large-scale networks underlying emotion perception. Compared to healthy individuals, schizophrenia patients had lower accuracy specifically for emotions that were congruent with prior expectations. At the neural level, schizophrenia patients had less engagement of right inferior frontal and parietal regions, as well as right amygdala dysconnectivity during discrimination of emotions congruent with prior expectations. The results indicate that individuals with schizophrenia may have aberrant prior expectations about emotional expressions, associated with under-activity in inferior frontoparietal regions and right amygdala dysconnectivity, which results in impaired perception of emotion.

Revisiting the Functional and Structural Connectivity of Large-Scale Cortical Networks.

Multimodal neuroimaging research has become increasingly popular, and structure-function correspondence is tacitly assumed. Researchers have not yet adequately assessed whether the functional connectivity (FC) and structural connectivity (SC) of large-scale cortical networks are in agreement. Structural magnetic resonance imaging (sMRI), resting-state functional MRI (rfMRI), and diffusion-weighted imaging (DWI) data sets from 36 healthy subjects (age 27.4) were selected from a Rockland sample (Enhanced Nathan Kline Institute). The cerebral cortex was parcellated into 62 regions according to the Desikan-Killiany atlas for FC and SC analyses. Thresholded correlations in rfMRI and tractography derived from DWI were used to construct FC and SC maps, respectively. A community detection algorithm was applied to reveal the underlying organization, and modular consistency was quantified to bridge cross-modal comparisons. The distributions of correlation coefficients in FC and SC maps were significantly different. Approximately one-fourth of the connections in the SC map were located at a correlation level below 0.2 (df 253). The index of modular consistency in the within-modality interindividual condition (either FC or SC) was considerably greater than that in the between-modality intraindividual analog. In addition, the SC-FC differential map (SC connections with lower correlations) revealed reliable modular structures. Based on these results, the hypothesized FC-SC agreement is partially valid. Contingent on extant neuroimaging tools and analytical conventions, the neural informatics of FC and SC should be regarded as complementary rather than concordant. Furthermore, the results verify the physiological significance of moderately (or mildly) correlated brain signals in rfMRI, which are often discarded by stringent thresholding.

Dynamics of Intersubject Brain Networks during Anxious Anticipation.

How do large-scale brain networks reorganize during the waxing and waning of anxious anticipation? Here, threat was dynamically modulated during human functional MRI as two circles slowly meandered on the screen; if they touched, an unpleasant shock was delivered. We employed intersubject correlation analysis, which allowed the investigation of network-level functional connectivity across brains, and sought to determine how network connectivity changed during periods of approach (circles moving closer) and periods of retreat (circles moving apart). Analysis of positive connection weights revealed that dynamic threat altered connectivity within and between the salience, executive, and task-negative networks. For example, dynamic functional connectivity increased within the salience network during approach and decreased during retreat. The opposite pattern was found for the functional connectivity between the salience and task-negative networks: decreases during approach and increases during approach. Functional connections between subcortical regions and the salience network also changed dynamically during approach and retreat periods. Subcortical regions exhibiting such changes included the putative periaqueductal gray, putative habenula, and putative bed nucleus of the stria terminalis. Additional analysis of negative functional connections revealed dynamic changes, too. For example, negative weights within the salience network decreased during approach and increased during retreat, opposite what was found for positive weights. Together, our findings unraveled dynamic features of functional connectivity of large-scale networks and subcortical regions across participants while threat levels varied continuously, and demonstrate the potential of characterizing emotional processing at the level of dynamic networks.

Brain networks of the imaginative mind: Dynamic functional connectivity of default and cognitive control networks relates to openness to experience.

Imagination and creative cognition are often associated with the brain's default network (DN). Recent evidence has also linked cognitive control systems to performance on tasks involving imagination and creativity, with a growing number of studies reporting functional interactions between cognitive control and DN regions. We sought to extend the emerging literature on brain dynamics supporting imagination by examining individual differences in large-scale network connectivity in relation to Openness to Experience, a personality trait typified by imagination and creativity. To this end, we obtained personality and resting-state fMRI data from two large samples of participants recruited from the United States and China, and we examined contributions of Openness to temporal shifts in default and cognitive control network interactions using multivariate structural equation modeling and dynamic functional network connectivity analysis. In Study 1, we found that Openness was related to the proportion of scan time (i.e., "dwell time") that participants spent in a brain state characterized by positive correlations among the default, executive, salience, and dorsal attention networks. Study 2 replicated and extended the effect of Openness on dwell time in a correlated brain state comparable to the state found in Study 1, and further demonstrated the robustness of this effect in latent variable models including fluid intelligence and other major personality factors. The findings suggest that Openness to Experience is associated with increased functional connectivity between default and cognitive control systems, a connectivity profile that may account for the enhanced imaginative and creative abilities of people high in Openness to Experience.

Characterizing the Instantaneous Connectivity of Large-Scale Urban Vehicular Networks

Understanding of the network topology is a basic building block towards the design of efficient networking solutions. In the context of vehicular networks, such a step is especially crucial due to the highly dynamic nature of vehicles that can lead to strong instantaneous variations in the structure of the network. This notwithstanding, and despite the soon-to-come real-world deployment of vehicle-to-vehicle communication technologies, we still lack a clear understanding of vehicular network topological properties. In this paper, we present a complex network analysis of the instantaneous topology of a realistic vehicular network in Cologne, Germany. Our study unveils a poorly connected topology, with very limited availability, reliability, and navigability. We also examine the vehicular network topology in a second scenario, i.e., Zurich, Switzerland. The comparative analysis shows how simplistic mobility models can lead to unrealistic overly connected topologies.

Further evidence of alerted default network connectivity and association with theory of mind ability in schizophrenia

BackgroundResting-state functional magnetic resonance imaging (rs-fMRI) has repeatedly shown evidence of altered functional connectivity of large-scale networks in schizophrenia. The relationship between these connectivity changes and behaviour (e.g. symptoms, neuropsychological performance) remains unclear. MethodsFunctional connectivity in 27 patients with schizophrenia or schizoaffective disorder, and 25 age and gender matched healthy controls was examined using rs-fMRI. Based on seed regions from previous studies, we examined functional connectivity of the default, cognitive control, affective and attention networks. Effects of symptom severity and theory of mind performance on functional connectivity were also examined. ResultsPatients showed increased connectivity between key nodes of the default network including the precuneus and medial prefrontal cortex compared to controls (p<0.01, FWE-corrected). Increasing positive symptoms and increasing theory of mind performance were both associated with altered connectivity of default regions within the patient group (p<0.01, FWE-corrected). DiscussionThis study confirms previous findings of default hyper-connectivity in schizophrenia spectrum patients and reveals an association between altered default connectivity and positive symptom severity. As a novel find, this study also shows that default connectivity is correlated to and predictive of theory of mind performance. Extending these findings by examining the effects of emerging social cognition treatments on both default connectivity and theory of mind performance is now an important goal for research.

Aberrant intra-salience network dynamic functional connectivity impairs large-scale network interactions in schizophrenia

Aberrant functional interactions between several large-scale networks, especially the central executive network (CEN), the default mode network (DMN) and the salience network (SN), have been postulated as core pathophysiologic features of schizophrenia; however, the attributing factors of which remain unclear. The study employed resting-state fMRI with 77 participants (42 patients and 35 controls). We performed dynamic functional connectivity (DFC) and functional connectivity (FC) analyses to explore the connectivity patterns of these networks. Furthermore, we performed a structural equation model (SEM) analysis to explore the possible role of the SN in modulating network interactions. The results were as follows: (1) The inter-network connectivity showed decreased connectivity strength and increased time-varying instability in schizophrenia; (2) The SN manifested schizophrenic intra-network dysfunctions in both the FC and DFC patterns; (3) The connectivity properties of the SN were effective in discriminating controls from patients; (4) In patients, the dynamic intra-SN connectivity negatively predicted the inter-network FC, and this effect was mediated by intra-SN connectivity strength. These findings suggest that schizophrenia show systematic deficits in temporal stability of large-scale network connectivity. Furthermore, aberrant network interactions in schizophrenia could be attributed to instable intra-SN connectivity and the dysfunction of the SN may be an intrinsic biomarker of the disease.