Dynamic Network Connectivity Research Articles

Altered cerebellar-cerebral dynamic functional connectivity in patients with pontine stroke: a resting-state fMRI study.

Potential changes in patterns of dynamic functional network connections at the cerebellar-cerebral level in pontine infarction (PI) patients remain unclear. The study aimed to investigate the abnormal patterns of dynamic functional connectivity (dFC) between the cerebellar subregions within networks and regions of the cerebral cortex in patients with PI. Forty-six chronic left pontine infarction (LPI), 32 chronic right pontine infarction (RPI), and 50 healthy controls (HCs) were recruited to undergo resting-state fMRI scans. Cerebellar-cerebral dFC was characterized using the sliding window method and seed-based connectivity analyses. Correlations between altered dFC values and clinical variables (The Rey Auditory Verbal Learning Test and Flanker task) in PI patients and healthy controls were investigated. Compared with HCs, the PI groups showed significantly aberrant cerebellar-cerebral dFC between cerebellar subregions within networks and supratentorial cerebral cortex, including executive, default-mode, and motor networks. Furthermore, Correlation analysis showed a decoupling between abnormal dFC and cognitive functions in PI patients. These findings indicate that PI patients are accompanied by damage to cerebellar subregions within networks and cerebellar-cerebral pathways, which may provide a potential target for treatment or an indication of therapeutic efficacy.

4D dynamic spatial brain networks at rest linked to cognition show atypical variability and coupling in schizophrenia.

Despite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain networks in human resting state functional magnetic resonance imaging (rsfMRI) data and evaluate the temporal changes in the volumes of these 4D networks. Our results show significant volumetric coupling (i.e., synchronized shrinkage and growth) between networks during the scan, that we refer to as dynamic spatial network connectivity (dSNC). We find that several features of such dynamic spatial brain networks are associated with cognition, with higher dynamic variability in these networks and higher volumetric coupling between network pairs positively associated with cognitive performance. We show that these networks are modulated differently in individuals with schizophrenia versus typical controls, resulting in network growth or shrinkage, as well as altered focus of activity within a network. Schizophrenia also shows lower spatial dynamical variability in several networks, and lower volumetric coupling between pairs of networks, thus upholding the role of dynamic spatial brain networks in cognitive impairment seen in schizophrenia. Our data show evidence for the importance of studying the typically overlooked voxel-wise changes within and between brain networks.

Abnormal static and dynamic brain network connectivity associated with chronic tinnitus

In order to comprehensively understand the changes of brain networks in patients with chronic tinnitus, this study combined static and dynamic analysis methods to explore the abnormalities of brain networks. Thirty-two patients with chronic tinnitus and 30 age-, sex- and education-matched healthy controls (HC) were recruited. Independent component analysis was used to identify resting-state networks (RSNs). Static and dynamic functional network connectivity (FNC) were performed. The temporal properties of brain network including mean dwell time (MDT), fraction time (FT) and numbers of transitions (NT) were calculated. Two-sample t test and Spearman’s correlation were used for group compares and correlation analysis. Four RSNs showed abnormal FNC including auditory network (AUN), default mode network (DMN), attention network (AN) and sensorimotor network (SMN). For static analysis, tinnitus patients showed significantly decreased FNC in AUN-DMN, AUN-AN, DMN-AN, and DMN-SMN than HC [p < 0.05, false discovery rate (FDR) corrected]. For dynamic analysis, tinnitus patients showed significantly decreased FNC in DMN-AN in state 3 (p < 0.05, FDR corrected). MDT in state 3 was significantly decreased in tinnitus patients (t = 2.039, P = 0.046). In the tinnitus group, the score of tinnitus functional index (TFI) was negatively correlated with MDT and FT in state 4, and the duration of tinnitus was positively correlated with FT in state 1 and NT. Chronic tinnitus causes abnormal brain network connectivity. These abnormal brain networks help to clarify the mechanism of tinnitus generation and chronicity, and provide a potential basis for the treatment of tinnitus.

Exploring the dynamic links, implications for hedging and investment strategies between Sukuk and commodity market volatility: Evidence from country level analysis

This research paper examines the influence of spillovers between volatility of commodities (including soft commodities, precious metals, industrial metals, along with energy) and returns of sukuk. Using a notable sample of fifteen sukuk country indices and sixteen products, we examine the time-varying criterion vector autoregression (TVP-VAR) based extended joint connectedness method and contribute to the correlation analysis literature by supplying a comprehensive as well as policy-oriented analysis of the connection between sukuks and also commodities. Our results disclose that the system-wide dynamic connectedness is slowly heterogeneous and driven by financial occasions. Next, we look at the potential determinants of connectivity between sukuk and commodity markets, we find that global risk factors significantly impact the degree of spillovers between markets. In particular, the negative impacts of risk factors on spillovers suggest that some risk-mitigating properties may be related to market leverage in the composite portfolio in bear market conditions. In addition, our results, using hedging efficiency and the Sharpe ratio, confirm the hypothesis of diversification opportunities between markets that leverage dynamic connectivity networks.

Dynamic Network Connectivity: from monkeys to humans.

Human brain imaging research using functional MRI (fMRI) has uncovered flexible variations in the functional connectivity between brain regions. While some of this variability likely arises from the pattern of information flow through circuits, it may also be influenced by rapid changes in effective synaptic strength at the molecular level, a phenomenon called Dynamic Network Connectivity (DNC) discovered in non-human primate circuits. These neuromodulatory molecular mechanisms are found in layer III of the macaque dorsolateral prefrontal cortex (dlPFC), the site of the microcircuits shown by Goldman-Rakic to be critical for working memory. This research has shown that the neuromodulators acetylcholine, norepinephrine, and dopamine can rapidly change the strength of synaptic connections in layer III dlPFC by (1) modifying the depolarization state of the post-synaptic density needed for NMDA receptor neurotransmission and (2) altering the open state of nearby potassium channels to rapidly weaken or strengthen synaptic efficacy and the strength of persistent neuronal firing. Many of these actions involve increased cAMP-calcium signaling in dendritic spines, where varying levels can coordinate the arousal state with the cognitive state. The current review examines the hypothesis that some of the dynamic changes in correlative strength between cortical regions observed in human fMRI studies may arise from these molecular underpinnings, as has been seen when pharmacological agents or genetic alterations alter the functional connectivity of the dlPFC consistent with the macaque physiology. These DNC mechanisms provide essential flexibility but may also confer vulnerability to malfunction when dysregulated in cognitive disorders.

Post-stroke upper limb recovery is correlated with dynamic resting-state network connectivity.

Motor recovery is still limited for people with stroke especially those with greater functional impairments. In order to improve outcome, we need to understand more about the mechanisms underpinning recovery. Task-unbiased, blood flow-independent post-stroke neural activity can be acquired from resting brain electrophysiological recordings and offers substantial promise to investigate physiological mechanisms, but behaviourally relevant features of resting-state sensorimotor network dynamics have not yet been identified. Thirty-seven people with subcortical ischaemic stroke and unilateral hand paresis of any degree were longitudinally evaluated at 3 weeks (early subacute) and 12 weeks (late subacute) after stroke. Resting-state magnetoencephalography and clinical scores of motor function were recorded and compared with matched controls. Magnetoencephalography data were decomposed using a data-driven hidden Markov model into 10 time-varying resting-state networks. People with stroke showed statistically significantly improved Action Research Arm Test and Fugl-Meyer upper extremity scores between 3 weeks and 12 weeks after stroke (both P < 0.001). Hidden Markov model analysis revealed a primarily alpha-band ipsilesional resting-state sensorimotor network which had a significantly increased life-time (the average time elapsed between entering and exiting the network) and fractional occupancy (the occupied percentage among all networks) at 3 weeks after stroke when compared with controls. The life-time of the ipsilesional resting-state sensorimotor network positively correlated with concurrent motor scores in people with stroke who had not fully recovered. Specifically, this relationship was observed only in ipsilesional rather in contralesional sensorimotor network, default mode network or visual network. The ipsilesional sensorimotor network metrics were not significantly different from controls at 12 weeks after stroke. The increased recruitment of alpha-band ipsilesional resting-state sensorimotor network at subacute stroke served as functionally correlated biomarkers exclusively in people with stroke with not fully recovered hand paresis, plausibly reflecting functional motor recovery processes.

Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics

Many studies have analyzed resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data to elucidate the effects of neurological and neuropsychiatric disorders upon the interactions of brain regions over time. Existing studies often use either machine learning classification or clustering algorithms. Additionally, several studies have used clustering algorithms to extract features related to brain states trajectories that can be used to train interpretable classifiers. However, the combination of explainable dFNC classifiers followed by clustering algorithms is highly underutilized. In this study, we show how such an approach can be used to study the effects of schizophrenia (SZ) upon brain activity. Specifically, we train an explainable deep learning model to classify between individuals with SZ and healthy controls. We then cluster the resulting explanations, identifying discriminatory states of dFNC. We lastly apply several novel measures to quantify aspects of the classifier explanations and obtain additional insights into the effects of SZ upon brain network dynamics. Specifically, we uncover effects of schizophrenia upon subcortical, sensory, and cerebellar network interactions. We also find that individuals with SZ likely have reduced variability in overall brain activity and that the effects of SZ may be temporally localized. In addition to uncovering effects of SZ upon brain network dynamics, our approach could provide novel insights into a variety of neurological and neuropsychiatric disorders in future dFNC studies.

Triple network connectivity changes in patients with major depressive disorder versus healthy controls via structural network imaging after electroconvulsive therapy treatment

ObjectiveTo investigate the effect of electroconvulsive treatment (ECT) on dynamic structural network connectivity in major depressive disorder (MDD), based on the triple-network model. MethodsTwenty-one first-episode, drug-naïve patients with MDD and 21 age- and sex-matched healthy subjects were recruited. Bilateral electrical stimulation was performed thrice a week for a total of 4–5 weeks in the MDD group. MRI data were obtained, and triple-network structural connectivity was evaluated using source-based morphometry (SBM) analysis. A paired t-test was used to analyze structural connectivity differences between pre- and post-ECT MDD groups, one-way analysis was used to calculate three intrinsic network differences between HCs, pre- and post-ECT groups, and partial least squares structural equation modelling was used to investigate dynamic structural network connectivity (dSNC) across groups. ResultsPre-ECT patients with MDD exhibited significantly lower salience network (SN) structural connectivity (p = 0.010) than the healthy control (HC) group and after ECT therapy SN structural connectivity was significantly elevated (p = 0.002) in post-ECT group compared with pre-ECT. PLS-SEM analysis conducted on inter-network connectivity in the triple-network model indicated a significant difference between SN and central executive network (CEN) in all three groups. The HC and post-ECT MDD groups showed notable direct connectivity between the SN and default mode network (DMN), while the pre-ECT MDD group showed consequential pathological connectivity between the CEN and DMN. A mediation analysis revealed a significant indirect effect of the SN on the DMN through the CEN (β = 0.363, p = 0.008) only in the pre-ECT MDD group. ConclusionsECT may be an effective and minimally invasive treatment for addressing structural changes in the SN and direct communication abnormalities between the three core brain networks in patients with MDD, with possible beneficial correction of indirect connections.

Altered dynamic functional network connectivity states in patients with acute basal ganglia ischemic stroke

BackgroundDynamic functional network connectivity (dFNC) patterns are successfully able to capture the time-varying features of intrinsic fluctuations throughout a scan. We explored dFNC alterations across the entire brain in patients with acute ischemic stroke (AIS) of the basal ganglia (BG). MethodResting-state functional magnetic resonance imaging data were acquired from 26 patients with first-ever AIS in the BG and 26 healthy controls (HCs). Independent component analysis, the sliding window method, and the K-means clustering method were used to obtain reoccurring dynamic network connectivity patterns. Moreover, temporal features across diverse dFNC states were compared between the two groups, and the local and global efficiencies across states were analyzed to explore the characteristics of the topological networks among states. ResultsFour dFNC states were characterized for comparison of dynamic brain network connectivity patterns. In contrast to the HC group, the AIS group spent a significantly higher fraction of time in State 1, which is characterized by a relatively weaker brain network connectome. Conversely, compared with HC, patients with AIS showed a lower mean dwell time in State 2, which was characterized by a relatively stronger brain network connectome. Additionally, functional networks exhibited variable efficiency of information transfer across 4 states. ConclusionsAIS not only altered the interaction between the different dynamic networks but also promoted characteristic alterations in the temporal and topological features of large-scale dynamic network connectivity.

Tracking functional network connectivity dynamics in the elderly.

Functional magnetic resonance imaging (fMRI) has shown that aging disturbs healthy brain organization and functional connectivity. However, how this age-induced alteration impacts dynamic brain function interaction has not yet been fully investigated. Dynamic function network connectivity (DFNC) analysis can produce a brain representation based on the time-varying network connectivity changes, which can be further used to study the brain aging mechanism for people at different age stages. This presented investigation examined the dynamic functional connectivity representation and its relationship with brain age for people at an elderly stage as well as in early adulthood. Specifically, the resting-state fMRI data from the University of North Carolina cohort of 34 young adults and 28 elderly participants were fed into a DFNC analysis pipeline. This DFNC pipeline forms an integrated dynamic functional connectivity (FC) analysis framework, which consists of brain functional network parcellation, dynamic FC feature extraction, and FC dynamics examination. The statistical analysis demonstrates that extensive dynamic connection changes in the elderly concerning the transient brain state and the method of functional interaction in the brain. In addition, various machine learning algorithms have been developed to verify the ability of dynamic FC features to distinguish the age stage. The fraction time of DFNC states has the highest performance, which can achieve a classification accuracy of over 88% by a decision tree. The results proved there are dynamic FC alterations in the elderly, and the alteration was found to be correlated with mnemonic discrimination ability and could have an impact on the balance of functional integration and segregation.

Altered electroencephalography resting state network coherence in remitted MDD

Individuals with remitted depression are at greater risk for subsequent depression and therefore may provide a unique opportunity to understand the neurophysiological correlates underlying the risk of depression. Research has identified abnormal resting-state electroencephalography (EEG) power metrics and functional connectivity patterns associated with major depression, however little is known about these neural signatures in individuals with remitted depression. We investigate the spectral dynamics of 64-channel EEG surface power and source-estimated network connectivity during resting states in 37 individuals with depression, 56 with remitted depression, and 49 healthy adults that did not differ on age, education, and cognitive ability across theta, alpha, and beta frequencies. Average reference spectral EEG surface power analyses identified greater left and midfrontal theta in remitted depression compared to healthy adults. Using Network Based Statistics, we also demonstrate within and between network alterations in LORETA transformed EEG source-space coherence across the default mode, fronto-parietal, and salience networks where individuals with remitted depression exhibited enhanced coherence compared to those with depression, and healthy adults. This work builds upon our currently limited understanding of resting EEG connectivity in depression, and helps bridge the gap between aberrant EEG power and brain network connectivity dynamics in this disorder. Further, our unique examination of remitted depression relative to both healthy and depressed adults may be key to identifying brain-based biomarkers for those at high risk for future, or subsequent depression.

An immune and epigenetics-related scoring model and drug candidate prediction for hepatic carcinogenesis via dynamic network biomarker analysis and connectivity mapping

Hepatocellular carcinoma (HCC) is a malignant tumor with high mortality. This study aimed to build a prognostic signature for HCC patients based on immune-related genes (IRGs) and epigenetics-related genes (EPGs). RNA-seq data from Gene Expression Omnibus were used for dynamic network biomarker (DNB) analysis to identify 56 candidate IRG–EPG–DNBs and their first-neighbor genes. These genes were screened using LASSO-Cox regression analysis to finally obtain five candidate genes—RNF2, YBX1, EZH2, CAD, and PSMD1—which constituted the prognostic signature panel. According to this panel, patients in The Cancer Genome Atlas and International Cancer Genome Consortium were divided into high- and low-risk groups. The prognosis, clinicopathological features, and immune cell infiltration significantly differed between the two risk groups. The prognostic ability of the signature panel and expression profiling were further validated using online databases. We used an independent cohort of patients to validate the expression profiles of the five genes using reverse transcription–PCR. CMap and CellMiner predicted four small molecule drug–protein pairs based on the five prognostic genes. Of them, two market drugs approved by the Food and Drug Administration (AT-13387 and KU-55933) have emerged as candidates for HCC study. This new signature panel may serve as a potential prognostic marker, engendering the possibility of novel personalized therapy with classification of HCC patients.

Fault Detection Method for Wi-Fi-Based Smart Home Devices

At present, the dynamic nature and unstable network connections in the deployment environments of Wi-Fi-based smart home devices make them susceptible to component damage, crashes, network disconnections, etc. To solve these problems, researchers have used various fault detection methods, such as alarming when monitored fault parameters exceed the preset values, model-based mathematical methods, device signal processing-based methods, and artificial intelligence-based methods. However, these methods require large numbers of fault parameters, the model are complex, and their fault detection accuracy is relatively poor. To more quickly and accurately detect faults in smart home devices and ensure the continuity of people’s daily work and lives, this paper analyzes both the Wi-Fi traffic characteristics of smart home devices and the complexity and difficulty of traditional fault detection methods and proposes a fault detection method based on TDD (Throughput and Delay Distribution). This method obtains throughput and data packet delay distribution by capturing Wi-Fi communication and sending test data. By dividing the throughput into heartbeat data and command information, we can calculate the real-time throughput and further calculate the similarity between the real-time throughput and the throughput in database. Also, the resulting delay distribution is compared with the probability distribution of delay in the database. When the throughput values are sufficiently similar and the delays are all in the normal range, the smart home secure devices are functioning properly. The experimental results show that the proposed TDD method can detect faults in household devices in real time and that it achieves high recall and good detection accuracy in Wi-Fi communication environment.

Temporal Stability of Dynamic Default Mode Network Connectivity Negatively Correlates with Suicidality in Major Depressive Disorder.

Previous studies have demonstrated that the suicidality in patients with major depressive disorder (MDD) is related to abnormal brain functional connectivity (FC) patterns. However, little is known about its relationship with dynamic functional connectivity (dFC) based on the assumption that brain FCs fluctuate over time. Temporal stabilities of dFCs within the whole brain and nine key networks were compared between 52 MDD patients and 21 age, sex-matched healthy controls (HCs) using resting-state functional magnetic resonance imaging and temporal correlation coefficients. The alterations in MDD were further correlated with the scores of suicidality item in the Hamilton Rating Scale for Depression (HAMD). Compared with HCs, the MDD patients showed a decreased temporal stability of dFC as indicated by a significantly decreased temporal correlation coefficient at the global level, as well as within the default mode network (DMN) and subcortical network. In addition, temporal correlation coefficients of the DMN were found to be significantly negatively correlated with the HAMD suicidality item scores in MDD patients. These results suggest that MDD may be characterized by excessive temporal fluctuations of dFCs within the DMN and subcortical network, and that decreased stability of DMN connectivity may be particularly associated with the suicidality in MDD.

A scientometrics investigation of magnetic nanofluids

The control of thermophysical, fluid flow, and structure of nanofluids using a magnetic field has attracted the focus of many recent researchers in the field. Despite the broad range and application of the magnetic nanofluids, there is no scientometric or thematic analysis revealing the number and distribution of the publications and citations in the field and dynamic maps of the connection of the authors, institutions, and countries. The current research aims to comprehensively analyze the publications and dynamic world network connections of authors, institutes, and countries. Recent five-year publications (2018–2022) were retrieved from the Clarivate database (2224 documents) and were analyzed using an in-house code. The VOSViwer software was also used for the plot of the maps. The results showed Chamkha, Ali J was the most productive author (104 publications) and also the most cited author in the field (2029 citations) with the highest H-Index (33). Babol Noshirvani University has the highest rank among top universities concerning two different parameters: total citations and citations per paper. King Khalid University and Islamic Azad University have the highest rank in total publications and h-index. The highest number of publications also belonged to India, Pakistan, and Saudi Arabia. Moreover, these countries were also influential contributors to MNF, and other countries were linked to these three countries.

Serum soluble mediator waves and networks along healthy ageing.

The ageing process is a complex phenomenon that impacts the immune system, leading to changes in the pattern of serum soluble mediators. In the present study, the serum levels of several chemokines, pro-inflammatory/regulatory cytokines and growth factors were quantified by high-throughput microbeads array in serum samples from 541 healthy subjects at distinct age ranges (3Yrs to >70Yrs). A broad increase in serum soluble mediators was observed at 6-10Yrs with subsequent decline at 11-20Yrs and 21-30Yrs followed by a second round of upregulation starting at 31-40Yrs, with a large increase at 51-60Yrs and a marked decline at age >70Yrs. Heatmap and signatures of serum soluble mediators demonstrated a bimodal profile with one peak at 6-10Yrs and a second wave around 61-70Yrs. A universal decline was observed later at age >70Yrs. In males, the second wave started earlier at 31-40Yrs with a peak at 51-60Yrs and a further smooth decline towards >70Yrs. Conversely, in females, the first peak extended from 3-5Yrs to 6-10Yrs and the second wave starting around 41-50Yrs with a peak at 61-70Yrs followed by a sharp decline at >70Yrs. Overall, CCL11, CXCL8, IL-1β, IL-6 were underscored as universal age-related biomarkers with higher levels observed at later age ranges (after 31-40Yrs) and TNF with increased levels starting at early age ranges. Data analysis demonstrated that the highest neighborhood connectivity amongst soluble mediators occurred at 3-5Yrs, with distinct declining and strengthening rhythm in males and females. Notably, rebuilding re-arrangements were usually earlier and more frequent in females (at 11-20Yrs, 51-60Yrs and >70Yrs) than in males (at 21-30Yrs, 61-70Yrs). Overall, this study provided a comprehensive landscape of evidence portrayed by distinct waves, rhythms and dynamic network connectivity along healthy ageing with differences in magnitude and timing reported for sexes.

Altered Functional Network Connectivity of Precuneus and Executive Control Networks in Type 2 Diabetes Mellitus Without Cognitive Impairment.

In epidemiological studies, type 2 diabetes mellitus (T2DM) has been associated with cognitive impairment and dementia, but studies about functional network connectivity in T2DM without cognitive impairment are limited. This study aimed to explore network connectivity alterations that may help enhance our understanding of damage-associated processes in T2DM. MRI data were analyzed from 82 patients with T2DM and 66 normal controls. Clinical, biochemical, and neuropsychological assessments were conducted in parallel with resting-state functional magnetic resonance imaging, and the cognitive status of the patients was assessed using the Montreal Cognitive Assessment-B (MoCA-B) score. Independent component analysis revealed a positive correlation between the salience network and the visual network and a negative connection between the left executive control network and the default mode network in patients with T2DM. The differences in dynamic brain network connectivity were observed in the precuneus, visual, and executive control networks. Internal network connectivity was primarily affected in the thalamus, inferior parietal lobe, and left precuneus. Hemoglobin A1c level, body mass index, MoCA-B score, and grooved pegboard (R) assessments indicated significant differences between the two groups (p < 0.05). Our findings show that key changes in functional connectivity in diabetes occur in the precuneus and executive control networks that evolve before patients develop cognitive deficits. In addition, the current study provides useful information about the role of the thalamus, inferior parietal lobe, and precuneus, which might be potential biomarkers for predicting the clinical progression, assessing the cognitive function, and further understanding the neuropathology of T2DM.

Serum Soluble Mediator Profiles and Networks During Acute Infection With Distinct DENV Serotypes.

A panoramic analysis of chemokines, pro-inflammatory/regulatory cytokines, and growth factors was performed in serum samples from patients with acute DENV infection (n=317) by a high-throughput microbeads array. Most soluble mediators analyzed were increased in DENV patients regardless of the DENV serotype. The substantial increase (≥10-fold) of CXCL10, IL-6, and IFN-γ, and decreased levels of PDGF (<0.4-fold) was universally identified in all DENV serotypes. Of note, increased levels of CXCL8, CCL4, and IL-12 (≥3-9-fold) were selectively observed in DENV2 as compared to DENV1 and DENV4. Heatmap and biomarker signatures further illustrated the massive release of soluble mediators observed in DENV patients, confirming the marked increase of several soluble mediators in DENV2. Integrative correlation matrices and networks showed that DENV infection exhibited higher connectivity among soluble mediators. Of note, DENV2 displayed a more complex network, with higher connectivity involving a higher number of soluble mediators. The timeline kinetics (Day 0-1, D2, D3, D4-6) analysis additionally demonstrated differences among DENV serotypes. While DENV1 triggers a progressive increase of soluble mediators towards D3 and with a decline at D4-6, DENV2 and DENV4 develop with a progressive increase towards D4-6 with an early plateau observed in DENV4. Overall, our results provided a comprehensive overview of the immune response elicited by DENV infection, revealing that infection with distinct DENV serotypes causes distinct profiles, rhythms, and dynamic network connectivity of soluble mediators. Altogether, these findings may provide novel insights to understand the pathogenesis of acute infection with distinct DENV serotypes.

Impact of mobility design on network connectivity dynamics in urban environment

Vehicular ad hoc networks have emerged as a promising research area for enabling various types of applications. However, VANETs are facing the biggest challenges in providing reliable communications due to unstable network connectivity caused by vehicle mobility. Several efforts have been carried out to analyze this problem. However, these efforts ignore the impact of intelligent mobility designs. This work highlights the relationship between intelligent mobility for transportation systems and connectivity dynamics. For this purpose, a framework of different mobility models is proposed based on the cellular automata (CA) approach to simulate vehicles mobility in a Manhattan two-dimensional network of roundabouts. To take into account intelligent aspects of mobility for drivers, a centralized path planning strategy based on the Bellman–Ford algorithm analyzes the travel time at road segments to provide the shortest paths for vehicles. Besides, we categorize three mobility models: In the first mobility model, vehicles are assigned the shortest paths in real-time with a periodic update. Each shortest path is defined as a set of turning movements (i.e., right, left, straight, …), where each turning step represents the driver’s decision at the next roundabout. At roundabouts, vehicles follow priority rules to avoid conflict with other traffics. The second mobility model is similar to the first one, but vehicles do have not the possibility to update their assigned shortest paths. The third model extends the first one by using traffic lights instead of priority rules at roundabouts. Extensive simulations based on both generated mobility traces and NS-2 analyze both network connectivity and reliability under several effective factors, including vehicles’ mobility model, vehicles’ speed, vehicles’ density, transmission range, and RSUs deployment strategy in terms of RSUs’ position and number. Several performance metrics of interest are introduced (such as packet delivery ratio, paths length, link duration, and end-to-end delay). The simulation results show that both the network performance and connectivity condition are sensitive to multiple factors, which reflect the V2V and V2I communications under varying conditions.

Time After Time: Electroconvulsive Therapy Modulates the Brain’s Functional Network Connectivity Dynamics

Time After Time: Electroconvulsive Therapy Modulates the Brain’s Functional Network Connectivity Dynamics