Network Connectivity Research Articles

Edge-based graph centrality measures with spatial analytics to support vulnerability assessment and maintenance planning in sewer networks

ABSTRACT In this study, the spatial relationship between critical pipes identified using edge-based centrality measures and pipes with higher failure probability-based vulnerability indicators were analysed in sanitary sewer networks. By analysing two sub-networks, one residential and the other a central network, significant spatial associations between pipes with high centrality values and those exhibiting adverse conditions (poor CCTV grades, previous blockages, and low self-cleaning capabilities) were identified. Path-based centrality measures, particularly edge betweenness and K-path edge centrality were less influenced by weights when identifying critical pipes. In contrast, non-path-based measures like nearest neighbour edge centrality could identify localised spatial patterns between critical pipes and pipes in adverse conditions within the sewer networks investigated. The results showed that the spatial patterns between critical pipes and pipes in adverse conditions were not random and could support proactive maintenance planning and the development of more resilient networks. Additionally, the impact of network structure, connectivity, and differences in the composition of pipe attributes could contribute to variations in the strength of observable spatial associations.

Characterising the low-cost carrier network in Southeast Asia: a complex network analysis

This study analyses the low-cost carrier network (LCCN) in Southeast Asia using complex network analysis. An analysis is conducted using the data from 2010 to 2018 covering 442 airports connected by 2,296 air routes. The LCCN is a scale-free network with a degree distribution following a power law in which fewer airports have high connectivity. It is a sparse network with a low network density. The network is efficient with a small average shortest path length, but low intra-connectivity amongst airports in the same cluster. This suggests that with good affordability and low costs, low-cost carriers play an important role in providing essential air connectivity to small and distanced destinations. Although low-cost carriers tend to use a point-to-point network instead of a hub-and-spoke network, major airports still play essential roles in shaping network connectivity and performance. Therefore, airports must have ample capacity and good infrastructure to ensure the network’s performance.

The role of the environment and connectivity on multiple facets of local fish diversity in tropical headwater streams

Abstract The identification of multiple facets of biodiversity (e.g., phylogenetic and functional) can help to understand how evolutionary and ecological mechanisms shape the assembly of biological communities. Here, we employed multiple regressions and variance partitions to investigate the effects of environmental conditions, connectivity and space on aspects of the functional and phylogenetic facets of fish fauna in tropical headwater streams of the Xingu River basin, in Eastern Amazonia. We found that the functional and phylogenetic diversities of fish communities are related to local environmental conditions and stream connectivity in the drainage network, with marked differences occurring between the rainy and dry periods. Furthermore, we discovered that functional uniqueness was influenced mainly by environmental factors, whereas phylogenetic uniqueness had a more complex relationship with the influence of local environmental factors and connectivity. Our findings indicate that the fish assemblages of small headwater streams of the Xingu River basin share a close phylogenetic history. This is probably because of the climatic and environmental stability experienced by Amazonian ichthyofauna over evolutionary time. Our findings emphasise the role of niche‐based processes in shaping the unique functional composition of fish assemblages in headwater streams, and highlight the importance of environmental heterogeneity in maintaining the ecological integrity of these freshwater ecosystems.

Effect of Li2O on the structure and properties of low‐boron aluminosilicate fiber glasses from molecular dynamics simulations and quantitative structure–property relationship analysis

AbstractMixed alkaline earth (MgO, CaO) aluminosilicate glass fibers (MCAS) with and without boron are commonly used as reinforcements in plastic composites, and the fundamental understanding of the thermal and mechanical properties is essential to the design of new glass compositions to satisfy the growing demands in applications from renewable energy to lightweight structural components. In this work, a series of Li2O containing low‐boron MCAS fiber glasses have been studied by using molecular dynamic (MD) simulations with recently developed effective partial charge composition dependent boron potentials. Structural characteristics, such as pair distribution function, bond angle distribution, and neutron/X‐ray diffraction structure factors, were calculated, as well as properties such as elastic moduli and vibrational density of states. The addition of Li2O was found to improve the elastic moduli of the fiber glasses in excellent agreement with experimental results we reported earlier. The simulation results showed that the weakened network connectivity and decrease of tri‐/bridging oxygen have positively affected the lowering of liquid temperature, owing to the transformation to more boron Q2 and silicon/aluminon Q3. It is found that higher oxygen packing density, coordinated aluminum/boron species such as [AlO5] and [BO4] units, larger‐membered oxide rings, and intensified connections of [AlOx] and [SiO4] are the main reasons that lead to improved mechanical properties. MD‐based quantitative structure–property relationship analyses were performed and showed excellent correlations to measured properties, indicating that it is a promising approach to understand glass properties and design new glass compositions for functional applications.

Enhancing mobile data security using red panda optimized approach with chaotic fuzzy encryption in mobile cloud computing

SummarySmartphone devices have occupied an indispensable place in human life. These devices have some restrictions, like short lifetime of battery, imperfect computation power, less memory size and unpredictable network connectivity. Hence, a number of methods previously presented to decrease these restrictions as well as increase the battery lifespan with the help of offloading strategy. This manuscript proposes a new enhancing mobile data security using red panda optimized approach with chaotic fuzzy encryption in mobile cloud computing (RPO‐CFE‐SMC) to offload intensive computation tasks from mobile device to the cloud. The proposed model utilizes a red panda optimization algorithm (RPOA) to scale dynamically the offloading decision under energy consumption, CPU utilization, execution time, memory usage parameters. Before the work is transferred to the cloud, an innovative security layer is applied for encrypting the data using AES chaotic fuzzy encryption (CFE) technology. The proposed RPO‐CFE‐SMC method provides 20.63%, 25.25%, 25.28%, and 32.47% lower encryption time and 23.66%, 24.25%, and 26.47% lower energy consumption compared with existing EFFORT‐SMC, EESH‐SMC, and CP‐ABE‐SMC models respectively. In conclusion, the simulation results prove that the improved efficiency of proposed model in offloading computation to the cloud with enhanced data protection using chaotic fuzzy encryption.

Differences Between Resting State fMRI BOLD Variability and Default Mode Network Connectivity in Healthy Older and Younger Adults.

Resting state fMRI analyses have been used to examine functional connectivity in the aging brain. Recently, fluctuations in the fMRI BOLD signal have been used as a potential marker of integrity in neural systems. Despite its increasing popularity, the results of BOLD variability analyses and mean based functional connectivity analyses have rarely been compared. The current study examined fMRI BOLD signal variability and default mode network seed-based analyses in healthy older and younger adults to better understand the unique contributions of these methodological approaches. 34 healthy participants were separated into a younger adult group (age 25-35, n=17) and an older adult group (age 65+, n=17). For each participant, a map of the standard deviation of the BOLD signal (SDBOLD) was derived. Group comparisons examined differences in resting-state SDBOLD in younger versus older adults. Seed-based analyses were used to examine differences between younger and older adults in the default mode network. Between-group comparisons revealed significantly greater BOLD variability in widespread brain regions in older relative to younger adults. There were no significant differences between younger and older adults in the default mode network connectivity. The current findings align with an increasing number of studies reporting greater BOLD variability in older relative to younger adults. The current results also suggest that the traditional resting state examination methods may not detect nuanced age-related differences. Further large-scale studies in an adult lifespan sample are needed to better understand the functional relevance of the BOLD variability in normative aging.

Analyzing the topological properties of resting-state brain function network connectivity based on graph theoretical methods in patients with high myopia

AimRecent imaging studies have found significant abnormalities in the brain’s functional or structural connectivity among patients with high myopia (HM), indicating a heightened risk of cognitive impairment and other behavioral changes. However, there is a lack of research on the topological characteristics and connectivity changes of the functional networks in HM patients. In this study, we employed graph theoretical analysis to investigate the topological structure and regional connectivity of the brain function network in HM patients.MethodsWe conducted rs-fMRI scans on 82 individuals with HM and 59 healthy controls (HC), ensuring that the two groups were matched for age and education level. Through graph theoretical analysis, we studied the topological structure of whole-brain functional networks among participants, exploring the topological properties and differences between the two groups.ResultsIn the range of 0.05 to 0.50 of sparsity, both groups demonstrated a small-world architecture of the brain network. Compared to the control group, HM patients showed significantly lower values of normalized clustering coefficient (γ) (P = 0.0101) and small-worldness (σ) (P = 0.0168). Additionally, the HM group showed lower nodal centrality in the right Amygdala (P < 0.001, Bonferroni-corrected). Notably, there is an increase in functional connectivity (FC) between the saliency network (SN) and Sensorimotor Network (SMN) in the HM group, while the strength of FC between the basal ganglia is relatively weaker (P < 0.01).ConclusionHM Patients exhibit reduced small-world characteristics in their brain networks, with significant drops in γ and σ values indicating weakened global interregional information transfer ability. Not only that, the topological properties of the amygdala nodes in HM patients significantly decline, indicating dysfunction within the brain network. In addition, there are abnormalities in the FC between the SN, SMN, and basal ganglia networks in HM patients, which is related to attention regulation, motor impairment, emotions, and cognitive performance. These findings may provide a new mechanism for central pathology in HM patients.

Solute transport characteristics of columnar volumetric contraction networks with mega column structure and aperture variability

Numerical simulations explore for the first time the role of mega columns and aperture variability on particle transport through mature volumetric contraction networks as informed by a unique synthesis of network propagation and maturity. Columnar fracture patterns are generated by updating a series of Voronoi centers to the midpoint of a generated polygon over many iterations, creating 250 network realizations. A DFN simulator solves for fluid flow and tracks conservative particle trajectories within each network. Dominant fracture attributes impacting fluid flow and solute transport in volumetric contraction networks are fracture orientation, density, and aperture/transmissivity. Ensemble plume snapshots generated by networks with equal fracture transmissivity define a baseline-level of dispersion that is solely attributed to network structure and connectivity. Longitudinal and transverse dispersion increase and the center of plume mass becomes delayed relative to the baseline case when fracture transmissivity is varied according to a lognormal distribution. The incorporation of highly-transmissive, large-aperture mega column fractures leads to plume snapshots with a more pronounced leading edge and an order of magnitude faster average breakthrough times. The breakthrough curves contain three peaks reflecting contrasting transport pathways in which particles are: (i) initially placed in mega column fractures and remain in these features until exiting the model domain, (ii) initially placed into small column fractures, incur additional time to migrate and enter a mega column fracture, and remain within those mega columns, and (iii) initially placed in small column fractures and remain in these fractures. Incorporating variability in fracture transmissivity for both small column and mega column fractures disrupts the binary distinction between small column and mega column fracture velocities and leads to dispersed breakthroughs over long time scales with a single peak. These results demonstrate that preferential flow paths emerge in volumetric contraction networks due to contracts in fracture transmissivity, not fracture connectivity as observed in tectonic networks.

Classification of mental workload with EEG analysis by using effective connectivity and a hybrid model of CNN and LSTM

Estimation of mental workload from electroencephalogram (EEG) signals aims to accurately measure the cognitive demands placed on an individual during multitasking mental activities. By analyzing the brain activity of the subject, we can determine the level of mental effort required to perform a task and optimize the workload to prevent cognitive overload or underload. This information can be used to enhance performance and productivity in various fields such as healthcare, education, and aviation. In this paper, we propose a method that uses EEG and deep neural networks to estimate the mental workload of human subjects during multitasking mental activities. Notably, our proposed method employs subject-independent classification. We use the “STEW” dataset, which consists of two tasks, namely “No task” and “simultaneous capacity (SIMKAP)-based multitasking activity”. We estimate the different workload levels of two tasks using a composite framework consisting of brain connectivity and deep neural networks. After the initial preprocessing of EEG signals, an analysis of the relationships between the 14 EEG channels is conducted to evaluate effective brain connectivity. This assessment illustrates the information flow between various brain regions, utilizing the direct Directed Transfer Function (dDTF) method. Then, we propose a deep hybrid model based on pre-trained Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for the classification of workload levels. The accuracy of the proposed deep model achieved 83.12% according to the subject-independent leave-subject-out (LSO) approach. The pre-trained CNN + LSTM approaches to EEG data have been found to be an accurate method for assessing the mental workload.

Advancements in mapping areas suitable for wetland habitats across the conterminous United States

Wetland habitats provide critical ecosystem services to the surrounding landscape, including nutrient and pollutant retention, flood mitigation, and carbon storage. Wetland connectivity to water bodies and related ecosystems is critical in habitat sustainability, but there are limited resources for landscape-level wetland planning. Considering the network connectivity of an ecosystem type can derive different benefits to the natural and built environment, as well as human health. The value that wetlands provide, along with incentive programs and conservation goals mandated by the government require new and improved wetland spatial data. Utilizing high quality, publicly available data, this study finds that the amount of land in the United States that could support built or restored wetlands is more than double the area of mapped existing wetlands.This study uses 17 input variables (i.e., features extracted from remotely sensed data and auxiliary datasets) at the 10-m resolution and the National Wetlands Inventory to train a random forest model to identify areas that may support a wetland habitat, or potential wetland areas. Models were calculated for each of 18 two-digit hydrologic units that encompass the conterminous United States, and model overall accuracy ranged from 78.0 % to 89.8 %. The models predicted that 21.1 % of the conterminous United States can be categorized as potential wetland area.Selecting input variables to predict areas with wetland potential, rather than to identify existing wetlands, using the random forest algorithm can be transferred to other locations, scales, and ecosystem types. Visualizing potential wetland areas using input data at the 10-m resolution and enhanced methodology improves previous work, as even slight changes in topography, soils, and landscape features can determine ecosystem connections. This product can be used to better place wetland restoration projects to serve ecosystem- and community-wide health by ensuring ecosystem success and targeting areas that face increased climate change impacts.

Resilient riverine social–ecological systems: A new paradigm to meet global conservation targets

AbstractThe United Nations' Convention on Biological Diversity set forth the 30 × 30 target, an agenda for countries to protect at least 30% of their terrestrial, inland water, and coastal and marine areas by 2030. With &lt;6 years to reach that goal, riverine conservation professionals are faced with the difficult decision of prioritizing which rivers or river segments should be conserved (protected and/or restored). While incorporating resilience into conservation planning is essential for enhancing, restoring, and maintaining the vital riverine ecosystem services (ES) most threatened by climate change and other environmental and human stresses, this paradigm is at odds with traditional conservation approaches that are either opportunistic or reactionary, where only unique and highly visible ecosystems have been prioritized. Barriers to implementing resilience‐based riverine conservation planning include: (1) difficulties in conceptualizing and quantifying resilience; (2) insufficient consideration of the social components of riverine systems; (3) the inapplicability of terrestrial‐only conservation models to aquatic systems; and (4) the traditional ad hoc and opportunistic approach to conservation. To overcome these barriers, we propose a resilience‐based riverine conservation framework that includes: (1) assessing riverine resilience using indicator frameworks; (2) considering rivers as dynamically coupled social–ecological systems; (3) explicitly incorporating terrestrial–aquatic network connectivity into conservation decision‐making; and (4) strategic systems planning using a novel resilience–conservation matrix as a tool. This framework has the potential to transform conservation practices around the globe to more effectively protect river systems and enhance their resilience to climate change and human development.This article is categorized under: Water and Life &gt; Conservation, Management, and Awareness Science of Water &gt; Water and Environmental Change Human Water &gt; Water Governance

Differences in gray matter atrophy and functional connectivity between motor subtypes of Parkinson's disease.

Parkinson's disease (PD) patients with postural gait abnormalities exhibit poorer motor function scores, more severe non-motor symptoms, faster cognitive function deterioration, and a less favorable response to drugs and surgery compared to PD patients with tremor. This discrepancy is believed to be associated with more pronounced gray matter atrophy and abnormal functional connectivity. To investigate the distinctive pathological mechanisms between PD subtypes, we examined gray matter volume (GMV) and functional connectivity in patients with Parkinson's disease presenting with postural instability/gait difficulty (PD-PIGD), patients with tremor-dominant Parkinson's disease (PD-TD), and healthy controls. Voxel-based morphometry (VBM) of T1-weighted images was conducted to compare GMV among 64 PD-PIGD patients, 44 PD-TD patients, and 32 controls. Subsequently, functional connectivity within regions showing reduced GMV was compared across the groups. We analyzed whether differences among the groups were associated with clinical characteristics and neuroimaging biomarkers using partial correlation and binary logistic regression. Our comparison between PD-PIGD and PD-TD patients revealed a link between PD-PIGD and more extensive frontotemporal atrophy, potentially indicating increased basal ganglia activity accompanied by decreased cerebellum activity. Furthermore, in addition to the smaller GMV in the left middle temporal gyrus, the increased functional connectivity between this brain region and the right caudate was also the independent risk factor for PD-PIGD. In addition, we compared brain network connectivity between the PIGD and TD subtypes, using an independent component analysis (ICA). We found that Compared to PD-TD, PD-PIGD patients showed an enhanced sensorimotor network (SMN) around the left supplementary motor area. These findings suggest that severe gray matter atrophy and abnormal functional connectivity and brain networks may serve as pathophysiological mechanisms distinguishing PD-PIGD patients from other subtypes.

The spatial mismatch between foreign direct investment and street networks: Evidence from Hanoi, Vietnam

This research investigates the spatial mismatch between the distribution of Foreign Direct Investment (FDI) and the configurational characteristics of street networks in Hanoi's urban districts. Two indicators are employed to represent the connectivity of street networks generated by space syntax methodology, global integration for vehicular movements and local integration for pedestrian traffic. Findings reveal that FDI clusters have preferred areas highly connected by arterial roads, including traditional commercial centres and new suburban locations, confirming that FDI is spatially matched with vehicle-oriented street networks. However, the major FDI clusters have been established without tight connections to street networks for pedestrians. The findings shed light on the new spatiality of global capital accumulation that expedites an unsustainable, vehicle-oriented urban structure.

IDS-DEC: A novel intrusion detection for CAN bus traffic based on deep embedded clustering

As the automotive industry advances towards greater automation, the proliferation of electronic control units (ECUs) has led to a substantial increase in the connectivity of in-vehicle networks with the external environment. However, the widely used Controller Area Network (CAN), which serves as the standard for in-vehicle networks, lacks robust security features, such as authentication or encrypted information transmission. This poses a significant challenge to the security of these networks. Despite the availability of powerful intrusion detection methods based on machine learning and deep learning, there are notable limitations in terms of stability and accuracy in the absence of a supervised learning process with labeled data. To address this issue, this paper introduces a novel in-vehicle intrusion detection system, termed IDS-DEC. This system combines a spatiotemporal self-coder employing LSTM and CNN (LCAE) with an entropy-based deep embedding clustering. Specifically, our approach involves encoding in-vehicle network traffic into windowed messages using a stream builder, designed to adapt to high-frequency traffic. These messages are then fed into the LCAE to extract a low-dimensional nonlinear spatiotemporal mapping from the initially high-dimensional data. The resulting low-dimensional mapping is subjected to a dual constraint in conjunction with our entropy-based pure deep embedding clustering module. This creates a bidirectional learning objective, addressing the optimization problem and facilitating an end-to-end training pattern for our model to adapt to diverse attack environments. The effectiveness of IDS-DEC is validated using both the benchmark Car Hacking dataset and the Car Hacking-Attack & Defense Challenge dataset. Experimental results demonstrate the model's high detection accuracy across various attacks, stabilizing at approximately 99% accuracy with a 0.5% false alarm rate. The F1 score also stabilizes at around 99%. In comparison with unsupervised methods based on deep stream clustering, LSTM-based self-encoder, and classification-based methods, IDS-DEC exhibits significant improvements across all performance metrics.

Maturity Assessment of District Health Information System Version 2 Implementation in Ethiopia: Current Status and Improvement Pathways.

Although Ethiopia has made remarkable progress in the uptake of the District Health Information System version 2 (DHIS2) for national aggregate data reporting, there has been no comprehensive assessment of the maturity level of the system. This study aims to assess the maturity level of DHIS2 implementation in Ethiopia and propose a road map that could guide the progress toward a higher level of maturity. We also aim to assess the current maturity status, implementation gaps, and future directions of DHIS2 implementation in Ethiopia. The assessment focused on digital health system governance, skilled human resources, information and communication technology (ICT) infrastructure, interoperability, and data quality and use. A collaborative assessment was conducted with the engagement of key stakeholders through consultative workshops using the Stages of Continuous Improvement tool to measure maturity levels in 5 core domains, 13 components, and 39 subcomponents. A 5-point scale (1=emerging, 2=repeatable, 3=defined, 4=managed, and 5=optimized) was used to measure the DHIS2 implementation maturity level. The national DHIS2 implementation's maturity level is currently at the defined stage (score=2.81) and planned to move to the manageable stage (score=4.09) by 2025. The domain-wise maturity score indicated that except for ICT infrastructure, which is at the repeatable stage (score=2.14), the remaining 4 domains are at the defined stage (score=3). The development of a standardized and basic DHIS2 process at the national level, the development of a 10-year strategic plan to guide the implementation of digital health systems including DHIS2, and the presence of the required competencies at the facility level to accomplish specific DHIS2-related tasks are the major strength of the Ministry of Health of Ethiopia so far. The lack of workforce competency guidelines to support the implementation of DHIS2; the unavailability of core competencies (knowledge, skills, and abilities) required to accomplish DHIS2 tasks at all levels of the health system; and ICT infrastructures such as communication network and internet connectivity at the district, zonal, and regional levels are the major hindrances to effective DHIS2 implementation in the country. On the basis of the Stages of Continuous Improvement maturity model toolkit, the implementation status of DHIS2 in Ethiopia is at the defined stage, with the ICT infrastructure domain being at the lowest stage as compared to the other 4 domains. By 2025, the maturity status is planned to move from the defined stage to the managed stage by improving the identified gaps. Various action points are suggested to address the identified gaps and reach the stated maturity level. The responsible body, necessary resources, and methods of verification required to reach the specified maturity level are also listed.

A GIS-Based emergency response and management support framework for Earthquake crisis: A case study of Antakya and Kahramanmaras earthquake in Turkey

Earthquakes can cause significant damage, including loss of lives, collapsed buildings, and blocked roads. However, the severity and frequency of earthquakes cannot be accurately predicted. To mitigate these effects, successful disaster management, particularly in improving post-earthquake road network connectivity, is crucial. Therefore, this study presents a novel satellite imagery-based approach for identifying post-earthquake road blockage and to assess its impact on urban infrastructure and emergency response management. This study utilised real-world pre- and post-earthquake satellite images of the earthquake that struck Turkey on February 6, 2023 using ArcGIS Pro. The results demonstrated that in the scenario when people in affected areas (incidents) tried to escape to emergency gathering zones, out of the initial 126 incident locations in Antakya, 101 of them experienced alterations in the selected routes after the earthquake. In Kahramanmaras, out of 35 incidents, 28 experienced changes in the selected routes. In the case of emergency vehicles (firefighting, medical aid, etc.) traveling to affected areas, some emergency vehicles were assigned a great number of incidents, whereas some vehicles had no incidents assigned to them. The findings of this study suggest that the developed framework can identify road blockage and inefficiencies in an emergency response framework with high accuracy. Therefore, the approach presented in this paper can be implemented to develop a practical geographic information system (GIS) based model to enhance the emergency response management for potential future earthquakes. Thereby, this study can facilitate the level of resilience of urban environments to withstand and adapt to future crises.

Cognitive and Salience Network Connectivity Changes following a Single Season of Repetitive Head Impact Exposure in High School Football.

During a season of high school football, adolescents with actively developing brains experience a considerable number of head impacts. Our aim was to determine whether repetitive head impacts in the absence of a clinically diagnosed concussion during a season of high school football produce changes in cognitive performance or functional connectivity of the salience network and its central hub, the dorsal anterior cingulate cortex. Football players were instrumented with the Head Impact Telemetry System during all practices and games, and the helmet sensor data were used to compute a risk-weighted exposure metric (RWEcp), accounting for the cumulative risk during the season. Participants underwent MRI and a cognitive battery (ImPACT) before and shortly after the football season. A control group of noncontact/limited-contact-sport athletes was formed from 2 cohorts: one from the same school and protocol and another from a separate, nearly identical study. Sixty-three football players and 34 control athletes were included in the cognitive performance analysis. Preseason, the control group scored significantly higher on the ImPACT Visual Motor (P = .04) and Reaction Time composites (P = .006). These differences increased postseason (P = .003, P < .001, respectively). Additionally, the control group had significantly higher postseason scores on the Visual Memory composite (P = .001). Compared with controls, football players showed significantly less improvement in the Verbal (P = .04) and Visual Memory composites (P = .01). A significantly greater percentage of contact athletes had lower-than-expected scores on the Verbal Memory (27% versus 6%), Visual Motor (21% versus 3%), and Reaction Time composites (24% versus 6%). Among football players, a higher RWEcp was significantly associated with greater increments in ImPACT Reaction Time (P = .03) and Total Symptom Scores postseason (P = .006). Fifty-seven football players and 13 control athletes were included in the imaging analyses. Postseason, football players showed significant decreases in interhemispheric connectivity of the dorsal anterior cingulate cortex (P = .026) and within-network connectivity of the salience network (P = .018). These decreases in dorsal anterior cingulate cortex interhemispheric connectivity and within-network connectivity of the salience network were significantly correlated with deteriorating ImPACT Total Symptom (P = .03) and Verbal Memory scores (P = .04). Head impact exposure during a single season of high school football is negatively associated with cognitive performance and brain network connectivity. Future studies should further characterize these short-term effects and examine their relationship with long-term sequelae.

Incorporating barriers restoration and stepping stones establishment to enhance the connectivity of watershed ecological security patterns

Enhancing the connectivity of watershed ecological security patterns (ESPs) is increasingly emphasized for preserving ecological processes. Yet the importance of small-scale conservation and restoration was ignored and few studies have quantitatively compared the contribution of barriers restoration and stepping stones establishment to landscape connectivity. In this study, taking Dongting Lake Basin as an example, a watershed ESP was constructed based on the minimum cumulative resistance model and optimized through graded barriers restoration and different stepping stones establishment. Then the enhancement effects of landscape connectivity were compared to identify the most cost-effective optimization scheme. The results showed that the average of six schemes only protected and repaired less than 1% of the total area, which could enhance corridor connectivity by about 12% and 16% for average corridor length and corridor cumulative resistance respectively. For the optimal ESP, establishing natural patches near the midpoint of longer corridors as stepping stones, increased the probability of connectivity by 21.05%, and reduced the average corridor length and corridor cumulative resistance of corridor connectivity by 17.99% and 15.48% respectively. It also increased network circuitry index of network connectivity from 0.541 to 0.570, compared with the original ESP, indicating the possibility of successful ecological flow increased effectively. It can be concluded that the connectivity enhancement of stepping stones approach was better than barriers restoration approach. This study highlights the importance of small-scale barriers restoration and stepping stones establishment in enhancing landscape connectivity.

Resting-State Network Analysis Reveals Altered Functional Brain Connectivity in Essential Tremor.

Introduction: Essential tremor (ET) comprises motor and non-motor-related features, whereas the current neuro-pathogenetic basis is still insufficient to explain the etiologies of ET. Although cerebellum-associated circuits have been discovered, the large-scale cerebral network connectivity in ET remains unclear. This study aimed to characterize the ET in terms of functional connectivity as well as network. We hypothesized that the resting-state network (RSN) within cerebrum could be altered in patients with ET. Methods: Resting-state functional magnetic resonance imaging (fMRI) was used to evaluate the inter- and intra-network connectivity as well as the functional activity in ET and normal control. Correlation analysis was performed to explore the relationship between RSN metrics and tremor features. Results: Comparison of inter-network connectivity indicated a decreased connectivity between default mode network and ventral attention network in the ET group (p < 0.05). Differences in functional activity (assessed by amplitude of low-frequency fluctuation, ALFF) were found in several brain regions participating in various RSNs (p < 0.05). The ET group generally has higher degree centrality over normal control. Correlation analysis has revealed that tremor features are associated with inter-network connectivity (|r| = 0.135-0.506), ALFF (|r| = 0.313-0.766), and degree centrality (|r| = 0.523-0.710). Conclusion: Alterations in the cerebral network of ET were detected by using resting-state fMRI, demonstrating a potentially useful approach to explore the cerebral alterations in ET.

Network-level enrichment provides a framework for biological interpretation of machine learning results

Abstract Machine learning algorithms are increasingly being utilized to identify brain connectivity biomarkers linked to behavioral and clinical outcomes. However, research often prioritizes prediction accuracy at the expense of biological interpretability, and inconsistent implementation of ML methods may hinder model accuracy. To address this, our paper introduces a network-level enrichment approach, which integrates brain system organization in the context of connectome-wide statistical analysis to reveal network-level links between brain connectivity and behavior. To demonstrate the efficacy of this approach, we used linear support vector regression (LSVR) models to examine the relationship between resting-state functional connectivity networks and chronological age. We compared network-level associations based on raw LSVR weights to those produced from the forward and inverse models. Results indicated that not accounting for shared family variance inflated prediction performance, the k-best feature selection via Pearson correlation reduced accuracy and reliability, and raw LSVR model weights produced network-level associations that deviated from the significant brain systems identified by forward and inverse models. Our findings offer crucial insights for applying machine learning to neuroimaging data, emphasizing the value of network enrichment for biological interpretation.