Network Metrics Research Articles

Intensive ground cover management and landscape simplification affect community-wide floral visitor-plant interactions in olive groves

Plant-pollinator interaction networks can provide insights about the impacts of agricultural practices on biodiversity. However, studies on the agricultural impact on these networks most often focus on specific pollinator guilds (i.e., wild bees), hindering our understanding of the effects at the entire insect visitor community level. Here, we evaluated the impact of intensive agriculture and the moderation effects of landscape context on the topology of community-wide plant-visitor interaction networks (381 insect species). We also compared the responses of wild bee-based networks and community-wide plant-floral visitor networks to agricultural intensification to examine whether the former can be a good proxy of the latter. To this aim, we sampled plant-pollinator interaction networks in 24 paired olive farms with contrasting herb cover management (low-intensity management, where ground herb cover is not removed during the flowering season, and intensive management, with herbs persistently removed by herbicides and/or recurrent tillage) across a gradient of landscape complexity in Andalusia (Spain). Results show that the topology of whole floral visitor interaction networks was affected by the herb cover management and the landscape simplification. Furthermore, we found that the effects of the herb cover management were often moderated through the interaction with the landscape context at both floral stand and landscape scale. However, the landscape scale at which these interaction effects occurred varied with the network metric. Non-bee floral visitors were fundamentally redundant for the network topology. We concluded that the implementation of low-intensity management of ground covers in olive groves, together with the enhancement of the flower stands at the local level (e.g., floral strips) and the maintenance and/or restoration of semi-natural habitats at the large landscape scale, would be beneficial to the preservation of healthy and robust floral visitor communities. Our study further supports that wild-bee based interaction networks may adequately reflect the response of the entire networks of plant-floral visitor, at least in this study system.

A lesion‐aware automated processing framework for clinical stroke magnetic resonance imaging

AbstractMagnetic resonance imaging (MRI) and MRI based computational modelling studies provide insights into severity and recovery of ischemic stroke patients. The presence of brain lesions, however, can heavily distort and impair state‐of‐the‐art processing frameworks due to abnormal intensity values and tissue distortions. In this study, we introduce and validate the novel “Lesion Aware automated Processing Pipeline (LeAPP).” LeAPP automatically processes clinical stroke MRI data while significantly reducing the impact of pathological artefacts on processing outputs such as structural and functional connectomes (SC, FC). This helps to advance the identification of biomarkers for recovery and mechanism‐based intervention planning using MRI as well as computational approaches. We extended existing frameworks, such as the Human Connectome Project (HCP) minimal processing pipeline, introducing correction steps, and implementing and modifying functional and diffusion processing to cope with MRI acquisition protocols more typical for the clinical context. A total of 51 participants (36 patients, 15 age‐matched controls) were processed across four time points for patients (3–5, 30–40, 85–95, 340–380 days after stroke onset) and one time point for controls. We validated performance using artificial lesioned brains (N = 81), derived from healthy brains and informed by real stroke lesions. The quality of reconstructing the ground truth was quantified on whole brain level and for lesion affected and unaffected regions‐of‐interest (ROIs) for brain parcellations and for SCs. Volume based agreement was evaluated using metrics such as dice coefficient, volume difference or ROI center‐of‐gravity distance while SC based agreement was defined as the difference in network metrics (e.g., node strength, clustering coefficient, or centrality). The observed deviations in reconstructed ground truth brain parcellations and structural connectomes from lesioned brains were significantly reduced for LeAPP, compared to the performance of existing pipelines such as HCP minimal processing pipeline. For instance, in the case of lesion affected ROIS we achieved a mean dice coefficient (where a value of one represents total agreement as defined by an exact overlap of both ROIs) of 0.81 with LeAPP, compared to 0.75 for HCP (p < .0001*). Additionally, the average measured ROI distance (where a value of zero represents no difference) for lesion ROIs was 0.87 for LeAPP in contrast to 1.7 for HCP (p < .0001*), indicating an overall superior performance of LeAPP. The pipeline generates standardized output files ready for brain network modelling for instance with The Virtual Brain software. This novel open‐source automated processing framework contributes to reproducible research and provides a robust framework for automated processing of clinical stroke MRI data, supporting the identification of brain network‐based biomarkers of stroke recovery.

Public attention to Indonesian dynastic politics: an analysis of social media networks

ABSTRACT This research aims to identify and map social networks on social media We took a dataset from as the unit of analysis. We did data mining from X to collect 19,231 Tweets, 20,023 re-tweets, and 11,890 nodes and 9466 edges. The research results concluded that the communication network map on social media X identified the Gibran node which has intermediary factors and relationships as well as the actor with the most centrality and is a link to other actors in the social media communication network X by becoming the trending topic ‘Gibran’. The communication network metrics graph also found the ‘Gibran’ account to be the actor with the most contacts on ‘X’. The ‘Jokowi’ account was the second most contacted. This research found a gap in the need to expand the analysis to different cultures or countries to understand differences and similarities in social interaction patterns. Another gap in this research exists in terms of expanding the analysis to different topics or communities to gain a broader understanding of social network dynamics.

Shedding light on overlooked pollinators: Global insights into floral interactions of velvet ants (Hymenoptera: Mutillidae and Myrmosidae)

AbstractPlant–animal interactions constitute a recurrent and central focus in ecological research, with pollination representing one of its most extensively studied aspects. While certain insect orders have traditionally received considerable attention due to their abundance as flower visitors and their efficiency in pollination, it is undeniable that the significance of other less popular and neglected flower visitors cannot be overlooked. In this regard, velvet ants (Hymenoptera: Mutillidae and Myrmosidae) constitute an excellent study model, as the knowledge of their ecology (e.g., their feeding preferences) is still very limited despite being reported as common flower visitors. In this study, we conducted a comprehensive global review of velvet ant floral visitation patterns using citizen science data, literature records and unpublished data. We used network metrics to explore their flower‐visit preferences on a global scale, as well as depending on the bioregion where the interaction was recorded and the sex of the velvet ants. In addition, we explored their potential role as pollen vectors examining the number of photographic records where velvet ants had pollen attached to their bodies. Our analyses revealed that velvet ants are generalist flower visitors of a wide range of plant families, with Apiaceae, Asteraceae, Euphorbiaceae, Rhamnaceae and Fabaceae as the most visited. Despite differences in flowering plant and velvet ant composition across bioregions causing differences in plant‐velvet ant interactions, velvet ants visited flowering plants in a generalistic way across the globe. Males and females seemed to visit different plant communities, with males being more generalist than females. Furthermore, 42.7% (likely an underestimation) of the photographic records of velvet ants visiting flowers showed pollen attached to their bodies in the same way as in other pollinating insects, suggesting the same potential role as pollinators. There remains ample scope for ongoing investigation to comprehensively assess the importance of numerous arthropods, including velvet ants, not only as flower visitors but also as potential pollinators.

Brain network topology and its cognitive impact in adult glioma survivors

Structural brain network topology can be altered in case of a brain tumor, due to both the tumor itself and its treatment. In this study, we explored the role of structural whole-brain and nodal network metrics and their association with cognitive functioning. Fifty WHO grade 2–3 adult glioma survivors (> 1-year post-therapy) and 50 matched healthy controls underwent a cognitive assessment, covering six cognitive domains. Raw cognitive assessment scores were transformed into w-scores, corrected for age and education. Furthermore, based on multi-shell diffusion-weighted MRI, whole-brain tractography was performed to create weighted graphs and to estimate whole-brain and nodal graph metrics. Hubs were defined based on nodal strength, betweenness centrality, clustering coefficient and shortest path length in healthy controls. Significant differences in these metrics between patients and controls were tested for the hub nodes (i.e. n = 12) and non-hub nodes (i.e. n = 30) in two mixed-design ANOVAs. Group differences in whole-brain graph measures were explored using Mann–Whitney U tests. Graph metrics that significantly differed were ultimately correlated with the cognitive domain-specific w-scores. Bonferroni correction was applied to correct for multiple testing. In survivors, the bilateral putamen were significantly less frequently observed as a hub (pbonf < 0.001). These nodes’ assortativity values were positively correlated with attention (r(90) > 0.573, pbonf < 0.001), and proxy IQ (r(90) > 0.794, pbonf < 0.001). Attention and proxy IQ were significantly more often correlated with assortativity of hubs compared to non-hubs (pbonf < 0.001). Finally, the whole-brain graph measures of clustering coefficient (r = 0.685), global (r = 0.570) and local efficiency (r = 0.500) only correlated with proxy IQ (pbonf < 0.001). This study demonstrated potential reorganization of hubs in glioma survivors. Assortativity of these hubs was specifically associated with cognitive functioning, which could be important to consider in future modeling of cognitive outcomes and risk classification in glioma survivors.

Using social networks to explore the social flexibility of urban vervet monkeys

Increasing urbanisation reduces available habitat and increases human-wildlife interactions, presenting social and ecological challenges for many species; however, flexible generalist species, such as the vervet monkey, Chlorocebus pygerythrus, thrive under these pressures. In the urban mosaic, human-food sources represent clumped, monopolisable food that can increase contest competition. Social network analysis (SNA) is a powerful tool to monitor changes in social structure, yet it has rarely been used to study urban wildlife. Using SNA, we investigated the effect of anthropogenic food and human-wildlife interactions on social cohesion in five vervet monkey groups in urban KwaZulu-Natal, South Africa. Over six months, we conducted group scan samples every 30-min on each group and recorded all humans-vervet monkey interactions during dawn to dusk follows. We analysed the effect of foraging on natural and human-related food sources and human-vervet monkey interactions on social network metrics for grooming and aggression at group (density, clustering coefficient and distance) and individual (eigenvector centrality and degree) levels, using linear mixed models. Anthropogenic food influenced almost all social metrics. At the group level, foraging on anthropogenic food was related to increased density and cohesion in both grooming and aggression networks. At the individual level, increasing anthropogenic food affected high-ranking monkeys most: eigenvector centrality and outdegree in aggression networks increased with rank. Social network analysis can be a useful tool to document urban effects on wildlife groups, and aids our understanding of wildlife behavioural flexibility, a key tool in developing educated and effective management strategies.

Exploring and mitigating hybrid rank attack in RPL-based IoT networks

Abstract Despite the widespread adoption of the Routing Protocol for Low-power and Lossy Networks (RPL) in IoT environments, its inherent limitations in addressing security vulnerabilities have left IoT networks vulnerable to ongoing attacks. This paper introduces a novel intrusion detection system tailored specifically for IoT networks, with a focus on mitigating attacks at the network’s edge. The study presents the Hybrid Rank Attack (HRA), a sophisticated threat exploiting RPL vulnerabilities by alternately advertising decreased and increased rank values in control messages. Extensive experimentation evaluates the detrimental effects of HRA on critical network metrics including exchanged messages, energy consumption, PDR, latency, and memory footprint. Additionally, a lightweight and distributed countermeasure algorithm is proposed to effectively mitigate the impact of HRA. Simulation-based evaluations demonstrate significant reductions in control overhead (68.7%) and energy consumption (61.83%), with minimal additional RAM utilization (1.05%). This lightweight solution enhances the resilience of RPL-based IoT networks against HRA threats.

Evaluation of the effectiveness of methodological approaches to hierarchical intelligent control of ground-air Ad-Hoc communication network

The article shows the features of intelligent management processes of a new type of ground-air communication networks that are rapidly being implemented. The dynamic nature of the conditions of operation and behavior of communication nodes, both ground and air, causes a rapid increase in the volume of service information necessary to ensure continuous and adaptive management in real time. One of the ways to solve this problem is the redistribution of management tasks at different stages of the management cycle, which is classically divided into the stages of planning, deployment and operational management. On the one hand, the increase in entropy at the planning stage complicates this process, but this approach will increase the probability of making "correct" management decisions from the standpoint of quality management (network metrics). The work investigates a new architecture of a hierarchical intelligent ground-air communication network control system based on the model-free Reinforcement learning algorithm as a Q-learning network agent and FA-OSELM online sequential extreme machine learning algorithms as node-level agents. The IСS model is presented, its adequacy is checked, and the process of its learning at the planning stage on various mobility models is shown. An important feature of the IСS training process is the application of the developed mobility model, disclosed in the article, which describes the interaction processes of communication nodes at a deeper level. The work conducted a study of the representativeness of the training sample, obtained using the developed mobility model, that was carried out relative to the existing ones, and it was determined that despite the smaller volume of the population of the initial data, it was possible to ensure a better quality of resource management.

Associations of glymphatic function with structural network and cognition in self-limited epilepsy with centrotemporal spikes

PurposeTo investigate glymphatic function by Virchow-Robin space (VRS) counts and volume in patients with newly diagnosed self-limited epilepsy with centrotemporal spikes (SeLECTS) and evaluate its relationship with structural connectivity and cognitive impairment. MethodsThirty-two children with SeLECTS and thirty-two age- and sex-matched typically developing (TD) children were enrolled in this study. VRS counts and volume were quantified. Structural networks were constructed and the topological metrics were analyzed. Wechsler Intelligence Scale (WISC) was used to assess cognitive function in all participants. Correlation analysis assessed the association between VRS counts and volume, network connectivity, and cognitive impairment. Mediation effects of topological metrics of the structural networks on the relationship between glymphatic function and cognitive impairment were explored. ResultsPatients with SeLECTS showed a higher VRS counts, VRS volume, and global shortest path length (Lp); they also showed a lower global efficiency (Eg). VRS counts and volume were significantly correlated with full-scale intelligence quotient (FIQ) (r_VRS counts = -0.520, r_VRS volume = -0.639), performance intelligence quotient (PIQ) (r_VRS counts = -0.693, r_VRS volume = -0.597), verbal intelligence quotient (VIQ) (r_VRS counts = -0.713, r_VRS volume = -0.699), Eg (r_VRS counts = -0.499, r_VRS volume = -0.490), and Lp (r_VRS volume = 0.671) in patients with SeLECTS. Eg mediated 24.59% of the effects for the relationship between VRS volume and FIQ. ConclusionGlymphatic function may be impaired in SeLECTS reflected by VRS counts and volume. Glymphatic dysfunction may result in cognitive impairment by disrupting structural connectivity in SeLECTS.

White matter tract density index is associated with disability in multiple sclerosis

BackgroundThe association between common neuroradiological markers of multiple sclerosis (MS) and clinical disability is weak. Given that the disability in patients with MS may depend on the underlying structural connectivity of the brain, our study aimed to examine the association between white matter tracts affected by MS and the patients' disability using a new tract density index (TDI). MethodThis study included 53 patients diagnosed with MS, examined between 2019 and 2020. Manual lesion segmentation was performed on fluid-attenuated inversion recovery (FLAIR) images, and the density of white matter tracts encompassing the lesion (i.e., TDI) was calculated. Correlation analysis was employed to assess the association between TDI and disability. Additionally, the relationship between disability, TDI, and lesion-derived network metrics was examined by computing a partial correlation network. ResultsThe TDI significantly correlated with the expanded disability status scale (EDSS) (r = 0.30, p = 0.03). Furthermore, the patient's disability is linked solely through TDI to lesion-derived network metrics —a key metric that ‘bridges’ the gap between the brain lesion and disability. ConclusionsIn this study, MS lesions encompassing regions with high white matter tract density were associated and linked with severe physical disability. These findings indicate that TDI may be an outcome predictor that may connect radiologic findings to clinical practice.

Power law of path multiplicity in complex networks.

Complex networks describe a wide range of systems in nature and society. As a fundamental concept of graph theory, the path connecting nodes and edges plays a vital role in network science. Rather than focusing on the path length or path centrality, here we draw attention to the path multiplicity related to decision-making efficiency, which is defined as the number of shortest paths between node pairs and thus characterizes the routing choice diversity. Notably, through extensive empirical investigations from this new perspective, we surprisingly observe a "hesitant-world" feature along with the "small-world" feature and find a universal power-law of the path multiplicity, meaning that a small number of node pairs possess high path multiplicity. We demonstrate that the power-law of path multiplicity is much stronger than the power-law of node degree, which is known as the scale-free property. Then, we show that these phenomena cannot be captured by existing classical network models. Furthermore, we explore the relationship between the path multiplicity and existing typical network metrics, such as average shortest path length, clustering coefficient, assortativity coefficient, and node centralities. We demonstrate that the path multiplicity is a distinctive network metric. These results expand our knowledge of network structure and provide a novel viewpoint for network design and optimization with significant potential applications in biological, social, and man-made networks.

What does the language system look like in pre-trained language models? A study using complex networks

Pre-trained language models has advanced the fields of natural language processing. The exceptional capabilities exhibited by PLMs in NLP tasks have been attracting researchers to explore the underlying factors responsible for their success. However, most of work primarily focus on studying some certain linguistic knowledge encoded in PLMs, rather than investigating how these models comprehend language from a holistic perspective. Furthermore, they cannot point out how PLMs organize the whole language system. Therefore, we adopt the complex network approach to represent the language system, and investigate how language elements are organized within the system. Specifically, we take the attention relationships among words as the research object, which are generated by attention heads within BERT models. Then, the words are treated as nodes, and the connections between words and their most-attending words are represented as edges. After obtaining these "words' attention networks", we analyze the network properties from various perspectives by calculating the network metrics. Many constructive conclusions are summarized, including: (1) The English attention networks demonstrate exceptional performance in organizing words; (2) Most words’ attention networks exhibit small-world property and scale-free behavior; (3) Some networks generated by multilingual BERT can reflect typological information well, achieving preferable clustering performance among language groups; (4) In cross-layer analysis, the networks from 8 to 10 layers in Chinese BERT and from 6 to 9 layers in English BERT exhibit more consistent characteristics. Our study provides a comprehensive explanation of how PLMs organize language systems, which can be utilized to evaluate and develop improved models.

Causal network topology analysis: Characterizing causal context for risk management.

The ways that risk assessments are commonly performed in organizations have limitations that undermine their quality. They typically focus on individual risk events one at a time but are weak at integrating their relevant causal context, into decision-making processes. Network topology analysis has previously been applied to address this weakness through quantitatively characterizing the importance of the causal interactions of risk events. However, there remains a lack of both clarity and consistency in terminology, methods, and interpretation of the results of this approach. This paper presents and formalizes causal network topology analysis, a methodology that contributes to (1) characterizing the causal context of a risk event to inform its management, (2) articulating the ontological concepts underpinning a repeatable topology network analysis, and (3) justifying the selection and usage of network metrics for this purpose. The theory and methodology are discussed, and an exemplar application to a mining project feasibility study is presented.

Web Application to Enable Online Social Interactions in a Parkinson Disease Risk Cohort: Feasibility Study and Social Network Analysis.

There is evidence that social interaction has an inverse association with the development of neurodegenerative diseases. PREDICT-Parkinson Disease (PREDICT-PD) is an online UK cohort study that stratifies participants for risk of future Parkinson disease (PD). This study aims to explore the methodological approach and feasibility of assessing the digital social characteristics of people at risk of developing PD and their social capital within the PREDICT-PD platform, making hypotheses about the relationship between web-based social engagement and potential predictive risk indicators of PD. A web-based application was built to enable social interaction through the PREDICT-PD portal. Feedback from existing members of the cohort was sought and informed the design of the pilot. Dedicated staff used weekly engagement activities, consisting of PD-related research, facts, and queries, to stimulate discussion. Data were collected by the hosting platform. We examined the pattern of connections generated over time through the cumulative number of posts and replies and ego networks using social network analysis. We used network metrics to describe the bonding, bridging, and linking of social capital among participants on the platform. Relevant demographic data and Parkinson risk scores (expressed as an odd 1:x) were analyzed using descriptive statistics. Regression analysis was conducted to estimate the relationship between risk scores (after log transformation) and network measures. Overall, 219 participants took part in a 4-month pilot forum embedded in the study website. In it, 200 people (n=80, 40% male and n=113, 57% female) connected in a large group, where most pairs of users could reach one another either directly or indirectly through other users. A total of 59% (20/34) of discussions were spontaneously started by participants. Participation was asynchronous, with some individuals acting as "brokers" between groups of discussions. As more participants joined the forum and connected to one another through online posts, distinct groups of connected users started to emerge. This pilot showed that a forum application within the cohort web platform was feasible and acceptable and fostered digital social interaction. Matching participants' web-based social engagement with previously collected data at individual level in the PREDICT-PD study was feasible, showing potential for future analyses correlating online network characteristics with the risk of PD over time, as well as testing digital social engagement as an intervention to modify the risk of developing neurodegenerative diseases. The results from the pilot suggest that an online forum can serve as an intervention to enhance social connectedness and investigate whether patterns of online engagement can impact the risk of developing PD through long-term follow-up. This highlights the potential of leveraging online platforms to study the role of social capital in moderating PD risk and underscores the feasibility of such approaches in future research or interventions.

Patient care in complex Sociotechnological ecosystems and learning health systems.

The learning health system (LHS) model was proposed to provide real-time, bi-directional flow of learning using data captured in health information technology systems to deliver rapid learning in healthcare delivery. As highlighted by the landmark National Academy of Medicine report "Crossing the Quality Chasm," the U.S. healthcare delivery industry represents complex adaptive systems, and there is an urgent need to develop innovative methods to identify efficient team structures by harnessing real-world care delivery data found in the electronic health record (EHR). We offer a discussion surrounding the complexities of team communication and how solutions may be guided by theories such as the Multiteam System (MTS) framework and the Multitheoretical Multilevel Framework of Communication Networks. To advance healthcare delivery science and promote LHSs, our team has been building a new line of research using EHR data to study MTS in the complex real world of cancer care delivery. We are developing new network metrics to study MTSs and will be analyzing the impact of EHR communication network structures on patient outcomes. As this research leads to patient care delivery interventions/tools, healthcare leaders and healthcare professionals can effectively use health IT data to implement the most evidence-based collaboration approaches in order to achieve the optimal LHS and patient outcomes.

Network Fairness Ambivalence: When does social network capital mitigate or amplify unfairness?

Social networks inherit societal biases present across lines of gender, race, socioeconomic status, and other factors. Networks can structurally perpetuate unequal access to information and opportunities through homophilous dynamics. While there is substantial knowledge about inequity in the diffusion of opportunities in a network where nodes seek them from their immediate neighbors, much less is known when considering beyond that first hop. In this paper, we leverage recent mathematical analysis of network fairness to prove that enabling simple multi-hop dissemination can reduce inequity towards a minority group in the network as long as homophily is sufficiently weak. Otherwise, our necessary and sufficient condition proves that multi-hop dissemination strategies amplify the bias already found amongst considering direct neighbors. We empirically validate these results on four social network datasets as well as present an example of a key application of our findings with a scenario of individuals who leverage their personal network to seek job referrals. Our results suggest that online platforms designing algorithms to promote opportunities to multi-hop connections must carefully take into account network metrics measuring group size and homophily in order to avoid amplifying bias against marginalized groups on their platforms.

Mapping analytical methods between input–output economics and network science

AbstractThe input–output (IO) model can be used to examine the flow of products and services within an economy, resembling a network with industries as nodes and transactions as links. Diverging significantly from commonly studied networks such as social, protein, and power grids, IO networks exhibit intricate interconnectivity, involving weighted nodes and both directional and weighted links. This uniqueness necessitates careful consideration when applying complex network analysis techniques to IO systems. We critically review current complex network metrics and attempt to link them with existing IO approaches. Based on our assessment, certain network metrics, such as degree centrality and eigenvector centrality, have been explicitly integrated into the IO theory. In contrast, there exist metrics whose definitions and interpretations expand when applied in the context of IO analysis, including closeness and betweenness centrality. Additionally, network metrics are usually used to study topological features, identify key sectors, and construct novel metrics to study related issues. Network metrics used in IO analysis can identify important driver and transmission sectors in resource flow and environmental emission network, facilitating the development of targeted and reliable strategies. Besides, network metrics are used to quantify topological features and structural changes of the IO network which help strengthen the supply chain and mitigate both direct and indirect impacts of disruptions. Our ultimate goal is to establish connections and offer a roadmap for developing network‐based tools in IO analysis.

Alteration of multilayer network perspective on gray and white matter connectivity in obstructive sleep apnea.

The objective of this research was to examine changes in the neural networks of both gray and white matter in individuals with obstructive sleep apnea (OSA) in comparison to those without the condition, employing a comprehensive multilayer network analysis. Patients meeting the criteria for OSA were recruited through polysomnography, while a control group of healthy individuals matched for age and sex was also assembled. Utilizing T1-weighted imaging, a morphometric similarity network was crafted to represent gray matter, while diffusion tensor imaging provided structural connectivity for constructing a white matter network. A multilayer network analysis was then performed, employing graph theory methodologies. We included 40 individuals diagnosed with OSA and 40 healthy participants in our study. Analysis revealed significant differences in various global network metrics between the two groups. Specifically, patients with OSA exhibited higher average degree overlap and average multilayer clustering coefficient (28.081 vs. 23.407, p < 0.001; 0.459 vs. 0.412, p = 0.004), but lower multilayer modularity (0.150 vs. 0.175, p = 0.001) compared to healthy controls. However, no significant differences were observed in average multiplex participation, average overlapping strength, or average weighted multiplex participation between the patients with OSA and healthy controls. Moreover, several brain regions displayed notable differences in degree overlap at the nodal level between patients with OSA and healthy controls. Remarkable alterations in the multilayer network, indicating shifts in both gray and white matter, were detected in patients with OSA in contrast to their healthy counterparts. Further examination at the nodal level unveiled notable changes in regions associated with cognition, underscoring the effectiveness of multilayer network analysis in exploring interactions across brain layers.

Disrupted single-subject gray matter networks are associated with cognitive decline and cortical atrophy in Alzheimer's disease.

Research has shown disrupted structural network measures related to cognitive decline and future cortical atrophy during the progression of Alzheimer's disease (AD). However, evidence regarding the individual variability of gray matter network measures and the associations with concurrent cognitive decline and cortical atrophy related to AD is still sparse. To investigate whether alterations in single-subject gray matter networks are related to concurrent cognitive decline and cortical gray matter atrophy during AD progression. We analyzed structural MRI data from 185 cognitively normal (CN), 150 mild cognitive impairment (MCI), and 153 AD participants, and calculated the global network metrics of gray matter networks for each participant. We examined the alterations of single-subject gray matter networks in patients with MCI and AD, and investigated the associations of network metrics with concurrent cognitive decline and cortical gray matter atrophy. The small-world properties including gamma, lambda, and sigma had lower values in the MCI and AD groups than the CN group. AD patients had reduced degree, clustering coefficient, and path length than the CN and MCI groups. We observed significant associations of cognitive ability with degree in the CN group, with gamma and sigma in the MCI group, and with degree, connectivity density, clustering coefficient, and path length in the AD group. There were significant correlation patterns between sigma values and cortical gray matter volume in the CN, MCI, and AD groups. These findings suggest the individual variability of gray matter network metrics may be valuable to track concurrent cognitive decline and cortical atrophy during AD progression. This may contribute to a better understanding of cognitive decline and brain morphological alterations related to AD.

A Network Interference Approach to Analyzing Change Propagation in Requirements

Abstract Requirements are frequently revised due to the iterative nature of the design process. If not properly managed, these changes may result in financial and time losses due to undesired propagating effect. Currently, predictive models to assist designers in making well-informed decisions prior to change implementation do not exist. Current modeling methods for managing requirements do not offer formal reasoning necessary to manage requirement change and its propagation. The ability to predict change during the design process may lead to valuable insights in designing artifacts more efficiently by minimizing unanticipated changes due to mismanaged requirement changes. Two research questions are addressed in this paper: (1) How do complex network metrics of requirements, considering both node and edge interferences, influence the predictability of requirement change propagation across different case studies? (2) How does the performance of the complex network metrics approach compare to the refined automated requirement change propagation prediction (R-ARCPP) tool, developed from our prior study, in accurately predicting requirement change propagation? Requirement changes are simulated by applying the node interference and the edge interference methods. It is found that complex network metrics can be used to predict requirement change propagation. Based on the studied data, the performance ranking of metrics is characterized by edge interference across the changes. The results reveal that the R-ARCPP tool ranks higher than comparatively performing complex network metrics.