Individual Network Research Articles

Principles of Computation by Competitive Protein Dimerization Networks.

Many biological signaling pathways employ proteins that competitively dimerize in diverse combinations. These dimerization networks can perform biochemical computations, in which the concentrations of monomers (inputs) determine the concentrations of dimers (outputs). Despite their prevalence, little is known about the range of input-output computations that dimerization networks can perform (their "expressivity") and how it depends on network size and connectivity. Using a systematic computational approach, we demonstrate that even small dimerization networks (3-6 monomers) are expressive, performing diverse multi-input computations. Further, dimerization networks are versatile, performing different computations when their protein components are expressed at different levels, such as in different cell types. Remarkably, individual networks with random interaction affinities, when large enough (≥8 proteins), can perform nearly all (~90%) potential one-input network computations merely by tuning their monomer expression levels. Thus, even the simple process of competitive dimerization provides a powerful architecture for multi-input, cell-type-specific signal processing.

E2SCNet: Efficient Multiobjective Evolutionary Automatic Search for Remote Sensing Image Scene Classification Network Architecture.

Remote sensing image scene classification methods based on deep learning have been widely studied and discussed. However, most of the network architectures are directly reliant on natural image processing methods and are fixed. A few studies have focused on automatic search mechanisms, but they cannot weigh the interpretation accuracy and the parameter quantity for practical application. As a result, automatic global search methods based on multiobjective evolutionary computation have more advantages. However, in the ranking process, the network individuals with large parameter quantities are easy to eliminate, but a higher accuracy may be obtained after full training. In addition, evolutionary neural architecture search methods often take several days. In this article, in order to solve the above concerns, we propose an efficient multiobjective evolutionary automatic search framework for remote sensing image scene classification deep learning network architectures (E2SCNet). In E2SCNet, eight kinds of lightweight operators are used to build a diversified search space, and the coding connection mode is flexible. In the search process, a large model retention mechanism is implemented through two-step multiobjective modeling and evolutionary search, where one step involves the "parameter quantity and accuracy," and the other step involves the "parameter quantity and accuracy growth quantity." Moreover, a super network is constructed to share the weight in the process of individual network evaluation and promote the search speed. The effectiveness of E2SCNet is proven by comparison with several networks designed by human experts and networks obtained by gradient and evolutionary computing-based search methods.

Endogenous preparatory control is associated with increased interaction between default mode and dorsal attention networks

Abstract It is increasingly recognized that cognitive control requires integration across large-scale brain networks anchored in frontal and parietal cortices. While the functional role of individual networks has been studied extensively, their cross-network interactions in the service of cognitive control are poorly understood. Beyond in-the-moment regulation of goal-relevant information processing (e.g., of sensory information), cognitive control encompasses preparatory processes in anticipation of upcoming stimuli and actions. Such preparatory control is often endogenous, that is, it is based on internal representations without relying on external cues or events. Here, we assessed network interactions that support such endogenously driven preparatory control. We recorded fMRI (N = 25) during a perceptual decision task with highly variable intertrial intervals. In half of the blocks, trial onset was cued, while in the remaining blocks, participants maintained readiness without relying on cues. We studied endogenous preparatory control in the intertrial period preceding uncued (vs. cued) trials. Behavioral outcomes confirmed heavier cognitive control demands in the uncued condition. Endogenous preparatory control was associated with increased activity of the dorsal attention network (DAN). This contrasted with in-the-moment control over stimulus-response processing during the trial itself, which was supported foremost by the right-hemispheric fronto-parietal network (FPN). Cross-network interactions were strengthened exclusively during endogenous preparatory control; the default mode network (DMN) showed more positive connectivity with the DAN and to a lesser degree the cingulo-opercular network (CON). Our results demonstrate that cross-networks interactions are particularly important for endogenously driven preparatory control. They further suggest that the DMN may be implicated in internally harnessing resources for cognitive control. This notion extends the DMN’s known role in internally-oriented processing to the domain of cognitive control when preparation cannot be aided by external events.

Impact of Enterprises Relationship on ML’s Ability to Forecast SME’s Credit Risk in SCF: A Research Based on Complex Network

ABSTRACT In this study, we investigate the impact of introducing enterprise relationship to train machine learning (ML) algorithms on improving its ability of forecasting small and medium-sized enterprises’ (SMEs) credit risk in supply chain finance (SCF). First, we incorporate attributes of SME node in the CEs-SMEs network, a bipartite network constructed with transaction data in the Chinese automobile industry, into financial indicators to train ML algorithms for improving forecasting performance. Second, we employ the weighted one-mode projection approach to extract a projected-SMEs network that reveals the relationship among SMEs from the CEs-SMEs network and examine whether the ML algorithms’ forecasting performances is improved when considering attributes in the projected-SMEs network. Third, we analyze the correlation between SME’s credit risk and individual network attribute to specifically understand the impact of enterprise relationship on credit risk. Overall, the empirical results indicate that the forecasting performance is obviously better when enterprise transaction relationship is considered, and it is further enhanced after we apply weighted one-mode projection approach. Meanwhile, the variable importance analysis and the binomial logistic regression demonstrate how significantly each network attribute is correlated with credit risk, and the discussion on partial dependence plot shows that SMEs with large degree are non-risky generally.

Fabrication and Characterization of Quad-Component Bioinspired Hydrogels to Model Elevated Fibrin Levels in Central Nervous Tissue Scaffolds.

Multicomponent interpenetrating polymer network (mIPN) hydrogels are promising tissue-engineering scaffolds that could closely resemble key characteristics of native tissues. The mechanical and biochemical properties of mIPNs can be finely controlled to mimic key features of target cellular microenvironments, regulating cell-matrix interactions. In this work, we fabricated hydrogels made of collagen type I (Col I), fibrin, hyaluronic acid (HA), and poly (ethylene glycol) diacrylate (PEGDA) using a network-by-network fabrication approach. With these mIPNs, we aimed to develop a biomaterial platform that supports the in vitro culture of human astrocytes and potentially serves to assess the effects of the abnormal deposition of fibrin in cortex tissue and simulate key aspects in the progression of neuroinflammation typically found in human pathologies such as Alzheimer's disease (AD), Parkinson's disease (PD), and tissue trauma. Our resulting hydrogels closely resembled the complex modulus of AD human brain cortex tissue (~7.35 kPa), promoting cell spreading while allowing for the modulation of fibrin and hyaluronic acid levels. The individual networks and their microarchitecture were evaluated using confocal laser scanning microscopy (CLSM) and scanning electron microscopy (SEM). Human astrocytes were encapsulated in mIPNs, and negligible cytotoxicity was observed 24 h after the cell encapsulation.

The Role of Virtual Communities in Encouraging Social Interaction in the Digital Era

Virtual communities have become a significant platform in facilitating social interaction in the digital era. With the development of information and communication technology, individuals can connect with each other without being limited by geographic boundaries. This article discusses the role of virtual communities in providing diverse social interactions, allowing individuals to share knowledge, interests, and experiences with fellow community members. Apart from that, this article will also discuss how virtual communities are able to facilitate the formation of strong social relationships, support the exchange of useful information, and expand individual social networks. Thus, virtual communities are not only spaces for social interaction, but also places where individuals can develop their social identities in a positive way.

Associative linking for collaborative thinking: Self-organization of content in online Q&A communities via user-generated links.

Virtual collaborative Q&A communities generate shared knowledge through the interaction of people and content. This knowledge is often fragmented, and its value as a collective, collaboratively formed product, is largely overlooked. Inspired by work on individual mental semantic networks, the current study explores the networks formed by user-added associative links as reflecting an aspect of self-organization within the communities' collaborative knowledge sharing. Using eight Q&A topic-centered discussions from the Stack Exchange platform, it investigated how associative links form internal structures within the networks. Network analysis tools were used to derive topological indicator metrics of complex structures from associatively-linked networks. Similar metrics extracted from 1000 simulated randomly linked networks of comparable sizes and growth patterns were used to generate estimated sampling distributions through bootstrap resampling, and 99% confidence intervals were constructed for each metric. The discussion-network indicators were compared against these. Results showed that participant-added associative links largely led to networks that were more clustered, integrated, and included posts with more connections than those that would be expected in random networks of similar size and growth pattern. The differences were observed to increase over time. Also, the largest connected subgraphs within the discussion networks were found to be modular. Limited qualitative observations have also pointed to the impacts of external content-related events on the network structures. The findings strengthen the notion that the networks emerging from associative link sharing resemble other information networks that are characterized by internal structures suggesting self-organization, laying the ground for further exploration of collaborative linking as a form of collective knowledge organization. It underscores the importance of recognizing and leveraging this latent mechanism in both theory and practice.

Reduced GM-WM concentration inside the Default Mode Network in individuals with high emotional intelligence and low anxiety: a data fusion mCCA+jICA approach.

The concept of emotional intelligence (EI) refers to the ability to recognize and regulate emotions to appropriately guide cognition and behaviour. Unfortunately, studies on the neural bases of EI are scant, and no study so far has exhaustively investigated grey matter (GM) and white matter (WM) contributions to it. To fill this gap, we analysed trait measure of EI and structural MRI data from 128 healthy participants to shed new light on where and how EI is encoded in the brain. In addition, we explored the relationship between the neural substrates of trait EI and trait anxiety. A data fusion unsupervised machine learning approach (mCCA + jICA) was used to decompose the brain into covarying GM-WM networks and to assess their association with trait-EI. Results showed that high levels trait-EI are associated with decrease in GM-WM concentration in a network spanning from frontal to parietal and temporal regions, among which insula, cingulate, parahippocampal gyrus, cuneus and precuneus. Interestingly, we also found that the higher the GM-WM concentration in the same network, the higher the trait anxiety. These findings encouragingly highlight the neural substrates of trait EI and their relationship with anxiety. The network is discussed considering its overlaps with the Default Mode Network.

Pulmonary arterial hypertension treatment: an individual participant data network meta-analysis.

Effective therapies that target three main signalling pathways are approved to treat pulmonary arterial hypertension (PAH). However, there are few large patient-level studies that compare the effectiveness of these pathways. The aim of this analysis was to compare the effectiveness of the treatment pathways in PAH and to assess treatment heterogeneity. A network meta-analysis was performed using individual participant data of 6811 PAH patients from 20 Phase III randomized clinical trials of therapy for PAH that were submitted to the US Food and Drug Administration. Individual drugs were grouped by the following treatment pathways: endothelin, nitric oxide, and prostacyclin pathways. The mean (±standard deviation) age of the sample was 49.2 (±15.4) years; 78.4% were female, 59.7% had idiopathic PAH, and 36.5% were on background PAH therapy. After covariate adjustment, targeting the endothelin + nitric oxide pathway {β: 43.7 m [95% confidence interval (CI): 32.9, 54.4]}, nitric oxide pathway [β: 29.4 m (95% CI: 22.6, 36.3)], endothelin pathway [β: 25.3 m (95% CI: 19.8, 30.8)], and prostacyclin pathway [oral/inhaled β: 19.1 m (95% CI: 14.2, 24.0), intravenous/subcutaneous β: 24.4 m (95% CI: 15.1, 33.7)] significantly increased 6 min walk distance at 12 or 16 weeks compared with placebo. Treatments also significantly reduced the likelihood of having clinical worsening events. There was significant heterogeneity of treatment effects by age, body mass index, hypertension, diabetes, and coronary artery disease. Drugs targeting the three traditional treatment pathways significantly improve outcomes in PAH, with significant treatment heterogeneity in patients with some comorbidities. Randomized clinical trials are warranted to identify the most effective treatment strategies in a personalized approach.

Temperature and strain rate effects on ultra‐high‐molecular‐weight‐polyethylene compression: An experimental and modeling approach

AbstractThis study aims to predict the compression behavior of ultra‐high molecular weight polyethylene (UHMWPE) at various temperatures (25, 40, and 55°C) and strain rates (~10−4/s and ~10−2/s) using a single set of three‐network (TN) viscoplastic model parameters. The TN model is made up of three parallel networks: networks A and B use nonlinear springs and dashpots to control the responses of the crystalline phase and confined amorphous phases, respectively, while network C captures the macromolecular amorphous networks using a single nonlinear spring. A single set of TN model parameters captures the yield and post‐yield hardening responses of the stress–strain curves at experimental temperatures and strain rates. When these TN model calibrated parameters are used as material property in a commercial finite element tool to simulate UHMWPE compression, the predicted and simulated results match well, showing the model's fidelity. Additionally, the model predicts experiments conducted at 40 and 70°C with loading rates of ~10−3/s and ~10−2/s, respectively. The study also correlates the deformations of UHMWPE's crystalline structure and macromolecular amorphous networks with its global stress versus strain response by extracting stresses from individual networks of the TN model at different strain rates and temperatures.Highlights UHMWPE's mechanical behavior predicted by a single set of TN model parameters TN model predicts mechanical response at various strain rates and temperatures. TN model explains microstructural crystalline and amorphous phase deformations.

A trust-centric approach to intrusion detection in edge networks for medical internet of thing Ecosystems

The Internet of Medical Things (IoMT) promises transformative benefits for patient care and remote monitoring but raises significant security and privacy concerns. Ensuring the trustworthiness and security of IoMT systems at the edge is a critical challenge. This research paper presents a novel framework to address the security and trust issues specific to IoMT environments. The primary objective of this framework is to establish a robust defense against intrusion attempts and to instill trust in edge networks, securing healthcare data and medical devices. To achieve this, the paper introduces an advanced Intrusion Detection System (IDS) optimized for edge networks in the context of IoMT. This paper emphasizes developing an evaluation methodology for appraising the trustworthiness of individual network components, developing an analytical technique for scrutinizing the participation of network entities in ongoing communications, establishing a means to quantify the trust level at the network's periphery or edge, and constructing an energy-efficient trust-based framework for the identification and mitigation of malicious entities operating within the IoMT. Through comprehensive testing, including real-world simulation scenarios, the paper evaluates the performance and effectiveness of the proposed framework, validating its ability to safeguard sensitive healthcare information and support the secure operation of IoMT devices.

Flower‐visitor and pollen‐load data provide complementary insight into species and individual network roles

Most animal pollination results from plant–insect interactions, but how we perceive these interactions may differ with the sampling method adopted. The two most common methods are observations of visits by pollinators to plants and observations of pollen loads carried by insects. Each method could favour the detection of different species and interactions, and pollen load observations typically reveal more interactions per individual insect than visit observations. Moreover, while observations concern plant and insect individuals, networks are frequently analysed at the level of species. Although networks constructed using visitation and pollen‐load data have occasionally been compared in relatively specialised, bee‐dominated systems, it is not known how sampling methodology will affect our perception of how species (and individuals within species) interact in a more generalist system. Here we use a Diptera‐dominated high‐Arctic plant–insect community to explore how sampling approach shapes several measures of species' interactions (focusing on specialisation), and what we can learn about how the interactions of individuals relate to those of species. We found that species degrees, interaction strengths, and species motif roles were significantly correlated across the two method‐specific versions of the network. However, absolute differences in degrees and motif roles were greater than could be explained by the greater number of interactions per individual provided by the pollen‐load data. Thus, despite the correlations between species roles in networks built using visitation and pollen‐load data, we infer that these two perspectives yield fundamentally different summaries of the ways species fit into their communities. Further, individuals' roles generally predicted the species' overall role, but high variability among individuals means that species' roles cannot be used to predict those of particular individuals. These findings emphasize the importance of adopting a dual perspective on bipartite networks, as based on the different information inherent in insect visits and pollen loads.

Transmission of depressive symptoms in the nuclear family: a cross-sectional and cross-lagged network perspective.

Children are more likely to develop depressive symptoms in families where parents have depressive symptoms. By conceptualizing the individual depressive symptom network of each family member as a whole, this study proposes a family symptom network model, and explored the mechanisms of transmission of depression within nuclear families at the symptom level. This study used four waves (2012, 2016, 2018, 2020) of data from the China Family Panel Studies (CFPS), which in wave one contained a representative sample of 1963 children (1038 boys; age = 12.60), 4763 mothers and 4614 fathers from China. Children with their parents completed the Center for Epidemiology Studies Depression Scale at each wave. Individual depressive symptom networks among children, fathers, and mothers were highly similar and stable across time. When considering depressive symptoms of all family members as a whole, there was a wide range of associations between child, father, and mother depressive symptom networks. The results of the cross-lagged network model suggest the bidirectional relationships between couples and parent-child depression. The current study provides preliminary validation of the family symptom network model. The model represents a further integration and extension of network theory of mental disorders and family systems theory, and points out the limitations of studying the intergenerational transmission of depression from a latent variable perspective. Thus, the family symptom network model proposed in this study could provide valuable new insights into understanding the intergenerational transmission of depression.

Trans-Net: A transferable pretrained neural networks based on temporal domain decomposition for solving partial differential equations

Physics-Informed Neural Networks (PINNs) has provided a novel direction for solving partial differential equations (PDEs) and has achieved significant advancements in the field of scientific computing. PINNs effectively incorporate the physical constraints of equations into the loss function, enabling neural networks to learn and approximate the behavior of physical systems by optimizing the loss function. According to the existing research, we propose a transferable pretrained neural networks framework based on temporal domain decomposition to solve partial differential equations. Specifically, we divide the domain into multiple subdomains according to time, each of which is solved using an individual neural network. Subsequently, the trained subnetwork and the predicted values at the common boundary are used as the pretrained model and initial values for the next subdomain, respectively. This not only improves the prediction accuracy of the subnetwork, but also reduces the training time. Finally, we present a series of classical numerical experiments including one-dimensional, two-dimensional, and three-dimensional partial differential equations. The experimental results indicate that the proposed method outperforms existing approaches in terms of accuracy and efficiency. Relevant data and code can be accessed on GitHub via https://github.com/karry-Zhang/Trans-Net.

Disrupted properties of functional brain networks in major depressive disorder during emotional face recognition: an EEG study via graph theory analysis.

Previous neuroimaging studies have revealed abnormal brain networks in patients with major depressive disorder (MDD) in emotional processing. While any cognitive task consists of a series of stages, little is yet known about the topology of functional brain networks in MDD for these stages during emotional face recognition. To address this problem, electroencephalography (EEG)-based functional brain networks of MDD patients at different stages of facial information processing were investigated in this study. First, EEG signals were collected from 16 patients with MDD and 18 age-, gender-, and education-matched normal subjects when performing an emotional face recognition task. Second, the global field power (GFP) method was employed to divide group-averaged event-related potentials into different stages. Third, using the phase transfer entropy (PTE) approach, the brain networks of MDD patients and normal individuals were constructed for each stage in negative and positive face processing, respectively. Finally, we compared the topological properties of brain networks of each stage between the two groups using graph theory approaches. The results showed that the analyzed three stages of emotional face processing corresponded to specific neurophysiological phases, namely, visual perception, face recognition, and emotional decision-making. It was also demonstrated that depressed patients showed abnormally decreased characteristic path length at the visual perception stage of negative face recognition and normalized characteristic path length in the stage of emotional decision-making during positive face processing compared to healthy subjects. Furthermore, while both the MDD and normal groups' brain networks were found to exhibit small-world network characteristics, the brain network of patients with depression tended to be randomized. Moreover, for patients with MDD, the centro-parietal region may lose its status as a hub in the process of facial expression identification. Together, our findings suggested that altered emotional function in MDD patients might be associated with disruptions in the topological organization of functional brain networks during emotional face recognition, which further deepened our understanding of the emotion processing dysfunction underlying MDD.

The Microverse: A Task-Oriented Edge-Scale Metaverse

Over the past decade, there has been a remarkable acceleration in the evolution of smart cities and intelligent spaces, driven by breakthroughs in technologies such as the Internet of Things (IoT), edge–fog–cloud computing, and machine learning (ML)/artificial intelligence (AI). As society begins to harness the full potential of these smart environments, the horizon brightens with the promise of an immersive, interconnected 3D world. The forthcoming paradigm shift in how we live, work, and interact owes much to groundbreaking innovations in augmented reality (AR), virtual reality (VR), extended reality (XR), blockchain, and digital twins (DTs). However, realizing the expansive digital vista in our daily lives is challenging. Current limitations include an incomplete integration of pivotal techniques, daunting bandwidth requirements, and the critical need for near-instantaneous data transmission, all impeding the digital VR metaverse from fully manifesting as envisioned by its proponents. This paper seeks to delve deeply into the intricacies of the immersive, interconnected 3D realm, particularly in applications demanding high levels of intelligence. Specifically, this paper introduces the microverse, a task-oriented, edge-scale, pragmatic solution for smart cities. Unlike all-encompassing metaverses, each microverse instance serves a specific task as a manageable digital twin of an individual network slice. Each microverse enables on-site/near-site data processing, information fusion, and real-time decision-making within the edge–fog–cloud computing framework. The microverse concept is verified using smart public safety surveillance (SPSS) for smart communities as a case study, demonstrating its feasibility in practical smart city applications. The aim is to stimulate discussions and inspire fresh ideas in our community, guiding us as we navigate the evolving digital landscape of smart cities to embrace the potential of the metaverse.

Three-year trajectories of social networks, face-to-face interactions, and non-face-to-face interactions among community-dwelling older adults in Japan during the COVID-19 pandemic: Otassha study

ObjectivesTo investigate the trajectories of social networks and interactions among older Japanese individuals during the COVID-19 pandemic. Study designWe used data from a cohort of community-dwelling older individuals from the Otassha study's 2019–2022 survey. Participants comprised 606 older individuals who participated in the 2019 survey and were followed up at least once. Main outcome measuresSocial networks were assessed using the Lubben Social Network Scale-6 (LSNS-6), whereas social interactions were assessed by the frequency of face-to-face or non-face-to-face contact with non-resident family members and friends. Trajectories of the LSNS-6 and face-to-face and non-face-to-face interactions were identified using group-based semiparametric mixture modeling. The trajectories of change in the LSNS-6 from 2019 were also identified. The factors associated with the changes in trajectory patterns were examined using logistic regression analysis. ResultsThree LSNS-6 trajectories were identified, with slightly decreasing patterns over time. Specifically, face-to-face interactions tended to decrease over time, whereas non-face-to-face interactions exhibited almost no change. The reduction in LSNS-6 in the decreased pattern failed to recover to its pre-pandemic level. The group of participants with the decreased pattern had a significantly higher proportion of people participating in group activities [odds ratio (95 % confidence interval): 2.27 (1.12–4.59)] and performing calisthenics twice a week than the group with the maintained pattern [2.08 (1.18–3.68)]. ConclusionsDuring the three years of the pandemic, no changes were observed in the social networks of community-dwelling older Japanese individuals.

Personalized functional network mapping for autism spectrum disorder and attention-deficit/hyperactivity disorder

Autism spectrum disorder (ASD) and Attention-deficit/hyperactivity disorder (ADHD) are two typical neurodevelopmental disorders that have a long-term impact on physical and mental health. ASD is usually comorbid with ADHD and thus shares highly overlapping clinical symptoms. Delineating the shared and distinct neurophysiological profiles is important to uncover the neurobiological mechanisms to guide better therapy. In this study, we aimed to establish the behaviors, functional connectome, and network properties differences between ASD, ADHD-Combined, and ADHD-Inattentive using resting-state functional magnetic resonance imaging. We used the non-negative matrix fraction method to define personalized large-scale functional networks for each participant. The individual large-scale functional network connectivity (FNC) and graph-theory-based complex network analyses were executed and identified shared and disorder-specific differences in FNCs and network attributes. In addition, edge-wise functional connectivity analysis revealed abnormal edge co-fluctuation amplitude and number of transitions among different groups. Taken together, our study revealed disorder-specific and -shared regional and edge-wise functional connectivity and network differences for ASD and ADHD using an individual-level functional network mapping approach, which provides new evidence for the brain functional abnormalities in ASD and ADHD and facilitates understanding the neurobiological basis for both disorders.

In patients with mild disability NMOSD: is the alteration in the cortical morphological or functional network topological properties more significant.

To assess the alteration of individual brain morphological and functional network topological properties and their clinical significance in patients with neuromyelitis optica spectrum disorder (NMOSD). Eighteen patients with NMOSD and twenty-two healthy controls (HCs) were included. The clinical assessment of NMOSD patients involved evaluations of disability status, cognitive function, and fatigue impact. For each participant, brain images, including high-resolution T1-weighted images for individual morphological brain networks (MBNs) and resting-state functional MR images for functional brain networks (FBNs) were obtained. Topological properties were calculated and compared for both MBNs and FBNs. Then, partial correlation analysis was performed to investigate the relationships between the altered network properties and clinical variables. Finally, the altered network topological properties were used to classify NMOSD patients from HCs and to analyses time- to-progression of the patients. The average Expanded Disability Status Scale score of NMOSD patients was 1.05 (range from 0 to 2), indicating mild disability. Compared to HCs, NMOSD patients exhibited a higher normalized characteristic path length (λ) in their MBNs (P = 0.0118, FDR corrected) but showed no significant differences in the global properties of FBNs (p: 0.405-0.488). Network-based statistical analysis revealed that MBNs had more significantly altered connections (P< 0.01, NBS corrected) than FBNs. Altered nodal properties of MBNs were correlated with disease duration or fatigue scores (P< 0.05/6 with Bonferroni correction). Using the altered nodal properties of MBNs, the accuracy of classification of NMOSD patients versus HCs was 96.4%, with a sensitivity of 93.3% and a specificity of 100%. This accuracy was better than that achieved using the altered nodal properties of FBNs. Nodal properties of MBN significantly predicted Expanded Disability Status Scale worsening in patients with NMOSD. The results indicated that patients with mild disability NMOSD exhibited compensatory increases in local network properties to maintain overall stability. Furthermore, the alterations in the morphological network nodal properties of NMOSD patients not only had better relevance for clinical assessments compared with functional network nodal properties, but also exhibited predictive values of EDSS worsening.

Regional emergency networks: how organizations shape technological collaborations under extreme conditions

ABSTRACT The aim of this research is to undertake explorative research and present propositions that would identify the drivers of regional emergency networks, i.e. regional technological collaborative agreements under extreme conditions. Based on previous studies on emergency management, we empirically evaluate how 49 organizations built 53 technological cooperative agreements within the context of COVID-19. We consider individual, dyadic and whole network level variables to build on theory about how these emergency networks are created. We propose that emergency networks are developed under embedded relationships, with physically proximate partners that are also indirectly connected. Secondly, we establish that emergency networks are characterized by heterarchical leadership with lower density connections between many different partners. Finally, we deliberate on the role of partner diversity in developing emergency networks, establishing alternative propositions based on the similarity in innovative capacity and institutional character of the partners.