A Comparative Analysis of Community Detection Algorithms on Artificial Networks

Symptoms of adjustment disorder during social unrest: The role of varied stances in Israel's sociopolitical climate

BACKGROUND

Civil unrest related to political change can have detrimental effects on the mental health of the population. This study investigated probable Adjustment Disorder (AjD) among individuals with different attitudes in the context of a highly controversial judicial reform in Israel. The relationships between AjD symptoms across three groups were explored: those who support the proposed changes, others who oppose them, and those who adopt a neutral stance.

METHODS

This study was conducted with a nationally representative sample of Israeli adults (n = 1999). A Symptoms Network analysis of AjD was performed using the International Adjustment Disorder Questionnaire, examining groups with different levels of social unrest engagement.

RESULTS

Participants actively opposing the judicial reform reported higher levels of probable AjD compared to neutral participants and those in favor of the reform. Similarly, neutral participants reported higher levels of probable AjD than those in favor of the reform. The network structure of AjD was similar across the three groups. Item five ("difficult to relax/feel calm since the social unrest") was the node with the highest strength centrality in all networks.

LIMITATIONS

This study utilized cross-sectional methodology and self-report measures.

CONCLUSIONS

Mental health needs should be actively addressed by policymakers and mental health professionals during social unrest, regardless of political stance. The high rates of probable AjD and the similarity in the structure of AjD across the groups suggest common underlying mechanisms and a significant adjustment crisis in a large portion of the population following social unrest.

New Perspective on Digital Well-Being by Distinguishing Digital Competency From Dependency: Network Approach

BACKGROUND

In the digital age, there is an emerging area of research focusing on digital well-being (DWB), yet conceptual frameworks of this novel construct are lacking. The current conceptualization either approaches the concept as the absence of digital ill-being, running the risk of pathologizing individual digital use, or follows the general subjective well-being framework, failing to highlight the complex digital nature at play.

OBJECTIVE

This preregistered study aimed to address this gap by using a network analysis, which examined the strength of the relationships among affective (digital stress and web-based hedonic well-being), cognitive (online intrinsic needs satisfaction), and social (online social connectedness and state empathy) dimensions of DWB and their associations with some major DWB protective and risk factors (ie, emotional regulation, nomophobia, digital literacy, self-control, problematic internet use, coping styles, and online risk exposure).

METHODS

The participants were 578 adults (mean age 38.7, SD 13.14 y; 277/578, 47.9% women) recruited from the United Kingdom and the United States who completed an online survey. Two network models were estimated. The first one assessed the relationships among multiple dimensions of DWB, and the second examined the relationships between DWB dimensions and related protective and risk factors.

RESULTS

The 2 resulting network structures demonstrated high stability, with the correlation stability coefficients being 0.67 for the first and 0.75 for the second regularized Gaussian graphical network models. The first network indicated that all DWB variables were positively related, except for digital stress, which was negatively correlated with the most central node-online intrinsic needs satisfaction. The second network revealed 2 distinct communities: digital competency and digital dependency. Emotional regulation emerged as the most central node with the highest bridge expected influence, positively associated with emotion-focused coping in the digital competency cluster and negatively associated with avoidant coping in the digital dependency cluster. In addition, some demographic differences were observed. Women scored higher on nomophobia (χ24=10.7; P=.03) and emotion-focused coping (χ24=14.9; P=.01), while men scored higher on digital literacy (χ24=15.2; P=.01). Compared with their older counterparts, younger individuals scored lower on both emotional regulation (Spearman ρ=0.27; P<.001) and digital self-control (Spearman ρ=0.35; P<.001) and higher on both digital stress (Spearman ρ=-0.14; P<.001) and problematic internet use (Spearman ρ=-0.25; P<.001).

CONCLUSIONS

The network analysis revealed how different aspects of DWB were interconnected, with the cognitive component being the most influential. Emotional regulation and adaptive coping strategies were pivotal in distinguishing digital competency from dependency.

Risk of What and Why? Disaggregating Pathways to Extremist Behaviours in Individuals Susceptible to Violent Extremism

Best practice in violent extremist risk assessment and management recommends adopting a Structured Professional Judgement (SPJ) approach. The SPJ approach identifies relevant, evidence-based risk and protective factors and requires experts to articulate hypotheses about a) what the person might do (risk of what), and b) how they've come to engage in the concerning behaviour (and why) (Logan 2021) to inform who, needs to do what, and when. Whilst the field continues to move towards adopting an SPJ approach, there remains a gap between what is known empirically and what is needed in practice. We apply psychometric network modelling to a sample of 485 individuals entered into Channel, the UK's preventing and countering violent extremism (P/CVE) program. We model the system of interactions from which susceptibility to violent extremism emerges, providing data driven evidence which speaks to risk of what and why. Our research highlights a way to generate evidence which captures the multifactorial nature of susceptibility to violent extremism, to support professional decision making in the context of an SPJ approach.

Exploring the relationships between complex post-traumatic stress disorder and depression symptoms in the context of childhood maltreatment through network analysis

BACKGROUND

Individuals with a history of childhood maltreatment commonly experience the co-occurrence of complex post-traumatic stress disorder (CPTSD) and depression, but the underlying mechanisms of their comorbidities remain unclear.

METHODS

We recruited 2740 college students, including 1366 who experienced childhood maltreatment to assess the co-occurrence network of CPTSD and depression symptoms. We constructed a Gaussian graphical model to visualize the associations between symptoms and a directed acyclic graph to explore inferred relationships among symptoms.

RESULTS

(1) We identified the following five subnetworks within the co-occurring network of CPTSD and depression symptoms: post-traumatic stress disorder (PTSD), disturbance in self-organization (DSO), depression with vegetative symptoms, depression with interpersonal problems, and lack of positive affect subnetworks. (2) Core symptoms, identified by their high expected influence, such as sadness, low spirits, and not feeling loved have the highest EI in the depression subnetwork, whereas failure, distant, avoiding clues, and avoiding thoughts have the highest EI in the DSO and PTSD subnetworks. Bridging symptoms in the childhood maltreatment network included failure, self-denial, startlement, and hyperactivity. (3) The inferred mechanism identified includes PTSD activating DSO, which subsequently triggers depression in the childhood maltreatment network.

LIMITATIONS

This study involved a non-clinical sample.

CONCLUSION

Our study contributes to a deeper understanding of the mechanisms of CPTSD and depression co-occurrence at a transdiagnostic level and has implications for better clinical interventions targeting influential symptoms.

Longitudinal evolution of the transdiagnostic prodrome to severe mental disorders: a dynamic temporal network analysis informed by natural language processing and electronic health records

Modelling the prodrome to severe mental disorders (SMD), including unipolar mood disorders (UMD), bipolar mood disorders (BMD) and psychotic disorders (PSY), should consider both the evolution and interactions of symptoms and substance use (prodromal features) over time. Temporal network analysis can detect causal dependence between and within prodromal features by representing prodromal features as nodes, with their connections (edges) indicating the likelihood of one feature preceding the other. In SMD, node centrality could reveal insights into important prodromal features and potential intervention targets. Community analysis can identify commonly occurring feature groups to define SMD at-risk states. This retrospective (2-year) cohort study aimed to develop a global transdiagnostic SMD network of the temporal relationships between prodromal features and to examine within-group differences with sub-networks specific to UMD, BMD and PSY. Electronic health records (EHRs) from South London and Maudsley (SLaM) NHS Foundation Trust were included from 6462 individuals with SMD diagnoses (UMD:2066; BMD:740; PSY:3656). Validated natural language processing algorithms extracted the occurrence of 61 prodromal features every three months from two years to six months before SMD onset. Temporal networks of prodromal features were constructed using generalised vector autoregression panel analysis, adjusting for covariates. Edge weights (partial directed correlation coefficients, z) were reported in autocorrelative, unidirectional and bidirectional relationships. Centrality was calculated as the sum of (non-autoregressive) connections leaving (out-centrality, cout) or entering (in-centrality, cin) a node. The three sub-networks (UMD, BMD, PSY) were compared using permutation analysis, and community analysis was performed using Spinglass. The SMD network revealed strong autocorrelations (0.04 ≤ z ≤ 0.10), predominantly positive connections, and identified aggression (cout = 0.103) and tearfulness (cin = 0.134) as the most central features. Sub-networks for UMD, BMD, and PSY showed minimal differences, with 3.5% of edges differing between UMD and PSY, 0.8% between UMD and BMD, and 0.4% between BMD and PSY. Community analysis identified one positive psychotic community (delusional thinking-hallucinations-paranoia) and two behavioural communities (aggression-cannabis use-cocaine use-hostility, aggression-agitation-hostility) as the most common. This study represents the most extensive temporal network analysis conducted on the longitudinal interplay of SMD prodromal features. The findings provide further evidence to support transdiagnostic early detection services across SMD, refine assessments to detect individuals at risk and identify central features as potential intervention targets.

Beyond Microbial Variability: Disclosing the Functional Redundancy of the Core Gut Microbiota of Farmed Gilthead Sea Bream from a Bayesian Network Perspective

The significant microbiota variability represents a key feature that makes the full comprehension of the functional interaction between microbiota and the host an ongoing challenge. To overcome this limitation, in this study, fish intestinal microbiota was analyzed through a meta-analysis, identifying the core microbiota and constructing stochastic Bayesian network (BN) models with SAMBA. We combined three experiments performed with gilthead sea bream juveniles of the same hatchery batch, reared at the same season/location, and fed with diets enriched on processed animal proteins (PAP) and other alternative ingredients (NOPAP-PP, NOPAP-SCP). Microbiota data analysis disclosed a high individual taxonomic variability, a high functional homogeneity within trials and highlighted the importance of the core microbiota, clustering PAP and NOPAP fish microbiota composition. For both NOPAP and PAP BNs, >99% of the microbiota population were modelled, with a significant proportion of bacteria (55-69%) directly connected with the diet variable. Functional enrichment identified 11 relevant pathways expressed by different taxa across the different BNs, confirming the high metabolic plasticity and taxonomic heterogeneity. Altogether, these results reinforce the comprehension of the functional bacteria-host interactions and in the near future, allow the use of microbiota as a species-specific growth and welfare benchmark of livestock animals, and farmed fish in particular.

Predicting lymph node metastasis in papillary thyroid carcinoma with Hashimoto's thyroiditis using regression and network analysis

The comprehensive study of the relationship between lymph node metastasis (LNM) and its associated factors in patients with concurrent papillary thyroid carcinoma (PTC) and Hashimoto's thyroiditis (HT) remains insufficient. Building upon the initial investigation of factors associated with LNM in patients with concurrent PTC and HT, we further analyzed the complex relationships between different severity indicators of LNM and these associated factors. This study included patients confirmed PTC with HT who underwent total thyroidectomy at Xiangya Hospital, from January 2020 to December 2021. A total of 271 patients from 2020 were used as the training set, and 300 patients from 2021 as the validation set. Univariate analysis and regression modeling were used to identify key factors associated with LNM. Model reliability was assessed using the area under the receiver operating characteristic curve (AUC). Network analysis was employed to explore associations between LNM severity and its related factors. The regression model indicated that age, calcification, free triiodothyronine (FT3), and tumor maximum diameter (TMD) are independent factors for LNM. The severity model showed free thyroxine (FT4) and hemoglobin (Hb) are independent protective factors for the region and quantity of LNM, respectively, while TMD is an independent risk factor for both. Network analysis revealed TMD has a closer relationship with LNM severity compared to other associated factors. This study innovatively combined regression models and network analysis to investigate factors related to LNM in patients with PTC and HT, providing a theoretical basis for predicting preoperative LNM in future clinical practice.

Uncovering migration systems through spatio-temporal tensor co-clustering

A central problem in the study of human mobility is that of migration systems. Typically, migration systems are defined as a set of relatively stable movements of people between two or more locations over time. While these emergent systems are expected to vary over time, they ideally contain a stable underlying structure that could be discovered empirically. There have been some notable attempts to formally or informally define migration systems. However, they have been limited by being hard to operationalize and defining migration systems in ways that ignore origin/destination aspects and fail to account for migration dynamics over time. In this work, we propose to employ spatio-temporal tensor co-clustering-that stems from signal processing and machine learning theory-as a novel migration system analysis tool. Tensor co-clustering is designed to cluster entities exhibiting similar patterns across multiple modalities and thus suits our purpose of analyzing spatial migration activities across time. To demonstrate its effectiveness in describing stable migration systems, we first focus on domestic migration between counties in the US from 1990 to 2018. We conduct three case studies on domestic migration, namely, (i) US Metropolitan Areas, (ii) the state of California, and (iii) Louisiana, in which the last focuses on detecting exogenous events such as Hurricane Katrina in 2005. In addition, we also examine a case study at a larger scale, using worldwide international migration data from 200 countries between 1990 and 2015. Finally, we conclude with a discussion of this approach and its limitations.

A multilayer network analysis of Alzheimer's disease pathogenesis: Roles for p-tau, synaptic peptides, and physical activity

INTRODUCTION

In the aging brain, cognitive abilities emerge from the coordination of complex pathways arising from a balance between protective lifestyle and environmental factors and accumulation of neuropathologies.

METHODS

As part of the Rush Memory and Aging Project (n = 440), we measured accelerometer-based actigraphy, cognitive performance, and after brain autopsy, selected reaction monitoring mass spectrometry. Multilevel network analysis was used to examine the relationships among the molecular machinery of vesicular neurotransmission, Alzheimer's disease (AD) neuropathology, cognition, and late-life physical activity.

RESULTS

Synaptic peptides involved in neuronal secretory function were the most influential contributors to the multilayer network, reflecting the complex interdependencies among AD pathology, synaptic processes, and late-life cognition. Older adults with lower physical activity evidenced stronger adverse relationships among phosphorylated tau peptides, markers of synaptic integrity, and tangle pathology.

DISCUSSION

Network-based approaches simultaneously model interdependent biological processes and advance understanding of the role of physical activity in age-associated cognitive impairment.

HIGHLIGHTS

Network-based approaches simultaneously model interdependent biological processes. Secretory synaptic peptides were influential contributors to the multilayer network. Older adults with lower physical activity had adverse relationships among pathology. There was interdependence among phosphorylated tau, synaptic integrity, and tangles. Network methods elucidate the role of physical activity in cognitive impairment.

Kernel machine tests of association using extrinsic and intrinsic cluster evaluation metrics

Modeling the network topology of the human brain within the mesoscale has become an increasing focus within the neuroscientific community due to its variation across diverse cognitive processes, in the presence of neuropsychiatric disease or injury, and over the lifespan. Much research has been done on the creation of algorithms to detect these mesoscopic structures, called communities or modules, but less has been done to conduct inference on these structures. The literature on analysis of these community detection algorithms has focused on comparing them within the same subject. These approaches, however, either do not accomodate a more general association between community structure and an outcome or cannot accommodate additional covariates that may confound the association of interest. We propose a semiparametric kernel machine regression model for either a continuous or binary outcome, where covariate effects are modeled parametrically and brain connectivity measures are measured nonparametrically. By incorporating notions of similarity between network community structures into a kernel distance function, the high-dimensional feature space of brain networks, defined on input pairs, can be generalized to non-linear spaces, allowing for a wider class of distance-based algorithms. We evaluate our proposed methodology on both simulated and real datasets.

Network visualization to interpret which healthcare services are central to people living with HIV

AIM

To employ network analysis to identify the central healthcare service needs of people living with HIV (PLWH) for integrated care.

DESIGN

Cross-sectional survey.

METHODS

A list of healthcare services was identified through literature reviews, expert workshops and validity evaluations by PLWH. A total of 243 PLWH participated at five hospitals and self-reported their need for healthcare services on a four-point Likert scale. Centrality of healthcare service needs was analysed using network analysis.

RESULTS

The mean score for 20 healthcare service needs was 3.53 out of 4. The highest scoring need, "Precaution for interaction between antiretroviral therapy and other drugs," received a rating of 3.73 but had a centrality of only 0.31. The most central node in the network of healthcare service needs, "Information and coping with opportunistic infections," had a strength centrality of 1.63 and showed significant relationships with "non-HIV-related medical services (e.g., health check-ups)" and "Regular dental services." The correlation stability coefficient, which quantifies the stability of centrality, was 0.44 with an acceptable value.

CONCLUSIONS

The most central need was information on opportunistic infections that had connections with many nodes in network analysis. By interpreting the relationships between needs, healthcare providers can design interventions with an integrative perspective.

IMPLICATIONS FOR PATIENT CARE

Network visualization provides dynamic relationships between needs that are unknown from the score scale by presenting them graphically and qualitatively.

IMPACT

Using network analysis to interpret need assessment offers an integrated nursing perspective. Coping with opportunistic infection is central to connecting the chain of healthcare. This study highlights the multifaceted understanding of patients' needs that nurses gain when they conduct network analysis.

REPORTING METHOD

We adhered to the STROBE checklist.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

Comprehensive analysis of clustering algorithms: exploring limitations and innovative solutions

This survey rigorously explores contemporary clustering algorithms within the machine learning paradigm, focusing on five primary methodologies: centroid-based, hierarchical, density-based, distribution-based, and graph-based clustering. Through the lens of recent innovations such as deep embedded clustering and spectral clustering, we analyze the strengths, limitations, and the breadth of application domains-ranging from bioinformatics to social network analysis. Notably, the survey introduces novel contributions by integrating clustering techniques with dimensionality reduction and proposing advanced ensemble methods to enhance stability and accuracy across varied data structures. This work uniquely synthesizes the latest advancements and offers new perspectives on overcoming traditional challenges like scalability and noise sensitivity, thus providing a comprehensive roadmap for future research and practical applications in data-intensive environments.

On the role of diffusion dynamics on community-aware centrality measures

Theoretical and empirical studies on diffusion models have revealed their versatile applicability across different fields, spanning from sociology and finance to biology and ecology. The presence of a community structure within real-world networks has a substantial impact on how diffusion processes unfold. Key nodes located both within and between these communities play a crucial role in initiating diffusion, and community-aware centrality measures effectively identify these nodes. While numerous diffusion models have been proposed in literature, very few studies investigate the relationship between the diffusive ability of key nodes selected by community-aware centrality measures, the distinct dynamical conditions of various models, and the diverse network topologies. By conducting a comparative evaluation across four diffusion models, utilizing both synthetic and real-world networks, along with employing two different community detection techniques, our study aims to gain deeper insights into the effectiveness and applicability of the community-aware centrality measures. Results suggest that the diffusive power of the selected nodes is affected by three main factors: the strength of the network's community structure, the internal dynamics of each diffusion model, and the budget availability. Specifically, within the category of simple contagion models, such as SI, SIR, and IC, we observe similar diffusion patterns when the network's community structure strength and budget remain constant. In contrast, the LT model, which falls under the category of complex contagion dynamics, exhibits divergent behavior under the same conditions.

Calling to the collective: contact calling rates within groups of disc-winged bats do not vary by kinship or association

Many group-living animals coordinate social behaviours using contact calls, which can be produced for all group members or targeted at specific individuals. In the disc-winged bat, Thyroptera tricolor, group members use 'inquiry' and 'response' calls to coordinate daily movements into new roosts (furled leaves). Rates of both calls show consistent among-individual variation, but causes of within-individual variation remain unknown. Here, we tested whether disc-winged bats produce more contact calls towards group members with higher kinship or association. In 446 experimental trials, we recorded 139 random within-group pairs of one flying bat (producing inquiry calls for roost searching) and one roosting bat (producing response calls for roost advertising). Using generalized linear mixed-effect models (GLMM), we assessed how response and inquiry calling rates varied by sender, receiver, genetic kinship and co-roosting association rate. Calling rates varied consistently across senders but not by receiver. Response calling was influenced by inquiry calling rates, but neither calling rate was higher when the interacting pair had higher kinship or association. Rather than dyadic calling rates indicating within-group relationships, our findings are consistent with the hypothesis that bats produce contact calls to maintain contact with any or all individuals within a group while collectively searching for a new roost site. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics'.

Extremists of a feather flock together? Community structures, transitivity, and patterns of homophily in the US Islamist co-offending network

Prior research suggests that members of terrorist groups prioritize forming network ties based on trust to improve their organizational and operational security. The homophily principle, which postulates that individuals tend to form relationships based on shared characteristics, can be a key mechanism through which people identify trustworthy associates. Next to homophily, the mechanism of establishing interconnected relationships through transitivity is also well-known to serve this purpose and shape community structures in social networks. We analyze the community structures of the Islamist co-offending network in the United States, which is highly violent, to assess whether homophily and transitivity determine which extremists form co-offending ties. We rely on a new database on the individual attributes and the co-offending relationships of 494 Islamist offenders radicalized in the United States between 1993 and 2020. Using community detection algorithms, we show that the US Islamist co-offending network is highly clustered, modular, and includes many small but only a few large communities. Furthermore, results from exponential random graph modeling show that transitive relationships as well as spatial proximity, ideological affiliation, and shared socio-cultural characteristics drive co-offending among US Islamist extremists. Overall, these findings demonstrate that the processes of homophily and transitivity shape violent social networks.

Identification of symptom clusters and sentinel symptoms during the first cycle of chemotherapy in patients with lung cancer

PURPOSE

To identify symptom clusters (SCs) in patients with lung cancer who are undergoing initial chemotherapy and to identify the sentinel symptoms of each SC.

METHODS

A convenience sampling method was used to recruit patients with lung cancer who were undergoing their initial chemotherapy treatment. Patient information was collected using the General Demographic Questionnaire, MD Anderson Symptom Inventory (including the lung cancer module) and a schedule documenting the initial occurrence of symptoms. The Walktrap algorithm was employed to identify SCs, while sentinel symptoms within each SC were identified using the Apriori algorithm in conjunction with the initial occurrence time of symptoms.

RESULTS

A total of 169 patients with lung cancer participated in this study, and four SCs were identified: the psychological SC (difficulty remembering, sadness, dry mouth, numbness or tingling, and distress), somatic SC (pain, fatigue, sleep disturbance, and drowsiness), respiratory SC (coughing, expectoration, chest tightness, and shortness of breath), and digestive SC (nausea, poor appetite, constipation, vomiting, and weight loss). Sadness, fatigue, and coughing were identified as sentinel symptoms of the psychological, somatic, and respiratory SCs, respectively. However, no sentinel symptom was identified for the digestive SC.

CONCLUSION

Patients with lung cancer who are undergoing chemotherapy encounter a spectrum of symptoms, often presenting as SCs. The sentinel symptom of each SC emerges earlier than the other symptoms and is characterized by its sensitivity, significance, and driving force. It serves as a vital indicator of the SC and assumes a sentry role. Targeting sentinel symptoms might be a promising strategy for determining the optimal timing of interventions and for mitigating or decelerating the progression of the other symptoms within the SC.

The evolutionary drivers and correlates of viral host jumps

Most emerging and re-emerging infectious diseases stem from viruses that naturally circulate in non-human vertebrates. When these viruses cross over into humans, they can cause disease outbreaks, epidemics and pandemics. While zoonotic host jumps have been extensively studied from an ecological perspective, little attention has gone into characterizing the evolutionary drivers and correlates underlying these events. To address this gap, we harnessed the entirety of publicly available viral genomic data, employing a comprehensive suite of network and phylogenetic analyses to investigate the evolutionary mechanisms underpinning recent viral host jumps. Surprisingly, we find that humans are as much a source as a sink for viral spillover events, insofar as we infer more viral host jumps from humans to other animals than from animals to humans. Moreover, we demonstrate heightened evolution in viral lineages that involve putative host jumps. We further observe that the extent of adaptation associated with a host jump is lower for viruses with broader host ranges. Finally, we show that the genomic targets of natural selection associated with host jumps vary across different viral families, with either structural or auxiliary genes being the prime targets of selection. Collectively, our results illuminate some of the evolutionary drivers underlying viral host jumps that may contribute to mitigating viral threats across species boundaries.

Proof of biased behavior of Normalized Mutual Information

The Normalized Mutual Information (NMI) metric is widely utilized in the evaluation of clustering and community detection algorithms. This study explores the performance of NMI, specifically examining its performance in relation to the quantity of communities, and uncovers a significant drawback associated with the metric's behavior as the number of communities increases. Our findings reveal a pronounced bias in the NMI as the number of communities escalates. While previous studies have noted this biased behavior, they have not provided a formal proof and have not addressed the causation of this problem, leaving a gap in the existing literature. In this study, we fill this gap by employing a mathematical approach to formally demonstrate why NMI exhibits biased behavior, thereby establishing its unsuitability as a metric for evaluating clustering and community detection algorithms. Crucially, our study exposes the vulnerability of entropy-based metrics that employ logarithmic functions to similar bias.

The relations of psychotic-like experiences (PLEs) and depressive symptoms and the bias of depressive symptoms during the clustering among Chinese adolescents: Findings from the network perspective

BACKGROUND

There are rare studies about the network structure of psychotic-like experiences (PLEs) and depressive symptoms among adolescents. Studies have widely acknowledged that PLEs in adolescents confer a higher risk of depressive symptoms, but the complex interactions remain inadequately understood. Our study aimed to examine the hierarchy and inter-associations of PLEs and depressive symptoms in a large adolescent sample from the network analysis perspective.

METHODS

A total of 5008 Chinese adolescents were enrolled in our sample. Community Assessment of Psychic Experiences-42 (CAPE-42) was applied to build the network. Centrality indexes were calculated to represent the significance of nodes in the network. Community detection was conducted to figure out the specific clustering of nodes. Demographic information was collected for the sub-network comparisons. Accuracy and stability of the network were also tested.

RESULTS

"Failure", "External control", and "Lack of activity" were the most central nodes. The main bridge nodes linking PLEs and depressive symptoms were "Failure", "Guilty", and "No future". Positive PLE "Odd looks" and negative PLE "Unable to terminate" are the two PLEs that were most relevant to depressive nodes. Community detection further demonstrated the bias of depressive nodes in the data-driven clustering. Comparative sub-network analysis suggested that age was the only demographic factor related to the current network.

CONCLUSION

In this study of a large adolescent sample, we first demonstrated the network structure and specific clustering preference of PLEs and depressive symptoms. Our findings may enhance the understanding of the relationship between PLE and depressive symptoms.

Community detection in the human connectome: Method types, differences and their impact on inference

Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method-dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting-state fMRI fromn $$ n $$ = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), andn $$ n $$ = 5338 synthetic networks with heterogeneous, data-inspired topologies, with the goal to investigate and compare three classes of community detection methods: (i) modularity maximization-based (Newman and Louvain), (ii) probabilistic (Bayesian inference within the framework of stochastic block modeling (SBM)), and (iii) geometric (based on graph Ricci flow). Extensive comparisons between methods and their individual accuracy (relative to the ground truth in synthetic networks), and reliability (when applied to multiple fMRI runs from the same brains) suggest that the underlying brain network topology plays a critical role in the accuracy, reliability and agreement of community detection methods. Consistent method (dis)similarities, and their correlations with topological properties, were estimated across fMRI runs. Based on synthetic graphs, most methods performed similarly and had comparable high accuracy only in some topological regimes, specifically those corresponding to developed connectomes with at least quasi-optimal community organization. In contrast, in densely and/or weakly connected networks with difficult to detect communities, the methods yielded highly dissimilar results, with Bayesian inference within SBM having significantly higher accuracy compared to all others. Associations between method-specific modularity and demographic, anthropometric, physiological and cognitive parameters showed mostly method invariance but some method dependence as well. Although method sensitivity to different levels of community structure may in part explain method-dependent associations between modularity estimates and parameters of interest, method dependence also highlights potential issues of reliability and reproducibility. These findings suggest that a probabilistic approach, such as Bayesian inference in the framework of SBM, may provide consistently reliable estimates of community structure across network topologies. In addition, to maximize robustness of biological inferences, identified network communities and their cognitive, behavioral and other correlates should be confirmed with multiple reliable detection methods.

Comparing community detection algorithms in psychometric networks: A Monte Carlo simulation

Identifying the correct number of factors in multivariate data is fundamental to psychological measurement. Factor analysis has a long tradition in the field, but it has been challenged recently by exploratory graph analysis (EGA), an approach based on network psychometrics. EGA first estimates a network and then applies the Walktrap community detection algorithm. Simulation studies have demonstrated that EGA has comparable or better accuracy for recovering the same number of communities as there are factors in the simulated data than factor analytic methods. Despite EGA's effectiveness, there has yet to be an investigation into whether other sparsity induction methods or community detection algorithms could achieve equivalent or better performance. Furthermore, unidimensional structures are fundamental to psychological measurement yet they have been sparsely studied in simulations using community detection algorithms. In the present study, we performed a Monte Carlo simulation using the zero-order correlation matrix, GLASSO, and two variants of a non-regularized partial correlation sparsity induction methods with several community detection algorithms. We examined the performance of these method-algorithm combinations in both continuous and polytomous data across a variety of conditions. The results indicate that the Fast-greedy, Louvain, and Walktrap algorithms paired with the GLASSO method were consistently among the most accurate and least-biased overall.

Identification of crosstalk genes relating to ECM-receptor interaction genes in MASH and DN using bioinformatics and machine learning

This study aimed to identify genes shared by metabolic dysfunction-associated fatty liver disease (MASH) and diabetic nephropathy (DN) and the effect of extracellular matrix (ECM) receptor interaction genes on them. Datasets with MASH and DN were downloaded from the Gene Expression Omnibus (GEO) database. Pearson's coefficients assessed the correlation between ECM-receptor interaction genes and cross talk genes. The coexpression network of co-expression pairs (CP) genes was integrated with its protein-protein interaction (PPI) network, and machine learning was employed to identify essential disease-representing genes. Finally, immuno-penetration analysis was performed on the MASH and DN gene datasets using the CIBERSORT algorithm to evaluate the plausibility of these genes in diseases. We found 19 key CP genes. Fos proto-oncogene (FOS), belonging to the IL-17 signalling pathway, showed greater centrality PPI network; Hyaluronan Mediated Motility Receptor (HMMR), belonging to ECM-receptor interaction genes, showed most critical in the co-expression network map of 19 CP genes; Forkhead Box C1 (FOXC1), like FOS, showed a high ability to predict disease in XGBoost analysis. Further immune infiltration showed a clear positive correlation between FOS/FOXC1 and mast cells that secrete IL-17 during inflammation. Combining the results of previous studies, we suggest a FOS/FOXC1/HMMR regulatory axis in MASH and DN may be associated with mast cells in the acting IL-17 signalling pathway. Extracellular HMMR may regulate the IL-17 pathway represented by FOS through the Mitogen-Activated Protein Kinase 1 (ERK) or PI3K-Akt-mTOR pathway. HMMR may serve as a signalling carrier between MASH and DN and could be targeted for therapeutic development.

Learning dynamic representations of the functional connectome in neurobiological networks

The static synaptic connectivity of neuronal circuits stands in direct contrast to the dynamics of their function. As in changing community interactions, different neurons can participate actively in various combinations to effect behaviors at different times. We introduce an unsupervised approach to learn the dynamic affinities between neurons in live, behaving animals, and to reveal which communities form among neurons at different times. The inference occurs in two major steps. First, pairwise non-linear affinities between neuronal traces from brain-wide calcium activity are organized by non-negative tensor factorization (NTF). Each factor specifies which groups of neurons are most likely interacting for an inferred interval in time, and for which animals. Finally, a generative model that allows for weighted community detection is applied to the functional motifs produced by NTF to reveal a dynamic functional connectome. Since time codes the different experimental variables (e.g., application of chemical stimuli), this provides an atlas of neural motifs active during separate stages of an experiment (e.g., stimulus application or spontaneous behaviors). Results from our analysis are experimentally validated, confirming that our method is able to robustly predict causal interactions between neurons to generate behavior.

Transdiagnostic clustering and network analysis for questionnaire-based symptom profiling and drug recommendation in the UK Biobank and a Korean cohort

Clinical decision support systems (CDSSs) play a critical role in enhancing the efficiency of mental health care delivery and promoting patient engagement. Transdiagnostic approaches that utilize raw psychological and biological data enable personalized patient profiling and treatment. This study introduces a CDSS incorporating symptom profiling and drug recommendation for mental health care. Among the UK Biobank cohort, we analyzed 157,348 participants for symptom profiling and 14,358 participants with a drug prescription history for drug recommendation. Among the 1307 patients in the Samsung Medical Center cohort, 842 were eligible for analysis. Symptom profiling utilized demographic and questionnaire data, employing conventional clustering and community detection methods. Identified clusters were explored using diagnostic mapping, feature importance, and scoring. For drug recommendation, we employed cluster- and network-based approaches. The analysis identified nine clusters using k-means clustering and ten clusters with the Louvain method. Clusters were annotated for distinct features related to depression, anxiety, psychosis, drug addiction, and self-harm. For drug recommendation, drug prescription probabilities were retrieved for each cluster. A recommended list of drugs, including antidepressants, antipsychotics, mood stabilizers, and sedative-hypnotics, was provided to individual patients. This CDSS holds promise for efficient personalized mental health care and requires further validation and refinement with larger datasets, serving as a valuable tool for mental healthcare providers.

vissE: a versatile tool to identify and visualise higher-order molecular phenotypes from functional enrichment analysis

Functional analysis of high throughput experiments using pathway analysis is now ubiquitous. Though powerful, these methods often produce thousands of redundant results owing to knowledgebase redundancies upstream. This scale of results hinders extensive exploration by biologists and can lead to investigator biases due to previous knowledge and expectations. To address this issue, we present vissE, a flexible network-based analysis and visualisation tool that organises information into semantic categories and provides various visualisation modules to characterise them with respect to the underlying data, thus providing a comprehensive view of the biological system. We demonstrate vissE's versatility by applying it to three different technologies: bulk, single-cell and spatial transcriptomics. Applying vissE to a factor analysis of a breast cancer spatial transcriptomic data, we identified stromal phenotypes that support tumour dissemination. Its adaptability allows vissE to enhance all existing gene-set enrichment and pathway analysis workflows, empowering biologists during molecular discovery.

SynTwin: A graph-based approach for predicting clinical outcomes using digital twins derived from synthetic patients

The concept of a digital twin came from the engineering, industrial, and manufacturing domains to create virtual objects or machines that could inform the design and development of real objects. This idea is appealing for precision medicine where digital twins of patients could help inform healthcare decisions. We have developed a methodology for generating and using digital twins for clinical outcome prediction. We introduce a new approach that combines synthetic data and network science to create digital twins (i.e. SynTwin) for precision medicine. First, our approach starts by estimating the distance between all subjects based on their available features. Second, the distances are used to construct a network with subjects as nodes and edges defining distance less than the percolation threshold. Third, communities or cliques of subjects are defined. Fourth, a large population of synthetic patients are generated using a synthetic data generation algorithm that models the correlation structure of the data to generate new patients. Fifth, digital twins are selected from the synthetic patient population that are within a given distance defining a subject community in the network. Finally, we compare and contrast community-based prediction of clinical endpoints using real subjects, digital twins, or both within and outside of the community. Key to this approach are the digital twins defined using patient similarity that represent hypothetical unobserved patients with patterns similar to nearby real patients as defined by network distance and community structure. We apply our SynTwin approach to predicting mortality in a population-based cancer registry (n=87,674) from the Surveillance, Epidemiology, and End Results (SEER) program from the National Cancer Institute (USA). Our results demonstrate that nearest network neighbor prediction of mortality in this study is significantly improved with digital twins (AUROC=0.864, 95% CI=0.857-0.872) over just using real data alone (AUROC=0.791, 95% CI=0.781-0.800). These results suggest a network-based digital twin strategy using synthetic patients may add value to precision medicine efforts.

Fantasy Made Flesh - A Network Analysis of the Reciprocal Relationship between Sexual Fantasies, Pornography Usage, and Sexual Behavior

Based on different theories in media research (3AM, catalyst model of violent crime, reinforcing spirals model), we further explore the relationship between pornography use, sexual fantasy, and behavior. We suggest that pornography use appears so persistent across time and culture because it is related to a human universal, the ability to fantasize. Consequently, pornography use seems to be an opportunity to acquire media-mediated sexual fantasies, and we believe that pornography use interacts with sexual fantasies and, to a much weaker extent, with sexual behavior. To assess our assumptions, we conducted a network analysis with a large and diverse sample of N = 1338 hetero- and bisexual participants from Germany. Analyses were done separately for men and women. Our network analysis clustered parts of the psychological processes around the interaction of sexual fantasies, pornography use, and behavior into communities of especially strong interacting items. We detected meaningful communities (orgasm-centered intercourse, BDSM) consisting of sexual fantasies and behavior, with some containing pornography. However, pornography use was not part of communities we perceive to account for mainstream/everyday sexuality. Instead, our results show that non-mainstream behavior (e.g., BDSM) is affected by pornography use. Our study highlights the interaction between sexual fantasies, sexual behavior, and (parts of) pornography use. It advocates for a more interactionist view of human sexuality and media use.

A new measure of node centrality on schedule-based space-time networks for the designation of spread potential

Node centrality is one of the most frequently revisited network theoretical concepts, which got many calculation method alternatives, each of them being conceived on different empirical or theoretical network abstractions. The vast majority of centrality measures produced up to date were conceived on static network abstractions (the so-called "snapshot" networks), which arguably are less realistic than dynamic (temporal) network abstractions. The new, temporal node centrality measure that we offer with this article, is based on an uncommon abstraction, of a space-time network derived from service schedules (timetables). The proposed measure was designed to rank nodes of a space-time network based on their spread or transmission potential, and was subsequently implemented on the network of sea ferry transportation derived from the aggregated schedules for sea ferry liner shipping services in Europe, as they occurred in the month of August, 2015. The main feature of our measure, named "the Spread Potential", is the evaluation of the potential of a node in the network for transmitting disease, information (e.g. rumours or false news), as well as other phenomena, whichever support a space-time network abstraction from regular and scheduled services with some known carrying capacities. Such abstractions are, for instance, of the transportation networks (e.g. of airline or maritime shipping or the wider logistics (delivery) networks), networks of medical (hospital) services, educational (teaching) services, and virtually, of any other scheduled networked phenomenon. The article also offers the perspectives of the measure's applicability on the non-scheduled space-time network abstractions.

Robustness of community structure under edge addition

Communities often represent key structural and functional clusters in networks. To preserve such communities, it is important to understand their robustness under network perturbations. Previous work in community robustness analysis has focused on studying changes in the community structure as a response of edge rewiring and node or edge removal. However, the impact of increasing connectivity on the robustness of communities in networked systems is relatively unexplored. Studying the limits of community robustness under edge addition is crucial to better understanding the cases in which density expands or false edges erroneously appear. In this paper, we analyze the effect of edge addition on community robustness in synthetic and empirical temporal networks. We study two scenarios of edge addition: random and targeted. We use four community detection algorithms, Infomap, Label Propagation, Leiden, and Louvain, and demonstrate the results in community similarity metrics. The experiments on synthetic networks show that communities are more robust when the initial partition is stronger or the edge addition is random, and the experiments on empirical data also indicate that robustness performance can be affected by the community similarity metric. Overall, our results suggest that the communities identified by the different types of community detection algorithms exhibit different levels of robustness, and so the robustness of communities depends strongly on the choice of detection method.

The network structure of mania symptoms differs between people with and without binge eating

OBJECTIVES

People with bipolar disorder who also report binge eating have increased psychopathology and greater impairment than those without binge eating. Whether this co-occurrence is related to binge eating as a symptom or presents differently across full-syndrome eating disorders with binge eating is unclear.

METHODS

We first compared networks of 13 lifetime mania symptoms in 34,226 participants from the United Kingdom's National Institute for Health and Care Research BioResource with (n = 12,104) and without (n = 22,122) lifetime binge eating. Second, in the subsample with binge eating, we compared networks of mania symptoms in participants with lifetime anorexia nervosa binge-eating/purging (n = 825), bulimia nervosa (n = 3737), and binge-eating disorder (n = 3648).

RESULTS

People with binge eating endorsed every mania symptom significantly more often than those without binge eating. Within the subsample, people with bulimia nervosa most often had the highest endorsement rate of each mania symptom. We found significant differences in network parameter statistics, including network structure (M = 0.25, p = 0.001) and global strength (S = 1.84, p = 0.002) when comparing the binge eating with no binge-eating participants. However, network structure differences were sensitive to reductions in sample size and the greater density of the latter network was explained by the large proportion of participants (34%) without mania symptoms. The structure of the anorexia nervosa binge-eating/purging network differed from the bulimia nervosa network (M = 0.66, p = 0.001), but the result was unstable.

CONCLUSIONS

Our results suggest that the presence and structure of mania symptoms may be more associated with binge eating as a symptom rather than any specific binge-type eating disorder. Further research with larger sample sizes is required to confirm our findings.

A cross-disasters comparison of psychological distress: Symptoms network analysis

Large-scale traumatic events have the potential to trigger psychological distress, particularly among those in the affected areas. However, the manifestation of psychological distress may vary across different types of disasters. This study thus aimed to compare the symptoms network structure of psychological distress as assessed by the Kessler Psychological Distress Scale across three types of disasters: Terror (n = 5842), COVID-19 (n = 2428), and a nature-related disaster (n = 1001). Across disasters, two communities representing depression and anxiety symptoms were revealed. However, while after a nature-related disaster and the COVID-19 pandemic depression and anxiety items were interconnected via hopelessness, a terror attack resulted in more separated manifestations of anxiety and depression. Examination of symptom centrality showed that while in the Terror and the COVID-19 networks "depressed/no cheering up" was most connected to other symptoms, for the nature-related disaster network, two items were most central: "depressed/no cheering up" and "restless or fidgety". The results may point to different mechanisms of psychological distress structures after different disasters. Depending on the type of disaster, trauma-focused interventions may require targeted support and treatment.

Defining human-AI teaming the human-centered way: a scoping review and network analysis

Introduction

With the advancement of technology and the increasing utilization of AI, the nature of human work is evolving, requiring individuals to collaborate not only with other humans but also with AI technologies to accomplish complex goals. This requires a shift in perspective from technology-driven questions to a human-centered research and design agenda putting people and evolving teams in the center of attention. A socio-technical approach is needed to view AI as more than just a technological tool, but as a team member, leading to the emergence of human-AI teaming (HAIT). In this new form of work, humans and AI synergistically combine their respective capabilities to accomplish shared goals.

Methods

The aim of our work is to uncover current research streams on HAIT and derive a unified understanding of the construct through a bibliometric network analysis, a scoping review and synthetization of a definition from a socio-technical point of view. In addition, antecedents and outcomes examined in the literature are extracted to guide future research in this field.

Results

Through network analysis, five clusters with different research focuses on HAIT were identified. These clusters revolve around (1) human and (2) task-dependent variables, (3) AI explainability, (4) AI-driven robotic systems, and (5) the effects of AI performance on human perception. Despite these diverse research focuses, the current body of literature is predominantly driven by a technology-centric and engineering perspective, with no consistent definition or terminology of HAIT emerging to date.

Discussion

We propose a unifying definition combining a human-centered and team-oriented perspective as well as summarize what is still needed in future research regarding HAIT. Thus, this work contributes to support the idea of the Frontiers Research Topic of a theoretical and conceptual basis for human work with AI systems.

Is ecology different when studied with citizen scientists? A bibliometric analysis

Ecology is broad and relies on several complementary approaches to study the mechanisms driving the distribution and abundance of organisms and their interactions. One of them is citizen science (CitSci), the co-production of scientific data and knowledge by nonprofessional scientists, in collaboration with, or under the direction of, professional scientists. CitSci has bloomed in the scientific literature over the last decade and its popularity continues to increase, but its qualitative contribution to the development of academic knowledge remains understudied. We used a bibliometric analysis to study whether the epistemic content of CitSci-based articles is different from traditional, non-CitSci ones within the field of ecology. We analyzed keywords and abstracts of articles published in ecology over the last decade, disentangling CitSci articles (those explicitly referring to citizen science) and non-CitSci articles. Keyword co-occurrence and thematic map analyses first revealed that CitSci and non-CitSci articles broadly focused on biodiversity, conservation, and climate change. However, CitSci articles did so in a more descriptive way than non-CitSci articles, which were more likely to address mechanisms. Conservation biology and its links with socio-ecosystems and ecosystem services was a central theme in the CitSci corpus, much less in the non-CitSci corpus. The situation was opposite for climate change and its consequences on species distribution and adaptation, which was a central theme in the non-CitSci corpus only. We only revealed subtle differences in the relative importance of particular themes and in the way these themes are tackled in CitSci and non-CitSci articles, thus indicating that citizen science is well integrated in the main, classical research themes of ecology.

Comparing professional communities: Opioid prescriber networks and Public Health Preparedness Districts

Problem opioid use and opioid-related drug overdoses remain a major public health concern despite attempts to reduce and monitor opioid prescriptions and increase access to office-based opioid treatment. Current provider-focused interventions are implemented at the federal, state, regional, and local levels but have not slowed the epidemic. Certain targeted interventions aimed at opioid prescribers rely on populations defined along geographic, political, or administrative boundaries; however, those boundaries may not align well with actual provider-patient communities or with the geographic distribution of high-risk opioid use. Instead of relying exclusively on commonly used geographic and administrative boundaries, we suggest augmenting existing strategies with a social network-based approach to identify communities (or clusters) of providers that prescribe to the same set of patients as another mechanism for targeting certain interventions. To test this approach, we analyze 1 year of prescription data from a commercially insured population in the state of Indiana. The composition of inferred clusters is compared to Indiana's Public Health Preparedness Districts (PHPDs). We find that in some cases the correspondence between provider networks and PHPDs is very high, while in other cases the overlap is low. This has implications for whether an intervention is reaching its intended provider targets efficiently and effectively. Assessing the best intervention targeting strategy for a particular outcome could facilitate more effective interventions to tackle the ongoing opioid use epidemic.

Associations of hospitalisation - admission, readmission and length to stay - with multimorbidity patterns by age and sex in adults and older adults: the ELSI-Brazil study

BACKGROUND

Although the association between multimorbidity (MM) and hospitalisation is known, the different effects of MM patterns by age and sex in this outcome needs to be elucidated. Our study aimed to analyse the association of hospitalisations' variables (occurrence, readmission, length of stay) and patterns of multimorbidity (MM) according to sex and age.

METHODS

Data from 8.807 participants aged ≥ 50 years sourced from the baseline of the Brazilian Longitudinal Study of Ageing (ELSI-Brazil) were analysed. Multimorbidity was defined as ≥ 2 (MM2) and ≥ 3 (MM3) chronic conditions. Poisson regression was used to verify the association between the independent variables and hospitalisation according to sex and age group. Multiple linear regression models were constructed for the outcomes of readmission and length of stay. Ising models were used to estimate the networks of diseases and MM patterns.

RESULTS

Regarding the risk of hospitalisation among those with MM2, we observed a positive association with male sex, age ≥ 75 years and women aged ≥ 75 years. For MM3, there was a positive association with hospitalisation among males. For the outcomes hospital readmission and length of stay, we observed a positive association with male sex and women aged ≥ 75 years. Network analysis identified two groups that are more strongly associated with occurrence of hospitalisation: the cardiovascular-cancer-glaucoma-cataract group stratified by sex and the neurodegenerative diseases-renal failure-haemorrhagic stroke group stratified by age group.

CONCLUSION

We conclude that the association between hospitalisation, readmission, length of stay, and MM changes when sex and age group are considered. Differences were identified in the MM patterns associated with hospitalisation according to sex and age group.

Anatomical network modules of the human central nervous-craniofacial skeleton system

Anatomical network analysis (AnNA) is a systems biological framework based on network theory that enables anatomical structural analysis by incorporating modularity to model structural complexity. The human brain and facial structures exhibit close structural and functional relationships, suggestive of a co-evolved anatomical network. The present study aimed to analyze the human head as a modular entity that comprises the central nervous system, including the brain, spinal cord, and craniofacial skeleton. An AnNA model was built using 39 anatomical nodes from the brain, spinal cord, and craniofacial skeleton. The linkages were identified using peripheral nerve supply and direct contact between structures. The Spinglass algorithm in the igraph software was applied to construct a network and identify the modules of the central nervous system-craniofacial skeleton anatomical network. Two modules were identified. These comprised an anterior module, which included the forebrain, anterior cranial base, and upper-middle face, and a posterior module, which included the midbrain, hindbrain, mandible, and posterior cranium. These findings may reflect the genetic and signaling networks that drive the mosaic central nervous system and craniofacial development and offer important systems biology perspectives for developmental disorders of craniofacial structures.

Social network structure and the spread of complex contagions from a population genetics perspective

Ideas, behaviors, and opinions spread through social networks. If the probability of spreading to a new individual is a nonlinear function of the fraction of the individuals' affected neighbors, such a spreading process becomes a "complex contagion." This nonlinearity does not typically appear with physically spreading infections, but instead can emerge when the concept that is spreading is subject to game theoretical considerations (e.g., for choices of strategy or behavior) or psychological effects such as social reinforcement and other forms of peer influence (e.g., for ideas, preferences, or opinions). Here we study how the stochastic dynamics of such complex contagions are affected by the underlying network structure. Motivated by simulations of complex contagions on real social networks, we present a framework for analyzing the statistics of contagions with arbitrary nonlinear adoption probabilities based on the mathematical tools of population genetics. The central idea is to use an effective lower-dimensional diffusion process to approximate the statistics of the contagion. This leads to a tradeoff between the effects of "selection" (microscopic tendencies for an idea to spread or die out), random drift, and network structure. Our framework illustrates intuitively several key properties of complex contagions: stronger community structure and network sparsity can significantly enhance the spread, while broad degree distributions dampen the effect of selection compared to random drift. Finally, we show that some structural features can exhibit critical values that demarcate regimes where global contagions become possible for networks of arbitrary size. Our results draw parallels between the competition of genes in a population and memes in a world of minds and ideas. Our tools provide insight into the spread of information, behaviors, and ideas via social influence, and highlight the role of macroscopic network structure in determining their fate.

Network Analysis of Anxiety Symptoms in Front-Line Medical Staff during the COVID-19 Pandemic

BACKGROUND

This research analyses the relations between anxiety symptoms from the network perspective to deepen the understanding of anxiety in front-line medical staff during the COVID-19 pandemic and can also provide a reference for determining potential goals of clinical interventions.

METHODS

A convenience sampling was adopted, and the Generalized Anxiety Disorder 7-item scale (GAD-7) was administered to front-line medical staff through online platforms. A regularized partial correlation network of anxiety was constructed and then we evaluated its accuracy and stability. The expected influence and predictability were used to describe the relative importance and the controllability, using community detection to explore community structure. The gender-based differences and the directed acyclic graph were implemented.

RESULTS

The connections between A1 "Feeling nervous, anxious or on edge" and A2 "Not being able to stop or control worrying", A6 "Becoming easily annoyed or irritable" and A7 "Feeling afraid as if something awful might happen", etc., were relatively strong; A2 "Not being able to stop or control worrying" and A3 "Worrying too much about different things" had the highest expected influence, and A2 "Not being able to stop or control worrying" had the highest predictability. The community detection identified two communities. The results of the gender network comparison showed the overall intensity of the anxiety network in women was higher than that in men; DAG indicated that A2 "Not being able to stop or control worrying" had the highest probabilistic priority; the lines from A2 "Not being able to stop or control worrying" to A1 "Feeling nervous, anxious or on edge" and A2 "Not being able to stop or control worrying" to A7 "Feeling afraid as if something awful might happen" represented the most important arrows.

CONCLUSION

There exist broad interconnections among anxiety symptoms of front-line medical staff on the GAD-7. A2 "Not being able to stop or control worrying" might be the core symptom and a potential effective intervention target. It was possible to bring an optimal result for the entire GAD symptom network by interfering with A2 "Not being able to stop or control worrying". GAD may have two "subsystems". The modes of interconnection among anxiety may be consistent between genders.

MDiGest: A Python package for describing allostery from molecular dynamics simulations

Many biological processes are regulated by allosteric mechanisms that communicate with distant sites in the protein responsible for functionality. The binding of a small molecule at an allosteric site typically induces conformational changes that propagate through the protein along allosteric pathways regulating enzymatic activity. Elucidating those communication pathways from allosteric sites to orthosteric sites is, therefore, essential to gain insights into biochemical processes. Targeting the allosteric pathways by mutagenesis can allow the engineering of proteins with desired functions. Furthermore, binding small molecule modulators along the allosteric pathways is a viable approach to target reactions using allosteric inhibitors/activators with temporal and spatial selectivity. Methods based on network theory can elucidate protein communication networks through the analysis of pairwise correlations observed in molecular dynamics (MD) simulations using molecular descriptors that serve as proxies for allosteric information. Typically, single atomic descriptors such as α-carbon displacements are used as proxies for allosteric information. Therefore, allosteric networks are based on correlations revealed by that descriptor. Here, we introduce a Python software package that provides a comprehensive toolkit for studying allostery from MD simulations of biochemical systems. MDiGest offers the ability to describe protein dynamics by combining different approaches, such as correlations of atomic displacements or dihedral angles, as well as a novel approach based on the correlation of Kabsch-Sander electrostatic couplings. MDiGest allows for comparisons of networks and community structures that capture physical information relevant to allostery. Multiple complementary tools for studying essential dynamics include principal component analysis, root mean square fluctuation, as well as secondary structure-based analyses.

Intersecting Relationships of Psychosocial and Structural Syndemic Problems Among People with HIV in South Africa: Using Network Analysis to Identify Influential Problems

In South Africa, little is known about interrelationships between syndemic problems among people with HIV (PWH). A better understanding of syndemic problems may yield important information regarding factors amenable to mitigation. We surveyed 194 PWH in Khayelitsha, outside of Cape Town, South Africa. We used network analysis to examine the frequency of 10 syndemic problems and their interrelationships. Syndemic problems among PWH in South Africa were common; 159 (82.8%) participants reported at least 2 co-occurring syndemic problems and 90 (46.9%) endorsed 4 or more. Network analysis revealed seven statistically significant associations. The most central problems were depression, substance use, and food insecurity. Three clusters of syndemics were identified: mood and violence; structural factors; and behavioral factors. Depression, substance use, and food insecurity commonly co-occur among PWH in sub-Saharan Africa and interfere with HIV outcomes. Network analysis can identify intervention targets to potentially improve HIV treatment outcomes.

Using network analysis to explore the validity and influential items of the Parkinson's Disease Questionnaire-39

Quality of life (QoL) in people with Parkinson´s disease (PD) is commonly measured with the PD questionnaire-39 (PDQ-39), but its factor structure and construct validity have been questioned. To develop effective interventions to improve QoL, it is crucial to understand the connection between different PDQ-39 items and to assess the validity of PDQ-39 subscales. With a new approach based on network analysis using the extended Bayesian Information Criterion Graphical Least Absolute Shrinkage and Selection Operator (EBICglasso) followed by factor analysis, we mostly replicated the original PDQ-39 subscales in two samples of PD patients (total N = 977). However, model fit was better when the "ignored" item was categorized into the social support instead of the communication subscale. In both study cohorts, "depressive mood", "feeling isolated", "feeling embarrassed", and "having trouble getting around in public/needing company when going out" were identified as highly connected variables. This network approach can help to illustrate the relationship between different symptoms and direct interventional approaches in a more effective manner.

Time series cluster analysis reveals individual assignment of microbiota in captive tiger (Panthera tigris) and wildebeest (Connochaetes taurinus)

Fecal microbiota variability and individuality are well studied in humans and also in farm animals (related to diet- or disease-specific influences), but very little is known for exotic zoo-housed animals. This includes a wide range of species that differ greatly in microbiota composition and variation. For example, herbivorous species show a very similar and constant fecal microbiota over time, whereas carnivorous species appear to be highly variable in fecal microbial diversity and composition. Our objective was to determine whether species-specific and individual-specific clustering patterns were observed in the fecal microbiota of wildebeest (Connochaetes taurinus) and tigers (Panthera tigris). We collected 95 fecal samples of 11 animal individuals that were each sampled over eight consecutive days and analyzed those with Illumina MiSeq sequencing of the V3-V4 region of the 16SrRNA gene. In order to identify species or individual clusters, we applied two different agglomerative hierarchical clustering algorithms - a community detection algorithm and Ward's linkage. Our results showed that both, species-specific and individual-specific clustering is possible, but more reliable results were achieved when applying dynamic time warping which finds the optimal alignment between different time series. Furthermore, the bacterial families that distinguish individuals from each other in both species included daily occurring core bacteria (e.g., Acidaminococcaceae in wildebeests or Clostridiaceae in tigers) as well as individual dependent and more fluctuating bacterial families. Our results suggest that while it is necessary to consider multiple consecutive samples per individual, it is then possible to characterize individual abundance patterns in fecal microbiota in both herbivorous and carnivorous species. This would allow establishing individual microbiota profiles of animals housed in zoos, which is a basic prerequisite to quickly detect deviations and use microbiome analysis as a non-invasive and cost-effective tool in animal welfare.

Vector communities under global change may exacerbate and redistribute infectious disease risk

Vector-borne parasites may be transmitted by multiple vector species, resulting in an increased risk of transmission, potentially at larger spatial scales compared to any single vector species. Additionally, the different abilities of patchily distributed vector species to acquire and transmit parasites will lead to varying degrees of transmission risk. Investigation of how vector community composition and parasite transmission change over space due to variation in environmental conditions may help to explain current patterns in diseases but also informs our understanding of how patterns will change under climate and land-use change. We developed a novel statistical approach using a multi-year, spatially extensive case study involving a vector-borne virus affecting white-tailed deer transmitted by Culicoides midges. We characterized the structure of vector communities, established the ecological gradient controlling change in structure, and related the ecology and structure to the amount of disease reporting observed in host populations. We found that vector species largely occur and replace each other as groups, rather than individual species. Moreover, community structure is primarily controlled by temperature ranges, with certain communities being consistently associated with high levels of disease reporting. These communities are essentially composed of species previously undocumented as potential vectors, whereas communities containing putative vector species were largely associated with low levels, or even absence, of disease reporting. We contend that the application of metacommunity ecology to vector-borne infectious disease ecology can greatly aid the identification of transmission hotspots and an understanding of the ecological drivers of parasite transmission risk both now and in the future.

A Network Analysis of the Relationship Between Mental and Physical Health in Unsheltered Homeless Persons in Los Angeles County

Homelessness is a public health crisis both nationally, in the United States, and internationally. Nevertheless, due to the hidden vulnerabilities of persons who are without shelter, little is known about their experiences during periods of homelessness. The present research adopts a network approach that conceptualizes how the major risk factors of homelessness interact, namely substance abuse problems, poor mental health, disability, and exposure to physical or sexual violence by an intimate partner. Our analysis draws on a large demographic survey of over 5,000 unsheltered homeless persons conducted in 2017 by the Los Angeles Homeless Services Authority. We estimated a network structure for 12 survey items tapping individual risk using the graphical least absolute shrinkage and selection operator algorithm. We then examined network centrality metrics and implemented a community detection algorithm to detect communities in the network. Our results indicated that mental illness and intimate partner violence (IPV) are central measures that connect all other mental and physical health variables together and that post-traumatic stress disorder and IPV are both highly affected by changes in any part of the network and, in turn, affect changes in other parts of the network. A community detection analysis derived four communities characterized by disability, sexual victimization and health, substance use, and mental health issues. Finally, a directed acyclic graph revealed that drug abuse and physical disability were key drivers of the overall system. We conclude with a discussion of the major implications of our findings and suggest how our results might inform programs aimed at homelessness prevention and intervention.

Association of burnout with depression in pharmacists: A network analysis

Background

Burnout and depression have overlapping symptoms, but the extent of overlap remains unclear, and the complex relationship between burnout and depression in pharmacists is rarely explored.

Methods

We investigated burnout and depression in 1,322 frontline pharmacists, and explored the complex relationship between burnout and depression in those pharmacists using network analysis.

Results

Network analysis showed that there were 5 communities. A partial overlap was found between burnout and depressive symptoms in pharmacists. The nodes MBI-6 (I have become more callous toward work since I took this job), D18 (My life is meaningless), and D10 (I get tired for no reason) had the highest expected influence value. D1 (I feel down-hearted and blue) and D14 (I have no hope for the future) were bridge symptoms connected with emotional exhaustion and reduced professional efficacy, respectively.

Conclusion

A partial overlap exists between burnout and depressive symptoms in pharmacists, mainly in the connection between the emotional exhaustion and reduced professional efficacy and the depressive symptoms. Potential core targets identified in this study may inform future prevention and intervention.

Childhood anxious/withdrawn behaviour and later anxiety disorder: a network outcome analysis of a population cohort

BACKGROUND

Several previous studies have identified a continuity between childhood anxiety/withdrawal and anxiety disorder (AD) in later life. However, not all children with anxiety/withdrawal problems will experience an AD in later life. Previous studies have shown that the severity of childhood anxiety/withdrawal accounts for some of the variability in AD outcomes. However, no studies to date have investigated how variation in features of anxiety/withdrawal may relate to continuity prognoses. The present research addresses this gap.

METHODS

Data were gathered as part of the Christchurch Health and Development Study, a 40-year population birth cohort of 1265 children born in Christchurch, New Zealand. Fifteen childhood anxiety/withdrawal items were measured at 7-9 years and AD outcomes were measured at various interviews from 15 to 40 years. Six network models were estimated. Two models estimated the network structure of childhood anxiety/withdrawal items independently for males and females. Four models estimated childhood anxiety/withdrawal items predicting adolescent AD (14-21 years) and adult AD (21-40 years) in both males and females.

RESULTS

Approximately 40% of participants met the diagnostic criteria for an AD during both the adolescent (14-21 years) and adult (21-40 years) outcome periods. Outcome networks showed that items measuring social and emotional anxious/withdrawn behaviours most frequently predicted AD outcomes. Items measuring situation-based fears and authority figure-specific anxious/withdrawn behaviour did not consistently predict AD outcomes. This applied across both the male and female subsamples.

CONCLUSIONS

Social and emotional anxious/withdrawn behaviours in middle childhood appear to carry increased risk for AD outcomes in both adolescence and adulthood.

Network analysis of depression in prostate cancer patients: Implications for assessment and treatment

OBJECTIVES

Many prostate cancer patients also suffer from depression, which can decrease their life satisfaction and also impede recovery from their cancer. This study described the network structure of depressive symptomatology in prostate cancer patients, with a view to providing suggestions for clinical interventions for depressed patients.

METHODS

Using a cross-sectional design, 555 prostate cancer patients completed the Patient Health Questionnaire-9 (PHQ-9).

RESULTS

Network analysis and multidimensional scaling indicated that anhedonia was the most central symptom for these men, and that several sets of depression symptoms were closely associated with each other. These included anhedonia-depressed mood; sleeping problems-fatigue/lethargy; and suicidal ideation-low self-worth-depressed mood. Other depression symptoms such as appetite problems, concentration problems, and motor problems, were less well-related with the remainder of the network. Patients receiving treatment for reocurring prostate cancer (PCa) had significantly higher PHQ9 scores than patients undergoing their initial treatment, but no major differences in their network structures. Implications for clinical practice were derived from the relationships between individual depression symptoms and the overall depression network by examining node predictability.

CONCLUSIONS

The use of total depression scores on an inventory does not reflect the underlying network structure of depression in PCa patients. Identification and treatment of the central symptom of anhedonia in PCa patients suggests the need to adopt specific therapies that are focussed upon this symptom.

Exploring the associations between psychodynamic constructs and psychopathology: A network approach

Psychodynamic therapy effectively reduces symptomatology by focusing on underlying (unconscious) processes instead of symptoms. Nevertheless, the exact interrelationship between psychodynamic constructs and psychopathology remains unclear. This study uses network analysis to explore these associations. We computed a cross-sectional partial correlation network between psychodynamic constructs (i.e., personality functioning, interpersonal relations, and active and passive modes of intrapsychic conflicts according to the Operationalized Psychodynamic Diagnostics [OPD] system) and psychopathology (i.e., depression and somatization) in a naturalistic sample of 341 adults registering for psychodynamic outpatient therapy. We estimated node centrality, node predictability, and bridge symptoms and used community detection analysis. Bootstrap methods were applied to assess network stability. Psychodynamic constructs and psychopathology resulted in separate but connected clusters. Personality functioning emerged as the most influential node in the network and was bridging the clusters. The network was found to be highly stable, allowing reliable interpretations. The results offer important insights on how psychodynamic constructs relate to psychopathology, which can be used to inform treatment approaches. The findings suggest that personality functioning may be an important intervention target. However, future research is needed to include a broader range of diagnoses. In addition, longitudinal studies may clarify the direction of causality.

Network-based validation of the psychometric questionnaire EDI-3 for the assessment of eating disorders

Assessing the validity of a psychometric test is fundamental to ensure a reliable interpretation of its outcomes. Few attempts have been made recently to complement classical approaches (e.g., factor models) with a novel technique based on network analysis. The objective of the current study is to carry out a network-based validation of the Eating Disorder Inventory 3 (EDI-3), a questionnaire designed for the assessment of eating disorders. Exploiting a reliable, open source sample of 1206 patients diagnosed with an eating disorder, we set up a robust validation process encompassing detection and handling of redundant EDI-3 items, estimation of the cross-sample psychometric network, resampling bootstrap procedure and computation of the median network of the replica samples. We then employed a community detection algorithm to identify the topological clusters, evaluated their coherence with the EDI-3 subscales and replicated the full validation analysis on the subpopulations corresponding to patients diagnosed with either anorexia nervosa or bulimia nervosa. Results of the network-based analysis, and particularly the topological community structures, provided support for almost all the composite scores of the EDI-3 and for 2 single subscales: Bulimia and Maturity Fear. A moderate instability of some dimensions led to the identification of a few multidimensional items that should be better located in the intersection of multiple psychological scales. We also found that, besides symptoms typically attributed to eating disorders, such as drive for thinness, also non-specific symptoms like low self-esteem and interoceptive deficits play a central role in both the cross-sample and the diagnosis-specific networks. Our work adds insights into the complex and multidimensional structure of EDI-3 by providing support to its network-based validity on both mixed and diagnosis-specific samples. Moreover, we replicated previous results that reinforce the transdiagnostic theory of eating disorders.

Co-occurrence of Common Biological and Behavioral Addictions: Using Network Analysis to Identify Central Addictions and Their Associations with Each Other

The present study used network analysis to examine the network properties (network graph, centrality, and edge weights) comprising ten different types of common addictions (alcohol, cigarette smoking, drug, sex, social media, shopping, exercise, gambling, internet gaming, and internet use) controlling for age and gender effects. Participants (N = 968; males = 64.3%) were adults from the general community, with ages ranging from 18 to 64 years (mean = 29.54 years; SD = 9.36 years). All the participants completed well-standardized questionnaires that together covered the ten addictions. The network findings showed different clusters for substance use and behavioral addictions and exercise. In relation to centrality, the highest value was for internet usage, followed by gaming and then gambling addiction. Concerning edge weights, there was a large effect size association between internet gaming and internet usage; a medium effect size association between internet usage and social media and alcohol and drugs; and several small and negligible effect size associations. Also, only 48.88% of potential edges or associations between addictions were significant. Taken together, these findings must be prioritized in theoretical models of addictions and when planning treatment of co-occurring addictions. Relatedly, as this study is the first to use network analysis to explore the properties of co-occurring addictions, the findings can be considered as providing new contributions to our understanding of the co-occurrence of common addictions.