Network analysis is an increasingly popular approach to study mental disorders in all their complexity. Multiple methods have been developed to extract networks from cross-sectional data, with these data being either continuous or binary. However, when it comes to time series data, most efforts have focused on continuous data. We therefore propose ConNEcT, a network approach for binary symptom data across time. ConNEcT allows to visualize and study the prevalence of different symptoms as well as their co-occurrence, measured by means of a contingency measure in one single network picture. ConNEcT can be complemented with a significance test that accounts for the serial dependence in the data. To illustrate the usefulness of ConNEcT, we re-analyze data from a study in which patients diagnosed with major depressive disorder weekly reported the absence or presence of eight depression symptoms. We first extract ConNEcTs for all patients that provided data during at least 104 weeks, revealing strong inter-individual differences in which symptom pairs co-occur significantly. Second, to gain insight into these differences, we apply Hierarchical Classes Analysis on the co-occurrence patterns of all patients, showing that they can be grouped into meaningful clusters. Core depression symptoms (i.e., depressed mood and/or diminished interest), cognitive problems and loss of energy seem to co-occur universally, but preoccupation with death, psychomotor problems or eating problems only co-occur with other symptoms for specific patient subgroups.

In many areas of psychology, correlation-based network approaches (i.e., psychometric networks) have become a popular tool. In this paper, we propose an approach that recursively splits the sample based on covariates in order to detect significant differences in the structure of the covariance or correlation matrix. Psychometric networks or other correlation-based models (e.g., factor models) can be subsequently estimated from the resultant splits. We adapt model-based recursive partitioning and conditional inference tree approaches for finding covariate splits in a recursive manner. The empirical power of these approaches is studied in several simulation conditions. Examples are given using real-life data from personality and clinical research.

How social networks influence human behavior has been an interesting topic in applied research. Existing methods often utilized scale-level behavioral data (e.g., total number of positive responses) to estimate the influence of a social network on human behavior. This study proposes a novel approach to studying social influence that utilizes item-level behavioral measures. Under the latent space modeling framework, we integrate the two latent spaces for respondents’ social network data and item-level behavior measures into a single space we call ‘interaction map’. The interaction map visualizes the association between the latent homophily among respondents and their item-level behaviors, revealing differential social influence effects across item-level behaviors. We also measure overall social influence by assessing the impact of the interaction map. We evaluate the properties of the proposed approach via extensive simulation studies and demonstrate the proposed approach with a real data in the context of studying how students’ friendship network influences their participation in school activities.

This commentary reflects on the articles included in the Psychometrika Special Issue on Network Psychometrics in Action. The contributions to the special issue are related to several possible future paths for research in this area. These include the development of models to analyze and represent interventions, improvement in exploratory and inferential techniques in network psychometrics, the articulation of psychometric theories in addition to psychometric models, and extensions of network modeling to novel data sources. Finally, network psychometrics is part of a larger movement in psychology that revolves around the analysis of human beings as complex systems, and it is timely that psychometricians start extending their rich modeling tradition to improve and extend the analysis of systems in psychology.

We offer an introduction to the five papers that make up this special section. These papers deal with a range of the methodological challenges that face researchers analyzing fMRI data—the spatial, multilevel, and longitudinal nature of the data, the sources of noise, and so on. The papers all provide analyses of data collected by a multi-site consortium, the Function Biomedical Informatics Research Network. Due to the sheer volume of data, univariate procedures are often applied, which leads to a multiple comparisons problem (since the data are necessarily multivariate). The papers in this section include interesting applications, such as a state-space model applied to these data, and conclude with a reflection on basic measurement problems in fMRI. All in all, they provide a good overview of the challenges that fMRI data present to the standard psychometric toolbox, but also to the opportunities they offer for new psychometric modeling.

Objective: Patients with cognitive disorders such as Alzheimer’s disease (AD) and mild cognitive impairment (MCI) frequently exhibit depressive symptoms. Depressive symptoms can be evaluated with various measures and questionnaires. Geriatric Depression Scale (GDS) is a scale that can be used to measure symptoms in geriatric age. Many questionnaires usually sum up symptom scales. However, core symptoms of depression in these patients and connections between these symptoms have not been fully explored yet. Thus, the objectives of this study were: 1) to determine core symptoms of two cognitive disorders, Alzheimer’s disease and mild cognitive impairment; and 2) to investigate the network structure of depressive symptomatology in individuals with cognitive impairment in comparison with those with Alzheimer’s disease.

Methods: This study encompassed 5,354 patients with cognitive impairments such as Alzheimer’s disease [n = 1,889] and mild cognitive impairment [n = 3,464]. The Geriatric Depression Scale, a self-administered questionnaire, was employed to assess depressive symptomatology. Using exploratory graph analysis (EGA), a network analysis was conducted and the network structure was evaluated through regularized partial correlation models. To determine the centrality of depressive symptoms within each cohort, network parameters such as strength, betweenness, and closeness were examined. Additionally, to explore differences in the network structure between Alzheimer’s disease and mild cognitive impairment groups, a network comparison test was performed.

Results: In the analysis of centrality indices, “worthlessness’’ was identified as the most central symptom in the Geriatric Depression Scale among patients with Alzheimer’s disease, whereas “emptiness’’ was found to be the most central symptom in patients with mild cognitive impairment. Despite these differences in central symptoms, the comparative analysis showed no statistical difference in the overall network structure between Alzheimer’s disease and mild cognitive impairment groups.

Conclusion: Findings of this study could contribute to a better understanding of the manifestation of depressive symptoms in patients with cognitive impairment. These results are expected to aid in identifying and prioritizing core symptoms in these patients. Further research should be conducted to explore potential interventions tailored to these core symptoms in patients with Alzheimer’s disease and mild cognitive impairment. Finding out core symptoms in those groups might have clinical importance in that appropriate treatment for neuropsychiatric symptoms in patients with cognitive impairment could help preclude progression tofurtherimpairment.

Anxiety and depression are highly prevalent and comorbid problems in childhood, which deserve greater understanding for effective prevention and treatment. The main aim of the present study was to explore the comorbidity between anxiety and depression symptoms using a novel and valuable approach to study comorbidity, such as network analysis. Specifically, the connectivity between symptoms and possible relevant symptoms was examined through comorbidity estimation and shortest pathway networks, as well as bridge symptoms. This study comprised 281 Spanish-speaking children aged 8–12 years (45.2% girls), whose anxiety and depression symptoms were assessed through specific brief parent-report measures. Analyses revealed that in the comorbidity network, the most central symptoms were related to depression (“No good anymore,” “Could never be as good,” “Hated self,” “Did everything wrong,” “Nobody loved him/her”) or anxiety (“Suddenly feels really scared”). Furthermore, it was found that the most central bridge symptoms, whose activation would play a key role in the activation of other domain symptoms, were anxiety symptoms such as “Trouble going to school” and “Suddenly feels really scared” and depression symptoms, such as “Could never be as good” and “Hated self.” Additionally, the shortest path network suggested the existence of different possible pathways of connection between anxiety and depression symptoms. Overall, these findings help to understand the complexity of the anxiety-depression comorbidity. It suggests the existence of central and bridge symptoms that complement previous studies, which may be potential targets for interventions to prevent and treat childhood anxiety and depression.

Ideal points of MPs in the UK House of Commons (HoC) are characteristically difficult to ascertain due to tight party discipline and strategic voting by opposition members. This research note generates left/right ideal point estimates for 591 British MPs sitting in the HoC as of 22/08/2022, ascertained through their social media followership. Specifically, estimates are derived by conducting correspondence analysis (CA) on MP Twitter (X) follower networks, which are subsequently validated against an expert survey, confirming that these estimates have a high degree of between-party (R2 = 0.93) and within-party (Con: r = 0.84; Lab: r = 0.81) accuracy. The informative value of these estimates is then demonstrated by predicting candidate endorsement in the September 2022 Conservative leadership contest, confirming that an MP's ideal point was a statistically significant predictor of candidate endorsement, with Liz Truss drawing support primarily from the further right of the party.

Judges are not the first political officials that come to mind when one considers the role of social media in modern politics. Following in the wake of some prominent judicial personalities adopting Twitter, however, a growing number of state high court judges have adopted and established more public personas on the platform. Judges use Twitter in substantively different ways than traditional elected officials (Curry and Fix 2019); however, little is understood about how the use of such social media platforms affects broader judicial networks. Recognizing that judges, like typical social media users, may aspire to expand their networks to build and appeal to broader audiences, we contend that active participation in judicial Twitterverse could yield personal and professional advantages. Here, we address a currently unexplored question: To what extent have judges formed a distinctive “judicial network,” on Twitter, and what discernible patterns present in these networks? Leveraging the unique structure of social media, we collect comprehensive network data on judging using Twitter and analyze what institutional and social factors impact greater power within the judicial network. We find that early adoption, electoral concerns, and connective links between judges all impact the strength of the judicial network, highlighting the complex motivations driving judicial Twitter engagement, and the significance of network building in judges’ social media strategies and its potential impact on career advancement.

Internalizing and externalizing problems tend to co-occur beginning in early childhood. However, the dynamic interplay of symptom-level internalizing and externalizing problems that may drive their co-occurrence is poorly understood. Within the frameworks of the Network Approaches to Psychopathology and the Developmental Cascade Perspective, this study used a panel network approach to examine how symptoms of internalizing and externalizing problems are related in early childhood both concurrently and longitudinally and whether the pattern may differ in American (N = 1,202) and Chinese (N = 180) preschoolers. Internalizing and externalizing problems were rated by mothers in two waves. Results from cross-sectional networks showed that the bridge symptoms underlying the co-occurrence of internalizing and externalizing problems were largely consistent in American and Chinese preschoolers (e.g., withdrawal, aggressive behavior, anxiety and depressive moods). Results from cross-lagged panel networks further showed that the co-occurrence was manifested by unidirectional relations from internalizing to subsequent externalizing symptoms in both American and Chinese preschoolers. The findings contribute needed cross-cultural evidence to better understand the co-occurrence of internalizing and externalizing problems and highlight the temporal heterogeneity of the symptom networks of internalizing and externalizing problems in early childhood.

Rates of youth suicidal thoughts and behaviors (STBs) are rising, and younger age at onset increases vulnerability to negative outcomes. However, few studies have investigated STBs in early adolescence (ages 10–13), and accurate prediction of youth STBs remains poor. Network analyses that can examine pairwise associations between many theoretically relevant variables may identify complex pathways of risk for early adolescent STBs. The present study applied longitudinal network analysis to examine interrelations between STBs and several previously identified risk and protective factors. Data came from 9,854 youth in the Adolescent Brain Cognitive Development Study cohort (Mage = 9.90 ± .62 years, 63% white, 53% female at baseline). Youth and their caregivers completed an annual measurement battery between ages 9–10 through 11–12 years. Panel Graphical Vector Autoregressive models evaluated associations between STBs and several mental health symptoms, socioenvironmental factors, life stressors, and substance use. In the contemporaneous and between-subjects networks, direct associations were observed between STBs and internalizing symptoms, substance use, family conflict, lower parental monitoring, and lower school protective factors. Potential indirect pathways of risk for STBs were also observed. Age-specific interventions may benefit from prioritizing internalizing symptoms and early substance use, as well as promoting positive school and family support.

Prior research emphasizes the benefits of university makerspaces, but overall, quantitative metrics to measure how a makerspace is doing have not been available. Drawing on an analogy to metrics used for the health of industrial ecosystems, this article evaluates changes during and after COVID-19 for two makerspaces. The COVID-19 pandemic disturbed normal life worldwide and campuses were closed. When students returned, campus life looked different, and COVID-19-related restrictions changed frequently. This study uses online surveys distributed to two university makerspaces with different restrictions. Building from the analysis of industrial ecosystems, the data were used to create bipartite network models with students and tools as the two interacting actor groups. Modularity, nestedness and connectance metrics, which are frequently used in ecology for mutualistic ecosystems, quantified the changing usage patterns. This unique approach provides quantitative benchmarks to measure and compare makerspaces. The two makerspaces were found to have responded very differently to the disruption, though both saw a decline in overall usage and impact on students and the space’s health and had different recoveries. Network analysis is shown to be a valuable method to evaluate the functionality of makerspaces and identify if and how much they change, potentially serving as indicators of unseen issues.