Pixels and perception: Mapping the association between digital media and psychotic-like experiences in adolescents.

INTRODUCTION: Psychotic-like experiences (PLEs) during adolescence can lead to psychotic disorders. Digital media usage has been suggested to link to PLEs, but research is limited on how different types of screen exposure may differentially relate to PLEs over time. This study aimed to examine longitudinal associations between screen usage patterns and PLEs in adolescents. METHODS: Participants comprised 11,876 adolescents assessed annually from ages 9-12 years as part of the Adolescent Brain Cognitive Development study (ABCD). Screen usage (TV, video games, online video, social media, texting, video chat) and PLEs were assessed via self-report. Longitudinal network analysis models were estimated to examine connections between screen usage types and PLEs across three time points. RESULTS: Two clusters were formed, including digital media for socializing (e.g., social media/texting/video chat) and digital media for entertainment (e.g., online video/video games/TV). Texting and online video(s) had the highest centrality at each time point, suggesting importance in the network. PLE symptoms of hallucinations and concentration difficulties exhibited higher centrality than other symptoms. Online video and TV were influential bridges between screen usage and PLEs. Network structure significantly differed between ages 9-10 and 10-12 years, but global strength was unchanged over time. DISCUSSION: Results highlight the importance of understanding the associations between specific screen usage types and PLE symptoms. Texting and online video usage appear most influential in the development of adolescent PLEs over time. Findings can inform targeted interventions to promote healthy screen habits and reduce PLEs in at-risk youth.

Interactive changes in depression and loneliness symptoms prior to and during the COVID-19 pandemic: A longitudinal network analysis.

OBJECTIVES: Depression and loneliness co-occur frequently. This study examined interactive changes between depression and loneliness among older adults prior to and during the COVID-19 pandemic from a longitudinal network perspective. METHODS: This network study was based on data from three waves (2016-2017, 2018-2019, and 2020) of the English Longitudinal Study of Ageing (ELSA). Depression and loneliness were measured with the eight-item version of the Center for Epidemiologic Studies Depression Scale (CESD-8) and three item version of the University of California Los Angeles (UCLA) Loneliness Scale, respectively. A network model was constructed using an Ising Model while network differences were assessed using a Network Comparison Test. Central symptoms were identified via Expected Influence (EI). RESULTS: A total of 4,293 older adults were included in this study. The prevalence and network of depression and loneliness did not change significantly between the baseline and pre-pandemic assessments but increased significantly from the pre-pandemic assessment to during COVID-19 assessment. The central symptom with the strongest increase from pre-pandemic to pandemic assessments was "Inability to get going" (CESD8) and the edge with the highest increase across depression-loneliness symptom communities was "Lack companionship" (UCLA1) - "Inability to get going" (CESD8). Finally, "Feeling depressed" (CESD1) and "Everything was an effort" (CESD2) were the most central symptoms over the three assessment periods. CONCLUSIONS: The COVID-19 pandemic was associated with significant changes in the depression-loneliness network model. The most changed symptoms and edges could be treatment targets for reducing the risk of depression and loneliness in older adults.

Conceptualising social media addiction: a longitudinal network analysis of social media addiction symptoms and their relationships with psychological distress in a community sample of adults.

BACKGROUND: Problematic social media use has been identified as negatively impacting psychological and everyday functioning and has been identified as a possible behavioural addiction (social media addiction; SMA). Whether SMA can be classified as a distinct behavioural addiction has been debated within the literature, with some regarding SMA as a premature pathologisation of ordinary social media use behaviour and suggesting there is little evidence for its use as a category of clinical concern. This study aimed to understand the relationship between proposed symptoms of SMA and psychological distress and examine these over time in a longitudinal network analysis, in order better understand whether SMA warrants classification as a unique pathology unique from general distress. METHOD: N = 462 adults (Mage = 30.8, SDage = 9.23, 69.3% males, 29% females, 1.9% other sex or gender) completed measures of social media addiction (Bergen Social Media Addiction Scale), and psychological distress (DASS-21) at two time points, twelve months apart. Data were analysed using network analysis (NA) to explore SMA symptoms and psychological distress. Specifically, NA allows to assess the 'influence' and pathways of influence of each symptom in the network both cross-sectionally at each time point, as well as over time. RESULTS: SMA symptoms were found to be stable cross-sectionally over time, and were associated with, yet distinct, from, depression, anxiety and stress. The most central symptoms within the network were tolerance and mood-modification in terms of expected influence and closeness respectively. Depression symptoms appeared to have less of a formative effect on SMA symptoms than anxiety and stress. CONCLUSIONS: Our findings support the conceptualisation of SMA as a distinct construct occurring based on an underpinning network cluster of behaviours and a distinct association between SMA symptoms and distress. Further replications of these findings, however, are needed to strengthen the evidence for SMA as a unique behavioural addiction.

Fostering interdisciplinary collaboration: A longitudinal social network analysis of the NIH mHealth Training Institutes.

BACKGROUND/OBJECTIVE: Growing recognition that collaboration among scientists from diverse disciplines fosters the emergence of solutions to complex scientific problems has spurred initiatives to train researchers to collaborate in interdisciplinary teams. Evaluations of collaboration patterns in these initiatives have tended to be cross-sectional, rather than clarifying temporal changes in collaborative dynamics. Mobile health (mHealth), the science of using mobile, wireless devices to improve health outcomes, is a field whose advancement needs interdisciplinary collaboration. The NIH-supported annual mHealth Training Institute (mHTI) was developed to meet that need and provides a unique testbed. METHODS: In this study, we applied a longitudinal social network analysis technique to evaluate how well the program fostered communication among the disciplinarily diverse scholars participating in the 2017-2019 mHTIs. By applying separable temporal exponential random graph models, we investigated the formation and persistence of project-based and fun conversations during the mHTIs. RESULTS: We found that conversations between scholars of different disciplines were just as likely as conversations within disciplines to form or persist in the 2018 and 2019 mHTI, suggesting that the mHTI achieved its goal of fostering interdisciplinary conversations and could be a model for other team science initiatives; this finding is also true for scholars from different career stages. The presence of team and gender homophily effects in certain years suggested that scholars tended to communicate within the same team or gender. CONCLUSION: Our results demonstrate the usefulness of longitudinal network models in evaluating team science initiatives while clarifying the processes driving interdisciplinary communications during the mHTIs.

Distinct trajectories of depression symptoms in early and middle adolescence: Preliminary evidence from longitudinal network analysis.

Adolescent depression is a clinically relevant concern that has major implications for mental and physical health. The trajectory of depressive symptoms among adolescents suggests that there is likely variability throughout this developmental period. The aim of the study was to assess the longitudinal relationship between individual symptoms of depression among early and middle adolescents to provide guidance for future research investigating targeted intervention efforts. Data were extracted from electronic medical records (2015-2017) from a pediatric primary care clinic in an urban setting. Cross-Lagged Panel Network analysis was used to evaluate symptoms of depression measured with the Patient Health Questionnaire (PHQ-9) measured twice over a 1-year period among early adolescents (ages 11-13 years; n = 309) and middle adolescents (ages 14-16 years; n = 255). The sample was predominantly Hispanic (90%) and 56% female. The analyses highlighted key differences and similarities between early and middle adolescence, largely focused on the role of suicidal ideation and tightly linked with feelings of failure and appetitive disturbance. In early adolescence suicidal ideation was highly likely to lead to other symptoms. In middle adolescence, however, suicidal ideation no longer had connections to other symptoms and instead the strongest connections were toward suicidal ideation. Interestingly, across both early and middle adolescence feelings of failure and appetitive disturbance were highly likely to lead to suicidal ideation. These exploratory findings highlight several longitudinal associations between early and middle adolescence that provide insight into differences and similarities regarding how symptoms might progress within those developmental periods. Taken together these results can provide direction for future research to evaluate brief, targeted interventions for adolescents.

CHARACTERIZING THE PROPAGATION PATTERN OF NEURODEGENERATION IN ALZHEIMER'S DISEASE BY LONGITUDINAL NETWORK ANALYSIS.

Converging evidence shows that Alzheimer's disease (AD) is a neurodegenerative disease that represents a disconnection syndrome, whereby a large-scale brain network is progressively disrupted by one or more neuropathological processes. However, the mechanism by which pathological entities spread across a brain network is largely unknown. Since pathological burden may propagate trans-neuronally, we propose to characterize the propagation pattern of neuropathological events spreading across relevant brain networks that are regulated by the organization of the network. Specifically, we present a novel mixed-effect model to quantify the relationship between longitudinal network alterations and neuropathological events observed at specific brain regions, whereby the topological distance to hub nodes, high-risk AD genetics, and environmental factors (such as education) are considered as predictor variables. Similar to many cross-section studies, we find that AD-related neuropathology preferentially affects hub nodes. Furthermore, our statistical model provides strong evidence that abnormal neuropathological burden diffuses from hub nodes to non-hub nodes in a prion-like manner, whereby the propagation pattern follows the intrinsic organization of the large-scale brain network.

School suspension and social selection: Labeling, network change, and adolescent, academic achievement.

The growing body of research detailing the pronounced effects of criminal stigma on inequality in the US underscores the importance of labeling theory. In spite of the renewed interest in labeling, little research has evaluated the theoretical mechanisms underlying the theory. Drawing on the labeling perspective, this article evaluates mechanisms underlying the relationship between school punishment and reductions in adolescent academic achievement. It uses recent innovations in longitudinal network analysis to examine the consequences of school punishment as a dynamic interplay of labeling, network selection, and group influence. Results indicate that school punishment facilitates selection into academically underperforming peer networks and that this change in network composition is largely responsible for the association between school punishment and reductions in adolescent academic achievement.

A Paradigm for Longitudinal Complex Network Analysis over Patient Cohorts in Neuroscience.

The study of complex brain networks, where structural or functional connections are evaluated to create an interconnected representation of the brain, has grown tremendously over the past decade. Much of the statistical network science tools for analyzing brain networks have been developed for cross-sectional studies and for the analysis of static networks. However, with both an increase in longitudinal study designs, as well as an increased interest in the neurological network changes that occur during the progression of a disease, sophisticated methods for longitudinal brain network analysis are needed. We propose a paradigm for longitudinal brain network analysis over patient cohorts, with the key challenge being the adaptation of Stochastic Actor-Oriented Models (SAOMs) to the neuroscience setting. SAOMs are designed to capture network dynamics representing a variety of influences on network change in a continuous-time Markov chain framework. Network dynamics are characterized through both endogenous (i.e., network related) and exogenous effects, where the latter include mechanisms conjectured in the literature. We outline an application to the resting-state fMRI setting with data from the Alzheimers Disease Neuroimaging Initiative (ADNI) study. We draw illustrative conclusions at the subject level and make a comparison between elderly controls and individuals with AD.