The mechanisms for maintenance of COVID Stress Syndrome symptom networks: a dynamic network analysis

BackgroundHealth anxiety among adolescents is understudied yet concerning. Health-related internet use (HRIU) is a common coping strategy, but disconcerting content may heighten, rather than mitigate, the health anxiety. While research has been conducted, within-person evidence for long-term fluctuations is lacking. Furthermore, adolescents with different base-level (initial) health anxiety may react differently to health-related content, making the effect dependent on health anxiety level compared to others.ObjectiveThis study focused on the longitudinal relationship between HRIU and health anxiety on the within-person level in adolescents. We also considered their initial health anxiety, comparing adolescents with low, medium, and high base-level health anxiety.MethodsWe analyzed data from 2500 Czech adolescents, aged 11-16 years (mean 13.43, SD 1.69 years; 1250/2500, 50% girls in Wave 1). Health anxiety was measured by the affective subscale of the Multidimensional Inventory of Hypochondriacal Traits, and HRIU was captured by 6 items reflecting health-related behaviors (eg, reading articles or watching videos with health-related content). The level of health anxiety in Wave 1 also served as a grouping factor. The data were collected in 3 waves, 6 months apart from June 2021 to June 2022, and analyzed using a random intercept cross-lagged panel model, chosen for its ability to separate within- and between-person effects.ResultsOn the between-person level, adolescents with higher base-level health anxiety were more frequent health-related internet users (β=.52; P<.001). On the within-person level, change in health anxiety did not predict change in HRIU, or vice versa, for those with high base-level health anxiety. In contrast, for adolescents with medium health anxiety, change in HRIU positively predicted health anxiety with large effects across all waves (β=.16; P=.03 from Wave 1 to Wave 2; β=.18; P=.01 from Wave 2 to Wave 3), and increase in health anxiety affected HRIU from Wave 2 to Wave 3 (β=.15; P=.03). For adolescents with low base-level health anxiety, change in their HRIU had large positive effect on changes in health anxiety from Wave 1 to Wave 2 (β=.17; P<.001), and health anxiety positively affected HRIU with medium effect from Wave 2 to Wave 3 (β=.11; P=.03).ConclusionsAdolescents with lower, rather than high, health anxiety are susceptible to the negative long-term effects of HRIU. For adolescents with high health anxiety, HRIU neither worsens nor relieves health anxiety over time, suggesting that counselors should recommend other coping strategies besides HRIU. Adolescents with medium to low health anxiety should be guided toward mindful HRIU to prevent increased health anxiety after HRIU. This includes fostering eHealth literacy (eg, recognizing personally irrelevant information and awareness of sensationalism in media) and coping mechanisms (eg, time or topic limits, or intentional rather than compulsive searching).

The temporal dynamics of social interactions were shown to influence the spread of disease. Here, we model the conditions of progression and competition for several viral strains, exploring various levels of cross-immunity over temporal networks. We use our interaction-driven contagion model and characterize, using it, several viral variants. Our results, obtained on temporal random networks and on real-world interaction data, demonstrate that temporal dynamics are crucial to determining the competition results. We consider two and three competing pathogens and show the conditions under which a slower pathogen will remain active and create a second wave infecting most of the population. We then show that when the duration of the encounters is considered, the spreading dynamics change significantly. Our results indicate that when considering airborne diseases, it might be crucial to consider the duration of temporal meetings to model the spread of pathogens in a population.

Disease development is viewed as a highly dynamic process, where tipping points happen in many complex diseases with irreversible sudden deteriorations in patients’ medical situations. Developing effective strategies for identifying temporal biomarkers, such as identifying temporal protein interaction networks and detecting tipping points, is significantly meaningful for designing an appropriate intervention to prevent the deteriorations. Existing studies inferred dynamical networks from gene expression profiles, uniquely based on static protein-protein interactions, and identified tipping points by a single outlier analysis of dynamic networks under certain assumptions.In this paper, we proposed to identify tipping points of dynamical protein-protein interaction networks through a thermodynamics-enriched ensemble framework In this framework, we constructed dynamical protein-protein interaction networks (DPPINs) by integrating tissue-based direct protein-protein interactions with multiple temporal or non-temporal sources. Through a thermodynamic analysis of these dynamic networks, we identified an abrupt change of free energy at a tipping point and validated this result by relative entropy of dynamic networks.

Health anxiety, death anxiety and coronaphobia: Predictors of postpartum depression symptomatology during the COVID-19 pandemic

Existing literature has reported negative parental attitudes toward LGBTQ+ individuals associated with their LGBTQ+ identity concealment and mental well-being. However, limited research has explored the dynamic network changes using intensive, repeated weekly diary data. This study aimed to model the associations between perceived parental attitude, anxiety, depression, and LGBTQ+ individuals' identity concealment within dynamic network analysis (DNA); 103 LGBTQ+ youth participated in the study. Participants' perceived parental attitudes toward LGBTQ+ identity and LGBTQ+ identity concealment, depression (by the 9-item Patient Health Questionnaire), and anxiety (by the 7-item Generalized Anxiety Disorder Questionnaire) were measured. Each was assessed four times at 1-week intervals for four consecutive weeks. The graphical vector autoregression explored the DNA of the internal relationships among perceived parental attitudes, identity concealment, depression, and anxiety. Findings in the between-subjects network revealed that poor perceived parental attitudes toward sexual and gender minorities were positively associated with depression, anxiety, and identity concealment. The contemporaneous network showed that the "expression" (one's identity concealment) was the direct trigger of "suicide" (one's depressive symptom), indicating depression was initiated earlier and subsequently exacerbated a sequence of other psychiatric reactions. The temporal network indicated that only parents' "general attitude" reduced participants' concealment ("self-disclosure"), which simultaneously stimulated mental benefits.

Background: Analysis of dynamic (temporal) networks allows an identification of important targets of treatment. Objective: This study examined the dynamic network of symptoms in patients diagnosed with post-traumatic stress disorder (PTSD) during exposure therapy. Method: Patients (n = 65) were randomized to either standard prolonged exposure, which includes imaginal exposure to the traumatic memory, or modified prolonged exposure, where imagery re-scripting of the memory replaced imaginal exposure, in a 10-week residential program. They completed a measure of DSM-IV PTSD symptoms weekly. The multilevel vector autoregressive (mlVAR) model was used to analyse the data, producing a temporal (dynamic), contemporaneous, and between-person network. Results: Physiological reactivity to reminders in a given week was positively related to distress reactivity and to flashbacks the subsequent week. Hypervigilance one week was positively related to startle response and external avoidance the subsequent week. In addition, sleep problems were positively predicted by previous week internal avoidance and negatively predicted by previous week flashbacks. Hypervigilance and physiological reactivation had the highest out-strength, indicating that they were the most predictive of other symptoms. Conclusions: The present within-person results make a preliminaryrect basis for the recommendation to monitor and facilitate change in physiological reactivation and hypervigilance in the treatment of PTSD. Future studies should examine the replicability of our temporal PTSD network and also include causal variables beyond symptoms.

Although the electrophysiological event-related potential in face processing (e.g. N170) is widely accepted as a face-sensitivity biomarker that is deficient in children with autism spectrum disorders, the time-varying brain networks during face recognition are still awaiting further investigation. To explore the social deficits in autism spectrum disorder, especially the time-varying brain networks during face recognition, the current study analyzed the N170, cortical activity, and time-varying networks under 3 tasks (face-upright, face-inverted, and house-upright) in autism spectrum disorder and typically developing children. The results revealed a smaller N170 amplitude in autism spectrum disorder compared with typically developing, along with decreased cortical activity mainly in occipitotemporal areas. Concerning the time-varying networks, the atypically stronger information flow and brain network connections across frontal, parietal, and temporal regions in autism spectrum disorder were reported, which reveals greater effort was exerted by autism spectrum disorder to obtain comparable performance to the typically developing children, although the amplitude of N170 was still smaller than that of the typically developing children. Different brain activation states and interaction patterns of brain regions during face processing were discovered between autism spectrum disorder and typically developing. These findings shed light on the face-processing mechanisms in children with autism spectrum disorder and provide new insight for understanding the social dysfunction of autism spectrum disorder.

This study examines the relationship between public debt and inflation rates in Kenya from 2011 to 2021 using the Vector Autoregressive (VAR) model. Despite the models likeAutoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) gaining popularity in time series analysis, the Vector Autoregressive model, being multivariate, is relevant in analyzing two or more time series variables simultaneously, benefiting from the bi-directional causality and providing a better outlook into the flow of the dynamic interaction between inflation and public debt. The main objectives are modelling the Vector Autoregressive model and forecasting future trends to provide insights for policymakers. Additionally, the methodological approach comprises descriptive statistics, stationarity tests, normality tests, and the Vector Autoregressive model. Descriptive statistics reveal significant variations, with public debt increasing from 1.35 trillion KES to a peak of 8.2 trillion KES, and inflation rates ranging from 3.2% to 19.72% for the period from 2011 to 2021. The Augmented Dickey-Fuller (ADF) test confirmed that both time series were stationary at their levels. The Vector Autoregressive model, chosen for its ability to analyze dynamic interactions, indicated a significant relationship between the variables, with inflation showing strong self-persistence (coefficient of 0.8731, &amp;lt;i&amp;gt;p&amp;lt;/i&amp;gt; &amp;lt; 2 × 10&amp;lt;sup&amp;gt;−16&amp;lt;/sup&amp;gt;), though public debt did not significantly impact inflation in the model (&amp;lt;i&amp;gt;p&amp;lt;/i&amp;gt; = 0.5592). The models R-squared values, 95.82% for public debt and 84.74% for inflation, highlight its strong explanatory power. Moreover, findings indicate that while public debt does not directly affect inflation within the model lag structure, inflation exhibits a strong self-persistence. The model R-squared values are 95.82% for public debt and 84.74% for inflation, demonstrating high explanatory power. Recommendations include the implementation of a robust debt management strategy, emphasizing sustainable borrowing and enhancing revenue generation to mitigate inflationary pressures. Further research is recommended to explore the broader macroeconomic impacts of public debt on economic growth and employment in Kenya.

Psychological Treatment of Health Anxiety and Hypochondriasis: A Biopsychosocial Approach

The question of how to best conceptualize health anxiety (HA) from a diagnostic and etiological perspective remains debated. The aim was to examine the relationship between HA and the symptoms of anxiety and obsessive-compulsive-related disorders in a normative twin population. Four hundred and ninety-six monozygotic adult twin pairs from the Australian Twin Registry participated in the study (age, 34.4 ± 7.72 years; 59% females). Validated scales were used to assess each domain. We applied a twin regression methodology-ICE FALCON-to determine whether there was evidence consistent with 'causal' relationships between HA and other symptoms by fitting and comparing model estimates. Estimates were consistent with higher levels of obsessing ('unwanted thoughts') (P = 0.008), social anxiety (P = 0.03), and body dysmorphic symptoms (P = 0.008) causing higher levels of HA symptoms, and with higher levels of HA symptoms causing higher levels of physical/somatic anxiety symptoms (P = 0.001). Obsessional thoughts, body dysmorphic concerns, and social anxiety symptoms may have a causal influence on HA. To report physical/somatic anxiety appears to be a consequence of the underlying presence of HA-related fears. Should our results be confirmed by longitudinal studies, the evaluation and treatment of HA may benefit from the consideration of these identified risk factors.

The covid-19 pandemic has influenced children and parents worldwide. The pandemic has also been suggested to especially affect and exacerbate health anxiety (HA) symptoms in children and adolescents. However, there is limited understanding of the potential mechanisms challenges of families where parents themselves experience mental health issues such as high degree of HA symptoms. The aim of this study was to explore parental experiences of pandemic life in families with continuously high levels of HA symptoms during the covid-19 pandemic. Six parents, identified with high levels of HA symptoms, participated in qualitative individual semi-structured interviews. Interviews were analysed according to Interpretative Phenomenological Analysis principles. Three main themes emerged. Theme 1) "Anxious children in a pandemic world" explores how pandemic - independent child factors including anxious temperament may have influenced the child pandemic experience. Theme 2) "Parental influences on child anxiety" describes parental reflections on their possible influence on child anxious thoughts. Theme 3) "Living with pandemic guidelines and restrictions" demonstrates the varying parental experiences of interventions and how these may affect HA thoughts. Parents who themselves experience HA symptoms see their children, who also experience HA symptoms, to be particularly susceptible and vulnerable to both content and rhetoric of pandemic information. These children may however, experience school lockdown to be anxiety relieving. Parents who themselves have illness-related fears may not see themselves as perpetuating for their child's anxious thoughts.

ObjectiveNegative affect, alexithymia, and other predisposing traits (such as health anxiety) can influence an individual's symptom perception. In this study, we used the affective picture paradigm (APP, Bogaerts et al., 2010) to induce symptoms using affective picture stimuli. We aimed to cross-sectionally test the effect of high vs low-frequency symptom queries and analyze the time course of the APP, including interactions with health anxiety and somatic symptom distress. MethodsParticipants (N = 124) completed a modified APP and filled out various questionnaires. In the APP, participants were randomized to either a highly-frequent-query condition (18 symptom checklists) or a less-frequent-query condition (6 checklists). Data were analyzed using ANOVAs, cross-lagged panel models, moderation models, and multilevel models. ResultsBoth groups had comparable symptom baseline values, but people in the highly frequent as opposed to less frequent condition reported significantly higher symptom levels once the experiment started (F (1,120) = 14.319, p < .001, η2=.107). Symptom levels stayed stable over the course of the experiment and were best predicted by symptom levels at earlier timepoints in the experiment (β = 0.43 and β = 0.68, both p < .001). Health anxiety levels significantly predicted symptom levels (F(1,121) = 10.054, p = .002, η2=.077) and moderated the relation between condition and symptom levels (F(2,121) = 16.253, p < .001, η2=.212). ConclusionIn terms of the predictive processing model (e.g.,[1]), repeated symptom queries following negative affective cues may activate prior beliefs about symptoms, resulting in elevated levels of symptom reports in interaction with health anxiety.

Functional near-infrared spectroscopy (fNIRS) offers a safe and portable signal source for brain–computer interface (BCI) applications, particularly in motor imagery (MI) decoding. However, its low sampling rate and hemodynamic delay pose challenges for temporal modeling and dynamic brain network analysis. To address these limitations in temporal dynamics, static graph modeling, and feature fusion interpretability, we propose TopoTempNet, an innovative topology-enhanced temporal network for biomedical signal decoding. TopoTempNet integrates multi-level graph features with temporal modeling through three key innovations: (1) multi-level topological feature construction using local and global functional connectivity metrics (e.g., connection strength, density, global efficiency); (2) a graph-modulated attention mechanism combining Transformer and Bi-LSTM to dynamically model key connections; and (3) a multimodal fusion strategy uniting raw signals, graph structures, and temporal representations into a high-dimensional discriminative space. Evaluated on three public fNIRS datasets (MA, WG, UFFT), TopoTempNet achieves superior accuracy (up to 90.04% ± 3.53%) and Kappa scores compared to state-of-the-art models. The ROC curves and t-SNE visualizations confirm its excellent feature discrimination and structural clarity. Furthermore, the statistical analysis of graph features reveals the model’s ability to capture task-specific functional connectivity patterns, enhancing the interpretability of decoding outcomes. TopoTempNet provides a novel pathway for building interpretable and high-performance BCI systems based on fNIRS.

BackgroundPrevious studies have revealed that deviant peer affiliation (DPA) and internet gaming disorder (IGD) are highly related and often co-occur. Nevertheless, the dynamic interactions among these symptoms, their bidirectional effects, and the underlying mechanisms contributing to their persistence remain poorly understood. Most existing research relies on cross-sectional designs or examines aggregated scores, limiting the ability to detect time-dependent symptom interactions. In particular, prior studies overlook the complex bidirectional relationships between specific IGD and DPA symptoms over time. As adolescence is a developmental period marked by rapid changes in peer relationships and behavioral regulation, distinguishing temporal associations between specific DPA and IGD symptoms at both the within- and between-person levels is important for clarifying mechanisms of their co-occurrence and informing personalized intervention strategies.ObjectiveThis study aims to examine the temporal and directional relationships between DPA and IGD at the symptom level and differentiate within-person dynamics from between-person differences in their co-occurrence. By focusing on specific symptom-level interactions, this study aims to unravel whether changes in one behavior drive changes in the other over time and to clarify whether their relationship is more influenced by individual changes or stable characteristics.MethodsData were drawn from a prospective cohort study including a representative sample of 3296 adolescents. Cross-lagged panel networks analysis was used to explore the directional and temporal associations between individual symptoms of DPA and IGD across time, disentangling within-person dynamics from between-person differences as well as identifying the most influential symptoms that drive their interactions over time.ResultsAt the within-person level, several IGD symptoms exhibited predictive effects on subsequent DPA symptoms. Specifically, jeopardized school or work performance due to gaming (IGD10) predicted deviant peer behaviors, including friends’ smoking (DPA1; β=.403) and friends’ stealing (DPA4; β=.393). In addition, losing significant relationships due to gaming (IGD9) strongly predicted friends’ misbehavior (DPA6; β=.475). However, no significant between-person associations were observed, suggesting that the co-occurrence of IGD and DPA is not primarily explained by stable trait-level differences, but rather by dynamic, time-varying processes occurring within individuals.ConclusionsOur findings provided valuable insights into the complex relationships between DPA and IGD symptoms, highlighting that the IGD-DPA relationship is primarily driven by within-person dynamic changes rather than stable trait-level differences. This suggests that interventions aimed at reducing IGD risk should focus on addressing the dynamic, time-dependent interactions between gaming behaviors and peer relationships. Prevention programs should focus on healthy peer relationships and adaptive coping strategies to reduce IGD risk and its social consequences. Targeted interventions could disrupt the cycle of maladaptive behavior by focusing on improving both peer and social functioning during critical developmental periods, ultimately preventing the exacerbation of these behavioral issues over time.

Symptom trajectories in infancy for the prediction of subsequent wheeze and asthma in the BILD and PASTURE cohorts: a dynamic network analysis