Explore the vast range of titles available.

[This corrects the article DOI: 10.3389/fmed.2025.1631207.].

To explore and visualize the relationships among multiple symptoms in patients with inflammatory bowel disease (IBD) and present empirical evidence for establishing personalized and precise symptom management strategies. This is a quantitative research study conducted between May 2024 and March 2025 using a correlational research design. A total of 324 individuals diagnosed with IBD and hospitalized in Shanghai completed the Symptom Cluster Scale for Inflammatory Bowel Disease (SCS-IBD). We conducted multiple linear regression analysis to investigate factors related to the severity of overall IBD symptoms. After accounting for covariates, contemporaneous networks were constructed using all 18 symptoms. It was determined that active IBD, years since IBD diagnosis, or those who have not received medication and surgery tend to have more severe IBD symptoms. Although fatigue was the most frequent (74.07%) and severe symptom (2.37 ± 1.161) in IBD, the strength centrality of fatigue was lower than that of weight loss and diarrhea. Weight loss (  = 4.414,  = 5.202) and diarrhea (  = 4.489,  = 5.109) are the core symptoms based on exhibiting the highest strength centrality values in both networks, regardless of whether covariates are included or not. Our findings identified that IBD experienced a heavy symptom burden of a severe nature, with weight loss and diarrhea being core symptoms, regardless of covariate adjustment.

The network approach to psychopathology posits that mental disorders can be conceptualized and studied as causal systems of mutually reinforcing symptoms. This approach, first posited in 2008, has grown substantially over the past decade and is now a full-fledged area of psychiatric research. In this article, we provide an overview and critical analysis of 363 articles produced in the first decade of this research program, with a focus on key theoretical, methodological, and empirical contributions. In addition, we turn our attention to the next decade of the network approach and propose critical avenues for future research in each of these domains. We argue that this program of research will be best served by working toward two overarching aims: (a) the identification of robust empirical phenomena and (b) the development of formal theories that can explain those phenomena. We recommend specific steps forward within this broad framework and argue that these steps are necessary if the network approach is to develop into a progressive program of research capable of producing a cumulative body of knowledge about how specific mental disorders operate as causal systems.

Background Symptom networks can provide empirical evidence for the development of personalized and precise symptom management strategies. However, few studies have explored the symptom networks of multidimensional symptom experiences in cancer survivors. The objectives of this study were to generate symptom networks of multidimensional symptom experiences in cancer survivors and explore the centrality indices and density in these symptom networks Methods Data from 1065 cancer survivors were obtained from the Shanghai CANcer Survivor (SCANS) Report. The MD Anderson Symptom Inventory was used to assess the prevalence and severity of 13 cancer‐related symptoms. We constructed contemporaneous networks with all 13 symptoms after controlling for covariates. Results Distress (rs = 9.18, rc = 0.06), sadness (rs = 9.05, rc = 0.06), and lack of appetite (rs = 9.04, rc = 0.06) had the largest values for strength and closeness. The density of the “less than 5 years” network was significantly different from that of the “5–10 years” and “over 10 years” networks (p < 0.001). We found that while fatigue was the most severe symptom in cancer survivorship, the centrality of fatigue was lower than that of the majority of other symptoms. Conclusion Our study demonstrates the need for the assessment of centrality indices and network density as an essential component of cancer care, especially for survivors with <5 years of survivorship. Future studies are warranted to develop dynamic symptom networks and trajectories of centrality indices in longitudinal data to explore causality among symptoms and markers of interventions. This study use data from 1065 cancer survivors from the Shanghai CANcer Survivor (SCANS) Report. We constructed contemporaneous networks with all 13 symptoms after controlling for covariates. We found that while fatigue was the most severe symptom in cancer survivorship, the centrality of fatigue was lower than that of the majority of other symptoms.

The symptoms of stroke jeopardize patients’ health and increase the burden on society and caregivers. Although the traditional symptom cluster research paradigm can enhance management efficiency, it fails to provide targets for intervention, thereby hindering the development of patient-centered precision medicine. However, the symptom network paradigm, as a novel research approach, addresses the limitations of traditional symptom management by identifying core symptoms and determining intervention targets, thereby enhancing the efficiency and precision of symptom management. This study. aims to explore the symptom network and core symptoms of acute-phase stroke patients. A convenience sample of 505 stroke patients was selected for this study. Symptoms were assessed by the Stroke Symptom Experience Scale.Exploratory factor analysis was utilized to extract symptom clusters, and network analysis was conducted to construct the symptom network and characterize its nodes. In this study, four symptom clusters were extracted through exploratory factor analysis. Based on the results of node predictability(re) and node centrality such as strength centrality (rs), it was found that the symptoms of “No interest in surroundings” (rs = 1.299, re = 1.081), “Be disappointed about future” (rs = 0.922, re = 0.901), and “Unable to maintain body balance” (rs = 0.747, re = 0.744) had the highest centrality and predictability values, indicating their core positions within the symptom network. No interest in surroundings, Be disappointed about future, and Unable to maintain body balance are core symptoms in the symptom network. In the future, intervention methods for core symptoms can be constructed and validated for their intervention effects to further demonstrate the benefits of core symptoms.

Resilience is still often viewed as a unitary personality construct that, as a kind of antinosological entity, protects individuals against stress-related mental problems. However, increasing evidence indicates that maintaining mental health in the face of adversity results from complex and dynamic processes of adaptation to stressors that involve the activation of several separable protective factors. Such resilience factors can reside at biological, psychological, and social levels and may include stable predispositions (such as genotype or personality traits) and malleable properties, skills, capacities, or external circumstances (such as gene-expression patterns, emotion-regulation abilities, appraisal styles, or social support). We abandon the notion of resilience as an entity here. Starting from a conceptualization of psychiatric disorders as dynamic networks of interacting symptoms that may be driven by stressors into stable maladaptive states of disease, we deconstruct the maintenance of mental health during stressor exposure into timevariant dampening influences of resilience factors onto these symptom networks. Resilience factors are separate additional network nodes that weaken symptom–symptom interconnections or symptom autoconnections, thereby preventing maladaptive system transitions. We argue that these hybrid symptom-and-resilience-factor networks provide a promising new way of unraveling the complex dynamics of mental health.

Background Symptom networks can provide empirical evidence for the development of personalized and precise symptom management strategies. However, few studies have established networks of symptoms experienced by older patients on maintenance hemodialysis. Our goal was to examine the type of symptom clusters of older maintenance hemodialysis patients during dialysis and construct a symptom network to understand the symptom characteristics of this population. Methods The modified Dialysis Symptom Index was used for a cross-sectional survey. Network analysis was used to analyze the symptom network and node characteristics, and factor analysis was used to examine symptom clusters. Results A total of 167 participants were included in this study. The participants included 111 men and 56 women with a mean age of 70.05 ± 7.40. The symptom burdens with the highest scores were dry skin, dry mouth, itching, and trouble staying asleep. Five symptom clusters were obtained from exploratory factor analysis, of which the clusters with the most severe symptom burdens were the gastrointestinal discomfort symptom cluster, sleep disorder symptom cluster, skin discomfort symptom cluster, and mood symptom cluster. Based on centrality markers, it could be seen that feeling nervous and trouble staying asleep had the highest strength, and feeling nervous and feeling irritable had the highest closeness and betweenness. Conclusions Hemodialysis patients have a severe symptom burden and multiple symptom clusters. Dry skin, itching, and dry mouth are sentinel symptoms in the network model; feeling nervous and trouble staying asleep are core symptoms of patients; feeling nervous and feeling irritable are bridge symptoms in this symptom network model. Clinical staff can formulate precise and efficient symptom management protocols for patients by using the synergistic effects of symptoms in the symptom clusters based on sentinel symptoms, core symptoms, and bridge symptoms.

The network theory of psychological disorders posits that systems of symptoms cause, or are associated with, the expression of other symptoms. Substantial literature on symptom networks has been published to date, although no systematic review has been conducted exclusively on symptom networks of schizophrenia, schizoaffective disorder, and schizophreniform (people diagnosed with schizophrenia; PDS). This study aims to compare statistics of the symptom network publications on PDS in the last 21 years and identify congruences and discrepancies in the literature. More specifically, we will focus on centrality statistics. Thirty-two studies met the inclusion criteria. The results suggest that cognition, and social, and occupational functioning are central to the network of symptoms. Positive symptoms, particularly delusions were central among participants in many studies that did not include cognitive assessment. Nodes representing cognition were most central in those studies that did. Nodes representing negative symptoms were not as central as items measuring positive symptoms. Some studies that included measures of mood and affect found items or subscales measuring depression were central nodes in the networks. Cognition, and social, and occupational functioning appear to be core symptoms of schizophrenia as they are more central in the networks, compared to variables assessing positive symptoms. This seems consistent despite heterogeneity in the design of the studies.

Although exposure and response prevention (EX/RP) is recommended as a first-line treatment for obsessive-compulsive disorder (OCD), responses vary among patients. This study was the first to use network analysis to examine how OCD symptom networks change with EX/RP and vary across different progress trajectories. Data from four clinical trials with 334 adults with OCD who received manualized EX/RP were pooled. The Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) was administered at baseline, midpoint, and post-treatment. OCD symptom networks were constructed using individual Y-BOCS items at these three time points, both for the entire sample and for three different progress trajectories (dramatic, moderate, and little-to-no progress) previously identified using growth mixture modeling. Network measures, including global efficiency, modularity, and weighted degree centrality, were computed to quantitatively assess network properties across treatment. Network analysis revealed two distinct modules at baseline: resistance/control and interference/distress. In the full sample, these two modules became integrated over time, as indicated by significant increases in global efficiency and weighted degree centrality and decreases in modularity; at post-treatment, the network shifted toward a fully connected network, and the strength of associations between nodes increased. These changes were most pronounced in the dramatic progress class. Our findings indicated that effective EX/RP treatment was associated with more integrated OCD symptom networks, which may serve as an indicator of treatment response. Future research should examine how these shifts in network connectivity correspond to changes in underlying brain circuitry and/or to early identification of treatment responders.

Abstract Background Arm symptoms commonly endure in post-breast cancer period and persist into long-term survivorship. However, a knowledge gap existed regarding the interactions among these symptoms. This study aimed to construct symptom networks and visualize the interrelationships among arm symptoms in breast cancer survivors (BCS) both with and without lymphedema (LE). Patients and Methods We conducted a secondary analysis of 3 cross-sectional studies. All participants underwent arm circumference measurements and symptom assessment. We analyzed 17 symptoms with a prevalence >15%, identifying clusters and covariates through exploratory factor and linear regression analysis. Contemporaneous networks were constructed with centrality indices calculated. Network comparison tests were performed. Results 1116 cases without missing data were analyzed, revealing a 29.84% prevalence of LE. Axillary lymph node dissection [ALND] (vs sentinel lymph node biopsy [SLNB]), longer post-surgery duration, and radiotherapy significantly impacted overall symptom severity (P < .001). “Lymphatic Stasis,” “Nerve Injury,” and “Movement Limitation” symptom clusters were identified. Core symptoms varied: tightness for total sample network, firmness for non-LE network, and tightness for LE network. LE survivors reported more prevalent and severe arm symptoms with stronger network connections than non-LE group (P = .010). No significant differences were observed among different subgroups of covariates (P > .05). Network structures were significantly different between ALND and SLNB groups. Conclusion Our study revealed arm symptoms pattern and interrelationships in BCS. Targeting core symptoms in assessment and intervention might be efficient for arm symptoms management. Future research is warranted to construct dynamic symptom networks in longitudinal data and investigate causal relationships among symptoms. This study aimed to construct symptom networks and visualize the interrelationships among arm symptoms in breast cancer survivors with and without lymphedema.