Network Structure Of Symptoms Research Articles

Network analysis of caffeine use disorder, withdrawal symptoms, and psychiatric symptoms

ObjectiveCaffeine Use Disorder (CUD) is not currently recognized as a formal diagnosis in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). However, recent studies within the DSM-5 context have explored this issue. Also, this disorder is closely associated with caffeine withdrawal symptoms, which are formally recognized as a diagnosis in the DSM-5. Additionally, there is limited evidence regarding the connection between caffeine-related issues and psychiatric symptoms. The main aim of the present study was to determine the network structure of CUD and caffeine withdrawal symptoms among the general population. Also, the bridge symptoms among CUD, psychiatric symptoms, and caffeine withdrawal have been estimated.MethodParticipants were 1228 adults (50.3% females, Mean age (x̄±sd) 35.49 ± 11.70 years) who completed Caffeine Use Disorder Questionnaire (CUDQ), Caffeine Withdrawal Symptoms Questionnaire (CWSQ), and Symptom Checklist-25 (SCL-25). All estimations were conducted according to the Gaussian Graphical Model.Results“Excessive consumption” and “role obligations” were central symptoms in the CUD network. Difficulty in concentration was the most central node in the caffeine withdrawal network. Also, the obsessive-compulsive symptom emerged as a central and highly influential node in the relationship between caffeine-related nodes and psychiatric symptoms.ConclusionsMental health providers should target these specific symptoms in clinical interventions to mitigate caffeine-related problems among individuals in the general population effectively.

Examining the effects of school-vacation transitions on depression and anxiety in adolescents: network analysis.

The school-vacation cycle may have impacts on the psychological states of adolescents. However, little evidence illustrates how transition from school to vacation impacts students' psychological states (e.g. depression and anxiety). To explore the changing patterns of depression and anxiety symptoms among adolescent students within a school-vacation transition and to provide insights for prevention or intervention targets. Social demographic data and depression and anxiety symptoms were measured from 1380 adolescent students during the school year (age: 13.8 ± 0.88) and 1100 students during the summer vacation (age: 14.2 ± 0.93) in China. Multilevel mixed-effect models were used to examine the changes in depression and anxiety levels and the associated influencing factors. Network analysis was used to explore the symptom network structures of depression and anxiety during school and vacation. Depression and anxiety symptoms significantly decreased during the vacation compared to the school period. Being female, higher age and with lower mother's educational level were identified as longitudinal risk factors. Interaction effects were found between group (school versus vacation) and the father's educational level as well as grade. Network analyses demonstrated that the anxiety symptoms, including 'Nervous', 'Control worry' and 'Relax' were the most central symptoms at both times. Psychomotor disturbance, including 'Restless', 'Nervous' and 'Motor', bridged depression and anxiety symptoms. The central and bridge symptoms showed variation across the school vacation. The school-vacation transition had an impact on students' depression and anxiety symptoms. Prevention and intervention strategies for adolescents' depression and anxiety during school and vacation periods should be differentially developed.

Exploring early postoperative core symptoms in Chinese patients with primary liver cancer: a cross-sectional and longitudinal network analysis.

Patients with primary liver cancer (PLC) experience a range of symptoms in the early postoperative period. Symptoms include cancer-related symptoms and adverse effects of treatment. Exploring the core symptoms and their dynamics in the early post-hepatectomy patients may help provide better symptom management programs. The purpose of this study was to identify the core symptoms in early post-hepatectomy patients and to explore the trajectory of their dynamics. During the period from March 2021 to September 2022, a total of 281 patients diagnosed with PLC and undergoing radical curative surgery were recruited from the hepatobiliary surgery departments of two hospitals in Eastern China, among whom 249 individuals (88.6%) agreed to participate in the study. A comprehensive symptom assessment was administered to the patients 1-2days after surgery (T1) and 1-2days before discharge (T2). Network analysis was used to identify core symptoms in early post-hepatectomy patients based on symptom severity. The network analysis identified the core symptoms of dry mouth, pain, abdominal distension, the lack of appetite, and nausea at T1. In contrast, at T2, the lack of appetite, pain, shortness of breath, and disturbed sleep were revealed. At the two early time points, while the results of the network structure test showed no statistically significant difference in the overall symptom network structure, there was a significant difference in the results for the core symptoms. Our study demonstrates the need to identify core symptoms in the early post-hepatectomy to improve symptom management in PLC patients. It is crucial to apply network analysis as an essential component of cancer care.

Network analysis of prolonged grief disorder and anxiety symptoms among bereaved Chinese parents who lost their only child

Parents who have lost their only child and cannot or do not wish to adopt or have another child are labeled Shidu parents. Network analysis is used to examine symptom-level interactions in mental disorders. This study aimed to investigate the comorbidity network structure of prolonged grief disorder (PGD) and anxiety symptoms among Shidu parents and compare network differences between child loss from natural and unnatural causes. Key findings revealed that faintness, feeling afraid, panic, and meaninglessness are central symptoms, while meaninglessness, inability to trust others, and nightmares are bridge symptoms. The strongest connection in the PGD–anxiety network is between avoidance and shock, and the edge between meaninglessness and weakness strongly links the two communities. Shidu parents who experience unnatural loss have a stronger edge between inability to trust others and bitterness/anger. Highlighting these symptoms may help interventions address the comorbidities associated with PGD and anxiety among Shidu parents.

Exercise moderates longitudinal group psychopathology networks in individuals with eating disorders

Individuals with eating disorders (EDs) often engage in exercise no matter potential negative long-term outcomes (e.g., weight loss, injury). Yet exercising may temporarily attenuate ED symptoms, but whether exercise also affects network structure and pairwise associations of ED symptoms remained unclear. We used a novel approach called Moderated Multilevel Graphical Vector Autoregression to estimate changes in psychopathology networks from before to after exercising in ecological momentary assessment data from 102 individuals with EDs across multiple days (M = 22.14, SD = 5.40; range: 6–22 days) at 4 times daily. Between-person and within-person temporal networks were computed, obtaining stable centrality coefficients for temporal networks only. In those, autoregressive effects of several symptoms, including binge-eating, overeating, or weighing oneself, were attenuated when participants previously exercised. Exercise mostly downregulated temporal effects of ED symptoms on other symptoms, including effects of binge eating and other compensatory behaviors on feeling guilty after the most recent meal, vomiting on weighing oneself, and overeating on fear of weight gain. Our study highlights the complex dynamic effects of exercise on ED symptoms in daily life and calls for novel studies investigating mechanisms of exercise to inform treatments targeting detrimental long-term effects of exercise in EDs.

Network structure of functional somatic symptoms

ObjectiveThe overlap among functional somatic syndromes (FSS) is substantial, which is why various empirical attempts at an improved understanding of related symptoms have been undertaken. Network analyses are particularly valuable from a clinical point of view, since they focus on the extent to which symptoms expression is co-dependent. The aim of this study was to provide the first estimation of the network structure of symptoms in 17 FSS. MethodsN = 3054 young adults participated in an online survey. The Questionnaire on Functional Somatic Syndromes (FSSQ) was used to diagnose FSS and to assess related symptoms. The Patient Health Questionnaire (PHQ-9) was used to assess (comorbid) depression. Various R packages were used for network analysis, which yielded correlations between symptoms (edges), symptom groups (communities), and measures of centrality for individual symptoms (e.g., node strength). ResultsThe final network had a relatively small number of edges, with small (46.5 %) or small- to medium-sized (47.1 %) correlations. Ten communities were identified: cognitive problems/fatigue/depression, sensory problems, facial pain, head/neck/upper back pain, dizziness/nausea, throat pain/problems with swallowing, chest pain, widespread pain, abdominal pain/problems with digestion, and genital pain. The highest node strength in the network was found for the symptoms “tired”, “down, depressed, or hopeless”, and “tired after minimal exertion”. ConclusionsThe network analyses pointed to ten distinct groups of moderately associated symptoms in individuals with FSS. Fatigue and depression emerged as important symptoms connecting groups. Future studies should test whether (transdiagnostic) interventions specifically targeting these symptoms are particularly potent in alleviating FSS.

Predicting the outcome of psychotherapy for chronic depression by person-specific symptom networks.

Psychotherapies are efficacious in the treatment of depression, albeit only with a moderate effect size. It is hoped that personalization of treatment can lead to better outcomes. The network theory of psychopathology offers a novel approach suggesting that symptom interactions as displayed in person-specific symptom networks could guide treatment planning for an individual patient. In a sample of 254 patients with chronic depression treated with either disorder-specific or non-specific psychotherapy for 48 weeks, we investigated if person-specific symptom networks predicted observer-rated depression severity at the end of treatment and one and two years after treatment termination. Person-specific symptom networks were constructed based on a time-varying multilevel vector autoregressive model of patient-rated symptom data. We used statistical parameters that describe the structure of these person-specific networks to predict therapy outcome. First, we used symptom centrality measures as predictors. Second, we used a machine learning approach to select parameters that describe the strength of pairwise symptom associations. We found that information on person-specific symptom networks strongly improved the accuracy of the prediction of observer-rated depression severity at treatment termination compared to common covariates recorded at baseline. This was also shown for predicting observer-rated depression severity at one- and two-year follow-up. Pairwise symptom associations were better predictors than symptom centrality parameters for depression severity at the end of therapy and one year later. Replication and external validation of our findings, methodological developments, and work on possible ways of implementation are needed before person-specific networks can be reliably used in clinical practice. Nevertheless, our results indicate that the structure of person-specific symptom networks can provide valuable information for the personalization of treatment for chronic depression.

Comparing network structures of depressive and anxiety symptoms between demographically-matched heart disease and heart disease free samples using propensity score matching: Findings from the Chinese Longitudinal Healthy Longevity Survey (CLHLS)

BackgroundOlder adults with heart disease often experience higher rates of comorbid anxiety and depression. This study examined depression and anxiety network structures among older adults with heart disease and their heart disease free peers. MethodsNetwork analyses of secondary cross-sectional data from the 2017 to 2018 wave of CLHLS were used to construct groups of older adults with and without heart disease using propensity score matching. Depression and anxiety symptoms were assessed using Center for Epidemiological Studies Depression Scale and Generalized Anxiety Disorder Scale, respectively. Central symptoms and bridge symptoms were identified using expected influence. Results1689 older adults with heart disease and matched control sample of 1689 older adults without heart disease were included. The prevalence and severity of depression and anxiety were significantly higher in older adults with heart disease compared to the control group. There was no significant difference in overall structures of depression and anxiety network models between two the groups. Key central symptoms and bridge symptoms within these groups were highly similar; GAD 2 “Uncontrollable worrying” and GAD 4 “Trouble relaxing” were identified as the most central symptoms, while GAD 1 “Nervousness” and CESD 1 “Feeling bothered” were identified as key bridge symptoms across both network models. ConclusionDepression and anxiety are more prevalent in older adults with heart disease than demographically-matched heart disease free controls. However, network structures of these symptoms do not differ between two groups. Accordingly, depression and anxiety psychosocial interventions developed for older adults without heart disease may also benefit older adults with heart disease.

Network structure of REM sleep behavior disorder symptoms in iRBD patients

ObjectiveEmploying the REM Sleep Behavior Disorder Questionnaire-Hong Kong (RBDQ-HK) to investigate symptoms and their severity in rapid eye movement (REM) sleep behavior disorder (RBD) patients, this study delves into the construct of RBD through the RBDQ-HK and its links to depression and sleep quality. MethodsData from the RBDQ-HK, the Geriatric Depression Scale (GDS), and the Pittsburgh Sleep Quality Index (PSQI) were compiled from individuals with isolated RBD (iRBD) confirmed by polysomnography. We constructed a network analysis of the RBDQ-HK, measured the centrality of each symptom (node), conducted Exploratory Graph Analysis (EGA) to unveil the dimension structure of the questionnaire, and calculated bridge expected influence (BEI) to identifying critical bridge. Multivariate linear regression was also employed to discover relationships between RBDQ-HK dimensions and variables such as PSQI and GDS. ResultsIn our cohort of 455 iRBD patients (299 males), the items in the RBDQ-HK were divided into three dimensions: dream, movement, and SRI/violence. The symptoms identified as most central to RBD were ‘shouting or yelling in sleep’, ‘dream-enacting movements’, and ‘talking during sleep’. The highest (BEI) was ‘violent and aggressive dreams’, which has the potential to bridge three dimensions within the symptom network. Depression was significantly correlated with the movement and dream dimensions of RBD, and sleep quality was predominantly related to the dream dimension score. ConclusionOur findings verify that the principal symptoms of the RBDQ-HK align with the established diagnostic criteria and reveal a three-dimensional structure within RBD symptoms. The relationships between the RBD symptoms, depression, and sleep quality need to be identified for the effective management of RBD patients.

Network structure of depressive symptoms, school anxiety and perfectionism in Peruvian adolescents

Network structure of depressive symptoms, school anxiety and perfectionism in Peruvian adolescents

Network analysis of depressive symptoms and C-reactive protein levels in major depressive disorder

BackgroundC-reactive protein (CRP) levels have been implicated in the severity and symptomatology of major depressive disorder (MDD). The aim of this study was to explore the structure of depressive symptoms in patients with MDD according to different groups of CRP levels using network analysis. MethodsThe study included 864 individuals (mean age = 54.05, 67.48 % male) diagnosed with MDD from the 2015–2020 National Health and Nutrition Examination Survey (NHANES). Analyses examined how depressive symptoms and CRP level were related to each other, and how the network structure of depressive symptoms differed across groups with different CRP levels. ResultsA direct positive correlation was observed between CRP levels and specific depressive symptoms (e.g., appetite change, energy loss, and feelings of worthlessness). Moreover, there was a stronger correlation between depressive symptoms in the medium CRP and high CRP groups compared to the low CRP group. Furthermore, it was observed that there were notable structural differences between the high-CRP and moderate-CRP groups. LimitationsThe study is based on cross-sectional data, which precludes the drawing of causal conclusions. Furthermore, it does not take into account confounding factors such as body mass index (BMI) and lifestyle. ConclusionsThe findings underscore the pivotal role of CRP as a marker of the severity of depressive symptoms. Routine CRP level testing and anti-inflammatory therapies may be beneficial for depressed patients with elevated CRP levels in clinical practice.

Unraveling the core and bridge menopausal symptoms of perimenopausal women: a network analysis.

Perimenopause is the period from the early menopausal transition to 12 months after the final menstrual period. The clustering of menopausal symptoms poses a challenge for perimenopausal management. Core symptoms are targets for interventions that can alleviate other related symptoms. Bridge symptoms are connectors that link related symptom clusters and can improve the effectiveness of interventions. This study aims to construct a network structure of menopausal symptoms and to identify core and bridge symptoms as a reference for future management. Two hundred forty-two Chinese perimenopausal women were included in the survey. The structure and associations of the menopausal symptoms assessed by the Greene Climacteric Scale were analyzed using a network analysis. We generated the network structure graph using R software and checked its accuracy and stability. In the menopausal transition, the most prevalent symptoms were feeling tired or lacking in energy, excitability, and irritability. Sexual dysfunction was common among early postmenopausal women. Irritability (S = 7.16, C = 0.0167, B = 8) was a core symptom of the network. The depressive symptom cluster was a core symptom cluster, most of which have high centrality indices. Excitability (B = 6) was a bridge symptom connecting the anxiety and depressive symptom clusters. Our study has highlighted the crucial significance of irritability and excitability in perimenopausal management. Overcoming the challenges of perimenopausal management requires the public to ameliorate the prejudice and stigma associated with emotional symptoms.

Comparison of Negative Symptom Network Structures Between Patients With Early and Chronic Schizophrenia: A Network and Exploratory Graph Analysis.

Despite the clinical relevance of negative symptoms in schizophrenia, our understanding of negative symptoms remains limited. Although various courses and stages of schizophrenia have been identified, variations in the negative symptom networks between distinct stages of schizophrenia remain unexplored. We examined 405 patients with early schizophrenia (ES) and 330 patients with chronic schizophrenia (CS) using the Scale for the Assessment of Negative Symptoms. Network analysis and exploratory graph analysis were used to identify and compare the network structures and community memberships of negative symptoms between the two groups. Further, associations between communities and social functioning were evaluated. The potential influences of other symptom domains and confounding factors were also examined. Multidimensional differences were found in the networks of negative symptoms between ES and CS. The global connectivity strength was higher in the network of ES than in the network of CS. In ES, central symptoms were mainly related to expressive deficits, whereas in CS they were distributed across negative symptom domains. A three-community structure was suggested across stages but with different memberships and associations with social functioning. Potential confounding factors and symptom domains, including mood, positive, disorganization, and excitement symptoms, did not affect the network structures. Our findings revealed the presence of stage-specific network structures of negative symptoms in schizophrenia, with negative symptom communities having differential significance for social functioning. These findings provide implications for the future development of tailored interventions to alleviate negative symptoms and improve functionality across stages.

A simulation-based network analysis of intervention targets for adolescent depressive and anxiety symptoms

Although previous research has well explored central and bridge symptoms of mental health problems, little examined whether these symptoms can serve as effective targets for intervention practices. Based on the Ising model, this study constructed a network structure of depressive and anxiety symptoms. The NodeIdentifyR algorithm (NIRA) was used to simulate interventions within this network, examining the effects of alleviating or aggravating specific symptoms on the network’s sum scores. In this study, a total of 15,569 participants were recruited from China (50.87 % females, Mage = 13.44; SD = 0.97). The Ising model demonstrated that “sad mood” had the highest expected influence, and “irritability” had the highest bridge expected influence. Alleviating interventions suggested that decreasing the symptom value of “nervousness” resulted in the greatest projected reduction in network symptom activation, which may be a potential target symptom for treatment. Aggravating interventions indicated that elevating the symptom value of “sad mood” had the most projected increase in network activation, which may be a potential target for prevention. Additionally, network structure indices (e.g., central or bridge symptoms) need to be interpreted with more caution as intervention targets, since they may not be exactly the same. These findings enriched the comprehension of the depressive and anxiety network in Chinese adolescents, offering valuable insights for designing effective interventions.

A network perspective on posttraumatic stress disorder and comorbid borderline personality disorder symptoms

ABSTRACT Background: Comorbidity between posttraumatic stress disorder (PTSD) and borderline personality disorder (BPD) is surrounded by diagnostic controversy and although various effective treatments exist, dropout and nonresponse are high. Objective: By estimating the network structure of comorbid PTSD and BPD symptoms, the current study illustrates how the network perspective offers tools to tackle these challenges. Method: The sample comprised of 154 patients with a PTSD diagnosis and BPD symptoms, assessed by clinician-administered interviews. A regularised partial correlation network was estimated using the GLASSO algorithm in R. Central symptoms and bridge symptoms were identified. The reliability and accuracy of network parameters were determined through bootstrapping analyses. Results: PTSD and BPD symptoms largely clustered into separate communities. Intrusive memories, physiological cue reactivity and loss of interest were the most central symptoms, whereas amnesia and suicidal behaviour were least central. Conclusions: Present findings suggest that PTSD and BPD are two distinct, albeit weakly connected disorders. Treatment of the most central symptoms could lead to an overall deactivation of the network, while isolated symptoms would need more specific attention during therapy. Further experimental, longitudinal research is needed to confirm these hypotheses. Trial registration: ClinicalTrials.gov identifier: NCT03833453.

The similarities and differences of network structures of posttraumatic stress symptoms among Chinese adolescents exposed to three different types of trauma.

The aim of this study is to conduct network analysis to identify the posttraumatic stress disorder (PTSD) symptoms network structures in adolescents across three types of trauma events. After screening 20,802 students aged 10-18 years old from 27 schools, our analysis enrolled 2,234 traumatized students who were exposed to accidents (n = 622), natural disasters (n = 211), and any severe disease (n = 1,401). We conducted a network analysis to compare the PTSD symptom structures across three types of trauma events. Our findings reveal that negative emotional symptoms, such as trauma-related negative emotions, are central symptoms in adolescents across three exposures. Strong connections (i.e., trauma-related amnesia [D1] and blame of self or others [D3]) in the network were observed in the accident exposure group. Furthermore, the overall network structures in adolescents may be across exposures while the strength in edges varied in different traumas. Negative connections (i.e., nightmares [B2] and blame of self or others [D3]) showed up in disease and disaster exposure groups, while there was no negative connection in the accident exposure group. Future studies should pay more attention to central symptoms like negative trauma-related emotions, which could be a target symptom of PTSD related to accidents, natural disasters, and any severe disease among adolescents. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Supplemental Material for The Similarities and Differences of Network Structures of Posttraumatic Stress Symptoms Among Chinese Adolescents Exposed to Three Different Types of Trauma

Supplemental Material for The Similarities and Differences of Network Structures of Posttraumatic Stress Symptoms Among Chinese Adolescents Exposed to Three Different Types of Trauma

Network Analysis of Depressive Symptoms in Chinese Sexual Minority Women During the COVID-19 Pandemic: An Intra-Group Perspective

ABSTRACT The prevalence of depression among sexual minority women is a significant concern, yet no prior research has conducted a network analysis of depressive symptoms in this population. This is the first study to address this gap by examining the network structure of depressive symptoms in Chinese sexual minority women during the COVID-19 pandemic, considering both sexual orientation and gender expression as part of an intra-group perspective. 1420 Chinese sexual minority women completed the Center for Epidemiologic Studies Depressive Symptoms (CES-D). Network analysis was employed to calculate edge and centrality measures, and the network structures of lesbians and bisexual women were compared based on sexual orientation and of femme, androgyny, and butch based on gender expression. Network analysis revealed that the core depressive symptoms of Chinese sexual minority women are “Felt depressed,” “Fatigue,” “Sad,” and “Failure.” Although no significant differences were found in the network structure and global strength of depressive symptoms between different sexual orientations and gender expressions, there were significant differences in the core symptoms. This study suggests the unique associations between depressive symptoms and social and historical contexts among sexual minority women and emphasizes the importance of considering these differences when providing targeted mental health interventions.

The impact of antidepressant treatment on the network structure of neurocognition and core emotional depressive symptoms among depressed individuals with a history of suicide attempt: An 8-week clinical study

BackgroundA more in-depth understanding of the relationship between depressive symptoms, neurocognition and suicidal behavior could provide insights into the prognosis and treatment of major depressive disorder (MDD) and suicide. We conducted a network analysis among depressed patients examining associations between history of suicide attempt (HSA), core emotional major depression disorder, and key neurocognitive domains. MethodDepressed patients (n = 120) aged 18–65 years were recruited from a larger randomized clinical trial conducted at the Douglas Institute in Montreal, Canada. They were randomly assigned to receive one of two antidepressant treatments (i.e., escitalopram or desvenlafaxine) for 8 weeks. Core emotional MDD and key neurocognitive domains were assessed pre-post treatment. ResultsAt baseline, an association between history of suicide attempt (HSA) and phonemic verbal fluency (PVF) suggested that HSA patients reported lower levels of the latter. After 8 weeks of antidepressant treatment, HSA became conditionally independent from PVF. Similar results were found for both the HAM-D and the QIDS-SR core emotional MDD/neurocognitive networks. ConclusionNetwork analysis revealed a pre-treatment relationship between a HSA and decreased phonemic VF among depressed patients, which was no longer present after 8 weeks of antidepressant treatment.

Network analysis of comorbid aggressive behavior and testosterone among bipolar disorder patients: a cross-sectional study

Testosterone has complex effects on psychological traits and behavior; it is associated with social dominance and competition and is a potential human sex pheromone. This study aimed to investigate the associations between testosterone levels, aggressive behavior, and manic symptoms using a network analysis among bipolar disorder (BD) patients in psychiatric emergency departments (PED). Data from January 2021 and March 2022 BD patients in PED were analyzed. Manic symptoms were assessed using the Young Mania Rating Scale (YMRS). Aggression was assessed with subscale of the PANSS scale (PANSS-AG). The undirected network structures of testosterone levels, aggressive behavior, and manic symptoms were estimated, and centrality and bridge centrality indices were examined. Network stability was examined using the case-dropping procedure. The Network Comparison Test (NCT) was conducted to evaluate whether network characteristics differed by gender. We recruited a total of 898 BD patients, with the mean YMRS score as 13.30 ± 9.58. The prevalence of level II aggression was 35.6% (95%CI = 32.5%–38.7%), level III aggression was 29.5% (95%CI = 26.3%–32.6%), and level VI aggression was 7.0% (95%CI = 5.4%–8.8%). The male participants had a mean testosterone level of 391.71 (Standard Deviation (SD):223.39) compared to 36.90 (SD:30.50) for female participants in the whole sample. Through network analysis, “Increased motor activity-energy” emerged as the central symptom, with the highest centrality expected influence, followed by “Emotional Instability” and “Disruptive/aggression behavior”. Notably, “Emotional Instability” appeared to be the bridge symptom linking manic symptoms to aggressive behavior. Within the flow network model, “Speech rate and amount” exhibited the strongest positive correlation with testosterone levels, followed closely by “Disruptive/aggression behavior”. The constructed network model demonstrated robust stability, with gender showing no significant impact on the structure. In this study, “Increased motor activity-energy” stood out as the most influential symptom, and “Speech rate and amount” acted as the main bridge symptom linking testosterone levels, aggressive behavior, and manic symptoms. Targeting the central and bridge symptoms may improve the outcomes of aggression interventions implemented among BD patients in psychiatric emergency care.