Estimating the symptom structure of bipolar disorder via network analysis: Energy dysregulation as a central symptom

Sleep, Mental Health, and the Need for Physical and Real-Life Social Contact with (Non-)Family Members during the COVID-19 Pandemic: A Bayesian Network Analysis

Background/Objectives: The forced social isolation implemented to prevent the spread of the COVID-19 virus was accompanied by a worsening of mental health, an increase in insomnia symptoms, and the emergence of 'skin hunger'-an increased longing for personal touch. This study aimed to enhance our understanding of the interconnection between sleep, mental health, and the need for physical (NPC) and real-life social contact (NRL-SC). Methods: A total of 2827 adults participated in an online survey during the second COVID-19 lockdown. A Bayesian Gaussian copula graphical model (BGCGM) and a Bayesian-directed acyclic graph (DAG) were estimated, and mixed ANOVAs were carried out. Results: NPC with non-family members (t(2091) = 12.55, p < 0.001, d = 0.27) and relational lifestyle satisfaction (t(2089) = 13.62, p < 0.001, d = 0.30) were lower during the second lockdown than before the pandemic. In our BGCGM, there were weak positive edges between the need for PC and RL-SC on one hand and sleep and mental health on the other. Conclusions: During the second lockdown, people craved less physical contact with non-family members and were less satisfied with their relational lifestyle than before the pandemic. Individuals with a greater need for PC and RL-SC reported poorer mental health (i.e., worry, depression, and mental fatigue).

The future of psychological treatments: The Marburg Declaration

Although psychological treatments are broadly recognized as evidence-based interventions for various mental disorders, challenges remain. For example, a substantial proportion of patients receiving such treatments do not fully recover, and many obstacles hinder the dissemination, implementation, and training of psychological treatments. These problems require those in our field to rethink some of our basic models of mental disorders and their treatments, and question how research and practice in clinical psychology should progress. To answer these questions, a group of experts of clinical psychology convened at a Think-Tank in Marburg, Germany, in August 2022 to review the evidence and analyze barriers for current and future developments. After this event, an overview of the current state-of-the-art was drafted and suggestions for improvements and specific recommendations for research and practice were integrated. Recommendations arising from our meeting cover further improving psychological interventions through translational approaches, improving clinical research methodology, bridging the gap between more nomothetic (group-oriented) studies and idiographic (person-centered) decisions, using network approaches in addition to selecting single mechanisms to embrace the complexity of clinical reality, making use of scalable digital options for assessments and interventions, improving the training and education of future psychotherapists, and accepting the societal responsibilities that clinical psychology has in improving national and global health care. The objective of the Marburg Declaration is to stimulate a significant change regarding our understanding of mental disorders and their treatments, with the aim to trigger a new era of evidence-based psychological interventions.

The metabolic overdrive hypothesis: hyperglycolysis and glutaminolysis in bipolar mania

Evidence from diverse areas of research including chronobiology, metabolomics and magnetic resonance spectroscopy indicate that energy dysregulation is a central feature of bipolar disorder pathophysiology. In this paper, we propose that mania represents a condition of heightened cerebral energy metabolism facilitated by hyperglycolysis and glutaminolysis. When oxidative glucose metabolism becomes impaired in the brain, neurons can utilize glutamate as an alternative substrate to generate energy through oxidative phosphorylation. Glycolysis in astrocytes fuels the formation of denovo glutamate, which can be used as a mitochondrial fuel source in neurons via transamination to alpha-ketoglutarate and subsequent reductive carboxylation to replenish tricarboxylic acid cycle intermediates. Upregulation of glycolysis and glutaminolysis in this manner causes the brain to enter a state of heightened metabolism and excitatory activity which we propose to underlie the subjective experience of mania. Under normal conditions, this mechanism serves an adaptive function to transiently upregulate brain metabolism in response to acute energy demand. However, when recruited in the long term to counteract impaired oxidative metabolism it may become a pathological process. In this article, we develop these ideas in detail, present supporting evidence and propose this as a novel avenue of investigation to understand the biological basis for mania.

The Role of Anxiety and Depression in Shaping the Sleep-Pain Connection in Patients with Nonspecific Chronic Spinal Pain and Comorbid Insomnia: A Cross-Sectional Analysis

(1) Background: This exploratory study aims to explore the relationship between nonspecific chronic spinal pain (nCSP) and insomnia symptoms, by examining the interconnections, strengths, and directional dependence of the symptoms. In addition, we aim to identify the key symptoms of the nCSP-insomnia relationship and shed light on the bidirectional nature of this relationship. (2) Methods: This study is a secondary analysis of the baseline data (cross-sectional) from a randomized controlled trial, which examined the added value of Cognitive Behavioral Therapy for Insomnia (CBT-I) combined with cognition-targeted exercise therapy, conducted in collaboration with the Universiteit Gent and Vrije Universiteit Brussel (Belgium). One hundred and twenty-three nCSP patients with comorbid insomnia were recruited through the participating hospitals, advertisements, announcements in local newspapers, pharmacies, publications from support groups, and primary care. To explore the interconnections and directionality between symptoms and the strengths of the relationships, we estimated a regularized Gaussian graphical model and a directed acyclic graph. (3) Results: We found only one direct, but weak, link between sleep and pain, namely, between average pain and difficulties maintaining sleep. (4) Conclusions: Despite the lack of strong direct links between sleep and pain, pain and sleep seem to be indirectly linked via anxiety and depression symptoms, acting as presumable mediators in the network of nCSP and comorbid insomnia. Furthermore, feeling slowed down and fatigue emerged as terminal nodes, implying their role as consequences of the network.

Machine Learning and Bayesian Network Analyses Identifies Psychiatric Disorders and Symptom Associations with Insomnia in a national sample of 31,285 Treatment-Seeking College Students

Background

A better understanding of the structure of relations among insomnia and anxiety, mood, eating, and alcohol-use disorders is needed, given its prevalence among young adults. Supervised machine learning provides the ability to evaluate the discriminative accuracy of psychiatric disorders associated with insomnia. Combined with Bayesian network analysis, the directionality between symptoms and their associations may be illuminated.

Methods

The current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. Firstly, an elastic net regularization model was trained to predict, via repeated 10-fold cross-validation, which psychiatric disorders were associated with insomnia severity. Seven disorders were included: major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder. Secondly, using a Bayesian network approach, completed partially directed acyclic graphs (CPDAG) built on training and holdout samples were computed via a Bayesian hill-climbing algorithm to determine symptom-level interactions of disorders most associated with insomnia [based on SHAP (SHapley Additive exPlanations) values)] and were evaluated for stability across networks.

Results

Of 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .449 (.016); RMSE = 5.00 [.081]), with comparable performance in accounting for variance explained in the holdout sample [R2 = .33; RMSE = 5.47). SHAP indicated the presence of any psychiatric disorder was associated with higher insomnia severity, with major depressive disorder demonstrated to be the most associated disorder. CPDAGs showed excellent fit in the holdout sample and suggested that depressed mood, fatigue, and self-esteem were the most important depression symptoms that presupposed insomnia.

Conclusion

These findings offer insights into associations between psychiatric disorders and insomnia among college students and encourage future investigation into the potential direction of causality between insomnia and major depressive disorder.

Trial registration

Trial may be found on the National Institute of Health RePORTER website: Project Number: R01MH115128-05.

Dynamic time warp analysis of individual symptom trajectories in individuals with bipolar disorder

BACKGROUND

Manic and depressive mood states in bipolar disorder (BD) may emerge from the non-linear relations between constantly changing mood symptoms exhibited as a complex dynamic system. Dynamic Time Warp (DTW) is an algorithm that may capture symptom interactions from panel data with sparse observations over time.

METHODS

The Young Mania Rating Scale and Quick Inventory of Depressive Symptomatology were repeatedly assessed in 141 individuals with BD, with on average 5.5 assessments per subject every 3-6 months. Dynamic Time Warp calculated the distance between each of the 27 × 27 pairs of standardized symptom scores. The changing profile of standardized symptom scores of BD participants was analyzed in individual subjects, yielding symptom dimensions in aggregated group-level analyses. Using an asymmetric time-window, symptom changes that preceded other symptom changes (i.e., Granger causality) yielded a directed network.

RESULTS

The mean age of the BD participants was 40.1 (SD 13.5) years old, and 60% were female participants. Idiographic symptom networks were highly variable between subjects. Yet, nomothetic analyses showed five symptom dimensions: core (hypo)mania (6 items), dysphoric mania (5 items), lethargy (7 items), somatic/suicidality (6 items), and sleep (3 items). Symptoms of the "Lethargy" dimension showed the highest out-strength, and its changes preceded those of "somatic/suicidality," while changes in "core (hypo)mania" preceded those of "dysphoric mania."

CONCLUSION

Dynamic Time Warp may help to capture meaningful BD symptom interactions from panel data with sparse observations. It may increase insight into the temporal dynamics of symptoms, as those with high out-strength (rather than high in-strength) could be promising targets for intervention.

Clarifying directional dependence among measures of early auditory processing and cognition in schizophrenia: leveraging Gaussian graphical models and Bayesian networks

BACKGROUND

Research using latent variable models demonstrates that pre-attentive measures of early auditory processing (EAP) and cognition may initiate a cascading effect on daily functioning in schizophrenia. However, such models fail to account for relationships among individual measures of cognition and EAP, thereby limiting their utility. Hence, EAP and cognition may function as complementary and interacting measures of brain function rather than independent stages of information processing. Here, we apply a data-driven approach to identifying directional relationships among neurophysiologic and cognitive variables.

METHODS

Using data from the Consortium on the Genetics of Schizophrenia 2, we estimated Gaussian Graphical Models and Bayesian networks to examine undirected and directed connections between measures of EAP, including mismatch negativity and P3a, and cognition in 663 outpatients with schizophrenia and 630 control participants.

RESULTS

Chain structures emerged among EAP and attention/vigilance measures in schizophrenia and control groups. Concerning differences between the groups, object memory was an influential variable in schizophrenia upon which other cognitive domains depended, and working memory was an influential variable in controls.

CONCLUSIONS

Measures of EAP and attention/vigilance are conditionally independent of other cognitive domains that were used in this study. Findings also revealed additional causal assumptions among measures of cognition that could help guide statistical control and ultimately help identify early-stage targets or surrogate endpoints in schizophrenia.

Identifying the bridge between depression and mania: A machine learning and network approach to bipolar disorder

OBJECTIVES

Although the cyclic nature of bipolarity is almost by definition a network system, no research to date has attempted to scrutinize the relationship of the two bipolar poles using network psychometrics. We used state-of-the-art network and machine learning methodologies to identify symptoms, as well as relations thereof, that bridge depression and mania.

METHODS

Observational study that used mental health data (12 symptoms for depression and 12 for mania) from a large, representative Canadian sample (the Canadian Community Health Survey of 2002). Complete data (N = 36,557; 54.6% female) were analysed using network psychometrics, in conjunction with a random forest algorithm, to examine the bidirectional interplay of depressive and manic symptoms.

RESULTS

Centrality analyses pointed to symptoms relating to emotionality and hyperactivity as being the most central aspects of depression and mania, respectively. The two syndromes were spatially segregated in the bipolar model and four symptoms appeared crucial in bridging them: sleep disturbances (insomnia and hypersomnia), anhedonia, suicidal ideation, and impulsivity. Our machine learning algorithm validated the clinical utility of central and bridge symptoms (in the prediction of lifetime episodes of mania and depression), and suggested that centrality, but not bridge, metrics map almost perfectly onto a data-driven measure of diagnostic utility.

CONCLUSIONS

Our results replicate key findings from past network studies on bipolar disorder, but also extend them by highlighting symptoms that bridge the two bipolar poles, while also demonstrating their clinical utility. If replicated, these endophenotypes could prove fruitful targets for prevention/intervention strategies for bipolar disorders.

Network analysis of posttraumatic stress disorder in a treatment-seeking sample of US firefighters and emergency medical technicians

BACKGROUND

First responders, including firefighters and emergency medical technicians (EMTs), are under extreme stress from repeated exposure to potentially traumatic events. To optimize treatment for this population, it is critical to understand how the various posttraumatic stress disorder (PTSD) symptom factors are associated with one another so these relations may be targeted in treatment.

METHOD

Using a sample of treatment-seeking firefighters/EMTs (N = 342), we conducted a partial correlation network analysis of the eight-factor model. A Bayesian directed acyclic graph (DAG) was used to estimate causal associations between clusters.

RESULTS

Approximately 37 % of the sample screened positive for probable PTSD. Internal re-experiencing and external re-experiencing had the strongest edges. In the DAG, internal re-experiencing was the parent node and was potentially predictive of external re-experiencing, negative affect, dysphoric arousal, and avoidance.

LIMITATIONS

Data were drawn from a treatment-seeking sample that may not generalize to all firefighters/EMTs.

CONCLUSIONS

The current findings are consistent with prior research suggesting re-experiencing plays a critical role in developing and maintaining PTSD symptoms. Future research should investigate non-treatment-seeking first responders, as well as EMTs and firefighters as individual populations.

Mapping the links between sexual addiction and gambling disorder: A Bayesian network approach

Contemporary literature and recent classification systems have expanded the field of addictions to include problematic behaviours such as gambling and sexual addiction. However, conceptualisation of behavioural addictions is poorly understood and gender-based differences have emerged in relation to how these behaviours are expressed. The current research conducted partial-correlation and Bayesian network analyses to assess the symptomatic structure of gambling disorder and sexual addiction. Convenience community sampling recruited 937 adults aged 18 to 64 years (315 females, Mage = 30.02; 622 males, Mage = 29.46). Symptoms of problematic behaviours were measured using the Online Gambling Disorder Questionnaire (OGDQ) and the Bergen Yale Sex Addiction Scale (BYSAS). Results indicate distinct gender-based differences in the symptom networks of sexual addiction and gambling disorder, with a more complex network observed amongst men for both conditions. Addiction salience, withdrawal and dishonesty/deception were important components of the addictive network. Interpersonal conflict was more central for women while intrapsychic conflict a more prominent issue for men. Differences in the two symptom networks indicate separate disorders as opposed to a single underlying construct. Treating practitioners and community initiatives aimed at addressing sexual addiction and disordered gambling should consider gender, when designing educational or therapeutic interventions.

Autistic Symptoms, Irritability, and Executive Dysfunctions: Symptom Dynamics from Multi-Network Models

Socio-cognitive difficulties in individuals with autism spectrum disorder (ASD) are heterogenuous and often co-occur with irritability symptoms, such as angry/grouchy mood and temper outbursts. However, the specific relations between individual symptoms are not well-represented in conventional methods analyzing aggregated autistic symptoms and ASD diagnosis. Moreover, the cognitive-behavioral mechanisms linking ASD to irritability are largely unknown. This study investigated the dynamics between autistic (Social Responsiveness Scale) and irritability (Affective Reactivity Index) symptoms and executive functions (Cambridge Neuropsychological Test Automated Battery) in a sample of children and adolescents with ASD, their unaffected siblings, and neurotypical peers (N = 345, aged 6-18 years, 78.6% male). Three complementary networks across the entire sample were computed: (1) Gaussian graphical network estimating the conditional correlations between symptom nodes; (2) Relative importance network computing relative influence between symptoms; (3) Bayesian directed acyclic graph estimating predictive directionality between symptoms. Networks revealed numerous partial correlations within autistic (rs = .07-.56) and irritability (rs = .01-.45) symptoms and executive functions (rs = -.83 to .67) but weak connections between clusters. This segregated pattern converged in all directed and supplementary networks. Plausible predictive paths were found between social communication difficulties to autism mannerisms and between "angry frequently" to "lose temper easily." Autistic and irritability symptoms are two relatively independent families of symptoms. It is unlikely that executive dysfunctions explain elevated irritability in ASD. Findings underscore the need for researching other mood and cognitive-behavioral bridge symptoms, which may inform individualized treatments for co-occurring irritability in ASD.

A Bayesian Network Approach to Social and Nonsocial Cognition in Schizophrenia: Are Some Domains More Fundamental than Others?

OBJECTIVES

Social and nonsocial cognition are defined as distinct yet related constructs. However, the relative independence of individual variables-and whether specific tasks directly depend on performance in other tasks-is still unclear. The current study aimed to answer this question by using a Bayesian network approach to explore directional dependencies among social and nonsocial cognitive domains.

STUDY DESIGN

The study sample comprised 173 participants with schizophrenia (71.7% male; 28.3% female). Participants completed 5 social cognitive tasks and the MATRICS Consensus Cognitive Battery. We estimated Bayesian networks using directed acyclic graph structures to examine directional dependencies among the variables.

STUDY RESULTS

After accounting for negative symptoms and demographic variables, including age and sex, all nonsocial cognitive variables depended on processing speed. More specifically, attention, verbal memory, and reasoning and problem solving solely depended on processing speed, while a causal chain emerged between processing speed and visual memory (processing speed → attention → working memory → visual memory). Social processing variables within social cognition, including emotion in biological motion and empathic accuracy, depended on facial affect identification.

CONCLUSIONS

These results suggest that processing speed and facial affect identification are fundamental domains of nonsocial and social cognition, respectively. We outline how these findings could potentially help guide specific interventions that aim to improve social and nonsocial cognition in people with schizophrenia.

Health-promoting behavior to enhance perceived meaning and control of life in chronic disease patients with role limitations and depressive symptoms: a network approach

The association between health-related role limitations in the mental and physical subdomains and clinical status (i.e., chronic disease and comorbid depressive symptoms) is mediated by health-promoting behaviors. To enhance health-promoting behaviors in adults with chronic disease, it is necessary to identify item-level associations among targets of health-related monitoring and management. Therefore, the current study used a network approach to examine associations among health-related role limitations, depressive symptoms, existential well-being, socioeconomic position, and health-promoting behavior in adults with chronic disease. A total of 535 adults (mean ± SD age = 62.9 ± 11.9 years; males, n = 231, females, n = 304) who were regularly visiting an outpatient clinic for chronic disease treatment participated in this cross-sectional study. Data on participant demographics, chronic disease diagnoses, socioeconomic status, health-related role limitations (12-item short form survey scores), depressive symptoms (patient health questionnaire-9 scores), existential well-being (scores for four items of the McGill quality of life questionnaire-Revised), and health-promoting behavior (Healthy Habits Questionnaire scores) were acquired. "Undirected regularized partial correlations" and "directional joint probability distributions" among these variables were calculated using a mixed graphical model (MGM) and directed acyclic graph (DAG). In the MGM, the most influential nodes were emotional well-being, feelings of failure, and health-related limitations affecting usual role and physical activities. According to both the MGM and DAG, the relationship between emotional well-being and feelings of failure mediated the relationships of health-related role limitations with concentration difficulty and suicidal ideation. A positive mindset was dependent on the probability distributions of suicidal ideation, controllability of life, and positive self-image. Both the meaning of life and a positive mindset had direct associations with proactive living. Specifically, proactive living was associated with a balanced diet, regular exercise, volunteering in the community, and nurturing intimacy in social interactions. The meaning and controllability of life in individuals with chronic diseases could mediate the relationships of health-promoting behavior with health-related limitations related to usual role activities, physical activities, and depressive symptoms. Thus, interventions targeting health-promoting behaviors should aim to enhance the meaning and controllability of life (as it pertains to limitations in usual role and physical activities), as well as promote proactive screening and timely psychiatric treatment of depressive symptoms including feelings of failure, concentration difficulties, and suicidal ideation.

Childhood trauma and depressive symptoms in bipolar disorder: A network analysis

BACKGROUND

Childhood trauma is related to an increased number of depressive episodes and more severe depressive symptoms in bipolar disorder. The evaluation of the networks of depressive symptoms-or the patterns of relationships between individual symptoms-among people with bipolar disorder with and without a history of childhood trauma may assist in further clarifying this complex relationship.

METHODS

Data from over 500 participants from the Heinz C. Prechter Longitudinal Study of Bipolar Disorder were used to construct a series of regularised Gaussian Graphical Models. The networks of individual depressive symptoms-self-reported (Patient Health Questionnaire-9; n = 543) and clinician-rated (Hamilton Depression Rating Scale-17; n = 529)-among participants with bipolar disorder with and without a history of childhood trauma (Childhood Trauma Questionnaire) were characterised and compared.

RESULTS

Across the sets of networks, depressed mood consistently emerged as a central symptom (as indicated by strength centrality and expected influence); regardless of participants' history of childhood trauma. Additionally, feelings of worthlessness emerged as a key symptom in the network of self-reported depressive symptoms among participants with-but not without-a history of childhood trauma.

CONCLUSION

The present analyses-although exploratory-provide nuanced insights into the impact of childhood trauma on the presentation of depressive symptoms in bipolar disorder, which have the potential to aid detection and inform targeted intervention development.

A network approach to climate change anxiety and its key related features

Research has pointed to startling worldwide rates of people reporting considerable anxiety vis-à-vis climate change. Yet, uncertainties remain regarding how climate anxiety's cognitive-emotional features and daily life functional impairments interact with one another and with climate change experience, pro-environmental behaviors, and general worry. In this study, we apply network analyses to examine the associations among these variables in an international community sample (n = 874). We computed two network models, a graphical Gaussian model to explore network structure, potential communities, and influential nodes, and a directed acyclic graph to examine the probabilistic dependencies among the variables. Both network models pointed to the cognitive-emotional features of climate anxiety as a potential hub bridging general worry, the experience of climate change, pro-environmental behaviors, and the functional impairments associated with climate anxiety. Our findings offer data-driven clues for the field's larger quest to establish the foundations of climate anxiety.

Depression and PTSD in the aftermath of strict COVID-19 lockdowns: a cross-sectional and longitudinal network analysis

Background: Post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) are two highly comorbid psychological outcomes commonly studied in the context of stress and potential trauma. In Hubei, China, of which Wuhan is the capital, residents experienced unprecedented stringent lockdowns in the early months of 2020 when COVID-19 was first reported. The comorbidity between PTSD and MDD has been previously studied using network models, but often limited to cross-sectional data and analysis. Objectives: This study aims to examine the cross-sectional and longitudinal network structures of MDD and PTSD symptoms using both undirected and directed methods. Methods: Using three types of network analysis - cross-sectional undirected network, longitudinal undirected network, and directed acyclic graph (DAG) - we examined the interrelationships between MDD and PTSD symptoms in a sample of Hubei residents assessed in April, June, August, and October 2020. We identified the most central symptoms, the most influential bridge symptoms, and causal links among symptoms. Results: In both cross-sessional and longitudinal networks, the most central depressive symptoms included sadness and depressed mood, whereas the most central PTSD symptoms changed from irritability and hypervigilance at the first wave to difficulty concentrating and avoidance of potential reminders at later waves. Bridge symptoms showed similarities and differences between cross-sessional and longitudinal networks with irritability/anger as the most influential bridge longitudinally. The DAG found feeling blue and intrusive thoughts the gateways to the emergence of other symptoms. Conclusions: Combining cross-sectional and longitudinal analysis, this study elucidated central and bridge symptoms and potential causal pathways among PTSD and depression symptoms. Clinical implications and limitations are discussed.

Comparison of Hypomanic Symptoms Between Bipolar I and Bipolar II Disorders: A Network Perspective

BACKGROUND

Hypomanic symptoms between bipolar-I disorder (BD-I) and bipolar-II disorder (BD-II) are often indistinguishable in clinical practice. This study compared the network structure of hypomanic symptoms between patients with BD-I and BD-II.

METHODS

The 32-item Hypomania Checklist (HCL-32) was used to assess hypomanic symptoms. Network model was generated in BD-I and BD-II patients. Centrality index of strength was used to quantify the importance of each symptom in the network. The Network Comparison Test (NCT) was used to assess the differences in hypomanic symptoms between BD-I and BD-II patients.

RESULTS

Altogether, 423 patients with BD (BD-I: 191 and BD-II: 232) were included. The most central symptom was HCL17 "I am more flirtatious and/or am more sexually active" (strength _BD-I = 5.21) and HCL12 "I have more ideas, I am more creative" (strength _BD-II = 6.84) in BD-I and BD-II samples, respectively. The results of NCT showed that four nodes (HCL12 "I have more ideas, I am more creative," HCL17 "I am more flirtatious and/or am more sexually active," HCL23 "My thoughts jump from topic to topic," and HCL31 "I drink more alcohol") were significantly different between the BD-I and BD-II samples. Two edges (HCL3 "I am more self-confident"-HCL17 "I am more flirtatious and/or am more sexually active," and HCL10 "I am physically more active (sport, etc.)"-HCL24 "I do things more quickly and/or more easily") were significantly stronger in BD-I compared to BD-II patients.

CONCLUSION

The network structure of hypomanic symptoms is different between BD-I and BD-II patients. Interventions targeting the respective central symptoms and edges should be developed for BD-I and BD-II separately.