A network structure of manic symptoms

Module control of network analysis in psychopathology

The network approach to characterizing psychopathology departs from traditional latent categorical and dimensional approaches. Causal interplay among symptoms contributed to dynamic psychopathology system. Therefore, analyzing the symptom clusters is critical for understanding mental disorders. Furthermore, despite extensive research studying the topological features of symptom networks, the control relationships between symptoms remain largely unclear. Here, we present a novel systematizing concept, module control, to analyze the control principle of the symptom network at a module level. We introduce Module Control Network (MCN) to identify key modules that regulate the network's behavior. By applying our approach to a multivariate psychological dataset, we discover that non-emotional modules, such as sleep-related and stress-related modules, are the primary controlling modules in the symptom network. Our findings indicate that module control can expose central symptom cluster governing psychopathology network, offering novel insights into the underlying mechanisms of mental disorders and individualized approach to psychological interventions.

Adaption and validation of the Rwandese version of the Young Mania Rating Scale to measure the severity of a manic or hypomanic episode

BACKGROUND

Bipolar Disorder is one of the most incapacitating diseases among young persons, leading to cognitive and functional impairment and raised mortality, particularly death by suicide. Managing a manic episode and developing new and more effective treatment modalities requires sensitive and reliable instruments. This study aims to translate the English version of the YMRS questionnaire into Kinyarwanda, adapt it to the Rwandan context, and assess its validity.

METHODS

The original English version of The Young Mania Rating Scale questionnaire was translated into Kinyarwanda. The translation process followed a standardized approach, including back-translation, cross-cultural adaptation, and final adjustments. A total of 130 inpatients with bipolar disorder in a manic episode from CARAES Ndera Teaching Hospital were included. The descriptive statistics and test-retest correlations were carried out, as well as the CFA for validation and Rasch-analysis.

RESULTS

The Rwandese version of The Young mania rating scale had an adequate internal consistency (Cronbach's alpha = 0.90). Item 11 provided the lowest standardized loading in both ratings (0.51 and 0.55). The second lowest loading involved the highly correlated item pairs 5 & 9, with item 5 loading 0.51 in rating 1 and item 9 loading 0.57 in rating 2. The remaining loadings ranged from 0.59 to 0.79. This relatively narrow range indicated that a fit to a Rasch model was plausible if excluding item 11.

CONCLUSION

The findings demonstrate that the translated YMRS, the R-YMRS, can be used as a reliable and valid instrument for assessing mania in the Rwandese population in clinical and research settings. However, the results supported using an unweighted total score of 32 and removing items 5, 9, and 11. Studies on this revised scale with an added interview guide for less-trained clinical staff are recommended.

Pathways between Child Maltreatment, Psychological Symptoms, and Life Satisfaction: A Network Analysis in Adolescent Inpatients

Child maltreatment is a risk factor for mental disorders and decreased life satisfaction during adolescence. We investigated whether child maltreatment would link to life satisfaction both directly and through psychological symptoms, whether these relations would change from admission to discharge after treatment, and which types of maltreatment, symptoms and facets of life satisfaction would be most influential in adolescent inpatients with internalizing mental disorders. N = 896 adolescent receiving inpatient psychotherapeutic treatment completed questionnaires on child maltreatment experiences, current psychopathology and subjective life satisfaction at admission and discharge (n = 765). Main diagnoses were affective (n = 322), eating (n = 447), obsessive-compulsive (n = 70) and anxiety disorders (n = 57). Network models of child maltreatment, psychopathology and life satisfaction nodes were estimated at admission and discharge and compared using network comparison tests. Potential causal shortest pathways were investigated using directed acyclic graphs.Network models were stable with no significant differences between admission and discharge. Strongest nodes of each cluster were "emotional abuse" (child maltreatment), "worthlessness", "thinking about dying" and "feeling lonely" (psychopathology) and "satisfied with life" (life satisfaction) at both admission and discharge. Emotional neglect showed direct connections to life satisfaction, indicating its relevance for therapeutic interventions. At both admission and discharge, "sexual abuse" indirectly predicted lower life satisfaction through psychological symptoms. In conclusion, child maltreatment is directly and indirectly connected to life satisfaction in adolescents with mental disorders. Emotional abuse and neglect were especially important in linking child maltreatment to life satisfaction and psychopathology.

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.

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.

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.

A network structure of manic symptoms

OBJECTIVES

The aim of this study is to explore mania as a network of its symptoms, inspired by the network approach to mental disorders.

METHODS

Network structures of both cross-sectional and temporal effects were measured at three time points (admission, middle of hospital stay, and discharge) in a sample of 100 involuntarily committed patients diagnosed with bipolar I disorder with severe manic features and hospitalized in a specialized psychiatric ward.

RESULTS

Elevated mood is the most interconnected symptom in the network on admission, while aggressive behavior and irritability are highly predictive of each other, as well as language-thought disorder and "content" (the presence of abnormal ideas or delusions). Elevated mood is influenced by many symptoms in the temporal network.

CONCLUSIONS

The investigation of manic symptoms with network analysis allows for identifying important symptoms that are better connected to other symptoms at a given moment and over time. The connectivity of the manic symptoms evolves over time. Central symptoms could be considered as targets for clinical intervention when treating severe mania.