A correlation network analysis of dissociative experiences

In response to rising rates of stress-related psychopathology, prevention programs have proliferated. These programs often target young adults, as the transition to adulthood marks a sensitive period for the emergence of psychiatric disorders. These programs typically target multiple processes and are therefore time-consuming. Here, we seek to advance our understanding of the relationships between multiple target processes and well-being as a first step toward simplifying these trainings to facilitate dissemination and scalability. We assessed common prevention targets (i.e., emotion regulation strategy use, reward responsiveness, working memory, and delay discounting) and subjective well-being in 463 healthy young adults. First, we utilized a regularized partial correlation network to estimate relationships between target processes and subjective well-being. Subsequently, we applied a directed acyclic graph (DAG) with a Bayesian hill-climbing algorithm to infer potential causal relationships between these constructs. Working memory was a key node associated with well-being across network models. Other nodes associated with well-being in the partial correlation network (i.e., reward responsiveness, reappraisal) appeared to be downstream consequences of well-being in the DAG. Though well-being was associated with greater habitual use of reappraisal and lower habitual use of rumination and catastrophizing in the partial correlation network, emotion regulation strategies were not directly linked to well-being in the DAG. Using a Bayesian network approach, our findings point toward working memory as a potential key mechanism of well-being. Future work is needed to replicate these findings and determine the robustness of the identified network.

The objective of the present study was to assess in detail the whole spectrum of normal and pathological dissociative experiences and dissociative disorder (DD) diagnoses in borderline personality disorder (BPD) as diagnosed with the Revised Diagnostic Interview for Borderlines. Dissociation was measured comprehensively in 21 BPD outpatients using the Structured Clinical Interview for DSM–IV Dissociative Disorders–Revised, the Multidimensional Inventory of Dissociation (MID), the Dissociative Experiences Scale pathological taxon analysis, and the Somatoform Dissociation Questionnaire. The frequencies of DDs in this BPD sample were as follows: 24% no DD, 29% mild DD (dissociative amnesia and depersonalization disorder), 24% DD Not Otherwise Specified (DDNOS), and 24% dissociative identity disorder. With regard to the dissociative experiences endorsed, almost all patients reported identity confusion, unexplained mood changes, and depersonalization. Even those BPD patients with mild DD reported derealization, depersonalization, and dissociative amnesia. BPD patients with DDNOS reported frequent depersonalization, frequent amnesia, and notable experiences of identity alteration. BPD patients with dissociative identity disorder endorsed severe dissociative symptoms in all categories. Analysis of the MID pathological dissociation items revealed that 32% of the items were endorsed at a clinically significant level of frequency by more than 50% of our BPD patients. In conclusion, the frequencies of Diagnostic and Statistical Manual of Mental Disorders (4th ed.) DDs in these patients with BPD were surprisingly high. Likewise, the “average” BPD patient endorsed a wide variety of recurrent pathological dissociative symptoms.

BackgroundsDissociative experiences are described as crucial psychological mechanisms involving the organism’s responses to severe psychological traumas and unpleasant past experiences. This research was conducted to examine the psychometric properties of the Dissociative Experiences Measure, Oxford (DEMO) in the Iranian general population.MethodsThis study used cross-sectional correlation, and the statistical population consisted of Iranians over 15 years old. Among them, 712 subjects were chosen as the study sample using the convenience sampling technique. Data were acquired between February and April 2022 using DEMO, Dissociative Experiences Scale II (DES-II), and Depression Anxiety and Stress Scale (DASS-21). In order to determine DEMO’s psychometric properties, Confirmatory Factor Analysis (CFA) was used to investigate the factorial structure, Cronbach’s alpha analyses to examine the internal consistency reliability, and Pearson correlation analyses to examine the relationships between DEMO’s subscales and the subscales of DES-II and DASS-21, indicating convergent validity. In order to analyze the data, LISREL 8.0 and SPSS-26 were used.ResultsBased on the results of the CFA, the proposed five-factor structure of DEMO showed an acceptable fit to the data (χ² = 1939.81, SRMR = 0.078, CFI = 0.96, RMSEA = 0.074). The internal reliability was satisfactory for the total scale (Cronbach’s alpha = 0.93) and the five subscales (Cronbach’s alpha = 0.89 for unreality, 0.87 for numb and disconnected, 0.80 for memory blanks, 0.85 for zoned out, and 0.79 for vivid internal world). The CFA results indicated that the five factors explained 60.69% of the variance. Significant correlations were observed between the DEMO subscales and the respective subscales of DES-II and DASS-21, confirming the measure’s convergent validity.ConclusionIt can be concluded from the results of this study that the DEMO has high reliability and validity among the Iranian general population.

ObjectiveWe investigated the performance of North Korean refugees on attention tasks, and the relationship between that performance and psychiatric symptoms.MethodsSustained and divided attention was assessed using the computerized Comprehensive Attention Test in North Korean refugees and in South Koreans. All participants also completed the Beck Depression Inventory, the Beck Anxiety Inventory, the Impact of Event Scale-Revised and the Dissociative Experiences Scale-II (DES-II).ResultsThe North Korean refugees showed slower reaction times (RTs) on the visual sustained attention task compared to the South Koreans after controlling for age and sex. North Korean refugees had a greater number of omission errors (OEs) on the divided attention task and a higher standard deviation (SD) of RT. Total DES-II scores of the North Korean refugees were associated with the number of OEs and the SD of RT on the sustained attention task, and with the number of OEs on the divided attention task.ConclusionNorth Korean refugees showed poorer performance on computerized attention tasks. In addition, attention deficit among North Korean refugees was associated with their dissociative experiences. Our results suggest that refugees may have attention deficits, which may be related to their psychiatric symptoms, particularly dissociation.

Cognitive Function and Dissociative Disorder Status Among Veteran Subjects With Chronic Posttraumatic Stress Disorder: A Preliminary Study

BackgroundDissociative experiences are psychological manifestations characterized by a loss of connection and continuity between thoughts, emotions, environment, behavior, and identity. Lebanon has been facing indescribable events in the last few years, including the COVID-19 pandemic, the Beirut explosion, a crushing economic crisis with the highest inflation rate the country has known in over three decades. The aim of this study was to evaluate the correlation between dissociative experiences and post-traumatic stress symptoms from the economic crisis, the Beirut blast, the COVID-19 pandemic, and other mental health issues in a sample of Lebanese university students.MethodsThis cross-sectional study enrolled 419 active university students (18–35 years) from all over Lebanon (May and August 2021). The respondents received the online soft copy of a survey by a snowball sampling technique through social media and messaging apps. The questionnaire included sociodemographic data, the Dissociative Experience Scale (DES-II), the PTSD Checklist Specific Version (PCL-S), the Financial Wellbeing Scale, the Beirut Distress Scale, the Lebanese Anxiety Scale, the Patient Health Questionnaire.ResultsThe two-factor model of the DES fitted best according to CFI, RMSEA and χ2/df values, but modestly according to TLI. The two factors were absorption and amnesia/depersonalization. Higher stress (Beta = 0.95) and more PTSD from the Beirut blast (Beta = 0.29) and from the economic crisis (Beta = 0.23) were significantly associated with more absorption. A personal history of depression (Beta = 6.03), higher stress (Beta = 0.36) and more PTSD from the Beirut blast (Beta = 0.27) and from the COVID-19 pandemic (Beta = 0.16) were significantly associated with more amnesia/depersonalization.ConclusionSignificant rates of dissociative experiences and their sub-manifestations (amnesia/depersonalization and absorption) were found among Lebanese university students, with remarkable co-occurrence of a traumatic/stressful pattern, whether on an individual (history of PTSD) or a collective level (Post-traumatic manifestations from Beirut blast, COVID-19 pandemic and/or economic crisis), or whether correlated to an acute single event or to certain chronic stressors, or even to a personal history of depression. Such findings must raise the attention to serious mental and psychosocial alteration in the Lebanese national identity.

Obsessive-compulsive disorder (OCD) is often co-morbid with depression. Using the methods of network analysis, we computed two networks that disclose the potentially causal relationships among symptoms of these two disorders in 408 adult patients with primary OCD and co-morbid depression symptoms. We examined the relationship between the symptoms constituting these syndromes by computing a (regularized) partial correlation network via the graphical LASSO procedure, and a directed acyclic graph (DAG) via a Bayesian hill-climbing algorithm. The results suggest that the degree of interference and distress associated with obsessions, and the degree of interference associated with compulsions, are the chief drivers of co-morbidity. Moreover, activation of the depression cluster appears to occur solely through distress associated with obsessions activating sadness - a key symptom that 'bridges' the two syndromic clusters in the DAG. Bayesian analysis can expand the repertoire of network analytic approaches to psychopathology. We discuss clinical implications and limitations of our findings.

BACKGROUNDSex addiction is a behavioural addiction that has received increasing scientific attention in recent years. Indeed, problematic sexual behaviours represent a growing clinical concern, often associated with significant emotional distress and interpersonal difficulties. In light of this, the present study aimed to investigate its psychological antecedents by exploring the role of alexithymia and dissociation, and by examining the effect of gender as a covariate.PARTICIPANTS AND PROCEDUREA sample of 326 participants completed validated self-report measures assessing alexithymia, dissociative experiences, and problematic sexual behaviour. Data analysis included ANOVAs, regression models, and a path analysis.RESULTSThe results showed that two subdimensions of alexithymia (difficulty in identifying feelings and difficulty in describing feelings) were significantly associated with higher levels of sex addiction. Moreover, the dissociation subdimension of absorption significantly mediated both relationships, while dissociative amnesia and depersonalization/derealization did not show significant mediation effects. Gender also played a role, with male participants reporting higher levels of problematic sexual behaviour.CONCLUSIONSThese findings offer novel insight into the emotional and dissociative processes underlying sex addiction. The results emphasize the central role of absorption (a form of dissociation involving deep immersion in internal experience) as a potential emotional avoidance strategy that may sustain compulsive sexual behaviours. Understanding these mechanisms can guide future research and inform the development of targeted clinical interventions. Additionally, the relevance of gender differences suggests the importance of considering sex-specific pathways in assessment and treatment planning.

Chapter 11 Bayesian Networks

Directed acyclic graphs (DAGs) play a large role in the modern approach to causal inference. DAGs describe the relationship between measurements taken at various discrete times including the effect of interventions. The causal mechanisms, on the other hand, would naturally be assumed to be a continuous process operating over time in a cause–effect fashion. How does such immediate causation, that is causation occurring over very short time intervals, relate to DAGs constructed from discrete observations? We introduce a time-continuous model and simulate discrete observations in order to judge the relationship between the DAG and the immediate causal model. We find that there is no clear relationship; indeed the Bayesian network described by the DAG may not relate to the causal model. Typically, discrete observations of a process will obscure the conditional dependencies that are represented in the underlying mechanistic model of the process. It is therefore doubtful whether DAGs are always suited to describe causal relationships unless time is explicitly considered in the model. We relate the issues to mechanistic modeling by using the concept of local (in)dependence. An example using data from the Swiss HIV Cohort Study is presented.

BackgroundDiagnosing urinary tract infections (UTIs) in children in the emergency department (ED) is challenging due to the variable clinical presentations and difficulties in obtaining a urine sample free from contamination. Clinicians need to weigh a range of observations to make timely diagnostic and management decisions, a difficult task to achieve without support due to the complex interactions among relevant factors. Directed acyclic graphs (DAG) and causal Bayesian networks (BN) offer a way to explicitly outline the underlying disease, contamination and diagnostic processes, and to further make quantitative inference on the event of interest thus serving as a tool for decision support.MethodsWe prospectively collected data on children present to ED with suspected UTIs. Through knowledge elicitation workshops and one-on-one meetings, a DAG was co-developed with clinical domain experts (the Expert DAG) to describe the causal relationships among variables relevant to paediatric UTIs. The Expert DAG was combined with prospective data and further domain knowledge to inform the development of an application-oriented BN (the Applied BN), designed to support the diagnosis of UTI. We assessed the performance of the Applied BN using quantitative and qualitative methods.ResultsWe summarised patient background, clinical and laboratory characteristics of 431 episodes of suspected UTIs enrolled from May 2019 to November 2020. The Expert DAG was presented with a narrative description, elucidating how infection, specimen contamination and management pathways causally interact to form the complex picture of paediatric UTIs. Parameterised using prospective data and expert-elicited parameters, the Applied BN achieved an excellent and stable performance in predicting Escherichia coli culture results, with a mean area under the receiver operating characteristic curve of 0.86 and a mean log loss of 0.48 based on 10-fold cross-validation. The BN predictions were reviewed via a validation workshop, and we illustrate how they can be presented for decision support using three hypothetical clinical scenarios.ConclusionCausal BNs created from both expert knowledge and data can integrate case-specific information to provide individual decision support during the diagnosis of paediatric UTIs in ED. The model aids the interpretation of culture results and the diagnosis of UTIs, promising the prospect of improved patient care and judicious use of antibiotics.

The key goals in learning Bayesian networks (BNs) from data are to identify significant statistical relationships between variables and to build a Directed Acyclic Graph (DAG) that represents these relationships through Joint Probability Distributions. Most research relies on score-based or conditional test methods for model selection. However, when using real-world data, it can be challenging to identify whether the learned DAG represents the underlying relations inherent in the limited datasets, particularly when evaluating data obtained from multiple independent sources. This study presents a methodology to assess the credible interval for both the existence and direction of each edge within Bayesian networks derived from data. Furthermore, it explores the fusion of models acquired from distinct and independent datasets. This approach enables the Bayesian learning of Bayesian Networks (BNs) from data by treating the uncertainty associated with the existence and orientation of each edge as a random variable. By evaluating the probability of the orientation of each edge, it is possible to suggest the existence of a potential latent variable within the dataset. If an edge exhibits equiprobable directions and is verified to exist, it becomes a plausible hypothesis for a latent variable. The Fast Causal Algorithm, originally introduced by [1], is the foundation of this approach. Finally, by employing a maximum a posteriori estimation, the most prominent edges and their respective orientations are identified and employed to create the leading DAG. We present our findings in simulated datasets with different length sizes. By comparing the structure of the learned DAGs with existing structures and evaluating the inference capabilities of the final BN, we establish that our approach achieves results comparable to the most recent studies in the field, while offering insights into the model’s reliability and improving the use of the data.

The curing process regulates metabolite transformations of leaves and significantly influences the formation of tobacco quality. This study investigated the major physicochemical compositions and metabolic profiles under normal curing (NC), excessive curing (EC), and insufficient curing (IC) treatments. The results indicated that the contents of nicotine, nitrogen, potassium, and chlorine remained stable among treatments, while the sugar content in EC was significantly lower than in IC. LC-MS/MS identified 845 metabolites, with flavonoids as the most abundant class. Comparative analyses identified a series of differentially expressed metabolites (DEMs) among fresh leaf, NC, EC, and IC leaves at the end of 42°C, 54°C, and 68°C, respectively. At the end of 68°C, 256 up-regulated and 241 down-regulated common DEMs across treatments were isolated in comparison to fresh leaf, underscoring the consistency of metabolic changes during curing. Notably, nonivamide varied markedly across treatments, suggesting its potential as a key curing indicator. NC_68°C displayed 11 up-regulated and 17 down-regulated unique DEMs, differing from EC_68°C and IC_68°C, suggesting their potential availability in evaluating tobacco leaf quality. KEGG pathway analysis revealed temporal shifts in metabolic pathways, particularly those involved in secondary metabolite biosynthesis (such as flavonoids, flavones, flavonols) and amino acid metabolism, during the transition from yellowing to color-fixing. Correlation analysis isolated the top 25 DEMs correlated with curing degree and stage, which might play pivotal roles in the curing process and could serve as potential biomarkers for assessing curing degree and stage. Specifically, D-(+)-cellobiose displayed the strongest negative correlation with curing degree, while 5,7-dihydroxychromone exhibited the highest positive correlation coefficient. Furthermore, curcurbitacin IIa showed the highest positive correlation with curing stage, followed by hesperetin and 8-shogaol. Additionally, random forest analysis emphasized morellic acid as a core molecular metabolite across curing degrees, suggesting its potential as a biomarker. Debiased sparse partial correlation (DSPC) network analysis further pinpointed hispidulin as a key metabolite, underscoring its significance in elucidating flavonoid metabolism during the curing process. Collectively, this study enhances the understanding of metabolite transformations underlying tobacco curing processes and provides a valuable reference for optimizing curing strategies to achieve desired outcomes.

Spectral Bayesian network theory

In this paper, we address the challenges of random label ordering and limited interpretability associated with Ensemble Classifier Chains (ECC) by introducing a novel ECC method, ECC-MOO&BN, which integrates Bayesian Networks (BN) and Multi-Objective Optimization (MOO). This approach is designed to concurrently overcome these ECC limitations. The ECC-MOO&BN method focuses on extracting diverse and interpretable label orderings for the ECC classifier. We initiated this process by employing mutual information to investigate label relationships and establish the initial structures of the BN. Subsequently, an enhanced NSGA-II algorithm was applied to develop a series of Directed Acyclic Graphs (DAGs) that effectively balance the likelihood and complexity of the BN structure. The rationale behind using the MOO method lies in its ability to optimize both complexity and likelihood simultaneously, which not only diversifies DAG generation but also helps avoid overfitting during the production of label orderings. The DAGs, once sorted topologically, yielded a series of label orderings, which were then seamlessly integrated into the ECC framework for addressing multi-label classification (MLC) problems. Experimental results show that when benchmarked against eleven leading-edge MLC algorithms, our proposed method achieves the highest average ranking across seven evaluation criteria on nine out of thirteen MLC datasets. The results of the Friedman test and Nemenyi test also indicate that the performance of the proposed method has a significant advantage compared to other algorithms.